ORCID Profile
0000-0003-1889-9336
Current Organisations
University of Queensland
,
King Abdullah University of Science and Technology
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Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-4068
Abstract: & & Detailed information on rock formations and assessment of their geological structures, such as joints, faults, shears and bedding planes, are required for evaluation of rock integrity and stability. Our research demonstrates a comprehensive approach for producing high spatial resolution 3D information of rock formations from unmanned aerial vehicle (UAV) imagery for rock joint identification, and presents an innovative technique for using Terrestrial Laser Scanning (TLS) data to derive ground control points (GCPs) for geo-referencing of UAV imagery of vertical rock walls. UAV imagery was collected from a freestanding 90 m tall rock formation with a 3.5 km perimeter, via oblique rock fa& #231 ade scans and also at-nadir, covering a ground and rock facade area of approximately 0.32 and 0.25 km& sup& & /sup& , respectively. Seventy-two GCPs were distributed around the rock for geo-referencing of the UAV imagery. As GCPs could not be deployed on vertical rock walls, a TLS system was used for identification of 93 distinct natural features as pseudo-GCPs on the rock walls. Forty scans were collected and geo-referenced from triplets of GCPs placed on the ground near the TLS system for each scan. A Real-Time Kinematic (RTK) Global Navigation Satellite System (GNSS) survey was performed on the GCPs, using a base station and a rover. Continuously Operating Reference Stations (CORS) data were used to fix the position of the base station and improve the absolute geometric position to an average and lowest accuracy of 3.3 and 18.6 mm for 177 GCPs. A total of 44 fa& #231 ade scans, as well as 14 low and 5 high altitude nadir-viewing UAV flights, were undertaken with a Zenmuse X3 RGB camera mounted to a DJI Matrice 100 platform, resulting in a collection of nearly 17,000 photos. The five high altitude flights were designed to include a larger area around the rock to incorporate additional GCPs, while the low altitude flights were to increase the spatial resolution of the imaged rock. Flight planning was undertaken with the Universal Ground Control Station Client application. Fa& #231 ade scans were flown horizontally and parallel to the rock walls at a distance of 20-30 m and at heights between 10-80 m with sidelaps & % between horizontal flight lines and & % forward overlap along flight lines. Fa& #231 ade scans were collected with a 4& #176 viewing angle to ensure the base of the rock was included. The Agisoft MetaShape software was initially used to generate a sparse point cloud using all fa& #231 ade scans and nadir-viewing imagery. Geo-referencing of the UAV imagery was based on 136 GCPs, which produced an accuracy of 0.1558 m, with an addition 100 control points kept aside for independent evaluation, yielding an accuracy of 0.2018 m. Subsequent image processing was split into 14 evenly sized & #8220 chunks& #8221 to enable more efficient processing of a dense point cloud (4.33 billion points) and 3D model (mesh with 129.5 million faces and texture layer) for the entire area. The produced 3D model was found suitable for identification of rock joints larger than 1 m in length.& &
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-6548
Abstract: The widespread use of diameter at breast height (DBH) and tree height attributes as a non-destructive indirect estimation of tree parameters (e.g., above-ground biomass, volume, age, and carbon stock) demands efficient and accurate surveying methods. However, traditional surveys, which are primarily manual, are often time-consuming, inaccurate, inconsistent, and might suffer from observer-bias. This study applies an agile quadruped robot, Spot from Boston Dynamics, and a mounted LiDAR system for mapping and measuring tree height, diameter at breast height (DBH), and tree volume. This project uses the Spot Enhanced Autonomy Payload (EAP) navigation module as the source of LiDAR data. The use of this module has two main advantages. First, Spot EAP's VLP-16 sensor is a low-beam LiDAR that, as demonstrated in previous research, is capable of estimating tree structural parameters while consuming less time and data than robust systems such as Terrestrial Laser Scanning (TLS). Second, using an existing payload as the primary source of data without disabling its default function results in more efficient payload capacity utilization and, as a result, lower energy consumption, in addition to making room for additional payloads. The experiment was conducted for 41 trees (23 Erythrina variegata and 18 Ficus altissima) in a park on the c us of King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. TLS data were used to compute the height and volume reference data, while manual measurements were used to obtain DBH reference data. The robot-derived point cloud generation methodology was based on a multiway registration approach in which a total of 76 scans were acquired from 4 different locations using multiple poses of the robot to overcome the short field of view of the LiDAR sensor. As a result of processing the scans, a point cloud for each of the trees was obtained. The height estimations, which consist of a difference within Z coordinates, obtained a mean absolute error (MAE) and a mean percentage error (MPE) of 6.71 cm and 1.31% respectively. The DBH estimation based on circle-fitting algorithms obtained an MAE and an MPE of 2.55 and 12.99% respectively. The volume estimation obtained a coefficient of determination of 0.93. When compared to the most recent approaches available in the literature, the results for height and volume were satisfactory, yielding higher accuracy than other studies in some cases. The results for DBH estimation were also comparable to those in the literature. The main sources of error were tree occlusion and inclined trees, both of which are solvable by including more scanning locations and increasing the robustness of software estimation. Consequently, the acquisition system is not a barrier to future improvements. This work successfully introduced one of the first methods for using agile robots in high throughput field phenotyping. The use of agile robots addresses some of the major challenges for deploying ground-based robotics in high throughput field phenotyping, allowing for a higher assessment frequency without causing soil compaction and damage, as well as bringing unprecedented adaptation to difficult terrains.&
Publisher: Elsevier BV
Date: 11-2013
Publisher: American Society for Photogrammetry and Remote Sensing
Date: 02-2010
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-6547
Abstract: Monitoring the water-uptake rate (WUR) in olive orchards is a key parameter for improving irrigation efficiency and represents an indicator of tree health and yield. Commercial olive orchards extend over large areas and therefore, the use of in-situ sensors to monitor tree WUR, such as the installation of sap flow meters, which are costly and time-consuming to install, are not feasible. The aim of this study is to investigate the potential of C-band Synthetic Aperture Radar (SAR) data acquired by Sentinel-1 with 6-days revisit time to estimate the WUR in very high-density olive orchards in the hot and arid desert climate of Saudi Arabia. A random forest regression (RFR) model was used to calibrate the SAR-derived metrics against WUR measurements recorded by sap flow meters in six plots in 2019, 2020, and 2021. Later, SAR-derived metrics and the coincident WUR measurements were used for RFR optimization and validation. A SAR-derived metric to predict the WUR in a plot at a given Sentinel-1 acquisition date was the difference between the SAR backscattering at that image date and the average SAR backscattering in the second-half of January, when WUR was negligible (around 0.1 L.h-1). The optimized RFR approach provided an accurate estimate of WUR (R2 = 0.86, RMSE = 0.13 [L.h-1]). The optimized RFR was used to operationally map the WUR at the plot level between 2019 and 2021 with a revisit time of 6 days. Results showed that the average WUR over the mapped area co-varied with the average daily air temperature (R2 = 0.82) and inversely co-varied with the average daily air humidity (R2 = 0.58), both recorded by a weather station installed at the study site. These observations support the operational mapping results as they are consistent with the principle of soil-plant-atmosphere interactions, where the WUR generally increases with air temperature and decreases with air humidity. Future work should focus on the assimilation of SAR-derived WUR into water-use models to evaluate the added value of SAR-derived WUR for water resource management in olive orchards.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-6789
Abstract: Biomass estimation is important in many applications, such as carbon sequestration and precision agriculture. Developing a reliable method for biomass estimation from satellite, airborne and near-surface remote sensing sensors is an ongoing task due to the large uncertainty in current methods, which are often related to sensor limitations. Indeed, signals from optical sensors and synthetic aperture radar at high and medium frequencies suffer from saturation issues at high biomass levels. The Cosmic-Ray Neutron Sensor (CRNS) is a new non-invasive near-surface sensor used primarily to estimate soil water content (SWC), but it has also shown potential for retrieving other hydrological and environmental parameters such as biomass water equivalent and snow depth. The CRNS detects and counts the number of neutrons controlled by hydrogen atoms in the soil, air just above the ground, and vegetation. Biomass attenuates the intensity of cosmic ray neutrons, hence the ability to estimate biomass from a CRNS. Recent studies have used CRNS measurements to estimate biomass changes in crop areas and forest stands, while the use of CRNSs in orchards is limited. The objective of this study is to explore the potential of two CRNSs to estimate the biomass variation in irrigated cherry and olive tree orchards. The olive tree orchard is located in an arid region in northern Saudi Arabia (plantation density of 1667 trees/hectare) with an average tree height of 3 m and canopy diameter of 2 m. The cherry field is located in southern France (plantation density of 260 trees/hectare) with an average tree height of 3.5 m and canopy diameter of 5.5 m. Several soil moisture probes recording soil water content (SWC) at 15-min intervals at both sites were installed at different depths within the CRNS footprint. SWC measurements were used to assess the variations in the sensitivity of CRNS to soil moisture with increasing biomass. Tree parameters (height, canopy width, canopy length, leaf area index, and diameter at breast height) were measured in situ to estimate biomass using allometric equations. In addition, repetitive Light Detection and Ranging (LiDAR) scanning was performed over the cherry field to detect canopy volume changes over time. The results showed that the CRNS is sensitive to SWC variation, and this sensitivity is controlled by biomass evolution, indicating that CNRS measurements can also be used to estimate biomass. The sensitivity of CRNS neutron counts to SWC in the early season (before blooming) was twice as high as that during the mid- and late growing seasons (maximum leaf cover). The Cornish Pasdy model& #173 , which models the measured neutron counts as a function of SWC and biomass contribution, was calibrated and then inverted to estimate the biomass in the cherry and olive tree orchards.&
