ORCID Profile
0000-0002-7446-8429
Current Organisation
University of Twente
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Environmental Science and Management | Aboriginal and Torres Strait Islander Environmental Knowledge | Geomorphology and Regolith and Landscape Evolution | Environmental Management
Ecosystem Assessment and Management of Coastal and Estuarine Environments | Conserving Aboriginal and Torres Strait Islander Heritage |
Publisher: Informa UK Limited
Date: 20-11-2017
Publisher: Informa UK Limited
Date: 2003
Publisher: Elsevier BV
Date: 02-2014
Publisher: Elsevier BV
Date: 05-2014
Publisher: Elsevier BV
Date: 10-2017
Publisher: Wiley
Date: 10-2005
Publisher: Elsevier BV
Date: 05-2008
Publisher: Elsevier BV
Date: 2013
DOI: 10.1016/J.JENVMAN.2012.10.029
Abstract: Surface water is one of the constraining resources for herbivore populations in semi-arid regions. Artificial waterpoints are constructed by wildlife managers to supplement natural water supplies, to support herbivore populations. The aim of this paper is to analyse how a landowner may realize his ecological and economic goals by manipulating waterpoints for the management of an elephant population, a water-dependent species in the presence of water-independent species. We develop a theoretical bio-economic framework to analyse the optimization of wildlife management objectives (in this case revenue generation from both consumptive and non-consumptive use and bio ersity conservation), using waterpoint construction as a control variable. The model provides a bio-economic framework for analysing optimization problems where a control has direct effects on one herbivore species but indirect effects on the other. A landowner may be interested only in maximization of profits either from elephant offtake and/or tourism revenue, ignoring the negative effects that could be brought about by elephants to bio ersity. If the landowner does not take the indirect effects of waterpoints into consideration, then the game reserve management, as the authority entrusted with the sustainable management of the game reserve, might use economic instruments such as subsidies or taxes to the landowners to enforce sound waterpoint management.
Publisher: Oxford University Press (OUP)
Date: 17-02-2010
Publisher: Elsevier BV
Date: 03-2017
Publisher: Elsevier BV
Date: 12-2017
Publisher: MDPI AG
Date: 15-03-2018
DOI: 10.3390/RS10030460
Publisher: Elsevier BV
Date: 02-2007
Publisher: Informa UK Limited
Date: 06-1996
Publisher: Elsevier BV
Date: 2014
Publisher: Elsevier BV
Date: 08-2016
Publisher: Elsevier BV
Date: 03-2013
Publisher: Wiley
Date: 25-01-2018
DOI: 10.1002/CNCR.31247
Abstract: The authors' systematic review indicated an increasing trend in the risk of second primary cancers (SPCs) from the 1980s to 2000 when considering studies from the United States and Australia. It is uncertain whether this trend has continued to increase since 2000. The current study was a population-based study of 51,802 in iduals with adult-onset cancers identified in the Tasmanian Cancer Registry. Patients with a first cancer diagnosis made between 1980 and 2009 were followed up to December 2013. SPC risks were quantified using standardized incidence ratios (SIRs) and absolute excess risks (AERs). Trends in SPC risk were assessed using multivariable Poisson models. With a median follow-up of 4.8 years (mean, 6.9 years), a total of 5339 SPCs were observed. The SIRs for any SPC increased from 0.98 (95% confidence interval, 0.90-1.07) after a first cancer diagnosis in 1980 through 1984 to 1.12 (95% confidence interval, 1.05-1.20) in 2005 through 2009. In multivariable Poisson models accounting for patient sex, age at the time of the first cancer diagnosis, follow-up interval, and first cancer type, the trend in SIRs increased significantly from 1980 through 2009 for all SPCs (P for trend <.001) and for specific SPCs of the head and neck, lung, digestive tract, and prostate (all P for trend <.05). From 2000 onward, the AER for specific SPCs after specific first cancers was highest for prostate cancer after first cancers of the urinary tract (AER, 54.3 per 10,000 person-years). In Tasmania, the risk of SPCs among survivors of adult-onset cancers has increased with periods of first cancer diagnosis from 1980 through 2009. Increased cancer screening and improved medical imaging may have contributed to the greater risk in recent years. Cancer 2018 :1808-18. © 2018 American Cancer Society.
Publisher: Elsevier BV
Date: 07-2002
Publisher: Elsevier BV
Date: 09-2018
Publisher: Consorci del Museu de Ciencies Naturals de Barcelona
Date: 2016
Publisher: Wiley
Date: 12-10-2019
DOI: 10.1002/CNCR.31806
Abstract: With improved cancer survivorship, cardiovascular disease (CVD) and other noncancer events compete with cancer as the underlying cause of death, but the risks of mortality in competing-risk settings have not been well characterized. The authors identified 21,637 in iduals who had a first cancer registered between 2006 and 2013, with follow-up to 2015, in the Australian population-based Tasmanian Cancer Registry. The cumulative incidence of deaths from specific competing events was assessed in competing-risk analyses. Standardized mortality ratios (SMRs) and absolute excess risks (AERs) for deaths from noncancer causes were calculated for comparison with the general population. Overall, 8844 deaths were observed, with 1946 (22%) from competing events. The cumulative incidence of deaths from CVD increased significantly with age at first cancer diagnosis and exceeded other competing events at age ≥65 years. The risk of death from CVD was more common than expected in the first year of follow-up (SMR, 1.44 [95% confidence interval, 1.26-1.64] AER, 36.8 per 10,000 person-years). The SMR and AER for CVD deaths varied by first cancer site, indicating increased risks after a first diagnosis of lung cancer, hematologic malignancy, and urinary tract cancer. For other noncancer events, the SMRs increased significantly for deaths from infectious disease and respiratory disease and were highest in the first year of follow-up. CVD was the leading cause of competing mortality among Tasmanian patients with cancer who were diagnosed from 2006 to 2013. The higher than expected occurrence of death from CVD and other noncancer events during the first year after a cancer diagnosis highlights the importance of early preventive interventions.
Publisher: Informa UK Limited
Date: 14-04-2014
Publisher: Korean Society for Preventive Medicine
Date: 31-03-2021
DOI: 10.3961/JPMPH.20.559
Publisher: Informa UK Limited
Date: 2002
Publisher: Springer Science and Business Media LLC
Date: 25-10-2021
Publisher: MDPI AG
Date: 20-03-2015
DOI: 10.3390/RS70303274
Publisher: Informa UK Limited
Date: 11-2016
Publisher: Elsevier BV
Date: 05-2018
Publisher: Elsevier BV
Date: 10-2016
Publisher: Wiley
Date: 17-05-2011
Publisher: Elsevier BV
Date: 04-2006
Publisher: MDPI AG
Date: 11-05-2015
DOI: 10.3390/RS70505901
Publisher: Springer Science and Business Media LLC
Date: 27-05-2023
DOI: 10.1038/S41467-023-38901-Y
Abstract: New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global bio ersity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transferable deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine-resolution (38-50 cm) satellite imagery. The results achieve accurate detection of nearly 500,000 in iduals across thousands of square kilometers and multiple habitat types, with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%). This research demonstrates the capability of satellite remote sensing and machine learning techniques to automatically and accurately count very large populations of terrestrial mammals across a highly heterogeneous landscape. We also discuss the potential for satellite-derived species detections to advance basic understanding of animal behavior and ecology.
Publisher: Elsevier BV
Date: 02-2019
Publisher: Informa UK Limited
Date: 2003
Publisher: Elsevier BV
Date: 10-2016
Publisher: MDPI AG
Date: 10-11-2020
DOI: 10.3390/RS12223690
Abstract: The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for bio ersity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geo ersity, but also to preserve bio ersity, as well as to improve bio ersity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting ex les of aeolian, fluvial, and coastal landforms. Different ex les for monitoring geomorphology as a crucial discipline of geo ersity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic ersity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in bio ersity monitoring.
Publisher: Informa UK Limited
Date: 05-04-2013
Publisher: Springer Science and Business Media LLC
Date: 13-05-2021
DOI: 10.1038/S41559-021-01451-X
Abstract: Monitoring global bio ersity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field observation. Although a framework of essential bio ersity variables (EBVs) is emerging for monitoring bio ersity, its poor alignment with remote sensing products hinders interpolation between field observations. This study compiles a comprehensive, prioritized list of remote sensing bio ersity products that can further improve the monitoring of geospatial bio ersity patterns, enhancing the EBV framework and its applicability. The ecosystem structure and ecosystem function EBV classes, which capture the biological effects of disturbance as well as habitat structure, are shown by an expert review process to be the most relevant, feasible, accurate and mature for direct monitoring of bio ersity from satellites. Bio ersity products that require satellite remote sensing of a finer resolution that is still under development are given lower priority (for ex le, for the EBV class species traits). Some EBVs are not directly measurable by remote sensing from space, specifically the EBV class genetic composition. Linking remote sensing products to EBVs will accelerate product generation, improving reporting on the state of bio ersity from local to global scales.
Publisher: Wiley
Date: 29-01-2013
Publisher: Elsevier BV
Date: 08-2006
Publisher: Wiley
Date: 06-2009
Publisher: Informa UK Limited
Date: 28-06-2011
Publisher: Wiley
Date: 06-08-2017
DOI: 10.1111/DDI.12607
Publisher: Elsevier BV
Date: 03-2004
Publisher: Elsevier BV
Date: 2001
Publisher: IEEE
Date: 07-2012
Publisher: Springer Science and Business Media LLC
Date: 11-09-2014
DOI: 10.1007/S10661-014-4017-X
Abstract: There is growing concern that increasing eutrophication causes degradation of coastal ecosystems. Studies in terrestrial ecosystems have shown that increasing the concentration of nitrogen in soils contributes to the acidification process, which leads to leaching of base cations. To test the effects of eutrophication on the availability of base cations in mangroves, we compared paired leaf and soil nutrient levels s led in Nypa fruticans and Rhizophora spp. on a severely disturbed, i.e. nutrient loaded, site (Mahakam delta) with s les from an undisturbed, near-pristine site (Berau delta) in East Kalimantan, Indonesia. The findings indicate that under pristine conditions, the availability of base cations in mangrove soils is determined largely by salinity. Anthropogenic disturbances on the Mahakam site have resulted in eutrophication, which is related to lower levels of foliar and soil base cations. Path analysis suggests that increasing soil nitrogen reduces soil pH, which in turn reduces the levels of foliar and soil base cations in mangroves.
Publisher: Informa UK Limited
Date: 1989
Publisher: Elsevier BV
Date: 07-2008
Publisher: Elsevier BV
Date: 06-2011
Publisher: Wiley
Date: 11-07-2016
DOI: 10.1111/AJE.12346
Publisher: Wiley
Date: 21-09-2016
DOI: 10.1111/PCE.12815
Abstract: Canopy chlorophyll content (CCC) is an essential ecophysiological variable for photosynthetic functioning. Remote sensing of CCC is vital for a wide range of ecological and agricultural applications. The objectives of this study were to explore simple and robust algorithms for spectral assessment of CCC. Hyperspectral datasets for six vegetation types (rice, wheat, corn, soybean, sugar beet and natural grass) acquired in four locations (Japan, France, Italy and USA) were analysed. To explore the best predictive model, spectral index approaches using the entire wavebands and multivariable regression approaches were employed. The comprehensive analysis elucidated the accuracy, linearity, sensitivity and applicability of various spectral models. Multivariable regression models using many wavebands proved inferior in applicability to different datasets. A simple model using the ratio spectral index (RSI R815, R704) with the reflectance at 815 and 704 nm showed the highest accuracy and applicability. Simulation analysis using a physically based reflectance model suggested the biophysical soundness of the results. The model would work as a robust algorithm for canopy-chlorophyll-metre and/or remote sensing of CCC in ecosystem and regional scales. The predictive-ability maps using hyperspectral data allow not only evaluation of the relative significance of wavebands in various sensors but also selection of the optimal wavelengths and effective bandwidths.
