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.
Highlights
As a major component of terrestrial ecosystems, vegetation plays an important role in material cycling and energy flows, and provides irreplaceable service functions that maintain the wellbeing of our planet and all the creatures that inhabit it
The hydrological variables simulated by the soil and water assessment tool (SWAT) model, as presented in our previous study [61], were adopted to analyze the relationship between hydrological variables and vegetation dynamics
The paper presents the results of applying the Mann–Kendall, Sen’s Slope, and Global and Local Moran’s I techniques to analyze vegetation changes and applying partial least squares regression (PLSR) and geographical and temporal weighted regression (GTWR) models to analyze the relationships between vegetation and climatic, anthropogenic, and hydrological factors based on meteorological data, remotely sensed information and hydrological variables simulated by the SWAT model
Summary
As a major component of terrestrial ecosystems, vegetation plays an important role in material cycling and energy flows, and provides irreplaceable service functions that maintain the wellbeing of our planet and all the creatures that inhabit it. Based on remote sensing products of these parameters at regional and global scales, scholars around the world have used different methods to study spatial–temporal changes in vegetation greenness (related to canopy structure) and vegetation productivity (related to canopy function) [26,27,28], such as trend analysis and spatial autocorrelation analysis
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