Improved Urban Flooding Mapping from Remote Sensing Images Using Generalized Regression Neural Network-Based Super-Resolution Algorithm

Li, Linyi; Xu, Tingbao; Chen, Yongqin David

doi:10.3390/rs8080625

Cited by 40 publications

(32 citation statements)

References 40 publications

(19 reference statements)

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“…All of the landscape metrics (see Table 1) were calculated using Fragstats 4.1 at the class level [54]. To facilitate our results being extrapolated to other sites and for comparative purposes with other previous studies [43,[55][56][57], we chose three resolutions of maps (i.e., 2 m, 10 m and 30 m) for analysis. The vector format of the land use and cover map was converted to the raster format with three resolutions for further landscape pattern analysis.…”

Section: Measurement Of the Spatial Pattern Of The Impervious Surfacementioning

confidence: 99%

Effects of Impervious Surface on the Spatial Distribution of Urban Waterlogging Risk Spots at Multiple Scales in Guangzhou, South China

Zhang

Cheng

et al. 2018

Sustainability

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An impervious surface is considered one of main factors affecting urban waterlogging. Previous studies found that spatial pattern (composition and configuration) of impervious surfaces affected urban waterlogging. However, their relative importance remains unknown, and the scale-effect of the spatial pattern on urban waterlogging has been ignored. To move forward, our research studied the relationship between spatial patterns on the impervious surface and its subcategories (building and pavement) on urban waterlogging risk spots using Pearson correlation, partial redundancy analysis and performed at three grid scales (1 km × 1 km, 3 km × 3 km, 5 km × 5 km) and the catchment scale based on different spatial resolution land cover maps (2 m, 10 m and 30 m). We identified positively-correlated metrics with urban waterlogging risk spots, such as the composition of impervious surface (i.e., total impervious surface, building, pavement) and the aggregation metric of the total impervious surface at most scales, as well as two negatively correlated metrics (i.e., proximity metric of building, fragmentation metric of total impervious surface). Furthermore, the total variance of urban waterlogging risk spots explained by the spatial pattern of the total impervious surface and its subcategories increased with studied grid and catchment scales while decreasing from a fine to a coarse resolution. The relative contribution of the impervious surface composition and configuration to the variation of urban waterlogging risk spots varied across scales and among impervious surface types. The composition contributed more than the configuration did for the total impervious surface at both grid and catchment scales. Similar to total impervious surface, the composition of buildings was more important than its configuration was at all the grid scales, while the configuration of buildings was more important at the catchment scale. Contrary to the total impervious surface, the configuration of pavement at both the grid and catchment scales mattered more than their compositions did. Furthermore, the composition of the building was more important than that of pavement, but its configuration mattered less. Our study could provide a multi-scale landscape perspective with detailed suggestions for controlling the area of impervious surface and optimizing its spatial configuration in urban waterlogging risk mitigation and urban planning.

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Section: Measurement Of the Spatial Pattern Of The Impervious Surfacementioning

confidence: 99%

Effects of Impervious Surface on the Spatial Distribution of Urban Waterlogging Risk Spots at Multiple Scales in Guangzhou, South China

Zhang

Cheng

et al. 2018

Sustainability

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“…This process is similar that which has occurred in Ho Chi Minh [2], Dhaka [1,[3][4][5][6]10], Delhi [7], Hanoi [8] and Ansan [9]. Numerous bare land areas emerging along with urbanization processes have cast irreversible impacts on the urban environment, such as air pollution and soil loss [51][52][53][54][55][56][57]. In the plum rain season, flash flooding hazards have become a serious issue and have caused human death and damages to urban infrastructure in the city [52], similar to that in Dhaka [3].…”

Section: Analyses On Urbanization Of Wuhanmentioning

confidence: 71%

“…Numerous bare land areas emerging along with urbanization processes have cast irreversible impacts on the urban environment, such as air pollution and soil loss [51][52][53][54][55][56][57]. In the plum rain season, flash flooding hazards have become a serious issue and have caused human death and damages to urban infrastructure in the city [52], similar to that in Dhaka [3]. One study [53] investigated the consequence of LULC change on thermal environment.…”

