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

Document Type : Research Article

Authors

• Mahin Sedpanah ¹
• Jamil Amanollahi ²

¹ PhD Student in Environment Science, Department of Environmental Sciences, University of Kurdistan, Iran.

² Associate Professor in Environmental Science, Department of Environmental Sciences, University of Kurdistan, Iran.

10.22067/geoeh.2024.86012.1446

Abstract

Extended Abstract
 
Introduction
Since the Remote Sensing Ecological Index (RSEI) is derived from various aspects and characteristics of an ecosystem, it can be used as a reliable index to show environmental quality or assess the condition of a region or ecosystem. The quality of urban ecosystems is determined by several indices, including the Normalized Difference Vegetation Index (NDVI), leaf area index (LAI), Land Surface Temperature (LST), Land Surface Moisture (LSM), Normalized Difference Built-up Index (NDBI), Index-Based Built-up Index (IBI), and Normalized Difference Impervious Surface Index (NDISI). However, most of these indicators are not comprehensive enough and cannot evaluate all the important and effective aspects of urban environmental quality. RSEI integrates various indices without requiring manual weighting. This index is a comprehensive measure derived from four indices—vegetation, humidity, dryness, and heat—using Principal Component Analysis (PCA).
Wang et al. (2022) investigated the environmental quality of eastern China using RSEI. The average RSEI values for the years 2000, 2005, 2010, 2015, and 2020 were reported as 0.67, 0.55, 0.59, 0.58, and 0.63, respectively, showing an initial decline followed by an increase. The purpose of the current research is to investigate the environmental quality of the Zaribar Lake basin using RSEI. The variables required to calculate this index were obtained under the conceptual model of Pressure-State-Response.
 
Material and Methods
Zaribar Lake, covering an area of 20 km² with an average depth of 5 m, is located 3 km west of Marivan city, Kurdistan province, at an altitude of 1,320 m above sea level. Landsat satellite images for 1998, 2010, and 2022 were downloaded from the United States Geological Survey website (https://earthexplorer.usgs.gov). After performing the pre-processing operations, land use maps were created using the supervised classification method and the maximum likelihood algorithm in ENVI 5.3 software. The maps categorized land into five classes: built-up areas, agricultural lands, lakes, forests, and reeds.
To verify the accuracy of the classified maps, a random sampling method was employed with ground truth points (control points). Control points were collected through Google Earth images, and classification accuracy was assessed using an error matrix and statistical parameters (kappa coefficient and overall accuracy). The RSEI was calculated using NDVI, LSM, LST, and NDBSI, representing greenness, humidity, heat, and dryness, respectively.
 
Results and Discussion
Considering the large water area of Zaribar Lake, the lake was excluded from the computational analysis. The results indicated that the specific values of Principal Component 1 (PC1) for each year exceeded 60%, ranging from 63.78% to 71.35%. Two contrasting groups of indicators were observed based on their contribution to environmental quality. NDVI and LSM indices were positively associated, while LST and NDBSI were negatively associated with environmental quality.
The average RSEI for 1998, 2010, and 2022 was 0.39, 0.38, and 0.37, respectively, showing a declining trend. The values suggest an average RSEI of less than 0.40, indicating poor environmental quality in the Zaribar Lake basin. Environmental quality improved with increased vegetation, humidity, and surface water content, while higher NDBSI and LST values indicated greater soil degradation and poorer conditions.
Between 1998 and 2022, NDVI decreased from 0.56 to 0.45, and LSM decreased from 0.64 to 0.62, both negatively impacting environmental quality. Conversely, NDBSI increased from 0.63 to 0.65, and LST rose from 0.77 to 0.87, contributing to environmental degradation. NDBSI reflects changes in urban construction and highlights how urbanization in the Zaribar Lake basin has significantly affected environmental quality.
 
Conclusion
The analysis of environmental quality changes in the Zaribar Lake basin for 1998, 2010, and 2022 using RSEI revealed that vegetation and humidity positively influenced the environment, whereas heat and dryness had negative effects. The inhibitory effects of NDBSI and LST were more pronounced than the positive effects of NDVI and LSM. The weak RSEI category occupied the largest area, while excellent and good levels were primarily observed in forest and reed lands around Zaribar Lake.
The findings demonstrate that environmental quality in the Zaribar Lake basin has significantly declined, with the average RSEI decreasing from 0.39 in 1998 to 0.37 in 2022. Land use changes were identified as a major factor contributing to the degradation of environmental quality. Therefore, effective urban planning and sustainable land management practices are essential to improve and maintain the ecological quality of the Zaribar Lake basin.

Keywords

• Remote Sensing Index
• Land Use Changes
• Vegetation Index
• Moran Index
• Environmental Quality Assessment

Main Subjects

• Ecology and environment

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