Fusion of Hyperspectral and LIDAR Remote Sensing Data for Classification of Complex Forest Areas

By Michele Dalponte, Lorenzo Bruzzone, Damiano Gianelle. IEEE Transactions on Geoscience and Remote Sensing, 2008

561 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/16074771ab071f2296cba921af73f520e4715e55

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Citations in the corpus

Support vector machines in remote sensing: A review (2011, unknown): “Looking into forest species classification, Dalponte et al. (2008) used SVMs and data fusion of hyperspectral (AISA) and LiDAR data.”
Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest (2014, unknown): “The joint use of hyperspectral and LiDAR remote sensing data for the classification of forests was investigated in [35], where a novel classification system was proposed to properly integrate multi-sensor information.”
Advances in fusion of optical imagery and LiDAR point cloud applied to photogrammetry and remote sensing (2017, background): “Similarly, Dalponte et al. (2008) integrate LiDAR-generated raster layers with hyperspectral imagery to discriminate tree species on the pixel basis in a complex forest.”
Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data (2012, methodology): “The delineation of tree crowns can, for example, be done by auxiliary 3-D information [52,61,63,69–72], for example obtained from Canopy Height Models.”
Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data (2010, methodology): “Recent studies discuss the use of SVM for spectral–spatial classification of urban hyperspectral data [10]–[12] and multisource classification [13]–[15] and extend the supervised SVM techniques by semisupervised concepts [16], [17].”
Observing Changing Ecological Diversity in the Anthropocene (2013, methodology): “The architectural information obtained from LiDAR can be used in process studies and to aid in identifying plant traits, and in mapping their distributions (Dalponte et al 2008).”
Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019, background): “[135] M.”
Key structural features of Boreal forests may be detected directly using L-moments from airborne lidar data (2017, background): “For this reason, studies on ALS-based forest structure characterization by statistical inductive methods, which relate ALS metrics to field attributes empirically, are commonplace (Hall et al., 2005; Lefsky et al., 2005; Dalponte et al., 2008; Pascual et al., 2008; Disney et al., 2010; Jaskierniak e”
Tree species classification using structural features derived from terrestrial laser scanning (2020, result): “Hyperspectral data has, for example, shown potential for tree species classification and shown to be complementary to LiDAR data (Dalponte et al., 2008; Jones et al., 2010; Alonzo et al., 2014).”
Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion (2018, background): “Previous studies have similarly found that tree height is a useful input to classification algorithms due to its limited sensitivity to segmentation error [46,47].”
Urban tree species mapping using hyperspectral and lidar data fusion (2014, methodology): “Tree height is the single most common lidar variable used in tandem with spectral information as a means to improve classification results (Dalponte et al., 2008; Jones et al., 2010; Koetz, Morsdorf, van der Linder, Curt, & Allgöwer, 2008).”