CORRECTING TREE COUNT BIAS FOR OBJECTS SEGMENTED FROM LIDAR POINT CLOUDS
Từ khóa:
Remote sensing, forest inventory, Lambert's W, LiDAR data, Poisson DistributionTóm tắt
We introduce a new statistical distribution for modeling the number of trees that fall in segmented LiDAR point clouds. Average tree count is modeled as a linear function of segment ground area, but the same methods can be used to fit more complex non-linear models. Although observed mean occurrences can be continuous, occurrences must be a non-negative integer implying the usual assumption of normal errors may not be adequate. The starting point for a new distribution is the Poisson. The Poisson is based on the premise of rare events from a large population. The probability a particular tree falls in a given point cloud segment is small and the number of segments is large, hence the Poisson distribution should be an appropriate error distribution for modeling the number of trees that fall in a point cloud segment. Deviation from the Poisson occurs as a result of the point cloud segmentation process. The purpose of segmentation is to provide segments that contain a single tree. This implies that a Poisson with deflated probability of zero occurrences and inflated probability of one occurrence is appropriate. LiDAR point cloud data on 20 stem mapped plots are used to show the utility of this approach.
Tài liệu tham khảo
P.B. Brito, F. Fabiao, and A. Staubyn. Euler, lambert, and the lambert w-function today. Mathematical Scientist, 33(2), 2008.
J.W. Flewelling. Probability models for individually segmented tree crown images in a sampling context. Proceedings of SilviLaser, 2008:8th, 2008.
J.W. Flewelling. Forest inventory predictions from individual tree crowns: regression modeling within a sample framework. In In: McRoberts, R.E.; Reams, G.A.; Van Deusen, P.C.; McWilliams, W.H., eds. Proceedings of the eighth annual forest inventory and analysis symposium; 2006 October 16-19; Monterey, CA. Gen. Tech. Report WO-79. Washington, DC: US Department of Agriculture, Forest Service. 203-210., volume 79, 2009.
K. Iles. Are models the answer? Mathematical and Computational Forestry & Natural Resource Sciences, 10(1):6, 2018.
S. Jeronimo, V. Kane, D. Churchill, R. McGaughey, and J. Franklin. Applying lidar individual tree detection to management of structurally diverse forest landscapes. Journal of Forestry, 116(4):336–346, 2018.
T. Lämås. The haglöf postex ultrasound instrument for the positioning of objects on forest sample plots. Technical Report. Ume, Sweden, Dept. of Forest Resource Management, Arbetsrapport / Sveriges lantbruksuniversitet, Institutionen fr skoglig resurshushllning och geomatik, 296, 2010.
R. Pope. Constructing aerial photo volume tables. USDA Forest Service PNW Old Series Research Paper No. 49: 1-25, 49, 1962.
J.R. Roussel, D. Auty, N. Coops, P. Tompal-ski, T. Goodbody, A.S. Meador, J. Bourdon, F. de Boissieu, and A. Achimlidr: An r package for analysis of airborne laser scanning (als) data. Remote Sensing of Environment, 251:112061, 2020.
R Core Team et al. R: A language and environment for statistical computing. 2020.
Lượt tải xuống
Đã Xuất bản
Số
Chuyên mục
Giấy phép
Authors who publish with this journal agree to the following terms:- Authors grant the journal right of first publication, with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) following the publication by the journal, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
