A forest vulnerability index based on drought and high temperatures

Increasing forest stress and tree mortality has been directly linked to combinations of drought and high temperatures. The climatic changes expected during the next decades – large increases in mean temperature, increased heat waves, and significant long-term regional drying in the western USA – will likely increase chronic forest stress and mortality. The aim of this research is to develop and apply a new forest vulnerability index (FVI) associated with drought and high temperatures across the Pacific Northwest region (PNW; Oregon and Washington) of the USA during the MODIS Aqua era (since 2003). Our technique incorporates the alterations to canopy water and energy exchange processes caused by drought and high temperatures with spatially continuous MODIS land surface temperature (LST) and evapotranspiration (ET), and with Parameter-elevation Relationships on Independent Slopes Model (PRISM) precipitation (P) data.With P and ET, we calculate a monthly water balance variable for each individual pixel normalized by forest type group (FTG), and then difference the water balance with the corresponding normalized monthly mean LST to calculate a monthly forest stress index (FSI). We then extract the pixel-specific (800-mresolution) statistically significant temporal trends of the FSI from 2003 to 2012 by month (April to October). The FVI is the slope of the monthly FSI across years, such that there is a FVI for each month. Statistically significant positive slopes indicate interannual increases in stress leading to expected forest vulnerability (positive FVI) for a given month. Positive FVI values were concentrated in the months of August and September, with peak vulnerability occurring at different times for different FTGs. Overall, increased vulnerability rates were the highest in drier FTGs such as Ponderosa Pine, Juniper, and Lodgepole Pine. Western Larch and Fir/Spruce/Mountain Hemlock groups occupy moister sites but also had relatively high proportion of positive FVI values. The Douglas-fir group had the second largest total area of increased vulnerability due to its large areal extent in the study area. Based on an analysis using imagery viewed in Google Earth, we confirm that areas with increased vulnerability are associated with greater amounts of stress and mortality. The FVI is a new way to conceptualize and monitor forest vulnerability based on first-order principles and has the potential to be generalized to other geographical areas

Geographic variability in lidar predictions of forest stand structure in the Pacific Northwest

Estimation of the amount of carbon stored in forests is a key challenge for understanding the global carbon cycle, one which remote sensing is expected to help address. However, carbon storage in moderate to high biomass forests is difficult to estimate with conventional optical or radar sensors. Lidar (light detection and ranging) instruments measure the vertical structure of forests and thus hold great promise for remotely sensing the quantity and spatial organization of forest biomass. In this study, we compare the relationships between lidar measured canopy structure and coincident field measurements of forest stand structure at five locations in the Pacific Northwest of the U.S.A. with contrasting composition. Coefficient of determination values (r2) ranged between 41% and 96%. Correlations for two important variables, LAI (81%) and above ground biomass (92%), were noteworthy, as was the fact that neither variable showed an asymptotic response. Of the 17 stand structure variables considered in this study, we were able to develop eight equations that were valid for all sites, including equations for two variables generally considered to be highly important (aboveground biomass and leaf area index). The other six equations that were valid for all sites were either related to height (which is most directly measured by lidar) or diameter at breast height (which should be closely related to height). Four additional equations (a total of 12) were applicable to all sites where either Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla) or Sitka spruce (Picea sitchensi) were dominant. Stand structure variables in sites dominated by true firs (Abies sp.) or ponderosa pine (Pinus ponderosa) had biases when predicted by these four additional equations. Productivity-related variables describing the edaphic, climatic and topographic environment of the sites where available for every regression, but only two of the 17 equations (maximum diameter at breast height, stem density) incorporated them. Given the wide range of these environmental conditions sampled, we conclude that the prediction of stand structure is largely independent of environmental conditions in this study area. Most studies of lidar remote sensing for predicting stand structure have depended on intensive data collections within a relatively small study area. This study indicates that the relationships between many stand structure indices and lidar measured canopy structure have generality at the regional scale. This finding, if replicated in other regions, would suggest that mapping of stand structure using lidar may be accomplished by distributing field sites extensively over a region, thus reducing the overall inventory effort required

