Multisensor Data Fusion for Improved Segmentation of Individual Tree Crowns in Dense Tropical Forests

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Ben Weinstein¹  Anthony Laybros²  James Ball³  Melaine Aubry-Kientz⁴  David Coomes⁵  Gregoire Vincent⁵  Toby Jackson⁶ 

[1] de la Guyane), Kourou, French Guiana;des Antilles, Universit&x00E9;AMAP, University of Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France;AgroParisTech, UMR EcoFoG (CNRS, Cirad, INRAE, Universit&x00E9;Department of Plant Sciences, University of Cambridge, Cambridge, U.K.;Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL, USA;

关键词: Airborne laser scanning (ALS);    data fusion;    deepforest;    high-resolution imagery;    hyperspectral;    3-D adaptive mean-shift (AMS3D);   

DOI  :  10.1109/JSTARS.2021.3069159

来源: DOAJ

【 摘 要 】

Automatic tree crown segmentation from remote sensing data is especially challenging in dense, diverse, and multilayered tropical forest canopies, and tracking mortality by this approach is even more difficult. Here, we examine the potential for combining airborne laser scanning (ALS) with multispectral and hyperspectral data to improve the accuracy of tree crown segmentation at a study site in French Guiana. We combined an ALS point cloud clustering method with a spectral deep learning model to achieve 83% accuracy at recognizing manually segmented reference crowns (with congruence >0.5). This method outperformed a two-step process that involved clustering the ALS point cloud and then using the logistic regression of hyperspectral distances to correct oversegmentation. We used this approach to map tree mortality from repeat surveys and show that the number of crowns identified in the first that intersected with height loss clusters was a good estimator of the number of dead trees in these areas. Our results demonstrate that multisensor data fusion improves the automatic segmentation of individual tree crowns and presents a promising avenue to study forest demography with repeated remote sensing acquisitions.

【 授权许可】

Unknown