Publisher: MDPI AG
Date: 02-09-2014
DOI: 10.3390/RS6098261
Publisher: Elsevier BV
Date: 06-2014
Publisher: IEEE
Date: 07-2011
Publisher: MDPI AG
Date: 21-05-2020
DOI: 10.3390/RS12101647
Abstract: Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) systems are useful tools for deriving horticultural tree structure estimates. However, there are limited studies to guide growers and agronomists on different applications of the two technologies for horticultural tree crops, despite the importance of measuring tree structure for pruning practices, yield forecasting, tree condition assessment, irrigation and fertilization optimization. Here, we evaluated ALS data against near coincident TLS data in avocado, macadamia and mango orchards to demonstrate and assess their accuracies and potential application for mapping crown area, fractional cover, maximum crown height, and crown volume. ALS and TLS measurements were similar for crown area, fractional cover and maximum crown height (coefficient of determination (R2) ≥ 0.94, relative root mean square error (rRMSE) ≤ 4.47%). Due to the limited ability of ALS data to measure lower branches and within crown structure, crown volume estimates from ALS and TLS data were less correlated (R2 = 0.81, rRMSE = 42.66%) with the ALS data found to consistently underestimate crown volume. To illustrate the effects of different spatial resolution, capacity and coverage of ALS and TLS data, we also calculated leaf area, leaf area density and vertical leaf area profile from the TLS data, while canopy height, tree row dimensions and tree counts) at the orchard level were calculated from ALS data. Our results showed that ALS data have the ability to accurately measure horticultural crown structural parameters, which mainly rely on top of crown information, and measurements of hedgerow width, length and tree counts at the orchard scale is also achievable. While the use of TLS data to map crown structure can only cover a limited number of trees, the assessment of all crown strata is achievable, allowing measurements of crown volume, leaf area density and vertical leaf area profile to be derived for in idual trees. This study provides information for growers and horticultural industries on the capacities and achievable mapping accuracies of standard ALS data for calculating crown structural attributes of horticultural tree crops.
Publisher: Elsevier BV
Date: 11-2020
Publisher: MDPI AG
Date: 03-2022
DOI: 10.3390/RS14051205
Abstract: Capturing and identifying field-based agricultural activities, such as the start, duration and end of irrigation, together with crop sowing/germination, growing period and time of harvest, offer informative metrics that can assist in precision agricultural activities in addition to broader water and food security monitoring efforts. While optically based band-ratios, such as the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), have been used as descriptors for monitoring crop dynamics, data are not always available due to the influence of clouds and other atmospheric effects on optical sensors. Satellite-based microwave systems, such as the synthetic aperture radar (SAR), offer an all-weather advantage in monitoring soil and crop conditions. In this paper, we leverage the relative strengths of both optical- and microwave-based approaches by combining high resolution Sentinel-1 SAR and Sentinel-2 optical imagery to monitor irrigation events and crop dynamics in a dryland agricultural landscape. A microwave backscatter model was used to analyze the responses of simulated backscatters to soil moisture, NDVI and NDWI (both are correlated with vegetation water content and can be regarded as vegetation descriptors), allowing an empirical relationship between these two platforms. A correlation analysis was also performed using Sentinel-1 SAR and Sentinel-2 optical data over crops of maize, alfalfa, carrot and Rhodes grass in Al Kharj farm of Saudi Arabia to identify an appropriate SAR-based vegetation descriptor. The results illustrate the relationship between SAR and both NDVI and NDWI and demonstrated the relationship between the cross-polarization ratio (VH/VV) and the two optical indices. We explore the capacity of this multi-platform and multi-sensor approach to inform on the spatio-temporal dynamics of a range of agricultural activities, which can be used to facilitate field-based management decisions.
Publisher: MDPI AG
Date: 27-01-2014
DOI: 10.3390/RS6020984
Publisher: SPIE-Intl Soc Optical Eng
Date: 28-10-2013
Publisher: MDPI AG
Date: 17-04-2015
DOI: 10.3390/RS70404753
Publisher: Springer Science and Business Media LLC
Date: 23-06-2016
Publisher: Elsevier BV
Date: 04-2016
Publisher: Royal Society of Chemistry (RSC)
Date: 2018
DOI: 10.1039/C8TA04818E
Abstract: Zr 4+ could fulfill an equal-tendency doping in layer-tunnel hybrid composite. The Na 0.6 Mn 0.98 Zr 0.02 O 2 delivered a high capacity of 81 mA h g −1 at 2 A g −1 with 75% retention after 1000 cycles.
Publisher: American Chemical Society (ACS)
Date: 11-03-2019
Abstract: In-sewer stability of illicit drug biomarkers has been evaluated by several reactor-based studies, but less has been done in sewer pipes. Experiments conducted in sewer pipes have advantages over lab-scale reactors in providing more realistic biomarker stability due to the flow and biological dynamics. This study assessed the transportation and transformation of seven illicit drug biomarker compounds in a pilot-scale rising main and a gravity sewer pipe. Biomarkers presented erse stability patterns in the pilot sewers, based on which a drug transformation model was calibrated. This model was subsequently validated using transformation data sets from the literature, aiming to demonstrate the predictability of the pilot-based transformation coefficients under varying sewer conditions. Furthermore, transformation coefficients for five investigated biomarkers were generated from four studies, and their prediction capabilities under the pilot-sewer conditions were jointly assessed using performance statistics. The transformation model was successful in simulating the in-sewer stability for most illicit drugs. However, further study is required to delineate the sources and pathways for those compounds with potential formations to be simulated in the transformation model. Overall, the transformation model calibrated using the pilot-sewer data is a credible tool for the application of wastewater-based epidemiology.
Publisher: Copernicus GmbH
Date: 12-11-2020
DOI: 10.5194/HESS-24-5251-2020
Abstract: Abstract. The agricultural sector in Saudi Arabia has witnessed rapid growth in both production and area under cultivation over the last few decades. This has prompted some concern over the state and future availability of fossil groundwater resources, which have been used to drive this expansion. Large-scale studies using satellite gravimetric data show a declining trend over this region. However, water management agencies require much more detailed information on both the spatial distribution of agricultural fields and their varying levels of water exploitation through time than coarse gravimetric data can provide. Relying on self-reporting from farm operators or sporadic data collection c aigns to obtain needed information are not feasible options, nor do they allow for retrospective assessments. In this work, a water accounting framework that combines satellite data, meteorological output from weather prediction models, and a modified land surface hydrology model was developed to provide information on both irrigated crop water use and groundwater abstraction rates. Results from the local scale, comprising several thousand in idual center-pivot fields, were then used to quantify the regional-scale response. To do this, a semi-automated approach for the delineation of center-pivot fields using a multi-temporal statistical analysis of Landsat 8 data was developed. Next, actual crop evaporation rates were estimated using a two-source energy balance (TSEB) model driven by leaf area index, land surface temperature, and albedo, all of which were derived from Landsat 8. The Community Atmosphere Biosphere Land Exchange (CABLE) model was then adapted to use satellite-based vegetation and related surface variables and forced with a 3 km reanalysis dataset from the Weather Research and Forecasting (WRF) model. Groundwater abstraction rates were then inferred by estimating the irrigation supplied to each in idual center pivot, which was determined via an optimization approach that considered CABLE-based estimates of evaporation and TSEB-based satellite estimates. The framework was applied over two study regions in Saudi Arabia: a small-scale experimental facility of around 40 center pivots in Al Kharj that was used for an initial evaluation and a much larger agricultural region in Al Jawf province comprising more than 5000 in idual fields across an area exceeding 2500 km2. Total groundwater abstraction for the year 2015 in Al Jawf was estimated at approximately 5.5 billion cubic meters, far exceeding any recharge to the groundwater system and further highlighting the need for a comprehensive water management strategy. Overall, this novel data–model fusion approach facilitates the compilation of national-scale groundwater abstractions while also detailing field-scale information that allows both farmers and water management agencies to make informed water accounting decisions across multiple spatial and temporal scales.
