Locally optimized separability enhancement indices for urban land cover mapping: Exploring thermal environmental consequences of rapid urbanization in Addis Ababa, Ethiopia

Feyisa, Gudina Legese; Meilby, Henrik; Jenerette, G. Darrel; Pauliet, Stephan

doi:10.1016/j.rse.2015.12.026

Cited by 68 publications

(47 citation statements)

References 40 publications

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“…Urban hot spots (UHS), the special urban thermal features, experience extreme heat stress mainly developed by man-made activities within a UHI zone (Chen, Zhao, Li, & Yin, 2006;Coutts, White, Tapper, Beringer, & Livesley, 2016;Feyisa, Meilby, Jenerette, & Pauliet, 2016;Lopez, Heider, & Scheffran, 2017;Pearsall, 2017;Ren et al, 2016). So, identifying these UHS for mitigation purpose becomes an important task to maintain the ecological balance within a city.…”

Section: Introductionmentioning

confidence: 99%

Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy

Guha

Govil

Dey³

et al. 2018

European Journal of Remote Sensing

407

153

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The present study focuses on determining the relationship of estimated land surface temperature (LST) with normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) for Florence and Naples cities in Italy using Landsat 8 data. The study also classifies different land use/land cover LU-LC) types using NDVI and NDBI threshold values, iterative self-organizing data analysis technique and maximum likelihood classifier, and analyses the relationship built by LST with the built-up area and bare land. Urban thermal field variance index was applied to determine the thermal and ecological comfort level of the city. Several urban heat islands (UHIs) were extracted as the most heated zones within the city boundaries due to increasing anthropogenic activities. The difference between the mean LST of UHI and non-UHI is 3.15°C and 3.31°C, respectively, for Florence and Naples. LST build a strong correlation with NDVI (negative) and NDBI (positive) for both the cities as a whole, especially for the non-UHIs. But, the strength of correlation becomes much weaker within the UHIs. Moreover, most of the UHIs (85.21% in Naples and 76.62% in Florence) are developed within the built-up area or bare land and are demarcated as an ecologically stressed zone. ARTICLE HISTORY

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Section: Introductionmentioning

confidence: 99%

Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy

Guha

Govil

Dey³

et al. 2018

European Journal of Remote Sensing

407

153

View full text Add to dashboard Cite

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“…To show the images of color coded indices instead of grayscale, a classification using Support Vector Machine (SVM) was carried out. SVM is well known in the field of classification for remote sensing and leads to better results (Feyisa et al 2016;Hazini & Hashim 2015). The Region Of Interest (ROI) are used as a spectral signature of land use and land cover categories namely built-up, barren, vegetation and water.…”

Section: Evaluation Of Some Existing Indicesmentioning

confidence: 99%

A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery

Bouhennache

Bouden

Taleb-Ahmed

et al. 2018

Geocarto International

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“…The user iteratively runs the process while changing the values of the three parameters until image objects are attained that visually correspond to features on the ground. Image segmentation was performed at three scales (5,15,40) with constant values of shape (0.1) and compactness (0.5) to produce image objects with different sizes for each Landsat imagery. Smaller scale parameters were used to capture ground features with fine and medium scales in the study area, such as small-scale farming fields with no crop cover and some informal built-up areas.…”

Section: Object-based Classificationmentioning

confidence: 99%

“…While it is more appealing to attribute the ULU classification problems to inadequate imagery spatial resolution, it is, however, mostly caused by the limitations of the commonly used imagery classification techniques (e.g., pixel-based classification) [4]. In fact, inconsistent recommendations are regularly made on the choice of classification techniques, despite many studies using different types of imagery data with varying spatial resolutions [5].…”

Section: Introductionmentioning

confidence: 99%

“…Other studies use ancillary data, such as geographical data [9,10] and census data [11,12], which is also either not available or inconsistent with remote sensing data in most SSA cities. Additionally, the variations in the level of complexity of urban land use/cover (LUC) among different localities limits most methods from being applied under a different set of conditions [5]. For example, most of the noted improvements in urban land use/cover classification using advanced classification techniques have been conducted in developed countries, which are characterised by a highly developed urban built-up environment and well-planned ULU system [13,14].…”

Section: Introductionmentioning

confidence: 99%

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Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia

Simwanda

Murayama

2017

IJGI

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For most sub-Saharan African (SSA) cities, in order to control the historically unplanned urban growth and stimulate sustainable future urban development, there is a need for accurate identification of the past and present urban land use (ULU). However, studies addressing ULU classification in SSA cities are lacking. In this study, we developed an integrated approach of remote sensing and Geographical Information System (GIS) techniques to classify ULU in the developing SSA city of Lusaka. First, we defined six ULU classes (i.e., unplanned high density residential; unplanned low density residential; planned medium-high density residential; planned low density residential; commercial and industrial; public institutions and service areas). ULU parcels, created using road networks as homogenous units separating ULU classes, were used to classify ULU. We utilised the combined detail of cadastral and land use data plus high-resolution Google Earth imagery to infer ULU and classify the parcels. For residential ULU, we also created density thresholds for accurate separation of the classes. We then used the classified ULU parcels for post-classification sorting of built-up pixels extracted from three Landsat TM/ETM+ imageries (1990, 2000, and 2010) into respective ULU classes. Three ULU maps were produced with overall accuracy values of 84.09% to 85.86%. The maps provide information that is relevant to urban planners and policy makers for sustainable future urban planning of Lusaka City. The study also provides an insight for ULU classification in SSA cities with complex urban landscapes similar to Lusaka.

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Locally optimized separability enhancement indices for urban land cover mapping: Exploring thermal environmental consequences of rapid urbanization in Addis Ababa, Ethiopia

Cited by 68 publications

References 40 publications

Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy

Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy

A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery

Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia

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