Forest Disturbance Monitoring Using Cloud-Based Sentinel-2 Satellite Imagery and Machine Learning
Problem
Forest damage has become more frequent in Hungary, and rapid, cost-effective monitoring of disturbances was needed.
Why it matters
Timely detection of drought, frost and other damage affects forest management and economic losses.
What the AI did
Sentinel-2 imagery and Google Earth Engine cloud computing produced NDVI-based disturbance maps, and a random forest classifier mapped the spatial distribution of dominant tree species.
Results
The cloud-based method successfully showed drought and frost damage in the Nagyerdő forest of Debrecen (2017–2020); accuracy assessed via confusion matrices, numbers not stated in source.
Impact
The method detected drought and frost damage and revealed species-specific reactions at the Hungarian study site.
Record
| Venue | Journal of Imaging |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Tamás Molnár, Géza Király |
| Citations | 17 (OpenAlex, at import time) |
| References | 52 |
| Model or method | random forest classifier |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/jimaging10010014 |
| Open access | gold |
| Keywords | forest disturbance, Sentinel-2, random forest, NDVI, Google Earth Engine |
Importance rating
real but narrower advance
Abstract states forest damage has become more frequent in Hungary; rapid, cost-effective satellite monitoring aids forest management, demonstrated on one Hungarian forest site.
More in Climate & Environment
| # | Paper | Year | Citations | Importance |
|---|---|---|---|---|
| 1 | Learning skillful medium-range global weather forecasting | 2023 | 1,416 |
8
|
| 2 | Accurate medium-range global weather forecasting with 3D neural networks | 2023 | 1,631 |
8
|
| 3 | FuXi: a cascade machine learning forecasting system for 15-day global weather forecast | 2023 | 355 |
8
|
| 4 | Skilful nowcasting of extreme precipitation with NowcastNet | 2023 | 451 |
8
|
| 5 | Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects | 2023 | 378 |
8
|