Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1299-1304, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1299-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1299-1304, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1299-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

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

E. Nasanbat1,3, S. Sharav2, T. Sanjaa2, O. Lkhamjav3, E. Magsar3,4, and B. Tuvdendorj5 E. Nasanbat et al.
  • 1Information and Research Institute of Meteorology, Hydrology and Environment, Juulchiny street-5, Ulaanbaatar 15160, Mongolia
  • 2School of Applied Sciences, Mongolian University of Science and Technology, Ulaanbaatar, Mongolia
  • 3Mongolian Geospatial Association, P.O. Box 24/38, Ulaanbaatar 15141, Mongolia
  • 4National Agency for Meteorology and Environmental Monitoring, Juulchiny street-5, Ulaanbaatar 15160, Mongolia
  • 5Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth(RADI), Chinese Academy of Sciences Olympic Village Science Park,W. Beichen Road, Beijing 100101, China

Keywords: MODIS-NDVI, Climate Parameter, Time-series trend analysis, Mann-Kendall

Abstract. 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.