Analysis and prediction of land cover changes using the land change modeler (LCM) in a semiarid river basin, Iran

Abstract

Predicting future land cover (LC) changes is an important step in the proper planning and management of watersheds. As a susceptible area to salinity and desertification, receiving only about 195 mm rainfall annually, the Hable‐Rud River basin is especially sensitive to land use/cover changes. Based on corrected LANDSAT satellite images for the years 1986, 2000, and 2017, the LC were extracted using the maximum likelihood (ML) method. LC changes were predicted by applying the land change modeler (LCM) for the basin. The kappa index for classification in 1986, 2000, and 2017 was 75, 78, and 81%, respectively. Using LCM, the prediction was accomplished for the year 2017 with a kappa index of above 74%. The LC map was predicted for year 2040. The analysis indicates that over the past 32 years, bare land, saline land, agricultural, industrial, and residential areas have increased by about 8, 6.2, 2.7, 0.63, and 0.48%, respectively; while rangeland area was decreased by 18%. Results also indicate that given the predicted LC for year 2040 in comparison with the reference year (2017), saline land, agricultural, industrial, and residential areas will be likely to continue to increase by about 3, 1.5, 0.7, and 0.8%, respectively, whereas bare land and rangeland will most likely decrease by about 4.55 and 1.45%, respectively. The findings of this study assist in analyzing the future trends of LC changes in the basin. This information can be used as a guide for land planners and managers in future land use planning of the area.

Historical and future dynamics of land use land cover and its drivers in Ajora‐Woybo watershed, Omo‐Gibe basin, Ethiopia

“…The implementation procedure of LCM involves three steps: the change analysis, transition potentials, and change predictions (Gharaibeh et al, 2020; Khoshnood Motlagh et al, 2021). The LULC change transition probability function was determined based on the difference between 2000, 2010, and 2020 to predict future LULC of the watershed.…”

“…Future LULC were predicted using Land Change Modeler (LCM) based on classified historical satellite images. This LCM model was created in TerrSet software version 18.31 by combining MLP‐NN and CA‐MC models (Girma et al, 2022; Khoshnood Motlagh et al, 2021; Leta et al, 2021).…”

“…The implementation procedure of LCM involves three steps: the change analysis, transition potentials, and change predictions (Gharaibeh et al, 2020;Khoshnood Motlagh et al, 2021).…”

Past and future of land use change in the Middle reaches of the Yellow River Basin in China

“…To map future LULC scenarios, the Markov model projection is performed by creating matrixes to estimate the transition probability and the area of each LULC class for future dates [38,39].Markov model was applied to forecast the future LULC in two scenarios (2025 and 2030), via a few main steps: (i) analysis of historical LULC maps (1995, 2005, 2015and2020) and associated changes, (ii) creation of transition probability matrixes, (iii) model validation, (iv) prediction of future LULC maps, accounting for possible driving forces. In this work, we de ne the probability transitional matrix as a matrix showing the transfer direction of LULC types from one category to other categories in a given year [40].…”

Historical and future land use and land cover changes in the Fincha watershed, Ethiopia

“…projection is performed by creating matrixes to estimate the transition probability and the area of each LULC class for future dates (Hasan et al 2020;Khoshnood Motlagh et al 2021). In this study, LCM was applied to forecast the future LULC in three scenarios (2030,2040,2050), via a few main steps: i) analysis of historical LULC maps (1989,2004,2019) and associated changes, ii) creation of transition probability matrixes, iii) model validation, iv) prediction of future LULC maps, accounted for possible driving forces.…”