Few studies have examined post-fire vegetation recovery in temperate forest ecosystems with Landsat time series analysis. We analyzed time series of Normalized Burn Ratio (NBR) derived from LandTrendr spectral-temporal segmentation fitting to examine post-fire NBR recovery for several wildfires that occurred in three different coniferous forest types in western North America during the years 2000 to 2007. We summarized NBR recovery trends, and investigated the influence of burn severity, post-fire climate, and topography on post-fire vegetation recovery via random forest (RF) analysis. NBR recovery across forest types averaged 30 to 44% five years post fire, 47 to 72% ten years post fire, and 54 to 77% 13 years post fire, and varied by time since fire, severity, and forest type. Recovery rates were generally greatest for several years following fire. Recovery in terms of percent NBR was often greater for higher-severity patches. Recovery rates varied between forest types, with conifer−oak−chaparral showing the greatest NBR recovery rates, mixed conifer showing intermediate rates, and ponderosa pine showing slowest rates. Between 1 and 28% of patches had recovered to pre-fire NBR levels 9 to 16 years after fire, with greater percentages of low-severity patches showing full NBR recovery. Precipitation decreased and temperatures generally remained the same or increased post fire. Pre-fire NBR and burn severity were important predictors of NBR recovery for all forest types, and explained 2 to 6% of the variation in post-fire NBR recovery. Post-fire climate anomalies were also important predictors of NBR recovery and explained an additional 30 to 41% of the variation in post-fire NBR recovery. Landsat time series analysis was a useful means of describing and analyzing post-fire vegetation recovery across mixed-severity wildfire extents. We demonstrated that a relationship exists between post-fire vegetation recovery and climate in temperate ecosystems of western North America. Our methods could be applied to other burned landscapes for which spatially explicit measurements of post-fire vegetation recovery are needed.

Wildfire behavior depends on the type, quantity, and condition of fuels, and the effect that bark beetle outbreaks have on fuels is a topic of current research and debate. Remote sensing can provide estimates of fuels across landscapes, although few studies have estimated surface fuels from remote sensing data. Here we predicted and mapped field-measured canopy and surface fuels from light detection and ranging (lidar) and Landsat time series explanatory variables via random forest (RF) modeling across a coniferous montane forest in Colorado, USA, which was affected by mountain pine beetles (Dendroctonus ponderosae Hopkins) approximately six years prior. We examined relationships between mapped fuels and the severity of tree mortality with correlation tests. RF models explained 59%, 48%, 35%, and 70% of the variation in available canopy fuel, canopy bulk density, canopy base height, and canopy height, respectively (percent root-mean-square error (%RMSE) = 12–54%). Surface fuels were predicted less accurately, with models explaining 24%, 28%, 32%, and 30% of the variation in litter and duff, 1 to 100-h, 1000-h, and total surface fuels, respectively (%RMSE = 37–98%). Fuel metrics were negatively correlated with the severity of tree mortality, except canopy base height, which increased with greater tree mortality. Our results showed how bark beetle-caused tree mortality significantly reduced canopy fuels in our study area. We demonstrated that lidar and Landsat time series data contain substantial information about canopy and surface fuels and can be used for large-scale efforts to monitor and map fuel loads for fire behavior modeling at a landscape scale.

Methods to accurately estimate spatially explicit fuel consumption are needed because consumption relates directly to fire behavior, effects, and smoke emissions. Our objective was to quantify sparkleberry (Vaccinium arboretum Marshall) shrub fuels before and after six experimental prescribed fires at Fort Jackson in South Carolina. We used a novel approach to characterize shrubs non-destructively from three-dimensional (3D) point cloud data collected with a terrestrial laser scanner. The point cloud data were reduced to 0.001 m^–3 voxels that were either occupied to indicate fuel presence or empty to indicate fuel absence. The density of occupied voxels was related significantly by a logarithmic function to 3D fuel bulk density samples that were destructively harvested (adjusted R² = .32, P < .0001). Based on our findings, a survey-grade Global Navigation Satellite System may be necessary to accurately associate 3D point cloud data to 3D fuel bulk density measurements destructively collected in small (submeter) shrub plots. A recommendation for future research is to accurately geolocate and quantify the occupied volume of entire shrubs as 3D objects that can be used to train models to map shrub fuel bulk density from point cloud data binned to occupied 3D voxels.

