A Drone-Powered Deep Learning Methodology for High Precision Remote Sensing in California’s Coastal Shrubs
<p>Map of research sites (yellow border)—UCSC Fort Ord Natural Reserve (black border). (<b>a</b>) Application Site 1 (8.42 ha), (<b>b</b>) Training Site (16 ha), (<b>c</b>) Application Site 2 (8.84 ha), (<b>d</b>) Application Site 3 (7.44 ha). Backdrop imagery source: World Imagery Esri, Maxar, Earthstar Geographics (2022). Research site imagery is displayed as RGB orthomosaic from UAV research flights.</p> "> Figure 2
<p>Manzanita dominated maritime chaparral (<b>left</b>) and coastal sage scrub transitioning to oak woodland (<b>right</b>). UCSC Fort Ord Natural Reserve, California.</p> "> Figure 3
<p>Orthomosiac raster displayed in RGB. (<b>a</b>) Application Site 1 (7.43 ha), (<b>b</b>) Training Site (16 ha), (<b>c</b>) Application Site 2 (9 ha), (<b>d</b>) Application Site 3 (8 ha). Resolution: 2.5 cm/pixel with WGS 84 UTM Zone 10 projection. Backdrop imagery source: World Imagery Esri, Maxar, Earthstar Geographics (2022).</p> "> Figure 4
<p>Normalized Digital Surface Model (nDSM) estimating canopy height (m). (<b>a</b>) Application Site 1 (7.43 ha), (<b>b</b>) Training Site (16 ha), (<b>c</b>) Application Site 2 (9 ha), (<b>d</b>) Application Site 3 (8 ha). Resolution: 5 cm/pixel (WGS 84 UTM Zone 10 projection).</p> "> Figure 5
<p>Slope model (degrees) generated from normalized digital surface model (nDSM) canopy height (WGS 84 UTM Zone 10 projection). (<b>a</b>) Application Site 1 (7.43 ha), (<b>b</b>) Training Site (16 ha), (<b>c</b>) Application Site 2 (9 ha), (<b>d</b>) Application Site 3 (8 ha).</p> "> Figure 6
<p>CNN generated probability heat maps for each feature class in the 16-ha training site. Red regions have a high probability (<span class="html-italic">p</span> = 1) of membership to the class and blue represents a null probability (<span class="html-italic">p</span> = 0) of membership to the feature class.</p> "> Figure 7
<p>CNN + OBIA land cover classification results (WGS 84 UTM Zone 10 projection). (<b>a</b>) Application Site 1 (7.43 ha), (<b>b</b>) Training Site (16 ha), (<b>c</b>) Application Site 2 (9 ha), (<b>d</b>) Application Site 3 (8 ha).</p> "> Figure 8
<p>Accuracy Assessment of CNN + OBIA deep learning landscape cover classification.</p> "> Figure A1
<p>Random Forest classification results (WGS 84 UTM Zone 10 projection). (<b>a</b>) Application Site 1 (7.43 ha), (<b>b</b>) Training Site (16 ha), (<b>c</b>) Application Site 2 (9 ha), (<b>d</b>) Application Site 3 (8 ha).</p> "> Figure A2
<p>Accuracy assessment of random forest landscape cover classification.</p> "> Figure A3
<p>Support Vector Machine classification results (WGS 84 UTM Zone 10 projection). (<b>a</b>) Application Site 1 (7.43 ha), (<b>b</b>) Training Site (16 ha), (<b>c</b>) Application Site 2 (9 ha), (<b>d</b>) Application Site 3 (8 ha).</p> "> Figure A4
<p>Accuracy assessment of support vector machine landscape cover classification.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. UAV Data Collection
2.3. UAV Image Processing
Flight Imagery
2.4. Field Sampling
2.5. Classification Modeling Development
2.6. Image Sampling
2.7. CNN Application and OBIA Classification
2.8. Segmentation
2.9. Accuracy Assessment
3. Results
3.1. Field Survey Results
3.2. Classification Model Results
4. Discussion
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
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Species | Common Name | Plant Communities |
---|---|---|
Adenostoma fasciculatum | Chamise | Maritime Chaparral |
Arctostaphylos pumila | Sandmat Manzanita | |
Arctostaphylos tomentosa | Woolyleaf Manzanita | |
Ceanothus rigidus | Monterey Ceanothus | |
Artemisia californica | California Sage | Coastal Sage Scrub |
Baccharis pilularis | Coyote Brush | |
Ericameria ericoides | Mock Heather | |
Salvia mellifera | Black Sage | |
Toxicodendron diversilobum | Poison Oak | |
Quercus agrifolia | Coast Live Oak | Oak Woodland |
Training Site | Application Site | ||||||
---|---|---|---|---|---|---|---|
Group | Species | Training | Testing | Corrected Testing | 1 | 2 | 3 |
Chaparral | A. fasciculatum | 174 | 74 | 67 | 244 | 9 | 303 |
A. pumila | 1545 | 662 | 562 | 387 | 492 | 247 | |
A. tomentosa | 1065 | 457 | 369 | 295 | 218 | 573 | |
C. rigidus | 162 | 69 | 64 | 16 | 87 | 33 | |
Sage Scrub | A. californica | 20 | 9 | 9 | 17 | 8 | 14 |
B. pilularis | 84 | 36 | 34 | 48 | 31 | 23 | |
E. ericoides | 286 | 123 | 119 | 69 | 91 | 36 | |
S. mellifera | 102 | 44 | 42 | 62 | 16 | 39 | |
T. diversilobum | 35 | 15 | 14 | 30 | 57 | 18 | |
Woodland | Q. agrifolia | 913 | 392 | 376 | 186 | 306 | 82 |
Gaps | 226 | 97 | 90 | 94 | 83 | 78 | |
Bareground | 340 | 146 | 136 | 70 | 112 | 72 | |
Total | 4952 | 2124 | 1882 | 1518 | 1510 | 1518 |
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Detka, J.; Coyle, H.; Gomez, M.; Gilbert, G.S. A Drone-Powered Deep Learning Methodology for High Precision Remote Sensing in California’s Coastal Shrubs. Drones 2023, 7, 421. https://doi.org/10.3390/drones7070421
Detka J, Coyle H, Gomez M, Gilbert GS. A Drone-Powered Deep Learning Methodology for High Precision Remote Sensing in California’s Coastal Shrubs. Drones. 2023; 7(7):421. https://doi.org/10.3390/drones7070421
Chicago/Turabian StyleDetka, Jon, Hayley Coyle, Marcella Gomez, and Gregory S. Gilbert. 2023. "A Drone-Powered Deep Learning Methodology for High Precision Remote Sensing in California’s Coastal Shrubs" Drones 7, no. 7: 421. https://doi.org/10.3390/drones7070421