Landscape change assessment and its prediction in a mountainous gradient with diverse land-uses

Raoof Mostafazadeh () and Hossein Talebi Khiavi
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Raoof Mostafazadeh: University of Mohaghegh Ardabili
Hossein Talebi Khiavi: University of Mohaghegh Ardabili

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2024, vol. 26, issue 2, No 44, 3941 pages

Abstract: Abstract Land-use change is one of the main threats to the environment and natural resources nowadays. Dealing with the impacts of landscape change and its better management needs up-to-date information and data about the rate and amount of changes in different land-use types and spatial configurations. Remote sensing and landscape assessment provide diverse metrics for analyzing spatiotemporal changes over different scales. The purpose of this study is to quantifying the spatiotemporal analysis of landscape change index (LCI) during 1984 to 2016, and predicting the trend of land-use in 2030 using CA–Markov model. Toward this attempt, the Meshgin-Shahr area was chosen as a region with high potential in agriculture and tourism that has undergone many changes in recent decades. Landsat images were used to change detection, and landscape indices were calculated. Also, the LCI has been quantified in different time intervals under study. Also, the CA–Markov model was employed to predict the trend of changes in 2032. The values of CONTAG_MN, AREA_MN, CONNECT, and edge density (ED) landscape metrics have been increased at the class level. Assessing the trend of changes in the landscape metrics showed that the number of patch (NP), patch density (PD), largest patch index (LPI), ED, and total edge (TE) had a decreasing trend at the landscape level, which shows a significant decrease from 2008 to 2016. Accordingly, the value of the LCI was 6.67 in 2008–2016 as the most considerable value among consequent periods. Eventually, the LCI for the predicted period (2016–2032) equals 4.97. The result provides a basis for predicting the landscape characteristics in the future and will help managers to develop effective policies for the conservation of landscape integrity and connectivity.

Keywords: Connectivity; Landscape fragmentation; Landsat image; Landscape change index; CA–Markov (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10668-022-02862-x

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