Sensitivity Analysis of Sentinel-1 Backscatter to Oil Palm Plantations at Pluriannual Scale: A Case Study in Gabon, Africa

Abstract

The present paper focuses on a sensitivity analysis of Sentinel-1 backscattering signatures from oil palm canopies cultivated in Gabon, Africa. We employed one Sentinel-1 image per year during the 2015–2021 period creating two separated time series for both the wet and dry seasons. The first images were almost simultaneously acquired to the initial growth stage of oil palm plants. The VH and VV backscattering signatures were analysed in terms of their corresponding statistics for each date and compared to the ones corresponding to tropical forests. The times series for the wet season showed that, in a time interval of 2–3 years after oil palm plantation, the VV/VH ratio in oil palm parcels increases above the one for forests. Backscattering and VV/VH ratio time series for the dry season exhibit similar patterns as for the wet season but with a more stable behaviour. The separability of oil palm and forest classes was also quantitatively addressed by means of the Jeffries–Matusita distance, which seems to point to the C-band VV/VH ratio as a potential candidate for discrimination between oil palms and natural forests, although further analysis must still be carried out. In addition, issues related to the effect of the number of samples in this particular scenario were also analysed. Overall, the outcomes presented here can contribute to the understanding of the radar signatures from this scenario and to potentially improve the accuracy of mapping techniques for this type of ecosystems by using remote sensing. Nevertheless, further research is still to be done as no classification method was performed due to the lack of the required geocoded reference map. In particular, a statistical assessment of the radar signatures should be carried out to statistically characterise the observed trends.

Evaluation of SAR and Optical Image Fusion Methods in Oil Palm Crop Cover Classification Using the Random Forest Algorithm

“…A high level of accuracy was found in the classification of coverages based on the SAR image, corroborating the research developed by [14], which evidences an increase in user accuracy (UA) compared with optical image classification. As mentioned in [57,62], the specific classification of the cover associated with oil palm crops is highly reliable when using only SAR images. However, the classification of bare soil and vegetation coverage was confusing, corroborating the results obtained by [54], where the main confusions of the classifications in their research were in these two coverages.…”

“…SAR indices allow us to obtain more information on land cover characteristics. Their application significantly increases the identification and differentiation of land covers, such as oil palm crop areas and natural forests [57,58]. The SAR indices implemented were: division ratio (DR) (1), cross-ratio (CR) (2), difference (DIF) (3), normalized difference bands (NDB) (4), radar vegetation index (RVI) (5), and radar square index (RSI) (6).…”