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Open AccessArticle

Improving Ecotope Segmentation by Combining Topographic and Spectral Data

by 1,*,†, 2,†, 2,†, 2,† and 1,†
1
Earth and Life Institute, Université catholique de Louvain, 1348 Louvain-la-Neuve, Belgium
2
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.
Remote Sens. 2019, 11(3), 354; https://doi.org/10.3390/rs11030354
Received: 14 January 2019 / Accepted: 30 January 2019 / Published: 11 February 2019
(This article belongs to the Special Issue Image Segmentation for Environmental Monitoring)
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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MDPI and ACS Style

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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