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http://dx.doi.org/10.7780/kjrs.2019.35.6.2.1

Forest Management Research using Optical Sensors and Remote Sensing Technologies  

Kim, Eun-sook (Forest Ecology and Climate Change Division, National Institute of Forest Science)
Won, Myoungsoo (Forest Ecology and Climate Change Division, National Institute of Forest Science)
Kim, Kyoungmin (Global Forestry Division, National Institute of Forest Science)
Park, Joowon (College of Agriculture & Life Science, Kyungpook National University)
Lee, Jung Soo (College of Forest & Environmental Sciences, Kangwon National University)
Publication Information
Korean Journal of Remote Sensing / v.35, no.6_2, 2019 , pp. 1031-1035 More about this Journal
Abstract
Nowadays, the utilization infrastructure of domestic satellite information is expanding rapidly. Especially, the development of agriculture and forestry satellite is expected to drastically change the utilization of satellite information in the forest sector. The launch of the satellite is expected in 2023. Therefore, NIFoS and academic experts in forest sectors have prepared "Special Issue on Forest Management Research using Optical Sensors and Remote Sensing Technologies" in order to understand new remote sensing technologies and suggest the future direction of forest research and decision-making. This special issue is focused on a variety of fields in forest remote sensing research, including forest resources survey, forest disaster detection, and forest ecosystem monitoring. The new research topics for remote sensing technologies in forest sector focuses on three points: development of new indicators and information for accurate detection of forest conditions and changes, the use of new information sources such as UAV and new satellites, and techniques for improving accuracy through the use of artificial intelligence techniques.
Keywords
Remote Sensing; Forest Management; Forest Resources; Forest Disaster; Forest Ecosystem; Optical Sensor;
Citations & Related Records
Times Cited By KSCI : 19  (Citation Analysis)
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1 Lee, S., S.-J. Park, G. Baek, H. Kim, and C.-W. Lee, 2019d. Detection of Damaged Pine Tree by the Pine Wilt Disease Using UAV Image, Korean Journal of Remote Sensing, 35(3): 359-373 (in Korean with English abstract).   DOI
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4 Lim, J., K.-M. Kim, and M.-K. Kim, 2019. The Development of Major Tree Species Classification Model using Different Satellite Images and Machine Learning in Gwangneung Area, Korean Journal of Remote Sensing, 35(6-2): 1037-1052 (in Korean with English abstract).   DOI
5 Park, J.M., W.D. Sim, and J.S. Lee, 2019a. Automatic classification by Land use category of national level LULUCF sector using Deep Learning model, Korean Journal of Remote Sensing, 35(6-2): 1053-1065 (in Korean with English abstract).   DOI
6 Park, S.-W., S.-J. Lee, C.-Y. Chung, S.-R. Chung, I. Shin, W.-C. Jung, H.-S. Mo, S.-I. Kim, and Y.-W. Lee, 2019b. Satellite-based Forest Withering Index for Detection of Fire Burn Area: Its Development and Application to 2019 Kangwon Wildfires, Korean Journal of Remote Sensing, 35(2): 343-346 (in Korean with English abstract).   DOI
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15 Kwon, S.-K., Y.-S. Lee, D.-S. Kim, and H.-S. Jung, 2019. Classification of Forest Vertical Structure Using Machine Learning Analysis, Korean Journal of Remote Sensing, 35(2): 229-239 (in Korean with English abstract).   DOI
16 Lee, B., E.-S. Kim, J.-H. Lim, M. Kang, and J. Kim, 2019a. Application of machine learning algorithm and remote-sensed data to estimate forest gross primary production at multi-sites level, Korean Journal of Remote Sensing, 35(6-2): 1117-1132 (in Korean with English abstract).   DOI
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