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Identifying tropical E cuadorian A ndean trees from inter‐crown pixel distributions in hyperspatial aerial imagery

2012; Wiley; Volume: 15; Issue: 4 Linguagem: Inglês

10.1111/j.1654-109x.2012.01196.x

1654-109X

Mika Peck, Ana Mariscal, Martin Padbury, Tim Cane, Dominic Kniveton, Miguel Angel Chinchero,

Remote Sensing and LiDAR Applications

Abstract Question Identification of tropical tree species from low‐cost, very high‐resolution ( VHR ) proximal canopy remote sensing imagery has great potential for improving our understanding of tropical forest ecology. We investigated whether inter‐crown pixel information from VHR imagery could be used for taxonomic identification of trees and characterization of successional stages of tropical A ndean mountain forest. Location S anta L ucia cloud forest reserve, NW E cuadorian A ndes. Methods We gathered digital camera imagery (0.05‐m spatial resolution), using a remote‐controlled helicopter platform, from primary and secondary forest and identified visible crowns to species before extracting digital crown samples (2‐m radius). Using an object‐based approach, histogram descriptors and diversity metrics of pixel intensity in red, green and blue ( RGB ) bands were calculated for crowns, and patterns in similarity explored using ordination. Predictive models were developed and validated using four decision tree models ( CHIAD , Exhaustive CHIAD , CRT and QUEST ). Results Aerial imagery represented 54% of families, 53% of genera and 56% of species sampled from the ground. Ordination (redundancy analysis) confirmed that inherent continuities, based on crown metrics, correlated with traditional species, genus and family groupings ( P < 0.05). Data were best described by histogram means in the green band. The best predictive model ( CRT ) generated a 47% probability of correct species identification for 41 species – with similar success at genus and family level. Predictive ability was highly species‐specific, ranging from zero for some taxa to 93% for C ecropia gabrielis C uatrec. Conclusions From the crown metrics tested, we found the mean pixel intensity in the green band was most effective in predicting species and species grouping of tropical mountain trees. This metric integrates species‐specific differences in leaf density of crowns and reflectance in the green waveband. High predictive success for indicators of primary ( C ornus peruviana J . F . M acbr.) and secondary forest ( C ecropia gabrielis C uatrec.) shows that VHR imagery can be used to identify species from pixel information to provide ecological information on successional status. Further research is needed to develop pattern and textural metrics specifically for hyperspatial digital imagery to identify tree species from crown imagery in diverse tropical forests.