Establishing a land degradation neutrality national baseline through trend analysis of GIMMS NDVI Time-series

2018 ◽  
Vol 29 (9) ◽  
pp. 2985-2997 ◽  
Author(s):  
Helene Gichenje ◽  
Sérgio Godinho
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Land Degradation ◽  
Ndvi Time Series ◽  
Gimms Ndvi

Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia

2015 ◽  
Vol 113 ◽  
pp. 16-28 ◽  
Author(s):  
Sandra Eckert ◽  
Fabia Hüsler ◽  
Hanspeter Liniger ◽  
Elias Hodel
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Land Degradation ◽  
Modis Ndvi ◽  
Ndvi Time Series

scholarly journals Assessment of Regional Vegetation Response to Climate Anomalies: A Case Study for Australia Using GIMMS NDVI Time Series between 1982 and 2006

2017 ◽  
Vol 9 (1) ◽  
pp. 34 ◽  
Author(s):  
Wanda De Keersmaecker ◽  
Stef Lhermitte ◽  
Michael Hill ◽  
Laurent Tits ◽  
Pol Coppin ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Response ◽  
Climate Anomalies ◽  
Ndvi Time Series ◽  
Gimms Ndvi

scholarly journals FREQUENCY ANALYSIS OF MODIS NDVI TIME SERIES FOR DETERMINING HOTSPOT OF LAND DEGRADATION IN MONGOLIA

2018 ◽  
Vol XLII-3 ◽  
pp. 1299-1304
Author(s):  
E. Nasanbat ◽  
S. Sharav ◽  
T. Sanjaa ◽  
O. Lkhamjav ◽  
E. Magsar ◽  
...  
Keyword(s):  
Time Series ◽  
Satellite Imagery ◽  
Frequency Analysis ◽  
Land Degradation ◽  
Temperature Changes ◽  
Modis Ndvi ◽  
Growing Seasons ◽  
Time Period ◽  
Ndvi Time Series ◽  
Land Degradation And Desertification

This study examines MODIS NDVI satellite imagery time series can be used to determine hotspot of land degradation area in whole Mongolia. The trend statistical analysis of Mann-Kendall was applied to a 16-year MODIS NDVI satellite imagery record, based on 16-day composited temporal data (from May to September) for growing seasons and from 2000 to 2016. We performed to frequency analysis that resulting NDVI residual trend pattern would enable successful determined of negative and positive changes in photo synthetically health vegetation. Our result showed that negative and positive values and generated a map of significant trends. Also, we examined long-term of meteorological parameters for the same period. The result showed positive and negative NDVI trends concurred with land cover types change representing an improve or a degrade in vegetation, respectively. Also, integrated the climate parameters which were precipitation and air temperature changes in the same time period seem to have had an affecting on huge NDVI trend area. The time series trend analysis approach applied successfully determined hotspot of an improvement and a degraded area due to land degradation and desertification.

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