ORCID Profile
0000-0002-4860-1679
Current Organisation
Australian National University
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Publisher: MDPI AG
Date: 11-11-2020
DOI: 10.3390/LAND9110443
Abstract: Rapid urbanisation in China has led to massive outmigration in rural regions, which has changed the regional labour force structure and can have various profound impacts as a result. This research used a case study in Southwest China to investigate how regional land use patterns have been changed in the context of rural outmigration and assessed the resulting dynamics on local ecological environment. The key findings include: (1) The local land conversion process was mainly characterised by the conversion of farmland (−18.3%) to residential area (+268.3%) and woodland (+55.6%) during 2000–2018 (2) about 83.7% of area showed a statistically significant increase in the normalised difference vegetation index (NDVI), which was not due to human interference factors (e.g., afforestation). Correlation analyses showed that depopulation (R = −0.514, p 0.01) and local mild temperature (R = 0.505, p 0.01) could be the main contributors. Only 2.5% of the area had decreased NDVI and this was directly caused by human activities (e.g., urban area expansion). These results implied that vegetation improvement can occur in the context of depopulation and farmland reduction, which did not significantly threaten the local agricultural sector. It then could be a good choice to allow those high-slope and biophysically poor farmlands to undergo forest succession rather than cultivation. Farmers in Southwest China should make a full use of the existing low-slope arable land to curb the declining trend of local farmland amount, in order to meet the future challenges brought by urbanisation. Enhanced agricultural infrastructure, mechanised farming and guide from local government can help achieve this goal. This study provided new insights and more realistic scenarios for rural development in Southwest China. The research findings are expected to provide a better understanding to enable sustainable land use management in Southwest China.
Publisher: MDPI AG
Date: 27-06-2020
DOI: 10.3390/RS12132068
Abstract: Urban flooding is one of the most costly and destructive natural hazards worldwide. Remote-sensing images with high temporal resolutions have been extensively applied to timely inundation monitoring, assessing and mapping, but are limited by their low spatial resolution. Sub-pixel mapping has drawn great attention among researchers worldwide and has demonstrated a promising potential of high-accuracy mapping of inundation. Aimed to boost sub-pixel urban inundation mapping (SUIM) from remote-sensing imagery, a new algorithm based on spatial attraction models and Elman neural networks (SAMENN) was developed and examined in this paper. The Elman neural networks (ENN)-based SUIM module was developed firstly. Then a normalized edge intensity index of mixed pixels was generated. Finally the algorithm of SAMENN-SUIM was constructed and implemented. Landsat 8 images of two cities of China, which experienced heavy floods, were used in the experiments. Compared to three traditional SUIM methods, SAMENN-SUIM attained higher mapping accuracy according not only to visual evaluations but also quantitative assessments. The effects of normalized edge intensity index threshold and neuron number of the hidden layer on accuracy of the SAMENN-SUIM algorithm were analyzed and discussed. The newly developed algorithm in this study made a positive contribution to advancing urban inundation mapping from remote-sensing images with medium-low spatial resolutions, and hence can favor urban flood monitoring and risk assessment.
Publisher: MDPI AG
Date: 24-05-2019
DOI: 10.3390/RS11101231
Abstract: Wetland flooding is significant for the flora and fauna of wetlands. High temporal resolution remote sensing images are widely used for the timely mapping of wetland flooding but have a limitation of their relatively low spatial resolutions. In this study, a novel method based on random forests and spatial attraction models (RFSAM) was proposed to improve the accuracy of sub-pixel mapping of wetland flooding (SMWF) using remote sensing images. A random forests-based SMWF algorithm (RM-SMWF) was developed firstly, and a comprehensive complexity index of a mixed pixel was formulated. Then the RFSAM-SMWF method was developed. Landsat 8 Operational Land Imager (OLI) images of two wetlands of international importance included in the Ramsar List were used to evaluate RFSAM-SMWF against three other SMWF methods, and it consistently achieved more accurate sub-pixel mapping results in terms of visual and quantitative assessments in the two wetlands. The effects of the number of trees in random forests and the complexity threshold on the mapping accuracy of RFSAM-SMWF were also discussed. The results of this study improve the mapping accuracy of wetland flooding from medium-low spatial resolution remote sensing images and therefore benefit the environmental studies of wetlands.
