Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning
| Autor: | Marco Lavalle, Mariano García, Eric Pottier, Naoto Yokoya, Junshi Xia, Heiko Balzter, Maryam Pourshamsi |
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| Rok vydání: | 2021 |
| Předmět: |
Canopy
Tree canopy 010504 meteorology & atmospheric sciences business.industry 0211 other engineering and technologies 02 engineering and technology Vegetation Machine learning computer.software_genre 01 natural sciences Atomic and Molecular Physics and Optics Computer Science Applications Random forest Support vector machine Lidar Forest ecology Environmental science Artificial intelligence Computers in Earth Sciences business Canonical correlation Engineering (miscellaneous) computer 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
| Zdroj: | ISPRS Journal of Photogrammetry and Remote Sensing. 172:79-94 |
| ISSN: | 0924-2716 |
| Popis: | Forest height is an important forest biophysical parameter which is used to derive important information about forest ecosystems, such as forest above ground biomass. In this paper, the potential of combining Polarimetric Synthetic Aperture Radar (PolSAR) variables with LiDAR measurements for forest height estimation is investigated. This will be conducted using different machine learning algorithms including Random Forest (RFs), Rotation Forest (RoFs), Canonical Correlation Forest (CCFs) and Support Vector Machine (SVMs). Various PolSAR parameters are required as input variables to ensure a successful height retrieval across different forest heights ranges. The algorithms are trained with 5000 LiDAR samples (less than 1% of the full scene) and different polarimetric variables. To examine the dependency of the algorithm on input training samples, three different subsets are identified which each includes different features: subset 1 is quiet diverse and includes non-vegetated region, short/sparse vegetation (0–20 m), vegetation with mid-range height (20–40 m) to tall/dense ones (40–60 m); subset 2 covers mostly the dense vegetated area with height ranges 40–60 m; and subset 3 mostly covers the non-vegetated to short/sparse vegetation (0–20 m) .The trained algorithms were used to estimate the height for the areas outside the identified subset. The results were validated with independent samples of LiDAR-derived height showing high accuracy (with the average R2 = 0.70 and RMSE = 10 m between all the algorithms and different training samples). The results confirm that it is possible to estimate forest canopy height using PolSAR parameters together with a small coverage of LiDAR height as training data. |
| Databáze: | OpenAIRE |
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