The rapid acceleration of global warming and intensifying human activities have exacerbated the fragility and climate sensitivity of ecosystems worldwide,particularly in arid regions.Vegetation,a key component of ecos...The rapid acceleration of global warming and intensifying human activities have exacerbated the fragility and climate sensitivity of ecosystems worldwide,particularly in arid regions.Vegetation,a key component of ecosystems,is critical in enhancing the ecological environment.The Ertix River Basin(ERB)is a transboundary watershed that spans multiple countries,mostly in arid regions.However,research on the fractional vegetation coverage(FVC)and its driving factors in the ERB remains limited.Investigating the spatiotemporal changes in the FVC and its relationship with various factors in the ERB can offer scientific support for optimizing regional vegetation restoration policies and promoting the coordinated development of human-environment interactions.The Moderate-resolution Imaging Spectroradiometer(MODIS)MYD13Q1 V6 data were obtained via the Google Earth Engine platform,and methods including the pixel dichotomy method,Theil-Sen median trend analysis,and Mann‒Kendall test were employed to examine the spatiotemporal dynamics of the FVC in the ERB from 2003 to 2023,with future trend forecast using the Hurst index.The impacts of natural and socioeconomic factors on the FVC were evaluated through the partial least squares-structural equation model(PLS-SEM).The results indicated that the FVC in the ERB showed a slight degradation trend with an average annual decrease of 0.046%during 2003-2023,with significant changes occurring in 2004,2010,and 2019.Spatially,53.380%of the study area was degraded,and the change in the FVC increased gradually from southeast to northwest.The FVC in 63.000%of the study area was highly stable and displayed long-term persistence;and the direct impact of natural factors(path coefficient of 0.617)on the FVC was significantly higher than that of socioeconomic factors(0.167).Among the natural factors,precipitation(0.999)was the most significant.This study reveals the significant impacts of natural and socioeconomic factors on vegetation dynamics in arid regions,and provides a scientific basis for transnational ecological conservation.展开更多
Estimation of NEE of Grasslands ecosystems becomes mandatory as these grasslands with their wide spread (almost 40% of land of the earth) and high plant diversity play a major role in global carbon balances and NEE at...Estimation of NEE of Grasslands ecosystems becomes mandatory as these grasslands with their wide spread (almost 40% of land of the earth) and high plant diversity play a major role in global carbon balances and NEE at both local and global scale. The present study has been focused on understanding the role of different plant species responsible for variation in NEE of the Banni Grasslands of India. These grasslands form a belt of arid grassland having low growing forbs, graminoids and scattered tree cover. Due to its wide spread and inaccessibility of Banni, this study utilized spatial approach for evaluating carbon emissions and NEE. Landsat data was utilized for vegetation type classification and SMAP data for extraction of NEE values proved their potential for categorising vegetation type and generating NEE values precisely. Three major plant types were identified from the study area <i>viz.</i>, Grasslands, Land with <i>Acacia</i> and Land with <i>Prosopis</i>. Grasses were dominant covering 77% and the rest of the area was occupied by the other two classes, <i>i.e. Acacia</i> and <i>Prosopis</i>. The NEE values were higher for the grasses when compared to the other two plant species proving to be the active sinks when compared to other plants. The differential contribution of NEE by species has been depicted in the present work.展开更多
High spatial resolution and high temporal frequency fractional vegetation cover(FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estima...High spatial resolution and high temporal frequency fractional vegetation cover(FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over China by taking advantage of the spatial and temporal information from different types of sensors: the 30-m resolution sensor on the Chinese environment satellite(HJ-1) and the 1-km Moderate Resolution Imaging Spectroradiometer(MODIS). The algorithm was implemented for each main vegetation class and each land cover type over China. First, the high spatial resolution and high temporal frequency normalized difference vegetation index(NDVI) was acquired by using the continuous correction(CC) data assimilation method. Then, FVC was generated with a nonlinear pixel unmixing model. Model coefficients were obtained by statistical analysis of the MODIS NDVI. The proposed method was evaluated based on in situ FVC measurements and a global FVC product(GEOV1 FVC). Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors(MEs) of forest, cropland, and grassland were-0.025, 0.133, and 0.160, respectively, indicating that the FVCs derived from the proposed algorithm were consistent with ground measurements [R2 = 0.809,root-mean-square deviation(RMSD) = 0.065]. An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial–temporal consistency and similar magnitude(RMSD approximates 0.1). Overall, the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.展开更多
基金funded by the Third Xinjiang Comprehensive Scientific Investigation Project,China(2022xjkk0702)the Western Young Scholars Project of the Chinese Academy of Sciences(2022-XBQNXZ-001)the Tianshan Talent Development Program,China(2022TSYCCX0006).
