Phenology and Spectral Unmixing-Based Invasive Kudzu Mapping: A Case Study in Knox County, Tennessee
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Datasets
2.2.1. Remote Sensing Images and Pre-Processing
2.2.2. Reference Data
2.3. Spectral Unmixing and Phenology-Based Kudzu Identification Method
2.3.1. Initial Estimation by Linear Unmixing
2.3.2. Phenology-Based Potential Kudzu Masking
2.3.3. Nonlinear Unmixing Refinement
2.3.4. Creating Kudzu Presence Maps
2.4. Accuracy Assessment
3. Results
3.1. Performance of the Initial Linear Unmixing Estimation for Kudzu Mapping
3.2. Performance of Phenology-Based Kudzu Masking
3.3. Performance of the Nonlinear Unmixing Refinements for Kudzu Mapping
3.4. Kudzu Presence in Knox County
4. Discussion
4.1. Misclassification with the Surrounding Vegetation
4.2. Spectral Unmixing Model Selection for Kudzu Mapping
4.3. Future Improvements of the Proposed Kudzu Classification Approach
5. Conclusions
- The spectral unmixing approach is appropriate for kudzu mapping at the county scale using Sentinel-2 images and allows for continuous monitoring of large areas;
- Linear unmixing provides high producer’s accuracy but low user’s accuracy due to the misclassification of grasslands as kudzu;
- A phenology-based mask can be created based on the differences of kudzu abundance estimated from linear spectral unmixing and NDVI derived from the Sentinel-2 images. The use of this phenology-based mask improves the kudzu classification accuracy and decreases the computing expense for nonlinear spectral unmixing;
- The nonlinear unmixing analysis can refine the kudzu abundance estimation and presence classification, although an appropriate nonlinear model should be selected based on the performance assessment on the datasets and the physical interpretation of the spectral mixing scenarios;
- The refined kudzu presence map for Knox County gives user’s accuracy, producer’s accuracy, Jaccard index, and Kappa index values of 0.858, 0.907, 0.789, and 0.725, respectively, based on an optimal abundance reclassification threshold of 0.6;
- Kudzu plants are scattered in small patches along forest edges, roads, and vegetation tops near houses and infrastructure, especially in the northwestern and southeastern parts of Knox County.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Band Number | Central Wavelength (nm) | Bandwidth (nm) | Spatial Resolution (m) |
---|---|---|---|
1—Coastal aerosol | 443 | 20 | 60 |
2—Blue | 490 | 65 | 10 |
3—Green | 560 | 35 | 10 |
4—Red | 665 | 30 | 10 |
5—Vegetation Red Edge | 705 | 15 | 20 |
6—Vegetation Red Edge | 740 | 15 | 20 |
7—Vegetation Red Edge | 783 | 20 | 20 |
8—Near-Infrared | 842 | 115 | 10 |
8b—Narrow Near-Infrared | 865 | 20 | 20 |
9—Water Vapor | 945 | 20 | 60 |
10—Cirrus | 1375 | 30 | 60 |
11—Short-wave Infrared | 1610 | 90 | 20 |
12—Short-wave Infrared | 2190 | 180 | 20 |
Thresholds | 1 | 0.9 | 0.8 | 0.7 | 0.6 | 0.5 | 0.4 | 0.3 | 0.2 | 0.1 |
---|---|---|---|---|---|---|---|---|---|---|
PA | 0.989 | 0.961 | 0.919 | 0.879 | 0.848 | 0.808 | 0.774 | 0.734 | 0.676 | 0.616 |
UA | 0.511 | 0.475 | 0.432 | 0.396 | 0.367 | 0.333 | 0.289 | 0.245 | 0.195 | 0.152 |
Jaccard | 0.508 | 0.466 | 0.416 | 0.376 | 0.344 | 0.309 | 0.266 | 0.225 | 0.179 | 0.138 |
Kappa | 0.560 | 0.585 | 0.545 | 0.503 | 0.466 | 0.421 | 0.364 | 0.305 | 0.234 | 0.170 |
Thresholds | 1 | 0.9 | 0.8 | 0.7 | 0.6 | 0.5 | 0.4 | 0.3 | 0.2 | 0.1 |
---|---|---|---|---|---|---|---|---|---|---|
PA | 1.000 | 0.989 | 0.976 | 0.951 | 0.922 | 0.884 | 0.849 | 0.809 | 0.748 | 0.687 |
UA | 0.802 | 0.801 | 0.785 | 0.764 | 0.738 | 0.689 | 0.632 | 0.572 | 0.530 | 0.506 |
Jaccard | 0.802 | 0.794 | 0.771 | 0.734 | 0.695 | 0.632 | 0.568 | 0.504 | 0.450 | 0.411 |
Kappa | 0.000 | 0.263 | 0.409 | 0.489 | 0.525 | 0.478 | 0.414 | 0.352 | 0.281 | 0.236 |
Procedure Step | Computation Areas for Unmixing Models |
---|---|
Step 1 Linear unmixing | Knox County vegetated area, 1126.06 km2 |
Step 3 Nonlinear unmixing | Phenology-based masked kudzu area, 134.19 km2 |
Thresholds | 1 | 0.9 | 0.8 | 0.7 | 0.6 | 0.5 | 0.4 | 0.3 | 0.2 | 0.1 |
---|---|---|---|---|---|---|---|---|---|---|
PA | 0.974 | 0.955 | 0.944 | 0.925 | 0.907 | 0.880 | 0.842 | 0.803 | 0.756 | 0.698 |
UA | 0.875 | 0.872 | 0.867 | 0.864 | 0.858 | 0.852 | 0.830 | 0.790 | 0.715 | 0.615 |
Jaccard | 0.855 | 0.837 | 0.824 | 0.807 | 0.789 | 0.763 | 0.718 | 0.662 | 0.581 | 0.486 |
Kappa | 0.126 | 0.454 | 0.618 | 0.698 | 0.725 | 0.723 | 0.690 | 0.633 | 0.548 | 0.433 |
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Shen, M.; Tang, M.; Li, Y. Phenology and Spectral Unmixing-Based Invasive Kudzu Mapping: A Case Study in Knox County, Tennessee. Remote Sens. 2021, 13, 4551. https://doi.org/10.3390/rs13224551
Shen M, Tang M, Li Y. Phenology and Spectral Unmixing-Based Invasive Kudzu Mapping: A Case Study in Knox County, Tennessee. Remote Sensing. 2021; 13(22):4551. https://doi.org/10.3390/rs13224551
Chicago/Turabian StyleShen, Ming, Maofeng Tang, and Yingkui Li. 2021. "Phenology and Spectral Unmixing-Based Invasive Kudzu Mapping: A Case Study in Knox County, Tennessee" Remote Sensing 13, no. 22: 4551. https://doi.org/10.3390/rs13224551