Spatiotemporal evolution of habitat quality and its response to landscape patterns in karst mountainous cities: a case study of Guiyang City in China

Wang, Zhijie; Liu, Shujun; Su, Yuan

doi:10.1007/s11356-023-30420-z

Cited by 5 publications

(1 citation statement)

References 80 publications

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“…In addition, the user's guide of the InVEST model applies the maximum threat factor impact range (MAX-DIST), threat factor weight (0-1), and decline index of threat factor impact on habitat in the model. This study referred to the recommended reference value of the model and other relevant studies [6,38,[51][52][53] as the assigned habitat suitability values to cropland, forest land, grassland, water bodies, construction land, and unused land (Table 1). In addition, intense human activities and environmental changes have had a profound impact on the land system [54].…”

Section: Invest Model and Hq Modelmentioning

confidence: 99%

Spatio-Temporal Differentiation and Driving Factors of Land Use and Habitat Quality in Lu’an City, China

Wang,

Zhao,

Jia

2024

Land

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The spatio-temporal evolution of land use/land cover (LULC) and habitat quality (HQ) is vital to maintaining ecological balance and realizing regional sustainable development. Using the InVEST and CA-Markov model, with the Kendall coefficient as the sensitivity value, LULC and HQ in Lu’an City from 2000 to 2030 are simulated and evaluated. Then, Spearman is used to analyze the correlation between HQ and driving factors. Finally, the influence of policy factors on HQ is discussed. The results show the following: (1) from 2000 to 2030, the LULC of Lu’an is mainly cropland (about 40%) and forest land (about 30%) which are transferred to construction land; (2) the kappa coefficient is 0.9097 (>0.75), indicating that the prediction results are valid; (3) the Spearman coefficient shows that DEM (0.706), SLOPE (0.600), TRI (0.681), and HFI (−0.687) are strongly correlated with HQ, while FVC (0.356) and GDP (−0.368) are weakly correlated with HQ; (4) the main reasons for the decrease in HQ are the increase in construction land area, the decrease in forest area, the vulnerability of artificial forests to threat factors, and their low biodiversity. This study outlines exploratory research from two perspectives of HQ factors and policy effects to provide policy suggestions for the sustainable development of Lu’an City.

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