Publisher: Elsevier BV
Date: 04-2015
DOI: 10.1016/J.TREE.2015.01.011
Abstract: Landscape structure and fragmentation have important effects on ecosystem services, with a common assumption being that fragmentation reduces service provision. This is based on fragmentation's expected effects on ecosystem service supply, but ignores how fragmentation influences the flow of services to people. Here we develop a new conceptual framework that explicitly considers the links between landscape fragmentation, the supply of services, and the flow of services to people. We argue that fragmentation's effects on ecosystem service flow can be positive or negative, and use our framework to construct testable hypotheses about the effects of fragmentation on final ecosystem service provision. Empirical efforts to apply and test this framework are critical to improving landscape management for multiple ecosystem services.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-13115
Abstract: Land surface temperature (LST) is crucial information that helps to understand and assess the interactions between the surface and the atmosphere. LST is a key parameter used in various applications including studies of irrigation, water use, vegetation health, urban heat island effects, and building insulation. In addition to several satellites that provide periodic images of surface temperature, unmanned aerial vehicle (UAV) platforms have been adapted to obtain higher spatio-temporal resolution thermal infrared (TIR) data. In fact, numerous research studies have investigated the accuracy and the processing method of UAV-based TIR images given its complexity and sensitivity to ambient conditions. However, the surface temperature is characterized by continuous and rapid variation over time, which is difficult to take into consideration in the processing of UAV-based orthomosaics. Here, we quantify this variation and discuss the environmental factors that lead to its lification. Thermal images were collected over a fixed hovering position during periods of 15-20 min, representing the common duration of UAV flights. At different times of the day, we flew at different altitudes over sand, water, grass and olive trees. Before the quantification of the surface temperature variation, the thermal infrared data were evaluated against field-based measurements using calibrated Apogee sensors. The evaluation showed a significant error in the UAV-based thermal infrared data linked to wind speed, which increased the bias from -1.02 to 3.86 & #176 C for 0.8 to 8.5 m/s winds, respectively. The assessment of the LST values collected over the different surfaces showed a temperature variation while hovering ranging between 1.4 and 5 & #176 C. In addition to wind effects, temperature variations while hovering were strongly linked to solar radiation, specifically radiation fluctuations occurring after sunrise and before sunset. This research provides insights into the LST variation expected for standard UAV flights of 15-20 min under different environmental conditions, which should be taken into account during UAV-based thermal infrared data processing and may help interpret and quantify inconsistencies in UAV-based orthomosaics of LST.
Publisher: IEEE
Date: 07-2013
Publisher: MDPI AG
Date: 18-08-2021
DOI: 10.3390/RS13163255
Abstract: The miniaturization of thermal infrared sensors suitable for integration with unmanned aerial vehicles (UAVs) has provided new opportunities to observe surface temperature at ultra-high spatial and temporal resolutions. In parallel, there has been a rapid development of software capable of streamlining the generation of orthomosaics. However, these approaches were developed to process optical and multi-spectral image data and were not designed to account for the often rapidly changing surface characteristics inherent in the collection and processing of thermal data. Although radiometric calibration and shutter correction of uncooled sensors have improved, the processing of thermal image data remains difficult due to (1) vignetting effects on the uncooled microbolometer focal plane array (2) inconsistencies between images relative to in-flight effects (wind-speed and direction) (3) unsuitable methods for thermal infrared orthomosaic generation. Here, we use thermal infrared UAV data collected with a FLIR-based TeAx camera over an agricultural field at different times of the day to assess inconsistencies in orthophotos and their impact on UAV-based thermal infrared orthomosaics. Depending on the wind direction and speed, we found a significant difference in UAV-based surface temperature (up to 2 °C) within overlapping areas of neighboring flight lines, with orthophotos collected with tail wind being systematically cooler than those with head wind. To address these issues, we introduce a new swath-based mosaicking approach, which was compared to three standard blending modes for orthomosaic generation. The swath-based mosaicking approach improves the ability to identify rapid changes of surface temperature during data acquisition, corrects for the influence of flight direction relative to the wind orientation, and provides uncertainty (pixel-based standard deviation) maps to accompany the orthomosaic of surface temperature. It also produced more accurate temperature retrievals than the other three standard orthomosaicking methods, with a root mean square error of 1.2 °C when assessed against in situ measurements. As importantly, our findings demonstrate that thermal infrared data require appropriate processing to reduce inconsistencies between observations, and thus, improve the accuracy and utility of orthomosaics.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-11293
Abstract: Driven in large part by policy initiatives designed to increase food security and realized via the construction of thousands of center-pivot irrigation fields since the 1970s, agriculture development in Saudi Arabia has undergone tremendous changes. However, little is known about the accurate number, acreage, and the changing dynamics of the fields. To bridge the knowledge gap between the political drivers and in-field response, we leveraged a hybrid machine learning framework by implementing Density-Based Spatial Clustering of Applications with Noise, Convolutional Neural Networks, and Spectral Clustering in a stepwise manner to delineate the center-pivot fields on a national scale in Saudi Arabia using historical Landsat imagery since 1990. The framework achieved producer's and user's accuracies larger than & .7% and 90.2%, respectively, when assessed against 28,000 manually delineated fields collected from different regions and periods. We explored multi-decadal dynamics of the agricultural development in Saudi Arabia by quantifying the number, acreage, and size distribution of center-pivot fields, along with the first and last detection year of the fields since 1990. The agricultural development in Saudi Arabia experienced four stages, including an initialization stage before 1990, a contraction stage from 1990 to 2010, an expansion stage from 2010 to 2016, and an ongoing contraction stage since 2016. Most of the fields predated 1990, representing over 8,800 km2 in that year, as a result of the policy initiatives to stimulate wheat production, promoting Saudi Arabia as the sixth largest exporter of wheat in the 1980s. A decreasing trend was observed from 1990 to 2010, with an average of 8,011 km2 of& fields detected during those two decades, which was a response to the policy initiative implemented to phase-out wheat after 1990. As a consequence of planting fodder crops to promote the dairy industry, the number and extent of fields increased rapidly from 2010 to 2015 and reached its peak in 2016, with 33,961 fields representing 9,400 km2. Agricultural extent has seen a continuous decline since 2016 to a level lower than 1990 values in 2020. This decline has been related to sustainable policy initiatives implemented for the Saudi Vision 2030. There is some evidence of an uptick in 2021 & #8212 also observed in an ongoing analysis for 2022 & #8212 which might be in response to global influences, such as the COVID-19 pandemic and the more recent conflict in the Ukraine, which has disrupted the international supply of agricultural products. The results provide a historical account of agricultural activity throughout the Kingdom and provide a basis for informed decision-making on sustainable irrigation and agricultural practices, helping to better protect and manage the nation's threatened groundwater resources, and providing insights into the resilience and elasticity of the Saudi Arabian food system to global perturbations.
