荆棘(建筑)
多光谱图像
黑荆
天蓬
阿拉伯树胶
农林复合经营
森林健康
Rust(编程语言)
林业
遥感
地理
环境科学
生物
生态学
计算机科学
程序设计语言
考古
作者
Kabir Peerbhay,Ilaria Germishuizen,Romano Lottering,Rowan Naicker
标识
DOI:10.1080/01431161.2022.2058891
摘要
The detection and monitoring of lethal pathogenic funguses are important for effectively deploying suppressive measures to sudden and severe outbreaks in plantation forestry. This study successfully investigated the utility of Landsat 8 multispectral satellite imagery to map Uromycladium acacia (wattle rust) induced canopy defoliation across black wattle plantations. The framework developed for the provincial assessment of rust damage proved to be effective by using data collected from field monitoring plots over the year 2015 and 2016. Using a powerful Gradient Boosting Machine (GBM) approach, rust occurrences were mapped at accuracies of 69% for March and 72% for November when using the 2015 dataset and 77% for March and 81% for November using the 2016 dataset. Individual class accuracies for varying levels of defoliation were also evaluated. When aggregating the field and image datasets, a two-year probability map revealed the likelihood of rust defoliation across the black wattle plantation region. Overall, the study showcased the robustness and cost-effectiveness of using multispectral remote sensing methodologies for repeatable forest health monitoring in key commercial forest plantations.
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