Shortwave infrared vegetation index-based modelling for aboveground vegetation biomass assessment in the arid steppes of Algeria

Abstract

Selecting the appropriate vegetation index for accurate biomass estimation is a prerequisite before and during the ecosystem management project. This study, aims to compare Vegetation Indices (VIs) that are combining both Visible and Near Infrared OLI bands (VNIR-VIs), Visible and Short Wave Infrared OLI bands and also NIR and Short Wave Infrared OLI bands (SWIR-VIs) in order to accurately model the Aboveground Biomass (AGB) of three widely-located study sites over the arid steppe lands in Algeria. The Simple Linear Model (SLM) and Support Vector Machine (SVM) were utilised as statistical learning techniques on data; firstly, from each study site separately, and secondly, from all study sites (pooled data). In all study sites, SVM improves R² with a mean of 4.5% and decreases the Root Mean Squared Error (RMSE) and Percentage of Error (PE), respectively, with 15.50 (kg DM ha⁻¹) and 1.33% on average. In all cases, the SWIR-VIs outperforms the VNIR-VIs with an improvement rate of 40% of R² and an average reduction of 362.36 kg DM ha⁻¹ and 25% of RMSE and PE, respectively. The principal main improvement was found to involve the pooled data-based model utilising normalised difference VI form, which combines OLI₂(0.452–0.512 μm) with OLI₇(2.107–2.294 μm), (R² = 0.840, p < 0.0005).

Keywords:

• cross-validation
• Landsat 8 OLI
• machine learning
• Stipa tenacissima L