Baryon Number Violating Transitions in String Backgrounds

We construct field configurations that interpolate between string background states of differing baryon number. Using these configurations we estimate the effect of the background fields on the energy barrier separating different vacua. In the background of a superconducting GUT string the energy barrier is increased, while in an electroweak string background or the electroweak layer of a non-superconducting string the energy barrier is reduced. The energy barrier depends sensitively on both the background gauge and scalar fields.Comment: 27 pages. Texing problems fixe

Comparing statistical techniques to classify the structure of mountain forest stands using CHM-derived metrics in Trento province (Italy)

In some cases a canopy height model (CHM) is the only available source of forest height information. For these cases it is important to understand the predictive power of CHM data for forest attributes. In this study we examined the use of lidar-derived CHM metrics to predict forest structure classes according to the amount of basal area present in understory, midstory, and overstory trees. We evaluated two approaches to predict sizebased forest classifications: in the first, we attempted supervised classification with both linear discriminant analysis (LDA) and random forest (RF); in the second, we predicted basal areas of lower, mid, and upper canopy trees from CHM-derived variables by k-nearest neighbour imputation (k-NN) and parametric regression, and then classified observations based on their predicted basal areas. We used leave-one-out cross-validation to evaluate our ability to predict forest structure classes from CHM data and in the case of prediction-based classification approach we look at the performances in predicting basal area. The strategies proved moderately successful with a best overall classification accuracy of 41% in the case of LDA. In general, we were most successful in predicting the basal areas of small and large trees (R 2 respectively of 71% and 69% in the case of k-NN imputation)

Thermal Anomalies Detect Critical Global Land Surface Changes

Measurements that link surface conditions and climate can provide critical information on important biospheric changes occurring in the Earth system. As the direct driving force of energy and water fluxes at the surface–atmosphere interface, land surface temperature (LST) provides information on physical processes of land-cover change and energy-balance changes that air temperature cannot provide. Annual maximum LST (LSTmax) is especially powerful at minimizing synoptic and seasonal variability and highlighting changes associated with extreme climatic events and significant land-cover changes. The authors investigate whether maximum thermal anomalies from satellite observations could detect heat waves and droughts, a melting cryosphere, and disturbances in the tropical forest from 2003 to 2014. The 1-km2 LSTmax anomalies peaked in 2010 when 20% of the global land area experienced anomalies of greater than 1 standard deviation and over 4% of the global land area was subject to positive anomalies exceeding 2 standard deviations. Positive LSTmax anomalies display complex spatial patterns associated with heat waves and droughts across the global land area. The findings presented herein show that entire biomes are experiencing shifts in their LSTmax distributions driven by extreme climatic events and large-scale land surface changes, such as melting of ice sheets, severe droughts, and the incremental effects of forest loss in tropical forests. As climate warming and land-cover changes continue, it is likely that Earth’s maximum surface temperatures will experience greater and more frequent directional shifts, increasing the possibility that critical thresholds in Earth’s ecosystems and climate system will be surpassed, resulting in profound and irreversible changes

Estimating forest aboveground biomass by low density lidar data in mixed broad-leaved forests in the Italian Pre-Alps

