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Remote Sens. 2019, 11(3), 354;

Improving Ecotope Segmentation by Combining Topographic and Spectral Data

Earth and Life Institute, Université catholique de Louvain, 1348 Louvain-la-Neuve, Belgium
Biodiversity and Landscape Unit, Gembloux Agro-Bio Tech, Université de Liège, 5030 Gembloux, Belgium
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 14 January 2019 / Accepted: 30 January 2019 / Published: 11 February 2019
(This article belongs to the Special Issue Image Segmentation for Environmental Monitoring)
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Ecotopes are the smallest ecologically distinct landscape features in a landscape mapping and classification system. Mapping ecotopes therefore enables the measurement of ecological patterns, process and change. In this study, a multi-source GEOBIA workflow is used to improve the automated delineation and descriptions of ecotopes. Aerial photographs and LIDAR data provide input for landscape segmentation based on spectral signature, height structure and topography. Each segment is then characterized based on the proportion of land cover features identified at 2 m pixel-based classification. The results show that the use of hillshade bands simultaneously with spectral bands increases the consistency of the ecotope delineation. These results are promising to further describe biotopes of high ecological conservation value, as suggested by a successful test on ravine forest biotope. View Full-Text
Keywords: GEOBIA; biodiversity; LIDAR; orthophoto; segmentation; classification; biotope distribution model GEOBIA; biodiversity; LIDAR; orthophoto; segmentation; classification; biotope distribution model

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Radoux, J.; Bourdouxhe, A.; Coos, W.; Dufrêne, M.; Defourny, P. Improving Ecotope Segmentation by Combining Topographic and Spectral Data. Remote Sens. 2019, 11, 354.

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