Assessing Forest Landscape Stability through Automatic Identification of Landscape Pattern Evolution in Shanxi Province of China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Methodology
2.2.1. Data Preparation
2.2.2. Calculation of the Forest Landscape Index
2.2.3. Segmenting Time Series Based on TICC
2.2.4. Extracting Short-Term Change Process
2.2.5. Assessing Landscape Stability
3. Results
3.1. Characteristics of Subsequence in Landscape Dynamic
3.2. Spatiotemporal Distribution of the Forest Change Processes
3.3. Characteristics of Landscape Stability
3.4. Landscape Stability Assessment of Representative Regions
4. Discussion
- (1)
- Classification accuracy affected the landscape index calculation in landscape patterns. The results of landscape evolution modes based on this classification could be recognized as long as the annual classification accuracy was accepted since the recognition of landscape stability assessment is based on the change processes that have occurred in the landscape indices from the land cover maps.
- (2)
- A variety of driving factors and their interactions will affect ecological land degradation-restoration [53], and it is necessary to combine driving factors to understand the mechanism of forest landscape evolution. In the future, multiple time series can be constructed that consider different driving factors (such as drought, fire, and deforestation) to determine the relationship between forest landscape dynamics and driving factors.
- (3)
- The landscape change process defined in this study was not universal but based on prior knowledge and actual conditions in the study area. Such criteria may not apply to other land-use types [54,55,56,57,58,59]. Hence, future research must use deep-learning algorithms to detect change processes more intelligently to extract landscape evolution modes of various land-use types.
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Index | Formula | Units | Ecological Significance |
---|---|---|---|
Forest cover area (CA) | CA = F() F:total forest area in landscape units () | ha | Forest area is the basis for maintaining forest ecosystem activities. The loss of forest represents the loss of biological habitat, which translates into reduced stability. The trend in CA is also an important basis for distinguishing different ecological processes. |
Patch density (PD) | PD = N: number of patches in landscape units A: area of landscape units (1 ) | number (1 ) | PD can reflect forest fragmentation, which is the most direct manifestation of forest landscape structural changes and is an important indicator to measure the activity of ecological processes. The increase in fragmentation often represents a decrease in stability. |
Change Process | Trend | Ecological Significance | |
---|---|---|---|
CA | PD | ||
Degradation | Negative | No trend | The decrease in the area of forest patches does not change the overall fragmentation, and the patches disappear from locations on the edges or inside of existing patches. Patch changes correspond to the change process of shrinkage and perforation. |
Degradation | Negative | Negative | Decreased area of forest patches leads to decreased overall fragmentation. Patch changes correspond to the change process of attrition. |
Degradation | Negative | Positive | Decrease in patch area fragments forest patches, leading to an increase in overall fragmentation. Patch changes correspond to the change process of division. |
Degradation | No trend | Positive | Although the patch area is stable, the degree of fragmentation increases and the integrity of the forest landscape decreases, which is considered degradation. |
Restoration | Positive | No trend | Increase in patch area does not affect overall fragmentation; the increase in patch area is located at the edge or inside of the patches. Patch changes correspond to the change processes of expansion and infilling. |
Restoration | Positive | Positive | An increase in the number of patches leads to an increase in forest area and fragmentation. Patch changes correspond to the change process of outlying. |
Restoration | Positive | Negative | Increase in the patch area reduces overall fragmentation. This is the process of landscape connectivity. |
Restoration | No trend | Negative | Patch area is stable, and the decrease in fragmentation implies that the integrity of the forest landscape has increased, which is considered restoration. |
Stable | No trend | No trend | Both the area and the fragmentation remain stable. |
Long-Term Evolution Mode | Description | Stability Measurement |
---|---|---|
No Change mode | There are no restoration or degradation processes in long-term evolution. Forest landscape pattern does not change. | Stable |
Decrease mode | There are at least one or more degradation processes but no restoration process in long-term evolution. | Unstable |
Increase mode | There are at least one or more restoration processes but no degradation processes in long-term evolution. | Stable |
Wave mode | There are both degradation and restoration processes in long-term evolution. For example, forest landscape patterns may first degrade, then recover to stable. | Cumulative time switching frequency restoration time |
Index | Number of Clusters | ||||||
---|---|---|---|---|---|---|---|
6 | 7 | 8 | 9 | 10 | 11 | 12 | |
BIC () | 5.791 | 5.966 | 6.131 | 5.804 | 7.163 | 7.474 | 8.017 |
DBI | 1.72 | 1.92 | 1.94 | 1.60 | 2.03 | 2.00 | 2.28 |
Cluster | CA (ha) | PD (Patch Number/1 Km2) | Change Process | ||||||
---|---|---|---|---|---|---|---|---|---|
Mean | Change Rate | St.dv | Trend | Mean | Change Rate | St.dv | Trend | ||
1 | 14.1 | 0.59 | 6.7 | Positive | 18.2 | −0.40 | 5.9 | Negative | Restoration |
2 | 14.2 | 0.64 | 6.0 | Positive | 31.8 | 1.66 | 10.5 | Positive | Restoration |
3 | 27.2 | 0.44 | 7.0 | Positive | 27.7 | 0.37 | 7.2 | Positive | Restoration |
4 | 42.9 | 0.38 | 8.0 | Positive | 19.4 | −0.02 | 5.4 | No trend | Restoration |
5 | 11.1 | −0.54 | 5.1 | Negative | 7.9 | −0.23 | 5.1 | Negative | Degradation |
6 | 15.7 | −0.75 | 2.9 | Negative | 16.7 | 0.57 | 7.1 | Positive | Degradation |
7 | 63.2 | 0.06 | 9.2 | No trend | 9.5 | −0.05 | 3.8 | No trend | Stable |
8 | 42.7 | 0.12 | 15.3 | No trend | 13.4 | 0.08 | 5.1 | No trend | Stable |
9 | 85.7 | 0.04 | 7.7 | No trend | 3.6 | −0.07 | 2.0 | No trend | Stable |
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Hou, B.; Wei, C.; Liu, X.; Meng, Y.; Li, X. Assessing Forest Landscape Stability through Automatic Identification of Landscape Pattern Evolution in Shanxi Province of China. Remote Sens. 2023, 15, 545. https://doi.org/10.3390/rs15030545
Hou B, Wei C, Liu X, Meng Y, Li X. Assessing Forest Landscape Stability through Automatic Identification of Landscape Pattern Evolution in Shanxi Province of China. Remote Sensing. 2023; 15(3):545. https://doi.org/10.3390/rs15030545
Chicago/Turabian StyleHou, Bowen, Caiyong Wei, Xiangnan Liu, Yuanyuan Meng, and Xiaoyue Li. 2023. "Assessing Forest Landscape Stability through Automatic Identification of Landscape Pattern Evolution in Shanxi Province of China" Remote Sensing 15, no. 3: 545. https://doi.org/10.3390/rs15030545
APA StyleHou, B., Wei, C., Liu, X., Meng, Y., & Li, X. (2023). Assessing Forest Landscape Stability through Automatic Identification of Landscape Pattern Evolution in Shanxi Province of China. Remote Sensing, 15(3), 545. https://doi.org/10.3390/rs15030545