Urban Remote Sensing. Группа авторов
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The authors develop a method to deal with the challenge caused by the urban geometry preventing a nadir‐looking radiometer to observe all urban facets, which makes the observed urban radiometric surface temperature (Tr) different from the urban complete surface temperature (Tc). Their method is based on numerical experiments with an urban microclimate model to help understand the thermal radiative transfer within the built‐up space and then the relationship between observed Tr and Tc. They further present a case study to demonstrate the methodology and discuss some issues for improving the applicability of thermal infrared remote sensing in urban areas. Chapter 18 targets remote sensing of air pollution in urban areas emphasizing the fundamental considerations in transforming satellite‐derived Aerosol Optical Depth (AOD) retrievals into Particulate Matter concentrations (PM) estimations at the ground level and by pixel. The author firstly discusses the complexity of air pollution monitoring from space and then proposes a comprehensive approach being built upon advanced technological development to examine different pollution sources and possible factors controlling their spatiotemporal variability. Chapter 19 focuses on remote sensing of urban after‐rain standing water bodies that can become mosquito larvae‐favorable environments and trigger disease transmission in high‐populated areas. The authors successfully develop a multilevel image analysis framework integrating multisource remote sensor data of weather and built environments from various spatial scales to detect urban standing water bodies. Such information can provide health authorities with reference guidelines to geographically prioritize the targets of mosquito larval control in urban areas. Chapter 20 provides a comprehensive review on the progress in remote sensing of urban green infrastructure. The authors firstly discuss the ecosystem services provided by urban green infrastructures, which include improvement of water and air quality, mitigation of the UHI, flood regulation, carbon sequestration and storage, and biodiversity conservation. They then provide an overview of how new advances in remote sensing can enhance urban green infrastructure knowledge and relevant planning. The last chapter (Chapter 21) discusses how remote sensing and EO can help quantify SDG indicators. The authors specifically consider UN SDG 11.7 (universal access to safe, inclusive, and accessible, green, and public spaces, etc). While aggregated green spaces can be effectively mapped and quantified by remote sensing, separating “public” versus “private” green spaces needs additional data. The authors take a close look at the relationship between urban green and urban climate as the latter can directly affect the quality of life. They survey the current remote sensing literature concerning UHI effects and cooling effects of green spaces and conclude that combining existing remote sensor data can support scientists in tackling current and future challenges.
1.4 SUMMARY AND CONCLUDING REMARKS
This chapter has discussed the rationale and motivation leading to the publication of this new edition on urban remote sensing. Then, it has provided an overview on some essential and emerging areas that are shifting the directions in urban remote sensing research over the past decade, followed by a preview of the book structure and the major topics covered in the book.
While exciting progress has been made in urban remote sensing during the past ten years, as discussed in this new edition, there are several major conceptual or technical areas deserving further attentions. Firstly, while a clear transition in urban remote sensing research from being technologically driven into being problem‐solving motivated has been observed, we herewith call for a systematic consideration of all components in a project, conceptual or technical, in order to obtain the best possible outcome. Secondly, urban remote sensing research has been shifting beyond observing physical patterns and into understanding underlying processes, and into pursuing toward urban sustainability. To accommodate this transformation, urban remote sensing researchers should be equipped with not only solid technical skills for monitoring, analysis, and modeling but also essential knowledge on cities including relevant core concepts, theoretical debates, and emerging methods. Thirdly, there has been an increasing trend for an urban remote sensing project to use data from diverse sources including not only multi‐temporal and multi‐sensor images but also big geotagged data from social sensing. It is important to maintain and probably strengthen research efforts in developing practical methods that can help derive reliable information from diverse and heterogenous datasets. Last, more efforts should be made in urban remote sensing education to train the next generation of interdisciplinary scientists who not only can develop essential knowledge to understanding cities but also link knowledge to action in search of a transition towards urban sustainability.
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