Automatic segmentation of forest stands using a canopy height model and aerial photography

Abstract Forest management planning is based on stand-level information. The stands are typically visually delineated based on aerial photographs. Because of visual interpretation, the estimation of stand boundaries is always subjective. Moreover, there are no definite criteria for the delineation. For one forest area, several different solutions could be obtained. Automated segmentation of digital imagery offers a possible solution to these problems, by producing more objective delineation, reducing time and costs, and increasing consistency of stand delineation. The aim of this study was to evaluate the applicability of a canopy height model (CHM) in automatic segmentation of forest stands. In addition, the mosaic produced with CHM was compared with one that was produced automatically using an aerial photograph (RGB). The usefulness of combining CHM and an aerial photograph in the automatic segmentation process was also examined. The results showed that delineation based on CHM is a good option compared with aerial photographs, when aiming for homogeneity of the delineated stands. The explained proportion of the whole variability of mean diameter was 74% and of mean height 83% in the CHM mosaic, compared with 65% and 73% in the RGB mosaic, and 60% and 73% in the reference. However, a visual interpreter was able to produce delineation almost as homogeneous as the reference with respect to volume, and better with respect to mean diameter and height.

[1]  J. Hyyppä,et al.  Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions , 2004 .

[2]  F. Ackermann.  Airborne laser scanning : present status and future expectations , 1999 .

[3]  Ljusk Ola Eriksson,et al.  Formation of harvest units with genetic algorithms. , 2000 .

[4]  A. Pekkarinen,et al.  Segment-level stand inventory for forest management , 2005 .

[5]  Raymond L. Czaplewski,et al.  Calibration of Remotely Sensed Proportion or Area Estimates for Misclassification Error , 1992 .

[6]  M. Roth.  A quantitative assessment , 1987 .

[7]  Arno Schäpe,et al.  Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation , 2000 .

[8]  Åsa Persson,et al.  Detecting and measuring individual trees using an airborne laser scanner , 2002 .

[9]  AUTOMATIC ESTIMATION OF FOREST INVENTORY PARAMETERS BASED ON LIDAR, MULTI-SPECTRAL AND FOGIS DATA , 2003 .

[10]  Kari T. Korhonen,et al.  Inventory by Compartments , 2006 .

[11]  E. Næsset.  Determination of mean tree height of forest stands using airborne laser scanner data , 1997 .

[12]  Julien Radoux,et al.  A quantitative assessment of boundaries in automated forest stand delineation using very high resolution imagery , 2007 .

[13]  J. Means,et al.  Predicting forest stand characteristics with airborne scanning lidar , 2000 .

[14]  Richard G. Oderwald,et al.  Forest Volume and Biomass Estimation Using Small-Footprint Lidar-Distributional Parameters on a Per-Segment Basis , 2006 .

[15]  J. Hyyppä,et al.  DETECTING AND ESTIMATING ATTRIBUTES FOR SINGLE TREES USING LASER SCANNER , 2006 .

[16]  Jing Li Wang,et al.  Color image segmentation: advances and prospects , 2001, Pattern Recognit..

[17]  P. Gessler,et al.  Automated estimation of individual conifer tree height and crown diameter via two-dimensional spatial wavelet analysis of lidar data , 2006 .

[18]  Zhe Jiang,et al.  Spatial Statistics , 2013 .

[19]  Peter Holmgren,et al.  Applying objectively estimated and spatially continuous forest parameters in tactical planning to obtain dynamic treatment units , 1997 .

[20]  Guangxing Wang,et al.  The calibration of digitized aerial photographs for forest stratification , 1998 .

[21]  Jouni Siipilehto,et al.  Improving the Accuracy of Predicted Basal-Area Diameter Distribution in Advanced Stands by Determining Stem Number , 1999 .

[22]  E. Næsset.  Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data , 2002 .

[23]  J. Holmgren,et al.  Estimation of Tree Height and Stem Volume on Plots Using Airborne Laser Scanning , 2003, Forest Science.

[24]  Simo Poso,et al.  Kuvioittaisen arvioimismenetelmän perusteita. , 1983 .

[25]  Mikko Inkinen,et al.  A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners , 2001, IEEE Trans. Geosci. Remote. Sens..

[26]  E. Næsset.  Practical large-scale forest stand inventory using a small-footprint airborne scanning laser , 2004 .

[27]  Brian D. Ripley,et al.  Spatial Statistics: Ripley/Spatial Statistics , 2005 .

[28]  S. Magnussen,et al.  Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators , 1998 .

[29]  W. Cohen,et al.  Surface lidar remote sensing of basal area and biomass in deciduous forests of eastern Maryland, USA , 1999 .

[30]  G. Hay,et al.  An automated object-based approach for the multiscale image segmentation of forest scenes , 2005 .

[31]  Jennifer L. R. Jensen,et al.  Estimation of biophysical characteristics for highly variable mixed-conifer stands using small-footprint lidar , 2006 .

[32]  Tero Heinonen,et al.  Developing spatial optimization in forest planning , 2007 .