Biases in morphological landscape features: challenges for environmental purposes in GIScience and related fields

During the last two decades, a wide range of geographical tools including the calculation of landscape metrics were transposed to ecological studies to build models for land-use dynamics. Currently, few studies have evaluated the biases which can occur during the rasterization step which could influ...

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Main Authors: Valentin Chardon, Quentin Poterek, Cybill Staentzel
Format: Article
Language:deu
Published: Unité Mixte de Recherche 8504 Géographie-cités 2021-05-01
Series:Cybergeo
Subjects:
Online Access:http://journals.openedition.org/cybergeo/36770
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spelling doaj-5b24c9979d794a8399bfb112748e198f2021-07-08T17:07:44ZdeuUnité Mixte de Recherche 8504 Géographie-citésCybergeo1278-33662021-05-0110.4000/cybergeo.36770Biases in morphological landscape features: challenges for environmental purposes in GIScience and related fieldsValentin ChardonQuentin PoterekCybill StaentzelDuring the last two decades, a wide range of geographical tools including the calculation of landscape metrics were transposed to ecological studies to build models for land-use dynamics. Currently, few studies have evaluated the biases which can occur during the rasterization step which could influence the results. The purpose of this study was to evaluate the influence of dataset rasterization on area and perimeter variables, which are frequently used to calculate landscape indices, according to (i) the rasterization cell size and (ii) the shape of geographic features. The Urban Atlas 2006 dataset focused on Bas-Rhin department (France) was used as a vector reference layer. Rasterization was performed for various cell sizes to evaluate the influence of spatial resolution on the errors injected into shape descriptors. Five morphological metrics were calculated for all geographic features. For the first time, a UMAP algorithm was performed to relate the rasterization relative errors at all spatial resolutions with morphological attributes. Results showed that low values of area errors were obtained for cell sizes lower than 5 m (<10%). For higher cell sizes, errors exceeding 10% appeared for linear and low width geographic features. For perimeter, significant errors were observed for cell sizes between 1 and 5 m (>10%) with an overestimation tendency. For cell sizes greater to 10 m, overestimations and underestimations were occurring according to the shape of geographic features. This study showed that sensitivity analyses must be performed before any study carried out on landscape changes estimation to define the best raster cell size as function to the morphological attributes of the geographic features, the predefined error threshold.http://journals.openedition.org/cybergeo/36770spatial resolutionstructural analysis (morphology)landscape metrictransition matrixtransformation
collection DOAJ
language deu
format Article
sources DOAJ
author Valentin Chardon
Quentin Poterek
Cybill Staentzel
spellingShingle Valentin Chardon
Quentin Poterek
Cybill Staentzel
Biases in morphological landscape features: challenges for environmental purposes in GIScience and related fields
Cybergeo
spatial resolution
structural analysis (morphology)
landscape metric
transition matrix
transformation
author_facet Valentin Chardon
Quentin Poterek
Cybill Staentzel
author_sort Valentin Chardon
title Biases in morphological landscape features: challenges for environmental purposes in GIScience and related fields
title_short Biases in morphological landscape features: challenges for environmental purposes in GIScience and related fields
title_full Biases in morphological landscape features: challenges for environmental purposes in GIScience and related fields
title_fullStr Biases in morphological landscape features: challenges for environmental purposes in GIScience and related fields
title_full_unstemmed Biases in morphological landscape features: challenges for environmental purposes in GIScience and related fields
title_sort biases in morphological landscape features: challenges for environmental purposes in giscience and related fields
publisher Unité Mixte de Recherche 8504 Géographie-cités
series Cybergeo
issn 1278-3366
publishDate 2021-05-01
description During the last two decades, a wide range of geographical tools including the calculation of landscape metrics were transposed to ecological studies to build models for land-use dynamics. Currently, few studies have evaluated the biases which can occur during the rasterization step which could influence the results. The purpose of this study was to evaluate the influence of dataset rasterization on area and perimeter variables, which are frequently used to calculate landscape indices, according to (i) the rasterization cell size and (ii) the shape of geographic features. The Urban Atlas 2006 dataset focused on Bas-Rhin department (France) was used as a vector reference layer. Rasterization was performed for various cell sizes to evaluate the influence of spatial resolution on the errors injected into shape descriptors. Five morphological metrics were calculated for all geographic features. For the first time, a UMAP algorithm was performed to relate the rasterization relative errors at all spatial resolutions with morphological attributes. Results showed that low values of area errors were obtained for cell sizes lower than 5 m (<10%). For higher cell sizes, errors exceeding 10% appeared for linear and low width geographic features. For perimeter, significant errors were observed for cell sizes between 1 and 5 m (>10%) with an overestimation tendency. For cell sizes greater to 10 m, overestimations and underestimations were occurring according to the shape of geographic features. This study showed that sensitivity analyses must be performed before any study carried out on landscape changes estimation to define the best raster cell size as function to the morphological attributes of the geographic features, the predefined error threshold.
topic spatial resolution
structural analysis (morphology)
landscape metric
transition matrix
transformation
url http://journals.openedition.org/cybergeo/36770
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AT quentinpoterek biasesinmorphologicallandscapefeatureschallengesforenvironmentalpurposesingiscienceandrelatedfields
AT cybillstaentzel biasesinmorphologicallandscapefeatureschallengesforenvironmentalpurposesingiscienceandrelatedfields
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