Long-term vegetation changes in semi-arid regions using remote sensing and spatial statistics functions in order to prepare a road map

Subject Areas : Agriculture, rangeland, watershed and forestry

Mohamad Mahdi Ali Molaee ¹ , marzeyh Rezai ² , Rasool Mahdavi ³ , Hamid Gholami ⁴ , Mohamad Kazemi ⁵

1 - Emdad committee
2 - Assistant Professor of Natural Resources Engineering, University of Hormozgan, Bandar Abbas, Iran
3 - Associate Professor of Natural Resources Engineering Group
4 - Department of natural resources engineering, University of Hormozgan
5 - Hormoz Research Center, University of Hormozgan

Received: 2022-09-03 Accepted : 2023-01-18

Keywords: Fars, remote sensing, statistical functions, Vegetation,

Abstract :

Currently, Remote sensing is a useful technology that can be used to extract different layers such as soil, rainfall, vegetation and so on. The ultimate goal in most remote sensing analysis operations that are used to investigate different factors of vegetation cover is that the data of various spectral bands that can represent data such as percentage of vegetation cover, biomass and leaf area index into a single value in each pixel. Reduce. Over time, environmental and human factors have caused positive and negative changes in the quantity and quality of vegetation; This situation will continue in the future. Temporal changes in vegetation may be in the form of increasing or decreasing trends. Recognizing these changes and determining their trends in the past and future can open the way for decision-making for the image of the land. One of the ways to study vegetation changes as the most important indicator of land degradation is remote sensing. Based on this, in this research, using the NDVI normalized vegetation difference index in HDF format and MODIS sensor with a pixel size of 250 meters in a 16-day period, monitoring the long-term changes in the vegetation cover of Fars province during a 20-year period from 2000 to 2020 and was investigated using spatial statistics functions in order to prepare a road map in the year 1400. For this purpose, using spatial statistics functions to prepare a road map. For this purpose, MADIS data has been used for a period of twenty years. Then the data were analyzed by classical statistics and spatial statistics. The results show the increasing trend of the vegetation level in a period of 20 years, and the distribution of the vegetation over time was clustered. At the end, a roadmap for long-term vegetation monitoring in Fars province was proposed.