NASA Develops Early Warning System for Detecting Forest Disturbances

To develop a near-real-time system for detecting and monitoring vegetation changes across the 48 contiguous states, NASA engineers needed to analyze multispectral images captured by the Aqua and Terra satellites, build multi-timescale baselines of the gathered data, and then compare incoming data with the baseline to identify anomalies.

每一天,MODIS数据属ted for 14 gridded tiles that span the 48 states, with each tile comprising 4800 x 4800 pixels. Twice a year the system processes 15 to 25 terabytes of data when updating the yearly baselines used to identify changes in vegetation.

NASA engineers needed to process and analyze large time-series data sets of images stored as mutidimensional arrays. They needed a development environment for interactively developing and rapidly evaluating new analysis algorithms. After demonstrating their algorithms, they wanted to deploy them into production without recoding.

美国宇航局工程师使用MATLAB开发FORWARN的两个关键组件：时间序列产品工具（TSPT），其在时间上处理MODIS数据，以及使用处理的数据来计算绿色级别和鉴别的鉴率参数估计工具（PPET）来计算绿色级别day of year for the peak of the growing season and for other phenological parameters of interest to the U.S. Forest Service.

对于TSPT，NASA的团队写了一个MATLAB脚本，它将在NASA存档中的分层数据格式（HDF）文件中的MATLAB数据检索，并将数据导入MATLAB。TSPT调用映射工具箱™功能，将导入的纬度和经度数据转换为投影地图坐标系。

The TSPT Band Processing module, also developed in MATLAB, generates the normalized difference vegetation index (NDVI) from the time-series data, as well as soil, moisture, water, and other indices.

在MATLAB工作，该团队开发了TSPT的算法，以消除云，阴影，视图Zenith角度和其他效果扭曲的像素。

After merging data from the Aqua and Terra satellites into a single time series, TSPT uses Signal Processing Toolbox™ functions to identify and remove spikes and other outlying data points.

一旦TSPT算法删除了异常值，它们就会使用优化工具箱™和图像处理工具箱™进一步过滤并重新采样时间序列。TSPT算法将Savitzky-Golay滤波器从信号处理工具箱应用以插入任何缺失像素的值。

The engineers used MATLAB, Optimization Toolbox, and Image Processing Toolbox to develop PPET, which performs curve fitting on the time-series data to identify vegetative states related to annual cyclical growing seasons, such as green-up, maturity, senescence, and dormancy. They later enhanced PPET to detect forest disturbances by identifying differences between daily satellite data and time-series baselines.

Using MATLAB Compiler™, the team created standalone executable versions of their MATLAB based algorithms, which can be run by users who do not have MATLAB installed.

The ForWarn team has won multiple awards for their efforts, including the Interagency Partnership Award, which recognizes federal employees from at least two different agencies who have “collaboratively accomplished outstanding work in transferring a technology.” The ForWarn team consists of the U.S. Forest Service, NASA, DOE Oak Ridge National Lab, U.S. Geological Survey, and University of North Carolina at Asheville.

• 在几小时内实施和测试的新想法。“Matlab中内置了信号和图像处理算法，因此我不必从头开始写入它们，”洛克希德马丁的计算机工程师Jerry Gasser说。“而不是写作和调试数百行的C代码来测试一个新的算法的想法，我使用MATLAB命令以交互方式验证，然后写一个脚本以测试多个参数值。我在几分钟或几小时内得到结果，而几个月或几周。“
• 多年的开发时间挽救了。“Matlab与多维数据的方式的方式非常适合分析卫星图像，”阿姆斯特朗说。“如果我们使用了传统的编程语言，如C，那将使我们更长时间。”
• 可重复使用的生产软件送到生长的用户社区。“Forwarn是美国大陆美国第一个近乎实时的森林威胁预警系统，”备注Armstrong。“其日益增长的用户基础包括超过7000林业专家，学生和土地资源经理。我们还在为其他客户提供Matlab开发的算法和模块，与世界各地的合作伙伴合作。“