Spatiotemporal patterns of habitat quality and its topographic gradient effects of Hubei Province based on the InVEST model

Xiao, Pengnan; Yong, Zhou; Li, Mengyao; Xu, Jie

doi:10.1007/s10668-022-02310-w

Cited by 32 publications

(16 citation statements)

References 47 publications

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“…The In-VEST model is designed to evaluate ecosystem service functions and simulate changes in ecosystem services under different landscape pattern scenarios. It presents results in raster charts and includes three significant modules for terrestrial, marine, and freshwater ecosystem assessments [38]. The HQ module of the In-VEST model is based on land-use data.…”

Section: Habitat Quality Assessment Based On In-vest Modelmentioning

confidence: 99%

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

Guo

Wang

et al. 2023

Land

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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.

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Section: Habitat Quality Assessment Based On In-vest Modelmentioning

confidence: 99%

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

Guo

Wang

et al. 2023

Land

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“…The CA-Markov model is often used for inertial simulation of historical changes, but the disadvantage is that it cannot flexibly simulate different scenarios (Mondal et al 2016, Mansour et al 2020, the CLUE-S model simulates spatiotemporal changes in land use at a small spatial scale (Liao et al 2022, Mohammady 2021, the GeoSOS-FLUS model is based on the system dynamics (SD) and cellular automata (CA) models, combined with artificial neural networks (ANN) in various mechanisms. It has significant advantages in land use scenario simulation under stress (Wang et al 2022a, Shao et al 2022, and the model has been successfully used to simulate land use in different scenarios such as cities and urban agglomerations (Xiao et al 2022a). At present, there is a relative lack of research on scenario simulation that considers policy binding factors under territorial spatial planning, and no relevant suggestions are mentioned on the limiting factors of research results (Liu et al 2018, Zuo et al 2022.…”

Section: Introductionmentioning

confidence: 99%

The spatiotemporal changes and trade-off synergistic effects of ecosystem services in the Jianghan Plain of China under different scenarios

Ren,

Zhang,

Peng

2024

Environ. Res. Commun.

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Disturbance from human activities has intensified the evolution of ecosystem structure in the Jianghan Plain of China, leading to intensified conflicts between ecosystem services. It is essential to clarify the trade-off synergies between ecosystem services in the Jianghan Plain of China to better coordinate the economic and social development and ecological protection of the region. Based on historical data and scenario predictions using the GeoSOS-FLUS model, the InVEST model was applied to five key ecosystem services: Carbon storage, crop production, habitat quality, soil conservation and water yield from 2000 to 2020. Spearman correlation analysis was used to explore the trade-off synergies between different ecosystem services in space and time. The results showed that arable land and water land areas are the most important land types in the Jianghan Plain of China. From 2000 to 2020, the increase in build-up land and water land areas was accompanied by a decrease in arable land, forest land and unused land, and an increase in forest land. The natural development scenario in 2035 continues this trend except forest land reduction, while the ecological protection scenario reverses this trend. From 2000 to 2020, crop production, water yield, and soil conservation increased in the Jianghan Plain of China, while carbon storage and habitat quality declined significantly, showing a spatial distribution pattern of higher in the northwest and lower in the southeast. The comprehensive ecosystem services simulated in 2035 showed a downward trend compared with 2020, and the ecological protection scenario has the smallest decrease. There is an overall synergistic relationship between the five ecosystem services in the Jianghan Plain of China, and the strongest synergistic relationship is between soil conservation and water yield.

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“…InVEST is extensively employed in HQ assessment owing to its simplicity, ease of data acquisition, spatial expressiveness, and high accuracy (Terrado et al, 2016;Xiao et al, 2023). Moreover, empirical evidence has substantiated the various advantages of InVEST in the domains of land planning and environmental management (Kunwar et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The second approach entails utilizing model‐based ecological assessment methods, the main assessment models are the Integrated Valuation of Ecosystem Services and Trade‐offs (InVEST) model (Chen, Zhao, et al, 2020; Chen, Zhu, et al, 2020), Social Values for Ecosystem Services (SolVES) model (Sherrouse et al, 2014), and habitat suitability index (HSI) model (Liu et al, 2017). InVEST is extensively employed in HQ assessment owing to its simplicity, ease of data acquisition, spatial expressiveness, and high accuracy (Terrado et al, 2016; Xiao et al, 2023). Moreover, empirical evidence has substantiated the various advantages of InVEST in the domains of land planning and environmental management (Kunwar et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Research on habitat quality in arid urban agglomerations: Influencing mechanisms and multi‐scenario simulations

Han,

Zheng,

Han

et al. 2024

Land Degrad Dev

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In the context of climate change and rapid urbanization, there have been unparalleled changes in land use and land cover (LULC), resulting in substantial impacts on the surrounding habitat quality (HQ), particularly in ecologically vulnerable arid regions. However, previous studies on the influencing mechanisms of HQ in arid urban agglomerations and future multi‐scenario simulations remain limited. To fill this knowledge gap, this study aimed to reveal the influencing mechanisms in HQ changes and to develop a multi‐scenario HQ assessment framework within arid urban agglomerations. We assessed the spatiotemporal variations in HQ using the InVEST model and three periods of LULC data for the urban agglomeration on the northern slope of the Tianshan Mountains (UANSTM), and the partial least squares structural equation model was introduced to explore the interactions between natural and non‐natural factors and their impacts on HQ. Additionally, we coupled multi‐objective programming and PLUS models to predict the LULC under different optimization scenarios (natural development scenario (NDS), ecological protection scenario (EPS), ecological–economic coordinated scenario, and economic development scenario) for the UANSTM in 2030, and to assess HQ. Results show that (1) the HQ index of the UANSTM was 0.507, 0.520, and 0.495 in 2000, 2010, and 2020 respectively, with a spatial distribution pattern of high values in the west, low values in the east, and high in the central and low in the north and south; (2) geomorphic, climatic, and LULC factors have direct positive effects on HQ, while socio‐economic factors have a direct negative effect on HQ. In addition, geomorphic, socio‐economic, and climatic factors also influence HQ through potential indirect paths. Climatic and LULC factors enhance the positive effect of geomorphic on HQ while counteracting the direct negative effect of socio‐economic factors on HQ. Climatic factors have the largest negative effect on HQ through their influence on LULC; (3) according to the four scenarios in 2030, the highest HQ index (increased by 0.13%) was found under the EPS, which also aligns more closely with SDGs. Conversely, NDS showed the lowest HQ index (declined by 2.59%). The research results could provide a scientific basis for promoting sustainable land management and ecological conservation for the UANSTM.

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Spatiotemporal patterns of habitat quality and its topographic gradient effects of Hubei Province based on the InVEST model

Cited by 32 publications

References 47 publications

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

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

The spatiotemporal changes and trade-off synergistic effects of ecosystem services in the Jianghan Plain of China under different scenarios

Research on habitat quality in arid urban agglomerations: Influencing mechanisms and multi‐scenario simulations

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