Using lidar and aerial photography to build a geographic object database tuned for ecological model

This study area covers approximately 16800 square kilometers with a very fragmented landscape. A dataset including aerial photographs at 0.25 cm resolution and LIDAR at 0.8 pts/m has been provided by the Walloon region for the study. The data were resampled at 2m resolution for the purpose of the an...

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Tác giả chính:	Radoux, J.
Định dạng:	BB
Ngôn ngữ:	eng
Thông tin xuất bản:	2020
Chủ đề:	topography segmentation biodiversity LIDAR Spatial regions
Truy cập trực tuyến:	http://tailieuso.tlu.edu.vn/handle/DHTL/5113
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spelling	oai:localhost:DHTL-51132020-03-30T02:14:20Z Using lidar and aerial photography to build a geographic object database tuned for ecological model Radoux, J. topography segmentation biodiversity LIDAR Spatial regions This study area covers approximately 16800 square kilometers with a very fragmented landscape. A dataset including aerial photographs at 0.25 cm resolution and LIDAR at 0.8 pts/m has been provided by the Walloon region for the study. The data were resampled at 2m resolution for the purpose of the analysis. The data processing workflow includes three steps : pixel-based image classification, image segmentation and object-based integration. Pixel-based image classification consists in a supervised classification with the spectral values from the aerial photographs (NIR/Red/green/blue), the Digital Height Model extracted from the LIDAR and the intensity of the first LIDAR return. This yielded a classification into broadleaved trees, needleleaved trees, grass, bare soil, crop, pavement, building, water and shadows with more than 80% overal accuracy. The image segmentation approach is the main novelty of this research. In order to fit with the biotopes, image segments indeed had to take the type of slope into account. This was achieved by computing pseudo-hillshades for North-South and West-East orientation and including those two files together with the spectral information from the aerial photographs. The result of this analysis is a set of topographically relevant ecotope delineation. The last step applied contextual decision rules to consistently aggregate the land cover information at the ecotope level and add more information from ancillary datasets. https://proceedings.utwente.nl/387/1/Radoux-Using%20LIDAR%20And%20Aerial%20Photography%20To%20Build%20A%20Geographic%20Object%20Database%20Tuned-14.pdf 2020-02-18T02:32:21Z 2020-02-18T02:32:21Z 2016 20190114153158.0 130605s2016 BB In: GEOBIA 2016 : Solutions and Synergies., 14 September 2016 - 16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation (ITC) . http://tailieuso.tlu.edu.vn/handle/DHTL/5113 eng
institution	Trường Đại học Thủy Lợi
collection	DSpace
language	eng
topic	topography segmentation biodiversity LIDAR Spatial regions
spellingShingle	topography segmentation biodiversity LIDAR Spatial regions Radoux, J. Using lidar and aerial photography to build a geographic object database tuned for ecological model
description	This study area covers approximately 16800 square kilometers with a very fragmented landscape. A dataset including aerial photographs at 0.25 cm resolution and LIDAR at 0.8 pts/m has been provided by the Walloon region for the study. The data were resampled at 2m resolution for the purpose of the analysis. The data processing workflow includes three steps : pixel-based image classification, image segmentation and object-based integration. Pixel-based image classification consists in a supervised classification with the spectral values from the aerial photographs (NIR/Red/green/blue), the Digital Height Model extracted from the LIDAR and the intensity of the first LIDAR return. This yielded a classification into broadleaved trees, needleleaved trees, grass, bare soil, crop, pavement, building, water and shadows with more than 80% overal accuracy. The image segmentation approach is the main novelty of this research. In order to fit with the biotopes, image segments indeed had to take the type of slope into account. This was achieved by computing pseudo-hillshades for North-South and West-East orientation and including those two files together with the spectral information from the aerial photographs. The result of this analysis is a set of topographically relevant ecotope delineation. The last step applied contextual decision rules to consistently aggregate the land cover information at the ecotope level and add more information from ancillary datasets.
format	BB
author	Radoux, J.
author_facet	Radoux, J.
author_sort	Radoux, J.
title	Using lidar and aerial photography to build a geographic object database tuned for ecological model
title_short	Using lidar and aerial photography to build a geographic object database tuned for ecological model
title_full	Using lidar and aerial photography to build a geographic object database tuned for ecological model
title_fullStr	Using lidar and aerial photography to build a geographic object database tuned for ecological model
title_full_unstemmed	Using lidar and aerial photography to build a geographic object database tuned for ecological model
title_sort	using lidar and aerial photography to build a geographic object database tuned for ecological model
publishDate	2020
url	http://tailieuso.tlu.edu.vn/handle/DHTL/5113
work_keys_str_mv	AT radouxj usinglidarandaerialphotographytobuildageographicobjectdatabasetunedforecologicalmodel
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Using lidar and aerial photography to build a geographic object database tuned for ecological model

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