Early Monitoring of Health Status of Plantation-Grown Eucalyptus pellita at Large Spatial Scale via Visible Spectrum Imaging of Canopy Foliage Using Unmanned Aerial Vehicles
Abstract
:1. Introduction
2. Methods
2.1. Study Area and Tree Health Data
2.2. Early Inventory Measurement
2.3. Unmanned Aerial Vehicle Image Acquisition
2.4. Image Processing
2.5. Image Analysis
2.6. Zonal Statistics
2.7. Normalized Difference Vegetation Index (NDVI)
2.8. Confusion Matrix and Kappa Coefficient
3. Results
3.1. Tree Health Data and VARI-Green Indices
3.2. Range of Index Map of VARI-Green as Benchmark for Detection of Health Status Using UAV
3.3. VARI-Green Pattern and Tree Health Status
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Pre-Processing Parameter | Values |
---|---|
Flying Height | 129 m |
Ground Sample distance | 3.5 cm |
Flight Line | Parallel |
Camera snapping | Auto |
Image Ratio | 3:02 |
ISO | 100 |
F-stop | 4.5 |
Shutter | 1/200 s |
Capture Rates | 3.02 |
Speed | Auto |
Flight Duration | 30 min |
Processing Parameters | Values |
---|---|
Alignment | |
Accuracy | High |
Generic Preselection | Yes |
Reference Preselection | No |
key point limit | 40,000 |
Tie point limit | 4000 |
Adaptive camera model fitting | Yes |
Matching time | 11 min 52 s |
Alignment time | 3 min 11 s |
Optimization | |
Parameters | F, b1, b2, cx, cy, k1-k4, p1, p2 |
Adaptive camera model fitting | No |
Optimization time | 8 s |
Dense point cloud | |
Points | 16,653,614 |
Point colors | 3 band (RGB), unit 8 |
Red | 625–700 nm |
Green | 500–565 nm |
Blue | 450–485 nm |
Reconstructions | |
Quality | High |
Depth filtering | Mild |
Depth maps generation time | 2 h 34 min |
Dense cloud generation time | 3 h 51 min |
Total Raw Images | 178 Pcs |
Tie Point Cloud | |
---|---|
Point | 102,671 of 108,813 |
Root Mean Square reprojection error | 0.162116 (1.16695 pix) |
Max reprojection error | 0.487534 (44.9617 pix) |
Mean key point size | 5.71308 pix |
Effective overlap | 3.46308 |
Dense Point Cloud | |
Point | 21,985,408 |
Geomatic Product | Size |
---|---|
Collected Image | 178 files (1.43 gb) |
Agisoft PhotoScan Project | 6.11 gb |
Full Orthoimage | 606 mb |
Nonground Orthoimage | 393.40 mb |
Full Orthoimage Affection Binary | - |
Validation Mask | - |
Red band | 639.21 mb |
Green band | 639.21 mb |
Blue band | 639.21 mb |
Class | Health Status | VARI-Green Value | Number of Trees | Symptoms | Causal Agent |
---|---|---|---|---|---|
1 | Dead | −2–0 | 7 | Trees no longer functional due to vascular and leaves discoloration | Pathogenic microorganism (Ralstonia solanacearum) |
2 | Severe infection | 0–0.05 | 5 | Splitting of trunk at stem with stippling leaves and discoloration | Stem borer (Zeuzera coffeae, Endoclita sp.) and phytophagous insects (Helopeltis sp.) |
3 | Mild infection | 0.06–0.16 | 967 | Distortion of foliage and shoots dieback | Sap-sucking insect (Helopeltis sp.) |
4 | Healthy | 0.17–2.00 | 10,090 | No sign of infestation on leaves and trunk |
Early Monitoring Measurement (EIM) | VARI-Green Analysis | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Plot No. | Up Slope (°) | Down Slope (°) | Plot Radius (M) | Live Trees | Dead Trees | Missing Tree (Qty) | Live Trees | Dead (Class 1) | Severe (Class 2) | Mild (Class 3) | Health (Class 4) | Missing Trees (Qty) |
