Mapping Potential Plant Species Richness over Large Areas with Deep Learning, MODIS, and Species Distribution Models
Abstract
:1. Introduction
2. Materials and Methods
2.1. Species Distribution Modeling
2.2. Species Richness (as a Response Variable)
2.3. MODIS Products: NDVI and LAI (as Input Variables)
2.4. Deep Learning-Based Species Richness Model
2.5. Independent Validation of Species Richness
3. Results
3.1. Species Richness Estimation from S-SDMs
3.2. Deep Learning-Based Species Richness Estimation Model Using Remote Sensing Data
3.3. Statistical Feature Importance
3.4. Independent Validation of Species Richness
4. Discussion
4.1. Deep Learning-Based Species Richness Estimation
4.2. Limitations and Recommendations
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Model Types | MAE (Mean Absolute Error) | RMSE (Root Mean Square Error) | Bias | Correlation |
---|---|---|---|---|
Random Forest | 61.1028 | 78.9512 | 0.5656 | 0.8843 |
Deep Learning | 28.8105 | 38.5759 | 10.2055 | 0.9752 |
DOY (Day of Year) | LAI (Leaf Area Index) | NDVI (Normalized Difference Vegetation Index) | |
---|---|---|---|
Spring | 97–177 | 46.46 (0.50) | 49.59 (0.61) |
Summer | 193–257 | 38.51 (0.38) | 35.06 (0.38) |
Fall | 273–321 | 28.19 (0.21) | 43.50 (0.49) |
Winter | 1–81; 337–353 | 20.84 (0.01) | 40.83 (0.41) |
Average | 32.64 (0.25) | 42.32 (0.47) |
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Choe, H.; Chi, J.; Thorne, J.H. Mapping Potential Plant Species Richness over Large Areas with Deep Learning, MODIS, and Species Distribution Models. Remote Sens. 2021, 13, 2490. https://doi.org/10.3390/rs13132490
Choe H, Chi J, Thorne JH. Mapping Potential Plant Species Richness over Large Areas with Deep Learning, MODIS, and Species Distribution Models. Remote Sensing. 2021; 13(13):2490. https://doi.org/10.3390/rs13132490
Chicago/Turabian StyleChoe, Hyeyeong, Junhwa Chi, and James H. Thorne. 2021. "Mapping Potential Plant Species Richness over Large Areas with Deep Learning, MODIS, and Species Distribution Models" Remote Sensing 13, no. 13: 2490. https://doi.org/10.3390/rs13132490