Challenges in Estimating Tropical Forest Canopy Height from Planet Dove Imagery
| Autor: | Pramukta Kumar, Ovidiu Csillik, Gregory P. Asner |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
LiDAR
Mean squared error Science canopy texture machine learning satellite images Peru PE&RC Lidar Climate change mitigation Laboratory of Geo-information Science and Remote Sensing Canopy texture Remote sensing (archaeology) Machine learning Reducing emissions from deforestation and forest degradation General Earth and Planetary Sciences Environmental science Laboratorium voor Geo-informatiekunde en Remote Sensing Ordination Satellite imagery Satellite images Image resolution Remote sensing |
| Zdroj: | Remote Sensing 12 (2020) 7 Remote Sensing, 12(7) Remote Sensing, Vol 12, Iss 1160, p 1160 (2020) Remote Sensing; Volume 12; Issue 7; Pages: 1160 |
| ISSN: | 2072-4292 |
| Popis: | Monitoring tropical forests using spaceborne and airborne remote sensing capabilities is important for informing environmental policies and conservation actions. Developing large-scale machine learning estimation models of forest structure is instrumental in bridging the gap between retrospective analysis and near-real-time monitoring. However, most approaches use moderate spatial resolution satellite data with limited capabilities of frequent updating. Here, we take advantage of the high spatial and temporal resolutions of Planet Dove images and aim to automatically estimate top-of-canopy height (TCH) for the biologically diverse country of Peru from satellite imagery at 1 ha spatial resolution by building a model that associates Planet Dove textural features with airborne light detection and ranging (LiDAR) measurements of TCH. We use and modify features derived from Fourier textural ordination (FOTO) of Planet Dove images using spectral projection and train a gradient boosted regression for TCH estimation. We discuss the technical and scientific challenges involved in the generation of reliable mechanisms for estimating TCH from Planet Dove satellite image spectral and textural features. Our developed software toolchain is a robust and generalizable regression model that provides a root mean square error (RMSE) of 4.36 m for Peru. This represents a helpful advancement towards better monitoring of tropical forests and improves efforts in reducing emissions from deforestation and forest degradation (REDD+), an important climate change mitigation approach. |
| Databáze: | OpenAIRE |
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