Evaluation of Spatial-Temporal Land Use Changes Based on Qualitative Ecological Indicators (Case Study: Zaribar Lake Basin)

جغرافیا و مخاطرات محیطی

Article 11, Volume 13, Issue 4 - Serial Number 52, December 2024, Pages 269-290   

Document Type: Research Article

DOI: 10.22067/geoeh.2024.86012.1446

Authors

Mahin Sedpanah¹; Jamil Amanollahi^{* 2}

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

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

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

References

Allen, R. G., Tasumi, M., Mors, A., & Terzza, R. (2002). Satellite-Based Evapotranspiration by Energy Balance for Western States Water Management. US Bureau Reclamation Evapotranspiration workshop. https://doi.org/10.1061/40792(173)556

Arekhi, S., Mahmoudian, A., & Emadaddian, S. (2022). Forest Degradation using GIS and Logistic Regression (Case Study: Forests of Sardasht). Journal of Geography and Environmental Hazards, 10(4), 69-92. [In Persian] https://doi.org/10.22067/geoeh.2021.70743.1070

Ariken, M., Zhang, F., Liu, K., Fang, C., & Kung, H. T. (2020). Coupling coordination analysis of urbanization and ecoenvironment in Yanqi Basin based on multi-source remote sensing data. Ecological Indicators, 114, 106331. https://doi.org/10.1016/j.ecolind.2020.106331

Esmaeili, H., Mirmousavi, S., & Soheili, E. (2022). Investigation of Agricultural Drought Time Series in Darab City using Remote Sensing and Google Earth Engine System. Journal of Geography and Environmental Hazards, 10(4), 185-203. [In Persian] https://doi.org/10.22067/geoeh.2021.69186.1029

Hang, X., Li, Y., Luo, X., Xu, M., & Han, X. (2020). Assessing the ecological quality of Nanjing during its urbanization process by using satellite, meteorological, and socioeconomic data. Journal of Meteorological Research, 34(2), 280-293. https://doi.org/10.1007/s13351-020-9150-6

Hang, X., Luo, X. C., Cao, Y., & Li, Y. C. (2020). Ecological quality assessment and urbanization impact based on RSEI model for Nanjing, Jiangsu Province, China. Chinese Journal of Applied Ecology, 31, 219-229. https://doi.org/10.13287/j.1001-9332.202001.030 

Hu, X., & Xu, H. (2018). A new remote sensing index for assessing the spatial heterogeneity in urban ecological quality: A case from Fuzhou City, China. Ecological Indicators, 89, 11-21. https://doi.org/10.1016/j.ecolind.2018.02.006

Jalilian, Z., & Amanollahi, J. (2023). Evaluating Ecological Degradation of Kosalan Protected Area using Remote Sensing and GIS. Journal of Geography and Environmental Hazards, 12(1), 41-61. [In Persian] https://doi.org/10.22067/geoeh.2022.75467.1184

Jing, Y., Zhang, F., He, Y., Kung, H., Johnson, V. C., & Arikena, M. (2020). Assessment of spatial and temporal variation of ecological environment quality in Ebinur Lake Wetland National Nature Reserve, Xinjiang, China. Ecological Indicators, 110, 105874. https://doi.org/10.1016/j.ecolind.2019.105874

Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202. https://doi.org/10.1098/rsta.2015.0202

Kafy, A. A., Rahman, M. S., Faisal, A. A., Hasan, M. M., & Islam, M. (2020). Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi, Bangladesh. Remote Sensing Applications: Society and Environment, 18, 100314. https://doi.org/10.1016/j.rsase.2020.100314

Karbalaei Saleh, S., Amoushahi, A., & Gholipour, M. (2021). Spatiotemporal ecological quality assessment of metropolitan cities: a case study of central Iran. Environmental Monitoring Assessment, 193(5), 305. https://doi.org/10.1007/s10661-021-09082-2

Li, J., Bai, Y., & Alatalo, J. M. (2020). Impacts of rural tourism-driven land use change on ecosystems services provision in Erhai Lake Basin, China. Ecosystem Services, 42, 101081. https://doi.org/10.1016/j.ecoser.2020.101081

