Using multiple remote sensing perspectives to identify and attribute land surface dynamics in Central Asia 2001–2013

Beurs, Kirsten M. de; Henebry, Geoffrey M.; Owsley, B.; Sokolik, I. N.

doi:10.1016/j.rse.2015.08.018

Cited by 142 publications

(83 citation statements)

References 63 publications

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“…The fundamental findings of this study support previous region-wide research [12,14,19]. Unlike the previous studies, we applied a novel integrated methodology to measure and compare the impact of different environmental factors on vegetation dynamics rather than by the more traditional approach using pair-wise correlations between vegetation and possible drivers used in the cited papers.…”

Section: Factors Of Inter-annual Dynamics Of Irrigated Croplandssupporting

confidence: 78%

“…The reported studies focused mainly on trend analysis of satellite-based vegetation indices and correlated those on a pair-wise basis with likely drivers such as in the recent study by [19]. Analyses of pair-wise correlations between vegetation trends and their drivers, though useful, neither provide information on the impacts of driving factors on vegetation dynamics on an annual basis, nor do they consider the cumulative impact of several factors on inter-annual vegetation changes.…”

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confidence: 99%

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Quantifying the Impacts of Environmental Factors on Vegetation Dynamics over Climatic and Management Gradients of Central Asia

et al. 2016

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Abstract:Currently there is a lack of quantitative information regarding the driving factors of vegetation dynamics in post-Soviet Central Asia. Insufficient knowledge also exists concerning vegetation variability across sub-humid to arid climatic gradients as well as vegetation response to different land uses, from natural rangelands to intensively irrigated croplands. In this study, we analyzed the environmental drivers of vegetation dynamics in five Central Asian countries by coupling key vegetation parameter "overall greenness" derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI time series data, with its possible factors across various management and climatic gradients. We developed nine generalized least-squares random effect (GLS-RE) models to analyze the relative impact of environmental factors on vegetation dynamics. The obtained results quantitatively indicated the extensive control of climatic factors on managed and unmanaged vegetation cover across Central Asia. The most diverse vegetation dynamics response to climatic variables was observed for "intensively managed irrigated croplands". Almost no differences in response to these variables were detected for managed non-irrigated vegetation and unmanaged (natural) vegetation across all countries. Natural vegetation and rainfed non-irrigated crop dynamics were principally associated with temperature and precipitation parameters. Variables related to temperature had the greatest relative effect on irrigated croplands and on vegetation cover within the mountainous zone. Further research should focus on incorporating the socio-economic factors discussed here in a similar analysis.

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Section: Factors Of Inter-annual Dynamics Of Irrigated Croplandssupporting

confidence: 78%

mentioning

confidence: 99%

Quantifying the Impacts of Environmental Factors on Vegetation Dynamics over Climatic and Management Gradients of Central Asia

et al. 2016

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“…Whereas, it could be assumed that changes in land management may also cause divergence between LOS and GSI (e.g., a change from one crop-type to another with different LOS/GSI characteristics), it is noteworthy that both converging trends and pixels with significant positive correlations between LOS and GSI are more pronounced for mixed forest than for the three natural forest biomes, which is also the case for croplands as compared to the three natural non-forest biomes ( Figures 4A and 6B). These differences may reflect anthropogenic management in the form of fertilization and irrigation that could alleviate climatic constraints on vegetation growth influencing concurrently on both phenology and productivity [74,76]. Moreover, it should be noted that areas of diverging trends type 2 in croplands are much smaller than it is the case for the three natural non-forest biomes ( Figure 4F).…”

Section: Converging/diverging Trends and Correlations Between Los Andmentioning

confidence: 96%

“…This is further supported by Xia [69] et al who revealed that the inter-biome variations in annual GPP can be better explained by the variations in seasonal maximal photosynthetic capacity than LOS. In addition to the impact from ongoing global warming and natural climate variability, the relationship between vegetation phenology and productivity could also be affected by changes in both/either vegetation metrics induced by anthropogenic land use/cover changes (e.g., deforestation/afforestation, land clearing, irrigation and fertilization, and changes in land management practices) [53,60,[74][75][76]. Whereas, it could be assumed that changes in land management may also cause divergence between LOS and GSI (e.g., a change from one crop-type to another with different LOS/GSI characteristics), it is noteworthy that both converging trends and pixels with significant positive correlations between LOS and GSI are more pronounced for mixed forest than for the three natural forest biomes, which is also the case for croplands as compared to the three natural non-forest biomes ( Figures 4A and 6B).…”