1 | 5 | 6 | 11.29 | 37 | 4 | 4 | 36 | 1 | - | 8 | 27 | 10 |
2 | 23 | 22 | 11.41 | 41 | - | 2 | 33 | - | 1 | 1 | 31 | 9 |
3 | 33 | 21 | 11.48 | 41 | 3 | 1 | 30 | - | - | - | 30 | 15 |
4 | 38 | 38 | 11.68 | 46 | 1 | 1 | 34 | - | - | - | 34 | 11 |
5 | 22 | 25 | 11.44 | 46 | 1 | - | 13 | - | - | - | 13 | 14 |
6 | 5 | 15 | 11.31 | 46 | - | - | 23 | - | - | 3 | 20 | 16 |
7 | 28 | 22 | 11.44 | 47 | 1 | 2 | 37 | - | - | - | 37 | 8 |
8 | 15 | 22 | 11.38 | 43 | - | - | 31 | - | - | 1 | 30 | 12 |
9 | 21 | 25 | 11.44 | 49 | 1 | - | 29 | - | - | - | 29 | 11 |
10 | 40 | 28 | 11.57 | 46 | 2 | 1 | 34 | - | - | 4 | 34 | 11 |
11 | 25 | 25 | 11.48 | 46 | 1 | - | 36 | - | - | 36 | 7 | |
12 | 25 | 20 | 11.44 | 48 | - | - | 35 | - | - | 5 | 30 | 6 |
13 | 30 | 33 | 11.57 | 48 | - | - | 24 | - | - | 1 | 23 | 14 |
14 | 26 | 24 | 11.48 | 47 | - | 1 | 32 | - | - | - | 32 | 9 |
15 | 25 | 21 | 11.44 | 46 | 1 | 2 | 35 | - | - | 1 | 34 | 11 |
16 | 38 | 30 | 11.62 | 45 | 2 | - | 31 | - | - | 4 | 27 | 12 |
17 | 27 | 25 | 11.48 | 35 | 3 | 4 | 34 | - | - | 13 | 21 | 5 |
18 | 30 | 20 | 11.48 | 43 | 1 | 1 | 26 | - | - | - | 26 | 14 |
19 | 23 | 35 | 11.53 | 51 | - | - | 36 | - | - | 2 | 34 | 10 |
20 | 30 | 30 | 11.57 | 48 | - | - | 35 | - | - | - | 35 | 6 |
21 | 17 | 15 | 11.35 | 43 | - | - | 36 | - | - | 3 | 33 | 6 |
22 | 12 | 17 | 11.33 | 45 | - | - | 26 | - | - | 6 | 20 | 11 |
23 | 17 | 13 | 11.35 | 39 | 1 | 2 | 28 | - | - | 5 | 23 | 12 |
24 | 17 | 13 | 11.35 | 39 | 3 | 2 | 28 | - | - | 1 | 27 | 15 |
1065 | 25 | 23 | 742 | 1 | 1 | 58 | 686 | 255 |
Predict | Class 1 | Class 2 | Class 3 | Class 4 | Total | User Accuracy |
---|---|---|---|---|---|---|
Dead | 7.00 | 1.00 | 0.00 | 0.00 | 8.00 | 0.86 |
Severe | 0.00 | 5.00 | 67.00 | 154.00 | 226.00 | 0.02 |
Mild | 0.00 | 1.00 | 939.00 | 627.00 | 1567.00 | 0.60 |
Health | 0.00 | 0.00 | 156.00 | 9112.00 | 9268.00 | 0.98 |
Total | 7.00 | 7.00 | 1162.00 | 9893.00 | 11,069.00 | |
Producer Accuracy | 1.00 | 0.71 | 0.81 | 0.92 |
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Megat Mohamed Nazir, M.N.; Terhem, R.; Norhisham, A.R.; Mohd Razali, S.; Meder, R. Early Monitoring of Health Status of Plantation-Grown Eucalyptus pellita at Large Spatial Scale via Visible Spectrum Imaging of Canopy Foliage Using Unmanned Aerial Vehicles. Forests 2021, 12, 1393. https://doi.org/10.3390/f12101393
Megat Mohamed Nazir MN, Terhem R, Norhisham AR, Mohd Razali S, Meder R. Early Monitoring of Health Status of Plantation-Grown Eucalyptus pellita at Large Spatial Scale via Visible Spectrum Imaging of Canopy Foliage Using Unmanned Aerial Vehicles. Forests. 2021; 12(10):1393. https://doi.org/10.3390/f12101393
Chicago/Turabian StyleMegat Mohamed Nazir, Megat Najib, Razak Terhem, Ahmad R. Norhisham, Sheriza Mohd Razali, and Roger Meder. 2021. "Early Monitoring of Health Status of Plantation-Grown Eucalyptus pellita at Large Spatial Scale via Visible Spectrum Imaging of Canopy Foliage Using Unmanned Aerial Vehicles" Forests 12, no. 10: 1393. https://doi.org/10.3390/f12101393
APA StyleMegat Mohamed Nazir, M. N., Terhem, R., Norhisham, A. R., Mohd Razali, S., & Meder, R. (2021). Early Monitoring of Health Status of Plantation-Grown Eucalyptus pellita at Large Spatial Scale via Visible Spectrum Imaging of Canopy Foliage Using Unmanned Aerial Vehicles. Forests, 12(10), 1393. https://doi.org/10.3390/f12101393