[논문]A Study on the Subdivision of Water Body in Watersheds Classified by Remote Sensing

Choi, Hyun

doi:10.7848/ksgpc.2020.38.2.87

A Study on the Subdivision of Water Body in Watersheds Classified by Remote Sensing 원문보기

한국측량학회지 = Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, v.38 no.2, 2020년, pp.87 - 95

Choi, Hyun (Dept. of Civil Engineering, Kyungnam University)

Abstract ▼ AI-Helper

South korea has been developing and managing the complete dimensions, around the rivers to rapid economic growth. In Korea, where water resources are scarce, administration and work are complicated and diversified in the computerization of related facilities and hydrologic data due to the indiscriminate development of river facilities. In general, dividing the water system based on object in remote sensing is relatively accurate in the image with the same spectral characteristics. However, the distinction between the reservoir and the river must be made manually due to the characteristics of remote sensing. Therefore, this study performed three classifications using GIS (Geographic Information System) to classify reservoirs and rivers. For the purpose of accuracy analysis, the land cover map provided by EGIS (Environmental Geographic Information Service) was used to evaluate the accuracy, and the average of 85.63% was found to be 75.40% of rivers, 89.50% of reservoirs, and 92.00% of others.

주제어

표/그림 (21)

그림 Fig. 1. Research method of water body subdivision
그림 Fig. 2. The locations and visual characteristics of the study area
표 Table 1. Summary of the study areas and input KOMPSAT-3A
그림 Fig. 3. Means and standard deviations of surface reflectance in the five-and-dime land cover type
그림 Fig. 4. WI (Water Index) value distributions for mixed water, pure water, vegetation, urban and mountain shadow surface categories.
그림 Fig. 5. A process of extracting water systems and catchment boundaries using a digital elevation model
그림 Fig. 6. Training designated areas polygon
그림 Fig. 7. A water-based classification and image overlap in the maximum likelihood classification
그림 Fig. 8. A digital elevation model where sinks and peaks were removed model using fill
그림 Fig. 9. Flow direction
그림 Fig. 10. Flow accumulation
그림 Fig. 11. Stream to feature
그림 Fig. 12. The water system classified with the maximum likelihood classification method
그림 Fig. 13. The rivers extracted from the water systems classified with the maximum likelihood classification method using spatial join
그림 Fig. 14. Classifying the images of the study target area with the maximum likelihood classification method and the results of the production of the river flow chart
그림 Fig. 15. The study target area and the reservoirs existing in the study target area extracted by classifying the land cover map data and images provided by the national land information platform of the national geographic information institute were superimposed
표 Table 2. Number of reservoir classiﬁcation
그림 Fig. 16. Error area of image classiﬁcation in research area
그림 Fig. 17. The result of water area extraction
표 Table 3. Number of river classiﬁcation accuracy
표 Table 4. Number of reservoir classiﬁcation accuracy

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문제 정의

The area is rugged and mountainous; the elevation ranges to 822m. Therefore, the assessment of the water body extraction method in this area focused on distinguishing between water bodies and mountain shadows. Table 1 summarizes the summary of the study areas and input KOMPSAT-3A.
Despite satisfactory results reports, these approaches are unnecessarily limited to water bodies with specific morphological characteristics due to the significant inconsistencies of assembled parameters and training samples across different scenarios. Therefore, this study aims to distinguish rivers and reservoirs by using GIS in watersheds classified in remote sensing.
Because the water body has the same spectral characteristics, it is difficult to efficiently manage water resources because it cannot be subdivided into rivers and reservoirs. Therefore, this study classifies water systems using remote sensing and GIS for efficient water resource management. The research method is shown in Fig.

