Multi-Dimensional Landscape Connectivity Index for Prioritizing Forest Cover Change Scenarios: A Case Study of Southeast China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources and Pre-Processing
2.3. Methods
2.3.1. Dynamics Model of Land Use/Cover Changes
2.3.2. Design of Multi-Scenarios
2.3.3. Simulation of Land Use/Cover Changes
2.3.4. Selection and Calculation of Forest Landscape Connectivity Indices
2.3.5. Construction of a Multi-Dimensional Landscape Connectivity Index
3. Results
3.1. Analysis of Land Use/Cover Change from 2000 to 2030
3.2. Comparison of Forest Landscape Connectivity Indices at the Global Scale
3.3. Comparison of Forest Landscape Connectivity Indices at the Local Scale
4. Discussion
5. Conclusions
- (1)
- By 2030, the FC in all scenarios is projected to surpass 61.4%, with growth observed only in SSP1 (+268.519 km2) and SSP4 (+1793.725 km2), while reductions were evident in SSP2 (−220.938 km2), SSP3 (−219.558 km2), and SSP5 (−520.379 km2). Notably, forest in SSP1 is primarily converted from cropland (99.6%), with the transformation predominantly occurring in Longyan and the northwest of Zhangzhou, Putian, and Quanzhou. In SSP4, the main forest transfers involve cropland (63.6%) and grassland (36.0%). Additionally, the forest loss in SSP4 amounts to 332.807 km2, with over 99.9% converted to cropland.
- (2)
- At a global scale, SSP4 outperforms the other scenarios. From 2020 to 2030, SSP4 consistently achieves high MLCI values across all thresholds. Specifically, at the CONNECT 90 m threshold, SSP1 attains the highest MLCI value of 2.569, followed by SSP4 at 2.092. At the 300, 600, and 1200 m thresholds, SSP4 records the highest MLCI values of 2.207, 2.183, and 2.302, respectively.
- (3)
- At a local scale, SSP4 also demonstrates significant superiority. When assessing MLCI values based on the mean FLC indices, SSP4 (3.907–6.219) consistently achieves the highest values across all thresholds, followed by SSP1 (3.111), and the lowest is SSP2 (0.579). Similarly, when considering MLCI values derived from the median FLC indices, SSP4 (4.519–7.573) maintains the highest value, surpassing SSP1 (3.457–5.762) that follows it, and the lowest value of SSP3 (0.354–1.188).
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Name | Resolution | Year | Sources |
---|---|---|---|
Land use/cover (LUC) | 30 m | 2000–2020 | RESDC (https://www.resdc.cn, accessed on 9 July 2023) |
Digital elevation model (DEM) | 30 m | — | Geospatial data cloud (http://www.gscloud.cn, accessed on 9 July 2023) |
Precipitation | 1 km | 2020 | National Earth system science data center (http://www.geodata.cn, accessed on 12 July 2023) |
Temperature | |||
Population | 1 km | 2019 | Resource environmental science data registry and publishing system (http://www.resdc.cn/DOI, accessed on 13 July 2023) |
Population and gross domestic product (GDP) | |||
Shared socio-economic pathways (SSPs) database | 0.5° | NUIST disaster risk research team of Prof. Jiang T., graduate school of management (https://www.scidb.cn/en/detail?dataSetId=73c1ddbd79e54638bd0ca2a6bd48e3ff, accessed on 15 July 2023) |
Scenarios | Conditions |
---|---|
SSP1 | Green and sustainable developments and strict regulation of LUCC. |
SSP2 | Continuing current social development and moderately regulating LUCC. |
SSP3 | Continuous regional competition and limited regulatory efforts for LUCC. |
SSP4 | Uneven development in different regions, well-regulated efforts for LUCC in middle-income and high-income countries, and poor development in low-income countries. |
