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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >A Statistical Framework for Near-Real Time Detection of Beetle Infestation in Pine Forests Using MODIS Data
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A Statistical Framework for Near-Real Time Detection of Beetle Infestation in Pine Forests Using MODIS Data

机译：利用MODIS数据近实时检测松林中甲虫侵害的统计框架

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Beetle infestations have caused significant damage to the pine forest in North America. Early detection of beetle infestation in near real time is crucial, in order to take appropriate steps to control the damage. In this letter, we consider near-real-time detection of beetle infestation in North American pine forests using high temporal resolution and coarse spatial resolution MODIS (eight-day 500-m) satellite data. We show that the parameter sequence of a stationary vegetation index time series, which is derived by fitting an underlying triply modulated cosine model over a sliding window using nonlinear least squares, resembles a martingale sequence. The advantage of such properties of the parameter sequence is that standard martingale central limit theorem and well-known Gaussian distribution statistics can be effectively used to detect any nonstationarity in the vegetation index time series with high accuracy. The proposed method exploits these properties of the parameter time series and, hence, does not require threshold tuning. The threshold is selected based on a well-documented procedure of $z$-value selection from the table of Gaussian distribution, depending upon the percentage of the distribution considered as outlier. The proposed framework is tested on different vegetation index data sets derived from MODIS eight-day 500-m image time series of beetle infestations in North America. The results show that the proposed framework can detect nonstationarities in the vegetation index time series accurately and performs the best on red–green index.
机译：甲虫的侵害对北美的松树林造成了严重破坏。为了采取适当措施控制损害，近乎实时地早期发现甲虫感染至关重要。在这封信中，我们考虑使用高时间分辨率和粗略的空间分辨率MODIS（八天500米）卫星数据，对北美松树林中的甲虫侵扰进行近实时检测。我们表明，通过使用非线性最小二乘法在滑动窗口上拟合底层三重调制余弦模型而得出的固定植被指数时间序列的参数序列类似于a序列。参数序列的这种性质的优点在于，可以有效地使用标准mar中心极限定理和众所周知的高斯分布统计量来高精度地检测植被指数时间序列中的任何非平稳性。所提出的方法利用了参数时间序列的这些特性，因此不需要阈值调整。根据高斯分布表中记录的$ z $值选择的充分记录的程序来选择阈值，具体取决于被视为异常值的分布百分比。拟议的框架在不同的植被指数数据集上进行了测试，这些数据集来自北美甲虫侵害的8天500米影像时间序列MODIS。结果表明，所提出的框架可以准确地检测植被指数时间序列中的非平稳性，并且在红绿指数方面表现最佳。

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