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【Objective】 The Urumqi River is one of the most important rivers in northwestern China. The objective of this paper is to study the change in vegetation coverage in its basin and the underlying determinants in attempts to help improve its management. 【Method】 The study is based on the Landsat TM/OLI remote sensing imageries from 2000 to 2020. Spatiotemporal variations in vegetation coverage (FVC) in the basin during this period are extracted using the image dichotomy and vegetation cover transfer matrix, from which we analyze the changes in land use and topographic features in the basin. The factors that are responsible for the change in FVC in the basin are calculated using the geo-detector model, reclassification and other methods. 【Result】 From 2000 to 2020, vegetation coverage in the basin decreased first and then increased. Vegetation coverage was high in the upper reaches and low in the middle and low reaches. Vegetation coverage in different land usage was ranked in the order of forest land > cropland > grassland > construction land > water bodies > unused land. The change in vegetation coverage was influenced by topographic factors and fluctuated with elevation, with the vegetation coverage being the highest in elevations air temperature > elevation > surface temperature > precipitation > soil moisture > slope > slope direction. The results of the interaction detection analysis show that elevation-land use, land use-temperature are the coupling factors that influenced vegetation coverages more than coupling of other factors. 【Conclusion】 The average vegetation coverage in the Urumqi River basin varied from 0.348 to 0.456 in 2000—2020; it varied spatially. Overall, it is high in the upper reach and low in the middle and lower reaches. Variation in land use explains the variation in the vegetation coverage more than any other natural factors.

【Objective】 To investigate the spatial and temporal variation patterns of net primary productivity (NPP) of vegetation and its influencing factors in the Manas River basin, Xinjiang. 【Method】 Based on MODIS remote sensing data as well as topography, meteorological factors and human activity data, the spatial and temporal change characteristics of NPP in the Manas River basin and its driving factors from 2001 to 2021 were analyzed by using slope trend analysis, correlation analysis and geoprobe. 【Result】 From 2001 to 2021, the multi-year average NPP in the Manas River basin was 125.63 g C/(m2·a), with a minimum of 98.80 g C/(m2·a) in 2008 and a maximum of 163.98 g C/(m2·a) in 2016, and the NPP generally showed an inter-annual increasing trend. The spatial distribution pattern of NPP in the Manas River basin was characterized by low in the Northern and Southern regions and high in the central region, with nearly 63.84% of the regions showing an increasing trend in NPP, of which 26.98% of the regions showed a significant increase in NPP (P land use > precipitation > slope > GDP > temperature > population density > nighttime light, among which the interaction between elevation and land use had the greatest influence on NPP. 【Conclusion】 The NPP in Manas River basin has obvious spatial and temporal differentiation characteristics, and elevation, land use and precipitation have important effects on the spatial and temporal distribution pattern of NPP.

[Objective] The spatial and temporal changes of soil erosion and its influencing factors in the north piedmont of Yinshan Mountain from 2000 to 2020 were analyzed in order to provide scientific guidance for soil erosion control and land space planning in this area. [Methods] Based on precipitation, land use, soil and remote sensing image data, the research was carried out by using GIS technology and the RUSLE model. [Results] ① From 2000 to 2020, soil erosion intensity in the north piedmont of Yinshan Mountain was mainly micro-grade erosion and moderate-grade erosion. The area of high-grade erosion continued to increase over time. The soil erosion status in the north piedmont of Yinshan Mountain was very severe. ② The areas of severe soil erosion in the north piedmont of Yinshan Mountain were mainly located along the Yinshan Mountain range and most areas of Duolun County, Inner Mongolia Autonomous Region. ③ Land use type was the main influencing factor of soil erosion in the north piedmont of Yinshan Mountain. The explanatory power of each factor followed the order of: land use type > vegetation coverage > rainfall > slope. The area consisting of cultivated land with vegetation coverage less than 0.3, slope of 15°~20°, and rainfall of 365~413 mm was the high risk erosion area. [Conclusion] The degree of soil erosion in the north piedmont of Yinshan Mountain was generally high. Cultivated land and grassland with low vegetation coverage should be the key areas for soil erosion control in the north piedmont of Yinshan Mountain. Soil erosion should be controlled by planting trees and grasses to expand the coverage of forests and grasslands, improve vegetation coverage, reduce surface runoff rate, and improve infiltration capacity.

