Browsing by Author "Benseghir, Louaï"

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    Estimation of aboveground biomass in conserved areas of Stipa tenacissima L. stands in the high steppes of western Algeria by mean of the Landsat 8 imagery-based vegetation indices
    (Wiley, 2019) Benseghir, Louaï; Kadi‐Hanifi, Halima; Bachari, Nour El Islam
    The main aim of this paper was to evaluate the use of OLI spectral data as a tool to assess the steppe vegetation in a conservation context. The field sampling was conducted for two specific areas of treatment (a) an exclosure area and (b) a free grazing area. After testing several vegetation indices (VIs), the optimal results were obtained for the Normalised Difference Vegetation Index (NDVI)-based aboveground biomass model with r2 = 0.61 and r2 = 0.72 for total and perennial biomass, respectively. No difference between observed and predicted total and perennial biomass was found (p = 0.700 and p = 0.306, respectively). The comparison between the two treatments using the field sampling revealed a significant difference on total plant cover (p = 0.016) and total biomass (p = 0.005), with a plant cover of 50.6% and a biomass of 325.771 kg dry matter per hectare (kg DM ha−1) on average in grazed area and 66.9%, 1,407.869 kg DM ha−1 in exclosure. Finally, a concordance is noted between the results obtained by the NDVI-based biomass model and the field sampling-based biomass
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    Shortwave infrared vegetation index-based modelling for aboveground vegetation biomass assessment in the arid steppes of Algeria
    (Taylor & Francis, 2021) Benseghir, Louaï; Bachari, Nour El Islam
    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)