Improved Wetland Mapping of a Highly Fragmented Agricultural Landscape Using Land Surface Phenological Features
Abstract
:1. Introduction
2. Methods
2.1. Study Area
2.2. Field Survey
2.3. Vegetation Compositional Types
2.4. Topographic Variables
2.5. Sentinel-2 Based Variables
2.5.1. Tasseled Cap Transformations (TCT)
2.5.2. Vegetation Indices and Calculation of Statistical and Phenological Features
2.6. Machine Learning Classification
2.6.1. Development of Random Forest Models
2.6.2. Classification Accuracy
2.7. Model Comparison
3. Results
3.1. Model Performance
3.1.1. Level One Classification
3.1.2. Level Two Classification
3.1.3. Level Three Classification
3.2. Model Comparison
3.3. Predicted Wetland Distribution
3.4. Key Predictors to Discriminate Wetland
4. Discussion
4.1. The Value of HANTS in Wetland Identification
4.2. The Potential and Limits of Using HANTS Features to Discriminate Wetland Types
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Level 1 | Level 2 | Level 3 | No. of Samples |
---|---|---|---|
L1: Wetland | L11: Forested wetland | L111: River red gum forest | 69 |
L12: Woodland wetland | L121: Coolabah wetland woodland | 115 | |
L122: Coolabah open woodland | 122 | ||
L123: Black box woodland | 29 | ||
L13: Shrubland wetland | L131: Lignum shrubland | 92 | |
L14: Marshes | L141: Common reed wetland | 29 | |
L142: Water couch wetland | 25 | ||
L143: Marsh club-rush wetland | 17 | ||
L15: Sedgeland | L144: Sedgeland | 73 | |
L2: Terrestrial upland | L21: Terrestrial upland | L211: Terrestrial upland | 104 |
L3: Cropland | L31: Cropland | L311: Cropland | 77 |
L4: Water | L41: Water | L411: Water | 19 |
Index | Formula | Relevance | Reference |
---|---|---|---|
kNDVI | kNDVI improves accuracy in monitoring vegetation parameters such as LAI and GPP. | [61] | |
NDRE | NDRE is sensitive not only to chlorophyll content but also to canopy structure and composition variations. Differences in leaf angle distribution, canopy density, and leaf area index (LAI) influence the reflectance properties in the red-edge region, which NDRE can capture. | [62] | |
IRECI | Highly correlated with leaf chlorophyll content. | [63] | |
NDMI | NDMI detects moisture levels in vegetation, providing an indicator for vegetation water stress levels. | [64] | |
MNDWI | MNDWI highlights water bodies and monitor their turbidity. | [65] | |
EMBI | and | EMBI enhances detecting bare soil areas, can be valuable to differentiate bare soil and other landcover types. Due to the high contrast between bare soil and vegetation, EMBI provides a continuum ranging from high vegetation cover to exposed soil. | [66] |
Models | M1 | M2 | M3 |
