Challenges in Estimating Tropical Forest Canopy Height from Planet Dove Imagery

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.

Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop–Livestock System Using Textural Information from PlanetScope Imagery

“…In our study, the uncertainty analysis for AGB and CH estimation was carried out following the procedure adopted by Csillik et al [68,69]. We grouped the estimated values of AGB and CH (separately) into 10 bins using the natural breaks method and computed the RMSE in percentage (%) for each bin.…”

Near-real time aboveground carbon emissions in Peru

“…The remaining 22–25% of unexplained variance can be minimized by using additional remotely sensed predictors and image processing techniques. For example, texture analysis on high resolution Planet data [ 18 , 54 ], hyperspectral data [ 55 ], or other multispectral imagery [ 56 – 58 ] have been successfully used for aboveground biomass and carbon estimation purposes. However, using extra predictors will require a trade-off between accuracy of the model, computational resources needed and the temporal frequency of the analysis.…”

Satellite-based estimation of roughness lengths and displacement heights for wind resource modelling

“…Although representing a major advancement in estimating 3D forest structure, these current spaceborne laser observations are restricted to very narrow footprints, which are insufficient to map wall-towall canopy heights over larger areas. Recent studies have shown that the laser-derived canopy heights can be extrapolated through different combinations with other satellite observations and machine learning techniques (Csillik et al, 2020;Fagua et al, 2019). Typically, the canopy heights from laser measurements are extrapolated using textural information from active microwave sensors (e.g.…”