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International Journal of Remote Sensing Volume 35, 2014 - Issue 1
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Articles

Unconstrained approach for isolating individual trees using high-resolution aerial imagery

Taejin ParkDivision of Environmental Science and Ecological Engineering, Korea University, Seoul, Republic of Korea
,
Jung-Kil ChoDivision of Environmental Science and Ecological Engineering, Korea University, Seoul, Republic of Korea
,
Jong-Yeol LeeDivision of Environmental Science and Ecological Engineering, Korea University, Seoul, Republic of Korea
,
Woo-Kyun LeeDivision of Environmental Science and Ecological Engineering, Korea University, Seoul, Republic of KoreaCorrespondence[email protected]
,
Sungho ChoiDepartment of Earth and Environment, Boston University, Boston, MA, USA
,
Doo-Ahn KwakDivision of Environmental Science and Ecological Engineering, Korea University, Seoul, Republic of Korea
&
Moon-Il KimDivision of Environmental Science and Ecological Engineering, Korea University, Seoul, Republic of Korea
 show all
Pages 89-114 | Received 17 Sep 2012, Accepted 16 Oct 2013, Published online: 28 Nov 2013

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Amrit Kathuria, Russell Turner, Christine Stone, Joaqin Duque-Lazo & Ron West. (2016) Development of an automated individual tree detection model using point cloud LiDAR data for accurate tree counts in a Pinus radiata plantation. Australian Forestry 79:2, pages 126-136.
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Articles from other publishers (3)

Lulu Hu, Xiaojun Xu, Juzhong Wang & Huaixing Xu. (2023) Individual tree crown width detection from unmanned aerial vehicle images using a revised local transect method. Ecological Informatics 75, pages 102086.
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Lia Duarte, Pedro Silva & Ana Teodoro. (2018) Development of a QGIS Plugin to Obtain Parameters and Elements of Plantation Trees and Vineyards with Aerial Photographs. ISPRS International Journal of Geo-Information 7:3, pages 109.
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Taichi Takayama & Akira Iwasaki. (2016) Selection of additional trainig data for improving accuracy of forest type classification using hyperspectral data. Selection of additional trainig data for improving accuracy of forest type classification using hyperspectral data.

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