Estimation of woody plant species diversity during a dry season in a savanna environment using the spectral and textural information derived from WorldView-2 imagery

PloS One
Emmanuel FundisiSolomon G Tesfamichael

Abstract

Remote sensing techniques are useful in the monitoring of woody plant species diversity in different environments including in savanna vegetation types. However, the performance of satellite imagery in assessing woody plant species diversity in dry seasons has been understudied. This study aimed to assess the performance of multiple Gray Level Co-occurrence Matrices (GLCM) derived from individual bands of WorldView-2 satellite imagery to quantify woody plant species diversity in a savanna environment during the dry season. Woody plant species were counted in 220 plots (20 m radius) and subsequently converted to a continuous scale of the Shannon species diversity index. The index regressed against the GLCMs using the all-possible-subsets regression approach that builds competing models to choose from. Entropy GLCM yielded the best overall accuracy (adjusted R2: 0.41-0.46; Root Mean Square Error (RMSE): 0.60-0.58) in estimating species diversity. The effect of the number of predicting bands on species diversity estimation was also explored. Accuracy generally increased when three-five bands were used in models but stabilised or gradually decreased as more than five bands were used. Despite the peak accuracies achieved with three-...Continue Reading

References

Jun 7, 2011·Photogrammetric Engineering and Remote Sensing·Emilio Federico Moran
Aug 30, 2012·Ecology·Elsa E ClelandElizabeth M Wolkovich
Nov 17, 2012·Trends in Ecology & Evolution·Riccardo BommarcoSimon G Potts
Mar 8, 2017·BMC Biology·Marc S M SosefThomas L P Couvreur
Oct 31, 2017·Environmental Monitoring and Assessment·Maliheh ArekhiYaşar Feyza Akyüz

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Software Mentioned

lmSubsets
VI
WorlView
ENVI
R
WorldView
eCognition Developer®
ArcGIS ArcGIS
QUick Atmospheric Correction ( QUAC )
DigitalGlobe

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