Temporal and Spatial Variations in Landscape Habitat Quality under Multiple Land-Use/Land-Cover Scenarios Based on the PLUS-InVEST Model in the Yangtze River Basin, China

Abstract

Despite the Yangtze River Basin (YRB)’s abundant land and forestry resources, there is still a dearth of research on forecasting habitat quality changes resulting from various geographic and environmental factors that drive landscape transformations. Hence, this study concentrates on the YRB as the focal area, with the aim of utilizing the Patch Landscape Upscaling Simulation model (PLUS) and the habitat quality model to scrutinize the spatial distribution of landscape patterns and the evolution of HQ under four scenarios: the natural development scenario (NDS), farmland protection scenario (CPS), urban development scenario (UDS), and ecological protection scenario (EPS), spanning from the past to 2030. Our results show that (1) from 2000 to 2020, the construction land in the YRB expanded at a high dynamic rate of 47.86% per year, leading to a decrease of 32,776 km2 in the cultivated land area; (2) the UDS had the most significant expansion of construction land, followed by the NDS, CPS, and EPS, which had higher proportions of ecologically used land such as forests and grasslands; (3) from 2000 to 2020, the HQ index ranged from 0.211 to 0.215 (low level), showing a slight upward trend, with the most drastic changes occurring in the low-level areas (−0.49%); (4) the EPS had the highest HQ (0.231), followed by the CPS (0.215), with the CPS increasing the HQ proportion of the lower-level areas by 2.64%; (5) and in addition to government policies, NDVI, DEM, GDP, and population were also significant factors affecting landscape pattern and changes in habitat quality.

Study on the coordination of flow and sediment transport, ecological environment, and crop production in the Lower Yellow River since 2000

“…Considering the important adjustment role of water and sediment regulation and ecological water replenishment to the wetlands, waters, and saline‐alkali land in the Lower Yellow River and estuarine deltas, the changes in the area of wetlands, waters, and saline‐alkali land were emphasized in the analysis of ecosystem pattern. The habitat quality was obtained by employing the Habitat Quality Model of InVEST model (He et al, 2023): $$${i}_{rxy}=1-\left(\frac{{d}_{xy}}{{d}_{r\max }}\right),$$$ $$${i}_{rxy}=\text{exp}\left(-\left(\frac{2.99}{{d}_{r\max }}\right){d}_{xy}\right),$$$ $$${D}_{xj}=\sum _{r=1}^{R}\sum _{y=1}^{{Y}_{r}}\left(\frac{{w}_{r}}{{\sum }_{r=1}^{R}{w}_{r}}\right){r}_{y}{i}_{rxy}{\beta }_{x}{S}_{jr},$$$where D xj , R, W r , y r , and r y represent the habitat degradation index, the number of threat factors, the weight of threat factor r , the grid number of threat factors and the value of threat factors on the grid, respectively; i rxy represents the distance between the habitat and the threat source and the impact of the threat on the space; β x is the factor that mitigates the impact of threats on habitats through various conservation policies (i.e., the degree of legal protection, in which 0 is for areas protected by law and 1 is for the rest); S jr is the sensitivity of habitat type j to threat factor r; d xy is the linear distance between grid x and grid y; d r max is the maximum threat distance of threat source r .…”

“…Considering the important adjustment role of water and sediment regulation and ecological water replenishment to the wetlands, waters, and saline-alkali land in the Lower Yellow River and estuarine deltas, the changes in the area of wetlands, waters, and saline-alkali land were emphasized in the analysis of ecosystem pattern. The habitat quality was obtained by employing the Habitat Quality Model of InVEST model (He et al, 2023):…”

Simulating the Land Use and Carbon Storage for Nature-Based Solutions (NbS) under Multi-Scenarios in the Three Gorges Reservoir Area: Integration of Remote Sensing Data and the RF–Markov–CA–InVEST Model

“…In comparison to frequently used single models such as CLUE-S and PLUS [85,86], this study combines the benefits of quantitative and spatial prediction to build the RF-Markov-CA coupled model. The Markov-CA model in this model combines the RF algorithm's highprecision screening mechanism, considerably minimising the effect of subjective elements and more correctly simulating complicated land transformation situations, hence enhancing the simulation accuracy.…”

A Study of the Spatiotemporal Evolution Patterns and Coupling Coordination between Ecosystem Service Values and Habitat Quality in Diverse Scenarios: The Case of Chengdu Metropolitan Area, China

“…Nonetheless, there exists a relative dearth of comprehensive investigations concerning large-scale, composite spatial entities like metropolitan areas. Furthermore, while tools like geographically weighted regression [23], geographic detectors [24], and PLUS have been used to analyze the changes in HQ and their driving forces, there are also many scholars who combine PLUS and other land use simulation software to explore the characteristics of HQ changes under multiple scenarios [25,26].…”