제안 방법

He tested the proposed algorithm on the various block sizes between 11×11 and 19×19 using KOMPSAT (Korea Multi-Purpose Satellite)-2 images for the accuracy assessment.
Second, rivers and reservoirs were classified using the maximum likelihood classification method and the area and accuracy of the land cover map were calculated, compared and reviewed. According to the results, the total area of the image was 201,524,214㎡, and the area of rivers was shown to be 726,753㎡ in the land cover map and 187,221㎡ when rivers were classified with the maximum likelihood classification method.
Thereafter, training was carried out to improve the accuracy of classification results when water systems are classified in KOMPSAT-3A high-performance and high resolution satellite images and water systems were extracted by classifying them using the maximum likelihood classification method, one of the supervised classifications. Since it was difficult to subdivide water systems because water systems had the same spectral characteristics, the objects in the two results were classified using GIS and this enabled the subdivision of water systems.
This study was conducted to distinguish rivers and reservoirs in water systems classified in KOMPSAT-3A images. The land cover map provided by the environmental geographic information service was used to compare and analyze the results of classification using the error matrix to evaluate the accuracy. Water systems were classified in satellite images using the maximum likelihood classification method, which is one of the supervised classifications, and the classified waters were superimposed on the river flow chart generated by GIS to subdivide watersheds with the same spectral characteristics.
The criterion for distinguishing between supervised classification and unsupervised classification is the presence or absence of a training phase, which is done before carrying out image classification so that it greatly affects classification results. The targets of classification were compared with those in the digital map, designated with polygons, and combined into the relevant buildings, forests, rice fields, fields, and water systems to compose a layer. As shown in Fig.
Third, the accuracy was calculated by comparing 150 random points in images classified with the maximum likelihood classification method with the land cover map to calculate the error matrices of rivers and reservoirs. According to the results, the accuracy of classification of rivers was shown to be 75.
This study was conducted to distinguish rivers and reservoirs in water systems classified in KOMPSAT-3A images. The land cover map provided by the environmental geographic information service was used to compare and analyze the results of classification using the error matrix to evaluate the accuracy.

대상 데이터

2 shows the locations and visual characteristics of the study area. Study Area is located in Jeollanamdo Province in South Korea and encompasses the Dam-yang reservoir and Youngsan river. Primarily deciduous coniferous forest and shrubs cover the land surface.
To describe the spectral differences between water bodies and land, 1000 samples were selected to represent the three land cover types (i.e., water, vegetation, soil and green vegetation) from the entire scene of the Damyang image, which was used as the source for study area. The mixed water pixels were sampled from narrow rivers and the edges of lakes or wide rivers.

이론/모형

17. In addition, the two results were compared to mark the reservoirs identically shown in the two results and the reservoirs not classified in the result of classification using the maximum likelihood classification method.
This study was conducted using KOMPSAT-3A images of regions near damyang lake in Jeollanam-do province. It is judged that classify water systems are classified using high performance and high resolution satellite images and GIS is useful for management of sewage treatment facilities as well water regulation and water control because the classification is more accurate than existing classification methods and geographic information of wide areas can be quickly updated.
The land cover map provided by the environmental geographic information service was used to compare and analyze the results of classification using the error matrix to evaluate the accuracy. Water systems were classified in satellite images using the maximum likelihood classification method, which is one of the supervised classifications, and the classified waters were superimposed on the river flow chart generated by GIS to subdivide watersheds with the same spectral characteristics. The following conclusions were obtained.

성능/효과

According to the results, as shown in Table. 2, there were 158 reservoirs in the land cover map and 145 reservoirs in the results of classification of the images using the maximum likelihood classification method, and among the foregoing reservoirs, 143 were identified in both the land cover map and the results of classification of the images using the maximum likelihood classification method.
Third, the accuracy was calculated by comparing 150 random points in images classified with the maximum likelihood classification method with the land cover map to calculate the error matrices of rivers and reservoirs. According to the results, the accuracy of classification of rivers was shown to be 75.95% and that of other objects was shown to be 78.87% so that the overall accuracy was 77.33% in the case of rivers. The accuracy of classification of reservoirs was shown to be 87.
Second, rivers and reservoirs were classified using the maximum likelihood classification method and the area and accuracy of the land cover map were calculated, compared and reviewed. According to the results, the total area of the image was 201,524,214㎡, and the area of rivers was shown to be 726,753㎡ in the land cover map and 187,221㎡ when rivers were classified with the maximum likelihood classification method. The area of reservoirs was shown to be 4,414,997㎡ in the land cover map and 197,908,834㎡ in the results of classification of reservoirs with the maximum likelihood classification method.
This study was conducted using KOMPSAT-3A images of regions near damyang lake in Jeollanam-do province. It is judged that classify water systems are classified using high performance and high resolution satellite images and GIS is useful for management of sewage treatment facilities as well water regulation and water control because the classification is more accurate than existing classification methods and geographic information of wide areas can be quickly updated.
The error matrix which compares reference data and classified images pixel by pixel was used as an accuracy evaluation method. One hundred fifty random points were set and producer accuracy, user accuracy, and overall accuracy were obtained. Thereafter, the kappa coefficients was obtained to evaluate the accuracy.
He tested the proposed algorithm on the various block sizes between 11×11 and 19×19 using KOMPSAT (Korea Multi-Purpose Satellite)-2 images for the accuracy assessment. The overall accuracy and kappa coefficient of the algorithm varied from 99.47% to 99.53% and from 95.07% to 95.80%, respectively.
05%. The user accuracy for reservoirs was shown to be 87.78%, the overall accuracy was shown to be 89.33%, and the kappa coefficient was shown to be 0.78.

참고문헌 (15)

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