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.
Highlights
The proposed methodology was designed to fill the following knowledge gaps: i) to determine the spectral changes that have taken place along the riparian reserve by using preprocessed and co-registered multi-date stack time series images built from S-1 Single Look Complex Synthetic Aperture Radar (SAR) data; ii) to explore the capability of the C2 covariance matrix derivatives (i.e. C11, C22 and Radar Vegetation Index (RVI)) to characterize Acacia xanthophloea along Lake Nakuru riparian reserve; iii) to explore the spatio-temporal changes that have occurred on the Acacia strands from 2015 to 2020
Out of the 2491 images detected along the riparian reserve via temporal instances used in the classification, 711 instances were classified profiles as Acacia Forest while 6 instances were confused as class Other
This study demonstrates how Sentinel-1 Single Look Complex (SLC) polarimetric bands σ(VV) and σ(VH) filtered through covariance matrix could improve wetland classification
Summary
Lake Nakuru situated along Kenya’s great Rift valley has been facing challenges due to the rising water levels (Onywere et al, 2013), causing destruction of the National Park’s natural and cultural features. Recent flooding of the lake are hytime of the day or night, penetrating through cloud cover, pothesized to have been caused by additional factors i.e climate producing images of high quality and integrity
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