Object-Based Change Detection on Acacia Xanthophloea Species Degradation Along Lake Nakuru Riparian Reserve

Abstract

Abstract. Automated mapping of heterogeneous riparian landscape is of high interest to assess our planet. Still, it remains a challenging task due to the occurrence of flooded vegetation. While both optical and radar images can be exploited, the latter has the advantage of being independent acquisition conditions. However, and despite their popularity, the threshold-based approaches commonly used present some drawbacks such as not taking into account the spatial context and providing mixed pixels within class boundaries. In this study, we propose a novel methodology to avoid such issues by using an object-based image analysis approach on polarimetric radar data. We use our workflow to map the degrading Acacia x. species along lake Nakuru Riparian reserve, and obtain highly-accurate results.

Forest image classification based on deep learning and ontologies.

“…A study by Osio et al (2018) uses OBIA-based monitoring of Riparian vegetation to assess the effect of flooding on the Lake Nakara Riparian Reserve vegetation species. An OBIA methodology was proposed (Osio et al 2018) to serve as the basis for the classification of Riparian vegetation. The methodology comprised four pillars: data capture, preprocessing, processing, and analysis.…”

“…Segmentation scales(Osio et al 2018). , and detection are the three key processes in edge detection(Jain et al 1995).…”