Normalized algorithm for mapping and dating forest disturbances and regrowth for the United States

Forest disturbances such as harvesting, wildfire and insect infestation are critical ecosystem processes affecting the carbon cycle. Because carbon dynamics are related to time since disturbance, forest stand age that can be used as a surrogate for major clear-cut/fire disturbance information has recently been recognized as an important input to forest carbon cycle models for improving prediction accuracy. In this study, forest disturbances in the USA for the period of ∼1990–2000 were mapped using 400+ pairs of re-sampled Landsat TM/ETM scenes in 500m resolution, which were provided by the Landsat Ecosystem Disturbance Adaptive Processing System project. The detected disturbances were then separated into two five-year age groups, facilitated by Forest Inventory and Analysis (FIA) data, which was used to calculate the area of forest regeneration for each county in the USA.In this study, a disturbance index (DI) was defined as the ratio of the short wave-infrared (SWIR, band 5) to near-infrared (NIR, band 4) reflectance. Forest disturbances were identified through the Normalized Difference of Disturbance Index (NDDI) between circa 2000 and 1990, where a positive NDDI means disturbance and a negative NDDI means regrowth. Axis rotation was performed on the plot between DIs of the two matched Landsat scenes in order to reduce any difference of DIs caused by non-disturbance factors. The threshold of NDDI for each TM/ETM pair was determined by analysis of FIA data. Minor disturbances affecting small areas may be omitted due to the coarse resolution of the aggregated Landsat data, but the major stand-clearing disturbances (clear-cut harvest, fire) are captured. The spatial distribution of the detected disturbed areas was validated by Monitoring Trends in Burn Severity fire data in four States of the western USA (Washington, Oregon, Idaho, and California). Results indicate omission errors of 66.9%.An important application of this remote sensing-based disturbance map is to associate with FIA forest age data for developing a US forest age map. The US forest age map was also combined with the Canadian forest age map to produce a continent-wide forest map, which becomes a remarkable data layer for North America carbon cycle modeling.

Research highlights▶ Forest disturbance detection algorithm is hindered by non-disturbance factors. ▶ Triangulation and axis rotation technique for disturbance index normalization. ▶ Forest inventory and analysis data are used to determined threshold automatically.