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Forests 2017, 8(7), 234; doi:10.3390/f8070234

Characterizing Forest Succession Stages for Wildlife Habitat Assessment Using Multispectral Airborne Imagery

1
Department of Earth and Space Science and Engineering, York University, 4700 Keele St., Toronto, ON M3J 1P3, Canada
2
Ontario Ministry of Natural Resources and Forestry, 3301 Trout Lake Road, North Bay, ON P1A 4L7, Canada
3
Wildlife Research and Monitoring Section, Ontario Ministry of Natural Resources and Forestry, Trent University DNA Building, 2140 East Bank Drive, Peterborough, ON K9L 0G2, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Christian Ginzler and Lars T. Waser
Received: 11 April 2017 / Revised: 21 June 2017 / Accepted: 22 June 2017 / Published: 30 June 2017
(This article belongs to the Special Issue Optimizing Forest Inventories with Remote Sensing Techniques)
View Full-Text   |   Download PDF [4001 KB, uploaded 30 June 2017]   |  

Abstract

In this study, we demonstrate the potential of using high spatial resolution airborne imagery to characterize the structural development stages of forest canopies. Four forest succession stages were adopted: stand initiation, young multistory, understory reinitiation, and old growth. Remote sensing metrics describing the spatial patterns of forest structures were derived and a Random Forest learning algorithm was used to classify forest succession stages. These metrics included texture variables from Gray Level Co-occurrence Measures (GLCM), range and sill from the semi-variogram, and the fraction of shadow and its spatial distribution. Among all the derived variables, shadow fractions and the GLCM variables of contrast, mean, and dissimilarity were the most important for characterizing the forest succession stages (classification accuracy of 89%). In addition, a LiDAR (Light Detection and Ranging) derived forest structural index (predicted Lorey’s height) was employed to validate the classification result. The classification using imagery spatial variables was shown to be consistent with the LiDAR derived variable (R2 = 0.68 and Root Mean Square Error (RMSE) = 2.39). This study demonstrates that high spatial resolution imagery was able to characterize forest succession stages with promising accuracy and may be considered an alternative to LiDAR data for this kind of application. Also, the results of stand development stages build a framework for future wildlife habitat mapping. View Full-Text
Keywords: stand complexity; spatial pattern analysis; GLCM; semi-variogram; VHR imagery; LiDAR stand complexity; spatial pattern analysis; GLCM; semi-variogram; VHR imagery; LiDAR
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhang, W.; Hu, B.; Woods, M.; Brown, G. Characterizing Forest Succession Stages for Wildlife Habitat Assessment Using Multispectral Airborne Imagery. Forests 2017, 8, 234.

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