Red needle cast (RNC), mainly caused by Phytophthora pluvialis, is a very damaging disease of the widely grown species radiata pine within New Zealand. Using a combination of satellite imagery and weather data, a novel methodology was developed to pre-visually predict the incidence of RNC on radiata pine within the Gisborne region of New Zealand over a five-year period from 2019 to 2023. Sentinel-2 satellite imagery was used to classify areas within the region as being disease-free or showing RNC expression from the difference in the red/green index (R/Gdiff) during a disease-free time of the year and the time of maximum disease expression in the upper canopy (early spring–September). Within these two classes, 1976 plots were extracted, and a classification model was used to predict disease incidence from mean monthly weather data for key variables during the 11 months prior to disease expression. The variables in the final random forest model included solar radiation, relative humidity, rainfall, and the maximum air temperature recorded during mid–late summer, which provided a pre-visual prediction of the disease 7–8 months before its peak expression. Using a hold-out test dataset, the final random forest model had an accuracy of 89% and an F1 score of 0.89. This approach can be used to mitigate the impact of RNC by focusing on early surveillance and treatment measures.

中文翻译：

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利用气候数据趋势和卫星观测对区域性红针铸件爆发进行早期预测

红针腐病 (RNC) 主要由雨生疫霉引起，是新西兰境内广泛种植的辐射松树种的一种极具破坏性的疾病。结合卫星图像和天气数据，开发了一种新方法，可以预先直观地预测 2019 年至 2023 年五年期间新西兰吉斯伯恩地区辐射松 RNC 的发生率。Sentinel-2 卫星图像用于根据一年中无病时间期间的红/绿指数 (R/Gdiff) 和上部疾病表达最大时间的差异，将该区域内的区域分类为无病或显示 RNC 表达树冠（早春至九月）。在这两类中，提取了 1976 个图，并使用分类模型根据疾病出现前 11 个月内关键变量的月平均天气数据来预测疾病发病率。最终随机森林模型中的变量包括太阳辐射、相对湿度、降雨量和夏季中后期记录的最高气温，这为疾病在发病高峰前7-8个月提供了预先视觉预测。使用保留测试数据集，最终的随机森林模型的准确度为 89%，F1 得分为 0.89。这种方法可通过注重早期监测和治疗措施来减轻 RNC 的影响。