Background: Estimation of forest biomass on the regional and global scale is of great importance. Many studies have demonstrated that lidar is an accurate tool for estimating forest aboveground biomass. However, results vary with forest types, terrain conditions and the quality of the lidar data. Methods: In this study, we investigated the utility of low density lidar data (<2 points∙m−2) for estimating forest aboveground biomass in the mountainous forests of northern Italy. As a study site we selected a 4 km2 area in the Valsassina mountains in Lombardy Region. The site is characterized by mixed and broad-leaved forests with variable stand densities and tree species compositions, being representative for the entire Pre-Alps region in terms of type of forest and geomorphology. We measured and determined tree height, DBH and tree species for 27 randomly located circular plots (radius =10 m) in May 2008. We used allometric equations to calculate total aboveground tree biomass and subsequently plot-level aboveground biomass (mg∙ha−1). Lidar data were collected in June 2004. Results: Our results indicate that low density lidar data can be used to estimate forest aboveground biomass with acceptable accuracies. The best height results show a R2 = 0.87 from final model and the root mean square error (RMSE) 1.02 m (8.3% of the mean). The best biomass model explained 59% of the variance in the field biomass. Leave-one-out cross validation yielded an RMSE of 30.6 mg∙ha−1 (20.9% of the mean). Conclusions: Low-density lidar data can be used to develop a forest aboveground biomass model from plot-level lidar height measurements with acceptable accuracies. In order to monitoring the National Forest Inventory, and respond to Kyoto protocol requirements, this analysis might be applied to a larger area. Keywords: LiDAR; Allometric equations; Plant height; Mixed fores

Automated cloud and cloud shadow identification in Landsat MSS imagery for temperate ecosystems

Automated cloud and cloud shadow identification algorithms designed for Landsat Thematic Mapper (TM) and Thematic Mapper Plus (ETM+) satellite images have greatly expanded the use of these Earth observation data by providing a means of including only clear-view pixels in image analysis and efficient cloud-free compositing. In an effort to extend these capabilities to Landsat Multispectal Scanner (MSS) imagery, we introduce MSS clear-view-mask (MSScvm), an automated cloud and shadow identification algorithm for MSS imagery. The algorithm is specific to the unique spectral characteristics of MSS data, relying on a simple, rule-based approach. Clouds are identified based on green band brightness and the normalized difference between the green and red bands, while cloud shadows are identified by near infrared band darkness and cloud projection. A digital elevation model is incorporated to correct for topography-induced illumination variation and aid in identifying water. Based on an accuracy assessment of 1981 points stratified by land cover and algorithm mask class for 12 images throughout the United States, MSScvm achieved an overall accuracy of 84.0%. Omission of thin clouds and bright cloud shadows constituted much of the error. Perennial ice and snow, misidentified as cloud, also contributed disproportionally to algorithm error. Comparison against a corresponding assessment of the Fmask algorithm, applied to coincident TM imagery, showed similar error patterns and a general reduction in accuracy commensurate with differences in the radiometric and spectral richness of the two sensors. MSScvm provides a suitable automated method for creating cloud and cloud shadow masks for MSS imagery required for time series analyses in temperate ecosystems.Keywords: Time series analysis, Landsat MSS, Automated cloud masking, Large area mapping, Change detectio

Detecting landscape changes in the interior of British Columbia from 1975 to 1992 using satellite imagery

To consider the regional scale effects of forest management requires complete and consistent data over large areas. We used Landsat Thematic Mapper and Multispectral Scanner (TM and MSS) imagery to map forest cover and detect major disturbances between 1975 and 1992 for a 4.2 x 106 ha area of interior British Columbia. Forested pixels were mapped into closed conifer, semiopen conifer, deciduous, and mixed forest classes, with further subdivision of the closed conifer type into three age-classes. The image-based estimate of harvested area was similar to an independent estimate from forest inventory data. Changes in landscape pattern from 1975 to 1992 were examined by calculating indices that describe overall landscape pattern and that of conifer and harvested patches in each biogeoclimatic zone. Harvesting affected 8.4% of the forest area outside provincial parks during the 17-year period. Harvested areas were consistently much smaller than conifer patches in all biogeoclimatic zones and had a lower percentage of interior area and perimeter/area ratio. Conifer patch-shape complexity varied between zones; harvested patches had simpler shapes and were similar in all zones. Results indicate that this landscape is only in the early stages of fragmentation, but a similar harvest pattern has been imposed on differing ecological zones.Keywords: land cover, forest harvesting, remote imagery, forest management, landscape pattern, patch complexit

