Remote Sensing Used to Detect Changes in Land Use and Forest Cover (A Case Study of Astara City)

Document Type : ترویجی

Authors

1 Professor, Department of Physical geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Iran.

2 Msc in Remote Sensing & Gis, Faculty of Social Sciences, Mohaghegh Ardabili University, Ardabil, Iran.

Abstract

Given the significance of the Hyrcanian Forests, inscribed by the UNESCO as a world heritage site, it is essential to monitor the changes in and the devastation of the forest cover in this ecoregion for the planning and management of national lands. It is the objective of the present study to monitor changes in both land use and forest cover in Astara region using Landsat TM, OLI 1 & 2 sensors for the years 1995 and 2022. For this purpose, images captured on days with cloud covers of less than 10% were selected over three time intervals and the relevant enhanced Vegetation Index (EVI) values were determined based on Landsat inter-band relations. In the next stage, the index values were combined to derive the land use map using the support vector machine (SVM) algorithm. The results of accuracy evaluation showed that the overall accuracy and Kappa coefficients of the land use map for the year 1995 were equal to 89 and 92% and those for 2022 were 0.86 and 0.75%, respectively, indicating acceptable results. The results of land use changes in Astara city during the period from 1995 to 2022 showed that residential land use had increased by 7% equal to 2954 ha while rangeland and agricultural uses had decreased by 1 to 2% equal to 258 and 997 ha, respectively. However, an important land use along the Caspian coast line – that is, forest cover - stretched over an area of 34283 ha equal to 80% of the study area, which declined in 2022 to 32522 ha equal to 76%, showing a decrease of 4% equal to 1761 ha. It is clear that, owing to its rich archive of satellite images, the GEE system can be used as a strong and useful tool for monitoring and managing forest lands.

Keywords

References

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Land Management Journal
Volume 11, Issue 2
January 2024
Pages 195-208

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