Identification of Ecological Restoration Approaches and Effects Based on the OO-CCDC Algorithm in an Ecologically Fragile Region
Abstract
:1. Introduction
2. Study Area
3. Materials and Methods
3.1. Data Preprocessing
3.1.1. Landsat Time Series Construction
3.1.2. Reference Sample Collection
Reference Sample of Ecological Restoration Approaches
Reference Sample of Ecological Restoration Detection
3.2. Classification of Ecological Restoration Approaches
3.2.1. Feature Extraction
3.2.2. Classification
3.3. Estimating Ecological Restoration Based on Object-Oriented Continuous Change Detection
3.3.1. Multiscale Segmentation
3.3.2. Object-Level Feature Sets Generation
3.3.3. Characterizing Vegetation Change Processes under Multiple Ecological Restoration Approaches
3.3.4. Accuracy Assessment
4. Results
4.1. Accuracy Assessment
4.2. Spatial Distribution of Multiple Ecological Restoration Approaches
4.3. The Ecological Restoration Effect for Multiple Ecological Restoration Approaches
5. Discussion
5.1. Efficient Framework for Assessing Effectiveness of Multiple Ecological Restoration Approaches
5.2. Comprehensive Evaluations of the OO-CCDC Algorithm
5.3. Spatiotemporal Patterns of Ecological Restoration Effects
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Natural Forests | Planted Forests | Natural Grassland | Others | ||
---|---|---|---|---|---|
User accuracy (%) | 97.94 | 86.07 | 88.64 | 90.14 | Overall accuracy = 90.73% |
Producer accuracy (%) | 97.94 | 86.07 | 88.65 | 89.24 | Kappa = 88.47% |
SCAL | LV | ROC-LV |
---|---|---|
100 | 138.34 | 1.17 |
200 | 146.25 | 0.82 |
300 | 152.67 | 0.87 |
400 | 159.00 | 0.77 |
500 | 164.80 | 0.79 |
600 | 170.36 | 0.69 |
700 | 175.46 | 0.46 |
800 | 179.11 | 0.73 |
900 | 183.67 | 0.44 |
1000 | 188.19 | 0.48 |
Vegetation Indices | Equations | References |
---|---|---|
Normalized Difference Vegetation Index (NDVI) | [27,28] | |
Enhanced Vegetation Index (EVI) | [41] | |
Normalized Difference Phenology Index (NDPI) | [29] |
Vegetation Indices | Equations |
---|---|
Contrast (CON) | |
Entropy (ENT) | |
Correlation (COR) | |
Variance (VAR) | |
Angular second moment (ASM) | |
Sum average (SAVG) | |
Dissimilarity (DIS) |
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Ecological Restoration Approaches | Field Survey Data | Image Data | Total Number |
---|---|---|---|
Natural forest protection | 623 | 911 | 1534 |
Planted forests restoration | 687 | 641 | 1328 |
Natural grassland protection | 532 | 498 | 1030 |
Others | 446 | 2063 | 2509 |
Total number | 2288 | 4113 | 6401 |
Ecological Restoration Approaches | Changed Pixels | Unchanged Pixels | Total Number |
---|---|---|---|
Natural forest protection | 21 | 39 | 60 |
Planted forest | 112 | 103 | 215 |
Grassland protection | 117 | 108 | 225 |
Total Number | 250 | 250 | 500 |
Parameters | Description | Value |
---|---|---|
Number of Trees | The number of decision trees created | 100 |
Variables Per Split | The number of variables per split | null |
Min Leaf Population | The minimum size of a terminal node | 1 |
Bag Fraction | The fraction of input to bag per tree | 0.5 |
Out of Bag Mode | Whether the classifier should run in out-of-bag mode | false |
Seed | Random seed | 0 |
Segmentation Scale | Number of Objects | Description | Splitting Effect |
---|---|---|---|
300 | 270,962 | The image is too fragmented, the edge of the cultivated land is blurred, and the segmented object is too small | |
500 | 215,414 | The division is relatively complete, the details are well preserved, and each object is relatively independent from the others | |
800 | 175,420 | Partitioned objects are too large, and some objects are divided together |
Ecological Restoration Approaches | Trend | Effects |
---|---|---|
Natural forest protection | increasing | Positive |
stable | Positive | |
decreasing | Negative | |
Plant forest | increasing | Positive |
stable | Positive | |
decreasing | Negative | |
Natural grassland protection | increasing | Positive |
stable | Positive | |
decreasing | Negative |
Category | Real Category/Pixel | Total Number of Pixels | -- | User Accuracy/% |
---|---|---|---|---|
-- | Changed pixels | Unchanged pixels | Total number of pixels | -- |
Changed pixels | 233 | 17 | 250 | 93.2 |
Unchanged pixels | 115 | 135 | 250 | 54.0 |
Total number of pixels | 348 | 152 | 500 | -- |
Producer accuracy/% | 66.9 | 88.8 | -- | Overall accuracy = 74.5% |
Category | -- | Real Category/pc | -- | User Accuracy/% |
---|---|---|---|---|
-- | Changed pixels | Unchanged pixels | Total number of pixels | -- |
Changed pixels | 225 | 25 | 250 | 90.0 |
Unchanged pixels | 53 | 197 | 250 | 78.8 |
Total number of pixels | 278 | 222 | 500 | -- |
Producer accuracy/% | 80.9 | 88.7 | -- | Overall accuracy = 84.4% |
Category | -- | Real Category/pc | -- | User Accuracy/% |
---|---|---|---|---|
-- | Changed objects | Unchanged objects | Total number of objects | -- |
Changed objects | 89 | 11 | 100 | 89 |
Unchanged objects | 5 | 45 | 50 | 90 |
Total number of objects | 94 | 56 | 150 | -- |
Producer accuracy/% | 95 | 80 | -- | Overall accuracy = 89.3% |
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Share and Cite
Wei, C.; Xue, X.; Tian, L.; Yang, Q.; Hou, B.; Wang, W.; Ma, D.; Meng, Y.; Liu, X. Identification of Ecological Restoration Approaches and Effects Based on the OO-CCDC Algorithm in an Ecologically Fragile Region. Remote Sens. 2023, 15, 4023. https://doi.org/10.3390/rs15164023
Wei C, Xue X, Tian L, Yang Q, Hou B, Wang W, Ma D, Meng Y, Liu X. Identification of Ecological Restoration Approaches and Effects Based on the OO-CCDC Algorithm in an Ecologically Fragile Region. Remote Sensing. 2023; 15(16):4023. https://doi.org/10.3390/rs15164023
Chicago/Turabian StyleWei, Caiyong, Xiaojing Xue, Lingwen Tian, Qin Yang, Bowen Hou, Wenlong Wang, Dawei Ma, Yuanyuan Meng, and Xiangnan Liu. 2023. "Identification of Ecological Restoration Approaches and Effects Based on the OO-CCDC Algorithm in an Ecologically Fragile Region" Remote Sensing 15, no. 16: 4023. https://doi.org/10.3390/rs15164023