Mapping and Characterizing Selected Canopy Tree Species at the Angkor World Heritage Site in Cambodia Using Aerial Data

Abstract

At present, there is very limited information on the ecology, distribution, and structure of Cambodia’s tree species to warrant suitable conservation measures. The aim of this study was to assess various methods of analysis of aerial imagery for characterization of the forest mensuration variables (i.e., tree height and crown width) of selected tree species found in the forested region around the temples of Angkor Thom, Cambodia. Object-based image analysis (OBIA) was used (using multiresolution segmentation) to delineate individual tree crowns from very-high-resolution (VHR) aerial imagery and light detection and ranging (LiDAR) data. Crown width and tree height values that were extracted using multiresolution segmentation showed a high level of congruence with field-measured values of the trees (Spearman’s rho 0.782 and 0.589, respectively). Individual tree crowns that were delineated from aerial imagery using multiresolution segmentation had a high level of segmentation accuracy (69.22%), whereas tree crowns delineated using watershed segmentation underestimated the field-measured tree crown widths. Both spectral angle mapper (SAM) and maximum likelihood (ML) classifications were applied to the aerial imagery for mapping of selected tree species. The latter was found to be more suitable for tree species classification. Individual tree species were identified with high accuracy. Inclusion of textural information further improved species identification, albeit marginally. Our findings suggest that VHR aerial imagery, in conjunction with OBIA-based segmentation methods (such as multiresolution segmentation) and supervised classification techniques are useful for tree species mapping and for studies of the forest mensuration variables.

Automatic Segment-Level Tree Species Recognition Using High Resolution Aerial Winter Imagery

“…There are only a few studies utilizing proposed method for crown segmentation in tropical ecosystems, where the forest is denser and has more diverse species composition. The highest overall accuracy for species classification in such conditions has been 83% and kappa coefficient 0.76 [Singh et al, 2015]. However, given that only the canopy segments visible to the camera need to be classified to support albedo modelling, the density of the forest does not matter, if the canopy-background separation can be performed reliably.…”

“…The task of separating the tree crowns from the background becomes more challenging when the background is not snowy, at least in boreal forests where the forest floor can be spectrally similar to the canopy. However, recent studies [Wang, 2010;Jing et al, 2012;Singh et al, 2015] showed that it is possible to extract the tree crowns from very high resolution optical data with high accuracy. Further classification can then be done utilizing the method described in this paper.…”

Airborne laser scanning as a method for exploring long-term socio-ecological dynamics in Cambodia

“…The application of ALS completely transformed our view of all of the covered areas, in some cases unexpectedly revealing entire urban landscapes beneath dense jungle canopy . The campaign has resulted in a range of significant research outcomes in archaeology as well as other domains such as forest ecology (Singh et al, 2015a(Singh et al, , 2015b, and ALS-derived datasets now play a key role in the management of the World Heritage site. Analysis and interpretation of the 2012 data will continue for years to come.…”

A comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification

“…The reason is that without this kind of knowledge, almost no results about tree properties can be comprehensively mapped (Naidoo et al, 2012), which may lead to wrong management decisions and incorrect scientific conclusions (Holopainen and Talvitie, 2006). Thereby, accurately identifying tree species is of great significance for tree-related scientific research and practical applications, and across the relevant Hong, 2008; Zhang and Hu, 2012;Singh et al, 2015). For instance, the feature parameters capable of characterizing crown structural profiles were derived from the spectral images and attempted for improving the performance of tree species classification (Zhang and Hu, 2012).…”