A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests

Insects are important forest disturbance agents, and mapping their effects on tree mortality and surface fuels represents a critical research challenge. Although various remote sensing approaches have been developed to monitor insect impacts, most studies have focused on single insect agents or single locations and have not related observed changes to ground-based measurements. This study presents a remote sensing framework to (1) characterize spectral trajectories associated with insect activity of varying duration and severity and (2) relate those trajectories to ground-based measurements of tree mortality and surface fuels in the Cascade Range, Oregon, USA. We leverage a Landsat time series change detection algorithm (LandTrendr), annual forest health aerial detection surveys (ADS), and field measurements to investigate two study landscapes broadly applicable to conifer forests and dominant insect agents of western North America. We distributed 38 plots across multiple forest types (ranging from mesic mixed-conifer to xeric lodgepole pine) and insect agents (defoliator [western spruce budworm] and bark beetle [mountain pine beetle]). Insect effects were evident in the Landsat time series as combinations of both short- and long-duration changes in the Normalized Burn Ratio spectral index. Western spruce budworm trajectories appeared to show a consistent temporal evolution of long-duration spectral decline (loss of vegetation) followed by recovery, whereas mountain pine beetle plots exhibited both short- and long-duration spectral declines and variable recovery rates. Although temporally variable, insect-affected stands generally conformed to four spectral trajectories: short-duration decline then recovery, short- then long-duration decline, long-duration decline, long-duration decline then recovery. When comparing remote sensing data with field measurements of insect impacts, we found that spectral changes were related to cover-based estimates (tree basal area mortality R(adj)(2);= 0.40, F(1.34) = 24.76, P<0.0001] and down coarse woody detritus [R(adj)(2) = 0.29, F(1.32) = 14.72. P = 0.0006]). In contrast, ADS changes were related to count-based estimates (e.g., ADS mortality from mountain pine beetle positively correlated with ground-based counts [R(adj)(2) = 037, F(1.22) = 14.71, P= 0.0009]). Fine woody detritus and forest floor depth were not well correlated with Landsat- or aerial survey-based change metrics. By characterizing several distinct temporal manifestations of insect activity in conifer forests, this study demonstrates the utility of insect mapping methods that capture a wide range of spectral trajectories. This study also confirms the key role that satellite imagery can play in understanding the interactions among insects, fuels, and wildfire. (C) 2011 Elsevier Inc. All rights reserved.Keywords: Mountain pine beetle, Spectral trajectory, Western spruce budworm, Landsat time series, Defoliator, Pacific Northwest, Bark beetle, Fuel, Change detection, Insect disturbance, Fir

United States Forest Disturbance Trends Observed Using Landsat Time Series

Disturbance events strongly affect the composition, structure, and function of forest ecosystems; however, existing U.S. land management inventories were not designed to monitor disturbance. To begin addressing this gap, the North American Forest Dynamics (NAFD) project has examined a geographic sample of 50 Landsat satellite image time series to assess trends in forest disturbance across the conterminous United States for 1985-2005. The geographic sample design used a probability-based scheme to encompass major forest types and maximize geographic dispersion. For each sample location disturbance was identified in the Landsat series using the Vegetation Change Tracker (VCT) algorithm. The NAFD analysis indicates that, on average, 2.77 Mha/yr of forests were disturbed annually, representing 1.09%/yr of US forestland. These satellite-based national disturbance rates estimates tend to be lower than those derived from land management inventories, reflecting both methodological and definitional differences. In particular the VCT approach used with a biennial time step has limited sensitivity to low-intensity disturbances. Unlike prior satellite studies, our biennial forest disturbance rates vary by nearly a factor of two between high and low years. High western US disturbance rates were associated with active fire years and insect activity, while variability in the east is more strongly related to harvest rates in managed forests. We note that generating a geographic sample based on representing forest type and variability may be problematic since the spatial pattern of disturbance does not necessarily correlate with forest type. We also find that the prevalence of diffuse, non-stand clearing disturbance in US forests makes the application of a biennial geographic sample problematic. Future satellite-based studies of disturbance at regional and national scales should focus on wall-to-wall analyses with annual time step for improved accuracy