Publisher: Elsevier BV
Date: 12-2015
Publisher: Elsevier BV
Date: 11-2013
Publisher: MDPI AG
Date: 20-06-2019
DOI: 10.3390/RS11121462
Abstract: The objective of this study is to provide complete information on the dynamic relationship between X-band (3.11 cm) backscattering intensity (σ°) and rice crop’s leaf area index (LAI) at all growth phases. Though the relationship between X-band σ° and LAI has been previously explored, details on the relationship at the reproductive phase remain unstudied. LAI at the reproductive phase is important particularly at the heading stage where LAI reaches its maximum as it is closely related to grain yield, and at flowering stage where the total leaf area affects the amount of photosynthates. Therefore, this study examined the relationship of increasing LAI (vegetative to reproductive phase) and decreasing LAI (ripening phase) with TerraSAR-X (TSX) ScanSAR (3.11 cm) σ° at HH polarisation and 45° incidence angle. The results showed a statistically significant (R2 = 0.51, p value 0.001) non-linear relationship of LAI with σ° at the vegetative to reproductive phase while no significant linear relationship was found at the ripening phase. This study completes the response curve of X-band σ° to LAI by filling in the information on the reproductive phase which more accurately characterises the dynamic relationship between the rice crop’s LAI and X-band’s σ°. This contributes to improved knowledge on the use of X-band data for estimating LAI for the whole crop cycle which is essential for the modelling of crop growth and estimation of yield.
Publisher: American Society for Photogrammetry and Remote Sensing
Date: 09-2014
Publisher: Springer Science and Business Media LLC
Date: 15-08-2014
DOI: 10.1007/S11356-014-3412-8
Abstract: The spatial separation of the Qinling Mountains from the western mountains has caused morphological and genetic distinctions of giant pandas. Could this separation also cause the pandas' behavior change? In this research, we focused on the pandas' movement pattern and selected two wild panda groups in Foping and Wolong Nature Reserves (NR) to represent the populations in the Qinling and Qionglai Mountains, respectively. We hypothesized that the Qinling pandas have developed a different seasonal movement pattern compared with the pandas in the western mountains. We analyzed the radio tracking data from two NRs by using GIS. Our results showed the following significant differences: (1) The Foping pandas live most of the year in the low elevation areas and move higher during June and remain through August while the Wolong pandas live most of the year in the high elevation areas and move lower in April and stay through June (2) Comparing their low and high elevational areas shows the distinct spatial patterns between reserves, forming two obviously separated clusters in Foping but a single-compact cluster in Wolong (3) Foping pandas move an average of 425 m ± 147 s.d. daily, while Wolong pandas move an average of 550 m ± 343 s.d. daily and (4) Three habitat factors (i.e., terrain, temperature, and bamboo nutrient) were taken as the driving forces and analyzed, and they showed a strong support explanation to these different movement behaviors of pandas in two NRs. Our findings have important implications for management, for instance, it needs to be careful considering the behavior difference of the pandas when reintroducing them to the wild.
Publisher: MDPI AG
Date: 23-08-2023
DOI: 10.3390/RS15174128
Abstract: Integrating multimodal remote sensing data can optimize the mapping accuracy of in idual trees. Yet, one issue that is not trivial but generally overlooked in previous studies is the spatial mismatch of in idual trees between remote sensing datasets, especially in different imaging modalities. These offset errors between the same tree on different data that have been geometrically corrected can lead to substantial inaccuracies in applications. In this study, we propose a novel approach to match in idual trees between aerial photographs and airborne LiDAR data. To achieve this, we first leveraged the maximum overlap of the tree crowns in a local area to determine the correct and the optimal offset vector, and then used the offset vector to rectify the mismatch on in idual tree positions. Finally, we compared our proposed approach with a commonly used automatic image registration method. We used pairing rate (the percentage of correctly paired trees) and matching accuracy (the degree of overlap between the correctly paired trees) to measure the effectiveness of results. We evaluated the performance of our approach across six typical landscapes, including broadleaved forest, coniferous forest, mixed forest, roadside trees, garden trees, and parkland trees. Compared to the conventional method, the average pairing rate of in idual trees for all six landscapes increased from 91.13% to 100.00% (p = 0.045, t-test), and the average matching accuracy increased from 0.692 ± 0.175 (standard deviation) to 0.861 ± 0.152 (p = 0.017, t-test). Our study demonstrates that the proposed tree-oriented matching approach significantly improves the registration accuracy of in idual trees between aerial photographs and airborne LiDAR data.
Publisher: Elsevier BV
Date: 08-2010
Publisher: Elsevier BV
Date: 03-2018
Publisher: Elsevier BV
Date: 11-2016
Publisher: MDPI AG
Date: 19-09-2022
DOI: 10.3390/JOF8090981
Abstract: Fungi are a hyper- erse kingdom that contributes significantly to the regulation of the global carbon and nutrient cycle. However, our understanding of the distribution of fungal ersity is often hindered by a lack of data, especially on a large spatial scale. Open bio ersity data may provide a solution, but concerns about the potential spatial and temporal bias in species occurrence data arising from different observers and s ling protocols challenge their utility. The theory of species accumulation curves predicts that the cumulative number of species reaches an asymptote when the s ling effort is sufficiently large. Thus, we hypothesize that open bio ersity data could be used to reveal large-scale macrofungal ersity patterns if these datasets are accumulated long enough. Here, we tested our hypothesis with 50 years of macrofungal occurrence records in Norway and Sweden that were downloaded from the Global Bio ersity Information Facility (GBIF). We first grouped the data into five temporal subs les with different cumulative s ling efforts (i.e., accumulation of data for 10, 20, 30, 40 and 50 years). We then predicted the macrofungal ersity and distribution at each subs le using the maximum entropy (MaxEnt) species distribution model. The results revealed that the cumulative number of macrofungal species stabilized into distinct distribution patterns with localized hotspots of predicted macrofungal ersity with s ling efforts greater than approximately 30 years. Our research demonstrates the utility and importance of the long-term accumulated open bio ersity data in studying macrofungal ersity and distribution at the national level.
Publisher: Springer Science and Business Media LLC
Date: 17-09-2018
DOI: 10.1038/S41559-018-0667-3
Abstract: Essential Bio ersity Variables (EBVs) allow observation and reporting of global bio ersity change, but a detailed framework for the empirical derivation of specific EBVs has yet to be developed. Here, we re-examine and refine the previous candidate set of species traits EBVs and show how traits related to phenology, morphology, reproduction, physiology and movement can contribute to EBV operationalization. The selected EBVs express intra-specific trait variation and allow monitoring of how organisms respond to global change. We evaluate the societal relevance of species traits EBVs for policy targets and demonstrate how open, interoperable and machine-readable trait data enable the building of EBV data products. We outline collection methods, meta(data) standardization, reproducible workflows, semantic tools and licence requirements for producing species traits EBVs. An operationalization is critical for assessing progress towards bio ersity conservation and sustainable development goals and has wide implications for data-intensive science in ecology, biogeography, conservation and Earth observation.
Publisher: Informa UK Limited
Date: 14-12-2010
Publisher: Asosiasi Peneliti dan Teknisi Kehutanan dan Lingkungan Hidup Indonesia
Date: 29-04-2017
Publisher: Elsevier BV
Date: 09-2017
Publisher: Elsevier BV
Date: 12-2007
Publisher: Informa UK Limited
Date: 26-11-2008
Publisher: Wiley
Date: 05-03-2019
DOI: 10.1002/RSE2.113
Publisher: Elsevier BV
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 06-09-2017
DOI: 10.1038/S41598-017-10898-7
Abstract: There is considerable uncertainty concerning changes in plant ersity of Chinese secondary forests, particularly with respect to ersity recovery following anthropogenic disturbance. Here we present a meta-analysis of the recovery of woody plant species richness in secondary forests in China, with nearby primary forests as a reference. A total of 125 pairs of secondary-primary forest data reported in 55 publications were identified across China. We analyzed the data by region and logging history to examine their influences on secondary forest recovery. Our results indicated that the woody plant richness of secondary forests in China was close to fully recovered when compared to the primary forest, with the recovery ratio being 85–103%. Higher recovery ratios were observed in central, northeast and southwest China, with lower recovery ratios seen in east, south and northwest China, and the recovery in central China significantly reached the primary forests (reference) level. Concerning logging histories, the recovery ratios showed two peak values, with one at 21–40 years after clear cutting and the other at 61–80 years. We reveal the fundamental recovery patterns of woody plant species richness in secondary forests in China. These patterns provide information for the sustainable management of secondary forest resources.
Publisher: Elsevier BV
Date: 2012
Publisher: Elsevier BV
Date: 12-2015
Publisher: Wiley
Date: 08-2010
DOI: 10.1890/09-1359.1
Abstract: Issues of residual spatial autocorrelation (RSA) and spatial scale are critical to the study of species-environment relationships, because RSA invalidates many statistical procedures, while the scale of analysis affects the quantification of these relationships. Although these issues independently are widely covered in the literature, only sparse attention is given to their integration. This paper focuses on the interplay between RSA and the spatial scaling of species-environment relationships. Using a hypothetical species in an artificial landscape, we show that a mismatch between the scale of analysis and the scale of a species' response to its environment leads to a decrease in the portion of variation explained by environmental predictors. Moreover, it results in RSA and biased regression coefficients. This bias stems from error-predictor dependencies due to the scale mismatch, the magnitude of which depends on the interaction between the scale of landscape heterogeneity and the scale of a species' response to this heterogeneity. We show that explicitly considering scale effects on RSA can reveal the characteristic scale of a species' response to its environment. This is important, because the estimation of species-environment relationships using spatial regression methods proves to be erroneous in case of a scale mismatch, leading to spurious conclusions when scaling issues are not explicitly considered. The findings presented here highlight the importance of examining the appropriateness of the spatial scales used in analyses, since scale mismatches affect the rigor of statistical analyses and thereby the ability to understand the processes underlying spatial patterning in ecological phenomena.
Publisher: Elsevier BV
Date: 06-2011
Publisher: Springer Science and Business Media LLC
Date: 19-11-2011
Publisher: Elsevier BV
Date: 2018
Publisher: MDPI AG
Date: 15-07-2022
DOI: 10.3390/RS14143399
Abstract: Plant functional traits at the community level (plant community traits hereafter) are commonly used in trait-based ecology for the study of vegetation–environment relationships. Previous studies have shown that a variety of plant functional traits at the species or community level can be successfully retrieved by airborne or spaceborne imaging spectrometer in homogeneous, species-poor ecosystems. However, findings from these studies may not apply to heterogeneous, species-rich ecosystems. Here, we aim to determine whether unmanned aerial vehicle (UAV)-based hyperspectral imaging could adequately estimate plant community traits in a species-rich alpine meadow ecosystem on the Qinghai–Tibet Plateau. To achieve this, we compared the performance of four non-parametric regression models, i.e., partial least square regression (PLSR), the generic algorithm integrated with the PLSR (GA-PLSR), random forest (RF) and extreme gradient boosting (XGBoost) for the retrieval of 10 plant community traits using visible and near-infrared (450–950 nm) UAV hyperspectral imaging. Our results show that chlorophyll a, chlorophyll b, carotenoid content, starch content, specific leaf area and leaf thickness were estimated with good accuracies, with the highest R2 values between 0.64 (nRMSE = 0.16) and 0.83 (nRMSE = 0.11). Meanwhile, the estimation accuracies for nitrogen content, phosphorus content, plant height and leaf dry matter content were relatively low, with the highest R2 varying from 0.3 (nRMSE = 0.24) to 0.54 (nRMSE = 0.20). Among the four tested algorithms, the GA-PLSR produced the highest accuracy, followed by PLSR and XGBoost, and RF showed the poorest performance. Overall, our study demonstrates that UAV-based visible and near-infrared hyperspectral imaging has the potential to accurately estimate multiple plant community traits for the natural grassland ecosystem at a fine scale.
