Assessment of vegetation degradation in mountainous pastures of the Western Tien-Shan, Kyrgyzstan, using eMODIS NDVI

Zhumanova, Munavar; Mönnig, Carsten; Hergarten, Christian; Darr, Dietrich; Wrage‐Mönnig, Nicole

doi:10.1016/j.ecolind.2018.07.060

Cited by 50 publications

(38 citation statements)

References 48 publications

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“…According to the climatic classification of Köppen, the climate of the region is characterized as semiarid (Bsh), and the average annual precipitation of the region is 588 mm. The soil of the study area was classified as Planosol according to the Agroecological Zoning of Pernambuco [31]. Soil characterization was performed according to the methods described by [6] in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Short-term grazing and its impacts on soil and pasture degradation

Batista

Almeida

Silva

et al. 2020

DYNA

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The objective was to verify possible modifications of the soil structure and the pattern of the spectral response of pasture vegetation cover to animal trampling. The study was carried out on a farm in the Agreste region of Pernambuco, Brazil in an area with continuous grazing by heifers. Soil samples were collected at 36 regular points, before and after the grazing period, where the physical properties of the soil were determined at a 0.00—0.10 m depth. Before and after grazing, images of the Sentinel-2A satellite were also obtained to observe the pasture vegetation response pattern over time through Vegetation Indexes. The soil attribute data were submitted to multivariate factorial analysis. The vegetation index maps were evaluated for spatial variability. The results showed that after the grazing, there was a significant change in soil attributes and pasture, which can indicate possible degradation processes.

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Section: Methodsmentioning

confidence: 99%

Short-term grazing and its impacts on soil and pasture degradation

Batista

Almeida

Silva

et al. 2020

DYNA

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“…Enhanced/expedited/expandable MODIS (eMODIS) data provides separate Geostationary Earth Orbit Tagged Image File Format (GeoTIFF) for each product in a 10 day interval, allowing the users to download only the les they need. For example, the eMODIS NDVI imagery for the month of June 2015 includes NDVI data from June 1 st -10 th , 6 th -15 th , 11 th -20 th , 16 th -25 th , 21 st -30 th , and 26 th -July 5 th (Zhumanova et al, 2018). In this study 21 st to 30 th day interval of eMODIS NDVI imagery were taken for analysis purpose for the growing season of crops.…”

Section: Data Source and Methods Of Data Collectionmentioning

confidence: 99%

Mapping the Spatial and Temporal Variation of Agricultural and Metrological Drought Using Geospatial Techniques, Ethiopia

Senamaw

Addisu

Suryabhagavan

2020

Preprint

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Background Geographic Information System (GIS) and Remote Sensing play an important role for near real time monitoring of drought condition over large areas. The objective of this study was to assess spatial and temporal variation of agricultural and metrological drought using temporal image of eMODIS NDVI based vegetation condition index (VCI) and standard precipitation index (SPI). To validate the strength of drought indices correlation analysis was made between VCI and crop yield anomaly as well as SPI and crop yield anomaly. The results revealed that the year 2009 and 2015 were drought years while the 2001 and 2007 were wet years. There was also a good correlation between NDVI and rainfall (r=0.71), VCI and crop yield anomaly (0.72), SPI and crop yield anomaly (0.74). Frequency of metrological and agricultural drought was compiled by using historical drought intensity map. ResultThe result shows that there was complex and local scale variation in frequency of drought events in the study period. There was also no year without drought in many parts of the study area. Combined drought risk map also showed that 8%, 56%, 35% and 8% of study area were vulnerable to very severe, severe and moderate drought condition respectively. Conclusion In conclusion, the study area is highly vulnerable to agricultural and meteorological drought. Thus besides mapping drought vulnerable areas, integrating socioeconomic data for better understand other vulnerable factors were recommended.

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“…We chose the maximum value composite (MVC) method to obtain the NDVI values for the year 2000 and 2015. The MVC method is a procedure used in satellite imaging, which is applied to vegetation studies [53][54][55][56]. It requires that a series of multitemporal geo-referenced satellite data be processed into NDVI images.…”

Section: Biogeographic Variablesmentioning

confidence: 99%

“…It requires that a series of multitemporal geo-referenced satellite data be processed into NDVI images. On a pixel-by-pixel basis, each NDVI value is examined, and only the highest value is retained for each pixel location [56].…”

Section: Biogeographic Variablesmentioning

confidence: 99%

Exploring Variability in Landscape Ecological Risk and Quantifying Its Driving Factors in the Amu Darya Delta

Bao

et al. 2019

IJERPH

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Examining the drivers of landscape ecological risk can provide scientific information for planning and landscape optimization. The landscapes of the Amu Darya Delta (ADD) have recently undergone great changes, leading to increases in landscape ecological risks. However, the relationships between landscape ecological risk and its driving factors are poorly understood. In this study, the ADD was selected to construct landscape ecological risk index (ERI) values for 2000 and 2015. Based on a geographically weighted regression (GWR) model, the relationship between each of the normalized difference vegetation index (NDVI), land surface temperature (LST), digital elevation model (DEM), crop yield, population density (POP), and road density and the spatiotemporal variation in ERI were explored. The results showed that the ERI decreased from the periphery of the ADD to the centre and that high-risk areas were distributed in the ADD’s downstream region, with the total area of high-risk areas increasing by 86.55% from 2000 to 2015. The ERI was spatially correlated with Moran’s I in 2000 and 2015, with correlation of 0.67 and 0.72, respectively. The GWR model indicated that in most ADD areas, the NDVI had a negative impact on the ERI, whereas LST and DEM had positive impacts on the ERI. Crop yield, road density and POP were positively correlated with the ERI in the central region of the ADD, at road nodes and in densely populated urban areas, respectively. Based on the findings of this study, we suggest that the ecological constraints of the aforementioned factors should be considered in the process of delta development and protection.

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Assessment of vegetation degradation in mountainous pastures of the Western Tien-Shan, Kyrgyzstan, using eMODIS NDVI

Cited by 50 publications

References 48 publications

Short-term grazing and its impacts on soil and pasture degradation

Short-term grazing and its impacts on soil and pasture degradation

Mapping the Spatial and Temporal Variation of Agricultural and Metrological Drought Using Geospatial Techniques, Ethiopia

Exploring Variability in Landscape Ecological Risk and Quantifying Its Driving Factors in the Amu Darya Delta

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