Urban Flood-Related Remote Sensing: Research Trends, Gaps and Opportunities

Abstract

As a result of urbanization and climate change, urban areas are increasingly vulnerable to flooding, which can have devastating effects on the loss of life and property. Remote sensing technology can provide practical help for urban flood disaster management. This research presents a review of urban flood-related remote sensing to identify research trends and gaps, and reveal new research opportunities. Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), the systematic literature search resulted in 347 documents classified as geography, disaster management application, and remote sensing data utilization. The main results include 1. most of the studies are located in high-income countries and territories and inland areas; 2. remote sensing for observing the environment was more popular than observing the building; 3. the most often applied disaster management activities were vulnerability assessment and risk modeling (mitigation) and rapid damage assessment (response); 4. DEM is often applied to simulate urban floods as software inputs. We suggest that future research directions include 1. coastal urban study areas in non-high-income countries/territories to help vulnerable populations; 2. understudied disaster management activities, which often need to observe the buildings in more urban areas; 3. data standardization will facilitate integration with international standard methods for assessing urban floods.

Runoff and Sediment Deposition Characteristics of Gravel-Mulched Land: An Experimental Study

“…Under all conditions in this research, the runoff discharge increased first and then tended to stabilize or continue to rise. This is because, at the early stage of runoff, the soil water content has not reached saturation, the soil infiltration capacity is still strong, and there is more rainwater infiltration rather than runoff [81,82]. As the rainfall continues, the soil water gradually becomes saturated, the permeability weakens, and more rainwater forms runoff [83,84], so the runoff discharge increases.…”

Radar-Based Precipitation Nowcasting Based on Improved U-Net Model

“…The optical flow method is an operational method for short-range rainfall forecasting, but the accuracy of the echo extrapolation decreases rapidly after 1 h [9,10]. In recent years, machine learning and deep learning have been widely used in many fields; some scholars have introduced them into the field of meteorology to carry out radar echo extrapolation and achieved significant results [11][12][13][14][15][16]. Agrawal et al [17] converted radar echo extrapolation into an image prediction task by introducing the U-Net model of the Convolutional Neural Network (CNN) for the first time to carry out echo prediction, and the results outperformed the traditional extrapolation methods.…”

Quantitative Model Construction for Sustainable Security Patterns in Social–Ecological Links Using Remote Sensing and Machine Learning

“…With global warming and urbanization, the frequency of extreme natural disasters has increased, posing challenges to regional ecological security [1][2][3]. Issues such as biodiversity reduction [4,5], habitat loss [6], and eutrophication [7,8] have become prominent, thereby hindering balanced development and emphasizing the importance of planning, protecting, and governing regional ecological security patterns (ESPs) [9][10][11]. As a vital ecological barrier in China, the Qinling Mountains region plays a crucial role in protecting the environment and establishing rational ESPs [12,13].…”