Publisher: MDPI AG
Date: 23-08-2017
DOI: 10.3390/RS9090878
Publisher: Elsevier BV
Date: 09-2006
Publisher: Elsevier BV
Date: 02-2012
Publisher: Springer Science and Business Media LLC
Date: 11-12-2011
Publisher: Elsevier BV
Date: 1999
Publisher: Wiley
Date: 29-11-2022
Abstract: Visibility (viewshed) plays a significant and erse role in animals' behaviour and fitness. Understanding how visibility influences animal behaviour requires the measurement of habitat visibility at spatial scales commensurate to in idual animal choices. However, measuring habitat visibility at a fine spatial scale over a landscape is a challenge, particularly in highly heterogeneous landscapes (e.g. forests). As a result, our ability to model the influence of fine‐scale visibility on animal behaviour has been impeded or limited. In this study, we demonstrate the application of the concept of three‐dimensional (3D) cumulative viewshed in the study of animal spatial behaviour at a landscape level. Specifically, we employed a newly described approach that combines terrestrial and airborne light detection and ranging (LiDAR) to measure fine‐scale habitat visibility (3D cumulative viewshed) on a continuous scale in forested landscapes. We applied the LiDAR‐derived visibility to investigate how visibility in forests affects the summer habitat selection and the movement of 20 GPS‐collared female red deer Cervus elaphus in a temperate forest in Germany. We used integrated step selection analysis to determine whether red deer show any preference for fine‐scale habitat visibility and whether visibility is related to the rate of movement of red deer. We found that red deer selected intermediate habitat visibility. Their preferred level of visibility during the day was substantially lower than that of night and twilight, whereas the preference was not significantly different between night and twilight. In addition, red deer moved faster in high‐visibility areas, possibly mainly to avoid predation and anthropogenic risk. Furthermore, red deer moved most rapidly between locations in the twilight. For the first time, the preference for intermediate habitat visibility and the adaption of movement rate to fine‐scale visibility by a forest‐dwelling ungulate species at a landscape scale was revealed. The LiDAR technique used in this study offers fine‐scale habitat visibility at the landscape level in forest ecosystems, which would be of broader interest in the fields of animal ecology and behaviour.
Publisher: Elsevier BV
Date: 10-2006
Publisher: Elsevier BV
Date: 05-2004
Publisher: Informa UK Limited
Date: 17-02-2010
Publisher: Informa UK Limited
Date: 07-1987
Publisher: MDPI AG
Date: 12-11-2021
DOI: 10.3390/RS13224548
Abstract: Marine plastic debris (MPD) is a globally relevant environmental challenge, with an estimated 8 million tons of synthetic debris entering the marine environment each year. Plastic has been found in all parts of the marine environment, including the surface layers of the ocean, within the water column, in coastal waters, on the benthic layer and on beaches. While research on detecting MPD using remote sensing is increasing, most of it focuses on detecting floating debris in open waters, rather than detecting MPD on beaches. However, beaches present challenges that are unique from other parts of the marine environment. In order to better understand the spectral properties of beached MPD, we present the SWIR reflectance of weathered MPD and virgin plastics over a sandy substrate. We conducted spectral feature analysis on the different plastic groups to better understand the impact that polymers have on our ability to detect synthetic debris at sub-pixel surface covers that occur on beaches. Our results show that the minimum surface cover required to detect MPD on a sandy surface varies between 2–8% for different polymer types. Furthermore, plastic composition affects the magnitude of spectral absorption. This suggests that variation in both surface cover and polymer type will inform the efficacy of beach litter detection methods.
Publisher: Elsevier BV
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 10-2007
Publisher: Informa UK Limited
Date: 10-01-2006
Publisher: Wiley
Date: 09-2001
Publisher: Elsevier BV
Date: 05-2007
Publisher: Elsevier BV
Date: 08-2007
DOI: 10.1016/J.WATRES.2007.05.018
Abstract: Remotely sensed assessment of dredging impacts on water turbidity is straightforward when turbidity plumes show up in clear water. However, it is more complicated in turbid waters as the spatial or temporal changes in turbidity might be of natural origin. The plausibility of attributing turbidity patterns to dredging activities would be greatly enhanced when demonstrating association between dredging infrastructure and water turbidity. This study investigated the possibility to strengthen the inference of dredging impact while simultaneously monitoring vessels and water turbidity in the northern Poyang Lake, China, where dredging was first introduced in 2001 and rapidly extended onwards. Time-series of Landsat TM and MODIS images of 2000-2005 were used to estimate the distribution and number of vessels as well as water turbidity. MODIS images revealed a significant increase in water turbidity from 2001 onwards. Landsat TM image analysis indicated a simultaneous increase in the number of vessels. Regression analysis further showed a highly significant positive relationship (R2=0.92) between water turbidity and vessel number. Visual interpretation of ship locations led to the conclusion that clear upstream waters developed turbidity plumes while passing the first cluster of vessels. We concluded that dredging caused the increase in water turbidity, and simultaneously monitoring the water turbidity and vessels enhanced the strength of evidence in remotely sensed dredging impact assessment.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2016
Publisher: Springer Science and Business Media LLC
Date: 22-07-2015
DOI: 10.1038/523403A
Publisher: MDPI AG
Date: 15-07-2018
DOI: 10.3390/RS10071120
Abstract: Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health (2) using remote sensing (RS) approaches to monitor forest health (3) coupling different monitoring approaches (4) using data science as a bridge between complex and multidimensional big forest health (FH) data and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles (iii) Semantic Web (iv) proof, trust, and uncertainties (v) tools for data science analysis and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.
Publisher: Informa UK Limited
Date: 03-2005
Publisher: SPIE
Date: 28-12-2008
DOI: 10.1117/12.815983
Publisher: Informa UK Limited
Date: 04-1988
Publisher: SPIE
Date: 28-12-2008
DOI: 10.1117/12.815981
Publisher: American Society for Photogrammetry and Remote Sensing
Date: 2009
Publisher: Elsevier BV
Date: 07-2015
Publisher: Elsevier BV
Date: 04-2013
Publisher: Public Library of Science (PLoS)
Date: 18-09-2014
Publisher: Elsevier BV
Date: 09-2016
Publisher: Elsevier BV
Date: 11-2017
Publisher: MDPI AG
Date: 15-02-2019
DOI: 10.3390/RS11040390
Abstract: Leaf area index (LAI) has been investigated in multiple studies, either by means of visible/near-infrared and shortwave-infrared or thermal infrared remotely sensed data, with various degrees of accuracy. However, it is not yet known how the integration of visible/near and shortwave-infrared and thermal infrared data affect estimates of LAI. In this study, we examined the utility of Landsat-8 thermal infrared data together with its spectral data from the visible/near and shortwave-infrared region to quantify the LAI of a mixed temperate forest in Germany. A field c aign was carried out in August 2015, in the Bavarian Forest National Park, concurrent with the time of the Landsat-8 overpass, and a number of forest structural parameters, including LAI and proportion of vegetation cover, were measured for 37 plots. A normalised difference vegetation index threshold method was applied to calculate land surface emissivity and land surface temperature and their relations to LAI were investigated. Next, the relation between LAI and eight commonly used vegetation indices were examined using the visible/near-infrared and shortwave-infrared remote sensing data. Finally, the artificial neural network was used to predict the LAI using: (i) reflectance data from the Landsat-8 operational land imager (OLI) sensor (ii) reflectance data from the OLI sensor and the land surface emissivity and (iii) reflectance data from the OLI sensor and land surface temperature. A stronger relationship was observed between LAI and land surface emissivity compared to that between LAI and land surface temperature. In general, LAI was predicted with relatively low accuracy by means of the vegetation indices. Among the studied vegetation indices, the modified vegetation index had the highest accuracy for LAI prediction (R2CV = 0.33, RMSECV = 1.21 m2m−2). Nevertheless, using the visible/near-infrared and shortwave-infrared spectral data in the artificial neural network, the prediction accuracy of LAI increased (R2CV = 0.58, RMSECV = 0.83 m2m−2). The integration of reflectance and land surface emissivity significantly improved the prediction accuracy of the LAI (R2CV = 0.81, RMSECV = 0.63 m2m−2). For the first time, our results demonstrate that the combination of Landsat-8 reflectance spectral data from the visible/near-infrared and shortwave-infrared domain and thermal infrared data can boost the estimation accuracy of the LAI in a forest ecosystem. This finding has implication for the prediction of other vegetation biophysical, or possibly biochemical variables using thermal infrared satellite remote sensing data, as well as regional mapping of LAI when coupled with a canopy radiative transfer model.
Publisher: Springer Science and Business Media LLC
Date: 06-2005
Publisher: Informa UK Limited
Date: 1997
Publisher: Elsevier BV
Date: 07-2012
Publisher: MDPI AG
Date: 23-02-2018
DOI: 10.3390/RS10020346
Publisher: Elsevier BV
Date: 12-2022
Publisher: Elsevier BV
Date: 09-2018
Publisher: Elsevier BV
Date: 02-2004
Publisher: Elsevier BV
Date: 2016
Publisher: Elsevier BV
Date: 02-2011
Publisher: Elsevier BV
Date: 12-2007
Publisher: IEEE
Date: 07-2014
Publisher: Informa UK Limited
Date: 15-07-2020
Publisher: MDPI AG
Date: 08-11-2019
DOI: 10.3390/RS11222614
Abstract: Knowledge about forest structures, particularly of deadwood, is fundamental for understanding, protecting, and conserving forest bio ersity. While in idual tree-based approaches using single wavelength airborne laserscanning (ALS) can successfully distinguish broadleaf and coniferous trees, they still perform multiple tree species classifications with limited accuracy. Moreover, the mapping of standing dead trees is becoming increasingly important for damage calculation after pest infestation or bio ersity assessment. Recent advances in sensor technology have led to the development of new ALS systems that provide up to three different wavelengths. In this study, we present a novel method which classifies three tree species (Norway spruce, European beech, Silver fir), and dead spruce trees with crowns using full waveform ALS data acquired from three different sensors (wavelengths 532 nm, 1064 nm, 1550 nm). The ALS data were acquired in the Bavarian Forest National Park (Germany) under leaf-on conditions with a maximum point density of 200 points/m 2 . To avoid overfitting of the classifier and to find the most prominent features, we embed a forward feature selection method. We tested our classification procedure using 20 s le plots with 586 measured reference trees. Using single wavelength datasets, the highest accuracy achieved was 74% (wavelength = 1064 nm), followed by 69% (wavelength = 1550 nm) and 65% (wavelength = 532 nm). An improvement of 8–17% over single wavelength datasets was achieved when the multi wavelength data were used. Overall, the contribution of the waveform-based features to the classification accuracy was higher than that of the geometric features by approximately 10%. Our results show that the features derived from a multi wavelength ALS point cloud significantly improve the detailed mapping of tree species and standing dead trees.
Publisher: Springer Science and Business Media LLC
Date: 13-12-2009
Publisher: Informa UK Limited
Date: 2001
Publisher: Public Library of Science (PLoS)
Date: 28-09-2012
Publisher: Wiley
Date: 09-2004
DOI: 10.1002/LDR.636
Publisher: Informa UK Limited
Date: 14-06-2008
Publisher: SPIE
Date: 25-10-2016
DOI: 10.1117/12.2240948
Publisher: Elsevier BV
Date: 06-2015
Publisher: Elsevier BV
Date: 02-2017
Publisher: Brill
Date: 2009
DOI: 10.1163/156853809788201207
Abstract: The biogeographic patterns in species density of herptiles were analysed in the Iberian Peninsula. Geoclimatic regions were identified using a PCA. In idual habitat suitability (HS) models for 23 hibians and 35 reptiles at 10 × 10 km scale were calculated with ENFA, using 12 environmental factors established with Remote Sensing (RS) techniques. The species presence proportion in each geoclimatic region was calculated through a cross-tabulation between each potential occurrence model and the geoclimatic regions. Species chorotypes were determined through Hierarchical Cluster Analysis using Jaccard's index as association measure and by the analysis of marginality and tolerance factors from in idual HS models. Predicted species density maps were calculated for each geoclimatic region. Probable under-s led areas were estimated through differences between the predicted species density maps and observed (Gap analysis). The selected PCA components ided the Iberian Peninsula in two major geoclimatic regions largely corresponding to the Atlantic and Mediterranean climates. The Jaccard's index clustered herptiles in two main taxonomic groups, with distribution similar to the Atlantic and Mediterranean geoclimatic regions (7 hibian + 13 reptile species in three Atlantic subgroups and 16 hibian + 22 reptile species in four Mediterranean subgroups). Marginality and tolerance factor scores identified species groups of herptile specialists and generalists. The highest observed and predicted species density areas were broadly located in identical regions. Predicted gaps are located in north-western, north-east and central Iberia. RS is a useful tool for biogeographical studies, as it provides consistent environmental data from large areas with high accuracy.
