Improving the classification of six evergreen subtropical tree species with multi-season data from leaf spectra simulated to WorldView-2 and RapidEye

Remote sensing offers a feasible means to monitor tree species at a regional level where species distribution and composition is affected by the impacts of global change. Furthermore, the temporal resolution of space-borne multispectral sensors offers the ability to combine phenologically important...

Full description

Saved in:
Bibliographic Details
Published in:International journal of remote sensing 2017-09, Vol.38 (17), p.4804-4830
Main Authors: van Deventer, H., Cho, M. A., Mutanga, O.
Format: Article
Language:English
Subjects:
Autumn
Canopies
Classification
Composition
Computer simulation
Data
Evergreen trees
Forests
Least squares method
Mathematical models
Optimization
Plant cover
Reflectance
Remote monitoring
Remote sensing
Seasons
Sensors
Species
Species classification
Spring (season)
Summer
Temporal resolution
Variance analysis
Winter
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Remote sensing offers a feasible means to monitor tree species at a regional level where species distribution and composition is affected by the impacts of global change. Furthermore, the temporal resolution of space-borne multispectral sensors offers the ability to combine phenologically important phases for the optimization of tree species classification. In this study, we determined whether multi-seasonal leaf-level spectral data (winter, spring, summer, and autumn) improved the classification of six evergreen tree species in the subtropical forest region of South Africa when compared to a single season, for hyperspectral data, and reflectance data simulated to the WorldView-2 (WV2) and RapidEye (RE) sensors. Classification accuracies of the test data were assessed using a Partial Least Square Random Forest algorithm. The accuracies were compared between single seasons and multi-season classification and across seasons using analysis of variance and post-hoc Tukey Honest Significant Difference tests. The average overall accuracy (OA) of the leaf-level hyperspectral data ranged from a minimum of 90 ± 3.5% in winter to a maximum of 92 ± 2.7% in summer, outperforming the simulated reflectance data for the WV2 and RE sensors with an average OA of between 8 and 10 percentage points (p 82% and showed no significant change using multi-season data. Multiple seasons may therefore be beneficial to multispectral sensors with ≤8 bands, yet remains to be tested at canopy level, for other species and climatic regions.
ISSN:0143-1161
1366-5901
DOI:10.1080/01431161.2017.1320445