Application of deep learning algorithm for estimating stand volume in South Korea : научное издание


Тип публикации: статья из журнала

Год издания: 2022

Аннотация: Current estimates of stand volume for South Korean forests are mostly derived from expensive field data. Techniques that allow reducing the amount of ground data with reliable accuracy would decrease the cost and time. The fifth National Forest Inventory (NFI) has been conducted annually for all forest areas in South Korea from 200Показать полностью6 to 2010 and using these data we can make a model for estimating the stand volume of forests. The purpose of this study is to test deep learning whether it is available for measurement of stand volume with satellite imageries and geospatial information. The spatial distribution of the stand volume of South Korean forests was predicted with the convolutional neural networks (CNNs) algorithm. NFI data were randomly sampled for training from 90% to 10%, with 10% decrement, and the rest of the area was estimated using satellite imagery and geospatial information. Consequently, we found that the error rate of total stand volume was

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Журнал: Journal of Applied Remote Sensing

Выпуск журнала: Vol. 16, Iss. 2

Издатель: SPIE


  • Sungeun Cha (National Institue of Forest Science (Korea, Republic of))
  • Hyun-Woo Jo (Korea Univ. (Korea, Republic of))
  • Moonil Kim (3Pyeongtaek Univ. (Korea, Republic of))
  • Cholho Song (Korea Univ. (Korea, Republic of))
  • Halim Lee (United Nations Univ. (Germany))
  • Eunbeen Park (Korea Univ. (Korea, Republic of))
  • Joongbin Lim (National Institute of Forest Science (Korea, Republic of))
  • Schepaschenko D. G. (Siberian Federal Univ. (Russian Federation))
  • Shvidenko Anatoly (International Institute for Applied Systems Analysis (Austria))
  • Woo-Kyun Lee (Korea Univ. (Korea, Republic of))

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