Publisher: SPIE
Date: 05-10-2017
DOI: 10.1117/12.2278179
Publisher: Elsevier BV
Date: 10-2021
Publisher: SPIE-Intl Soc Optical Eng
Date: 21-08-2014
Publisher: Cold Spring Harbor Laboratory
Date: 17-08-2023
DOI: 10.1101/2023.08.15.553433
Abstract: Soil salinity is a major environmental stressor affecting agricultural productivity worldwide. Understanding plant responses to salt stress is crucial for developing resilient crop varieties. Wild relatives of cultivated crops, such as wild tomato, Solanum pimpinellifolium , can serve as a useful resource to further expand the resilience potential of the cultivated germplasm, S. lycopersicum . In this study, we employed high-throughput phenotyping in the greenhouse and field conditions to explore salt stress responses of a S. pimpinellifolium ersity panel. Our study revealed extensive phenotypic variations in response to salt stress, with traits such as transpiration rate, shoot mass, and ion accumulation showing significant correlations with plant performance. We found that while transpiration was a key determinant of plant performance in the greenhouse, shoot mass strongly correlated with yield under field conditions. Conversely, ion accumulation was the least influential factor under greenhouse conditions. Through a Genome Wide Association Study, we identified candidate genes not previously associated with salt stress, highlighting the power of high-throughput phenotyping in uncovering novel aspects of plant stress responses. This study contributes to our understanding of salt stress tolerance in S. pimpinellifolium and lays the groundwork for further investigations into the genetic basis of these traits, ultimately informing breeding efforts for salinity tolerance in tomato and other crops.
Publisher: Informa UK Limited
Date: 2006
DOI: 10.5589/M06-020
Publisher: Wiley
Date: 27-07-2016
Publisher: Copernicus GmbH
Date: 25-02-2020
DOI: 10.5194/HESS-2020-50
Abstract: Abstract. The agricultural sector in Saudi Arabia has witnessed rapid growth in both production and area under cultivation over the last few decades. This has prompted some concern over the state and future availability of fossil groundwater resources, which have been used to drive this expansion. Large-scale studies using satellite gravimetric data show a declining trend over this region. However, water management agencies require much more detailed information on both the spatial distribution of agricultural fields, and their varying levels of water exploitation through time, than coarse gravimetric data can provide. Relying on self-reporting from farm operators or sporadic data collection c aigns to obtain needed information are not feasible options, nor do they allow for retrospective assessments. In this work, a water accounting framework that combines satellite data, meteorological output from weather prediction models, and a modified land surface hydrology model, was developed to provide information on both irrigated crop-water use and groundwater abstraction rates. Results from the local-scale, comprising several thousand in idual center-pivot fields, were then used to quantify the regional-scale response. To do this, a semi-automated approach for the delineation of center-pivot fields using a multi-temporal statistical analysis of Landsat 8 data was developed. Next, actual crop evaporation rates were estimated using a two-source energy balance (TSEB) model driven by leaf area index, land surface temperature, and albedo inputs, all of which were derived from Landsat 8. The Community Atmosphere Biosphere Land Exchange (CABLE) model was then adapted to use satellite-based vegetation and related surface variables, and forced with a 3 km reanalysis dataset from the Weather Research and Forecasting (WRF) model. Groundwater abstraction rates were then inferred by estimating the irrigation supplied to each in idual center-pivot, which was determined via an optimization approach that considered CABLE-based estimates of evaporation and TSEB-based satellite estimates. The framework was applied over two study regions in Saudi Arabia: a small-scale experimental facility of around 40 center-pivots in Al Kharj that was used for an initial evaluation, and a much larger agricultural region in Al Jawf province comprising more than 5,000 in idual fields across an area exceeding 2,500 km2. Total groundwater abstraction for the year 2015 in Al Jawf were estimated at approximately 5.5 billion cubic meters, far exceeding any recharge to the groundwater system and further highlighting the need for a comprehensive water management strategy. Overall, this novel data-model fusion approach facilitates the compilation of national-scale groundwater abstractions, while also detailing field-scale information that allows both farmers and water management agencies to make informed water accounting decisions across multiple spatial and temporal scales.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-9489
Abstract: Hyperspectral (HS) images obtained from space are useful for monitoring different natural phenomena on regional to global scales. The Environmental Mapping and Analysis Program (EnMAP) is a satellite recently launched by Germany to monitor the environment and explore the capabilities of hyperspectral sensors in the 420 and 2450 nm range of the spectrum. However, the data captured by the EnMAP mission have a ground s ling distance (GSD) of 30 m. This limits the use of the data for some applications that require higher spatial resolution ( m). This study examines the potential for improving the resolution of hyperspectral data using high resolution multispectral (MS) data obtained by Cubesats. Specifically, this work uses the data captured by the PlanetScope constellation, which has more than 150 CubeSats in low Earth orbit, with a high spatial and temporal resolution. The approach adopted leverages (1) the spectral capability of the hyperspectral EnMAP sensor, with a bandwidth of 6.5 nm in the visible and near infrared (VNIR) range (420& #8211 nm) and 10 nm in the SWIR range (900& #8211 nm), and (2) the spatial capability of the multispectral PlanetScope data, with a GSD of 3 meters, to enable significant spatial improvements due to its high spatial resolution. The main components of this work include: (i) area of interest clipping (ii) data co-registration, (iii) HS-MS data fusion, and (iv) quality assessments using the Jointly Spectral and Spatial Quality Index (QNR). In this study, a 2 km x 2 km area of interest was selected in the Malaucene region of France, where six state-of-the-art HS-MS fusion methods were evaluated: (1) fast multi-band image fusion algorithm (FUSE), (2) coupled nonnegative matrix factorization (CNMF), (3) smoothing filtered-based intensity modulation (SFIMHS), (4) maximum a posteriori stochastic mixing model (MAPSMM), (5) Hyperspectral Superresolution (HySure), and (6) generalized laplacian pyramid hypersharpening (GLPHS). Quality assessments of the enhanced data showed that high spectral and spatial fidelity are maintained, with the best performing fusion method being FUSE with a QNR of 0.625 followed by the MAPSMM method with a QNR of 0.604. Overall, this study advocates the benefits associated with the fusion of hyperspectral and multispectral data to obtain enhanced EnMAP data at 3 m GSD.&
Publisher: Elsevier BV
Date: 11-2010
Publisher: Elsevier BV
Date: 2010
Publisher: Elsevier BV
Date: 05-2020
Publisher: American Society for Photogrammetry and Remote Sensing
Date: 09-2009
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-6776
Abstract: Integrating remote sensing technology into crop growth models is a viable approach for water resources management and agricultural sustainability assurance since it allows crop water requirements and yield within agricultural fields to be estimated. Saudi Arabia has severely limited renewable water resources and non-renewable groundwater reserves that are rapidly depleting. Unlike rain-fed agriculture, the majority of agricultural water demand in Saudi Arabia is pumped from deep aquifers (up to 1000 m) to irrigate center pivots. This situation entails continuous monitoring of agricultural water use to enhance agricultural water productivity (i.e., producing more crops per drop) and preserve the equilibrium among the water, food, and energy sectors. The main purpose of this study is to calibrate the AquaCrop-OSPy model (an open-source Python implementation of the FAO crop-water productivity model, known as AquaCrop) using field data and Sentinel-2 (S2) images for operational mapping of crop yield, water demand, and water productivity. Another objective is to spatiotemporally estimate the energy requirements and associated CO2 emissions related to groundwater pumping for irrigation. The study area is located in the north of Saudi Arabia. It is a commercial farm with an area of 30,000 hectares comprising more than 200 agricultural fields with center-pivot irrigation systems. The crops cultivated on the farm are wheat and tomato. Field data were collected over three consecutive growing seasons (2019-2020, 2020-2021, and 2021-2022) and include information on wells, pumps, irrigation technique, field management practices, soil parameters, crop parameters, daily meteorological data, actual crop yield, and water use. The AquaCrop-OSPy model was first calibrated and validated using the collected field data as well as S2 images over the three seasons. Subsequently, the fractional vegetation cover (FVC) derived from S2 images was assimilated into the AquaCrop-OSPy model by direct insertion in place of AquaCrop-OSPy's simulated canopy cover (CC). Later, the energy requirements and CO2 emissions associated with irrigation groundwater pumping were estimated using crop water demand information calculated with the calibrated AquaCrop-OSPy model along with pumps and wells data.& Coupling the S2-derived FVC and the AquaCrop-OSPy model improved AquaCrop-OSPy predictions of crop water demand, yield, and water productivity as S2 images provide spatialized FVC information every 6-days. This integration further permitted a robust quantification of the energy requirements and CO2 emissions associated with groundwater pumping for irrigation. These results, when applied to larger scales and multiple crops, can help develop a comprehensive understanding of the water-energy-agriculture nexus and indicate potential improvements in AquaCrop-OSPy estimates that could be achieved once remote sensing data are integrated.&
Publisher: MDPI AG
Date: 23-01-2023
DOI: 10.3390/RS15030679
Abstract: The determination of key phenological growth stages of banana plantations, such as flower emergence and plant establishment, is difficult due to the asynchronous growth habit of banana plants. Identifying phenological events assists growers in determining plant maturity, and harvest timing and guides the application of time-specific crop inputs. Currently, phenological monitoring requires repeated manual observations of in idual plants’ growth stages, which is highly laborious, time-inefficient, and requires the handling and integration of large field-based data sets. The ability of growers to accurately forecast yield is also compounded by the asynchronous growth of banana plants. Satellite remote sensing has proved effective in monitoring spatial and temporal crop phenology in many broadacre crops. However, for banana crops, very high spatial and temporal resolution imagery is required to enable in idual plant level monitoring. Unoccupied aerial vehicle (UAV)-based sensing technologies provide a cost-effective solution, with the potential to derive information on health, yield, and growth in a timely, consistent, and quantifiable manner. Our research explores the ability of UAV-derived data to track temporal phenological changes of in idual banana plants from follower establishment to harvest. In idual plant crowns were delineated using object-based image analysis, with calculations of canopy height and canopy area producing strong correlations against corresponding ground-based measures of these parameters (R2 of 0.77 and 0.69 respectively). A temporal profile of canopy reflectance and plant morphology for 15 selected banana plants were derived from UAV-captured multispectral data over 21 UAV c aigns. The temporal profile was validated against ground-based determinations of key phenological growth stages. Derived measures of minimum plant height provided the strongest correlations to plant establishment and harvest, whilst interpolated maxima of normalised difference vegetation index (NDVI) best indicated flower emergence. For pre-harvest yield forecasting, the Enhanced Vegetation Index 2 provided the strongest relationship (R2 = 0.77) from imagery captured near flower emergence. These findings demonstrate that UAV-based multitemporal crop monitoring of in idual banana plants can be used to determine key growing stages of banana plants and offer pre-harvest yield forecasts.
