Spatial–Temporal Vegetation Dynamics and Their Relationships with Climatic, Anthropogenic, and Hydrological Factors in the Amur River Basin

Abstract

Information about the growth, productivity, and distribution of vegetation, which are highly relied on and sensitive to natural and anthropogenic factors, is essential for agricultural production management and eco-environmental sustainability in the Amur River Basin (ARB). In this paper, the spatial–temporal trends of vegetation dynamics were analyzed at the pixel scale in the ARB for the period of 1982–2013 using remotely sensed data of long-term leaf area index (LAI), fractional vegetation cover (FVC), and terrestrial gross primary productivity (GPP). The spatial autocorrelation characteristics of the vegetation indexes were further explored with global and local Moran’s I techniques. The spatial–temporal relationships between vegetation and climatic factors, land use/cover types and hydrological variables in the ARB were determined using a geographical and temporal weighted regression (GTWR) model based on the observed meteorological data, remotely sensed vegetation information, while the simulated hydrological variables were determined with the soil and water assessment tool (SWAT) model. The results suggest that the variation in area-average annual FVC was significant with an increase rate of 0.0004/year, and LAI, FVC, and GPP all exhibited strong spatial heterogeneity trends in the ARB. For LAI and FVC, the most significant changes in local spatial autocorrelation were recognized over the Sanjiang Plain, and the low–low agglomeration in the Sanjiang Plain decreased continuously. The GTWR model results indicate that natural and anthropogenic factors jointly took effect and interacted with each other to affect the vegetated regime of the region. The decrease in the impact of precipitation to vegetation growth over the Songnen Plain was determined as having started around 1991, which was most likely attributed to dramatic changes in water use styles induced by local land use changes, and corresponded to the negative correlation between pasture areas and vegetation indexes during the same period. The analysis results presented in this paper can provide vital information to decision-makers for use in managing vegetation resources.

Investigating Relationships between Runoff–Erosion Processes and Land Use and Land Cover Using Remote Sensing Multiple Gridded Datasets

“…The statistical indicators of the calibration and validation phases show the satisfactory performance of the model throughout the basin, mainly upstream and downstream. The obtained results follow other studies, which emphasize the good performance of the SWAT model in modeling Cerrado basins (e.g., [21,27,28,30,31,71]); therefore, the impacts of LULCC on the streamflow time series can be assessed using the calibrated and validated SWAT model. 12 compares annual differences in sediment yield for scenarios S1, OS, and PS.…”

“…The problem of the impacts of agricultural expansion and its implications on runofferosion processes in the Cerrado biome has been widely studied [23][24][25][26]; however, studies involving the flow behavior and the sediment yield in hypothetical LULC scenarios at a basin-scale in this Brazilian biome are still scarce [27][28][29]. For these reasons, the impacts of LULCC on streamflow and sediment yield still need to be further investigated in the Cerrado biome, which is of extreme importance for water resources and electrical production in Brazil [30].…”

Spatiotemporal green water dynamics and their responses to variations of climatic and underlying surface factors: A case study in the Sanjiang Plain, China

“…Besides, related studies showed that the calculated potential evapotranspiration of crop was also decreasing for the SJP (Ren et al, 2019). On the other hand, the prolongation of plant growth period and the increase of biomass caused by climate warming would also increase water consumption (Zhou et al, 2021). However, the plant growth period set in ESSI-3 model for this study was constant.…”

Monitoring long‐term vegetation dynamics over the Yangtze River Basin, China, using multi‐temporal remote sensing data

“…Variations in land cover are closely related to the human living environment (Gao et al, 2022; Zhang, Jiang, et al, 2022). Vegetation dynamics are mainly influenced by both climate change and human activities (Ge et al, 2021; Li et al, 2022; Zhou et al, 2021). In terms of greening trends, 35% is attributed to climate change, while 65% is associated with human factors (Qiao et al, 2021).…”