Volume 11, Issue 1 (5-2024)
Journal of Spatial Analysis Environmental Hazards 2024, 11(1): 97-111 |
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Rahmani M, Ahmadi Mirghaed ّ, Mollaaghajanzadeh S. Measuring spatiotemporal change in land use and habitat quality through remote sensing: A case study of the Tajan watershed, north of Iran. Journal of Spatial Analysis Environmental Hazards 2024; 11 (1) : 6
URL: http://jsaeh.khu.ac.ir/article-1-3376-en.html
URL: http://jsaeh.khu.ac.ir/article-1-3376-en.html
1- Mazandaran University , m.rahmani@umz.ac.ir
2- Mazandaran University
2- Mazandaran University
Abstract: (6324 Views)
This study aimed to assess the habitat quality of the Tajan watershed in northern Iran through land use changes from 1992 to 2052 and to investigate its relationship with landscape metrics, including number of patches (NP), patch density (PD), edge density (ED), largest patch index (LPI), landscape shape index (LSI), and splitting index (SPLIT). Landsat 8 and 4 images were processed to produce land use maps for 1992, 2022, and 2052 using maximum likelihood, cross-combination, and CA-Markov methods in ENVI and TerrSet software. Habitat quality was also assessed using InVEST in three scenarios based on the land use maps. Relationships were analyzed using least squares regression and Spearman's correlation test. The results showed that from 1992 to 2052, forest and agricultural areas had the most decreasing (-82,460 hectares) and increasing (76,392 hectares) changes, respectively. Habitats in the central part of the watershed had higher quality than those in the northern and southern parts. The relationship between habitat quality and land use changes is significant and inverse (-0.95
Article number: 6
Type of Study: Research |
Subject:
Special
Received: 2023/05/12 | Accepted: 2024/06/19 | Published: 2024/06/19
Received: 2023/05/12 | Accepted: 2024/06/19 | Published: 2024/06/19
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46. Sallustio, L.; A. De Toni, A. Strollo, M. Di Febbraro, E. Gissi, L. Casella, D. Geneletti, M. Munafo, M. Vizzarri, and M. Marchetti. 2017. Assessing habitat quality in relation to the spatial distribution of protected areas in Italy. Journal of Environmental Management, 201: 129–137.
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48. Sisay, G.; B. Gessesse, C. Fürst, M. Kassie, B. Kebede. 2023. Modeling of land use/land cover dynamics using artificial neural network and cellular automata Markov chain algorithms in Goang watershed, Ethiopia. Heliyon, 9(9).
49. TEEB. 2010. Main streaming the economics of nature: A synthesis of the approach, conclusions and recommendations of TEEB (The Economics of Ecosystems and Biodiversity). Earthscan.
50. USGS. 2020. United States geological survey website. https://earthexplorer.usgs.gov.
51. Wei, L.; L. Zhou, D. Sun, B. Yuan, and F. Hu. 2022a. Evaluating the impact of urban expansion on the habitat quality and constructing ecological security patterns: A case study of Jiziwan in the Yellow River Basin, China. Ecological Indicators, 145: 109544.
52. Wei, Q.; M. Abudureheman, A. Halike, K. Yao, L. Yao, H. Tang, and B. Tuheti. 2022b. Temporal and spatial variation analysis of habitat quality on the PLUS-InVEST model for Ebinur Lake Basin, China. Ecological Indicators, 145: 109632.
53. Wu, L.; C. Sun, and F. Fan. 2021. Estimating the Characteristic Spatiotemporal Variation in Habitat Quality Using the InVEST Model—A Case Study from Guangdong–Hong Kong–Macao Greater Bay Area. Remote Sensing, 13(5): 1008.
54. Zhang, C.; and S. Fang. 2021. Identifying and Zoning Key Areas of Ecological Restoration for Territory in Resource-Based Cities: A Case Study of Huangshi City, China. Sustainability, 13(7): 3931.
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