TY - JOUR
T1 - Unconstrained approach for isolating individual trees using high-resolution aerial imagery
AU - Park, Taejin
AU - Cho, Jung Kil
AU - Lee, Jong Yeol
AU - Lee, Woo Kyun
AU - Choi, Sungho
AU - Kwak, Doo Ahn
AU - Kim, Moon Il
N1 - Funding Information:
This study was carried out with the support of Forest Science and Technology Projects (Project No. S120909L010130 and S210912L010100) provided by the Korea Forest Service and a research grant (Project No. T1101191) from the E3 Corporation (Eco, Environment, Energy).
PY - 2014
Y1 - 2014
N2 - This study outlines an algorithm that can be used for individual tree detection and crown delineation; it was applied to coniferous forest using aerial imagery. This article explains the assumptions and processes involved in the algorithm, presents the results of the applications, and discusses possible limitations. The algorithm, which adopts contextual analysis that excludes the need to specify window size, was applied to detect and delineate individual trees based on morphological and reflective characteristics. The preprocessing steps included suppression of the non-coniferous area (i.e. non-forest and leaf-off deciduous forest) and the creation of appropriately smoothed imagery using an optimal smoothing level based on accuracy index (AI); thereafter, unconstrained directional peak- and edge-finding algorithms were processed separately. To assess the tree detection and crown delineation processes, the results of the algorithms were evaluated carefully against visually interpreted crowns in six square plots using several statistical measures based on tree top correspondence, positional difference of tree top, directional crown width, and crown area assessment. The average tree top correspondence had an AI of 88.83%. The positional difference between detected and visually interpreted tree tops was measured and its average was 0.6 m. For our 0.5 m/pixel aerial imagery, the average root mean square error (RMSE) of crown width in six sample plots was found to be 2.8 m, and crown area estimation resulted in RMSE of approximately 9.23 m2 (23.25%). In general, this study highlights the potentiality of the proposed algorithm to efficiently and automatically acquire forest information such as tree numbers, crown width, and crown area.
AB - This study outlines an algorithm that can be used for individual tree detection and crown delineation; it was applied to coniferous forest using aerial imagery. This article explains the assumptions and processes involved in the algorithm, presents the results of the applications, and discusses possible limitations. The algorithm, which adopts contextual analysis that excludes the need to specify window size, was applied to detect and delineate individual trees based on morphological and reflective characteristics. The preprocessing steps included suppression of the non-coniferous area (i.e. non-forest and leaf-off deciduous forest) and the creation of appropriately smoothed imagery using an optimal smoothing level based on accuracy index (AI); thereafter, unconstrained directional peak- and edge-finding algorithms were processed separately. To assess the tree detection and crown delineation processes, the results of the algorithms were evaluated carefully against visually interpreted crowns in six square plots using several statistical measures based on tree top correspondence, positional difference of tree top, directional crown width, and crown area assessment. The average tree top correspondence had an AI of 88.83%. The positional difference between detected and visually interpreted tree tops was measured and its average was 0.6 m. For our 0.5 m/pixel aerial imagery, the average root mean square error (RMSE) of crown width in six sample plots was found to be 2.8 m, and crown area estimation resulted in RMSE of approximately 9.23 m2 (23.25%). In general, this study highlights the potentiality of the proposed algorithm to efficiently and automatically acquire forest information such as tree numbers, crown width, and crown area.
UR - http://www.scopus.com/inward/record.url?scp=84890942262&partnerID=8YFLogxK
U2 - 10.1080/01431161.2013.862603
DO - 10.1080/01431161.2013.862603
M3 - Article
AN - SCOPUS:84890942262
SN - 0143-1161
VL - 35
SP - 89
EP - 114
JO - International Journal of Remote Sensing
JF - International Journal of Remote Sensing
IS - 1
ER -