ICOMP ...

URL: http://portal.igg.ac.mn/dataset/3037b39f-7822-4cb9-aca5-eed8383c0c69/resource/f9aed67e-26ed-4b1a-89fc-a0c72b732469/download/icomp-2020_munkhdulam_renchinmyadag_bayanjargal.pdf

Accurate assessment of land degradation is a global need, especially in arid and semi-arid regions. In practice, synergistic uses that integrate field and long term observational studies are more significant. Observation data with long term as vegetation can acquire from remotely sensed data that can be used for assessing land degradation. Because satellite sensors including light detection and ranging (LiDAR), radio detection and ranging (RADAR), and multi-spectral images provide highly informative remote measurements at different spectral, spatial, and temporal resolutions which can be used to detect mapping and monitor of land degradation for decision-makers dealing with environmental management. The aim of this study is to assess land degradation using trend and sen’s slope. Time series analysis used MODIS normalized difference vegetation index (NDVI) data with a resolution of 250 m, 16-day composites for the period 2002-2019. For detection of land degradation used trend analysis methods including linear regression model, and Sen’s Slope. Regression slope values were derived, and trend maps covering Mongolia at a 250 m were generated that takes account of each time series vector significance.

There are no views created for this resource yet.

Additional Information

Field Value
Data last updated October 12, 2020
Metadata last updated October 12, 2020
Created October 12, 2020
Format application/pdf
License Creative Commons Attribution
createdover 4 years ago
format.pdf
idf9aed67e-26ed-4b1a-89fc-a0c72b732469
last modifiedover 4 years ago
mimetypeapplication/pdf
on same domainTrue
package id3037b39f-7822-4cb9-aca5-eed8383c0c69
revision id2d3919f0-03d7-46e1-828e-cd7de17d413b
size7.2 MiB
stateactive
url typeupload