Publisher: Elsevier BV
Date: 07-2015
Publisher: Elsevier BV
Date: 10-2020
Publisher: American Chemical Society (ACS)
Date: 21-01-2022
Abstract: Wastewater-based epidemiology is a potential complementary technique for monitoring the use of performance- and image-enhancing drugs (PIEDs), such as anabolic steroids and selective androgen receptor modulators (SARMs), within the general population. Assessing in-sewer transformation and degradation is critical for understanding uncertainties associated with wastewater analysis. An electrospray ionization liquid chromatography mass spectrometry method for the quantification of 59 anabolic agents in wastewater influent was developed. Limits of detection and limits of quantification ranged from 0.004 to 1.56 μg/L and 0.01 to 4.75 μg/L, respectively. Method performance was acceptable for linearity (
Publisher: Elsevier BV
Date: 05-2018
DOI: 10.1016/J.SCITOTENV.2017.11.255
Abstract: Urban areas are sources of land use change and CO
Publisher: Copernicus GmbH
Date: 23-08-2017
DOI: 10.5194/ISPRS-ARCHIVES-XLII-2-W6-165-2017
Abstract: Abstract. The lychee tree is native to China and produce small fleshy fruit up to 5 cm in diameter. Lychee production in Australia is worth $20 million annually. Pruning of trees encourages new growth, has a positive effect on fruiting of lychee, makes fruit-picking easier, and may increase yield, as it increases light interception and tree crown surface area. The objective of this research was to assess changes in tree structure, i.e. tree crown circumference, width, height and Plant Projective Cover (PPC) using multi-spectral Remotely Piloted Aircraft System (RPAS) imagery collected before and after pruning of a lychee plantation. A secondary objective was to assess any variations in the results as a function of various flying heights (30, 50 and 70 m). Pre- and post-pruning results showed significant differences in all measured tree structural parameters, including an average decrease in: tree crown circumference of 1.94 m tree crown width of 0.57 m tree crown height of 0.62 m and PPC of 14.8 %. The different flying heights produced similar measurements of tree crown width and PPC, whereas tree crown circumference and height measurements decreased with increasing flying height. These results show that multi-spectral RPAS imagery can provide a suitable means of assessing pruning efforts undertaken by contractors based on changes in tree structure of lychee plantations and that it is important to collect imagery in a consistent manner, as varying flying heights may cause changes to tree structural measurements.
Publisher: Elsevier BV
Date: 10-2015
Publisher: MDPI AG
Date: 30-10-2022
DOI: 10.3390/RS14215467
Abstract: This research investigates the capability of field-based spectroscopy (350–2500 nm) for discriminating banana plants (Cavendish subgroup Williams) infested with spider mites from those unaffected. Spider mites are considered a major threat to agricultural production, as they occur on over 1000 plant species, including banana plant varieties. Plants were grown under a controlled glasshouse environment to remove any influence other than the imposed treatment (presence or absence of spider mites). The spectroradiometer measurements were undertaken with a leaf clip over three infestation events. From the resultant spectral data, various classification models were evaluated including partial least squares discriminant analysis (PLSDA), K-nearest neighbour, support vector machines and back propagation neural network. Wavelengths found to have a significant response to the presence of spider mites were extracted using competitive adaptive reweighted s ling (CARS), sub-window permutation analysis (SPA) and random frog (RF) and benchmarked using the classification models. CARS and SPA provided high detection success (86% prediction accuracy), with the wavelengths found to be significant corresponding with the red edge and near-infrared portions of the spectrum. As there is limited access to operational commercial hyperspectral imaging and additional complexity, a multispectral camera (Sequoia) was assessed for detecting spider mite impacts on banana plants. Simulated multispectral bands were able to provide a high level of detection accuracy (prediction accuracy of 82%) based on a PLSDA model, with the near-infrared band being most important, followed by the red edge, green and red bands. Multispectral vegetation indices were trialled using a simple threshold-based classification method using the green normalised difference vegetation index (GNDVI), which achieved 82% accuracy. This investigation determined that remote sensing approaches can provide an accurate method of detecting mite infestations, with multispectral sensors having the potential to provide a more commercially accessible means of detecting outbreaks.
Publisher: MDPI AG
Date: 16-04-2018
DOI: 10.20944/PREPRINTS201804.0198.V1
Abstract: Unmanned aerial vehicles (UAV) provide an unprecedented capacity to monitor the development and dynamics of tree growth and structure through time. It is generally thought that the pruning of tree crops encourages new growth, has a positive effect on fruiting, makes fruit-picking easier, and may increase yield, as it increases light interception and tree crown surface area. To establish the response of pruning in an orchard of lychee trees, an assessment of changes in tree structure, i.e. tree crown perimeter, width, height, area and Plant Projective Cover (PPC), was undertaken using multi-spectral UAV imagery collected before and after a pruning event. While tree crown perimeter, width and area could be derived directly from the delineated tree crowns, height was estimated from a produced canopy height model and PPC was most accurately predicted based on the NIR band. Pre- and post-pruning results showed significant differences in all measured tree structural parameters, including an average decrease in tree crown perimeter of 1.94 m, tree crown width of 0.57 m, tree crown height of 0.62 m, tree crown area of 3.5 m2, and PPC of 14.8%. In order to provide guidance on data collection protocols for orchard management, the impact of flying height variations was also examined, offering some insight into the influence of scale and the scalability of this UAV based approach for larger orchards. The different flying heights (i.e. 30, 50 and 70 m) produced similar measurements of tree crown width and PPC, while tree crown perimeter, area and height measurements decreased with increasing flying height. Overall, these results illustrate that routine collection of multi-spectral UAV imagery can provide a means of assessing pruning effects on changes in tree structure in commercial orchards, and highlight the importance of collecting imagery with consistent flight configurations, as varying flying heights may cause changes to tree structural measurements.
Publisher: MDPI AG
Date: 20-03-2020
DOI: 10.3390/RS12061001
Abstract: With the increasing role that unmanned aerial systems (UAS) are playing in data collection for environmental studies, two key challenges relate to harmonizing and providing standardized guidance for data collection, and also establishing protocols that are applicable across a broad range of environments and conditions. In this context, a network of scientists are cooperating within the framework of the Harmonious Project to develop and promote harmonized mapping strategies and disseminate operational guidance to ensure best practice for data collection and interpretation. The culmination of these efforts is summarized in the present manuscript. Through this synthesis study, we identify the many interdependencies of each step in the collection and processing chain, and outline approaches to formalize and ensure a successful workflow and product development. Given the number of environmental conditions, constraints, and variables that could possibly be explored from UAS platforms, it is impractical to provide protocols that can be applied universally under all scenarios. However, it is possible to collate and systematically order the fragmented knowledge on UAS collection and analysis to identify the best practices that can best ensure the streamlined and rigorous development of scientific products.
