Spatiotemporal evolution and prediction of habitat quality in Hohhot City of China based on the InVEST and CA-Markov models

Abstract

With the acceleration of urbanization, changes in the urban ecological environment and landscape pattern have led to a series of prominent ecological environmental problems. In order to better coordinate the balanced relationship between city and ecological environment, we selected land use change data to evaluate the habitat quality in Hohhot City of China, which is of great practical significance for regional urban and economic development. Thus, the integrated valuation of ecosystem services and tradeoffs (InVEST) and Cellular Automata-Markov (CA-Markov) models were used to analyze, predict, and explore the Spatiotemporal evolution path and characteristics of urban land use, and forecast the typical evolution pattern of land use in 2030. The results showed that the land use types in Hohhot City changed significantly from 2000 to 2020, and the biggest change took place in cultivated land, grassland, shrub, and artificial surface. The decrease of cultivated land area and the increase of artificial surface area were the main impact trend of land use change. The average value of habitat quality had been decreasing continuously from 2000 to 2020, and the values of habitat degradation were 0.2605, 0.2494, and 0.2934 in 2000, 2010, and 2020, respectively, showing a decreasing trend. The decrease of habitat quality was caused by the needs of economic development and urban construction, as well as the impact of land occupation. During this evolution, many cultivated land and urban grassland had been converted into construction land. The simulated land use changes in 2030 are basically the same as those during 2000-2020, and the habitat quality will still be declining. The regional changes are influenced by the urban rapid development and industrial layout. These results can provide decision-making reference for regional urban planning and management as well as habitat quality evaluation.

Major Role of Natural Wetland Loss in the Decline of Wetland Habitat Quality—Spatio-Temporal Monitoring and Predictive Analysis

“…In terms of model coupling, the CA-Markov and InVEST models integrate new land use expansion analysis strategies to simulate changes in different types of land use patches with high accuracy. Using this combination, the problem of the accuracy of simulation data in the field of large-scale research can be overcome [64,65]. This study explored the dynamics of habitat quality changes in Jingxin Wetland from 1964 to 2019, and also predicted future habitat quality under different scenarios based on government ecological protection policies and economic development trends.…”

Land-Use and Habitat Quality Prediction in the Fen River Basin Based on PLUS and InVEST Models

“…However, the CLUE-S, FLUS, CA-Markov models, which are referred to as traditional models, have some shortcomings. The CA-Markov model considers only the influence of cell number and structure on landuse simulation (Chu et al, 2018;Jana et al, 2022;Luan et al, 2023;Zhou et al, 2020), CLUE-S model does not consider the nonlinearity between land-use change-driven data (Kucsicsa et al, 2019), and FLUS model requires a coordinate system, resolution, and row and column numbers of all raster data to be unified (Liu et al, 2017).…”

Comprehensive Evaluation of Island Habitat Quality Based on the Invest Model and Terrain Diversity: A Case Study of Haitan Island, China

“…At this scale, scholars employ 3S technologies (Remote Sensing, Geographic Information System, and Global Positioning System) and associated models to achieve research goals. For instance, researchers use remote sensing data and GIS platforms in combination with the GUMBO model [13], IDRISI biodiversity [14,15], Maxent model [16,17], CLIMEX model [18], habitat suitability model HIS [19], SoIVES model [20], C-Plan model [21], and Invest model [21][22][23] to obtain significant results in evaluating ecosystems and habitat quality. The Invest model encompasses an autonomous habitat quality evaluation module [24], recognized for its prompt analysis and evaluation pace, and the availability of pertinent data.…”