Simulation of Land Use Based on Multiple Models in the Western Sichuan Plateau

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  • Xinran Yu
  • Jiangtao Xiao
  • Huang, Ke
  • Yuanyuan Li
  • Yang Lin
  • Gang Qi
  • Tao Liu
  • Ping Ren

Many single-land-use simulation models are available to simulate and predict Land Use and Land Cover Change (LUCC). However, few studies have used multiple models to simulate LUCC in the same region. The paper utilizes the CA-Markov model, Land Change Modeler (LCM), and Patch-generating Land Use Simulation model (PLUS) with natural and social driving factors to simulate the LUCC on the Western Sichuan Plateau, using Kappa coefficient, overall accuracy (OA), and Figure of Merit (FoM) to verify the accuracy of the model, and selects a suitable model to predict the LUCC and landscape pattern in the study area from 2020 to 2070. The results are as follows: (1) The LCM has the highest simulation effect, and its Kappa coefficient, OA, and FoM are higher than the other two models. (2) The area of land types other than grassland and wetland will increase from 2020 to 2070. Among them, the grassland area will decrease, but is still most prominent land category in this region. The proportion of wetland areas remains unchanged. The fragmentation degree of forest (F), grassland (GL), shrubland (SL), water bodies (WBs), bare areas (BAs), and permanent ice and snow (PIS) decreases, and the distribution shows a trend of aggregation. The dominance of F and C decreases but still dominates in the landscape. The overall landscape aggregation increased and complexity decreased, and each landscape type’s diversity, evenness, and richness increased, presenting as a more reasonable development. Using multiple models to simulate the LUCC in the same region, and choosing the most suitable local land model is of great significance to scientifically manage and effectively allocate the land resources in the field.

OriginalsprogEngelsk
Artikelnummer3629
TidsskriftRemote Sensing
Vol/bind15
Udgave nummer14
Antal sider18
ISSN2072-4292
DOI
StatusUdgivet - 2023

Bibliografisk note

Funding Information:
This research is supported by the Sichuan Science and Technology Program (2023NSFSC1979, 2023NSFSC0191), and the National Natural Science Foundation of China (41801185).

Publisher Copyright:
© 2023 by the authors.

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