Publisher: Elsevier BV
Date: 03-2007
Publisher: Elsevier BV
Date: 09-2007
Publisher: Elsevier BV
Date: 02-2020
Publisher: Springer Science and Business Media LLC
Date: 09-2007
Publisher: Informa UK Limited
Date: 13-11-2008
Publisher: MDPI AG
Date: 29-09-2018
DOI: 10.20944/PREPRINTS201809.0584.V1
Abstract: UAS-based multi-spectral imagery is becoming increasingly popular for the improved monitoring and managing of various horticultural crops. However, for UAS data to be used as an industry standard for assessing tree structure and condition as well as production parameters, it is imperative that the appropriate data collection and pre-processing protocols are established to enable multi-temporal comparison. There are several UAS-based radiometric correction methods commonly used for precision agricultural purposes. However, their relative accuracies have not been assessed for data acquired in complex horticultural environments. This study assessed the variations in estimated surface reflectance values of different radiometric corrections applied to multi-spectral UAS imagery acquired in both avocado and banana orchards. We found that inaccurate calibration panel measurements, inaccurate signal-to-reflectance conversion, and high variation in geometry between illumination, surface, and sensor viewing produced significant radiometric variations in at-surface reflectance estimates. Potential solutions to address these limitations included appropriate panel deployment, site-specific sensor calibration, and appropriate BRDF correction. Future UAS based horticultural crop monitoring can benefit from the proposed solutions to radiometric corrections to ensure they are using comparable image-based maps of multi-temporal biophysical properties.
Publisher: Copernicus GmbH
Date: 04-06-2019
DOI: 10.5194/ISPRS-ARCHIVES-XLII-2-W13-407-2019
Abstract: Abstract. Biomass and yield are important variables used for assessing agricultural production. However, these variables are difficult to estimate for in idual plants at the farm scale and may be affected by abiotic stressors such as salinity. In this study, the wild tomato species, Solanum pimpinellifolium, was evaluated through field and UAV-based assessment of 600 control and 600 salt-treated plants. The aim of this research was to determine, if UAV-based imagery, collected one, two, four, six, seven and eight weeks before harvest could predict fresh shoot mass, tomato fruit numbers, and yield mass at harvest and if predictions varied for control and salt-treated plants. A Random Forest approach was used to model biomass and yield. The results showed that shape features such as plant area, border length, width and length had the highest importance in the random forest models. A week prior to harvest, the explained variance of fresh shoot mass, number of fruits and yield mass were 86.60%, 59.46% and 61.09%, respectively. The explained variance was reduced as a function of time to harvest. Separate models may be required for predicting yield of salt-stressed plants, whereas the prediction of yield for control plants was less affected if the model included salt-stressed plants. This research demonstrates that it is possible to predict biomass and yield of tomato plants up to four weeks prior to harvest, and potentially earlier in the absence of severe weather events.
Publisher: MDPI AG
Date: 06-2018
DOI: 10.3390/RS10060854
Publisher: MDPI AG
Date: 29-10-2019
DOI: 10.3390/S19214699
Abstract: The use of unmanned aerial vehicles (UAVs) for Earth and environmental sensing has increased significantly in recent years. This is particularly true for multi- and hyperspectral sensing, with a variety of both push-broom and snap-shot systems becoming available. However, information on their radiometric performance and stability over time is often lacking. The authors propose the use of a general protocol for sensor evaluation to characterize the data retrieval and radiometric performance of push-broom hyperspectral cameras, and illustrate the workflow with the Nano-Hyperspec (Headwall Photonics, Boston USA) sensor. The objectives of this analysis were to: (1) assess dark current and white reference consistency, both temporally and spatially (2) evaluate spectral fidelity and (3) determine the relationship between sensor-recorded radiance and spectroradiometer-derived reflectance. Both the laboratory-based dark current and white reference evaluations showed an insignificant increase over time ( %) across spatial pixels and spectral bands for .5% of pixel–waveband combinations. Using a mercury/argon (Hg/Ar) l , the hyperspectral wavelength bands exhibited a slight shift of 1-3 nm against 29 Hg/Ar wavelength emission lines. The relationship between the Nano-Hyperspec radiance values and spectroradiometer-derived reflectance was found to be highly linear for all spectral bands. The developed protocol for assessing UAV-based radiometric performance of hyperspectral push-broom sensors showed that the Nano-Hyperspec data were both time-stable and spectrally sound.
Publisher: American Chemical Society (ACS)
Date: 09-05-2019
Abstract: Ferric (Fe
Publisher: Copernicus GmbH
Date: 18-07-2012
DOI: 10.5194/ISPRSANNALS-I-4-101-2012
Abstract: Abstract. Subsidence, resulting from underground coal mining can alter the structure of overlying rock formations changing hydrological conditions and potentially effecting ecological communities found on the surface. Of particular concern are impacts to endangered and/or protected sw communities and sw species sensitive to changes in hydrologic conditions. This paper describes a monitoring approach that uses UAVs with modified digital cameras and object-based image analysis methods to characterise sw landcover on the Newnes plateau in the Blue Mountains near Sydney, Australia. The characterisation of sw spatial distribution is key to identifying long term changes in sw condition. In this paper we describe i) the characteristics of the UAV and the sensor, ii) the pre-processing of the remote sensing data with sub-decimeter pixel size to derive visible and near infrared multispectral imagery and a digital surface model (DSM), and iii) the application of object-based image analysis in eCognition using the multi-spectral data and DSM to map sw extent. Finally, we conclude with a discussion of the potential application of remote sensing data derived from UAVs to conduct environmental monitoring.
Publisher: Informa UK Limited
Date: 20-12-2016
Publisher: Elsevier BV
Date: 06-2022
Publisher: MDPI AG
Date: 26-09-2014
DOI: 10.3390/RS6109213
Publisher: American Chemical Society (ACS)
Date: 18-01-2018
Abstract: Systematic s ling and analysis of wastewater s les are increasingly adopted for estimating drug consumption in communities. An understanding of the in-sewer transportation and transformation of illicit drug biomarkers is critical for reducing the uncertainty of this evidence-based estimation method. In this study, biomarkers stability was investigated in lab-scale sewer reactors with typical sewer conditions. Kinetic models using the Bayesian statistics method were developed to simulate biomarkers transformation in reactors. Furthermore, a field-scale study was conducted in a real pressure sewer pipe with the systematical spiking and s ling of biomarkers and flow tracers. In-sewer degradation was observed for some spiked biomarkers over typical hydraulic retention time (i.e., a few hours). Results indicated that sewer biofilms prominently influenced biomarker stability with the retention time in wastewater. The fits between the measured and the simulated biomarkers transformation demonstrated that the lab-based model could be extended to estimate the changes of biomarkers in real sewers. Results also suggested that the variabilities of biotransformation and analytical accuracy are the two major contributors to the overall estimation uncertainty. Built upon many previous lab-scale studies, this study is one critical step forward in realizing wastewater-based epidemiology by extending biomarker stability investigations from laboratory reactors to real sewers.
Publisher: Elsevier BV
Date: 08-2020
Publisher: Elsevier BV
Date: 07-2022
DOI: 10.1016/J.WATRES.2022.118531
Abstract: Sub-daily tracking of dynamic features and events using high spatial resolution satellite imagery has only recently become possible, with advanced observational capabilities now available through tasking of satellite constellations. Here, we provide a first of its kind demonstration of using sub-daily 0.50 m resolution SkySat imagery to track coastal water flows, combining these data with object-based detection and a machine-learning approach to map the extent and concentration of two dye plumes. Coincident high-frequency unmanned aerial vehicle (UAV) imagery was also employed for quantitative modeling of dye concentration and evaluation of the sub-daily satellite-based dye tracking. Our results show that sub-daily SkySat imagery can track dye plume extent with low omission (8.73-16.05%) and commission errors (0.32-2.77%) and model dye concentration (coefficient of determination = 0.73 root mean square error = 28.68 ppb) with the assistance of high-frequency UAV data. The results also demonstrate the capabilities of using UAV imagery for scaling between field data and satellite imagery for tracking coastal water flow dynamics. This research has implications for monitoring of water flows and nutrient or pollution exchange, and it also demonstrates the capabilities of higher temporal resolution satellite data for delivering further insights into dynamic processes of coastal systems.
