• Hwankyungkyoyuk
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• v.23 no.2
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• pp.97-112
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• 2010

The study aims to comprehend the effect of the wetland ecology education on the elementary school students' changes of recognition about wetland through the wetland ecology map program. In this study, the literary research, the experimental research and the survey methods were operated. Through the literary research, the environmental factors were extracted, and the writing item of ecology map was reconstructed based on the literary research, so the experimental research was operated with the wetland ecology map program. Through four areas of test items such as the information and knowledge, values and attitudes, development and conservation, behavior and participation, and the analysis of children's study results, the effect of the wetland ecology map program on changes of recognition about wetland was verified quantitatively and qualitatively. Wetland ecology map program would be able to be an educational approach which can achieve the 'personalization of environment' setting up predictable environmental improvement goals and satisfying the needs of spatial information of the appropriate regions from the holistic perspective that students themselves plan and participate beyond a one-time experience program. Production of ecological map through continuous monitoring is expected to improve the possibility of subjective environmental actions by operating self-directed learning. Based on the conclusion of this study, we would suggest the following. For wetland ecology map program to be supplemented and utilized, the basic education of wetland should be organized in regular school curriculum, ecology map program including various teaching learning methods be prepared actively, and in future studies, studies of ecosystem-wide wetland ecology map program including animals like birds and fish are necessary.

• Korean Journal of Environment and Ecology
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• v.32 no.6
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• pp.686-697
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• 2018

Due to the advance in remote sensing technology, it has become easier to more frequently obtain high resolution imagery to detect delicate changes in an extensive area, particularly including forest which is not readily sub-classified. Time-series analysis on high resolution images requires to collect extensive amount of ground truth data. In this study, the potential of land coverage mapas ground truth data was tested in classifying high-resolution imagery. The study site was Wonju-si at Gangwon-do, South Korea, having a mix of urban and natural areas. KOMPSAT-3A imagery taken on March 2015 and land coverage map published in 2017 were used as source data. Two pixel-based classification algorithms, Support Vector Machine (SVM) and Random Forest (RF), were selected for the analysis. Forest only classification was compared with that of the whole study area except wetland. Confusion matrixes from the classification presented that overall accuracies for both the targets were higher in RF algorithm than in SVM. While the overall accuracy in the forest only analysis by RF algorithm was higher by 18.3% than SVM, in the case of the whole region analysis, the difference was relatively smaller by 5.5%. For the SVM algorithm, adding the Majority analysis process indicated a marginal improvement of about 1% than the normal SVM analysis. It was found that the RF algorithm was more effective to identify the broad-leaved forest within the forest, but for the other classes the SVM algorithm was more effective. As the two pixel-based classification algorithms were tested here, it is expected that future classification will improve the overall accuracy and the reliability by introducing a time-series analysis and an object-based algorithm. It is considered that this approach will contribute to improving a large-scale land planning by providing an effective land classification method on higher spatial and temporal scales.