• Title/Summary/Keyword: land classification

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SHADOW EXTRACTION FROM ASTER IMAGE USING MIXED PIXEL ANALYSIS

  • Kikuchi, Yuki;Takeshi, Miyata;Masataka, Takagi
    • Proceedings of the KSRS Conference
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    • 2003.11a
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    • pp.727-731
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    • 2003
  • ASTER image has some advantages for classification such as 15 spectral bands and 15m ${\sim}$ 90m spatial resolution. However, in the classification using general remote sensing image, shadow areas are often classified into water area. It is very difficult to divide shadow and water. Because reflectance characteristics of water is similar to characteristics of shadow. Many land cover items are consisted in one pixel which is 15m spatial resolution. Nowadays, very high resolution satellite image (IKONOS, Quick Bird) and Digital Surface Model (DSM) by air borne laser scanner can also be used. In this study, mixed pixel analysis of ASTER image has carried out using IKONOS image and DSM. For mixed pixel analysis, high accurated geometric correction was required. Image matching method was applied for generating GCP datasets. IKONOS image was rectified by affine transform. After that, one pixel in ASTER image should be compared with corresponded 15×15 pixel in IKONOS image. Then, training dataset were generated for mixed pixel analysis using visual interpretation of IKONOS image. Finally, classification will be carried out based on Linear Mixture Model. Shadow extraction might be succeeded by the classification. The extracted shadow area was validated using shadow image which generated from 1m${\sim}$2m spatial resolution DSM. The result showed 17.2% error was occurred in mixed pixel. It might be limitation of ASTER image for shadow extraction because of 8bit quantization data.

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Micro-Landform Classification and Topographic Property of Tidal Flat in Julpo-Bay Using Satellite Image (위성영상을 이용한 줄포만 간석지의 미지형 분류와 지형적특성)

  • 조명희;조화룡
    • Korean Journal of Remote Sensing
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    • v.15 no.3
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    • pp.217-225
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    • 1999
  • Through the ISODATA method of unsupervised classification, the micro-landform of Julpo-Bay tidal flat was classified into mudflat, mixedflat, and sandflat using Landsat TM image. Each showed an apparent differences in its topographical characteristics and grain size composition. Mudflat occupied innermost part of the tidal flat, sandflat located closest to the entrance of the bay and mixed flat in the center is. For example, mudlflats are formed with flat faces and tidal channel. Topographically, mudflat consist of tidal channels and flat intermediate surface. Its average relief of them is about 2 meter. Meanwhile, sandflat comprised very flat landform with well-developed ripple marks of less than 10cm average relief. And Mixed flat stood in between. In addition, Out of 7 bands of Landsat TM images, band 5 and 7 provided the highest power level for discrimination between micro-landforms of the tidal flat. Band 4 showed a clear boundary between the land and tidal flat, and band 3 did its share by showing well a boundary between the sea surface and the tidal flat.

Study on SCS CN Estimation and Flood Flow Characteristics According to the Classification Criteria of Hydrologic Soil Groups (수문학적 토양군의 분류기준에 따른 SCS CN 및 유출변화특성에 관한 연구)

  • Ahn, Seung-Seop;Park, Ro-Sam;Ko, Soo-Hyun;Song, In-Ryeol
    • Journal of Environmental Science International
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    • v.15 no.8
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    • pp.775-784
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    • 2006
  • In this study, CN value was estimated by using detailed soil map and land cover characteristic against upper basin of Kumho watermark located on the upper basin of Kumho river and the hydrologic morphological characteristic factors were extracted from the basin by using the DEM document. Also the runoff analysis was conducted by the WMS model in order to study how the assumed CN value affects the runoff characteristic. First of all, as a result of studying the soil type in this study area, mostly D type soil was Identified by the application of the 1987 classification criteria. However, by that in 1995, B type soil and C type soil were distributed more widely in that area. When CN value was classified by the 1995 classification criteria, it was estimated lower than in 1987, as a result of comparing the estimated CNs by those standars. Also it was assumed that CN value was underestimated when the plan for Geum-ho river maintenance was drawn up. As a result of the analysis of runoff characteristic, the pattern of generation of the classification criteria of soil groups appeared to be similar, but in the case of the application of the classification criteria in 1995, the peak rate of runoff was found to be smaller on the whole than in the case of the application of the classification criteria in 1987. Also when the statistical data such as the prediction errors, the mean squared errors, the coefficient of determination and other data emerging from the analysis, was looked over in total, it seemed appropriate to apply the 1995 classification criteria when hydrological soil classification group was applied. As the result of this study, however, the difference of the result of the statistical dat was somewhat small. In future study, it is necessary to follow up evidence about soil application On many more watersheds and in heavy rain.

