Satellite image fusion to detect changing surface permeability and emerging urban heat islands in a fast-growing city

Abstract

Rapid and extensive urbanization has adversely impacted humans and ecological entities in the recent decades through a decrease in surface permeability and the emergence of Urban Heat Islands (UHI). While detailed and continuous assessments of surface permeability and UHI are crucial for urban planning and management of landuse zones, they mostly involve time consuming and expensive field studies and single sensor derived large scale aerial and satellite imageries. We demonstrated the advantage of fusing imageries from multiple sensors for landuse and landcover (LULC) change assessments as well as for assessing surface permeability and temperature and UHI emergence in a fast growing city, i.e. Tirunelveli, Tamilnadu, India. IRS-LISSIII and Landsat-7 ETM+ imageries were fused for 2007 and 2017, and classified using a Rotation Forest (RF) algorithm. Surface permeability and temperature were then quantified using Soil-Adjusted Vegetation Index (SAVI) and Land Surface Temperature (LST) index, respectively. Finally, we assessed the relationship between SAVI and LST for entire Tirunelveli as well as for each LULC zone, and also detected UHI emergence hot spots using a SAVI-LST combined metric. Our fused images exhibited higher classification accuracies, i.e. overall kappa coefficient values, than non-fused images. We observed an overall increase in the coverage of urban (dry, real estate plots and built-up) areas, while a decrease for vegetated (cropland and forest) areas in Tirunelveli between 2007 and 2017. The SAVI values indicated an extensive decrease in surface permeability for Tirunelveli overall and also for almost all LULC zones. The LST values showed an overall increase of surface temperature in Tirunelveli with the highest increase for urban built-up areas between 2007 and 2017. LST also exhibited a strong negative association with SAVI. Southeastern built-up areas in Tirunelveli were depicted as a potential UHI hotspot, with a caution for the Western riparian zone for UHI emergence in 2017. Our results provide important metrics for surface permeability, temperature and UHI monitoring, and inform urban and zonal planning authorities about the advantages of satellite image fusion.

Using Satellite Image Fusion to Evaluate the Impact of Land Use Changes on Ecosystem Services and Their Economic Values

“…While in the present study, the increase of ESV was mostly due to the transformation of farmland into grass and woodland. It is important to notice that, farmland can sometimes provide more valuable ES depending on the agricultural system and the type of crops that are allocated [24]. For this specific case, the conversion of one unit of farmland into grass or woodland add more value into the ESV, than the opposite.…”

“…As a result, there have been a growing number of studies attempting to measure landscape enhancement and the value of the different ES [8][9][10]14,22]. Yet, only a few studies address the impact of those programs on landscape recovering [23,24], and the ecological values arise from the enhancement of ES [4]. The correctness of this assessment technique is still low and does not conform to China's national conditions [25].…”

Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

“…Urban sprawl contributes to an increase in impervious surfaces, such as buildings, roads, parking lots, technical infrastructure, etc., which are the indicators of the degree of urbanisation, reflecting the environmental quality of urban areas. In turn, the growth of built-up land affects, among other things, the formation of urban heat islands [7][8][9][10], increased pollution [11], water management [12] and the structure and functioning of the city [13][14][15]. Thus, land cover types and the spatial configuration of their structures affect the quality of human life [16].…”

Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion

“…For example, spectral indexes have been widely applied in the classification of low-and medium-spatial-resolution remote sensing images, as they can accurately reflect statistical information regarding pixel spectral values. Commonly used indexes include the vegetation index [6][7][8][9][10][11][12][13], water index [14], normalized difference building index [15], ecological index [16], normalized difference vegetation index (NDVI) [10,13], and derivative indexes such as the re-normalized difference vegetation index [12] or the growing season normalized difference vegetation index [13]. Taking account of the advantage of the short time period of low-and medium-spatial-resolution remote sensing images, some researchers have applied spectral indexes to time-series images [17,18].…”