Detecting Forest Changes Using Dense Landsat 8 and Sentinel-1 Time Series Data in Tropical Seasonal Forests
Detecting Forest Changes Using Dense Landsat 8 and Sentinel-1 Time Series Data in Tropical Seasonal Forests
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DOI：
10.3390/rs11161899
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
Katsuto Shimizu;T. Ota;N. Mizoue
中科院分区：
文献类型：
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作者：
Katsuto Shimizu;T. Ota;N. Mizoue
准确、及时地发现森林扰动可以为有效的森林管理提供有价值的信息。结合光学和合成孔径雷达卫星的密集时间序列观测有可能改善大面积森林监测。对于各种干扰，机器学习算法可以准确地描述森林的变化。然而，特别是在使用机器学习算法通过结合不同数据源的混合方法检测森林干扰方面的知识有限。本研究基于机器学习算法，利用密集的Landsat 8和Sentinel-1时间序列数据检测热带季节性森林的扰动。随机森林算法利用调和回归模型的变量预测Landsat 8和Sentinel-1观测数据的扰动概率，该模型具有季节性和扰动相关变化的特征。然后结合两个传感器的时间序列干扰概率来检测每个像素上的森林干扰。结果表明，Landsat 8与Sentinel-1组合对干扰检测的总体精度为83.6%，高于单独使用Landsat 8（78.3%）或Sentinel-1（75.5%）的干扰检测。此外，结合Landsat 8和Sentinel-1，可以更及时地检测到干扰。采伐引起的小尺度扰动导致了大尺度扰动的遗漏；然而，其他干扰的检测精度相对较高。虽然本研究中仅使用Sentinel-1数据进行干扰检测精度较低，但结合Landsat 8数据提高了检测精度，说明密集的Landsat 8和Sentinel-1时间序列数据对于及时准确检测干扰的价值。
The accurate and timely detection of forest disturbances can provide valuable information for effective forest management. Combining dense time series observations from optical and synthetic aperture radar satellites has the potential to improve large-area forest monitoring. For various disturbances, machine learning algorithms might accurately characterize forest changes. However, there is limited knowledge especially on the use of machine learning algorithms to detect forest disturbances through hybrid approaches that combine different data sources. This study investigated the use of dense Landsat 8 and Sentinel-1 time series data for detecting disturbances in tropical seasonal forests based on a machine learning algorithm. The random forest algorithm was used to predict the disturbance probability of each Landsat 8 and Sentinel-1 observation using variables derived from a harmonic regression model, which characterized seasonality and disturbance-related changes. The time series disturbance probabilities of both sensors were then combined to detect forest disturbances in each pixel. The results showed that the combination of Landsat 8 and Sentinel-1 achieved an overall accuracy of 83.6% for disturbance detection, which was higher than the disturbance detection using only Landsat 8 (78.3%) or Sentinel-1 (75.5%). Additionally, more timely disturbance detection was achieved by combining Landsat 8 and Sentinel-1. Small-scale disturbances caused by logging led to large omissions of disturbances; however, other disturbances were detected with relatively high accuracy. Although disturbance detection using only Sentinel-1 data had low accuracy in this study, the combination with Landsat 8 data improved the accuracy of detection, indicating the value of dense Landsat 8 and Sentinel-1 time series data for timely and accurate disturbance detection.