With earth's surface temperature and human population both on the rise a new emphasis has been placed on monitoring changes to forested ecosystems the world over. In the United States the U.S. Forest Service Forest Inventory and Analysis (FIA) program monitors the forested land base with field data collected over a permanent network of sample plots. Although these plots are visited repeatedly through time there are large temporal gaps (e.g. 5-10 years) between remeasurements such that many forest canopy disturbances go undetected. In this paper we demonstrate how Landsat time series (LTS) can help improve FIA's capacity to estimate disturbance by 1.) incorporating a new, downward looking response variable which is more sensitive to picking up change and 2.) providing historical disturbance maps which can reduce the variance of design-based estimates via post-stratification. To develop the LTS response variable a trained analyst was used to manually interpret 449 forested HA plots located in the Uinta Mountains of northern Utah, USA. This involved recording cause and timing of disturbances based on evidence gathered from a 26-year annual stack of Landsat images and an 18-year, periodically spaced set of high resolution (similar to 1 m) aerial photographs (e.g. National Aerial Image Program, NAIP and Google Earth). In general, the Landsat data captured major disturbances (e.g. harvests, fires) while the air photos allowed more detailed estimates of the number of trees impacted by recent insect outbreaks. Comparing the LTS and FIA field observations, we found that overall agreement was 73%, although when only disturbed plots were considered agreement dropped to 40%. Using the non-parametric Mann-Whitney test, we compared distributions of live and disturbed tree size (height and DBH) and found that when LTS and FIA both found non-stand clearing disturbance the median disturbed tree size was significantly larger than undisturbed trees, whereas no significant difference was found on plots where only FIA detected disturbance. This suggests that LTS interpretation and FIA field crews both detect upper canopy disturbances while RA crews alone add disturbances occurring at or below canopy level. The analysis also showed that plots with only LTS disturbance had a significantly greater median number of years since last FIA measurement (6 years) than plots with both FIA and LTS disturbances (2.5 years), indicating that LTS improved detection on plots which had not been field sampled for several years. Next, to gauge the impact of incorporating LTS disturbances into the FIA estimation process we calculated design-based estimates of disturbance (for the period 1995-2011) using three response populations 1.) LTS observations, 2.) FIA field observations, and 3.) Combination of FIA and LTS observations. The results showed that combining the FIA and LTS observations led to the largest and most precise (i.e. smallest percent standard error) estimates of disturbance. In fact, the estimate based on the combined observations (486,458 ha, +/-47,101) was approximately 65% more than the estimate derived solely with FIA data (294,295 ha, +/-44,242). Lastly, a Landsat forest disturbance map was developed and tested for its ability to post-stratify the design-based estimates. Based on relative efficiency (RE), we found that stratification mostly improved the estimates derived with the LTS response data. Aside from insects (RE = 1. 26), the estimates of area affected by individual agents saw minimal gain, whereas the LTS and combined FIA + LTS estimates of total disturbance saw modest improvement, with REs of 1.43 and 1.50 respectively. Overall, our results successfully demonstrate two ways LTS can improve the completeness and precision of disturbance estimates derived from FIA inventory data. Published by Elsevier Inc.
