Quantitative study of spatiotemporal changes in ecology to monitor land degradation in Alborz Province.

Document Type : Research Paper

Authors

1 Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran

2 Assistance Professors, Faculty of Natural Resources ,University of Tehran ,Karaj, Iran.

Abstract

The expansion of human activities has caused widespread disturbances in ecosystems worldwide, necessitating the development of effective tools to monitor and quantify these changes. Remote sensing stands as a powerful tool for monitoring and quantifying ecological changes over time and space. In this study, a remote sensing-based ecological index (RSEI) was used to investigate land degradation and desertification in Alborz province during the period 2000-2020. After examining land use changes, the trend of RSEI outputs was evaluated using the Mann-Kendall test and Theil-Sen estimator. The examination of land use changes during the period 2000-2020 showed that barren lands, rangelands, shrublands and forests decreased by 2.30%, 6.25%, 1.53%, and 0.18%, respectively, while crop lands, built-up lands, and dam increased by 8.23%, 1.85%, and 0.18%, respectively. The analysis of the trend of changes in the RSEI using the Mann-Kendall test showed that the changes in 16.27% of Alborz province was decreasing, of which about 0.5% was statistically significant. This decreasing trend was also shown by the Theil-Sen estimator in 13% of this region. The results of this study using the Mann-Kendall test also showed that the RSEI values increased in 80.73% of the study area, of which about 4% increased significantly. The analysis of changes in this index using the Theil-Sen estimator showed that this index increased in 87% of this region. This study suggests that the RSEI approach performs effectively in quantifying and detecting ecological changes and, as a result, land degradation at various scales.

Keywords


 
Alcaraz-Segura, D., A. Lomba, R. Sousa-Silva, D. Nieto-Lugilde, P. Alves, D. Georges, J. R. Vicente, J. P. Honrado, 2017. Potential of satellite-derived ecosystem functional attributes to anticipate species range shifts. International Journal of Applied Earth Observation and Geoinformation, 57; 86-92. https://doi.org/https://doi.org/10.1016/j.jag.2016.12.009
Badapalli, P. K., K. Raghu Babu, M. Rajasekhar, M. Ramachandra, 2019. Assessment of aeolian desertification near Vedavathi river channel in Central part of Andhra Pradesh: Remote Sensing Approach. Remote Sensing of Land, 3(1); 39-49.
Baldocchi, D., 2008. Breathing of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems. Australian Journal of Botany, 56(1); 1-26. https://doi.org/https://doi.org/10.1071/BT07151
Caccamo, G., L. A. Chisholm, R. A. Bradstock, M. L. Puotinen, 2011. Assessing the sensitivity of MODIS to monitor drought in high biomass ecosystems. Remote Sensing of Environment, 115(10); 2626-2639. https://doi.org/https://doi.org/10.1016/j.rse.2011.05.018
Coutts, A. M., R. J. Harris, T. Phan, S. J. Livesley, N. S. G. Williams, N. J. Tapper, 2016. Thermal infrared remote sensing of urban heat: Hotspots, vegetation, and an assessment of techniques for use in urban planning. Remote Sensing of Environment, 186; 637-651. https://doi.org/https://doi.org/10.1016/j.rse.2016.09.007
de Araujo Barbosa, C. C., P. M. Atkinson, J. A. Dearing, 2015. Remote sensing of ecosystem services: A systematic review. Ecological Indicators, 52; 430-443. https://doi.org/https://doi.org/10.1016/j.ecolind.2015.01.007
Du, J., Z. Quan, S. Fang, C. Liu, J. Wu, Q. Fu, 2020. Spatiotemporal changes in vegetation coverage and its causes in China since the Chinese economic reform. Environmental Science and Pollution Research, 27(1); 1144-1159. https://doi.org/10.1007/s11356-019-06609-6
Dubinin, V., T. Svoray, M. Dorman, A. Perevolotsky, 2018. Detecting biodiversity refugia using remotely sensed data. Landscape Ecology, 33(10); 1815-1830. https://doi.org/10.1007/s10980-018-0705-1
 
