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–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 (3×3, 5×5 and 7×7) 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.
Highlights
Natural environments provide vital ecosystem services such as primary energy, habitat to fauna, maintaining of hydrological cycles, protection of biological diversity, medicinal benefits, recreation, aesthetic values, etc [1,2,3,4]
This study explored the performances of eight Gray Level Co-occurrence Matrices (GLCM) statistical measures derived from reflectance values of WorlView-2 individual bands to estimate species diversity during a dry season
An exhaustive analysis using the all-possible-subset regression approach showed that entropy GLCM statistic performed better than other statistics in capturing plot level species diversity expressed in the Shannon index scale
Summary
Natural environments provide vital ecosystem services such as primary energy, habitat to fauna, maintaining of hydrological cycles, protection of biological diversity, medicinal benefits, recreation, aesthetic values, etc [1,2,3,4]. Maintaining the heterogeneity and complexity of vegetation composition in these environments is critical to ensure the sustained functioning of such ecosystems [3]. Anthropogenic activities and natural processes are threatening biodiversity and associated ecosystem services, in areas close to human settlements [4,5,6,7]. Field inventory for characterising vegetation composition is expensive, labour intensive and timeconsuming, and is inefficient in complex heterogeneous ecosystems [3,5,9,10,11,12]
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