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Spatial Analysis and Modeling in Urban Remote Sensing

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Dear Colleagues,

Urban environments are essential to human beings, with over half of the world’s population residing in urban and suburban areas. In recent decades, urban environments have experienced rapid modifications due to population growth and migration from rural to urban areas. As a result, numerous urban issues, including urban sprawl, congestion, air and water pollution, segregation, flooding, etc., have attracted attention from scholars and urban planners.

Remote sensing technologies provide an innovative means for monitoring, synthesizing, and modeling urban environments. Especially, recent progress in very-high-resolution, hyperspectral, Lidar, and UAV remote sensing techniques, together with traditional medium-resolution and coarse-resolution imagery, have made the comprehensive understanding and modeling of urban environments possible.

This Special Issue emphasizes the application of spatial analysis and modeling techniques for monitoring, synthesizing, and modeling urban environments. Topics of interest include, but are not limited to, the following topics:

Urban land use/land cover mapping and change analysis;
New sensors’ (e.g., Lidar, UAV, and hyperspectral) applications in analyzing urban environments;
Integration of social sensing and/or GeoAI techniques and urban remote sensing;
Image processing algorithm development for monitoring and modeling urban environments;
Socio-economic applications (e.g., population, housing, crime, etc.);
Public health applications (e.g., heat stress);
Urban planning practices;
Analysis and modeling of environmental impacts (e.g., flooding, air and water pollution, biodiversity, eco-environment, etc.).

Dr. Wei Fan
Prof. Dr. Changshan Wu
Guest Editors

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Keywords

remote sensing
urban environment monitoring and modeling
unmanned aerial vehicles
lidar
hyperspectral sensor
imaging spectroscopy
social sensing
GeoAI
image processing algorithm
land use/land cover mapping and change analysis
socio-economic applications
public health applications
urban planning practices
environmental modeling
environmental impacts

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Research

25 pages, 11762 KiB

Open AccessArticle

Exploring Seasonal and Diurnal Variations of the Thermal Environment in Metropolitan Scale Analysis Based on Remote Sensing Data

by Danyal Rahimi, Masanobu Kii, Hikari Shimadera and Francesco Causone

Remote Sens. 2025, 17(7), 1210; https://doi.org/10.3390/rs17071210 - 28 Mar 2025

Viewed by 310

Abstract

Urban morphology, including land surface, building heights, vegetation, water bodies, and terrain, exerts a significant influence on the urban thermal environment. The complex and nonlinear pathways through which these factors exert influence present significant challenges in urban climate studies. However, existing studies of statistical approaches to the urban thermal environment have primarily focused on linear relationships, often overlooking the complex and nonlinear effects of these factors. Additionally, previous research on those approaches has not adequately addressed the seasonal and diurnal variations in land surface temperature, nor has it examined the extent to which urban morphology influences these variations. While simulation-based approaches can address these nonlinearities and temporal variations, they require large parameter sets and extensive high-resolution input data, making them computationally demanding. This gap limits the ability to develop targeted and effective urban heat mitigation strategies. Recent advancements in remote sensing technologies have revolutionized our ability to analyze these complexities using medium-resolution data products. In this study, we apply a polynomial regression model with an elastic net to represent the impact of terrain and urban morphological factors on the urban thermal environment, considering its seasonal and diurnal variations, taking the case of the Osaka Metropolitan Area. This approach is unique in terms of capturing the nonlinearity of the impacts based on earth observation data by remote sensing and efficiently captures complex relationships while maintaining interpretability and reducing computational overhead. The study leverages MODIS Terra thermal infrared data from 2018, supplemented by Sentinel-2 and Copernicus Land Cover data. The results reveal significant seasonal and diurnal variations in the thermal environment, indicating that building height influences LST non-monotonically, with daytime cooling effects in dense urban areas (0.12 to 0.19 °C decrease) but nighttime heat retention in suburban zones (0.06 to 0.13 °C increase). Similarly, vegetation coverage reduces nighttime LST more effectively, particularly beyond a critical density threshold (NDVI > 0.4). These findings suggest that by optimizing urban design by considering building height effects, strategic design of vegetation coverage can help mitigate heat/cold stress and improve thermal comfort throughout seasons. These findings contribute to sustainable urban development and heat mitigation efforts by providing data-driven insights into urban morphology’s impact on the thermal environment. Full article

(This article belongs to the Special Issue Spatial Analysis and Modeling in Urban Remote Sensing)

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