 
city). Journal of RS and GIS for Natural Resources, 14(1); 86-100. https://doi.org/10.30495/girs.2023.686944
Goodarzi, M., F. S. Mortazavizadeh, 2020. Assessing Climate Change Impacts on Groundwater Fluctuations Using RCP Scenarios. Iranian journal of Ecohydrology, 7(3); 801-814. https://doi.org/10.22059/ije.2020.302639.1330
Hu, X., H. Xu, 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/https://doi.org/10.1016/j.ecolind.2018.02.006
Huang, G., M. L. Cadenasso, 2016. People, landscape, and urban heat island: dynamics among neighborhood social conditions, land cover and surface temperatures. Landscape Ecology, 31(10); 2507-2515. https://doi.org/10.1007/s10980-016-0437-z
Ivits, E., G. Buchanan, L. Olsvig-Whittaker, M. Cherlet, 2011. European Farmland Bird Distribution Explained by Remotely Sensed Phenological Indices. Environmental Modeling & Assessment, 16(4); 385-399. https://doi.org/10.1007/s10666-011-9251-9
Kumar, B. P., K. R. Babu, M. Rajasekhar, M. Ramachandra, 2020. Identification of land degradation hotspots in semiarid region of Anantapur district, Southern India, using geospatial modeling approaches. Modeling Earth Systems and Environment, 6(3); 1841-1852. https://doi.org/10.1007/s40808-020-00794-x
Levin, S. A. 1992. The Problem of Pattern and Scale in Ecology: The Robert H. MacArthur Award Lecture. Ecology, 73(6); 1943-1967. https://doi.org/https://doi.org/10.2307/1941447
McDonnell, M. J., I. MacGregor-Fors, 2016. The ecological future of cities. Science, 352(6288); 936-938. https://doi.org/doi:10.1126/science.aaf3630
Mishra, N. B., K. A. Crews, N. Neeti, T. Meyer, K. R. Young, 2015. MODIS derived vegetation greenness trends in African Savanna: Deconstructing and localizing the role of changing moisture availability, fire regime and anthropogenic impact. Remote Sensing of Environment, 169; 192-204. https://doi.org/https://doi.org/10.1016/j.rse.2015.08.008
Naseri, N., R. Mostafazadeh, 2023. Spatial relationship of Remote Sensing Ecological Indicator (RSEI) and landscape metrics under urban development intensification. Earth Science Informatics, 16(4); 3797-3810. https://doi.org/10.1007/s12145-023-01119-z
Pourebrahim, S., M. Hadipour, Z. Emlaei, H. Heidari, C. T. Goh, K. E. Lee, 2023. Analysis of Environmental Carrying Capacity Based on the Ecological Footprint for the Sustainable Development of Alborz, Iran. Sustainability, 15(10); 7935. https://www.mdpi.com/2071-1050/15/10/7935
Qiu, B., G. Chen, Z. Tang, D. Lu, Z. Wang, C. Chen, 2017. Assessing the Three-North Shelter Forest Program in China by a novel framework for characterizing vegetation changes. ISPRS Journal of Photogrammetry and Remote Sensing, 133; 75-88.  https://doi.org/https://doi.org/10.1016/j.isprsjprs.2017.10.003
Rhee, J., J. Im, G. J. Carbone, 2010. Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sensing of Environment, 114(12); 2875-2887. https://doi.org/https://doi.org/10.1016/j.rse.2010.07.005
Safari, F., A. Shahbazi, H. Ketabchi, 2019. Quality analysis and nitrate map of groundwater resources in Alborz province (Hashtgerd Plain). Journal of Water and Soil Conservation, 26(5); 113-130. https://doi.org/10.22069/jwsc.2020.16571.3185
Tiner, R. W. 2004. Remotely-sensed indicators for monitoring the general condition of “natural habitat” in watersheds: an application for Delaware’s Nanticoke River watershed. Ecological Indicators, 4(4); 227-243. https://doi.org/https://doi.org/10.1016/j.ecolind.2004.04.002
Wang, M., Z. Zhang, T. Hu, X. Liu, 2019. A Practical Single-Channel Algorithm for Land Surface Temperature Retrieval: Application to Landsat Series Data. Journal of Geophysical Research: Atmospheres, 124(1); 299-316. https://doi.org/https://doi.org/10.1029/2018JD029330
Williams, M., B. Longstaff, C. Buchanan, R. Llansó, W. Dennison, 2009. Development and evaluation of a spatially-explicit index of Chesapeake Bay health. Marine Pollution Bulletin, 59(1); 14-25. https://doi.org/https://doi.org/10.1016/j.marpolbul.2008.11.018
Willis, K. S. 2015. Remote sensing change detection for ecological monitoring in United States protected areas. Biological Conservation, 182; 233-242. https://doi.org/https://doi.org/10.1016/j.biocon.2014.12.006
Xu, H., M. Wang, T. Shi, H. Guan, C. Fang, Z. Lin, 2018. Prediction of ecological effects of potential population and impervious surface increases using a remote sensing based ecological index (RSEI). Ecological Indicators, 93; 730-740. https://doi.org/https://doi.org/10.1016/j.ecolind.2018.05.055
Xu, H., Y. Wang, H. Guan, T. Shi, X. Hu, 2019. Detecting Ecological Changes with a Remote Sensing Based Ecological Index (RSEI) Produced Time Series and Change Vector Analysis. Remote Sensing, 11(20); 2345. https://www.mdpi.com/2072-4292/11/20/2345
Zhang, K., R. Feng, Z. Zhang, C. Deng, H. Zhang, K. Liu, 2022. Exploring the Driving Factors of Remote Sensing Ecological Index Changes from the Perspective of Geospatial Differentiation: A Case Study of the Weihe River Basin, China. International Journal of Environmental Research and Public Health, 19(17); 10930. https://www.mdpi.com/1660-4601/19/17/10930