Publisher: MDPI AG
Date: 21-12-2018
DOI: 10.20944/PREPRINTS201812.0261.V1
Abstract: Tree condition, pruning and orchard management practices within intensive horticultural tree crop systems can be determined via measurements of tree structure. Multi-spectral imagery acquired from an unmanned aerial system (UAS) has been demonstrated as an accurate and efficient platform for measuring various tree structural attributes, but research in complex horticultural environments has been limited. This research established a methodology for accurately estimating tree crown height, extent, plant projective cover (PPC) and condition of avocado tree crops, from a UAS platform. In idual tree crowns were delineated using object-based image analysis. In comparison to field measured canopy heights, an image-derived canopy height model provided a coefficient of determination (R2) of 0.65 and relative root mean squared error of 6%. Tree crown length perpendicular to the hedgerow was accurately mapped. PPC was measured using spectral and textural image information and produced an R2 value of 0.62 against field data. A random forest classifier was applied to assign tree condition into four categories in accordance with industry standards, producing out-of-bag accuracies & %. Our results demonstrate the potential of UAS-based mapping for the provision of information to support the horticulture industry and facilitate orchard-based assessment and management.
Publisher: Elsevier BV
Date: 07-2020
Publisher: MDPI AG
Date: 28-05-2021
DOI: 10.3390/RS13112123
Abstract: Unoccupied aerial vehicles (UAVs) have become increasingly commonplace in aiding planning and management decisions in agricultural and horticultural crop production. The ability of UAV-based sensing technologies to provide high spatial ( m) and temporal (on-demand) resolution data facilitates monitoring of in idual plants over time and can provide essential information about health, yield, and growth in a timely and quantifiable manner. Such applications would be beneficial for cropped banana plants due to their distinctive growth characteristics. Limited studies have employed UAV data for mapping banana crops and to our knowledge only one other investigation features multi-temporal detection of banana crowns. The purpose of this study was to determine the suitability of multiple-date UAV-captured multi-spectral data for the automated detection of in idual plants using convolutional neural network (CNN), template matching (TM), and local maximum filter (LMF) methods in a geographic object-based image analysis (GEOBIA) software framework coupled with basic classification refinement. The results indicate that CNN returns the highest plant detection accuracies, with the developed rule set and model providing greater transferability between dates (F-score ranging between 0.93 and 0.85) than TM (0.86–0.74) and LMF (0.86–0.73) approaches. The findings provide a foundation for UAV-based in idual banana plant counting and crop monitoring, which may be used for precision agricultural applications to monitor health, estimate yield, and to inform on fertilizer, pesticide, and other input requirements for optimized farm management.
Publisher: MDPI AG
Date: 24-08-2021
Abstract: Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-1745
Abstract: Growing forests is an effective way of removing CO2 from the atmosphere. Forestation projects were started in China, Germany, and the Middle East. Saudi Arabia announced its ambitious & #8220 Saudi Green Initiative,& #8221 intending to plant ten billion trees. Given the insufficient rainfall to support the initiative, vegetated areas will require irrigation, effectively increasing evaporation. In addition, those areas have a lower albedo than bare land, absorbing more solar radiation. Enhancing precipitation due to the recycling of evaporated water is important as it reduces the amount of freshwater required for irrigation.In this study, we focus on the regional climate impact of irrigated forested or vegetated areas on temperature and precipitation over the Arabian Peninsula to quantify their effect on livability and evaluate the water recycling potential. First, we studied the climate effect of irrigated farming developing over vast areas in Saudi Arabia since the 1980s. The agricultural areas were mapped using available satellite-based observations from the Landsat platforms, which capture optical and thermal data every 16 days at a resolution of 30 m to 100 m. Second, we projected the climate impact of widespread forestation over the Arabian Peninsula.The analysis of the long-term precipitation changes caused by irrigated farming is hindered by the lack of in situ observations and the limitations of global-scale observation data sets. Most reanalysis products have contradictory evaporation trends and indicate an overall reduction in rainfall since the 1980s. The recycled precipitation cannot be estimated reliably because of reanalysis increments and background rainfall variability. Presumably, the local increase in rains occurs downstream of the irrigated areas rather than over them. Along with the analysis of observations, we conducted numerical experiments mimicking the effect of irrigated agricultural fields using a non-hydrostatic regional meteorological model (WRF), covering the whole Arabian Peninsula by a 9x9 km2 grid, with 3x3 km2 nesting over the irrigated areas. Irrigation water is accounted for by tagging moisture evaporated from agricultural regions. The amount of tagged water vapor falling as rain represents recycled precipitation. The simulated evaporation and local temperature response strongly depends on the level of irrigation. Large-scale subsidence suppresses the local deep convection over most parts of the Arabian Peninsula. Strong turbulence quickly mixes evaporated water vapor within a six km thick atmospheric boundary layer, preventing precipitation in shallow convection so that the fraction of recycled rainfall appears to be low.
Publisher: Informa UK Limited
Date: 06-2010
Publisher: American Society for Photogrammetry and Remote Sensing
Date: 2006
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-7009
Abstract: Mangroves are essential ecosystems composed of salt-tolerant plants that grow in tropical and subtropical intertidal zones, acting as a vital link between aquatic and terrestrial ecosystems. Interest in mangrove preservation and restoration has been increasing in recent years due to their important role in climate regulation by capturing and preserving carbon. Despite their importance, these ecosystems are under huge pressure due to human activities. It is estimated that these environments have lost up to half of the area occupied under pristine conditions. Leaf area index (LAI) is a well-known biophysical parameter related to plant health, as it provides information on the water, energy, and CO2 exchange between plants and the atmosphere. Unmanned aerial vehicles (UAVs) have emerged in recent years as a viable solution for ecosystem monitoring, as they allow for rapid and frequent data acquisition of a wide range of wavelengths. In this work, we evaluated the potential of multispectral images acquired by a UAV to estimate the LAI of a mangrove (Avicennia marina) forest located in the coastal area of the Red Sea in the Kingdom of Saudi Arabia. Multicollinearity assessment was performed to select significant variables suited for estimating LAI, including five multispectral bands, a canopy height model, and eight vegetation indices. Multicollinearity assessment was performed with three approaches: the Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF) for variable selection, and Hierarchical Cluster Analysis (HCA). The capability of significant variables to estimate LAI was assessed using the Generalized Linear Model (GLM), RF and Support Vector Machine (SVM). Results showed high estimation accuracy of LAI (R& #178 = 0.91 for GLM, R& #178 = 0.89 for RF and R& #178 = 0.90 for SVM). However, further analysis showed that it is possible to estimate LAI of the mangrove forest with reasonable accuracy (R& #178 = 0.87 for GLM, R& #178 = 0.78 for RF and R& #178 = 0.87 for SVM) using only two variables, the canopy height model and the GreenNDVI. The same variables were used to estimate LAI at another mangrove site and similar results were obtained (R& #178 = 0.74 for GLM, R& #178 = 0.73 for RF and R& #178 = 0.68 for SVM).&
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-7923
Abstract: Updated crop type information is essential for agricultural monitoring and irrigation management applications. However, despite their importance, crop-type datasets, especially recent in-season data are still unavailable in most of developing countries where food security is a major concern. The lack of information on crop types is due to the limitations of traditional field surveys, which are generally infeasible, expensive, and inconsistent over larger spatial scales. Although remote sensing and machine learning approaches can provide cost-effective solutions for automatic crop-type mapping, huge amounts of high-quality training data are required in order to develop such applications. Therefore, semi-supervised or unsupervised learning methods become a potential solution to overcome the issue of lacking labeled training data.& In this work, we explored semi-supervised and self-supervised techniques to map crop types in data-poor regions such as Saudi Arabia. The Self-supervised Transformer with Energy-based Graph Optimization (STEGO) method is a transformer-based segmentation technique that has the capability of both discovering and segmenting objects without the need for labeled data or human intervention. We evaluated the capability of STEGO to classify crop fields using Sentinel2 images. These images consisted of the derived maximum normalized difference vegetation index (NDVI), the standard deviation of NDVI, and the green chlorophyll vegetation index (GCVI).& The STEGO approach uses a novel contrastive loss that helps to distill pre-trained unsupervised visual features into semantic clusters, which is reported to outperform other unsupervised clustering methods. We set k=5, where k is the number of classes that reflects 4 crop types and a background class.& Preliminary results of STEGO applied to small agricultural regions in Aljouf which is located in the north of Saudi Arabia captured the difference between crops when analyzed visually. Furthermore, assigning a crop-type label to each cluster class can be a challenging task. As for now, a brute force approach is followed to find the best assignment, and that is the assignment that provides the best results. As well as referring to previous knowledge of major crops grown in the region of interest, for this region, the major crops were wheat, olives, and tomato, in addition to & #8220 other& #8221 and & #8220 background& #8221 classes, which make up the 5 classes. Further work includes quantifying the accuracy of the clustering performance using the mean intersection over union metric (mIoU) and examining the effects of regional and national upscaling on the performance.&
Publisher: Informa UK Limited
Date: 06-2008
Publisher: Author(s)
Date: 2016
DOI: 10.1063/1.4958480
Publisher: Wiley
Date: 23-04-2008
Publisher: Informa UK Limited
Date: 22-12-2017
Publisher: MDPI AG
Date: 10-06-2020
DOI: 10.3390/S20113316
Abstract: Thermal infrared cameras provide unique information on surface temperature that can benefit a range of environmental, industrial and agricultural applications. However, the use of uncooled thermal cameras for field and unmanned aerial vehicle (UAV) based data collection is often h ered by vignette effects, sensor drift, ambient temperature influences and measurement bias. Here, we develop and apply an ambient temperature-dependent radiometric calibration function that is evaluated against three thermal infrared sensors (Apogee SI-11(Apogee Electronics, Santa Monica, CA, USA), FLIR A655sc (FLIR Systems, Wilsonville, OR, USA), TeAx 640 (TeAx Technology, Wilnsdorf, Germany)). Upon calibration, all systems demonstrated significant improvement in measured surface temperatures when compared against a temperature modulated black body target. The laboratory calibration process used a series of calibrated resistance temperature detectors to measure the temperature of a black body at different ambient temperatures to derive calibration equations for the thermal data acquired by the three sensors. As a point-collecting device, the Apogee sensor was corrected for sensor bias and ambient temperature influences. For the 2D thermal cameras, each pixel was calibrated independently, with results showing that measurement bias and vignette effects were greatly reduced for the FLIR A655sc (from a root mean squared error (RMSE) of 6.219 to 0.815 degrees Celsius (℃)) and TeAx 640 (from an RMSE of 3.438 to 1.013 ℃) cameras. This relatively straightforward approach for the radiometric calibration of infrared thermal sensors can enable more accurate surface temperature retrievals to support field and UAV-based data collection efforts.
