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Michele Dalponte

Researcher at University of Cambridge

Publications -  96
Citations -  4372

Michele Dalponte is an academic researcher from University of Cambridge. The author has contributed to research in topics: Hyperspectral imaging & Lidar. The author has an hindex of 28, co-authored 82 publications receiving 3243 citations. Previous affiliations of Michele Dalponte include University of Trento & Norwegian University of Life Sciences.

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

TL;DR: The elevation channel of the first LIDAR return was very effective for the separation of species with similar spectral signatures but different mean heights, and the SVM classifier proved to be very robust and accurate in the exploitation of the considered multisource data.
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Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data

TL;DR: In this paper, the authors analyzed two multi-sensor set-ups: (1) airborne high spatial resolution hyperspectral images combined with LiDAR data; and (2) high spatial-resolution satellite multispectral image combined with Lidar data.
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Tree Species Classification in Boreal Forests With Hyperspectral Data

TL;DR: Evaluating the potential of two high spectral and spatial resolution hyperspectral sensors, operating at different wavelengths, for tree species classification of boreal forests showed that the HySpex VNIR 1600 sensor is effective in borealTree species classification with kappa accuracies over 0.8.
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Allometric equations for integrating remote sensing imagery into forest monitoring programmes.

TL;DR: A global database of 108753 trees for which stem diameter, height and crown diameter have all been measured is compiled and it is found that a single equation predicts stem diameter from these two variables across the world's forests.
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Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data

TL;DR: In this article, the authors analyzed the ITC level classification accuracy of boreal tree species (Pine, Spruce and Broadleaves), considering two remote sensing data types: hyperspectral and airborne laser scanning (ALS).