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Open AccessArticle

Detecting Forest Changes Using Dense Landsat 8 and Sentinel-1 Time Series Data in Tropical Seasonal Forests

1
Faculty of Agriculture, Kyushu University, 744 Motooka, Fukuoka 819-0395, Japan
2
Research Fellow of Japan Society for the Promotion of Science, Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan
3
Institute of Decision Science for a Sustainable Society, Kyushu University, 744 Motooka, Fukuoka 819-0395, Japan
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(16), 1899; https://doi.org/10.3390/rs11161899
Received: 1 July 2019 / Revised: 31 July 2019 / Accepted: 12 August 2019 / Published: 14 August 2019
(This article belongs to the Section Forest Remote Sensing)
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Abstract

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. View Full-Text
Keywords: SAR; time series; fusion; PlanetScope; Google Earth Engine; CCDC; probability SAR; time series; fusion; PlanetScope; Google Earth Engine; CCDC; probability
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Shimizu, K.; Ota, T.; Mizoue, N. Detecting Forest Changes Using Dense Landsat 8 and Sentinel-1 Time Series Data in Tropical Seasonal Forests. Remote Sens. 2019, 11, 1899.

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