Publisher: Informa UK Limited
Date: 07-2012
Publisher: Elsevier BV
Date: 07-2017
Publisher: Wiley
Date: 03-05-2021
Abstract: Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, ersity patterns and change, metapopulation dynamics, population connectivity or gene flow. In this paper, we present a new R package— raster —to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. The raster package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open‐source algorithms.
Publisher: Springer Science and Business Media LLC
Date: 24-05-2021
Publisher: Elsevier BV
Date: 12-2016
Publisher: Wiley
Date: 06-2009
Publisher: MDPI AG
Date: 23-09-2015
DOI: 10.3390/RS70912563
Publisher: Proceedings of the National Academy of Sciences
Date: 21-06-2012
Abstract: A speech of then-Vice President Al Gore in 1998 created a vision for a Digital Earth, and played a role in stimulating the development of a first generation of virtual globes, typified by Google Earth, that achieved many but not all the elements of this vision. The technical achievements of Google Earth, and the functionality of this first generation of virtual globes, are reviewed against the Gore vision. Meanwhile, developments in technology continue, the era of “big data” has arrived, the general public is more and more engaged with technology through citizen science and crowd-sourcing, and advances have been made in our scientific understanding of the Earth system. However, although Google Earth stimulated progress in communicating the results of science, there continue to be substantial barriers in the public’s access to science. All these factors prompt a reexamination of the initial vision of Digital Earth, and a discussion of the major elements that should be part of a next generation.
Publisher: Elsevier BV
Date: 08-2012
Publisher: Informa UK Limited
Date: 10-2004
Publisher: Elsevier BV
Date: 04-2013
Publisher: MDPI AG
Date: 31-10-2020
DOI: 10.3390/RS12213573
Abstract: Chlorophyll content, as the primary pigment driving photosynthesis, is directly affected by many natural and anthropogenic disturbances and stressors. Accurate and timely estimation of canopy chlorophyll content (CCC) is essential for effective ecosystem monitoring to allow for successful management interventions to occur. Hyperspectral remote sensing offers the possibility to accurately estimate and map canopy chlorophyll content. In the past, research has predominantly focused on the use of hyperspectral data on canopy chlorophyll content retrieval of crops and grassland ecosystems. Therefore, in this study, a temperate mixed forest, the Bavarian Forest National Park in Germany, was chosen as the study site. We compared different statistical models (narrowband vegetation indices (VIs), partial least squares regression (PLSR) and random forest (RF)) in their accuracy to predict CCC using airborne hyperspectral data. The airborne hyperspectral imagery was acquired by the AisaFenix sensor (623 bands 3.5 nm spectral resolution in the visible near-infrared (VNIR) region, and 12 nm spectral resolution in the shortwave infrared (SWIR) region 3 m spatial resolution) on July 6, 2017. In situ leaf chlorophyll content and leaf area index measurements were s led from the upper canopy of coniferous, mixed, and deciduous forest stands in July and August 2017. The study yielded the highest retrieval accuracies with PLSR (root mean square error (RMSE) = 0.25 g/m2, R2 = 0.66). It further indicated specific spectral regions within the visible (390–400 nm and 470–540 nm), red edge (680–780 nm), near-infrared (1050–1100 nm) and shortwave infrared regions (2000–2270 nm) that were important for CCC retrieval. The results showed that forest CCC can be mapped with relatively high accuracies using image spectroscopy.
Publisher: Informa UK Limited
Date: 26-02-2020
Publisher: Informa UK Limited
Date: 04-2006
Publisher: Informa UK Limited
Date: 02-2009
Publisher: Elsevier BV
Date: 05-2007
Publisher: Informa UK Limited
Date: 21-09-2007
Publisher: Springer Science and Business Media LLC
Date: 2002
Publisher: Elsevier BV
Date: 02-2006
Publisher: Elsevier BV
Date: 12-2006
Publisher: Wiley
Date: 04-2008
DOI: 10.2193/2007-197
Publisher: Wiley
Date: 04-05-2009
Publisher: Informa UK Limited
Date: 07-1997
Publisher: Informa UK Limited
Date: 27-06-2016
Publisher: Wiley
Date: 15-08-2021
Abstract: Effects of climate change‐induced events on forest ecosystem dynamics of composition, function and structure call for increased long‐term, interdisciplinary and integrated research on bio ersity indicators, in particular within strictly protected areas with extensive non‐intervention zones. The long‐established concept of forest supersites generally relies on long‐term funds from national agencies and goes beyond the logistic and financial capabilities of state‐ or region‐wide protected area administrations, universities and research institutes. We introduce the concept of data pools as a smaller‐scale, user‐driven and reasonable alternative to co‐develop remote sensing and forest ecosystem science to validated products, bio ersity indicators and management plans. We demonstrate this concept with the Bohemian Forest Ecosystem Data Pool, which has been established as an interdisciplinary, international data pool within the strictly protected Bavarian Forest and Šumava National Parks and currently comprises 10 active partners. We demonstrate how the structure and impact of the data pool differs from comparable cases. We assessed the international influence and visibility of the data pool with the help of a systematic literature search and a brief analysis of the results. Results primarily suggest an increase in the impact and visibility of published material during the life span of the data pool, with highest visibilities achieved by research conducted on leaf traits, vegetation phenology and 3D‐based forest inventory. We conclude that the data pool results in an efficient contribution to the concept of global bio ersity observatory by evolving towards a training platform, functioning as a pool of data and algorithms, directly communicating with management for implementation and providing test fields for feasibility studies on earth observation missions.
Publisher: Elsevier BV
Date: 09-2004
Publisher: Informa UK Limited
Date: 25-03-2008
Publisher: MDPI AG
Date: 27-06-2012
DOI: 10.3390/S120708755
Publisher: Informa UK Limited
Date: 13-07-2016
Publisher: MDPI AG
Date: 11-03-2022
DOI: 10.3390/F13030445
Abstract: It remains difficult to compare the state of conservation of forests of different nations. Essential Bio ersity Variables (EBVs) are a set of variables designed as a framework for harmonizing bio ersity monitoring. Methods to monitor forest bio ersity are traditional monitoring (according to conservation policy requirements), remote sensing, environmental DNA, and the information products that are derived from them (RS/eDNA bio ersity products). However, it is not clear to what extent indicators from conservation policies align with EBVs and RS/eDNA bio ersity products. This research evaluated current gaps in harmonization between EBVs, RS/eDNA bio ersity products and forest conservation indicators. We compared two sets of bio ersity variables: (1) forest conservation indicators and (2) RS/eDNA bio ersity products, within the context of the Essential Bio ersity Variables framework. Indicators derived from policy documents can mostly be categorized within the EBV ‘ecosystem vertical profile’, while ‘ecosystem function’ remains underrepresented. RS/eDNA bio ersity products, however, can provide information about ‘ecosystem function’. Integrating RS/eDNA bio ersity products that monitor ecosystem functioning into monitoring programs will lead to a more comprehensive and balanced reporting on forest bio ersity. In addition, using the same variables and similar RS/eDNA products for forest bio ersity and conservation policies is a requirement for harmonization and international policy reporting.
Publisher: Informa UK Limited
Date: 10-1989
Publisher: Elsevier BV
Date: 06-2012
Publisher: Informa UK Limited
Date: 10-2020
Publisher: Elsevier BV
Date: 03-2014
Publisher: Wiley
Date: 22-09-2014
DOI: 10.1111/ECOG.00812
Publisher: Wiley
Date: 18-05-2012
Publisher: Elsevier BV
Date: 02-2007
Publisher: Informa UK Limited
Date: 20-09-2005
Publisher: Elsevier BV
Date: 06-2006
Publisher: Elsevier BV
Date: 03-2009
Publisher: Elsevier BV
Date: 04-2011
Publisher: Informa UK Limited
Date: 2001
Publisher: Informa UK Limited
Date: 10-2004
Publisher: Ubiquity Press, Ltd.
Date: 2021
DOI: 10.5334/IJIC.5581
Publisher: Cold Spring Harbor Laboratory
Date: 10-02-2021
DOI: 10.1101/2021.02.09.430391
Abstract: Ecosystem heterogeneity has been widely recognized as a key ecological feature, influencing several ecological functions, since it is strictly related to several ecological functions like ersity patterns and change, metapopulation dynamics, population connectivity, or gene flow. In this paper, we present a new R package - raster - to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. The raster package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open source algorithms.
Publisher: Springer Science and Business Media LLC
Date: 08-08-2015
Publisher: Elsevier BV
Date: 02-2015
Publisher: Wiley
Date: 06-2001
Publisher: MDPI AG
Date: 10-05-2021
DOI: 10.3390/RS13091850
Abstract: Floating and washed ashore marine plastic debris (MPD) is a growing environmental challenge. It has become evident that secluded locations including the Arctic, Antarctic, and remote islands are being impacted by plastic pollution generated thousands of kilometers away. Optical remote sensing of MPD is an emerging field that can aid in monitoring remote environments where in-person observation and data collection is not always feasible. Here we evaluate MPD spectral features in the visible to shortwave infrared regions for detecting varying quantities of MPD that have accumulated on beaches using a spectroradiometer. Measurements were taken from a range of in situ MPD accumulations ranging from 0.08% to 7.94% surface coverage. Our results suggest that spectral absorption features at 1215 nm and 1732 nm are useful for detecting varying abundance levels of MPD in a complex natural environment, however other absorption features at 931 nm, 1045 nm and 2046 nm could not detect in situ MPD. The reflectance of some in situ MPD accumulations was statistically different from s les that only contained organic debris and sand between 1.56% and 7.94% surface cover however other s les with similar surface cover did not have reflectance that was statistically different from s les containing no MPD. Despite MPD being detectable against a background of sand and organic beach debris, a clear relationship between the surface cover of MPD and the strength of key absorption features could not be established. Additional research is needed to advance our understanding of the factors, such as type of MPD assemblage, that contribute to the bulk reflectance of MPD contaminated landscapes.
Publisher: Elsevier BV
Date: 06-2009
Publisher: Informa UK Limited
Date: 1987
Publisher: Informa UK Limited
Date: 03-2004
Publisher: Springer Science and Business Media LLC
Date: 2013
Publisher: American Society for Photogrammetry and Remote Sensing
Date: 12-2010
Publisher: Wiley
Date: 18-08-2021
DOI: 10.1111/GEB.13372
Abstract: Knowledge concerning species distribution is important for bio ersity conservation and environmental management. Fungi form a large and erse group of species and play a key role in nutrient cycling and carbon storage. However, our understanding of fungal ersity and distribution remains limited, particularly at large spatial scales. Here, we predicted the ersity and distribution of ectomycorrhizal and saprotrophic macrofungi at relatively fine spatial resolution at a continental scale and examined the importance of variables that affect the distribution of these two functional groups. Europe. 1990–2018. Macrofungi. From observations of 1,845 macrofungal species, we predicted the ersity and distribution of two functional groups of macrofungi at a resolution of 5 km across eight European countries based on 25 environmental variables using the MaxEnt model. We determined the importance of variables that affect the distribution of these two functional groups of macrofungi using the built‐in jackknife test in the model. Analysis of the modelling results showed that eastern Denmark and southern Sweden are bio ersity hotspots for both functional groups of macrofungal species. Tree species and human disturbance (i.e., the human footprint index) were found to be the two most important predictor variables explaining the distribution of ectomycorrhizal and saprotrophic macrofungi. Overall, our study demonstrates that tree species and human disturbance have played a more important role than climatic factors in determining the ersity and distribution of macrofungi at the continental scale. Our study suggests that fungal ersity and distribution might change considerably if the strongest predictors (i.e., tree species) were to be affected by climate change and/or human activity. Changes in fungal ersity might, in turn, influence other processes, because fungi are important in driving ecosystem processes, such as nutrient and carbon cycling.
