Evaluation of spatial-temporal land use changes based on qualitative ecological indicators (Case study: Zaribar Lake Basin)

Document Type : Research Article

Authors

1 Ph.D. Student in Environmental Sciences, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran

2 Department of Environment Sciences, Faculty of Natural Resources, University of Kurdistan, Iran

10.22067/geoeh.2024.86012.1446

Abstract

Remote sensing technology can objectively and quantitatively evaluate spatial-temporal changes in environmental quality related to land use changes. Understanding regional environmental quality and ecological changes is very important for environmental monitoring and management and urban construction planning. In order to evaluate the spatial-temporal changes in environmental quality in the Zaribar lake basin, Landsat images of 1998, 2010, and 2022 were used to extract four indicators of vegetation cover, humidity, heat, and dryness. Then, the remote sensing ecological index was obtained by principal component analysis. The spatial heterogeneity of the remote sensing ecological index in the studied period was evaluated by Moran's index. The findings showed that the inhibiting effect of NDBSI and LST is significantly more than the promoting effect of NDVI and LSM on the environment of the studied area. According to RSEI results, the floor area with poor environmental quality in 1998, 2010, and 2022 was 59.36, 65.49 and 56.02% respectively. The regions with high and moderate RSEI (Risk-Screening Environmental Indicators) levels were primarily comprised of forest and reed lands. The average RSEI values suggested a decline in environmental quality in the Zaribar Lake basin. The global Moran's index scatter charts showed values of 0.86, 0.85, and 0.71 in 1998, 2010, and 2022, respectively, indicating a decrease in spatial homogeneity over this period. Given the intricate nature of the environment, we can evaluate its status using four RSEI indicators. To enhance future research, it would be advantageous to integrate a wider range of spatial data, including primary net productivity and airborne particles.

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