[Objective] The driving forces of the spatial distribution and spato-temporal changes in fractional vegetation coverage (FVC) in the Pearl River delta were analyzed in order to provide a scientific reference for the protection of the ecological environment in the region. [Methods] A binary pixel model was used with Landsat 5 TM and Landsat 8 OLI data to invert the vegetation coverage of the Pearl River delta in 2000, 2005, 2010, 2015, and 2020. The spatial pattern and spatio-temporal changes in FVC in the Pearl River delta were analyzed. Annual precipitation, average annual temperature, population density, and land use data during the five study periods were analyzed using correlation coefficients and the geographical detector method. [Results] ① Vegetation coverage in the Pearl River delta was lower in the middle of the region and higher in the marginal regions. Vegetation coverage was lower in Foshan, Zhongshan, Zhuhai, Southwestern Guangzhou, Dongguan City, and Shenzhen City, and higher in Zhaoqing, Jiangmen City, and Huizhou City. Overall, vegetation coverage increased over time, with 64.99% of the total area showing increases in vegetation coverage. There were stage differences in time, and the area with highest vegetation coverage (more than 80%) increased most significantly during 2010—2015. ② There were obvious regional differences in the driving factors of FVC. Annual precipitation and land use had more inhibiting effects than promoting effects, and average annual temperature and population density had more promoting effects than inhibiting effects. ③ The spatial pattern factor detection of FVC showed that the explanatory power of land use degree was the strongest, while the interactive detection showed that the explanatory power of annual average temperature and land use degree interaction was the highest. For the explanatory power of annual precipitation, annual precipitation and annual temperature interaction showed a weakening trend in the time series of 2000, 2005, 2010, 2015, and 2020. The explanatory power of other influencing factors and their interactions showed an increasing trend. The spatial-temporal FVC change factor detection also indicated that the explanatory power of land use degree change was the strongest, while the interactive detection indicated that annual precipitation change and land use degree interaction had the highest explanatory power. [Conclusion] Land use degree was the dominant factor affecting the temporal and spatial changes of vegetation coverage in the Pearl River delta. Human influence continues to increase, and the interaction of the two factors is significantly greater than the effect of a single factor.

[Objective] The temporal and spatial variation characteristics of cultivated land types and their influencing factors were studied in order to provide a theoretical basis for policy-making regarding cultivated land protection, sustainable agricultural development, and soil and water conservation in this region. [Methods] The study was conducted for Laiyang City in Shandong Province. High-resolution remote sensing data from 2019 to 2021 were combined with field observations in order to divide cultivated land types into five categories: irrigated land, arid flat land, arid sloping land, arid terrace land, and abandoned land. We determined the spatio-temporal distribution and variation of different types of cultivated land and their influencing factors, and identified the variable regions by using dynamic change degree, land use transfer matrix, and geographic detector. [Results] ① Cultivated land of Laiyang City accounted for 54.44% of the total land area, with an overall distribution pattern of more cultivated land in the southern and western areas of the city, and less in the northern and eastern areas of the city. Arid terrace land accounted for 61.19% of the total cultivated land area, which was followed by irrigated land, arid flat land, and arid sloping land. Abandoned land accounted for only 0.33% of the total cultivated land area. ② The analysis of single factor detector showed that landform type, slope, and soil type were the three main factors affecting the distribution of cultivated land type, and the (q values) were all above 0.3 when the factors interacted with each other. ③ From 2019 to 2021, the net rate of cultivated land conversion was -0.25%. The total area decreased 4.73 km2, mainly due to conversion of arid terrace land, arid sloping land, arid flat land, and irrigated land to construction land. This conversion mostly occurred in the urban area located in the low hilly area with a slope of less than 5° and a brown soil type. [Conclusion] The mainly cultivated land type is dry terraced field. Cultivated land distributed in low hilly area with gentle slopes (representing cultivated land with good quality) is easy to change. As the area continues to decrease, the protection measures of high-quality cultivated land should be strengthened.