Publisher: Elsevier BV
Date: 02-2014
Publisher: Oxford University Press (OUP)
Date: 17-02-2010
Publisher: Elsevier BV
Date: 04-2019
DOI: 10.1016/J.SCITOTENV.2018.12.223
Abstract: Bio ersity loss and variation in species responses to climate and land use change have been found across broad taxonomic groups. However, whether species from the same taxonomic group with distinct geographical ranges will respond differently is poorly understood. The aim of this study is to predict the potential impacts of future climate and land use change on the distribution of narrow- and wide-ranging Rhododendron species, and estimate their relative contribution in China. We applied the presence-only ecological niche model MaxEnt to predict the distribution of 10 narrow-ranging and 10 wide-ranging Rhododendron species for the year 2070, using three general circulation models and three scenarios of climate and land use change. We measured the predicted distribution change of each species using change ratio, distance and direction of core range shifts, and niche overlap using Schoener's D. We found that the distribution areas of six narrow-ranging species would decrease, of which one species would go extinct. The remaining four narrow-ranging species would experience range expansion. Distribution of all the wide-ranging Rhododendron species would decrease. All Rhododendrons will shift to the northwest. We conclude that Rhododendron species generally will be negatively affected by the climatic and land use change expected in 2070 from the three scenarios evaluated in this study, but some narrow-ranging species may be positively influenced. Narrow-ranging Rhododendron species are more vulnerable compared to wide-ranging Rhododendron species. This study demonstrated that the effects of climate and land use change on alpine and subalpine plant species is species-specific, thereby strengthening our understanding of the impacts of climate and land use change on plant distribution.
Publisher: JSTOR
Date: 10-2002
DOI: 10.2307/3802951
Publisher: Informa UK Limited
Date: 2012
Publisher: Elsevier BV
Date: 12-2015
Publisher: Elsevier BV
Date: 02-2011
Publisher: Informa UK Limited
Date: 2001
Publisher: American Society for Photogrammetry and Remote Sensing
Date: 08-2015
Abstract: Pulse density, the number of laser pulses that intercept a surface per unit area, is a key consideration when acquiring an Airborne Laser Scanning (ALS) dataset. This study compares area-based vegetation structure metrics derived from multi-return ALS simulated at six pulse densities (0.05 to 4 pl m -2 ) across a range of forest types: from savannah woodlands to dense rainforests. Results suggest that accurate measurement of structure metrics (canopy height, canopy cover, and vertical canopy structure) can be achieved with a pulse density of 0.5 pl m -2 across all forest types when compared to a dataset of 10 pl m -2 . For pulse densities .5 pl m -2 , two main sources of error lead to inaccuracies in estimation: the poor identification of the ground surface and sparse vegetation cover leading to under s ling of the canopy profile. This analysis provides useful information for land managers determining capture specifications for large-area ALS acquisitions.
Publisher: MDPI AG
Date: 27-07-2022
DOI: 10.3390/RS14153605
Abstract: In complex classification tasks, such as the classification of heterogeneous vegetation covers, the high similarity between classes can confuse the classification algorithm when assigning the correct class labels to unlabelled s les. To overcome this problem, this study aimed to develop a classification method by integrating graph-based semi-supervised learning (SSL) and an expert system (ES). The proposed method was applied to vegetation cover classification in a wetland in the Netherlands using Sentinel-2 and RapidEye imagery. Our method consisted of three main steps: object-based image analysis (OBIA), integration of SSL and an ES (SSLES), and finally, random forest classification. The generated image objects and the related features were used to construct the graph in SSL. Then, an independently developed and trained ES was used in the labelling stage of SSL to reduce the uncertainty of the process, before the final classification. Different spectral band combinations of Sentinel-2 were then considered to improve the vegetation classification. Our results show that integrating SSL and an ES can result in significantly higher classification accuracy (83.6%) compared to a supervised classifier (64.9%), SSL alone (71.8%), and ES alone (69.5%). Moreover, utilisation of all Sentinel-2 red-edge spectral band combinations yielded the highest classification accuracy (overall accuracy of 83.6% with SSLES) compared to the inclusion of other band combinations. The results of this study indicate that the utilisation of an ES in the labelling process of SSL improves the reliability of the process and provides robust performance for the classification of vegetation cover.
Publisher: Informa UK Limited
Date: 03-2000
Publisher: Informa UK Limited
Date: 1991
Publisher: Informa UK Limited
Date: 06-2006
Publisher: Informa UK Limited
Date: 23-11-2009
Publisher: Elsevier BV
Date: 03-2014
Publisher: Elsevier BV
Date: 12-2009
Publisher: Wiley
Date: 09-2000
Publisher: Elsevier BV
Date: 08-2007
Publisher: Informa UK Limited
Date: 26-11-2008
Publisher: Elsevier BV
Date: 10-2012
DOI: 10.1016/J.SCITOTENV.2012.08.025
Abstract: Leaf water content determines plant health, vitality, photosynthetic efficiency and is an important indicator of drought assessment. The retrieval of leaf water content from the visible to shortwave infrared spectra is well known. Here for the first time, we estimated leaf water content from the mid to thermal infrared (2.5-14.0 μm) spectra, based on continuous wavelet analysis. The dataset comprised 394 spectra from nine plant species, with different water contents achieved through progressive drying. To identify the spectral feature most sensitive to the variations in leaf water content, first the Directional Hemispherical Reflectance (DHR) spectra were transformed into a wavelet power scalogram, and then linear relations were established between the wavelet power scalogram and leaf water content. The six in idual wavelet features identified in the mid infrared yielded high correlations with leaf water content (R(2)=0.86 maximum, 0.83 minimum), as well as low RMSE (minimum 8.56%, maximum 9.27%). The combination of four wavelet features produced the most accurate model (R(2)=0.88, RMSE=8.00%). The models were consistent in terms of accuracy estimation for both calibration and validation datasets, indicating that leaf water content can be accurately retrieved from the mid to thermal infrared domain of the electromagnetic radiation.
Publisher: MDPI AG
Date: 23-08-2021
DOI: 10.3390/RS13163325
Abstract: The in situ leaf area index (LAI) measurement plays a vital role in calibrating and validating satellite LAI products. Digital hemispherical photography (DHP) is a widely used in situ forest LAI measurement method. There have been many software programs encompassing a variety of algorithms to estimate LAI from DHP. However, there is no conclusive study for an accuracy comparison among them, due to the difficulty in acquiring forest LAI reference values. In this study, we aim to use virtual (i.e., computer-simulated) broadleaf forests for the accuracy assessment of LAI algorithms in commonly used LAI software programs. Three commonly used DHP programs, including Can_Eye, CIMES, and Hemisfer, were selected since they provide estimates of both effective LAI and true LAI. In idual tree models with and without leaves were first reconstructed based on terrestrial LiDAR point clouds. Various stands were then created from these models. A ray-tracing technique was combined with the virtual forests to model synthetic DHP, for both leaf-on and leaf-off conditions. Afterward, three programs were applied to estimate PAI from leaf-on DHP and the woody area index (WAI) from leaf-off DHP. Finally, by subtracting WAI from PAI, true LAI estimates from 37 different algorithms were achieved for evaluation. The performance of these algorithms was compared with pre-defined LAI and PAI values in the virtual forests. The results demonstrated that without correcting for the vegetation clumping effect, Can_Eye, CIMES, and Hemisfer could estimate effective PAI and effective LAI consistent with each other (R2 0.8, RMSD 0.2). After correcting for the vegetation clumping effect, there was a large inconsistency. In general, Can_Eye more accurately estimated true LAI than CIMES and Hemisfer (with R2 = 0.88 0.72, 0.49 RMSE = 0.45 0.7, 0.94 nRMSE = 15.7% 24.21%, 32.81%). There was a systematic underestimation of PAI and LAI using Hemisfer. The most accurate algorithm for estimating LAI was identified as the P57 algorithm in Can_Eye which used the 57.5° gap fraction inversion combined with the finite-length averaging clumping correction. These results demonstrated the inconsistency of LAI estimates from DHP using different algorithms. It highlights the importance and provides a reference for standardizing the algorithm protocol for in situ forest LAI measurement using DHP.
Publisher: MDPI AG
Date: 11-10-2019
DOI: 10.3390/RS11202356
Abstract: In the face of rapid global change it is imperative to preserve geo ersity for the overall conservation of bio ersity. Geo ersity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to bio ersity on all scales of ecosystem organization. Despite the great importance of geo ersity, there is a lack of suitable monitoring methods. Compared to conventional in-situ techniques, remote sensing (RS) techniques provide a pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, as well as standardized monitoring of continuous geo ersity on the local to global scale. This paper gives an overview of the state-of-the-art approaches for monitoring soil characteristics and soil moisture with unmanned aerial vehicles (UAV) and air- and spaceborne remote sensing techniques. Initially, the definitions for geo ersity along with its five essential characteristics are provided, with an explanation for the latter. Then, the approaches of spectral traits (ST) and spectral trait variations (STV) to record geo ersity using RS are defined. LiDAR (light detection and ranging), thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soil characteristics and soil moisture are also presented. Furthermore, the paper discusses current and future satellite-borne sensors and missions as well as existing data products. Due to the prospects and limitations of the characteristics of different RS sensors, only specific geotraits and geo ersity characteristics can be recorded. The paper provides an overview of those geotraits.
Publisher: Wiley
Date: 13-06-2016
DOI: 10.1111/JVS.12421
Publisher: University of Chicago Press
Date: 02-2002
DOI: 10.1086/324791
Abstract: Recent theoretical studies have shown that spatial redistribution of surface water may explain the occurrence of patterns of alternating vegetated and degraded patches in semiarid grasslands. These results implied, however, that spatial redistribution processes cannot explain the collapse of production on coarser scales observed in these systems. We present a spatially explicit vegetation model to investigate possible mechanisms explaining irreversible vegetation collapse on coarse spatial scales. The model results indicate that the dynamics of vegetation on coarse scales are determined by the interaction of two spatial feedback processes. Loss of plant cover in a certain area results in increased availability of water in remaining vegetated patches through run-on of surface water, promoting within-patch plant production. Hence, spatial redistribution of surface water creates negative feedback between reduced plant cover and increased plant growth in remaining vegetation. Reduced plant cover, however, results in focusing of herbivore grazing in the remaining vegetation. Hence, redistribution of herbivores creates positive feedback between reduced plant cover and increased losses due to grazing in remaining vegetated patches, leading to collapse of the entire vegetation. This may explain irreversible vegetation shifts in semiarid grasslands on coarse spatial scales.