文摘The rapid acceleration of global warming and intensifying human activities have exacerbated the fragility and climate sensitivity of ecosystems worldwide,particularly in arid regions.Vegetation,a key component of ecosystems,is critical in enhancing the ecological environment.The Ertix River Basin(ERB)is a transboundary watershed that spans multiple countries,mostly in arid regions.However,research on the fractional vegetation coverage(FVC)and its driving factors in the ERB remains limited.Investigating the spatiotemporal changes in the FVC and its relationship with various factors in the ERB can offer scientific support for optimizing regional vegetation restoration policies and promoting the coordinated development of human-environment interactions.The Moderate-resolution Imaging Spectroradiometer(MODIS)MYD13Q1 V6 data were obtained via the Google Earth Engine platform,and methods including the pixel dichotomy method,Theil-Sen median trend analysis,and Mann‒Kendall test were employed to examine the spatiotemporal dynamics of the FVC in the ERB from 2003 to 2023,with future trend forecast using the Hurst index.The impacts of natural and socioeconomic factors on the FVC were evaluated through the partial least squares-structural equation model(PLS-SEM).The results indicated that the FVC in the ERB showed a slight degradation trend with an average annual decrease of 0.046%during 2003-2023,with significant changes occurring in 2004,2010,and 2019.Spatially,53.380%of the study area was degraded,and the change in the FVC increased gradually from southeast to northwest.The FVC in 63.000%of the study area was highly stable and displayed long-term persistence;and the direct impact of natural factors(path coefficient of 0.617)on the FVC was significantly higher than that of socioeconomic factors(0.167).Among the natural factors,precipitation(0.999)was the most significant.This study reveals the significant impacts of natural and socioeconomic factors on vegetation dynamics in arid regions,and provides a scientific basis for transnational ecological conservation.
文摘Estimation of NEE of Grasslands ecosystems becomes mandatory as these grasslands with their wide spread (almost 40% of land of the earth) and high plant diversity play a major role in global carbon balances and NEE at both local and global scale. The present study has been focused on understanding the role of different plant species responsible for variation in NEE of the Banni Grasslands of India. These grasslands form a belt of arid grassland having low growing forbs, graminoids and scattered tree cover. Due to its wide spread and inaccessibility of Banni, this study utilized spatial approach for evaluating carbon emissions and NEE. Landsat data was utilized for vegetation type classification and SMAP data for extraction of NEE values proved their potential for categorising vegetation type and generating NEE values precisely. Three major plant types were identified from the study area <i>viz.</i>, Grasslands, Land with <i>Acacia</i> and Land with <i>Prosopis</i>. Grasses were dominant covering 77% and the rest of the area was occupied by the other two classes, <i>i.e. Acacia</i> and <i>Prosopis</i>. The NEE values were higher for the grasses when compared to the other two plant species proving to be the active sinks when compared to other plants. The differential contribution of NEE by species has been depicted in the present work.
基金Supported by the National Key Research and Development Program of China (2018YFC1506501, 2018YFA0605503, and2016YFB0501502)Special Program of Gaofen Satellites (04-Y30B01-9001-18/20-3-1)National Natural Science Foundation of China (41871230 and 41871231)。
文摘High spatial resolution and high temporal frequency fractional vegetation cover(FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over China by taking advantage of the spatial and temporal information from different types of sensors: the 30-m resolution sensor on the Chinese environment satellite(HJ-1) and the 1-km Moderate Resolution Imaging Spectroradiometer(MODIS). The algorithm was implemented for each main vegetation class and each land cover type over China. First, the high spatial resolution and high temporal frequency normalized difference vegetation index(NDVI) was acquired by using the continuous correction(CC) data assimilation method. Then, FVC was generated with a nonlinear pixel unmixing model. Model coefficients were obtained by statistical analysis of the MODIS NDVI. The proposed method was evaluated based on in situ FVC measurements and a global FVC product(GEOV1 FVC). Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors(MEs) of forest, cropland, and grassland were-0.025, 0.133, and 0.160, respectively, indicating that the FVCs derived from the proposed algorithm were consistent with ground measurements [R2 = 0.809,root-mean-square deviation(RMSD) = 0.065]. An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial–temporal consistency and similar magnitude(RMSD approximates 0.1). Overall, the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.
