Can a Remote Sensing Approach with Hyperspectral Data Provide Early Detection and Mapping of Spatial Patterns of Black Bear Bark Stripping in Coast Redwoods?
Abstract
:1. Introduction
2. Materials and Methods
2.1. Site Location
2.2. Experimental Design/Treatment Details
2.3. Field Survey
2.4. UAV Data Collection and Processing
2.5. Crown Delineation and Spectral Signature Extraction
2.6. Feature Selection and Vegetation Indices Based on Class Separability
2.7. Classification
2.8. Accuracy Assessment
3. Results
3.1. Feature Selection
3.2. Classification Accuracy
3.3. Model Prediction
4. Discussion
4.1. Redwood Tree Characteristics
4.2. Feature Selection, Variable Importance and Early Detection of Damage
4.3. UAV-Based Image Acquisition in Forest Health Monitoring
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Imaging Sensor | Headwall Nano-Hyperspec |
---|---|
Spectral bands | 273 spectral bands from 398 to 1001 nm |
Focal Length | 4.8 mm |
FWHM | 6 nm |
Bit depth | 12-bit |
Spatial bands | 640 |
Ground sampling distance (GSD) | 2.5 cm |
Flight height | 90 m |
Flight speed | 4 m/s |
ID | Damage Class | ITCs | Pixels | Pixels/ITC |
---|---|---|---|---|
1 | No stress | 45 | 188,752 | 4194 |
2 | Fresh Damage 1 | 25 | 68,695 | 2747 |
3 | Old Damage | 38 | 111,607 | 2937 |
Vegetation Indices | Equation | Reference |
---|---|---|
Normalized Difference Vegetation Index (NDVI) | Rouse et al. [39] | |
Modified Chlorophyll Absorption Ratio Index (MCARI) | Daughtry et al. [40] | |
Red-edge Normalized Difference Vegetation Index (RENDVI) | Gitelson and Merzlyak [21] | |
Plant Senescing Reflectance Index (PSRI) | Merzlyak et al. [21] | |
Vogelmann “red edge” Index (VREI1)Normalized Channel Ratio (NCR) | Vogelmann et al. [41] | |
Coops et al. [26] |
Features | Accuracy (%) SVM | Kappa SVM | Accuracy (%) RF | Kappa RF |
---|---|---|---|---|
VNIR | 83.8 | 0.75 | 73.4 | 0.60 |
VIs | 57.6 | 0.36 | 54.8 | 0.32 |
λ685; λ750 | 49.6 | 0.24 | 43.1 | 0.15 |
NDVI | 45 | 0.17 | 38.8 | 0.08 |
MCARI | 33.9 | 0.09 | 36.5 | 0.04 |
RENDVI | 47.4 | 0.21 | 42.3 | 0.13 |
PSRI | 45.5 | 0.18 | 38.4 | 0.07 |
VREI 1 | 45.8 | 0.18 | 37.8 | 0.06 |
NCR | 45.1 | 0.26 | 38.1 | 0.09 |
full | 78.1 | 0.67 | 77.9 | 0.66 |
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Magstadt, S.; Gwenzi, D.; Madurapperuma, B. Can a Remote Sensing Approach with Hyperspectral Data Provide Early Detection and Mapping of Spatial Patterns of Black Bear Bark Stripping in Coast Redwoods? Forests 2021, 12, 378. https://doi.org/10.3390/f12030378
Magstadt S, Gwenzi D, Madurapperuma B. Can a Remote Sensing Approach with Hyperspectral Data Provide Early Detection and Mapping of Spatial Patterns of Black Bear Bark Stripping in Coast Redwoods? Forests. 2021; 12(3):378. https://doi.org/10.3390/f12030378
Chicago/Turabian StyleMagstadt, Shayne, David Gwenzi, and Buddhika Madurapperuma. 2021. "Can a Remote Sensing Approach with Hyperspectral Data Provide Early Detection and Mapping of Spatial Patterns of Black Bear Bark Stripping in Coast Redwoods?" Forests 12, no. 3: 378. https://doi.org/10.3390/f12030378
APA StyleMagstadt, S., Gwenzi, D., & Madurapperuma, B. (2021). Can a Remote Sensing Approach with Hyperspectral Data Provide Early Detection and Mapping of Spatial Patterns of Black Bear Bark Stripping in Coast Redwoods? Forests, 12(3), 378. https://doi.org/10.3390/f12030378