Publisher: Elsevier BV
Date: 07-2014
Publisher: Elsevier BV
Date: 02-2018
Publisher: Public Library of Science (PLoS)
Date: 23-09-2014
Publisher: Wiley
Date: 08-2018
Abstract: Human activities exert stress on and create disturbances to ecosystems, decreasing their ersity, resilience and ultimately the health of ecosystems and their vegetation. In environments with rapid changes in vegetation health (VH), progress is needed when it comes to monitoring these changes and underlying causes. There are different approaches to monitoring VH such as in situ species approaches and the remote‐sensing approach. Here we provide an overview of in situ species approaches, that is, the biological, the phylogenetic, and the morphological species concept, as well as an overview of the remote‐sensing spectral trait/spectral trait variations concept to monitor the status of VH as well as processes of stress, disturbances, and resource limitations affecting VH. The approaches are compared with regard to their suitability for monitoring VH, and their advantages, disadvantages, potential, and requirements for being linked are discussed. No single approach is sufficient to monitor the complexity and multidimensionality of VH over the short to long term and on local to global scales. Rather, every approach has its pros and cons, making it all the more necessary to link approaches. In this paper, we present a framework and list crucial requirements for coupling approaches and integrating additional monitoring elements to form a multisource vegetation health monitoring network (MUSO‐VH‐MN). When it comes to linking the different approaches, data, information, models or platforms in a MUSO‐VH‐MN, big data with its complexity and syntactic and semantic heterogeneity and the lack of standardized approaches and VH protocols pose the greatest challenge. Therefore, Data Science with the elements of (a) digitalization, (b) semantification, (c) ontologization, (d) standardization, (e) Open Science, as well as (f) open and easy analyzing tools for assessing VH are important requirements for monitoring, linking, analyzing, and forecasting complex and multidimensional changes in VH.
Publisher: Acta Herpetologica
Date: 2012
Publisher: Elsevier BV
Date: 07-2013
Publisher: Elsevier BV
Date: 03-2018
Publisher: Informa UK Limited
Date: 12-09-2015
Publisher: Elsevier BV
Date: 12-2017
Publisher: American Association for the Advancement of Science (AAAS)
Date: 29-11-2019
Abstract: Bastin et al . (Reports, 5 July 2019, p. 76) claim that 205 gigatonnes of carbon can be globally sequestered by restoring 0.9 billion hectares of forest and woodland canopy cover. Reinterpreting the data from Bastin et al ., we show that the global land area actually required to sequester human-emitted CO 2 is at least a factor of 3 higher, representing an unrealistically large area.
Publisher: Copernicus GmbH
Date: 20-06-2016
DOI: 10.5194/ISPRSARCHIVES-XLI-B7-99-2016
Abstract: Leaf area index (LAI) is an important essential bio ersity variable due to its role in many terrestrial ecosystem processes such as evapotranspiration, energy balance, and gas exchanges as well as plant growth potential. A novel approach presented here is the retrieval of LAI using thermal infrared (8–14 μm, TIR) measurements. Here, we evaluate LAI retrieval using TIR hyperspectral data. Canopy emissivity spectral measurements were recorded under controlled laboratory conditions using a MIDAC (M4401-F) illuminator Fourier Transform Infrared spectrometer for two plant species during which LAI was destructively measured. The accuracy of retrieval for LAI was then assessed using partial least square regression (PLSR) and narrow band index calculated in the form of normalized difference index from all possible combinations of wavebands. The obtained accuracy from the PLSR for LAI retrieval was relatively higher than narrow-band vegetation index (0.54 & R& sup& & /sup& & 0.74). The results demonstrated that LAI may successfully be estimated from hyperspectral thermal data. The study highlights the potential of hyperspectral thermal data for retrieval of vegetation biophysical variables at the canopy level for the first time.
Publisher: Informa UK Limited
Date: 11-2012
Publisher: Public Library of Science (PLoS)
Date: 25-09-2015
Publisher: Springer Science and Business Media LLC
Date: 08-04-2015
Publisher: Informa UK Limited
Date: 2002
Publisher: Informa UK Limited
Date: 1998
Publisher: Elsevier BV
Date: 03-2012
Publisher: Elsevier BV
Date: 05-2018
Publisher: Informa UK Limited
Date: 02-11-2012
Publisher: Informa UK Limited
Date: 16-02-2021
Publisher: Wiley
Date: 04-09-2013
Publisher: MDPI AG
Date: 23-03-2023
DOI: 10.3390/RS15071734
Abstract: The Australian ‘New South Wales Estuary health assessment and bio ersity monitoring program’ has set state-wide targets for estuary health. A selection of water bodies is being monitored by in situ chlorophyll a concentration and turbidity measurements, indicators for water quality. We investigate whether the current monitoring program can benefit from the use of remote sensing derived data, analyzing chlorophyll a and water clarity estimates by the C2RCC and ACOLITE products based on Sentinel-2 MSI imagery for three lakes along the New South Wales coast. The C2RCC and ACOLITE products were partly successful in predicting chlorophyll a concentration and water clarity. Estimates based on Sentinel-2 MSI imagery were in the range of in situ measurements. However, results varied across years and lakes, and a significant correlation could not be found in every case. It is likely that the physical differences between the systems, such as nutrient input, tannins, and suspended algae/sediment matrix, influence the output of the algorithm. This may preclude the application of a ‘one size fits all’ monitoring approach, given the importance of local ecological phenomena in both influencing remote sensing observations and the nature of appropriate targets. However, the design of a monitoring program that incorporates remote sensing provides a way forward.
Publisher: Elsevier BV
Date: 10-2013
Publisher: Elsevier BV
Date: 10-2015
Publisher: Copernicus GmbH
Date: 12-09-2017
DOI: 10.5194/ISPRS-ANNALS-IV-2-W4-35-2017
Abstract: Abstract. Airborne Laser Scanning (ALS) is a widespread method for forest mapping and management purposes. While common ALS techniques provide valuable information about the forest canopy and intermediate layers, the point density near the ground may be poor due to dense overstory conditions. The current study highlights a new method for detecting stems of single trees in 3D point clouds obtained from high density ALS with a density of 300 points/m2. Compared to standard ALS data, due to lower flight height (150–200 m) this elevated point density leads to more laser reflections from tree stems. In this work, we propose a three-tiered method which works on the point, segment and object levels. First, for each point we calculate the likelihood that it belongs to a tree stem, derived from the radiometric and geometric features of its neighboring points. In the next step, we construct short stem segments based on high-probability stem points, and classify the segments by considering the distribution of points around them as well as their spatial orientation, which encodes the prior knowledge that trees are mainly vertically aligned due to gravity. Finally, we apply hierarchical clustering on the positively classified segments to obtain point sets corresponding to single stems, and perform ℓ1-based orthogonal distance regression to robustly fit lines through each stem point set. The ℓ1-based method is less sensitive to outliers compared to the least square approaches. From the fitted lines, the planimetric tree positions can then be derived. Experiments were performed on two plots from the Hochficht forest in Oberösterreich region located in Austria.We marked a total of 196 reference stems in the point clouds of both plots by visual interpretation. The evaluation of the automatically detected stems showed a classification precision of 0.86 and 0.85, respectively for Plot 1 and 2, with recall values of 0.7 and 0.67.
Publisher: Oxford University Press (OUP)
Date: 15-10-2010
Publisher: Wiley
Date: 23-06-2016
DOI: 10.1111/DDI.12460
Publisher: Elsevier BV
Date: 10-2013
Publisher: Elsevier BV
Date: 10-2014
Publisher: Elsevier BV
Date: 04-2006
Publisher: Elsevier BV
Date: 10-2005
Publisher: Springer Science and Business Media LLC
Date: 19-07-2021
Publisher: Elsevier BV
Date: 11-2015
Publisher: Wiley
Date: 08-04-2009
Publisher: Elsevier BV
Date: 06-2015
Publisher: Springer Science and Business Media LLC
Date: 24-08-2023
Publisher: Elsevier BV
Date: 12-2015
Publisher: Wiley
Date: 30-08-2013
Publisher: Wiley
Date: 02-08-2018
DOI: 10.1111/BRV.12359
Abstract: Much bio ersity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing bio ersity status and trends. The concept of Essential Bio ersity Variables (EBVs) was introduced to structure bio ersity monitoring globally, and to harmonize and standardize bio ersity data from disparate sources to capture a minimum set of critical variables required to study, report and manage bio ersity change. Here, we assess the challenges of a 'Big Data' approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence-only or presence-absence data are s led repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi-source data sets across space, time, taxa and different s ling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying s ling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or s ling methods, applying statistical tools and models for spatial inter- or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Bio ersity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete ex les from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi-source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA-based techniques and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows and the production process/cycle as well as approaching technical interoperability among research infrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturing consistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free from restrictions on use, modification and sharing. Addressing these challenges is critical for bio ersity research and for assessing progress towards conservation policy targets and sustainable development goals.
Publisher: Faculty of Engineering, Chulalongkorn University
Date: 14-01-2011
Publisher: Elsevier BV
Date: 11-2014
Publisher: SPIE
Date: 10-10-2005
DOI: 10.1117/12.651781
Publisher: Elsevier BV
Date: 02-2018
Publisher: SPIE
Date: 31-08-2006
DOI: 10.1117/12.681640
Publisher: Wiley
Date: 25-03-2016
DOI: 10.1002/RSE2.15
Publisher: Informa UK Limited
Date: 03-2011
Publisher: Springer Science and Business Media LLC
Date: 2001
Publisher: Elsevier BV
Date: 02-2018
Publisher: Elsevier BV
Date: 10-2012
Publisher: MDPI AG
Date: 09-05-2022
DOI: 10.3390/RS14092279
Abstract: Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorpho ersity with RS, namely: geomorphic genesis ersity, geomorphic trait ersity, geomorphic structural ersity, geomorphic taxonomic ersity, and geomorphic functional ersity. In this respect, geomorphic trait ersity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous ex les from various RS technologies. Methods for classifying the five characteristics of geomorpho ersity using RS, as well as the constraints of monitoring the ersity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorpho ersity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorpho ersity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorpho ersity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorpho ersity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorpho ersity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed.
Publisher: Wiley
Date: 07-06-2011
Publisher: Wiley
Date: 18-12-2016
Abstract: The vertical arrangement of forest canopies is a key descriptor of canopy structure, a driver of ecosystem function and indicative of forest successional stage. Yet techniques to attribute for canopy vertical structure across large and potentially heterogeneously forested areas remain elusive. This study introduces a new technique to estimate the Number of Strata ( NoS ) that comprise a canopy profile, using discrete‐return Airborne Laser Scanning (ALS) data. Vertically resolved gap probability ( P gap ) aggregated over a plot is generalized with a nonparametric cubic spline regression ( P s ). Subsequently a count of the positive zero‐crossings of second derivative of 1 − P s is used to estimate NoS . Comparison with inventory derived estimates at 24 plots across three erse study areas shows a good agreement between the two techniques (RMSE = 0·41 strata). Furthermore, this is achieved without altering model parameters, indicating the transferability of the technique across erse forest types. NoS values ranged from 0 to 4 at a further 239 plots, emphasizing the need for a method to quantify canopy vertical structure across forested landscapes. Comparison of NoS with other commonly derived ALS descriptors of canopy structure (canopy height, canopy cover and return height coefficient of determination) returned only a moderate correlation ( r 2 0·4). It is proposed the presented method provides a primary descriptor of canopy structure to complement canopy height and cover, as well as a candidate Ecological Bio ersity Variable for characterizing habitat structure.