Bark beetle outbreaks have killed large numbers of trees across North America in recent years. Lidar remote sensing can be used to effectively estimate forest biomass, but prediction of both live and dead standing biomass in beetle-affected forests using lidar alone has not been demonstrated. We developed Random Forest (RF) models predicting total, live, dead, and percent dead basal area (BA) from lidar metrics in five different beetle-affected coniferous forests across western North America. Study areas included the Kenai Peninsula of Alaska, southeastern Arizona, north-central Colorado, central Idaho, and central Oregon, U.S.A. We created RF models with and without intensity metrics as predictor variables and investigated how intensity normalization affected RF models in Idaho. RF models predicting total BA explained the most variation, whereas RF models predicting dead BA explained the least variation, with live and percent dead BA models explaining intermediate levels of variation. Important metrics v... Au cours de ces dernières années, l'infestation de scolytes ont tué un grand nombre d'arbres à travers l'Amérique du Nord. La télédétection par lidar (laser) peut être utilisée pour estimer efficacement la biomasse forestière, mais la prévision de la biomasse des pieds vivants et morts dans les forêts infestées en utilisant le lidar seul reste à démontrée. Nous avons développé des Modèles de forêts aléatoires (RF) pour prédire la surface terrière (BA) totale, vivante, et le pourcentage de la surface terrière morte à partir de mesures lidar dans cinq différentes forêts de conifères touchées par les scolytes à travers l'ouest de l'Amérique du Nord. Les zones d’étude comprenaient la péninsule de Kenai en Alaska, l'Arizona du sud, le centre-nord du Colorado, le centre de l'Idaho, et le centre de l'Oregon aux Etats-Unis d'Amérique. Nous avons créé des modèles RF avec et sans mesures d'intensité comme variables prédictives et étudié les effets de la normalisation de l'intensité sur les modèles RF en Idaho. Les ...

Forest insect outbreaks have caused and will continue to cause extensive tree mortality worldwide, affecting ecosystem services provided by forests. Remote sensing is an effective tool for detecting and mapping tree mortality caused by forest insect outbreaks. In this study, we map insect-caused tree mortality across three coniferous forests in the Western United States for the years 1984 to 2018. First, we mapped mortality at the tree level using field observations and high-resolution multispectral imagery collected in 2010, 2011, and 2018. Using these high-resolution maps of tree mortality as reference images, we then classified moderate-resolution Landsat imagery as disturbed or undisturbed and for disturbed pixels, predicted percent tree mortality with random forest (RF) models. The classification approach and RF models were then applied to time series of Landsat imagery generated with Google Earth Engine (GEE) to create annual maps of percent tree mortality. We separated disturbed from undisturbed forest with overall accuracies of 74% to 80%. Cross-validated RF models explained 61% to 68% of the variation in percent tree mortality within disturbed 30-m pixels. Landsat-derived maps of tree mortality were comparable to vector aerial survey data for a variety of insect agents, in terms of spatial patterns of mortality and annual estimates of total mortality area. However, low-level tree mortality was not always detected. We conclude that our methodology has the potential to generate reasonable estimates of annual tree mortality across large extents.

Bark beetles cause significant tree mortality in western North America. The United States Forest Service coordinates annual insect and disease surveys (IDS) by observers in airplanes to map and quantify the tree mortality caused by beetles. The subjective nature of these surveys means that accuracy evaluation is important for characterizing uncertainty. Furthermore, the metric reported for quantifying tree mortality recently changed (2012–2018 depending in region) from killed trees per acre to percent tree mortality within damage polygons, posing challenges for linking older and newer records. Here we evaluated IDS severity estimates in a beetle-affected forest in northern Idaho, USA using fine-resolution satellite imagery, which permitted greater areal coverage than field data. We first used well-established methods to map beetle-caused tree mortality in two WorldView-2 (WV2) images with a high accuracy relative to field observations. Trees-per-acre measurements within collocated IDS polygons were then converted to percent mortality using three methods and evaluated with the WV2 maps. The overall accuracies for the three methods ranged from 35–38% (for methods that used five percent-mortality classes) and 49–56% (three classes). When IDS and WV2 estimates of mortality severity that were within ±15% of each other were considered accurate, overall accuracies were 71–78%. Within the aerial survey damage polygons, the total mortality area tended to be overestimated relative to WV2. WV2 imagery identified ~50% more mortality across the study region compared with the IDS methods, with most of the difference occurring where damage was low severity or in wilderness areas. Severity of Douglas-fir beetle-caused tree mortality was estimated the most accurately, whereas severity of mountain pine beetle-caused tree mortality was estimated the least accurately. Future studies that control for temporal ambiguity between IDS and satellite imagery, as well as IDS spatial error, might provide better assessments of IDS severity accuracy. Our study increases the usefulness of the rich aerial survey database by providing estimates of uncertainty in the IDS database of tree mortality severity.