Comparing Different Approaches for Mapping Urban Vegetation Cover from Landsat ETM+ Data: A Case Study on Brussels

Urban growth and its related environmental problems call for sustainable urban management policies to safeguard the quality of urban environments. Vegetation plays an important part in this as it provides ecological, social, health and economic benefits to a city’s inhabitants. Remotely sensed...

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Main Authors: Frank Canters, Jeroen Vlaeminck, Tim Van de Voorde
Format: Article
Language:English
Published: MDPI AG 2008-06-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/8/6/3880/
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spelling doaj-440978c7646846c4925391d0ae3d3ca52020-11-25T00:26:35ZengMDPI AGSensors1424-82202008-06-018638803902Comparing Different Approaches for Mapping Urban Vegetation Cover from Landsat ETM+ Data: A Case Study on BrusselsFrank CantersJeroen VlaeminckTim Van de VoordeUrban growth and its related environmental problems call for sustainable urban management policies to safeguard the quality of urban environments. Vegetation plays an important part in this as it provides ecological, social, health and economic benefits to a city’s inhabitants. Remotely sensed data are of great value to monitor urban green and despite the clear advantages of contemporary high resolution images, the benefits of medium resolution data should not be discarded. The objective of this research was to estimate fractional vegetation cover from a Landsat ETM+ image with sub-pixel classification, and to compare accuracies obtained with multiple stepwise regression analysis, linear spectral unmixing and multi-layer perceptrons (MLP) at the level of meaningful urban spatial entities. Despite the small, but nevertheless statistically significant differences at pixel level between the alternative approaches, the spatial pattern of vegetation cover and estimation errors is clearly distinctive at neighbourhood level. At this spatially aggregated level, a simple regression model appears to attain sufficient accuracy. For mapping at a spatially more detailed level, the MLP seems to be the most appropriate choice. Brightness normalisation only appeared to affect the linear models, especially the linear spectral unmixing.http://www.mdpi.com/1424-8220/8/6/3880/urban vegetation coverspectral mixture analysismulti-layer perceptrons
collection DOAJ
language English
format Article
sources DOAJ
author Frank Canters
Jeroen Vlaeminck
Tim Van de Voorde
spellingShingle Frank Canters
Jeroen Vlaeminck
Tim Van de Voorde
Comparing Different Approaches for Mapping Urban Vegetation Cover from Landsat ETM+ Data: A Case Study on Brussels
Sensors
urban vegetation cover
spectral mixture analysis
multi-layer perceptrons
author_facet Frank Canters
Jeroen Vlaeminck
Tim Van de Voorde
author_sort Frank Canters
title Comparing Different Approaches for Mapping Urban Vegetation Cover from Landsat ETM+ Data: A Case Study on Brussels
title_short Comparing Different Approaches for Mapping Urban Vegetation Cover from Landsat ETM+ Data: A Case Study on Brussels
title_full Comparing Different Approaches for Mapping Urban Vegetation Cover from Landsat ETM+ Data: A Case Study on Brussels
title_fullStr Comparing Different Approaches for Mapping Urban Vegetation Cover from Landsat ETM+ Data: A Case Study on Brussels
title_full_unstemmed Comparing Different Approaches for Mapping Urban Vegetation Cover from Landsat ETM+ Data: A Case Study on Brussels
title_sort comparing different approaches for mapping urban vegetation cover from landsat etm+ data: a case study on brussels
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2008-06-01
description Urban growth and its related environmental problems call for sustainable urban management policies to safeguard the quality of urban environments. Vegetation plays an important part in this as it provides ecological, social, health and economic benefits to a city’s inhabitants. Remotely sensed data are of great value to monitor urban green and despite the clear advantages of contemporary high resolution images, the benefits of medium resolution data should not be discarded. The objective of this research was to estimate fractional vegetation cover from a Landsat ETM+ image with sub-pixel classification, and to compare accuracies obtained with multiple stepwise regression analysis, linear spectral unmixing and multi-layer perceptrons (MLP) at the level of meaningful urban spatial entities. Despite the small, but nevertheless statistically significant differences at pixel level between the alternative approaches, the spatial pattern of vegetation cover and estimation errors is clearly distinctive at neighbourhood level. At this spatially aggregated level, a simple regression model appears to attain sufficient accuracy. For mapping at a spatially more detailed level, the MLP seems to be the most appropriate choice. Brightness normalisation only appeared to affect the linear models, especially the linear spectral unmixing.
topic urban vegetation cover
spectral mixture analysis
multi-layer perceptrons
url http://www.mdpi.com/1424-8220/8/6/3880/
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