Using Landsat 8 data to compare percent impervious surface area and normalized difference vegetation index as indicators of urban heat island effects in Connecticut, USA

Yang, Zhiyuan; Witharana, Chandi; Hurd, James D.; Wang, Kao; Run-mei, Hao; Tong, Siqin

doi:10.1007/s12665-020-09159-0

Cited by 14 publications

(6 citation statements)

References 38 publications

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“…These studies employ remote sensing and GIS techniques to assess the extent and distribution of impervious surfaces within the city. The results of these studies have been instrumental in informing urban planning and environmental management strategies [18].…”

Section: Study Areamentioning

confidence: 99%

Optical–SAR Data Fusion Based on Simple Layer Stacking and the XGBoost Algorithm to Extract Urban Impervious Surfaces in Global Alpha Cities

Ahmad,

Shao,

Javed

et al. 2024

Remote Sensing

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This study proposes a fusion approach to enhancing urban remote sensing applications by integrating SAR (Sentinel-1) and optical (Landsat-8) satellite datasets. The fusion technique combines feature-based fusion and simple layer stacking (SLS) to improve the accuracy of urban impervious surface (UIS) extraction. SAR textures and modified indices are used for feature extraction, and classification is performed using the XGBoost machine learning algorithm in Python and Google Earth Engine. The study focuses on four global cities (New York, Paris, Tokyo, and London) with heterogeneous climatic zones and urban dynamics. The proposed method showed significant results. The accuracy assessment using random validation points shows an overall accuracy of 86% for UIS classification with the SLS method, outperforming single-data classification. The proposed approach achieves higher accuracy (86%) compared to three global products (ESA, ESRI, and Dynamic World). New York exhibits the highest overall accuracy at 88%. This fusion approach with the XGBoost classifier holds potential for new applications and insights into UIS mapping, with implications for environmental factors such as land surface temperature, the urban heat island effect, and urban pluvial flooding.

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Section: Study Areamentioning

confidence: 99%

Optical–SAR Data Fusion Based on Simple Layer Stacking and the XGBoost Algorithm to Extract Urban Impervious Surfaces in Global Alpha Cities

Ahmad,

Shao,

Javed

et al. 2024

Remote Sensing

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“…It is not sufficient to adopt a uniform ecological index to assess ecological quality due to the complexity and diversity of the ecological system [46,47]. The remote sensing ecological index (RSEI), which integrates four indicators (greenness, wetness, dryness, and heat) through the principal component analysis (PCA) method [44,48,49], has been extensively applied to objectively evaluate eco-environmental quality without requiring manual parameter setting [44,50,51].…”

Section: Assessment Of Eqmentioning

confidence: 99%

Construction of an Ecological Network Based on an Integrated Approach and Circuit Theory: A Case Study of Panzhou in Guizhou Province

et al. 2022

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Protecting ecological security has become the backbone of social and economic development since declines in ecological quality due to an increase in human dominance over the natural environment. The establishment of ecological networks is an effective, comprehensive spatial regulation means to ensure regional ecological security. Panzhou city, as a case study, is a typical karst county and has been confronted with the pressure of ecological degradation in recent decades. In this study, an integrated approach combining ecological quality (EQ), ecosystem function importance (EFI), and morphological spatial pattern analysis (MSPA) was developed to determine the ecological sources. Ecological corridors, ecological pinch areas, and ecological barriers were extracted using circuit theory to identify the restored and conserved priority areas of ecological security patterns. The results showed that (1) the remote sensing ecological index (RSEI) and EFI exhibited typical geographical distributions, with the highest values concentrated in the northern and southern parts of the study area and the lowest values scattered in the middle part; (2) 26 patches with forestland, grassland, and waterbodies as the main land cover types were selected as the ecological sources; (3) 63 ecological corridors, composed of 45 key ecological corridors and 18 inactive ecological corridors, were extracted, accounting for 203.12 km and 163.31 km, respectively; (4) 82.76 km2 of pinch areas and 320.29 km2 of barriers were identified, both of which were distributed on key ecological corridors and played different roles in ecological security; and (5) 4 types of ecological security zones were established according to ecological sources, corridors, pinch areas, and barriers. This integrated approach provides a scientific method for the identification and implementation of ecological networks that can contribute to protecting regional ecological security. Our findings can serve as applicable and reasonable guidance to land administrators and policy-makers for adopting suitable territorial spatial planning, urban planning, green cities, etc.

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“…UHIs have a negative impact on the microclimate and daily weather of cities, human health and behaviour, electricity consumption, water consumption, and the use of other resources and facilities (Chan 2011;Mohan and Kandya 2015;NDMA 2016;Shi and Zhang 2018). However, the intensity and effect of UHIs are not distributed uniformly over the city, as different biophysical landscapes respond differently to heat radiation, absorption, and storage, which, in turn, influences the LST (Priyankara et al 2019;Yang et al 2020). This leads to the generation of urban hotspots (UHSs) wherein urban heating is highest and necessitates monitoring and planning.…”

Section: Introductionmentioning

confidence: 99%

Identifying Urban Hotspots and Cold Spots in Delhi Using the Biophysical Landscape Framework

Gupta

2024

Ecol. Econ. Soc.

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Urban heat islands (UHIs), which are formed by biophysical landscape transformations, have significant adverse effects on environmental quality as well as human health, resources, and facilities. Variations in UHI intensity give rise to urban hotspots (UHSs) and cold spots in different parts of the city. This study identifies such hotspots and cold spots in Delhi by classifying the city into zones of different UHI intensities using biophysical landscapes. The data on the selected biophysical landscapes were obtained from satellite images and secondary sources. The impact of different biophysical landscapes on UHI intensity was calculated using the weighted overlay method performed using ArcGIS software. The city of Delhi was thus divided into four zones, based on UHI intensity. It was found that UHSs cover about 45% of the total area and are mostly located in eastern and central Delhi. While built-up areas form the major source landscape, vegetation cover is the major sink landscape as per land surface temperature (LST) and UHI intensity. The findings of this study will help urban planners and policymakers identify UHSs and adopt suitable policies and measures to mitigate UHIs based on the different intensity zones.

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Using Landsat 8 data to compare percent impervious surface area and normalized difference vegetation index as indicators of urban heat island effects in Connecticut, USA

Cited by 14 publications

References 38 publications

Optical–SAR Data Fusion Based on Simple Layer Stacking and the XGBoost Algorithm to Extract Urban Impervious Surfaces in Global Alpha Cities

Optical–SAR Data Fusion Based on Simple Layer Stacking and the XGBoost Algorithm to Extract Urban Impervious Surfaces in Global Alpha Cities

Construction of an Ecological Network Based on an Integrated Approach and Circuit Theory: A Case Study of Panzhou in Guizhou Province

Identifying Urban Hotspots and Cold Spots in Delhi Using the Biophysical Landscape Framework

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