Section: Analyses On Urbanization Of Wuhanmentioning

confidence: 99%

“…Urbanization process of the city was monitored and analyzed from the maps, since urban growth is crucial for local environmental and ecological management. The city of Wuhan has undergone one of the most rapid economic, industrial and urban development in its history, similar to other cities in China, India, Bangladesh, Vietnam and other developing counties [1][2][3][4][5][6][7][8][9][10][51][52][53][54][55][56][57]. The study revealed that the urban expansion in Wuhan has been rapid, with the majority of built-up and bare land acquired by converting areas that were previously agricultural land, forest and water bodies.…”

Section: Analyses On Urbanization Of Wuhanmentioning

confidence: 99%

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Mapping Typical Urban LULC from Landsat Imagery without Training Samples or Self-Defined Parameters

Wang

Zhong

et al. 2017

Remote Sensing

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Abstract:Land use/land cover (LULC) change is one of the most important indicators in understanding the interactions between humans and the environment. Traditionally, when LULC maps are produced yearly, most existing remote-sensing methods have to collect ground reference data annually, as the classifiers have to be trained individually in each corresponding year. This study presented a novel strategy to map LULC classes without training samples or assigning parameters. First of all, several novel indices were carefully selected from the index pool, which were able to highlight certain LULC very well. Following this, a common unsupervised classifier was employed to extract the LULC from the associated index image without assigning thresholds. Finally, a supervised classification was implemented with samples automatically collected from the unsupervised classification outputs. Results illustrated that the proposed method could achieve satisfactory performance, reaching similar accuracies to traditional approaches. Findings of this study demonstrate that the proposed strategy is a simple and effective alternative to mapping urban LULC. With the proposed strategy, the budget and time required for remote-sensing data processing could be reduced dramatically.

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“…Additionally, ANN is capable of speedy classification and incorporates different types of data [60]. ANN is used in many extensive urban studies (to mention a few, [55,[61][62][63][64][65]). …”

Section: Methodsmentioning

confidence: 99%

Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging

Nimbalkar

Jarocińska

Zagajewski

2018

Journal of Spectroscopy

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Imaging spectroscopy in the remote sensing is an ever emerging platform that has offered the hyperspectral imaging (HSI) which delivers the Earth's object information in hundreds of bands. HSI integrates conventional imaging with spectroscopy to get rich spectral and spatial features of the object. However, the challenges associated with HSI are its huge dimensionality and data redundancy that requests huge space, complex computations, and lengthier processing time. Therefore, this study aims to find the optimal bands to characterize the roof surfaces using supervised classifiers. To deal with high dimensionality of hyperspectral data, this study assesses the band selection method over data transformation methods. This study provides the comparison between data reduction methods and used classifiers. The height information from LiDAR was used to characterize urban roofs above the height of 2.5 meters. The optimal bands were investigated using supervised classifiers such as artificial neural network (ANN), support vector machine (SVM), and spectral angle mapper (SAM) by comparing accuracies. The classification result shows that ANN and SVM classifiers outperform whereas SAM performed poorly in roof characterization. The band selection method worked efficiently than the transformation methods. The classification algorithm successfully identifies the optimum bands with significant accuracy.

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Improved Urban Flooding Mapping from Remote Sensing Images Using Generalized Regression Neural Network-Based Super-Resolution Algorithm

Cited by 40 publications

References 40 publications

Effects of Impervious Surface on the Spatial Distribution of Urban Waterlogging Risk Spots at Multiple Scales in Guangzhou, South China

Effects of Impervious Surface on the Spatial Distribution of Urban Waterlogging Risk Spots at Multiple Scales in Guangzhou, South China

Mapping Typical Urban LULC from Landsat Imagery without Training Samples or Self-Defined Parameters

Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging

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