---|---|---|---|
Predictors | Topographic, TCT, statistical and HANTS features | Topographic, TCT, statistical features | Topographic, TCT, HANTS features |
Class | Validation | M1 | M2 | M3 | M1 | M2 | M3 | M1 | M2 | M3 |
---|---|---|---|---|---|---|---|---|---|---|
F1 | Precision | Recall | ||||||||
L1: Wetland | Training | 0.936 | 0.921 | 0.933 | 0.933 | 0.925 | 0.933 | 0.940 | 0.918 | 0.932 |
Testing | 0.933 | 0.916 | 0.903 | 0.943 | 0.916 | 0.926 | 0.923 | 0.916 | 0.881 | |
L2: Terrestrial upland | Training | 0.706 | 0.634 | 0.692 | 0.731 | 0.635 | 0.725 | 0.682 | 0.633 | 0.662 |
Testing | 0.750 | 0.654 | 0.632 | 0.700 | 0.654 | 0.581 | 0.808 | 0.654 | 0.692 | |
L3: Cropland | Training | 0.950 | 0.945 | 0.919 | 0.952 | 0.936 | 0.880 | 0.948 | 0.955 | 0.962 |
Testing | 0.889 | 0.865 | 0.872 | 0.941 | 0.889 | 0.850 | 0.842 | 0.842 | 0.895 | |
L4: Water | Training | 0.859 | 0.875 | 0.875 | 0.795 | 0.824 | 0.824 | 0.933 | 0.933 | 0.933 |
Testing | 0.667 | 0.667 | 0.667 | 0.600 | 0.600 | 0.600 | 0.750 | 0.750 | 0.750 | |
Weighted F1 | Training | 0.915 | 0.898 | 0.903 | ||||||
Testing | 0.928 | 0.918 | 0.878 | |||||||
Overall accuracy | Training | 0.916 | 0.898 | 0.916 | ||||||
Testing | 0.927 | 0.917 | 0.927 |
Class | Validation | M1 | M2 | M3 | M1 | M2 | M3 | M1 | M2 | M3 |
---|---|---|---|---|---|---|---|---|---|---|
F1 | Precision | Recall | ||||||||
L11: Forested wetland | Training | 0.702 | 0.693 | 0.695 | 0.669 | 0.647 | 0.665 | 0.738 | 0.746 | 0.727 |
Testing | 0.789 | 0.800 | 0.800 | 0.714 | 0.696 | 0.778 | 0.882 | 0.941 | 0.824 | |
L12: Woody wetland | Training | 0.797 | 0.718 | 0.802 | 0.835 | 0.773 | 0.841 | 0.763 | 0.671 | 0.767 |
Testing | 0.784 | 0.764 | 0.760 | 0.845 | 0.839 | 0.790 | 0.731 | 0.701 | 0.731 | |
L13: Shrub wetland | Training | 0.608 | 0.531 | 0.571 | 0.565 | 0.503 | 0.528 | 0.658 | 0.562 | 0.620 |
Testing | 0.744 | 0.727 | 0.711 | 0.800 | 0.762 | 0.727 | 0.696 | 0.696 | 0.696 | |
L14: Marshes | Training | 0.834 | 0.772 | 0.816 | 0.842 | 0.798 | 0.818 | 0.826 | 0.748 | 0.815 |
Testing | 0.865 | 0.882 | 0.865 | 0.800 | 0.882 | 0.800 | 0.941 | 0.882 | 0.941 | |
L15: Sedgeland | Training | 0.643 | 0.539 | 0.606 | 0.638 | 0.490 | 0.620 | 0.647 | 0.600 | 0.593 |
Testing | 0.686 | 0.605 | 0.563 | 0.706 | 0.520 | 0.643 | 0.667 | 0.722 | 0.500 | |
L21: Terrestrial upland | Training | 0.715 | 0.662 | 0.664 | 0.729 | 0.679 | 0.669 | 0.703 | 0.646 | 0.659 |
Testing | 0.714 | 0.760 | 0.643 | 0.667 | 0.792 | 0.600 | 0.769 | 0.731 | 0.692 | |
L31: Cropland | Training | 0.937 | 0.936 | 0.936 | 0.926 | 0.909 | 0.909 | 0.948 | 0.966 | 0.966 |
Testing | 0.895 | 0.865 | 0.865 | 0.895 | 0.889 | 0.889 | 0.895 | 0.842 | 0.842 | |