Section: Converging/diverging Trends and Correlations Between Los Andmentioning

confidence: 99%

Temporal Changes in Coupled Vegetation Phenology and Productivity are Biome-Specific in the Northern Hemisphere

Wang

Fensholt

2017

Remote Sensing

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Global warming has greatly stimulated vegetation growth through both extending the growing season and promoting photosynthesis in the Northern Hemisphere (NH). Analyzing the combined dynamics of such trends can potentially improve our current understanding on changes in vegetation functioning and the complex relationship between anthropogenic and climatic drivers. This study aims to analyze the relationships (long-term trends and correlations) of length of vegetation growing season (LOS) and vegetation productivity assessed by the growing season NDVI integral (GSI) in the NH (>30 • N) to study any dependency of major biomes that are characterized by different imprint from anthropogenic influence. Spatial patterns of converging/diverging trends in LOS and GSI and temporal changes in the coupling between LOS and GSI are analyzed for major biomes at hemispheric and continental scales from the third generation Global Inventory Monitoring and Modeling Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) dataset for a 32-year period . A quarter area of the NH is covered by converging trends (consistent significant trends in LOS and GSI), whereas diverging trends (opposing significant trends in LOS and GSI) cover about 6% of the region. Diverging trends are observed mainly in high latitudes and arid/semi-arid areas of non-forest biomes (shrublands, savannas, and grasslands), whereas forest biomes and croplands are primarily characterized by converging trends. The study shows spatially-distinct and biome-specific patterns between the continental land masses of Eurasia (EA) and North America (NA). Finally, areas of high positive correlation between LOS and GSI showed to increase during the period of analysis, with areas of significant positive trends in correlation being more widespread in NA as compared to EA. The temporal changes in the coupled vegetation phenology and productivity suggest complex relationships and interactions that are induced by both ongoing climate change and increasingly intensive human disturbances.

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“…The third generation GIMMS NDVI is a 15-day Maximum Value Composite that was acquired from seven different NOAA satellites (7,9,11,14,16,17,18), which have been processed using an adaptive Empirical Mode Decomposition. NDVI3g is appropriate for long-term studies of land surface trends in vegetation, seasonality and coupling between climate variability and vegetation over the last three decades [31,32].…”

Section: Normalized Difference Vegetation Indexmentioning

confidence: 99%

Towards an Improved Environmental Understanding of Land Surface Dynamics in Ukraine Based on Multi-Source Remote Sensing Time-Series Datasets from 1982 to 2013

et al. 2016

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Ukraine has experienced immense environmental and institutional changes during the last three decades. We have conducted this study to analyze important land surface dynamics and to assess processes underlying the changes. This research was conducted in two consecutive steps. To analyze monotonic changes we first applied a Mann-Kendall trend analysis of the Normalized Difference Vegetation Index (NDVI3g) time series. Gradual and abrupt changes were studied by fitting a seasonal trend model and detecting the breakpoints. Secondly, essential environmental factors were used to quantify their possible relationships with land surface changes. These factors included soil moisture as well as gridded air temperature and precipitation data. This was done using partial rank correlation analysis based on annually aggregated time-series. Our results demonstrate that positive NDVI trends characterize approximately one-third of Ukraine's land surface, located in the northern and western areas of the country. Negative trends occurred less frequently, covering less than 2% of the area and are distributed irregularly across the country. Monotonic trends were rarely found; shifting trends were identified with a greater frequency. Trend shifts were seen to occur with an increased frequency following the period of the 2000s. We determined that land surface dynamics and climate variability are functionally interdependent; however, the relative influence of the drivers varies in different locations. Among the factors analyzed, the air temperature variable explains the largest portion of NDVI variability. High air temperature/NDVI correlation coefficients (r = 0.36´0.77) are observed over the entire country. The soil moisture content is of significant influence in the eastern portion of Ukraine (r = 0.68); precipitation (r = 0.65) was most influential in the central regions of the country. These results increase our understanding of ecosystem responses to climatic changes and anthropogenic activities.

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Using multiple remote sensing perspectives to identify and attribute land surface dynamics in Central Asia 2001–2013

Cited by 142 publications

References 63 publications

Quantifying the Impacts of Environmental Factors on Vegetation Dynamics over Climatic and Management Gradients of Central Asia

Quantifying the Impacts of Environmental Factors on Vegetation Dynamics over Climatic and Management Gradients of Central Asia

Temporal Changes in Coupled Vegetation Phenology and Productivity are Biome-Specific in the Northern Hemisphere

Towards an Improved Environmental Understanding of Land Surface Dynamics in Ukraine Based on Multi-Source Remote Sensing Time-Series Datasets from 1982 to 2013

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