1,626 research outputs found
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
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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
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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
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Mapping post-fire habitat characteristics through the fusion of remote sensing tools
Post-fire snags provide important resources for cavity nesting communities as well as being subject to timber removal through salvage logging practices. Tools that can characterize their distributions along with other features important as wildlife habitat, such as woody shrub cover, would be useful for research and management purposes. Three dimensional lidar data and Landsat time series disturbance products have both shown varying promise in their ability to characterize aspects of dead biomass and understory cover, but studies exploring the combination of the remote sensing datasets calibrated with field data to model difficult to map habitat components are limited. The purpose of this study was to 1) relate lidar and Landsat time series products to field-collected calibration data to produce maps of important post-fire wildlife habitat components including snags of varying sizes and the availability of a woody shrub layer; and 2) compare the individual performance of the Landsat and lidar datasets for predicting the distributions of these difficult to map forest habitat features. Using 164 field calibration plots and remote sensing predictors, we modeled and mapped the distributions of our response variables including snag classes (dbh ≥ 40 cm, ≥ 50 cm, and ≥ 75 cm) and woody shrub cover thresholds (≥ 30 and ≥ 50% cover) at 10 m resolutions. Remote sensing predictors included various lidar structure and topography variables and Landsat time series products representing the pre-fire forest, disturbance magnitude, and current forest condition. A model was chosen for mapping purposes using AIC model selection and then by comparing leave-one-out-cross-validation error matrices to choose among competing models. We were able to predict and map all response variables with moderate accuracies and variable sensitivity (true positive) and specificity (true negative) rates. All snag and shrub models were considered to have “good” predictive performance as indicated by area under the curve values (0.74–0.91), with percent correctly classified values ranging from 69–85% when a probability threshold is chosen that balances false positive and false negative errors. Landsat models performed marginally better than lidar structure models according to classification statistics. Landsat-only models had slightly less accuracy than models that included lidar and Landsat data, but often with greater errors than the combined model. The ability to map the response variables with moderate errors and acceptable accuracies for many applications was through the fusion of these remote sensing datasets.Keywords: Lidar, Post-fire, Habitat, Maps, Snags, Landsat time series, Shrub coverKeywords: Lidar, Post-fire, Habitat, Maps, Snags, Landsat time series, Shrub cove
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Landsat-based monitoring of annual wetland change in the Willamette Valley of Oregon, USA from 1972 to 2012
n Oregon’s Willamette Valley, remaining wetlands are at high risk to loss and degradation from agricultural activity and urbanization. With an increased need for fine temporal-scale monitoring of sensitive wetlands, we used annual Landsat MSS and TM/ETM+ images from 1972 to 2012 to manually interpret loss, gain, and type conversion of wetland area in the floodplain of the Willamette River. By creating Tasseled Cap Brightness, Greenness, and Wetness indices for MSS data that visually match TM/ETM+ Tasseled Cap images, we were able to construct a complete and consistent, annual time series and utilize the entire Landsat archive. With an extended time series we were also able to compare annual trends of net change in wetland area before and after the no-net-loss policy established under Section 404 of the Clean Water Act in 1990 using a Theil-Sen Slope estimate analysis. Vegetated wetlands experienced a 314 ha net loss of wetland area and non-vegetated wetlands experienced a 393 ha net gain, indicating higher functioning wetlands were replaced in area by non-vegetated wetland habitats such as agricultural and quarry ponds. The majority of both gain and loss in the study area was attributed to gains and losses of agricultural land. After 1990 policy implementations, the rate of wetland area lost slowed for some wetland categories and reversed into trends of gain in wetland area for others, perhaps representative of the success of increased regulations. Overall accuracy of land use classification through manual interpretation was at 80 %. This accuracy increased to 91.1 % when land use classes were aggregated to either wetland or upland categories, indicating that our methodology was more accurate at distinguishing between general upland and wetland than finer categorical classes.Keywords: Remote sensing, No-net-loss, Change detection, MSS, Tasseled Cap, WetlandsKeywords: Remote sensing, No-net-loss, Change detection, MSS, Tasseled Cap, Wetland
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