L41: Water | Training | 0.875 | 0.899 | 0.903 | 0.824 | 0.855 | 0.875 | 0.933 | 0.947 | 0.933 |
Testing | 0.600 | 0.545 | 0.545 | 0.500 | 0.429 | 0.429 | 0.750 | 0.750 | 0.750 | |
Weighted F1 | Training | 0.771 | 0.728 | 0.750 | ||||||
Testing | 0.803 | 0.798 | 0.801 | |||||||
Overall | Training | 0.769 | 0.725 | 0.769 | ||||||
Testing | 0.802 | 0.797 | 0.802 |
Class | Validation | M1 | M2 | M3 | M1 | M2 | M3 | M1 | M2 | M3 |
---|---|---|---|---|---|---|---|---|---|---|
F1 | Precision | Recall | ||||||||
L111: River red gum forest | Training | 0.762 | 0.751 | 0.755 | 0.707 | 0.694 | 0.735 | 0.827 | 0.819 | 0.777 |
Testing | 0.667 | 0.571 | 0.667 | 0.769 | 0.556 | 0.688 | 0.588 | 0.588 | 0.647 | |
L121: Coolabah wetland woodland | Training | 0.612 | 0.565 | 0.578 | 0.718 | 0.632 | 0.651 | 0.533 | 0.510 | 0.520 |
Testing | 0.583 | 0.462 | 0.500 | 0.700 | 0.500 | 0.500 | 0.500 | 0.429 | 0.500 | |
L122: Coolabah open woodland | Training | 0.594 | 0.551 | 0.558 | 0.613 | 0.602 | 0.574 | 0.576 | 0.508 | 0.544 |
Testing | 0.633 | 0.433 | 0.633 | 0.655 | 0.448 | 0.655 | 0.613 | 0.419 | 0.613 | |
L123: Black box woodland | Training | 0.577 | 0.494 | 0.565 | 0.522 | 0.425 | 0.528 | 0.645 | 0.591 | 0.609 |
Testing | 0.833 | 0.364 | 0.727 | 1.000 | 0.500 | 1.000 | 0.714 | 0.286 | 0.571 | |
L131: Lignum shrubland | Training | 0.609 | 0.622 | 0.623 | 0.576 | 0.588 | 0.593 | 0.646 | 0.661 | 0.655 |
Testing | 0.596 | 0.449 | 0.638 | 0.583 | 0.423 | 0.625 | 0.609 | 0.478 | 0.652 | |
L141: Common reed wetland | Training | 0.684 | 0.590 | 0.637 | 0.661 | 0.573 | 0.628 | 0.709 | 0.609 | 0.645 |
Testing | 0.556 | 0.615 | 0.526 | 0.455 | 0.667 | 0.417 | 0.714 | 0.571 | 0.714 | |
L142: Water couch wetland | Training | 0.896 | 0.866 | 0.866 | 0.849 | 0.821 | 0.821 | 0.947 | 0.916 | 0.916 |
Testing | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | |
L143: Marsh club-rush Wetlands | Training | 0.775 | 0.774 | 0.763 | 0.781 | 0.667 | 0.758 | 0.769 | 0.923 | 0.769 |
Testing | 0.667 | 0.333 | 0.400 | 1.000 | 0.250 | 1.000 | 0.500 | 0.500 | 0.250 | |
L144: Sedgeland | Training | 0.625 | 0.549 | 0.561 | 0.614 | 0.518 | 0.558 | 0.636 | 0.585 | 0.564 |
Testing | 0.432 | 0.457 | 0.438 | 0.421 | 0.471 | 0.500 | 0.444 | 0.444 | 0.389 | |
L211: Terrestrial upland | Training | 0.718 | 0.654 | 0.689 | 0.728 | 0.725 | 0.694 | 0.708 | 0.595 | 0.685 |
Testing | 0.702 | 0.588 | 0.679 | 0.645 | 0.600 | 0.667 | 0.769 | 0.577 | 0.692 | |
L311: Cropland | Training | 0.940 | 0.906 | 0.908 | 0.938 | 0.898 | 0.870 | 0.941 | 0.914 | 0.948 |
Testing | 0.864 | 0.927 | 0.837 | 0.760 | 0.864 | 0.750 | 1.000 | 1.000 | 0.947 | |