Publisher: MDPI AG
Date: 28-02-2023
DOI: 10.3390/RS15051373
Abstract: Satellite precipitation products (SPPs) have emerged as an important information source of precipitation with high spatio-temporal resolutions, with great potential to improve catchment water resource management and hydrologic modelling, especially in data-sparse regions. As an indirect precipitation measurement, satellite-derived precipitation accuracy is of major concern. There have been numerous evaluation/validation studies worldwide. However, a convincing systematic evaluation/validation of satellite precipitation remains unrealized. In particular, there are still only a limited number of hydrologic evaluations/validations with a long temporal period. Here we present a systematic evaluation of eight popular SPPs (CHIRPS, CMORPH, GPCP, GPM, GSMaP, MSWEP, PERSIANN, and SM2RAIN). The evaluation area used, using daily data from 2007 to 2020, is the Xiangjiang River basin, a mountainous catchment with a humid sub-tropical monsoon climate situated in south China. The evaluation was conducted at various spatial scales (both grid-gauge scale and watershed scale) and temporal scales (annual and seasonal scales). The evaluation paid particular attention to precipitation intensity and especially its impact on hydrologic modelling. In the evaluation of the results, the overall statistical metrics show that GSMaP and MSWEP rank as the two best-performing SPPs, with KGEGrid ≥ 0.48 and KGEWatershed ≥ 0.67, while CHIRPS and SM2RAIN were the two worst-performing SPPs with KGEGrid ≤ 0.25 and KGEWatershed ≤ 0.42. GSMaP gave the closest agreement with the observations. The GSMaP-driven model also was superior in depicting the rainfall-runoff relationship compared to the hydrologic models driven by other SPPs. This study further demonstrated that satellite remote sensing still has difficulty accurately estimating precipitation over a mountainous region. This study provides helpful information to optimize the generation of algorithms for satellite precipitation products, and valuable guidance for local communities to select suitable alternative precipitation datasets.
Publisher: Elsevier BV
Date: 06-2022
DOI: 10.1016/J.MARENVRES.2022.105635
Abstract: Continuing global warming and intensification of human activities have substantially disturbed the balance of coastal marine ecosystems, potentially creating favorable conditions for algal blooms. Using serial remote sensing data and various national and provincial statistics, we investigated the spatial and temporal variations of the environmental driving forces for algal blooms in the Southern Yellow Sea between 2003 and 2017. The findings suggest that (1) Continual warming was observed in the Southern Yellow Sea. The study area evidenced more than three times the warming speed (0.41 °C/decade) of the global oceans (0.12 °C/decade) during the same period. There was an apparent warming zone in the region where macroalgal blooms tended to spread, with a heating of 1.0-1.5 °C (May-June). (2) Nutrient loadings have erse patterns, characterized by fast-growing aquaculture activities and declining nutrients from land-based agriculture fertilizers and sewage discharge (based on published national and provincial statistics). (3) Growing expansion of algal blooms in the Southern Yellow Sea was confirmed by the relative increases in average May-June chlorophyll-a concentration of 46.7% and floating biomass area from 3.3% in 2003 to 13.4% in 2017. (4) While spatial correlation analysis showed a positive influence of the ocean surface temperature on chlorophyll-a, their relatively moderate (r = 0.40, p < 0.15) and declining correlations suggest that nutrient enrichment could be comparatively more influential on macroalgal blooms. Nutrient loading from the discharge of wastewater sourced from coastal aquaculture and organic residuals from land-sourcing sewage and industrial pollution, even though declining as reported, is still upholding a high level of nutrient enrichment in the study area. In addition, the fixed facilities for seaweed mariculture in the region provide vast breeding surfaces for algae. Consequently, the Southern Yellow Sea has become an ideal marine area for algal blooms.
Publisher: Public Library of Science (PLoS)
Date: 11-2011
No related grants have been discovered for TINGBAO XU.