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

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 R 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 (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 setups using widely available moderate-resolution imagery.

Remote sensing of savanna woody species diversity: A systematic review of data types and assessment methods

“…Numerous research in the field of forestry and ecology have exhausted this approach more specifically in enumerating species diversity. Though this approach is the most favoured information extraction method, it can under-or-overestimate the number of species that potentially exist on the ground [ 48 ]. In contrast, continuous-scale assessment/modelling converts unique species data into a continuous diversity scale, thus allowing stretching the ability to estimate as many species as possible.…”

“…They, then, used linear regression to assess the relationship between the diversity index and eight Grey Level Co-occurrence Matrices (GLCM) extracted from Landsat-7 achieving R 2 = 0.01–0.60. [ 48 ] estimated woody plant species diversity in Montane savanna area (651 ha), dominated by morphologically similar woody plant species. The study applied the all-subsets regression to correlate Shannon diversity index quantified from field inventory of 15 m radius plots with GLCM derived from individual bands of WorldView-2 imagery.…”

Two Decades Progress on the Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review

“…The relevance and application of remote sensing for natural forest studies have been widely demonstrated by various authors. These applications include questions linked to forest biophysical parameter inventory, forest biochemical mapping [20], tree species discrimination and mapping [21,22], carbon stocks estimation [23][24][25], forest land cover change mapping [26][27][28][29], biodiversity assessment and monitoring [30][31][32], assessment of forest extent [33], and tree crown delineation [34][35][36][37].…”