L411: Water | Training | 0.875 | 0.875 | 0.877 | 0.824 | 0.824 | 0.850 | 0.933 | 0.933 | 0.907 |
Testing | 0.667 | 0.667 | 0.667 | 0.600 | 0.600 | 0.600 | 0.750 | 0.750 | 0.750 | |
Weighted F1 | Training | 0.703 | 0.696 | 0.698 | ||||||
Testing | 0.690 | 0.649 | 0.655 | |||||||
Overall | Training | 0.706 | 0.700 | 0.702 | ||||||
Testing | 0.691 | 0.654 | 0.665 |
Level | Metric | M1 vs. M2 | M1 vs. M3 | M2 vs. M3 | |||
---|---|---|---|---|---|---|---|
Difference | p-Value | Difference | p-Value | Difference | p-Value | ||
L1 | Accuracy | 0.024 | <0.001 | 0.007 | 0.014 | −0.017 | 0.004 |
F1 | 0.021 | <0.001 | 0.009 | 0.023 | −0.011 | 0.117 | |
Mean precision | 0.022 | 0.005 | 0.013 | 0.009 | −0.008 | 0.366 | |
Mean recall | 0.021 | <0.001 | 0.006 | 0.192 | −0.016 | 0.007 | |
L2 | Accuracy | 0.059 | <0.001 | 0.015 | 0.002 | −0.044 | <0.001 |
F1 | 0.042 | <0.001 | 0.013 | 0.007 | −0.028 | <0.001 | |
Mean precision | 0.048 | <0.001 | 0.010 | 0.110 | −0.038 | <0.001 | |
Mean recall | 0.035 | <0.001 | 0.017 | <0.001 | −0.018 | 0.005 | |
L3 | Accuracy | 0.035 | <0.001 | 0.025 | <0.001 | −0.010 | 0.196 |
F1 | 0.038 | <0.001 | 0.024 | 0.001 | −0.016 | 0.034 | |
Mean precision | 0.044 | <0.001 | 0.02 | 0.009 | −0.034 | 0.002 | |
Mean recall | 0.019 | 0.009 | 0.027 | <0.001 | 0.008 | 0.329 |
Class | Area (ha) | Percentage |
---|---|---|
L111: River red gum forest | 5775 | 1.33 |
L121: Coolabah wetland woodland | 11,353 | 2.61 |
L122: Coolabah open woodland | 40,727 | 9.38 |
L123: Black box woodland | 23,063 | 5.31 |
L131: Shrubland | 11,308 | 2.60 |
L141: Common reed wetland | 6037 | 1.39 |
L142: Water couch wetland | 991 | 0.23 |
L143: Marsh club-rush wetland | 1694 | 0.39 |
L144: Sedgeland | 25,230 | 5.81 |
L211: Terrestrial | 21,622 | 4.98 |
L311: Cropland | 276,005 | 63.55 |
L411: Water | 10,511 | 2.42 |
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Share and Cite
Wen, L.; Mason, T.; Powell, M.; Ling, J.; Ryan, S.; Bernich, A.; Gufu, G. Improved Wetland Mapping of a Highly Fragmented Agricultural Landscape Using Land Surface Phenological Features. Remote Sens. 2024, 16, 1786. https://doi.org/10.3390/rs16101786
Wen L, Mason T, Powell M, Ling J, Ryan S, Bernich A, Gufu G. Improved Wetland Mapping of a Highly Fragmented Agricultural Landscape Using Land Surface Phenological Features. Remote Sensing. 2024; 16(10):1786. https://doi.org/10.3390/rs16101786
Chicago/Turabian StyleWen, Li, Tanya Mason, Megan Powell, Joanne Ling, Shawn Ryan, Adam Bernich, and Guyo Gufu. 2024. "Improved Wetland Mapping of a Highly Fragmented Agricultural Landscape Using Land Surface Phenological Features" Remote Sensing 16, no. 10: 1786. https://doi.org/10.3390/rs16101786