Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series
Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series
复制标题
DOI：
10.1016/j.rse.2015.02.012
复制
发表时间：
2015-05-01
影响因子：
13.5
通讯作者：
Herold, Martin
中科院分区：
文献类型：
--
作者：
DeVries, Ben;Verbesselt, Jan;Herold, Martin
遥感数据在监测森林变化方面发挥着重要作用。需要有方法来提供森林损失的客观估计，以支持各种规模的监测工作，随着遥感数据的日益公开，高时间分辨率的准确毁林测量变得更加现实。虽然最近在文献中描述了几种基于时间序列的方法，但很少有研究集中在热带森林地区，那里的数据可用性低和复杂的变化过程对森林干扰监测提出了挑战。在这里，我们提出了一个强大的数据驱动的方法来跟踪热带森林砍伐和退化的陆地卫星时间序列数据的基础上。基于先前报道的附加季节和趋势监测（BFAST监测）方法（Verbesselt等人，2012年），我们表明，BFAST监测，当应用于Landsat NDVI雾凇系列数据使用顺序定义的监测期，可用于跟踪小规模的森林干扰，每年在埃塞俄比亚南部的非洲山地森林系统。使用有序逻辑回归（OLR）的方法，变化幅度，计算的基础上观测值和预期值之间的差异，在监测期间，被认为是一个重要的预测变量的干扰。在应用NDVI变化幅度阈值为-0.065后，总体准确度估计为78%，生产者和用户的干扰类准确度估计为73%。这里介绍的方法和结果与参与REDD +的热带国家有关，因为这些国家的数据可用性和复杂的森林变化动态限制了可靠跟踪长期森林扰动的能力。(C)2015 Elsevier Inc. All rights reserved.
Remote sensing data play an important role in the monitoring of forest changes. Methods are needed to provide objective estimates of forest loss to support monitoring efforts at various scales, and with increasing public availability of remote sensing data, accurate deforestation measurements at high temporal resolution are becoming more realistic. While several time series based methods have recently been described in the literature, there are few studies focusing on tropical forest areas, where low data availability and complex change processes present challenges to forest disturbance monitoring. Here, we present a robust data-driven method to track tropical deforestation and degradation based on Landsat time series data. Based on the previously reported Breaks For Additive Season and Trend Monitor (BFAST Monitor) method (Verbesselt et al, 2012), we show that BFAST Monitor, when applied to Landsat NDVI rime series data using sequentially defined monitoring periods, can be used to track small-scale forest disturbances annually in an Afromontane forest system in southern Ethiopia. Using an ordinal logistic regression (OLR) approach, change magnitude, calculated based on differences between observed and expected values in a monitoring period, was found to be an essential predictor variable for disturbances. After applying a NDVI change magnitude threshold of -0.065, overall accuracy was estimated to be 78%, and both producer's and user's accuracy of the disturbance class were estimated to be 73%. The method and results presented here are relevant to tropical countries engaged in REDD + for whom data availability and complex forest change dynamics limit the ability to reliably track forest disturbances over time. (C) 2015 Elsevier Inc. All rights reserved.