Spatial and temporal variations in vegetation coverage observed using AVHRR GIMMS and Terra MODIS data in the mainland of China

Zhang, Yahai; Ye, Aizhong

doi:10.1080/01431161.2020.1714781

Cited by 42 publications

(16 citation statements)

References 96 publications

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“…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. These function services include food provision, climate regulation, carbon sequestration, timber production, biodiversity preservation, and soil protection [1][2][3][4][5][6][7][8]. Vegetation growth affects the ecological balance, the terrestrial carbon cycle, water circulation, and other biochemical processes [2,9].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…(2014) divided the mainland of China into 17 large hydroclimatic regions (Figure 1) based on the climate classifications and watershed divisions standard, including (1) Inland rivers in Xinjiang; (2) Inland rivers in northern Tibet; (3) Inland rivers in Inner Mongolia; (4) Yellow River; (5) Upper Yellow River; (6) Hai River; (7) Songhua River; (8) Liao River; (9) Upper Yangtze River; (10) Huai River; (11) Southwest rivers in southern Tibet; (12) Southwest rivers in Yunnan; (13) Yangtze River; (14) Middle Yangtze River; (15) Lower Yangtze River; (16) Pearl River; and (17) Southeast rivers. The same hydroclimatic regions have been used in some subsequent studies (see Ma et al., 2015; Y. Zhang & Ye, 2020). This study evaluated SM products in the 17 subregions (regions 1–17) and the whole of China (region 18).…”

Section: Methodsmentioning

confidence: 99%

“…Considering shaping the boundaries with similar regional climate and hydrological characteristics, Lang et al (2014) 17) Southeast rivers. The same hydroclimatic regions have been used in some subsequent studies (see Ma et al, 2015;Y. Zhang & Ye, 2020).…”

Section: Study Areamentioning

confidence: 99%

InterComparison and Evaluation of MultiSource Soil Moisture Products in China

Zhang

et al. 2021

Earth and Space Science

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As an important variable of the climate system, soil moisture (SM) plays a key role in the surface runoff, evapotranspiration, energy balance, and carbon cycle. And its heterogeneity and variability in space and time bring in continuous influences on the simulation and prediction of atmospheric circulation, surface processes, and land-atmosphere interactions (Fan et al., 2020;Schwingshackl et al., 2017;S. I. Seneviratne et al., 2010). SM is also a key variable in surface water fluxes and is critical for drought monitoring and assessment (Velpuri et al., 2016).Compared with other climate variables (e.g., precipitation and temperature), the measure of SM is more difficult and complicated. Moreover, due to the sparse distribution of gauge observation, the spatiotemporal coverage and representation are not well obtained. Thus, it prevents us from conducting reliable quantitative analysis and comprehensive studies. With the rapid development of remote sensing technology and the continuous improvement of relevant models, various SM estimation products (such as reanalysis, Land Data Assimilation Systems [LDAS], and satellite retrieval) are more widely used in the existing research because of their good spatiotemporal continuity. But due to the differences in data source, algorithms, coverage, and spatiotemporal resolution, all these products are subject to great uncertainties Chen et al., , 2021. On the other hand, many different products (e.g., ERA-Interim and ERA5) are constantly being iterated and updated. Therefore, effective evaluation of SM products is a prerequisite for more appropriate and targeted use of SM products in different regions and time scales. And it also provides scientific support for the application of SM products in hydrological research and drought monitoring (Chen &

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“…Recently, geospatial technologies have been used extensively for spatiotemporal monitoring of environmental phenomena, including land use/land cover changes [6,[9][10][11][12][13][14][15], understanding the ecosystem functions [16,17], identifying agricultural systems and crop mapping [3,8,18,19], estimating fractional crop cover and crop residue [20], estimating the impacts of urbanization on agricultural dynamics [3], identifying the karst cavities in agricultural areas [21][22][23], and water balance assessments at regional and local scales [6,24]. Many investigations have shown a great deal of potential in terms of different machine learning approaches in imagery classification, such as vector machine support [3,[25][26][27][28][29] and random forest [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Agricultural Water Requirements for Semi-Arid Areas: A Case Study of the Boufakrane River Watershed (Morocco)

et al. 2021

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This work was undertaken to develop a low-cost but reliable assessment method for agricultural water requirements in semi-arid locations based on remote sensing data/techniques. In semi-arid locations, water resources are often limited, and long-term water consumption may exceed the natural replenishment rates of groundwater reservoirs. Sustainable land management in these locations must include tools that facilitate assessment of the impact of potential future land use changes. Agricultural practices in the Boufakrane River watershed (Morocco) were used as a case study application. Land use practices were mapped at the thematic resolution of individual crops, using a total of 13 images generated from the Sentinel-2 satellites. Using a supervised classification scheme, crop types were identified as cereals, other crops followed by cereals, vegetables, olive trees, and fruit trees. Two classifiers were used, namely Support vector machine (SVM) and Random forest (RF). A validation of the classified parcels showed a high overall accuracy of 89.76% for SVM and 84.03% for RF. Results showed that cereal is the most represented species, covering 8870.43 ha and representing 52.42% of the total area, followed by olive trees with 4323.18 ha and a coverage rate of 25%. Vegetables and other crops followed by cereals cover 1530.06 ha and 1661.45 ha, respectively, representing 9.4% and 9.8% of the total area. In the last rank, fruit trees occupy only 3.67% of the total area, with 621.06 ha. The Food and Agriculture Organization (FAO) free software was used to overlay satellite data images with those of climate for agricultural water resources management in the region. This process facilitated estimations of irrigation water requirements for all crop types, taking into account total potential evapotranspiration, effective rainfall, and irrigation water requirements. Results showed that olive trees, fruit trees, and other crops followed by cereals are the most water demanding, with irrigation requirements exceeding 500 mm. The irrigation requirements of cereals and vegetables are lower than those of other classes, with amounts of 300 mm and 150 mm, respectively.

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Spatial and temporal variations in vegetation coverage observed using AVHRR GIMMS and Terra MODIS data in the mainland of China

Cited by 42 publications

References 96 publications

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

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

InterComparison and Evaluation of MultiSource Soil Moisture Products in China

Assessment of Agricultural Water Requirements for Semi-Arid Areas: A Case Study of the Boufakrane River Watershed (Morocco)

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