Vegetation Coverage Prediction for the Qinling Mountains Using the CA–Markov Model

Dados Bibliográficos

ID22032291
AutoresLu Cui (0000-0003-1484-6042, Chang'an University), Yonghua Zhao (0000-0003-2427-8931, Chang'an University, autor correspondente), Jianchao Liu (0000-0001-5647-3518, Chang'an University), Huanyuan Wang (0000-0002-5207-6213, Ministry of Natural Resources), Ling Han (0000-0002-3088-0374, Chang'an University), Juan Li (0000-0002-8639-6100, Ministry of Natural Resources), Zenghui Sun (0009-0005-6450-7905, Ministry of Natural Resources)
Ano2021
Volume10
Fascículo10
Páginas679
Data de publicação2021-10-08
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoISPRS International Journal of Geo-Information (JOURNAL)
Identificadores do periódicoISSN: 2220-9964 • E-ISSN: 2220-9964
EditoraMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi10100679
OpenAlexW3203937552
IdiomaEN
Citações recebidas1
Referências citadas49

The Qinling Mountains represent the dividing line of the natural landscape of north-south in China. The prediction on vegetation coverage is important for protecting the ecological environment of the Qinling Mountains. In this paper, the data accuracy and reliability of three vegetation index data (GIMMS NDVI, SPOT NDVI, and MODIS NDVI) were compared at first. SPOT, NDVI, and MODIS NDVI were used for calculating the vegetation coverage in the Qinling Mountains. Based on the CA–Markov model, the vegetation coverage grades in 2008, 2010, and 2013 were used to simulate the vegetation coverage grade in 2025. The results show that the grades of vegetation coverage of the Qinling Mountains calculated by SPOT, NDVI, and MODIS NDVI are highly similar. According to the prediction results, the grade of vegetation coverage in the Qinling Mountains has a rising trend under the guidance of the policy, particularly in urban areas. Most of the vegetation coverage transit from low vegetation coverage to middle and low vegetation coverage. The grades of the vegetation coverage, which were predicted by the CA–Markov model using SPOT, NDVI, and MODI NDVI, are consistent in spatial distribution and temporal variation

Climate change · Enhanced vegetation index · Geography · Markov chain · Normalized Difference Vegetation Index · Physical geography · Remote sensing · Statistics · Vegetation Classification · Vegetation Index · Environmental Science · Land Use and Ecosystem Services · Mathematics · Remote Sensing and Land Use · Remote Sensing in Agriculture · Ecology