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

• Published in: PloS one Vol. 15; no. 6; p. e0234158
• Main Authors: Fundisi, Emmanuel, Musakwa, Walter, Ahmed, Fethi B, Tesfamichael, Solomon G
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 08-06-2020; Public Library of Science (PLoS)
• Subjects: Biodiversity; Biological monitoring; Biology and Life Sciences; Botanical research; Distribution; Dry season; Ecology and Environmental Sciences; Ecosystems; Entropy; Environmental monitoring; Estimation; Flowers & plants; Methods; Natural history; Observations; Physical Sciences; Plant diversity; Regression analysis; Remote sensing; Remote sensing techniques; Research and Analysis Methods; Root-mean-square errors; Satellite imagery; Satellite remote sensing; Satellites; Savannahs; Savannas; Seasons; Sensing techniques; Spatial discrimination; Spatial resolution; Species diversity; Studies; Vegetation; Winter; Woody plants; South Africa; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0234158

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 R.sup.2 : 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-five bands, performances still fared well for models that used fewer bands, showing the relevance of few bands for species diversity estimation. We also assessed the effect of GLCM window size (3x3, 5x5 and 7x7) on species diversity estimation and generally found inconsistent conclusions. These findings demonstrate the capability of GLCMs combined with high spatial resolution imagery in estimating woody plants species diversity in a savanna environment during the dry period. It is important to test the performance of species diversity estimation of similar environmental set-ups using widely available moderate-resolution imagery.