Abstract
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.
Highlights
Tropical forests are an important component in the global carbon cycle and for mitigating climate change, but continued forest use [1] is transforming tropical forests into sources of atmospheric carbon [2]
top-of-canopy height (TCH) can be estimated from a variety of sensors, like Moderate Resolution Imaging Spectroradiometer (MODIS) [11,12] and Landsat [13,14], which have been used to scale-up light detection and ranging (LiDAR) measurements of TCH
We presented the technical and scientific challenges involved in the generation of reliable mechanisms for estimating TCH from Planet Dove satellite image spectral and textural features
Summary
Tropical forests are an important component in the global carbon cycle and for mitigating climate change, but continued forest use [1] is transforming tropical forests into sources of atmospheric carbon [2]. Developing large-scale estimation models using higher spatial and temporal resolution will be instrumental in bridging the gap between retrospective analysis and near-real-time monitoring [16] Such a high spatial and temporal capability is provided by the largest fleet of small cube satellites, Planet Dove, imaging the globe daily at 3.7 m resolution [17]. Moving from Landsat collections to the Planet Dove data source presents a substantial challenge in that Planet Dove data does not possess the same spectral resolution or stability It does, provide substantially higher spatial and temporal resolution than Landsat, necessary to detect rapid changes in the underlying tropical forest vertical structure. This will improve efforts in reducing emissions from deforestation and forest degradation (REDD+), an important climate change mitigation approach [4]
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