Capability of Remote Sensing Images to Distinguish the Urban Surface Materials: A Case Study of Venice City

Rosa Maria Cavalli

doi:10.3390/rs13193959

Abstract

Many countries share an effort to understand the impact of growing urban areas on the environment. Spatial, spectral, and temporal resolutions of remote sensing images offer unique access to this information. Nevertheless, their use is limited because urban surface materials exhibit a great diversity of types and are not well spatially and spectrally distinguishable. This work aims to quantify the effect of these spatial and spectral characteristics of urban surface materials on their retrieval from images. To avoid other sources of error, synthetic images of the historical center of Venice were analyzed. A hyperspectral library, which characterizes the main materials of Venice city and knowledge of the city, allowed to create a starting image at a spatial resolution of 30 cm and spectral resolution of 3 nm and with a spectral range of 365–2500 nm, which was spatially and spectrally resampled to match the characteristics of most remote sensing sensors. Linear spectral mixture analysis was applied to every resampled image to evaluate and compare their capabilities to distinguish urban surface materials. In short, the capability depends mainly on spatial resolution, secondarily on spectral range and mixed pixel percentage, and lastly on spectral resolution; impervious surfaces are more distinguishable than pervious surfaces. This analysis of capability behavior is very important to select more suitable remote sensing images and/or to decide the complementarity use of different data.

Highlights

The world’s population that resides in urban areas is growing rapidly: the urban population was equal to 751 million in 1950 and equal to 4.2 billionin 2018 [1]
Since the Kling–Gupta efficiency (KGE) value, representing the capability to classify the urban surface materials, is inversely proportional to the errors (i.e., Total Errors, 100–50% Errors, and 49–0% Errors), the shared premise requires that the first value decreases and the second values increase as spectral resolutions, spatial resolutions, and spectral ranges decrease
The results show that the capability to distinguish the urban surface materials depends on the spatial and spectral resolution, the spectral range of the images, and the percentage of mixed pixels

Summary

Introduction

The world’s population that resides in urban areas is growing rapidly: the urban population was equal to 751 million (about 30%) in 1950 and equal to 4.2 billion (about 55%)in 2018 [1]. The world’s population that resides in urban areas is growing rapidly: the urban population was equal to 751 million (about 30%) in 1950 and equal to 4.2 billion (about 55%). United Nations predicted that this movement will concern another 2.5 billion people (about 68%) in 2050 and that the largest urban growth will take place in Africa and. The rapid urban development has made clear to the scientific and policy-making community that do cities play a key role in social, economic, and environmental systems [1,2,3] and their rapid growth impacts negatively on land and aquatic ecosystems, the climate, and the territory [1,4,5,6,7,8].

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