Urban Object Classification Using Object Subclass Classification Fusion and Normalized Difference Vegetation Index (객체 서브 클래스 분류 융합과 정규식생지수를 이용한 도심지역 객체 분류)

  • Chul-Soo Ye
    • Korean Journal of Remote Sensing
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    • v.39 no.2
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    • pp.223-232
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    • 2023
  • A widely used method for monitoring land cover using high-resolution satellite images is to classify the images based on the colors of the objects of interest. In urban areas, not only major objects such as buildings and roads but also vegetation such as trees frequently appear in high-resolution satellite images. However, the colors of vegetation objects often resemble those of other objects such as buildings, roads, and shadows, making it difficult to accurately classify objects based solely on color information. In this study, we propose a method that can accurately classify not only objects with various colors such as buildings but also vegetation objects. The proposed method uses the normalized difference vegetation index (NDVI) image, which is useful for detecting vegetation objects, along with the RGB image and classifies objects into subclasses. The subclass classification results are fused, and the final classification result is generated by combining them with the image segmentation results. In experiments using Compact Advanced Satellite 500-1 imagery, the proposed method, which applies the NDVI and subclass classification together, showed an overall accuracy of 87.42%, while the overall accuracy of the subchannel classification technique without using the NDVI and the subclass classification technique alone were 73.18% and 81.79%, respectively.

Analyzing off-line Noah land surface model spin-up behavior for initialization of global numerical weather prediction model (전지구수치예측모델의 토양수분 초기화를 위한 오프라인 Noah 지면모델 스핀업 특성분석)

  • Jun, Sanghee;Park, Jeong-Hyun;Boo, Kyung-On;Kang, Hyun-Suk
    • Journal of Korea Water Resources Association
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    • v.53 no.3
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    • pp.181-191
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    • 2020
  • In order to produce accurate initial condition of soil moisture for global Numerical Weather Prediction (NWP), spin-up experiment is carried out using Noah Land Surface Model (LSM). The model is run repeatedly through 10 years, under the atmospheric forcing condition of 2008-2017 until climatological land surface state is achieved. Spin-up time for the equilibrium condition of soil moisture exhibited large variability across Koppen-Geiger climate classification zone and soil layer. Top soil layer took the longgest time to equilibrate in polar region. From the second layer to the fourth layer, arid region equilibrated slower (7 years) than other regions. This result means that LSM reached to equilibrium condition within 10 year loop. Also, spin-up time indicated inverse correlation with near surface temperature and precipitation amount. Initialized from the equilibrium state, LSM was spun up to obtain land surface state in 2018. After 6 months from restarted run, LSM simulates soil moisture, skin temperature and evaportranspiration being similar land surface state in 2018. Based on the results, proposed LSM spin-up system could be used to produce proper initial soil moisture condition despite updates of physics or ancillaries for LSM coupled with NWP.

A Study on Mitigation Plan of Urban Heat Island Phenomenon Using Landsat Time Series Imagery - Focusing on Cheongna International City - (시계열 Landsat 위성영상을 활용한 도시 열섬 현상 완화 방안에 관한 연구 - 청라 국제도시를 중심으로 -)

  • BAEK, Seon-Uk;KIM, Dong-Hyun;KIM, Hung-Soo;GU, Bon-Yup;CHOUNG, Yun-Jae
    • Journal of the Korean Association of Geographic Information Studies
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    • v.25 no.3
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    • pp.1-16
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    • 2022
  • Areas developed through land reclamation projects have huge economic advantages in terms of supplying lands that can be used for farmlands, urban areas and etc., however have relatively small areas of grasslands and densely located buildings compared to inland cities. Hence, an urban heat island is occurring in these areas due to this characteristic, and in particular, the urban heat island in Cheongna International City is getting serious. In this study, the urban heat island in Cheongna International City was evaluated and analyzed by classified into the three periods after the reclamation project: farmland(2001-2008), development(2009-2013) and artificial grassland(2014-2020). The land cover map and Landsat time-series imagery were utilized for measuring the differences of the land surface temperatures between the urbanized areas and the grassland/forest areas in Cheongna International City. The statistical results showed that the differences in the land surface temperature between these areas were calculated to be at most 0℃ during the period of farmland, at most 3.60℃ during the period of development, and at most 2.51℃ during the period of grassland. This study proved that the urban heat island phenomenon increased when the urbanized areas increased, and the urban heat island phenomenon decreased when the artificial grassland areas increased in Cheongna International City where the reclamation project was carried out. The statistical results derived through this research can be used as the reference data for identifying the urban heat island problem in urban planning and establishing the reduction plan.

Landslide Susceptibility Analysis in Jeju Using Artificial Neural Network(ANN) and GIS (인공신경망기법과 GIS를 이용한 제주도 산사태 취약성분석)

  • Quan, He-Chun;Lee, Byung-Gul;Cho, Eun-Il
    • Journal of Environmental Science International
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    • v.17 no.6
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    • pp.679-687
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    • 2008
  • In this study, we implemented landslide distribution of Jeju Island using ANN and GIS, respectively. To do this, we first get the counter line from 1:2,5000 digital map and use this counter line to make the DEM. for the evaluate the land slide susceptibility. Next, we abstracted slop map and aspect map from the DEM and get the land use map using ISODATA classification method from Landsat 7 images. In the computation processes of landslide analysis, we make the class to the soil map, tree diameter map, Isohyet map, geological map and so on. Finally, we applied the ANN method to the landslide one and calculated its weighted values. GIS results can be calculated by using Acrview program and produced Jeju landslide susceptibility map by usign Weighted Overlay method. Based on our results, we found the relatively weak points of landslide ware concentrated to the top of Halla mountains.

