在线森林砍伐检测

摘要：

利用卫星图像探测森林砍伐情况可对森林管理作出重要贡献。目前的方法可以大致分为那些比较在一年中的类似时期拍摄的两张图像和那些通过使用在生长季节拍摄的多张图像来监测变化的方法。Zhu等人（2012）中描述的CMFDA算法是一种基于后一类的算法，通过实施长达一年的连续时间序列方法来监测图像。该算法是为30米分辨率，16天的频率反射率数据从陆地卫星。在这项工作中，我们适应的算法，1公里，16天的频率反射率数据的modis传感器搭载的Terra卫星。CMFDA算法由两个子模型组成，这两个子模型是在逐像素的基础上拟合的。第一个估计的表面反射率的数量作为一年中的一天的函数。第二种方法通过比较最后几个预测值和实际反射率值来估计森林砍伐事件的发生。为了进行比较，首先将六个不同波段的反射率观测值组合成森林指数。然后将森林指数的实际值和预测值进行比较，并将连续观测日期的高绝对差异标记为毁林事件。我们的适应算法也使用两个模型框架。然而，由于所使用的modis 13A2数据集包括与Landsat数据集不同的光谱带的反射率数据，因此我们无法构建森林指数。相反，我们提出了两个对比的方法：一个多元和一个指数的方法类似于CMFDA。

英文摘要：

Deforestation detection using satellite images can make an important contribution to forest management. Current approaches can be broadly divided into those that compare two images taken at similar periods of the year and those that monitor changes by using multiple images taken during the growing season. The CMFDA algorithm described in Zhu et al. (2012) is an algorithm that builds on the latter category by implementing a year-long, continuous, time-series based approach to monitoring images. This algorithm was developed for 30m resolution, 16-day frequency reflectance data from the Landsat satellite. In this work we adapt the algorithm to 1km, 16-day frequency reflectance data from the modis sensor aboard the Terra satellite. The CMFDA algorithm is composed of two submodels which are fitted on a pixel-by-pixel basis. The first estimates the amount of surface reflectance as a function of the day of the year. The second estimates the occurrence of a deforestation event by comparing the last few predicted and real reflectance values. For this comparison, the reflectance observations for six different bands are first combined into a forest index. Real and predicted values of the forest index are then compared and high absolute differences for consecutive observation dates are flagged as deforestation events. Our adapted algorithm also uses the two model framework. However, since the modis 13A2 dataset used, includes reflectance data for different spectral bands than those included in the Landsat dataset, we cannot construct the forest index. Instead we propose two contrasting approaches: a multivariate and an index approach similar to that of CMFDA.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。