TY - JOUR
T1 - Predicting the spatial pattern of trees by airborne laser scanning
AU - Packalen, Petteri
AU - Vauhkonen, Jari
AU - Kallio, Eveliina
AU - Peuhkurinen, Jussi
AU - Pitkänen, Juho
AU - Pippuri, Inka
AU - Strunk, Jacob
AU - Maltamo, Matti
N1 - Funding Information:
This study was supported by the strategic funding of the University of Eastern Finland.
PY - 2013/7
Y1 - 2013/7
N2 - The spatial pattern of trees can be defined as a property of their location in relation to each other. In this study, the spatial pattern was summarized into three categories, regular, random, and clustered, using Ripley's L-function. The study was carried out at 79 sample plots located in a managed forest in Finland. The goal was to study how well the spatial pattern of trees can be predicted by airborne laser scanning (ALS) data. ALS-derived predictions were based upon individual tree detection (ITD), semi-individual tree detection (semi-ITD), and plot-level metrics calculated from the canopy height model, AREA. The kappa value for ITD was almost zero, which indicates no agreement. The semi-ITD and AREA methods performed better, although kappa values were only 0.34 and 0.24, respectively. It appears difficult to detect a particularly clustered spatial pattern.
AB - The spatial pattern of trees can be defined as a property of their location in relation to each other. In this study, the spatial pattern was summarized into three categories, regular, random, and clustered, using Ripley's L-function. The study was carried out at 79 sample plots located in a managed forest in Finland. The goal was to study how well the spatial pattern of trees can be predicted by airborne laser scanning (ALS) data. ALS-derived predictions were based upon individual tree detection (ITD), semi-individual tree detection (semi-ITD), and plot-level metrics calculated from the canopy height model, AREA. The kappa value for ITD was almost zero, which indicates no agreement. The semi-ITD and AREA methods performed better, although kappa values were only 0.34 and 0.24, respectively. It appears difficult to detect a particularly clustered spatial pattern.
UR - http://www.scopus.com/inward/record.url?scp=84876323181&partnerID=8YFLogxK
U2 - 10.1080/01431161.2013.787501
DO - 10.1080/01431161.2013.787501
M3 - Article
AN - SCOPUS:84876323181
SN - 0143-1161
VL - 34
SP - 5154
EP - 5165
JO - International Journal of Remote Sensing
JF - International Journal of Remote Sensing
IS - 14
ER -