Evaluating urban heat island in the critical local climate zones of an Indian city

Kotharkar, Rajashree; Bagade, Anurag

doi:10.1016/j.landurbplan.2017.08.009

Cited by 152 publications

(40 citation statements)

References 58 publications

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“…When the "trees" class and "low plants" class have the largest two and similar ratios, this block could be classified as "scattered trees". In this regard, the newly generated local climate zone classification map provides auxiliary data for applications such as urban climate studies [62] and urban planning [63].…”

Section: Implication Of Our African Land Cover Mapmentioning

confidence: 99%

Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine

Qiu

et al. 2020

Remote Sensing

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The remote sensing based mapping of land cover at extensive scales, e.g., of whole continents, is still a challenging task because of the need for sophisticated pipelines that combine every step from data acquisition to land cover classification. Utilizing the Google Earth Engine (GEE), which provides a catalog of multi-source data and a cloud-based environment, this research generates a land cover map of the whole African continent at 10 m resolution. This land cover map could provide a large-scale base layer for a more detailed local climate zone mapping of urban areas, which lie in the focus of interest of many studies. In this regard, we provide a free download link for our land cover maps of African cities at the end of this paper. It is shown that our product has achieved an overall accuracy of 81% for five classes, which is superior to the existing 10 m land cover product FROM-GLC10 in detecting urban class in city areas and identifying the boundaries between trees and low plants in rural areas. The best data input configurations are carefully selected based on a comparison of results from different input sources, which include Sentinel-2, Landsat-8, Global Human Settlement Layer (GHSL), Night Time Light (NTL) Data, Shuttle Radar Topography Mission (SRTM), and MODIS Land Surface Temperature (LST). We provide a further investigation of the importance of individual features derived from a Random Forest (RF) classifier. In order to study the influence of sampling strategies on the land cover mapping performance, we have designed a transferability analysis experiment, which has not been adequately addressed in the current literature. In this experiment, we test whether trained models from several cities contain valuable information to classify a different city. It was found that samples of the urban class have better reusability than those of other natural land cover classes, i.e., trees, low plants, bare soil or sand, and water. After experimental evaluation of different land cover classes across different cities, we conclude that continental land cover mapping results can be considerably improved when training samples of natural land cover classes are collected and combined from areas covering each Köppen climate zone.

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Section: Implication Of Our African Land Cover Mapmentioning

confidence: 99%

Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine

Qiu

et al. 2020

Remote Sensing

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“…Each variable represents a layer and the gridded layers are then combined using rules to generate LCZ types. This method has been used to generate LCZ maps for Hong Kong (Zheng et al, 2017), for Nagpur in India (Kotharkar and Bagade, 2018), for three medium-sized Central European cities, Brno, Hradec Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Generating WUDAPT Level 0 data – Current status of production and evaluation

et al. 2019

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The World Urban Database and Access Portal Tools (WUDAPT) project has grown out of the need for better information on the form and function of cities globally. Cities are described using Local Climate Zones (LCZ), which are associated with a range of key urban climate model parameters and thus can serve as inputs to high resolution urban climate models. We refer to this as level 0 data for each city. The LCZ level 0 product is produced using freely available Landsat imagery, crowdsourced training areas from the community, and the open source SAGA software. This paper outlines the protocol by which LCZ maps generated by different members of the community are produced and evaluated. In particular, the quality assessment comprises cross-validation, review, and cross-comparison with other data sets. To date, the results from the different quality assessments show that the LCZ maps are generally of moderate quality, i.e. 50-60% overall

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“…Besides, [5] reported a unneglected intra-LCZ temperature variability, which is possibly due to the intra-LCZ variation of urban structures and microscale heterogeneity of the surroundings of an LCZ. Therefore, LCZ sub-classes are suggested to be used in order to exploit the full potential of the LCZ concept with respect to intra-urban distinction of local-scale environments, which is also suggested by [8], especially in high-density cities. A similar necessary adjustment regarding the LCZ scheme is also suggested by [11] in arid cities.…”

Section: Confusion Among Lczsmentioning

confidence: 99%

“…The scheme contains ten "built" and seven "natural" classes, which are depicted in Figure 1, intended to be universal and applicable in cities all over the world, offering the possibility to compare different areas of different cities with trenchant distinctions representing the heterogeneous thermal behavior within an urban environment [2]. Besides the increasing impact on urban climate science worldwide [3][4][5][6][7][8], recently, researchers have started to use the LCZ scheme to classify the internal structure of urban areas, providing promising information for various applications such as infrastructure planning, disaster mitigation and population assessment [9] in this increasingly urbanized world [10]. Recently, it has been shown that useful insights can also be gained by investigating land surface temperature in different LCZs [11], even though LCZs are associated with air temperature when being proposed.…”

Section: Introductionmentioning

confidence: 99%

Feature Importance Analysis for Local Climate Zone Classification Using a Residual Convolutional Neural Network with Multi-Source Datasets

et al. 2018

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Global Local Climate Zone (LCZ) maps, indicating urban structures and land use, are crucial for Urban Heat Island (UHI) studies and also as starting points to better understand the spatio-temporal dynamics of cities worldwide. However, reliable LCZ maps are not available on a global scale, hindering scientific progress across a range of disciplines that study the functionality of sustainable cities. As a first step towards large-scale LCZ mapping, this paper tries to provide guidance about data/feature choice. To this end, we evaluate the spectral reflectance and spectral indices of the globally available Sentinel-2 and Landsat-8 imagery, as well as the Global Urban Footprint (GUF) dataset, the OpenStreetMap layers buildings and land use and the Visible Infrared Imager Radiometer Suite (VIIRS)-based Nighttime Light (NTL) data, regarding their relevance for discriminating different Local Climate Zones (LCZs). Using a Residual convolutional neural Network (ResNet), a systematic analysis of feature importance is performed with a manually-labeled dataset containing nine cities located in Europe. Based on the investigation of the data and feature choice, we propose a framework to fully exploit the available datasets. The results show that GUF, OSM and NTL can contribute to the classification accuracy of some LCZs with relatively few samples, and it is suggested that Landsat-8 and Sentinel-2 spectral reflectances should be jointly used, for example in a majority voting manner, as proven by the improvement from the proposed framework, for large-scale LCZ mapping.

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Evaluating urban heat island in the critical local climate zones of an Indian city

Cited by 152 publications

References 58 publications

Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine

Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine

Generating WUDAPT Level 0 data – Current status of production and evaluation

Feature Importance Analysis for Local Climate Zone Classification Using a Residual Convolutional Neural Network with Multi-Source Datasets

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