Abstract
Abstract. Mapping tree species is essential for sustainable planning as well as to improve our understanding of the role of different trees as different ecological service. However, crown-level tree species automatic classification is a challenging task due to the spectral similarity among diversified tree species, fine-scale spatial variation, shadow, and underlying objects within a crown. Advanced remote sensing data such as airborne Light Detection and Ranging (LiDAR) and hyperspectral imagery offer a great potential opportunity to derive crown spectral, structure and canopy physiological information at the individual crown scale, which can be useful for mapping tree species. In this paper, an innovative approach was developed for tree species classification at the crown level. The method utilized LiDAR data for individual tree crown delineation and morphological structure extraction, and Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery for pure crown-scale spectral extraction. Specifically, four steps were include: 1) A weighted mean filtering method was developed to improve the accuracy of the smoothed Canopy Height Model (CHM) derived from LiDAR data; 2) The marker-controlled watershed segmentation algorithm was, therefore, also employed to delineate the tree-level canopy from the CHM image in this study, and then individual tree height and tree crown were calculated according to the delineated crown; 3) Spectral features within 3 × 3 neighborhood regions centered on the treetops detected by the treetop detection algorithm were derived from the spectrally normalized CASI imagery; 4) The shape characteristics related to their crown diameters and heights were established, and different crown-level tree species were classified using the combination of spectral and shape characteristics. Analysis of results suggests that the developed classification strategy in this paper (OA = 85.12 %, Kc = 0.90) performed better than LiDAR-metrics method (OA = 79.86 %, Kc = 0.81) and spectral-metircs method (OA = 71.26, Kc = 0.69) in terms of classification accuracy, which indicated that the advanced method of data processing and sensitive feature selection are critical for improving the accuracy of crown-level tree species classification.
Highlights
Over 10 million km2 of agricultural land has a tree cover greater than 10% (Li et al, 2003)
Hyperspectral data is considered effective for mapping tree species as it can measure subtle variability in spectral reflectance from leaf to crown scales, largely due to their very high spectral resolution and wide range of electromagnetic spectrum (George et al, 2014)
With the spectral-metrics classification, the crown-scale spectrum and spectral indexes of an individual tree was extracted from the pixel at treetop location in the crown region, and a c5.0 classifier was applied to classify the individual tree to a particular species
Summary
Over 10 million km of agricultural land has a tree cover greater than 10% (Li et al, 2003). Crown-level tree species automatic classification is a challenging task due to crown-scale spectra have lower purity due to the interference of mixed pixel problem and double-side illumination problem (Shang and Chisholm, 2014; Zhang and Qiu, 2012). This lower purity of crown spectra, may contribute to the lower classification of tree species at the crown level. In order to address this issue, we developed an innovative method using the combination of spectral and shape characteristics of different tree species, which may potentially improve tree species classification accuracy. In order to assess the improvement of this method in classifying tree species, the LiDAR-metrics and spectral-metrics approaches will be carried out for a comparative analysis
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