Publisher: Informa UK Limited
Date: 11-07-2019
Publisher: National Inquiry Services Center (NISC)
Date: 22-03-2017
Publisher: Elsevier BV
Date: 04-2017
Publisher: Wiley
Date: 02-2003
Publisher: Elsevier BV
Date: 04-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Elsevier BV
Date: 02-2009
Publisher: Wiley
Date: 03-01-2011
Publisher: Springer Science and Business Media LLC
Date: 29-01-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2015
Publisher: MDPI AG
Date: 10-2023
DOI: 10.3390/RS15194797
Publisher: Elsevier BV
Date: 11-2011
Publisher: Elsevier BV
Date: 03-2016
Publisher: Elsevier BV
Date: 12-2019
Publisher: Elsevier BV
Date: 12-2016
Publisher: Wiley
Date: 24-04-2012
Publisher: Springer Science and Business Media LLC
Date: 24-06-2017
Publisher: Wiley
Date: 03-08-2012
Publisher: American Society for Photogrammetry and Remote Sensing
Date: 12-2010
Publisher: MDPI AG
Date: 28-08-2018
DOI: 10.3390/RS10091368
Abstract: An integral part of any remotely sensed fire detection and attribution method is an estimation of the target pixel’s background temperature. This temperature cannot be measured directly independent of fire radiation, so indirect methods must be used to create an estimate of this background value. The most commonly used method of background temperature estimation is through derivation from the surrounding obscuration-free pixels available in the same image, in a contextual estimation process. This method of contextual estimation performs well in cloud-free conditions and in areas with homogeneous landscape characteristics, but increasingly complex sets of rules are required when contextual coverage is not optimal. The effects of alterations to the search radius and s le size on the accuracy of contextually derived brightness temperature are heretofore unexplored. This study makes use of imagery from the AHI-8 geostationary satellite to examine contextual estimators for deriving background temperature, at a range of contextual window sizes and percentages of valid contextual information. Results show that while contextual estimation provides accurate temperatures for pixels with no contextual obscuration, significant deterioration of results occurs when even a small portion of the target pixel’s surroundings are obscured. To maintain the temperature estimation accuracy, the use of no less than 65% of a target pixel’s total contextual coverage is recommended. The study also examines the use of expanding window sizes and their effect on temperature estimation. Results show that the accuracy of temperature estimation decreases significantly when expanding the examined window, with a 50% increase in temperature variability when using a larger window size than 5 × 5 pixels, whilst generally providing limited gains in the total number of temperature estimates (between 0.4%–4.4% of all pixels examined). The work also presents a number of case study regions taken from the AHI-8 disk in more depth, and examines the causes of excess temperature variation over a range of topographic and land cover conditions.
Publisher: Elsevier BV
Date: 03-2002
Publisher: Wiley
Date: 09-12-2015
DOI: 10.1111/GEB.12405
Publisher: Wiley
Date: 28-03-2011
Publisher: Informa UK Limited
Date: 2010
Publisher: MDPI AG
Date: 19-08-2019
DOI: 10.3390/RS11161936
Abstract: Leaf dry matter content (LDMC), the ratio of leaf dry mass to its fresh mass, is a key plant trait, which is an indicator for many critical aspects of plant growth and survival. Accurate and fast detection of the spatiotemporal dynamics of LDMC would help understanding plants’ carbon assimilation and relative growth rate, and may then be used as an input for vegetation process models to monitor ecosystems. Satellite remote sensing is an effective tool for predicting such plant traits non-destructively. However, studies on the applicability of remote sensing for LDMC retrieval are scarce. Only a few studies have looked into the practicality of using remotely sensed data for the prediction of LDMC in a forest ecosystem. In this study, we assessed the performance of partial least squares regression (PLSR) plus 11 widely used vegetation indices (VIs), calculated based on different combinations of Sentinel-2 bands, in predicting LDMC in a coastal wetland. The accuracy of the selected methods was validated using LDMC, destructively measured in 50 randomly distributed s le plots at the study site in Schiermonnikoog, the Netherlands. The PLSR applied to canopy reflectance of Sentinel-2 bands resulted in accurate prediction of LDMC (coefficient of determination (R2) = 0.71, RMSE = 0.033). PLSR applied to the studied VIs provided an R2 of 0.70 and RMSE of 0.033. Four vegetation indices (enhanced vegetation index(EVI), specific leaf area vegetation index (SLAVI), simple ratio vegetation index (SRVI), and visible atmospherically resistant index (VARI)) computed using band 3 (green) and band 11 of the Sentinel-2 performed equally well and achieved a good measure of accuracy (R2 = 0.67, RMSE = 0.034). Our findings demonstrate the feasibility of using Sentinel-2 surface reflectance data to map LDMC in a coastal wetland.
Publisher: Elsevier BV
Date: 11-2019
Publisher: MDPI AG
Date: 06-2020
DOI: 10.3390/RS12111788
Abstract: Accurate measurement of canopy chlorophyll content (CCC) is essential for the understanding of terrestrial ecosystem dynamics through monitoring and evaluating properties such as carbon and water flux, productivity, light use efficiency as well as nutritional and environmental stresses. Information on the amount and distribution of CCC helps to assess and report bio ersity indicators related to ecosystem processes and functional aspects. Therefore, measuring CCC continuously and globally from earth observation data is critical to monitor the status of the biosphere. However, generic and robust methods for regional and global mapping of CCC are not well defined. This study aimed at examining the spatiotemporal consistency and scalability of selected methods for CCC mapping across biomes. Four methods (i.e., radiative transfer models (RTMs) inversion using a look-up table (LUT), the biophysical processor approach integrated into the Sentinel application platform (SNAP toolbox), simple ratio vegetation index (SRVI), and partial least square regression (PLSR)) were evaluated. Similarities and differences among CCC products generated by applying the four methods on actual Sentinel-2 data in four biomes (temperate forest, tropical forest, wetland, and Arctic tundra) were examined by computing statistical measures and spatiotemporal consistency pairwise comparisons. Pairwise comparison of CCC predictions by the selected methods demonstrated strong agreement. The highest correlation (R2 = 0.93, RMSE = 0.4371 g/m2) was obtained between CCC predictions of PROSAIL inversion by LUT and SNAP toolbox approach in a wetland when a single Sentinel-2 image was used. However, when time-series data were used, it was PROSAIL inversion against SRVI (R2 = 0.88, RMSE = 0.19) that showed greatest similarity to the single date predictions (R2 = 0.83, RMSE = 0.17 g/m2) in this biome. Generally, the CCC products obtained using the SNAP toolbox approach resulted in a systematic over/under-estimation of CCC. RTMs inversion by LUT (INFORM and PROSAIL) resulted in a non-biased, spatiotemporally consistent prediction of CCC with a range closer to expectations. Therefore, the RTM inversion using LUT approaches particularly, INFORM for ‘forest’ and PROSAIL for ‘short vegetation’ ecosystems, are recommended for CCC mapping from Sentinel-2 data for worldwide mapping of CCC. Additional validation of the two RTMs with field data of CCC across biomes is required in the future.
Publisher: Informa UK Limited
Date: 12-2006
Publisher: Informa UK Limited
Date: 12-2004
Publisher: Springer Science and Business Media LLC
Date: 22-11-2012
Publisher: American Society for Photogrammetry and Remote Sensing
Date: 06-2004
Publisher: Elsevier BV
Date: 02-2010
Publisher: Elsevier BV
Date: 02-2010
DOI: 10.1016/J.SCITOTENV.2009.10.062
Abstract: Simulating soil erosion variation with a temporal land use database reveals long-term fluctuations in landscape patterns, as well as priority needs for soil erosion conservation. The application of a multi-year land use database in support of a Soil Water Assessment Tool (SWAT) led to an accurate assessment, from 1977 to 2006, of erosion in the upper watershed of the Yellow River. At same time, the impacts of land use and landscape service features on soil erosion load were assessed. A series of supervised land use classifications of Landsat images characterized variations in land use and landscape patterns over three decades. The SWAT database was constructed with soil properties, climate and elevation data. Using water flow and sand density data as parameters, regional soil erosion load was simulated. A numerical statistical model was used to relate soil erosion to land use and landscape. The results indicated that decadal decrease of grassland areas did not pose a significant threat to soil erosion, while the continual increase of bare land, water area and farmland increased soil erosion. Regional landscape variation also had a strong relationship with erosion. Patch level landscape analyses demonstrated that larger water area led to more soil erosion. The patch correlation indicated that contagious grassland patches reduced soil erosion yield. The increased grassland patches led to more patch edges, in turn increasing the sediment transportation from the patch edges. The findings increase understanding of the temporal variation in soil erosion processes, which is the basis for preventing local pollution.
Publisher: MDPI AG
Date: 16-02-2017
DOI: 10.3390/RS9020163
Publisher: Public Library of Science (PLoS)
Date: 31-12-2014
Publisher: Elsevier BV
Date: 08-2013
Publisher: Copernicus GmbH
Date: 29-04-2015
DOI: 10.5194/ISPRSARCHIVES-XL-7-W3-277-2015
Abstract: Abstract. Quantification of vegetation properties plays an indispensable role in assessments of ecosystem function with leaf dry mater content (LDMC) and specific leaf area (SLA) being two important vegetation properties. Methods for fast, reliable and accurate measurement of LDMC and SLA are still lacking. In this study, the inversion of the PROSPECT radiative transfer model was used to estimate these two leaf parameters. Inversion of PROSPECT traditionally aims at quantifying its direct input parameters rather than identifying the parameters which can be derived indirectly from the input parameters. The technique has been tested here to indirectly model these parameters for the first time. Biophysical parameters such as leaf area, as well as fresh and dry weights of 137 leaf s les were measured during a field c aign in July 2013 in the mixed mountain forests of the Bavarian Forest National Park, Germany. Reflectance and transmittance of the leaf s les were measured using an ASD field spec III equipped with an integrating sphere. The PROSPECT model was inverted using a look-up table (LUT) approach for the NIR/SWIR region of the spectrum. The retrieved parameters were evaluated using their calculated R2 and normalized root mean square error (nRMSE) values with the field measurements. Among the retrieved variables the lowest nRMSE (0.0899) was observed for LDMC. For both traits higher R2 values (0.83 for LDMC and 0.89 for SLA) were discovered. The results indicate that the leaf traits studied can be quantified as accurately as the direct input parameters of PROSPECT. The strong correlation between the estimated traits and the NIR/SWIR region of the electromagnetic spectrum suggests that these leaf traits could be assessed at canopy and in the landscape by using hyperspectral remote sensing data.
Publisher: Informa UK Limited
Date: 1987
Publisher: Elsevier BV
Date: 02-2003
Publisher: ACM
Date: 13-09-2014
Publisher: Springer Science and Business Media LLC
Date: 09-2016
DOI: 10.1038/537307E
Publisher: Elsevier BV
Date: 02-2018
Publisher: PAGEPress Publications
Date: 11-2009
DOI: 10.4081/GH.2009.211
Abstract: The global spread of highly pathogenic avian influenza H5N1 in poultry, wild birds and humans, poses a significant pandemic threat and a serious public health risk. An efficient surveillance and disease control system relies on the understanding of the dispersion patterns and spreading mechanisms of the virus. A space-time cluster analysis of H5N1 outbreaks was used to identify spatio-temporal patterns at a global scale and over an extended period of time. Potential mechanisms explaining the spread of the H5N1 virus, and the role of wild birds, were analyzed. Between December 2003 and December 2006, three global epidemic phases of H5N1 influenza were identified. These H5N1 outbreaks showed a clear seasonal pattern, with a high density of outbreaks in winter and early spring (i.e., October to March). In phase I and II only the East Asia Australian flyway was affected. During phase III, the H5N1 viruses started to appear in four other flyways: the Central Asian flyway, the Black Sea Mediterranean flyway, the East Atlantic flyway and the East Africa West Asian flyway. Six disease cluster patterns along these flyways were found to be associated with the seasonal migration of wild birds. The spread of the H5N1 virus, as demonstrated by the space-time clusters, was associated with the patterns of migration of wild birds. Wild birds may therefore play an important role in the spread of H5N1 over long distances. Disease clusters were also detected at sites where wild birds are known to overwinter and at times when migratory birds were present. This leads to the suggestion that wild birds may also be involved in spreading the H5N1 virus over short distances.
Publisher: Elsevier BV
Date: 11-2013
DOI: 10.1016/J.MARPOLBUL.2013.09.033
Abstract: Conversion of mangroves to shrimp ponds creates fragmentation and eutrophication. Detection of the spatial variation of foliar nitrogen is essential for understanding the effect of eutrophication on mangroves. We aim (i) to estimate nitrogen variability across mangrove landscapes of the Mahakam delta using airborne hyperspectral remote sensing (HyMap) and (ii) to investigate links between the variation of foliar nitrogen mapped and local environmental variables. In this study, multivariate prediction models achieved a higher level of accuracy than narrow-band vegetation indices, making multivariate modeling the best choice for mapping. The variation of foliar nitrogen concentration in mangroves was significantly influenced by the local environment: (1) position of mangroves (seaward/landward), (2) distance to the shrimp ponds, and (3) predominant mangrove species. The findings suggest that anthropogenic disturbances, in this case shrimp ponds, influence nitrogen variation in mangroves. Mangroves closer to the shrimp ponds had higher foliar nitrogen concentrations.
