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

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

Fusion de données LiDAR et hyperspectrales pour la gestion forestière - une revue

International audienceAccording to the Intergovernmental Panel on Climate Change (IPCC), forests represent an essential source of all carbon stocks in vegetation for maintaining life conditions of many organisms in the terrestrial biosphere. The utilization of strategies for forest characterization and monitoring, plays an imperative role to develop a proper sustainable management. Current research in the field is focused on sensor potentiality and data processing. Recent advances in remote sensing afford valuable information to describe forests at tree level. On the one hand, hyperspectral images contain meaningful reflectance attributes of plants or spectral traits. On the other hand, LiDAR data offers alternatives for analyzing structural properties of canopy. A convenient selection of fusion methods provide better and more robust estimation of the variable of interest. This work presents a literature review for the integration of hyperspectral images and LiDAR data by considering applications related to forestry monitoring. Although different authors propose a variety of taxonomies for data fusion, we classified our reviewed methods according to three levels of fusion based on data processing: Low level or observation level, medium level or feature level, and high level or decision level. Fusion at observation level preserves most of the original information from both modalities by handling data at the same spatial dimension. Canopy Height Model (CHM) is the most used two-dimensional representation of LiDAR point cloud for the registration with hyperspectral images. Fusion at feature level seeks to complement information by exploiting the original data. The most relevant features extracted from hyperspectral or LiDAR data are statistical, morphological, structural, vegetation indexes, textural, among others. Some of these feature descriptors are stacked to be fused at higher level, or these are normalized to be integrated through methods of dimension reduction or feature selection. Fusion at decision level is directly associated to the forestry application and implies tasks of thresholding, segmentation, classification, or regression analysis. This review examines a relationship between the three levels of fusion and the methods used in each considered approach. The most important applications listed in this work are oriented to individual tree crown delineation, tree specie classification, landcovermaps, aboveground biomass estimation, and biophysical parameters

Fusion of hyperspectral imaging and LiDAR for forest monitoring

Effective strategies for forest characterization and monitoring are important to support sustainable management. Recent advances in remote sensing, like hyperspectral and LiDAR sensors, provide valuable information to describe forests at stand, plot, and tree level. Hyperspectral imaging contains meaningful reflectance attributes of plants or spectral traits, while LiDAR data offer alternatives for analyzing structural properties of canopy. The fusion of these two data sources can improve forest characterization. The method to use for the data fusion should be chosen according to the variables to predict. This work presents a literature review on the integration of hyperspectral imaging and LiDAR data by considering applications related to forest monitoring. Although different authors propose a variety of taxonomies for data fusion, we classified our reviewed methods according to three levels of fusion: low level or observation level, medium level or feature level, and high level or decision level. This review examines the relationship between the three levels of fusion and the methods used in each considered approac