Fusion of UAV Hyperspectral Imaging and LiDAR for the Early Detection of EAB Stress in Ash and a New EAB Detection Index—NDVI(776,678)
Abstract
:1. Introduction
2. Materials
2.1. Study Area
2.2. Ground Truth data Collection
2.2.1. Chlorophyll Fluorescence
2.2.2. Crown Thinning
2.2.3. D-Shaped Exit Holes and Woodpecker Pecking Holes
2.2.4. Canopy Color
2.2.5. UAV-Based Hyperspectral Imagery and LiDAR Acquisition
3. Methods
3.1. Individual Tree Decline Ratings
3.2. Individual Tree Segmentation and Feature Extraction
3.3. Separation of Raw Hyperspectral Bands
3.4. Ratio and Normalized Analysis of Sensitive Bands
3.5. Comparison with Other Vegetation Indices
4. Results
4.1. EAB-Sensitive Bands of Hyperspectral Image
4.2. Comparison of NDVI(776,678) with Existing Vegetation Indices and LiDAR Metrics
4.3. Relationship between Canopy Color and Tree Damage
4.4. Tree Damage Mapping
5. Discussion
5.1. Remote Sensing Monitoring Window for Broad-Leaved Trees
5.2. Ash Canopy Color and Damage Stages
5.3. Method for the Early Detection of EAB
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
References
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Decline Ratings Quantile | Measured QY | LAI | Number of Exit Holes | Number of Woodpecker Feeding Holes |
---|---|---|---|---|
4 | 0.606–0.85 | 2.88–4.51 | 0–4 | 0–3 |
3 | 0.493–0.605 | 2.11–2.87 | 5–10 | 4–9 |
2 | 0.158–0.492 | 1.62–2.10 | 11–18 | 10–16 |
1 | 0–0.157 | 0–1.61 | 19–31 | 17–32 |
Damage Stage | Average Tree Decline Rating Value |
---|---|
Healthy (H) | 3.17–4 |
Light (L) | 2.39–3.16 |
Moderate (M) | 1.39–2.38 |
Severe (S) | 1–1.38 |
Variables | Formula | Reference |
---|---|---|
CSC | R605/R760 | [42] |
GI | R554/R677 | [45] |
NPQI | (R415 − R435)/(R415 + R435) | [46] |
GMB | R750/R700 | [43] |
WBI | R970/R900 | [47] |
ACH | SUM(R600:R700)/SUM(R500:R600) | [48] |
NWI | (R970 − R850)/(R970 + R850) | [49] |
Cse | R605/R760 | [42] |
NDVI | (R800 − R670)/(R800 + R670) | [41] |
NDVI(810,450) | (R810 − R450)/(R810 + R450) | [50] |
Range (690, 740) | MAX(R690:R740) − MIN(R690:R740) | [51] |
Metrics | Definition |
---|---|
CC | Canopy cover |
GF | Gap fraction |
AIH_99th | Accumulate interquartile height of 99th |
AIH_95th | Accumulate interquartile height of 95th |
AIH_kurtosis | Kurtosis of accumulate interquartile height |
AIH_max | Maximum point cloud height |
AIH_min | Maximum point cloud height |
Int_p90 | 90th percentile of crown return intensity |
Int_p95 | 95th percentile of crown return intensity |
Int_p99 | 99th percentile of crown return intensity |
Model | Healthy | Lightly | Moderately | Severely | All Stages | |||||
---|---|---|---|---|---|---|---|---|---|---|
UA (%) | PA (%) | UA (%) | PA (%) | UA (%) | PA (%) | UA (%) | PA (%) | OA (%) | ||
Include NDVI(776,678) | HI + LIDAR | 86.17 | 90.00 | 64.89 | 76.25 | 83.33 | 58.33 | 100 | 100 | 82.90 |
HI | 84.04 | 87.78 | 56.67 | 68.92 | 76.09 | 53.03 | 100 | 100 | 79.03 | |
LIDAR | 82.22 | 68.51 | 48.00 | 60.00 | 46.42 | 43.33 | 74.47 | 72.92 | 70.32 | |
Exclude NDVI(776,678) | HI + LIDAR | 81.19 | 91.11 | 59.76 | 61.25 | 76.92 | 61.54 | 100 | 100 | 79.68 |
HI | 75.24 | 87.78 | 53.83 | 52.50 | 73.33 | 56.90 | 100 | 100 | 75.97 | |
LIDAR | 66.67 | 83.33 | 30.56 | 34.38 | 30.00 | 12.50 | 54.55 | 56.25 | 50.00 |
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Zhou, Q.; Yu, L.; Zhang, X.; Liu, Y.; Zhan, Z.; Ren, L.; Luo, Y. Fusion of UAV Hyperspectral Imaging and LiDAR for the Early Detection of EAB Stress in Ash and a New EAB Detection Index—NDVI(776,678). Remote Sens. 2022, 14, 2428. https://doi.org/10.3390/rs14102428
Zhou Q, Yu L, Zhang X, Liu Y, Zhan Z, Ren L, Luo Y. Fusion of UAV Hyperspectral Imaging and LiDAR for the Early Detection of EAB Stress in Ash and a New EAB Detection Index—NDVI(776,678). Remote Sensing. 2022; 14(10):2428. https://doi.org/10.3390/rs14102428
Chicago/Turabian StyleZhou, Quan, Linfeng Yu, Xudong Zhang, Yujie Liu, Zhongyi Zhan, Lili Ren, and Youqing Luo. 2022. "Fusion of UAV Hyperspectral Imaging and LiDAR for the Early Detection of EAB Stress in Ash and a New EAB Detection Index—NDVI(776,678)" Remote Sensing 14, no. 10: 2428. https://doi.org/10.3390/rs14102428