[Objective] The regional landscape ecological risks were evaluated, and their spatiotemporal variation was analyzed in order to provide important support for reducing regional ecological risks, maintaining regional ecological security, and promoting regional green development. [Methods] We constructed a landscape ecological risk assessment model based on land use change, and determined the temporal and spatial change characteristics of landscape ecological risk for the Ya’an-Kangding Expressway crossing counties and cities from 2000 to 2020. A geographic detector model with optimized parameters was used to quantitatively analyze the driving factors of landscape ecological risk change. We used the PLUS model to simulate the spatial distribution characteristics and changing trends of landscape ecological risks for the Ya’an-Kangding Expressway passing through counties and cities in 2035. [Results] ① From 2000 to 2020, the main landscape types in the study area were forest land, grassland, and cultivated land, with the fastest growth rate occurring for the impervious surface area (expressway), and the largest increase occurring in forest land area. ② Low and medium landscape ecological risk grades were the main factors. The risk grades spread outward from high to low. ③ Natural factors such as NDVI, elevation, and average annual precipitation were the main driving factors for changes in landscape ecological risk. ④ In 2035, the areas of medium, high, and high risk grades in the study area will decrease under the two different scenarios. The area of significant decline will be particularly obvious under the ecological protection scenario. [Conclusion] The landscape ecological risk levels in the study area were relatively low, mainly low, lower, and medium risk levels, and the ecological environment was gradually improving. The ecological protection scenario was more consistent with the concept of regional sustainable development.

[Objective] The temporal and spatial characteristics of carbon storage in the Poyang Lake wetland ecosystem were analyzed in order to provide scientifically based recommendations to protect Poyang Lake wetlands in the future and to produce regional “carbon peak and carbon neutrality”. [Methods] InVEST and GeoSoS-FLUS models were combined to calculate carbon storage of typical wetlands in the Poyang Lake region from 2000 to 2020, and to predict carbon storage changes in 2030 under natural development scenarios and ecological protection scenarios. The factors driving carbon storage changes were determined by means of the geographic detector model. [Results] ① The carbon reserves of typical wetlands in the Poyang Lake region in 2000, 2010 and 2020 were 2.42×106 t 2.48×106 t and 2.46×106 t, respectively. ② The high carbon reserves were concentrated in the central and western forests, while the low carbon reserves were concentrated in the east, central, western and northern lakes. ③ Land use was the dominant factor affecting carbon storage transfer. The explanatory power of vegetative cover type with respect to carbon storage transfer followed the order of marsh grassland> marshland>forest land>cultivated land. ④ Compared with the natural development scenario, the change rate of carbon storage for the ecological protection scenario changed from -17.81% to -1.09% during the period from 2020 to 2030. [Conclusion] Reasonable ecological protection policies can effectively guarantee the carbon sequestration capacity of wetlands. Land use control practices should be strengthened and ecological protection measures should be implemented as as to guarantee improvement in regional carbon storage capacity.

