The Use of Remote Sensing to Quantitatively Assess the Visual Effect of Urban Landscape—A Case Study of Zhengzhou, China

Xi, Chaofan; Guo, Yulong; He, Ruizhen; Mu, Bo; Zhang, Peixuan; Li, Yuan

doi:10.3390/rs14010203

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…It has been designated by the State Council as an important core city in central China and a major national transportation hub [ 27 ]. Zhengzhou comprises six municipal districts, five county-level cities, and one county [ 28 ], as shown in Figure 1 . It has a northern temperate continental monsoon climate, with an average annual rainfall amount of 640.9 mm and precipitation levels that, in general, decrease in a south-to-north direction.…”

Section: Methodsmentioning

confidence: 99%

Influencing Factors and Risk Assessment of Precipitation-Induced Flooding in Zhengzhou, China, Based on Random Forest and XGBoost Algorithms

Liu

Zhou

Lin

et al. 2022

IJERPH

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Due to extreme weather phenomena, precipitation-induced flooding has become a frequent, widespread, and destructive natural disaster. Risk assessments of flooding have thus become a popular area of research. In this study, we studied the severe precipitation-induced flooding that occurred in Zhengzhou, Henan Province, China, in July 2021. We identified 16 basic indicators, and the random forest algorithm was used to determine the contribution of each indicator to the Zhengzhou flood. We then optimised the selected indicators and introduced the XGBoost algorithm to construct a risk index assessment model of precipitation-induced flooding. Our results identified four primary indicators for precipitation-induced flooding in the study area: total rainfall for three consecutive days, extreme daily rainfall, vegetation cover, and the river system. The Zhengzhou storm and flood risk evaluation model was constructed from 12 indicators: elevation, slope, water system index, extreme daily rainfall, total rainfall for three consecutive days, night-time light brightness, land-use type, proportion of arable land area, gross regional product, proportion of elderly population, vegetation cover, and medical rescue capacity. After streamlining the bottom four indicators in terms of contribution rate, it had the best performance, with an accuracy rate reaching 91.3%. Very high-risk and high-risk areas accounted for 11.46% and 27.50% of the total area of Zhengzhou, respectively, and their distribution was more significantly influenced by the extent of heavy rainfall, direction of river systems, and land types; the medium-risk area was the largest, accounting for 33.96% of the total area; the second-lowest-risk and low-risk areas together accounted for 27.09%. The areas with the highest risk of heavy rainfall and flooding in Zhengzhou were in the Erqi, Guanchenghui, Jinshui, Zhongyuan, and Huizi Districts and the western part of Xinmi City; these areas should be given priority attention during disaster monitoring and early warning and risk prevention and control.

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Section: Methodsmentioning

confidence: 99%

Influencing Factors and Risk Assessment of Precipitation-Induced Flooding in Zhengzhou, China, Based on Random Forest and XGBoost Algorithms

Liu

Zhou

Lin

et al. 2022

IJERPH

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“…Operational definition: the urban landscape is one of the variables of this research, according to the definitions provided and the index proposed by the experts, four elements were selected as urban landscape elements to be studied in this research; 1) the type of design of streets and squares, 2) the quantity and quality of vegetation in the neighborhood, 3) urban furniture and street lighting and the state of facades, 4) the flooring of streets and surface water atmosphere. In choosing these elements, attention has been paid to their comprehensiveness and also the presence of these elements in the studied statistical population (Xi et al, 2022).…”

Section: Urban Landscapementioning

confidence: 99%

Evaluating the impact of urban landscape elements on the sense of security and local belonging-case study: Tongdejie, China

Zhu,

2024

Front. Environ. Sci.

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Introduction: The urban landscape is a pivotal element shaping the urban space and significantly influences its desirability or undesirability. It has a profound connection with people and their living environment, playing a crucial role in providing identity and a sense of peace to citizens. As urbanization expands and population density increases, the importance of security as a basic need within the urban structure becomes even more significant. A confusing, unattractive, or monotonous urban space can adversely affect citizens’ feelings and reduce their sense of security.Methods: In this research, we investigated the relationship between urban landscape components and the sense of local belonging, as well as the relationship between the sense of security and the urban landscape. We sought to understand how the design and planning of the urban landscape can have a positive impact on urban life and increase citizens’ sense of belonging to the city and its surroundings.Results: Our findings revealed a positive and significant correlation between the sense of local belonging and the sense of security. Living in a safe, attractive, and desirable environment fosters and strengthens feelings of belonging to the environment. This strengthening of the sense of belonging increases residents’ satisfaction and enhances the quality of life in cities.Discussion: The urban landscape serves as a powerful tool that, when optimally designed, can positively influence urban life and enhance citizens’ sense of belonging to their city and its surrounding environment. By prioritizing security and creating aesthetically pleasing and engaging urban spaces, cities can significantly improve the well-being and quality of life of their residents.

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“…Semantic segmentation performs a vital role in the interpretation and analysis of HSRRSIs. Furthermore, semantic segmentation can effectively perform target recognition and information extraction, and the study of semantic segmentation based on HSRRSIs has become a hot topic in the field of remote sensing, including building mapping, 1 sea and land segmentation, 2 vegetation cover assessment, 3 road extraction, 4 old city transformation, 5 and extraction of specific ground object information. 6,7 Traditional image segmentation methods divide images into patches for subsequent analysis based on the underlying feature information of the image.…”

Section: Introductionmentioning

confidence: 99%

U-shaped contourlet network for high-spatial-resolution remote sensing images segmentation

Zhao,

Cao,

Dong

2023

J. Appl. Rem. Sens.

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.Accurate semantic segmentation of images has long been a research priority in remote sensing. However, the presence of geometrically complex and spatially diverse objects increases the difficulty in simultaneously obtaining coherent and accurate labeling result. To solve this challenge, our study combined multiscale geometric feature extraction with convolutional neural network and proposed a new U-shaped contourlet network (USCNet) for segmentation from high-spatial-resolution remote sensing images (HSRRSIs). This network is designed to learn and characterize the geometric features present in HSRRSIs. The USCNet first transforms the original dataset into a pyramidal structure containing multiscale and multidirectional geometric information and then fuses the spatial and geometric features to extract high-level semantic information. This network has two advantages: (1) coarse-to-fine spatial features are learned efficiently using a hierarchical learning structure and (2) the multiscale learning scheme captures geometric information in different directions. The results of extensive experiments conducted on two remote sensing datasets (the International Society for Photogrammetry and Remote Sensing Vaihingen and Potsdam challenge datasets) show that the proposed approach outperforms several state-of-the-art semantic segmentation methods.

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The Use of Remote Sensing to Quantitatively Assess the Visual Effect of Urban Landscape—A Case Study of Zhengzhou, China

Cited by 10 publications

References 26 publications

Influencing Factors and Risk Assessment of Precipitation-Induced Flooding in Zhengzhou, China, Based on Random Forest and XGBoost Algorithms

Influencing Factors and Risk Assessment of Precipitation-Induced Flooding in Zhengzhou, China, Based on Random Forest and XGBoost Algorithms

Evaluating the impact of urban landscape elements on the sense of security and local belonging-case study: Tongdejie, China

U-shaped contourlet network for high-spatial-resolution remote sensing images segmentation

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