Vegetation Structure and Management Planning of Mountain Type Urban Green Space in Inchon, Korea : a case study of land area (인천광역시 산지형 도시녹지의 식생구조 및 관리계획: 육지지역을 중심으로)

  • Cho, Woo
    • Journal of the Korean Institute of Landscape Architecture
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    • v.26 no.2
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    • pp.15-27
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    • 1998
  • The purposes of this study were to investigate vegetation structure and present management planning of mountain type green space using the green space changes during the 20 years, actual vegetation, and plant community structure in land area of Inchon, Korea. The actual vegetation area in survey sites was consisted of Quercus acutissima community, Robinia pseudoacacia forest, Pinus rigida forest, Q. mongolica-Pinus rigida community, P. rigida-Q. mongolica community, Q. monogolica community and so on. According to the classification by TWINSPAN, 61 survey plots were divided into 9 groups; Q. mongolica-Alnus japonica-R. pseudoacacia-P. densiflora, R. pseudoacacia-Styrax japonica, P. rigida-R. pseudoacacia-Q. mongolica, R. pseudoacacia-P. rigida-Q. mongolica-A. hirusta, Q. mongolica-P. thunbergii, and prunus sargentii-Zelkova serrata community. From this result, ecological succession trend of vegetation seems to be change from artificial result, ecological succession trend of vegetation seems to be change from artificial planting forest to native plant community which was dominated by Quercus spp.. This study area need to manage for the increase of biodiversity through the restoration of naturalness by ecological management of artificial planting forest and ecological planting of injured green space.

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Land Cover Classification of Image Data Using Artificial Neural Networks (인공신경망 모형을 이용한 영상자료의 토지피복분류)

  • Kang, Moon-Seong;Park, Seung-Woo;Kwang, Sik-Yoon
    • Journal of Korean Society of Rural Planning
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    • v.12 no.1 s.30
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    • pp.75-83
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    • 2006
  • 본 연구에서는 최대우도법과 인공신경망 모형에 의해 카테고리 분류를 수행하고 각각의 분류 성능을 비교 평가하였다. 인공신경망 모형은 오류역전파 알고리즘을 이용한 것으로서 학습을 통한 은닉층의 최적노드수를 결정하여 카테고리 분류를 수행하도록 하였다. 인공신경망 최적 모형은 입력층의 노드수가 7개, 은닉층의 최적노드수가 18개, 그리고 출력층의 노드수가 5개인 것으로 구성하였다. 위성영상은 1996년에 촬영된 Landsat TM-5 영상을 사용하였고, 최대우도법과 인공신경망 모형에 의한 카테고리 분류를 위하여 각각의 카테고리에 대한 분광특성을 대표하는 지역을 절취하였다. 분류 정확도는 인공신경망 모형에 의한 방법이 90%, 최대우도법이 83%로서, 인공신경망 모형의 분류 성능이 뛰어난 것으로 나타났다. 카테고리 분류 항목인 토지 피복 상태에 따른 분류는 두 가지 방법에서 밭과 주거지의 분류오차가 큰 것으로 나타났다. 특히, 최대우도법에 의한 밭에서의 태만오차는 62.6%로서 매우 큰 값을 보였다. 이는 밭이나 주거지의 특성이 위성영상 촬영시기에 따라 나지의 형태로 분류되거나 산림, 또는 논으로도 분류되는 경향이 있기 때문인 것으로 보인다. 차후에 카테고리 분류를 위한 각각의 클래스의 보조적인 정보를 추가한다면, 카테고리 분류 향상이 이루어질 것으로 기대된다.

Development of Precise Vectorizing Tools for Digitization of Cadastral Maps (지적도면 수치화를 위한 정밀 벡터라이징 도구 개발)

  • 정재준;오재홍;김용일
    • Spatial Information Research
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    • v.8 no.1
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    • pp.69-83
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    • 2000
  • Cadastral map is the basic data that prescribe a lot number, the classification of land category, a boundary and ownerships of the parcels. Because the analogue cadastral map is not appropriate for the Parcel Based Land Information System, computerization of cadastral map is needed. When considering other automatic vectorizing softwares, we conclude that they can not satisfy the accuracy needed in cadastral map. Also screen digitizing methods demand lots of time. So we developed semi-automatic vectorizing program that realized almost capacities, such as overlay display which is needed for screen digitizing , window link, vector file generation , and so forth. As comparing screen digitizing method using AutoCAD with our developed program, we could obtain not only almost same accuracy , but also 35 minute reduction in vectorizing.

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