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Open AccessArticle

Challenges in Estimating Tropical Forest Canopy Height from Planet Dove Imagery

1
Center for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ 85287, USA
2
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Wageningen 6708 PB, The Netherlands
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(7), 1160; https://doi.org/10.3390/rs12071160
Received: 13 March 2020 / Revised: 1 April 2020 / Accepted: 2 April 2020 / Published: 4 April 2020
(This article belongs to the Special Issue Lidar Remote Sensing of Forest Structure, Biomass and Dynamics)
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. View Full-Text
Keywords: canopy texture; LiDAR; machine learning; satellite images; Peru canopy texture; LiDAR; machine learning; satellite images; Peru
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MDPI and ACS Style

Csillik, O.; Kumar, P.; Asner, G.P. Challenges in Estimating Tropical Forest Canopy Height from Planet Dove Imagery. Remote Sens. 2020, 12, 1160.

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