A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Characterizing Forest Succession Stages for Wildlife Habitat Assessment Using Multispectral Airborne Imagery
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.
Characterizing Forest Succession Stages for Wildlife Habitat Assessment Using Multispectral Airborne Imagery
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.
Characterizing Forest Succession Stages for Wildlife Habitat Assessment Using Multispectral Airborne Imagery
Wen Zhang (author) / Baoxin Hu (author) / Murray Woods (author) / Glen Brown (author)
2017
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
A TEST OF AIRBORNE MULTISPECTRAL VIDEOGRAPHY FOR ASSESSING THE ACCURACY OF WILDLIFE HABITAT MAPS
| Online Contents | 1995
DEVELOPMENT OF A FOREST HEALTH INDEX USING MULTISPECTRAL AIRBORNE DIGITAL CAMERA IMAGERY
| Online Contents | 2000
Characterizing Forest Succession in Central Ontario Using Lidar Derived Indices
| Online Contents | 2011