利用遥感技术改善城市建筑环境:工具、方法和差距综述

IF 3.8 Q2 ENVIRONMENTAL SCIENCES
Naga Venkata Sai Kumar Manapragada , Moshe Mandelmilch , Elena Roitberg , Fadi Kizel , Jonathan Natanian
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引用次数: 0

摘要

城市尺度的环境绩效评价对于设计有效应对气候变化和快速城市化的城市至关重要。遥感(RS)技术提供跨多个相互关联的环境标准的高分辨率、多尺度和时间评估。尽管RS在城市可持续发展中的应用越来越多,但对RS在多标准决策中的作用的全面审查仍然缺乏。本文分析了124篇研究论文,探讨了RS在城市空气质量、城市热量、室外热舒适、建筑能耗和太阳能潜力等5个相互关联的环境标准中的时空分析、影响评价、缓解策略评估和预测建模方面的应用。RS有助于整合形态、热力和气象数据,从而能够评估城市的相互依赖性,例如城市形态对空气污染扩散、保热和能源需求的影响。机器学习和人工智能增强模型改进了空气质量预测、城市减热策略、能源预测和太阳能潜力评估。无人机、激光雷达和纳米卫星技术进一步加强了更精细空间尺度上的实时城市气候监测,支持动态规划干预。尽管在数据分辨率、时间覆盖和实时监测方面存在挑战,但人工智能驱动的缩小规模、数字孪生和纳米卫星网络的进步继续扩展RS功能。通过促进多标准决策,RS使城市设计师和政策制定者能够开发气候适应性、节能和弹性城市,为可持续设计和规划提供可操作的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Remote sensing for environmentally responsive urban built environment: A review of tools, methods and gaps
Urban-scale environmental performance evaluations are essential for designing cities that effectively respond to climate change and rapid urbanization. Remote Sensing (RS) technologies provide high-resolution, multi-scale, and temporal assessments across multiple interlinked environmental criteria. Despite its growing adoption in urban sustainability, a comprehensive review of RS's role in multi-criteria decision-making is still lacking. This review analyzes 124 research articles to explore RS applications in spatio-temporal analysis, impact evaluation, mitigation strategy assessment, and predictive modeling across five interconnected environmental criteria: urban air quality, urban heat, outdoor thermal comfort, building energy consumption, and solar potential. RS facilitates the integration of morphological, thermal, and meteorological data, enabling the evaluation of urban interdependence, such as the influence of urban form on air pollution dispersion, heat retention, and energy demand. Machine learning and AI-enhanced models improve air quality predictions, urban heat mitigation strategies, energy forecasting, and solar potential assessments. UAVs, LiDAR, and nanosatellite technologies further enhance real-time urban climate monitoring at finer spatial scales, supporting dynamic planning interventions. Despite challenges in data resolution, temporal coverage, and real-time monitoring, advancements in AI-driven downscaling, digital twins, and nano satellite networks continue to expand RS capabilities. By facilitating multi-criteria decision-making, RS empowers urban designers and policymakers to develop climate-adaptive, energy-efficient, and resilient cities, offering actionable insights for sustainable design and planning.
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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
期刊介绍: The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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