【Objective】 Winter wheat is the second-largest stable crop in China and comprehending its growth and the factors affecting it on a large scale is crucial for food security. This paper aims to investigate the feasibility of using satellite imagery to accomplish this objective. 【Method】 The study is based on Sentinel-2 images. The spatial distribution of winter wheat planted from 2018 to 2020 in the studied region was extracted using the random forest method, which were then used to analyze the changes in wheat growth in rejuvenation, jointing, pregnant ear pumping, and flowering stages in each year. For comparison, we divided the growth into health growth, normal growth and poor growth. Wheat growth was linked to 11 abiotic and geographic factors, including temperature, precipitation, slope of the lands, slope aspect, elevation, soil type, soil moisture, sunshine time, population density, rural labor resources and GDP. 【Result】 Compared with 2018—2019, wheat in 2020 grew better during the greening and jointing stages in more than 90% of the studied area, but worse in the pregnant ear pumping stage in more than 20% of the studied area. Wheat growth was normal during the flowering stage in 80% of the studied area. The factors which affect winter wheat growth were ranked in the following order based on their significance: rural labor resources> soil moisture> precipitation> temperature> sunshine time. It was also found that the interaction between different factors in their impact on wheat growth is manifested as a bifold or nonlinear enhancement. 【Conclusion】 The change in winter wheat growth in the studied region is due to the complex interplay of multiple factors.

[Objective] The temporal and spatial variation characteristics and driving factors of vegetation in the Guanzhong Plain urban agglomeration from 2000 to 2020 were studied in order to provide scientific guidance for the construction of regional ecological civilization. [Methods] Based on the Google Earth Engine (GEE) cloud platform, we used the enhanced vegetation index (EVI) data calculated from Landsat images from 2000 to 2020 combined with trend analysis, hot spot analysis, geographic detector model, and other methods to analyze the change pattern and driving factors of the annual maximum EVI (EVImax) in the Guanzhong Plain urban agglomeration. [Results] ① Annual EVImax showed a significant upward trend in the Guanzhong Plain urban agglomeration from 2000 to 2020. Annual EVImax values for the unchanged land use/cover types showed a fluctuating upward trend, among which the rate of increase for grassland was the largest. ② Spatially, the EVImax values showed a decreasing trend from south to north, and the high-value areas were mainly located in the Qinling Mountains in the southern Guanzhong Plain urban agglomeration. Trend analysis results showed that the areas where the annual EVImax increased and decreased significantly accounted for 70.16% and 3.61%, respectively, of the total area. ③ The spatial agglomeration characteristics of annual EVImax showed that the number of hot and cold spots showed a slight decrease and a significant downward trend, and the cold spots gradually transformed into sub-cold or sub-hot spots. ④ Precipitation was the most important factor affecting the spatial distribution of EVImax in the Guanzhong Plain urban agglomeration, and the interaction of each influencing factor was characterized as nonlinear enhancement or two-factor enhancement. [Conclusion] The vegetation of the Guanzhong Plain urban agglomeration showed an overall upward trend from 2000 to 2020, and annual precipitation was an important factor that determined vegetation growth status.

[Objective] The spatio-temporal evolution of land use patterns in Chongqing City was analyzed in order to provide a scientific basis for promoting the sustainable use of land resources and sustainable social and economic development in the region. [Methods] Remote sensing monitoring data of land use for Chongqing City (located in a mountainous high-density area) from 1995 to 2020 were quantitatively analyzed to determine the spatio-temporal evolution characteristics of land use in Chongqing City from the aspects of quantitative structure and spatial layout. The driving forces of land use change in Chongqing City under different time scales were analyzed by geographical detectors. [Results] The rate of land use change in Chongqing City accelerated during the study period. The land used by urban and rural residents and the land used for industrial and mining construction continued to increase. The grassland area was greatly reduced and concentrated by the conversion to forest land. For 25 years, the degree of land use in Chongqing City has increased year by year, with the entire aera being in the development period. Hot spots of development were mainly concentrated in the main city and its surrounding areas. The density of paddy fields in Western Yuxi District decreased. The high-value density area of forest in Southeast Chongqing City increased, but the high-value density area of grassland decreased significantly. The high-density core of urban and rural residential land was located in the main urban area, and the high-density core of industrial and mining construction land exhibited a sattered distribution having a large growth rate. Socio-economic factors had a significant impact on land use change in Chongqing City, of which population density was the dominant factor. [Conclusion] Regional differences in land use change in Chongqing City were observed over time, and land use change was mainly affected by socio-economic factors.