Study on Forest Extraction and Ecological Network Construction of Remote Sensing Images Combined with Dynamic Large Kernel Convolution
Abstract
1. Introduction
2. Study Area and Data
2.1. Overview of the Study Area
2.2. Research Data
2.3. Experimental Data
3. Research Methods
3.1. Dynamic Large Kernel Convolution
3.2. Dynamic Large Kernel Segformer Forest Extraction Modeling
3.3. Morphological Spatial Pattern Analysis (MSPA)
3.4. Ecological Network Theory
- (1)
- Circuit Theory
- (2)
- Minimum Cumulative Resistance
- (3)
- Linkage Mapper Analysis
3.5. Accuracy Evaluation Indicators
4. Experimental Studies and Analyses
4.1. Model Training
4.2. Experimental Findings
5. Discussion
5.1. Spatiotemporal Change in Forest Land in Liaoning Province
5.2. Forest Ecological Network Construction
- (1)
- Ecological sources: Ecological sources are the core carriers of ecosystems and play a decisive role in maintaining biodiversity and guaranteeing the habitat and reproduction of species. They usually need to have a certain scale to support stable ecological processes. In this study, ecological source areas were delineated according to the significance of regional ecological services and habitat quality and identified from the dimensions of connectivity and importance. By analyzing the data on vegetation cover, species distribution, ecological service function, and landscape patterns, we identified 15 ecological source sites in Liaoning Province, which are the Core areas of biological habitats with high ecological integrity and species richness. In total, 216 potential source sites were identified, which provide a direction of expansion for the subsequent ecological conservation and rehabilitation and help improve the stability of the regional ecosystem.
- (2)
- Ecological corridors: Ecological corridors are linear/belt-shaped ecological patches connecting ecological source areas. They are the core part of ecosystem connectivity, with the key functions of species migration, energy flow, and material circulation. In this study, we used circuit theory, the synthesized topography, land use, ecological resistance, and other factors to simulate the movement paths of ecological flow and identified a total of 23 ecological corridors. Among these, 12 important corridors are spatially distributed across the northeastern and central parts of Liaoning Province, connecting the core ecological sources, with a high density of ecological flows and dominating the regional ecological connectivity, while 11 general corridors assist in improving the ecological network. The centralized distribution of the corridors in the northeast and central part of the study area not only reflects the characteristics of the regional ecological source layout but also highlights the active nature of the ecological process in the region, which is of great significance in promoting biological migration and maintaining ecological balance.
- (3)
- Ecological nodes: The key ecological nodes in this study consisted of 65 ecological pinch points and 37 ecological barrier points. The ecological pinch points are located within the ecological corridors, which are key channels of ecological flow, but due to the high resistance value of the periphery, they are easily impacted by human activities and become a “bottleneck” for biological migration. The ecological obstacle points are mostly located in the central and western parts of the study area and affected by the factors of land use and transportation networks, forming a barrier to ecological flow and hindering the ecological balance. Together, the two types of nodes affect the connectivity and stability of the ecological network, and their precise identification provides important guidance to optimize the ecological network and enhance the ecosystem function, which is the core concern of ecological protection and restoration.
5.3. Study Limitations and Directions for Future Research
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Normal State | Strong Light | Cropland-Like Disturbance | Cloud Disturbance | Negative Sample |
---|---|---|---|---|
Model | Precision (%) | Recall (%) | IoU (%) | F1 (%) |
---|---|---|---|---|
Segformer | 87.33 | 86.13 | 76.56 | 86.73 |
Deformable Segformer | 89.29 | 88.63 | 80.58 | 88.96 |
Remote Sensing Images | Ground Truth Labeling | Segformer | Deformable Segformer |
---|---|---|---|
Classification | 2019 (km2) | 2023 (km2) | Change Value (km2) |
---|---|---|---|
Branch | 1556.154 | 1782.0648 | 225.9108 |
Bridge | 1897.3908 | 2260.0692 | 362.6784 |
Core | 40,206.5244 | 40,831.3152 | 624.7908 |
Edge | 6398.7444 | 6837.4836 | 438.7392 |
Islet | 744.2496 | 1019.5128 | 275.2632 |
Loop | 2529.7776 | 2783.2176 | 253.44 |
Perforation | 1492.3836 | 1425.6216 | −66.762 |
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Wang, F.; Yang, F.; Chang, X.; Ye, Y. Study on Forest Extraction and Ecological Network Construction of Remote Sensing Images Combined with Dynamic Large Kernel Convolution. Forests 2025, 16, 1342. https://doi.org/10.3390/f16081342
Wang F, Yang F, Chang X, Ye Y. Study on Forest Extraction and Ecological Network Construction of Remote Sensing Images Combined with Dynamic Large Kernel Convolution. Forests. 2025; 16(8):1342. https://doi.org/10.3390/f16081342
Chicago/Turabian StyleWang, Feiyue, Fan Yang, Xinyue Chang, and Yang Ye. 2025. "Study on Forest Extraction and Ecological Network Construction of Remote Sensing Images Combined with Dynamic Large Kernel Convolution" Forests 16, no. 8: 1342. https://doi.org/10.3390/f16081342
APA StyleWang, F., Yang, F., Chang, X., & Ye, Y. (2025). Study on Forest Extraction and Ecological Network Construction of Remote Sensing Images Combined with Dynamic Large Kernel Convolution. Forests, 16(8), 1342. https://doi.org/10.3390/f16081342