PDF(Pubmed)
Abstract：
The remarkable adaptability and rapid proliferation of Prosopis juliflora have led to its invasive status in the rangelands of Kenya, detrimentally impacting native vegetation and biodiversity. Exacerbated by human activities such as overgrazing, deforestation, and land degradation, these conditions make the spread and management of this species a critical ecological concern. This study assesses the effectiveness of artificial intelligence (AI) and remote sensing in monitoring the invasion of Prosopis juliflora in Baringo County, Kenya. We investigated the environmental drivers, including weather conditions, land cover, and biophysical attributes, that influence its distinction from native vegetation. By analyzing data on the presence and absence of Prosopis juliflora, coupled with datasets on weather, land cover, and elevation, we identified key factors facilitating its detection. Our findings highlight the Decision Tree/Random Forest classifier as the most effective, achieving a 95% accuracy rate in instance classification. Key variables such as the Normalized Difference Vegetation Index (NDVI) for February, precipitation, land cover type, and elevation were significant in the accurate identification of Prosopis juliflora. Community insights reveal varied perspectives on the impact of Prosopis juliflora, with differing views based on professional experiences with the species. Integrating these technological advancements with local knowledge, this research contributes to developing sustainable management practices tailored to the unique ecological and social challenges posed by this invasive species. Our results highlight the contribution of advanced technologies for environmental management and conservation within rangeland ecosystems.
摘要：
Juliflora的显着适应性和快速增殖导致其在肯尼亚牧场的入侵状态，对原生植被和生物多样性产生不利影响。过度放牧等人类活动加剧，森林砍伐,土地退化,这些条件使得该物种的传播和管理成为一个关键的生态问题。这项研究评估了人工智能（AI）和遥感在监测Baringo县Prosopisjuliflora入侵中的有效性，肯尼亚。我们调查了环境驱动因素，包括天气条件,土地覆盖,和生物物理属性，这影响了它与原生植被的区别。通过分析是否存在Juliflora的数据，加上天气数据集，土地覆盖,和海拔，我们确定了促进其检测的关键因素。我们的发现强调决策树/随机森林分类器是最有效的，实例分类准确率达到95%。关键变量，如2月份归一化植被指数(NDVI)，降水,土地覆盖类型,和海拔高度对朱草的准确鉴定具有重要意义。社区见解揭示了对Prosopisjuliflora影响的不同观点，根据该物种的专业经验，有不同的观点。将这些技术进步与当地知识相结合，这项研究有助于开发针对这种入侵物种带来的独特生态和社会挑战的可持续管理实践。我们的结果强调了先进技术对牧场生态系统中环境管理和保护的贡献。
