Journal Article

A Deep Learning Approach for High-Resolution Canopy Height Mapping in Indonesian Borneo by Fusing Multi-Source Remote Sensing Data

Webb, Kinari, Siregar, Iskandar Zulkarnaen, Sokolow, Susanne H., Setiawan, Yudi, Liu, Zac Yung-Chun, Cross, Christopher G. L., De Leo, Giulio A., Pourtois, Julie, Hopkins, Skylar R., Chamberlin, Andrew J., Nurrochmat, Dodik Ridho

2025

Overview

Accurate mapping of forest canopy height is essential for monitoring forest structure, assessing biodiversity, and informing sustainable management practices. However, obtaining high-resolution canopy height data across large tropical landscapes remains challenging and prohibitively expensive. While machine learning approaches like Random Forest have become standard for predicting forest attributes from remote sensing data, deep learning methods remain underexplored for canopy height mapping despite their potential advantages. To address this limitation, we developed a rapid, automatic, scalable, and cost-efficient deep learning framework that predicts tree canopy height at fine-grained resolution (30 × 30 m) across Indonesian Borneo’s tropical forests. Our approach integrates diverse remote sensing data, including Landsat-8, Sentinel-1, land cover classifications, digital elevation models, and NASA Carbon Monitoring System airborne LiDAR, along with derived vegetation indices, texture metrics, and climatic variables. This comprehensive data pipeline produced over 300 features from approximately 2 million observations across Bornean forests. Using LiDAR-derived canopy height measurements from ~100,000 ha as training data, we systematically compared multiple machine learning approaches and found that our neural network model achieved canopy height predictions with R2 of 0.82 and RMSE of 4.98 m, substantially outperforming traditional machine learning approaches such as Random Forest (R2 of 0.57–0.59). The model performed particularly well for forests with canopy heights between 10–40 m, though systematic biases were observed at the extremes of the height distribution. This framework demonstrates how freely available satellite data can be leveraged to extend the utility of limited LiDAR coverage, enabling cost-effective forest structure monitoring across vast tropical landscapes. The approach can be adapted to other forest regions worldwide, supporting applications in ecological research, conservation planning, and forest loss mitigation.

• Publisher: MDPI AG
• Subject: Algorithms / artificial neural network / Bioclimatology / Biodiversity / California / Canopies / Carbon / Carbon footprint / Climate change / Conservation / Cost effectiveness / Data integration / Data points / Datasets / Deep learning / Deforestation / Digital Elevation Models / Earth resources technology satellites / Ecological research / Environmental accounting / forest canopy height / Forest ecosystems / Fourier transforms / google earth engine / High resolution / Indonesia / Land cover / Land degradation / Landsat / Learning algorithms / LiDAR / Machine learning / Mapping / Monitoring / Monitoring systems / Neural networks / Old growth forests / Optical radar / Peat / Remote sensing / Satellite tracking / Sensors / Sustainability management / Sustainability reporting / Sustainable practices / Terrestrial ecosystems / Topography / Tropical environment / Tropical environments / Tropical forests / United States / Vegetation / Vegetation index