Remote sensing pipeline for tree segmentation and classification in a mixed softwood and hardwood system

PeerJ

article

Conor A. McMahon¹ 

[1] Department of Mechanical Engineering, University of Texas at Austin

关键词: Remote sensing;    Forestry;    Lidar;    Hyperspectral camera;    Segmentation;    Classification;    Alignment;    Ecology;    Data science;    Biogeography;   

DOI  :  10.7717/peerj.5837

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

The National Institute of Standards and Technology data science evaluation plant identification challenge is a new periodic competition focused on improving and generalizing remote sensing processing methods for forest landscapes. I created a pipeline to perform three remote sensing tasks. First, a marker-controlled watershed segmentation thresholded by vegetation index and height was performed to identify individual tree crowns within the canopy height model. Second, remote sensing data for segmented crowns was aligned with ground measurements by choosing the set of pairings which minimized error in position and in crown area as predicted by stem height. Third, species classification was performed by reducing the dataset’s dimensionality through principle component analysis and then constructing a set of maximum likelihood classifiers to estimate species likelihoods for each tree. Of the three algorithms, the classification routine exhibited the strongest relative performance, with the segmentation algorithm performing the least well.

【 授权许可】

CC BY