Publisher: MDPI AG
Date: 06-11-2018
DOI: 10.3390/RS10111750
Abstract: Vegetation metrics, such as leaf area (LA), leaf area density (LAD), and vertical leaf area profile, are essential measures of tree-scale biophysical processes associated with photosynthetic capacity, and canopy geometry. However, there are limited published investigations of their use for horticultural tree crops. This study evaluated the ability of light detection and ranging (LiDAR) for measuring LA, LAD, and vertical leaf area profile across two mango, macadamia and avocado trees using discrete return data from a RIEGL VZ-400 Terrestrial Laser Scanning (TLS) system. These data were collected multiple times for in idual trees to align with key growth stages, essential management practices, and following a severe storm. The first return of each laser pulse was extracted for each in idual tree and classified as foliage or wood based on TLS point cloud geometry. LAD at a side length of 25 cm voxels, LA at the canopy level and vertical leaf area profile were calculated to analyse tree crown changes. These changes included: (1) pre-pruning vs. post-pruning for mango trees (2) pre-pruning vs. post-pruning for macadamia trees (3) pre-storm vs. post-storm for macadamia trees and (4) tree leaf growth over a year for two young avocado trees. Decreases of 34.13 m2 and 8.34 m2 in LA of mango tree crowns occurred due to pruning. Pruning for the high vigour mango tree was mostly identified between 1.25 m and 3 m. Decreases of 38.03 m2 and 16.91 m2 in LA of a healthy and unhealthy macadamia tree occurred due to pruning. After flowering and spring flush of the same macadamia trees, storm effects caused a 9.65 m2 decrease in LA for the unhealthy tree, while an increase of 34.19 m2 occurred for the healthy tree. The tree height increased from 11.13 m to 11.66 m, and leaf loss was mainly observed between 1.5 m and 4.5 m for the unhealthy macadamia tree. Annual increases in LA of 82.59 m2 and 59.97 m2 were observed for two three-year-old avocado trees. Our results show that TLS is a useful tool to quantify changes in the LA, LAD, and vertical leaf area profiles of horticultural trees over time, which can be used as a general indicator of tree health, as well as assist growers with improved pruning, irrigation, and fertilisation application decisions.
Publisher: Elsevier BV
Date: 05-2013
Publisher: MDPI AG
Date: 26-01-2023
DOI: 10.3390/RS15030731
Abstract: Agricultural intensification has resulted in the depletion of groundwater resources in many regions of the world. A prime ex le is Saudi Arabia, which witnessed dramatic agricultural expansion since the 1970s. To explore the influence of policy interventions aimed to better manage water resources, accurate information on the changes in the number and acreage of center-pivot fields is required. To quantify these metrics, we apply a hybrid machine learning framework, consisting of Density-Based Spatial Clustering of Applications with Noise, Convolutional Neural Networks, and Spectral Clustering, to the annual maximum Normalized Differential Vegetation Index maps obtained from Landsat imagery collected between 1990 to 2021. When evaluated against more than 28,000 manually delineated fields, the approach demonstrated producer’s accuracies ranging from 83.7% to 94.8% and user’s accuracies ranging from 90.2% to 97.9%. The coefficient of determination (R2) between framework-delineated and manually delineated fields was higher than 0.97. Nationally, we found that most fields pre-dated 1990 (covering 8841 km2 in that year) and were primarily located within the central regions covering Hail, Qassim, Riyadh, and Wadi ad-Dawasir. A small decreasing trend in field acreage was observed for the period 1990–2010. However, by 2015, the acreage had increased to approximately 33,000 fields covering 9310 km2. While a maximum extent was achieved in 2016, recent decreases have seen levels return to pre-1990 levels. The gradual decrease between 1990 to 2010 was related to policy initiatives designed to phase-out wheat, while increases between 2010 to 2015 were linked to fodder crop expansion. There is evidence of an agricultural uptick starting in 2021, which is likely in response to global influences such as the COVID-19 pandemic or the conflict in Ukraine. Overall, this work offers the first detailed assessment of long-term agricultural development in Saudi Arabia, and provides important insights related to production metrics such as crop types, crop water consumption, and crop phenology and the overarching impacts of agricultural policy interventions.
Publisher: MDPI AG
Date: 30-05-2011
DOI: 10.3390/RS3061139
Publisher: Elsevier BV
Date: 07-2010
Publisher: MDPI AG
Date: 30-01-2019
DOI: 10.3390/RS11030269
Abstract: Tree condition, pruning and orchard management practices within intensive horticultural tree crop systems can be determined via measurements of tree structure. Multi-spectral imagery acquired from an unmanned aerial system (UAS) has been demonstrated as an accurate and efficient platform for measuring various tree structural attributes, but research in complex horticultural environments has been limited. This research established a methodology for accurately estimating tree crown height, extent, plant projective cover (PPC) and condition of avocado tree crops, from a UAS platform. In idual tree crowns were delineated using object-based image analysis. In comparison to field measured canopy heights, an image-derived canopy height model provided a coefficient of determination (R2) of 0.65 and relative root mean squared error of 6%. Tree crown length perpendicular to the hedgerow was accurately mapped. PPC was measured using spectral and textural image information and produced an R2 value of 0.62 against field data. A random forest classifier was applied to assign tree condition into four categories in accordance with industry standards, producing out-of-bag accuracies %. Our results demonstrate the potential of UAS-based mapping for the provision of information to support the horticulture industry and facilitate orchard-based assessment and management.
Publisher: Elsevier BV
Date: 12-2019
DOI: 10.1016/J.WATRES.2019.115068
Abstract: Wastewater contains a wealth of information about the population who contribute to it including biological and chemical markers of human activity and exposures. F
Publisher: MDPI AG
Date: 02-10-2023
Publisher: Informa UK Limited
Date: 06-06-2022
Publisher: Elsevier BV
Date: 11-2015
Location: Saudi Arabia
No related grants have been discovered for Kasper Johansen.