Forest Disturbance Monitoring Based on Time Series of Landsat Data

Objective: Considering the character of forest disturbance in China is frequent and complicated, we use the series trajectory analysis method to monitor forest disturbance which can provide references for carbon cycle and carbon accumulation of terrestrial forest ecosystems and climate change research. It is of important significance to the terrestrial carbon cycle and climate change research. Method: We conduct a study in Changting county base on 15 Landsat time-series data from 2000 to 2016 by adopting a time-series trajectory analysis technique(LandTrendr)to identify forest disturbance which apply segment properly and fit linearly a time sequence on the time-series trajectory. The results are verified based on field survey and pixel of interference by using the Google image and global 30 m resolution of GDEM digital elevation products. Result: The total area of forest disturbance is 192.49 km2 and the mean area is 12.83 km2 within the area from 2000 to 2016, of which that in 2001 is the minimum and less than l km2. The most serious disturbance occurred in 2004, 2008 and 2009. The area all over 30 km2 and occupy about 1.3% of forest area in those years. In the three years, the total area represent 50% of the forest disturbed area and the biggest disturbance area is 32.85 km2 in 2004. In 2003, 2006, 2007 and 2010-2011, the disturbance area was slightly larger than 10 km2, but less 0.6% of forest area in this year. It's much less than 10 km2 for remaining years. The area of forest disturbance fluctuates greatly in individual years, but it is decreasing over time. The duration of forest disturbance is mainly about 1 to 3 years and the largest disturbance area occurred in 1 year which up to 82%. Disturbance is mainly concentrated in the east of Changting where is near the non-forest area. It's obvious that disturbance area is declined with the elevation and more than 60% of disturbance occurred at low-middle altitudes. Combined with visual interpretation of Google images, it is shown that the forest disturbance in Changting is mainly caused by forest fires and artificial deforestation, which mainly occurs in low altitude areas near the non-forest areas. Conclusion: The results of field survey and pixel of interference are consistent with the results of monitoring. Disturbance patch can be extract completely and the boundary is accurate and distinct, so is the fine disturbance. Base on pixel scale accuracy verification to monitor forest disturbance, it is showed that the overall accuracy reaches 96.26%, with a Kappa coefficient of 0.92. In all years, the accuracy of users is over 80%., and the accuracy of producers is more than 75% except several years. The results require a high monitoring accuracy, indicating a significant potential of the technique for forest disturbance monitoring. © 2020, Editorial Department of Scientia Silvae Sinicae. All right reserved.