SSP5 | Development relying mainly on fossil fuels and moderate supervision of LUCC. |
Cropland | Forest | Grassland | Water Area | Built-Up Land | Unused Land | |
---|---|---|---|---|---|---|
SSP1 | ||||||
Cropland | 1 | 1 | 1 | 0 | 1 | 1 |
Forest | 0 | 1 | 0 | 0 | 0 | 1 |
Grassland | 0 | 1 | 1 | 0 | 0 | 1 |
Water area | 0 | 0 | 0 | 1 | 1 | 1 |
Built-up land | 1 | 1 | 1 | 0 | 1 | 1 |
Unused land | 1 | 1 | 1 | 1 | 1 | 1 |
SSP2 | ||||||
Cropland | 1 | 0 | 0 | 0 | 1 | 1 |
Forest | 0 | 1 | 0 | 0 | 1 | 1 |
Grassland | 0 | 0 | 1 | 0 | 1 | 1 |
Water area | 0 | 0 | 0 | 1 | 1 | 1 |
Built-up land | 0 | 0 | 0 | 0 | 1 | 1 |
Unused land | 1 | 1 | 1 | 1 | 1 | 1 |
SSP3 | ||||||
Cropland | 1 | 0 | 0 | 0 | 1 | 1 |
Forest | 1 | 1 | 1 | 0 | 1 | 1 |
Grassland | 1 | 0 | 1 | 0 | 1 | 1 |
Water area | 0 | 0 | 0 | 1 | 0 | 1 |
Built-up land | 1 | 0 | 0 | 0 | 1 | 1 |
Unused land | 1 | 1 | 1 | 1 | 1 | 1 |
SSP4 | ||||||
Cropland | 1 | 1 | 1 | 0 | 1 | 1 |
Forest | 0 | 1 | 1 | 0 | 0 | 1 |
Grassland | 0 | 1 | 1 | 0 | 0 | 1 |
Water area | 0 | 0 | 0 | 1 | 0 | 1 |
Built-up land | 0 | 0 | 0 | 0 | 1 | 1 |
Unused land | 1 | 1 | 1 | 1 | 1 | 1 |
SSP5 | ||||||
Cropland | 1 | 1 | 0 | 0 | 0 | 1 |
Forest | 1 | 1 | 1 | 0 | 1 | 1 |
Grassland | 1 | 0 | 1 | 0 | 1 | 1 |
Water area | 0 | 0 | 0 | 1 | 0 | 1 |
Built-up land | 1 | 0 | 0 | 0 | 1 | 1 |
Unused land | 1 | 1 | 1 | 0 | 1 | 1 |
Indices | Formulas | Description |
---|---|---|
Class area (CA) | CA represents the area of the patch type, measured in hectares (hm2), and is calculated by dividing the sum of patch type areas (m2) by 10,000. aij denotes the area of patch ij (m2). | |
Patch density (PD) | PD refers to the patch density, indicating the number of patches per 100 hectares. The higher the PD value, the higher the forest landscape fragmentation. ni signifies the value of patch i. A represents the total area of the landscape (m2). | |
Landscape division index (DIVISION) | DIVISION represents the extent of dispersion of similar patches, serving as a metric to quantify the level of landscape fragmentation. A higher DIVISION value indicates greater landscape segregation. Value range: [0,1). | |
Mean nearest-neighbor index (ENN_MN) | ENN_MN denotes the distance (m) between similar patches, judging if they are structurally connected. A larger value represents a larger distance between patches. hij is the nearest proximity of the patch, ij, to the same class patch. | |
Connectance index (CONNECT) | CONNECT represents the number of nodes between specific patch classes divided by the number of potential nodes. The higher the patch connectivity, the greater the CONNECT value. Cijk denotes the connection between patches j and k linked to i within a critical distance. |
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He, Z.; Lin, Z.; Xu, Q.; Ding, S.; Bao, X.; Li, X.; Hu, X.; Li, J. Multi-Dimensional Landscape Connectivity Index for Prioritizing Forest Cover Change Scenarios: A Case Study of Southeast China. Forests 2024, 15, 1490. https://doi.org/10.3390/f15091490
He Z, Lin Z, Xu Q, Ding S, Bao X, Li X, Hu X, Li J. Multi-Dimensional Landscape Connectivity Index for Prioritizing Forest Cover Change Scenarios: A Case Study of Southeast China. Forests. 2024; 15(9):1490. https://doi.org/10.3390/f15091490
Chicago/Turabian StyleHe, Zhu, Zhihui Lin, Qianle Xu, Shanshan Ding, Xiaochun Bao, Xuefei Li, Xisheng Hu, and Jian Li. 2024. "Multi-Dimensional Landscape Connectivity Index for Prioritizing Forest Cover Change Scenarios: A Case Study of Southeast China" Forests 15, no. 9: 1490. https://doi.org/10.3390/f15091490