<b>Impacts of the Wenchuan Earthquake on the Chaping River upstream channel change</b>

Gong, Jianhua; Yue, Yujuan; Jian, Zhu; Wen, Yuming; Yi, Li; Zhou, Jie; Wang, Dongchuan; Yu, Caihong

doi:10.1080/01431161.2011.636767

Cited by 18 publications

(8 citation statements)

References 18 publications

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“…Compared with satellite remote sensing and aerial photogrammetry, UAV has its own merits which make it a very active field nowadays [19][20][21][22][23][24][25][26]. Specifically, UAV can be deployed easily and frequently to satisfy the requirements of rapid monitoring, assessment and mapping in natural resources at a user-defined spatio-temporal scale.…”

Section: Unmanned Aerial Vehicle (Uav) Remote Sensingmentioning

confidence: 99%

“…However, studies of using UAV images for urban vegetation detection are still rare. Current studies [19][20][21][22][23][24][25][26] mainly focused on forest mapping, rangeland monitoring and riparian vegetation extraction, etc. In this context, we are highly motivated to justify the performance of UAV remote sensing in differentiating vegetated land covers in complex urban landscapes.…”

Section: Uav Remote Sensing For Urban Vegetation Mappingmentioning

confidence: 99%

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UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis

2015

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Unmanned aerial vehicle (UAV) remote sensing has great potential for vegetation mapping in complex urban landscapes due to the ultra-high resolution imagery acquired at low altitudes. Because of payload capacity restrictions, off-the-shelf digital cameras are widely used on medium and small sized UAVs. The limitation of low spectral resolution in digital cameras for vegetation mapping can be reduced by incorporating texture features and robust classifiers. Random Forest has been widely used in satellite remote sensing applications, but its usage in UAV image classification has not been well documented. The objectives of this paper were to propose a hybrid method using Random Forest and texture analysis to accurately differentiate land covers of urban vegetated areas, and analyze how classification accuracy changes with texture window size. Six least correlated second-order texture measures were calculated at nine different window sizes and added to original Red-Green-Blue (RGB) images as ancillary data. A Random Forest classifier consisting of 200 decision trees was used for classification in the spectral-textural feature space. Results indicated the following: (1) Random Forest outperformed traditional Maximum Likelihood classifier and showed similar performance to object-based image analysis in urban vegetation classification; (2) the inclusion of texture features improved classification accuracy significantly; (3) classification accuracy followed an inverted U relationship with texture window size. The results demonstrate that UAV provides an efficient and ideal platform for urban vegetation mapping. The hybrid OPEN ACCESSRemote Sens. 2015, 7 1075 method proposed in this paper shows good performance in differentiating urban vegetation mapping. The drawbacks of off-the-shelf digital cameras can be reduced by adopting Random Forest and texture analysis at the same time.

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Section: Unmanned Aerial Vehicle (Uav) Remote Sensingmentioning

confidence: 99%

Section: Uav Remote Sensing For Urban Vegetation Mappingmentioning

confidence: 99%

UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis

2015

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“…Compared with space-borne observations, aerial remote sensing is immune to extensive clouds and revisit limitations, which manifests itself as an ideal tool for flood monitoring [14][15][16][17]. In addition to this, aerial images are acquired at decimeter and sub-decimeter levels, representing much more abundant details than satellite data.…”

Section: Introductionmentioning

confidence: 99%

“…The main drawback of piloted aircrafts in urban flood mapping is that it is always difficult to find ideal taking-off and landing places for the aircrafts, especially for fixed-wing airplanes. However, mini-UAVs provide a much safer and more convenient way to acquire data in disaster areas because they can be launched by hand and landed on a small piece of flat ground [15,16]. In addition, UAV can be deployed easily and frequently to satisfy the requirements of rapid monitoring and mapping in flooded areas at a user-defined spatio-temporal scale.…”

Section: Introductionmentioning

confidence: 99%

Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China

Feng

Liu

Gong

2015

Water

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Flooding is a severe natural hazard, which poses a great threat to human life and property, especially in densely-populated urban areas. As one of the fastest developing fields in remote sensing applications, an unmanned aerial vehicle (UAV) can provide high-resolution data with a great potential for fast and accurate detection of inundated areas under complex urban landscapes. In this research, optical imagery was acquired by a mini-UAV to monitor the serious urban waterlogging in Yuyao, China. Texture features derived from gray-level co-occurrence matrix were included to increase the separability of different ground objects. A Random Forest classifier, consisting of 200 decision trees, was used to extract flooded areas in the spectral-textural feature space. Confusion matrix was used to assess the accuracy of the proposed method. Results indicated the following: (1) Random Forest showed good performance in urban flood mapping with an overall accuracy of 87.3% and a Kappa coefficient of 0.746; (2) the inclusion of texture features improved classification accuracy significantly; (3) Random Forest outperformed maximum likelihood and artificial neural network, and showed a similar performance to support vector machine. The results demonstrate that UAV can provide an ideal platform for urban flood monitoring and the proposed method shows great capability for the accurate extraction of inundated areas.

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“…More than 5 million buildings collapsed and other 21 million damaged at different degrees across a wide area (USGS 2008) and the number of earthquake casualties reached 87,000 dead or missing and 374,000 injured. After Wenchuan earthquake, multi-satellite sensor data were used to analyze landslides (Liu & Yamazaki 2008;Ren & Lin 2010), barrier lakes (Pan & Tang 2010), river change (Liou et al 2010;Xu et al 2010;Gong et al 2012) and debris flow (Zhuang et al 2010), building damage detection (Wang & Wang 2009;Li et al 2010;Vu & Ban 2010). Most object identification of the seismic damage is relatively single, only focused on the destruction or collapse of buildings, especially residential buildings (Tiede et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Distributed and hierarchical object-based image analysis for damage assessment: a case study of 2008 Wenchuan earthquake, China

Sun

2016

Geomatics, Natural Hazards and Risk

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Impacts of the Wenchuan Earthquake on the Chaping River upstream channel change

Cited by 18 publications

References 18 publications

UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis

UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis

Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China

Distributed and hierarchical object-based image analysis for damage assessment: a case study of 2008 Wenchuan earthquake, China

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