Lin, L., Hao, Z., Post, C. J., & Mikhailova, E. A. (2022). Monitoring Ecological Changes on a Rapidly Urbanizing Island Using a Remote Sensing-Based Ecological Index Produced Time Series. Remote Sensing, 14(22), 5773. https://doi.org/10.3390/rs14225773

Lin, L., Hao, Z., Post, C. J., Mikhailova, E. A., Yu, K., Yang, L., & Liu, J. (2020). Monitoring land cover change on a rapidly urbanizing island using Google Earth Engine. Applied Sciences, 10(20), 7336. https://doi.org/10.3390/app10207336

Liu, Q., Yu, F., & Mu, X. (2022). Evaluation of the Ecological Environment Quality of the Kuye River Source Basin Using the Remote Sensing Ecological Index. International Journal of Environmental Research and Public Health, 19(19), 12500. https://doi.org/10.3390/ijerph191912500

Mishra, S. P., Taraphder, S., Swain, D., & Laishram, M. (2017). Multivariate statistical data analysis-principal component analysis (PCA). International Journal of Livestoke Research, 7(5), 60-78. http://dx.doi.org/10.5455/ijlr.20170415115235

Sadeghi, A., Khaledi, J., & Nyman, P. (2018). Rainfall Variability and Land-Use Change in Arid Basins with Traditional Agricultural Practices; Insight from Ground Water Monitoring in Northwestern Iran. Journal of Earth Science and Climatic Change, 9(11), 2-7. https://doi.org/10.4172/2157-7617.1000498

Wang, S., Zhang, M., & Xi, X. (2022). Ecological Environment Evaluation Based on Remote Sensing Ecological Index: A Case Study in East China over the Past 20 Years. Sustainability, 14(23), 15771. https://doi.org/10.3390/su142315771

Wu, A., Zhao, Y., Qin, Y., Liu, X, & Shen, H. (2023). Analysis of Ecological Environment Quality and Its Driving Factors in the Beijing-Tianjin-Hebei Region of China.  Sustainability, 15(10), 7898. https://doi.org/10.3390/su15107898

Xiong, Y., Xu, W., Lu, N., Huang, S., Wu, C., Wang, L., … & Kou, W. (2021). Assessment of spatial–temporal changes of ecological environment quality based on RSEI and GEE: A case study in Erhai Lake Basin, Yunnan province, China. Ecological Indicators, 125, 107518. https://doi.org/10.1016/j.ecolind.2021.107518

Xu, H. Q. (2013). A remote sensing index for assessment of regional ecological changes. China Environmental Science, 33(5), 889-897.

Yang, Z., Tian, J., Su, W., Wu, J., Liu, J., Liu, W., & Guo, R. (2022). Analysis of ecological environmental quality change in the yellow river basin using the remote-sensing-based ecological index. Sustainability, 14(17), 10726. https://doi.org/10.3390/su141710726

Zawadzki, J., Przeździecki, K., & Miatkowski, Z. (2016). Determining the area of influence of depression cone in the vicinity of lignite mine by means of triangle method and LANDSAT TM/ETM+ satellite images. Journal of Environmental Management, 166, 605-614. https://doi.org/10.1016/j.jenvman.2015.11.010

Zhai, H., Xie, W., Li, S., & Zhang, Q. (2019). Urban Ecological Environment Construction Based on Remote Sensing Ecological Index. Ekoloji Dergisi, (108), 1583-1588.

Zhang, Z., Cai, Z., Yang, J., & Guo, X. (2022). Ecological environmental quality assessment of Chinese estuarine wetlands during 2000–2020 based on a remote sensing ecological index. Frontiers in Marine Science, 9, 981139. https://doi.org/10.3389/fmars.2022.981139

Zhu, D., Chen, T., Zhen, N., & Niu, R. (2020). Monitoring the effects of open-pit mining on the eco-environment using a moving window-based remote sensing ecological index. Environmental Science and Pollution Research, 27(13), 15716-15728. https://doi.org/10.1007/s11356-020-08054-2