2 research outputs found
Mapping Opuntia stricta in the arid and semi-arid environment of Kenya using sentinel-2 imagery and ensemble machine learning classifiers
Globally, grassland biomes form one of the largest terrestrial covers and present critical social–ecological benefits. In Kenya, Arid and Semi-arid Lands (ASAL) occupy 80% of the landscape and are critical for the livelihoods of millions of pastoralists. However, they have been invaded by Invasive Plant Species (IPS) thereby compromising their ecosystem functionality. Opuntia stricta, a well-known IPS, has invaded the ASAL in Kenya and poses a threat to pastoralism, leading to livestock mortality and land degradation. Thus, identification and detailed estimation of its cover is essential for drawing an effective management strategy. The study aimed at utilizing the Sentinel-2 multispectral sensor to detect Opuntia stricta in a heterogeneous ASAL in Laikipia County, using ensemble machine learning classifiers. To illustrate the potential of Sentinel-2, the detection of Opuntia stricta was based on only the spectral bands as well as in combination with vegetation and topographic indices using Extreme Gradient Boost (XGBoost) and Random Forest (RF) classifiers to detect the abundance. Study results showed that the overall accuracies of Sentinel 2 spectral bands were 80% and 84.4%, while that of combined spectral bands, vegetation, and topographic indices was 89.2% and 92.4% for XGBoost and RF classifiers, respectively. The inclusion of topographic indices that enhance characterization of biological processes, and vegetation indices that minimize the influence of soil and the effects of atmosphere, contributed by improving the accuracy of the classification. Qualitatively, Opuntia stricta spatially was found along river banks, flood plains, and near settlements but limited in forested areas. Our results demonstrated the potential of Sentinel-2 multispectral sensors to effectively detect and map Opuntia stricta in a complex heterogeneous ASAL, which can support conservation and rangeland management policies that aim to map and list threatened areas, and conserve the biodiversity and productivity of rangeland ecosystems
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Understanding the spatiotemporal heterogeneity in grassland dynamics in Kenya’s semi-arid pastures
Grassland biomes are one of the largest terrestrial ecosystems on the planet, providing critical ecological, social, and economic benefits. However, they are subjected to natural and anthropogenic stresses such as precipitation, temperature variability, and widespread land degradation. Invasive plant species, for example, pose enormous challenges regarding biodiversity loss and degradation. Therefore, we need to keep up with and improve our knowledge of how they change, especially over space and time, to make good decisions about their productivity, management, and conservation.
However, questions remain in (i) mapping and invasion science regarding a methodological framework for mapping invasive plant species (Opuntia stricta) using satellite remote sensing, (ii) understanding the spatiotemporal relationship between grassland greenness, communities, precipitation, temperature, and grazing factors, and (iii) our understanding of the spatial variation in grassland community types and their palatability probability. As a result, this study aimed to better understand the spatiotemporal dynamics in Kenya’s heterogeneous semi-arid grasslands by characterising the grassland into grassland communities and palatable and non-palatable plants. Additionally, it evaluates the intra-seasonal drivers of grassland changes at a site-specific level in 2019.
The results show that combining Sentinel-2 spectral data, vegetation, and topographic indices is sufficient to map Opuntia stricta in a complex, heterogeneous semi-arid landscape. Additionally, precipitation, temperature and grazing, though at different times, are the major drivers of intra-seasonal grassland dynamics in semi-arid areas. Furthermore, the study found Sentinel-2 imagery to be adequate in achieving fine-scale spatial variations in grassland communities and inferring palatability probability in heterogeneous semi-arid grasslands. Finally, these findings and recommendations can help us better understand grassland dynamics and uncertainty modelling, as well as improve our understanding of plant-animal interactions, which can lead to management implications for rangeland management in terms of productivity, conservation, and rehabilitation