Estimation of Flooded Area Using Satellite Imagery and DSM Terrain Data

Kim, Soohyun; Lee, Sangku; Kim, Tae‐Woong; Kim, Dongkyun

doi:10.9798/kosham.2019.19.7.471

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…최근 이런 문제를 극복하기 위해 광범위한 지역에서 지표수 (Chen et al, 2004;Wu and Liu, 2014;Jawak et al, 2015;Ahmad and Kim, 2019)와 지하수 (Rodell and Famiglietti, 2002;Tiwari et al, 2009;Frappart and Ramillien, 2018;Chanu et al, 2020) 등 수자원관리를 위해 인공위성 기술을 활용하여 주기적으로 관측하기 시작 하였다. 인공위성은 광범위한 영역을 일시에 촬영할 수 있고 (Kim et al, 2019), 북한이나 동남아 등 미계측 지역의 유역 및 하천 특성을 동시에 고려할 수 있다는 점이 특징이다 (Sheffield et al, 2018). 인공위성 기반의 하천수 관측 연구는 주로 SRTM (Shuttle Radar Topography Mission)이나 Landsat 위성영상자료를 활용하여 지구 전체 (Yamazaki et al, 2014;Allen and Pavelsky, 2018;Lin et al, 2020) 또는 특정 대륙(북미: Allen and Pavelsky, 2015;Isikdogan et al, 2017, 오세아니아: Hou et al, 2019, 중국: Yang et al, 2020의 공간적 범위에서 큰 규모의 하천을 대상으로 연구가 이루어졌다.…”

Section: 서 론unclassified

Small- to Medium-Sized Stream Waterbody Extraction Using Sentinel-1 Images Based on Shortest Plane Distance

Kim¹,

Kim²

2022

J. Korean Soc. Hazard Mitig

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In this study, a method for extracting the waterbody of small- to medium-sized streams is verified using SAR (Synthetic Aperture Radar) images based on the shortest plane distance (DST). The areas investigated are the Hantangang River and Jungnangcheon stream. To verify the waterbody extraction method, SAR satellite image data (Sentinel-1) and PlanetScope, which is a high-resolution optical satellite, are used simultaneously. After preprocessing the Sentinel-1 images, their brightness distribution is equalized via histogram matching. To realize an efficient stream extraction, a weight, which is the DST from the stream centerline, is added. After combining this weight value and Sentinel-1's VH and VV polarizations, the optimal parameter value is obtained using the k-means method. This value allows the waterbody to be extracted from Sentinel-1 images with maximum accuracy, depending on the resolution limit. Based on the correlation among the VV, VH, and DST, the waterbody extraction can be calculated using an elliptic equation. Results show that the average accuracy is 0.45-0.75, and the average Kappa coefficient is 0.60-0.85. This study shows that the waterbody area can be estimated using the DST. Additionally, the proposed method extracts the waterbody more simply and rapidly than the existing waterbody extraction method.

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Section: 서 론unclassified

Small- to Medium-Sized Stream Waterbody Extraction Using Sentinel-1 Images Based on Shortest Plane Distance

Kim¹,

Kim²

2022

J. Korean Soc. Hazard Mitig

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“…The performance of segmentation models has improved significantly with the advent of FCN (Fully Convolutional Network) series models, and, for this reason, FCN series models transfer the pretrained classification model as a backbone, and comparison studies have been conducted widely [18,19]. The FCN series models include U-Net, Link-Net, Seg-Net, and DeepLabv3+, and there have been many studies comparing their performances [20][21][22][23][24][25]. Of the FCN models, U-Net series models have shown slightly better performances, but research on comparing U-Net with Link-Net was not common.…”

Section: Introductionmentioning

confidence: 99%

Application of Closed-Circuit Television Image Segmentation for Irrigation Channel Water Level Measurement

Kim,

Choi

2023

Water

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Measuring water levels in an irrigation channel is an important task in irrigation system decision making and estimating the quantity of irrigation water supplies. This study aimed to measure water levels with image information from an irrigation channel. Images were obtained from a CCTV (closed-circuit television) camera and manually annotated to create ground-truth mask images. A comparative analysis was performed using four backbone models (ResNet-18, ResNet-50, VGGNet-16, and VGGNet-19) and two segmentation models (U-Net and Link-Net). ROIs (Regions of Interest), mostly related to the water levels, were selected for converting water pixels to water levels. The U-Net with ResNet-50 backbone model outperformed other combinations in terms of the F1 score and robustness, and selecting an ROI and using a quadratic line between water pixels and water levels showed an R2 of 0.99, MAE (Mean Absolute Error) of 0.01 m, and ME (Maximum Error) of 0.05 m. The F1 score of 313 test datasets was 0.99, indicating that the water surface was sufficiently segmented and the water level measurement errors were within the irrigation system’s acceptable range. Although this methodology requires initial work to build the datasets and the model, it enables an accurate and low-cost water level measurement.

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“…Specifically, street trees connect green areas in networks and reduce concentrations of both direct and indirect carbon emissions from their closest sources [15]. As the role of street trees as carbon reduction sources has become increasingly important, numerous studies [15][16][17][18][19][20][21][22][23][24][25][26][27][28] have investigated the efficacy of this strategy worldwide.…”

Section: Introductionmentioning

confidence: 99%

Estimating Carbon Budget from Growth and Management of Urban Street Trees in South Korea

Kim

2022

Sustainability

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Although street trees take up carbon by vegetation growth, they emit carbon directly or indirectly into the atmosphere due to the need for regular management, such as pruning and, irrigation, etc. This study estimates the annual carbon budget from the growth and management of urban street trees in South Korea and suggests management strategies to minimize the degree of management and annual carbon emissions. Annual carbon emissions from management practices such as pruning, irrigation, pesticide and fungicide application, and fertilization were estimated based on maintenance data, interviews with managers, and actual measurements. Annual carbon emissions from the management of urban street trees were calculated to be 2.4 kg/tree/year. From this total, pruning was the largest contributor at 91%. The average net carbon uptake for each street tree was 5.5 kg/tree/year. The street trees included in this study played an important role in carbon uptake, which was equivalent to 90–550% of the annual carbon emissions from street tree management. Based on these results, suggested management strategies include pruning, irrigation, pesticide and fungicide application, and fertilization. These study results will be useful for designing practical low-carbon landscapes with minimal management and carbon emissions.

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Estimation of Flooded Area Using Satellite Imagery and DSM Terrain Data

Cited by 5 publications

References 22 publications

Small- to Medium-Sized Stream Waterbody Extraction Using Sentinel-1 Images Based on Shortest Plane Distance

Small- to Medium-Sized Stream Waterbody Extraction Using Sentinel-1 Images Based on Shortest Plane Distance

Application of Closed-Circuit Television Image Segmentation for Irrigation Channel Water Level Measurement

Estimating Carbon Budget from Growth and Management of Urban Street Trees in South Korea

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