• Title/Summary/Keyword: disaster-detecting

Search Result 94, Processing Time 0.019 seconds

Artificial Intelligence-Based Detection of Smoke Plume and Yellow Dust from GEMS Images (인공지능 기반의 GEMS 산불연기 및 황사 탐지)

  • Yemin Jeong;Youjeong Youn;Seoyeon Kim;Jonggu Kang;Soyeon Choi;Yungyo Im;Youngmin Seo;Jeong-Ah Yu;Kyoung-Hee Sung;Sang-Min Kim;Yangwon Lee
    • Korean Journal of Remote Sensing
    • /
    • v.39 no.5_2
    • /
    • pp.859-873
    • /
    • 2023
  • Wildfires cause a lot of environmental and economic damage to the Earth over time. Various experiments have examined the harmful effects of wildfires. Also, studies for detecting wildfires and pollutant emissions using satellite remote sensing have been conducted for many years. The wildfire product for the Geostationary Environmental Monitoring Spectrometer (GEMS), Korea's first environmental satellite sensor, has not been provided yet. In this study, a false-color composite for better expression of wildfire smoke was created from GEMS and used in a U-Net model for wildfire detection. Then, a classification model was constructed to distinguish yellow dust from the wildfire smoke candidate pixels. The proposed method can contribute to disaster monitoring using GEMS images.

Spatial Anaylsis of Agro-Environment of North Korea Using Remote Sensing I. Landcover Classification from Landsat TM imagery and Topography Analysis in North Korea (위성영상을 이용한 북한의 농업환경 분석 I. Landsat TM 영상을 이용한 북한의 지형과 토지피복분류)

  • Hong, Suk-Young;Rim, Sang-Kyu;Lee, Seung-Ho;Lee, Jeong-Cheol;Kim, Yi-Hyun
    • Korean Journal of Environmental Agriculture
    • /
    • v.27 no.2
    • /
    • pp.120-132
    • /
    • 2008
  • Remotely sensed images from a satellite can be applied for detecting and quantifying spatial and temporal variations in terms of landuse & landcover, crop growth, and disaster for agricultural applications. The purposes of this study were to analyze topography using DEM(digital elevation model) and classify landuse & landcover into 10 classes-paddy field, dry field, forest, bare land, grass & bush, water body, reclaimed land, salt farm, residence & building, and others-using Landsat TM images in North Korea. Elevation was greater than 1,000 meters in the eastern part of North Korea around Ranggang-do where Kaemagowon was located. Pyeongnam and Hwangnam in the western part of North Korea were low in elevation. Topography of North Korea showed typical 'east-high and west-low' landform characteristics. Landcover classification of North Korea using spectral reflectance of multi-temporal Landsat TM images was performed and the statistics of each landcover by administrative district, slope, and agroclimatic zone were calculated in terms of area. Forest areas accounted for 69.6 percent of the whole area while the areas of dry fields and paddy fields were 15.7 percent and 4.2 percent, respectively. Bare land and water body occupied 6.6 percent and 1.6 percent, respectively. Residence & building reached less than 1 percent of the country. Paddy field areas concentrated in the A slope ranged from 0 to 2 percent(greater than 80 percent). The dry field areas were shown in the A slope the most, followed by D, E, C, B, and F slopes. According to the statistics by agroclimatic zone, paddy and dry fields were mainly distributed in the North plain region(N-6) and North western coastal region(N-7). Forest areas were evenly distributed all over the agroclimatic regions. Periodic landcover analysis of North Korea based on remote sensing technique using satellite imagery can produce spatial and temporal statistics information for future landuse management and planning of North Korea.

Sentinel-1 SAR image-based waterbody detection technique for estimating the water storage in agricultural reservoirs (농업저수지의 저수량 추정을 위한 Sentinel-1 SAR 영상 기반 수체탐지 기법)

  • Jeong, Jaehwan;Oh, Seungcheol;Lee, Seulchan;Kim, Jinyoung;Choi, Minha
    • Journal of Korea Water Resources Association
    • /
    • v.54 no.7
    • /
    • pp.535-544
    • /
    • 2021
  • Agricultural water occupies 48% of water demand, and management of agricultural reservoirs is essential for water resources management within agricultural basins. For more efficient use of agricultural water, monitoring the distribution of water resources in agricultural reservoirs and agricultural basins is required. Therefore, in this study, three threshold determination methods (i.e., fixed threshold, Otsu threshold, Kittler-Illingworth (KI) threshold) were compared to detect terrestrial water bodies using Sentinel-1 images for 3 years from 2018 to 2020. The purpose of this study was to evaluate methods for determining threshold values to more accurately estimate the reservoir area. In addition, by analyzing the relationship between the water surface and water storage at the Edong, Gosam, and Giheung reservoirs, water storage based on the SAR image was estimated and validated with observations. The thresholding method for detecting a waterbody was found to be the most accurate in the case of the KI threshold, and the water storage estimated by the KI threshold indicated a very high agreement (r = 0.9235, KGE' = 0.8691). Although the seasonal error characteristics were not observed, the problem of underestimation at high water levels may occur; the relationship between the water surface and the water storage could change rapidly. Therefore, it is necessary to understand the relationship between the water surface area and water storage through ground observation data for a more accurate estimation of water storage. If the use of SAR data through water resources satellites becomes possible in the future, based on the results of this study, it is judged that it will be beneficial for monitoring water storage and managing drought.

Evaluation of Application Possibility for Floating Marine Pollutants Detection Using Image Enhancement Techniques: A Case Study for Thin Oil Film on the Sea Surface (영상 강화 기법을 통한 부유성 해양오염물질 탐지 기술 적용 가능성 평가: 해수면의 얇은 유막을 대상으로)

  • Soyeong Jang;Yeongbin Park;Jaeyeop Kwon;Sangheon Lee;Tae-Ho Kim
    • Korean Journal of Remote Sensing
    • /
    • v.39 no.6_1
    • /
    • pp.1353-1369
    • /
    • 2023
  • In the event of a disaster accident at sea, the scale of damage will vary due to weather effects such as wind, currents, and tidal waves, and it is obligatory to minimize the scale of damage by establishing appropriate control plans through quick on-site identification. In particular, it is difficult to identify pollutants that exist in a thin film at sea surface due to their relatively low viscosity and surface tension among pollutants discharged into the sea. Therefore, this study aims to develop an algorithm to detect suspended pollutants on the sea surface in RGB images using imaging equipment that can be easily used in the field, and to evaluate the performance of the algorithm using input data obtained from actual waters. The developed algorithm uses image enhancement techniques to improve the contrast between the intensity values of pollutants and general sea surfaces, and through histogram analysis, the background threshold is found,suspended solids other than pollutants are removed, and finally pollutants are classified. In this study, a real sea test using substitute materials was performed to evaluate the performance of the developed algorithm, and most of the suspended marine pollutants were detected, but the false detection area occurred in places with strong waves. However, the detection results are about three times better than the detection method using a single threshold in the existing algorithm. Through the results of this R&D, it is expected to be useful for on-site control response activities by detecting suspended marine pollutants that were difficult to identify with the naked eye at existing sites.