Publisher: Elsevier BV
Date: 2018
DOI: 10.1016/J.SCITOTENV.2017.08.191
Abstract: Dryland bio ersity plays important roles in the fight against desertification and poverty, but is highly vulnerable to the impacts of environmental change. However, little research has been conducted on dual pressure from climate and land cover changes on bio ersity in arid and semi-arid environments. Concequntly, it is crutial to understand the potential impacts of future climate and land cover changes on dryland bio ersity. Here, using the Chinese Altai Mountains as a case study area, we predicted the future spatial distributions and local assemblages of nine threatened mammal species under projected climate and land cover change scenarios for the period 2010-2050. The results show that remarkable declines in mammal species richness as well as high rates of species turnover are seen to occur across large areas in the Chinese Altai Mountains, highlighting an urgent need for developing protection strategies for areas outside of current nature reserve network. The selected mammals are predicted to lose more than 50% of their current ranges on average, which is much higher than species' range gains (around 15%) under future climate and land cover changes. Most of the species are predicted to contract their ranges while moving eastwards and to higher altitudes, raising the need for establishing cross-border migration pathways for species. Furthermore, the inclusion of land cover changes had notable effects on projected range shifts of in idual species under climate changes, demonstrating that land cover changes should be incorporated into the assessment of future climate impacts to facilitate bio ersity conservation in arid and semi-arid environments.
Publisher: MDPI AG
Date: 10-06-2016
DOI: 10.3390/RS8060491
Publisher: Elsevier BV
Date: 09-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2017
Publisher: Informa UK Limited
Date: 30-10-2018
Publisher: Wiley
Date: 04-11-2010
DOI: 10.1111/J.1365-2656.2010.01764.X
Abstract: 1. Understanding and accurately predicting the spatial patterns of habitat use by organisms is important for ecological research, bio ersity conservation and ecosystem management. However, this understanding is complicated by the effects of spatial scale, because the scale of analysis affects the quantification of species-environment relationships. 2. We therefore assessed the influence of environmental context (i.e. the characteristics of the landscape surrounding a site), varied over a large range of scales (i.e. ambit radii around focal sites), on the analysis and prediction of habitat selection by African elephants in Kruger National Park, South Africa. 3. We focused on the spatial scaling of the elephants' response to their main resources, forage and water, and found that the quantification of habitat selection strongly depended on the scales at which environmental context was considered. Moreover, the inclusion of environmental context at characteristic scales (i.e. those at which habitat selectivity was maximized) increased the predictive capacity of habitat suitability models. 4. The elephants responded to their environment in a scale-dependent and perhaps hierarchical manner, with forage characteristics driving habitat selection at coarse spatial scales, and surface water at fine spatial scales. 5. Furthermore, the elephants exhibited sexual habitat segregation, mainly in relation to vegetation characteristics. Male elephants preferred areas with high tree cover and low herbaceous biomass, whereas this pattern was reversed for female elephants. 6. We show that the spatial distribution of elephants can be better understood and predicted when scale-dependent species-environment relationships are explicitly considered. This demonstrates the importance of considering the influence of spatial scale on the analysis of spatial patterning in ecological phenomena.
Publisher: Elsevier BV
Date: 12-2018
Publisher: Elsevier BV
Date: 03-2006
Publisher: Wiley
Date: 14-07-2015
DOI: 10.1002/RSE2.4
Publisher: Wiley
Date: 21-03-2011
DOI: 10.1111/J.1365-2656.2011.01819.X
Abstract: 1. Elucidation of the mechanism determining the spatial scale of patch selection by herbivores has been complicated by the way in which resource availability at a specific scale is measured and by vigilance behaviour of the herbivores themselves. To reduce these complications, we studied patch selection by an animal with negligible predation risk, the African elephant. 2. We introduce the concept of nutrient load as the product of patch size, number of patches and local patch nutrient concentration. Nutrient load provides a novel spatially explicit expression of the total available nutrients a herbivore can select from. 3. We hypothesized that elephant would select nutrient-rich patches, based on the nutrient load per 2500 m(2) down to the in idual plant scale, and that this selection will depend on the nitrogen and phosphorous contents of plants. 4. We predicted that elephant would cause more adverse impact to trees of lower value to them in order to reach plant parts with higher nutrient concentrations such as bark and root. However, elephant should maintain nutrient-rich trees by inducing coppicing of trees through re-utilization of leaves. 5. Elephant patch selection was measured in a homogenous tree species stand by manipulating the spatial distribution of soil nutrients in a large field experiment using NPK fertilizer. 6. Elephant were able to select nutrient-rich patches and utilized Colophospermum mopane trees inside these patches more than outside, at scales ranging from 2500 down to 100 m(2) . 7. Although both nitrogen and phosphorus contents of leaves from C. mopane trees were higher in fertilized and selected patches, patch choice correlated most strongly with nitrogen content. As predicted, stripping of leaves occurred more in nutrient-rich patches, while adverse impact such as uprooting of trees occurred more in nutrient-poor areas. 8. Our results emphasize the necessity of including scale-dependent selectivity in foraging studies and how elephant foraging behaviour can be used as indicators of change in the availability of nutrients.
Publisher: Elsevier BV
Date: 04-2003
Publisher: Informa UK Limited
Date: 20-09-2008
Publisher: Elsevier BV
Date: 12-2006
Publisher: Informa UK Limited
Date: 07-2010
Publisher: Elsevier BV
Date: 07-2015
Publisher: Springer International Publishing
Date: 25-11-2016
Publisher: Elsevier BV
Date: 10-2016
Publisher: Elsevier BV
Date: 2000
Publisher: Elsevier BV
Date: 2010
Publisher: Elsevier BV
Date: 08-2013
Publisher: Informa UK Limited
Date: 25-03-2008
Publisher: Springer International Publishing
Date: 25-11-2017
Publisher: Public Library of Science (PLoS)
Date: 29-05-2014
Publisher: Informa UK Limited
Date: 09-1997
Publisher: MDPI AG
Date: 19-09-2022
DOI: 10.3390/RS14184664
Abstract: A pixel-based algorithm for multi-temporal Landsat (TM/ETM+/OLI/OLI-2) imagery between 1990 and 2022 monitored mangrove dynamics and detected their changes in the three provinces (i.e., Thai Binh, Nam Dinh and Hai Phong), which are located on the Northern coast of Vietnam, through the Google Earth Engine (GEE) cloud computing platform. Results showed that the mangrove area in the study area decreased from 2960 ha in 1990 to 2408 ha in 1995 and then significantly increased to 4435 ha in 2000 but later declined to 3502 ha in 2005. The mangrove areas experienced an increase from 4706 ha in 2010 to 10,125 ha in 2020 and reached a highest peak of 10,630 ha in 2022. In 2022, Hai Phong province had the largest area of mangrove (3934 ha), followed by Nam Dinh (3501 ha) and Thai Binh (3195 ha) provinces. The overall accuracies for 2020 and 2022 were 94.94% and 91.98%, while the Kappa coefficients were 0.90 and 0.84, respectively. The mangrove restoration programs and policies by the Vietnamese government and local governments are the key drivers of this increase in mangroves in the three provinces from 1990 to 2022. The results also demonstrated that the combination of Landsat time series images, a pixel-based algorithm, and the GEE platform has a high potential for monitoring long-term change of mangrove forests during 32 years in the tropics. Moreover, the obtained mangrove forest maps at a 30-m spatial resolution can serve as a useful and up-to-date dataset for sustainable management and conservation of these mangrove forests in the Red River Delta, Vietnam.
Publisher: MDPI AG
Date: 10-05-2013
DOI: 10.3390/S130506054
Publisher: Elsevier BV
Date: 15-12-2010
DOI: 10.1016/J.SCITOTENV.2010.10.020
Abstract: Soil erosion is a significant concern when considering regional environmental protection, especially in the Yellow River Basin in China. This study evaluated the temporal-spatial interaction of land cover status with soil erosion characteristics in the Longliu Catchment of China, using the Soil and Water Assessment Tool (SWAT) model. SWAT is a physical hydrological model which uses the RUSLE equation as a sediment algorithm. Considering the spatial and temporal scale of the relationship between soil erosion and sediment yield, simulations were undertaken at monthly and annual temporal scales and basin and sub-basin spatial scales. The corresponding temporal and spatial Normalized Difference Vegetation Index (NDVI) information was summarized from MODIS data, which can integrate regional land cover and climatic features. The SWAT simulation revealed that the annual soil erosion and sediment yield showed similar spatial distribution patterns, but the monthly variation fluctuated significantly. The monthly basin soil erosion varied from almost no erosion load to 3.92 t/ha and the maximum monthly sediment yield was 47,540 tones. The inter-annual simulation focused on the spatial difference and relationship with the corresponding vegetation NDVI value for every sub-basin. It is concluded that, for this continental monsoon climate basin, the higher NDVI vegetation zones prevented sediment transport, but at the same time they also contributed considerable soil erosion. The monthly basin soil erosion and sediment yield both correlated with NDVI, and the determination coefficients of their exponential correlation model were 0.446 and 0.426, respectively. The relationships between soil erosion and sediment yield with vegetation NDVI indicated that the vegetation status has a significant impact on sediment formation and transport. The findings can be used to develop soil erosion conservation programs for the study area.
Publisher: Wiley
Date: 05-2010
Publisher: MDPI AG
Date: 17-11-2015
DOI: 10.3390/RS71115340
Publisher: MDPI AG
Date: 22-12-2014
DOI: 10.3390/RS61212789
Publisher: Informa UK Limited
Date: 11-2012
Publisher: Springer Science and Business Media LLC
Date: 03-09-2013
Publisher: Elsevier BV
Date: 06-1999
Publisher: Elsevier BV
Date: 02-2019
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 21-03-2023
Abstract: The origins of sex differences in cardiovascular diseases are not well understood. We examined the contribution of childhood risk factors to sex differences in adult carotid artery plaques and intima‐media thickness (carotid IMT). Children in the 1985 Australian Schools Health and Fitness Survey were followed up when they were aged 36 to 49 years (2014–19, n=1085–1281). Log binomial and linear regression examined sex differences in adult carotid plaques (n=1089) or carotid IMT (n=1283). Childhood sociodemographic, psychosocial, and biomedical risk factors that might contribute to sex differences in carotid IMT laques were examined using purposeful model building with additional adjustment for equivalent adult risk factors in sensitivity analyses. Women less often had carotid plaques (10%) than men (17%). The sex difference in the prevalence of plaques (relative risk [RR] unadjusted 0.59 [95% CI, 0.43 to 0.80]) was reduced by adjustment for childhood school achievement and systolic blood pressure (RR adjusted 0.65 [95% CI, 0.47 to 0.90]). Additional adjustment for adult education and systolic blood pressure further reduced sex difference (RR adjusted 0.72 [95% CI, 0.49 to 1.06]). Women (mean±SD 0.61±0.07) had thinner carotid IMT than men (mean±SD 0.66±0.09). The sex difference in carotid IMT (β unadjusted −0.051 [95% CI, −0.061 to −0.042]) reduced with adjustment for childhood waist circumference and systolic blood pressure (β adjusted −0.047 [95% CI, −0.057 to −0.037]) and further reduced with adjustment for adult waist circumference and systolic blood pressure (β adjusted −0.034 [95% CI, −0.048 to −0.019]). Some childhood factors contributed to adult sex differences in plaques and carotid IMT. Prevention strategies across the life course are important to reduce adult sex differences in cardiovascular diseases.
Publisher: Springer Science and Business Media LLC
Date: 02-2005
Start Date: 11-2020
End Date: 12-2024
Amount: $507,000.00
Funder: Australian Research Council
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