scholarly journals Spectral unmixing of urban land cover using a generic library approach

2016 ◽  
Author(s):  
Jeroen Degerickx ◽  
Marian-Daniel Iordache ◽  
Akpona Okujeni ◽  
Martin Hermy ◽  
Sebastian van der Linden ◽  
...  
Keyword(s):  
Land Cover ◽  
Urban Land ◽  
Spectral Unmixing ◽  
Urban Land Cover ◽  
Generic Library

scholarly journals A Novel Spectral Library Pruning Technique for Spectral Unmixing of Urban Land Cover

2017 ◽  
Vol 9 (6) ◽  
pp. 565 ◽  
Author(s):  
Jeroen Degerickx ◽  
Akpona Okujeni ◽  
Marian-Daniel Iordache ◽  
Martin Hermy ◽  
Sebastian van der Linden ◽  
...  
Keyword(s):  
Land Cover ◽  
Urban Land ◽  
Spectral Unmixing ◽  
Spectral Library ◽  
Urban Land Cover ◽  
Pruning Technique

scholarly journals URBAN MORPHOLOGICAL DYNAMICS IN SANTIAGO (CHILE): PROPOSING SUSTAINABLE INDICATORS FROM REMOTE SENSING

2016 ◽  
Vol XLI-B8 ◽  
pp. 873-877 ◽  
Author(s):  
H. J. Hernández ◽  
M. A. Gutiérrez ◽  
M. P. Acuña
Keyword(s):  
Land Cover ◽  
Urban Land ◽  
Spectral Unmixing ◽  
City Centre ◽  
Landsat 8 ◽  
Land Cover Types ◽  
Urban Land Cover ◽  
End Members ◽  
S Model ◽  
The City

Latin America is one of the world’s most urbanised regions, with more than 80% of inhabitants living in urban areas and over 50 cities with at least 1 million inhabitants. The concept of urban structure types (UST) allows the dynamics of a growing urban environment to be captured in its quantity and quality. They are defined as areas of homogenous appearance in the urban matrix with a recognisable mixture of built-up areas and open spaces. We used the vegetation-impervious-soil (V-I-S) model approach to classify and monitor different types of USTs in Santiago (~800 km2), Chile between 1985 and 2015. The V-I-S model is based on a simplification of the large diversity of urban land cover types in three general categories: vegetation, impervious surfaces and soil. These categories were obtained by processing Landsat-5 TM and Landsat-8 OLI images. First, we applied standard radiometric calibration and co-registration methods to all datasets. Second, using a linear spectral unmixing algorithm we performed a soft classification of urban land cover types (<i>end members</i>): trees, shrubs, herbaceous plants, soils, buildings, roads and water bodies. All <i>end members</i> were validated using a combination of photointerpretation on high-resolution images (~1 m) and field data collection (only for 2015). In each pixel we used the resulting probability scores, logically grouped, to obtain final values for each V-I-S component. Third, we used statistical clustering of V-I-S values to create a set of eight pixel groups, which we interpreted as USTs and mapped them for each date. The overall accuracy for V-I-S components in 1985 and 2015 were 78% and 82%, respectively, and errors did not exhibit any spatial correlation. The main sources of differentiation between USTs were the trade-off proportions between vegetation and impervious components, whereas soil proportions remained near 5% across the city in both dates. To analyse the change in UST spatial configuration between dates, we used a set of selected landscape metrics and discussed their use as indicators for sustainable urban development. These indicators relate to the dispersion pattern of urban growth, the connectivity of open green space and the complexity in the composition of the UST types within the different sectors of the city. We were able to identify, using the dynamics exhibited by the USTs, three main zones: (1) city centre, where USTs of high-intensity development predominate, (2) eastern high-income areas whose spatial structure is marked by a relatively high urbanisation intensity with a very large proportion of vegetated spaces, and (3) peripheral areas, with significant changes in composition and configuration of USTs, in recent decades, showing high rates of urbanisation, shifting from low-medium to high densities. We concluded that these patterns and their dynamics are mainly determined by the spatial socio-economic stratification of the population.

scholarly journals URBAN MORPHOLOGICAL DYNAMICS IN SANTIAGO (CHILE): PROPOSING SUSTAINABLE INDICATORS FROM REMOTE SENSING

2016 ◽  
Vol XLI-B8 ◽  
pp. 873-877
Author(s):  
H. J. Hernández ◽  
M. A. Gutiérrez ◽  
M. P. Acuña
Keyword(s):  
Land Cover ◽  
Urban Land ◽  
Spectral Unmixing ◽  
City Centre ◽  
Landsat 8 ◽  
Land Cover Types ◽  
Urban Land Cover ◽  
End Members ◽  
S Model ◽  
The City

Latin America is one of the world’s most urbanised regions, with more than 80% of inhabitants living in urban areas and over 50 cities with at least 1 million inhabitants. The concept of urban structure types (UST) allows the dynamics of a growing urban environment to be captured in its quantity and quality. They are defined as areas of homogenous appearance in the urban matrix with a recognisable mixture of built-up areas and open spaces. We used the vegetation-impervious-soil (V-I-S) model approach to classify and monitor different types of USTs in Santiago (~800 km2), Chile between 1985 and 2015. The V-I-S model is based on a simplification of the large diversity of urban land cover types in three general categories: vegetation, impervious surfaces and soil. These categories were obtained by processing Landsat-5 TM and Landsat-8 OLI images. First, we applied standard radiometric calibration and co-registration methods to all datasets. Second, using a linear spectral unmixing algorithm we performed a soft classification of urban land cover types (<i>end members</i>): trees, shrubs, herbaceous plants, soils, buildings, roads and water bodies. All <i>end members</i> were validated using a combination of photointerpretation on high-resolution images (~1 m) and field data collection (only for 2015). In each pixel we used the resulting probability scores, logically grouped, to obtain final values for each V-I-S component. Third, we used statistical clustering of V-I-S values to create a set of eight pixel groups, which we interpreted as USTs and mapped them for each date. The overall accuracy for V-I-S components in 1985 and 2015 were 78% and 82%, respectively, and errors did not exhibit any spatial correlation. The main sources of differentiation between USTs were the trade-off proportions between vegetation and impervious components, whereas soil proportions remained near 5% across the city in both dates. To analyse the change in UST spatial configuration between dates, we used a set of selected landscape metrics and discussed their use as indicators for sustainable urban development. These indicators relate to the dispersion pattern of urban growth, the connectivity of open green space and the complexity in the composition of the UST types within the different sectors of the city. We were able to identify, using the dynamics exhibited by the USTs, three main zones: (1) city centre, where USTs of high-intensity development predominate, (2) eastern high-income areas whose spatial structure is marked by a relatively high urbanisation intensity with a very large proportion of vegetated spaces, and (3) peripheral areas, with significant changes in composition and configuration of USTs, in recent decades, showing high rates of urbanisation, shifting from low-medium to high densities. We concluded that these patterns and their dynamics are mainly determined by the spatial socio-economic stratification of the population.

Subpixel Urban Land Cover Estimation

2008 ◽  
Vol 74 (10) ◽  
pp. 1213-1222 ◽  
Author(s):  
Jeffrey T. Walton
Keyword(s):  
Land Cover ◽  
Urban Land ◽  
Urban Land Cover
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