The phytosanitary status of Tectona grandis plantations are monitored conventionally with periodic data collection in the field, which is often costly and has low efficiency. The objective of this research was to develop a methodology to predict the canopy cover of T. grandis plantations using multispectral images of the Sentinel-2 (S2) satellite and photographic imagery. The study was carried out in a T. grandis plantation of seminal origin, in Cáceres, Mato Grosso state, Brazil. Hemispherical photographic (HP) images of the plant canopy were obtained with a digital camera coupled to a “fisheye” lens fixed at 1.3 m high at two dates in the rainy and the dry season. Cloudless and no shadow images of the S2 satellite bands were concurrently obtained with the field images. Multivariate permutative analysis of variance (PERMANOVA) and partial least squares regression (PLSR) were used to predict canopy cover percentage. The accuracy of the predicted T. grandis canopy cover (%) by the PLSR model approach was 77.8 ± 0.09%. The results indicate that a PLS model calibrated with 28 HP sample images can accurately estimate the percentage canopy cover for a continuous area of T. grandis plantations and facilitate mapping of canopy heterogeneity to monitor threats of diseases, mortality, fires, pests and other disturbances.

中文翻译：

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Tectona Grandis 冠层盖度遥感预测

Tectona grandis 人工林的植物检疫状态通过在田间定期收集数据进行常规监测，这通常成本高昂且效率低下。本研究的目的是开发一种方法，使用 Sentinel-2 (S2) 卫星和摄影图像的多光谱图像来预测 T. grandis 种植园的冠层覆盖。该研究是在巴西马托格罗索州卡塞雷斯的一个精髓种植园中进行的。植物冠层的半球摄影 (HP) 图像是在雨季和旱季的两个日期使用数码相机与固定在 1.3 m 高的“鱼眼镜头”镜头相结合获得的。S2卫星波段的无云和无阴影图像与现场图像同时获得。多元置换方差分析 (PERMANOVA) 和偏最小二乘回归 (PLSR) 用于预测冠层覆盖百分比。通过 PLSR 模型方法预测的 T. grandis 冠层覆盖率 (%) 的准确性为 77.8 ± 0.09%。结果表明，使用 28 个 HP 样本图像校准的 PLS 模型可以准确估计连续区域的林冠覆盖率，并有助于绘制冠层异质性以监测疾病、死亡率、火灾、害虫和其他干扰的威胁。