1,075 research outputs found
Detecting mountain pine beetle red attack damage with EO-1 Hyperion moisture indices
No full textThe mountain pine beetle (Dendroctonus ponderosae) is the most destructive insect of mature pine forests in western North America. Time series of wetness transformations generated from Landsat imagery have been used to detect mountain pine beetle red attack damage over large areas. With the recent availability of high spatial (QuickBird) and high spectral (Hyperion) resolution satellite sensor imagery, the relationship between spectral moisture indices and levels of red attack damage may be investigated. Six moisture indices were generated from Hyperion data and were compared to the proportion of the Hyperion pixel having red attack damage. Results indicate the Hyperion moisture indices incorporating both the shortwave infrared (SWIR) and near infrared (NIR) regions of the electromagnetic spectrum concurrently, such as the Moisture Stress Index, were significantly correlated to levels of damage (r =0.51; p=0.0001). The results corroborate the hypothesis that changes in foliage moisture resulting from mountain pine beetle attack are driving the broad-scale temporal variation in Landsat derived wetness indices. Furthermore, the results suggest that Hyperion data may be used to map low levels of mountain pine beetle red attack damage over large areas that are not consistently captured with Landsat data
Characterizing stand-replacing disturbance in western Alberta grizzly bear habitat, using a satellite-derived high temporal and spatial resolution change sequence
No full textTimely and accurate mapping of anthropogenic and natural disturbance patterns can be used to better understand the nature of wildlife habitats, distributions and movements. One common approach to map forest disturbance is by using high spatial resolution satellite imagery, such as Landsat 5 Thematic Mapper (TM) or Landsat 7 Enhanced Thematic Mapper plus (ETM+) imagery acquired at a 30 m spatial resolution. However, the low revisit times of these sensors acts to limit the capability to accurately determine dates for a sequence of disturbance events, especially in regions where cloud contamination is a frequent occurrence. As wildlife habitat use can vary significantly seasonally, annual patterns of disturbance are often insufficient in assessing relationships between disturbance and foraging behaviour or movement patterns.The Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH) allows the generation of high-spatial (30 m) and -temporal (weekly or bi-weekly) resolution disturbance sequences using fusion of Landsat TM or ETM+ and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The STAARCH algorithm is applied here to generate a disturbance sequence representing stand-replacing events (disturbances over 1 ha in area) for the period 2001–2008, over almost 6 million ha of grizzly bear habitat along the eastern slopes of the Rocky Mountains in Alberta. The STAARCH algorithm incorporates pairs of Landsat images to detect the spatial extent of disturbances; information from the bi-weekly MODIS composites is used in this study to assign a date of disturbance (DoD) to each detected disturbed area. Dates of estimated disturbances with areas over 5 ha are validated by comparison with a yearly Landsat-based change sequence, with producer's accuracies ranging between 15 and 85% (average overall accuracy 62%, kappa statistic of 0.54) depending on the size of the disturbance event. The spatial and temporal patterns of disturbances within the entire region and in smaller subsets, representative of the size of a grizzly bear annual home range, are then explored. Disturbance levels are shown to increase later in the growing season, with most disturbances occurring in late August and September. Individual events are generally small in area (<10 ha) except in the case of wildfires, with, on average, 0.4% of the total area disturbed each year. The application of STAARCH provides unique high temporal and spatial resolution disturbance information over an extensive area, with significant potential for improving understanding of wildlife habitat use
Dynamics of spectral bio-indicators and their correlations with light use efficiency using directional observations at a Douglas-fir forest
No full textThe carbon science community must rely on satellite remote sensing to obtain global estimates of photosynthetic activity, typically expressed as net primary production (NPP), gross primary production (GPP) or light use efficiency (LUE). The photochemical reflectance index (PRI), calculated as a normalized difference reflectance index using a physiologically active green band (~531 nm) and another physiologically insensitive green reference band (~570 nm), denoted as PRI(570), has been confirmed in many studies as being strongly related to LUE. Here, we examined the potential of utilizing PRI(570) observations under different illumination conditions for canopy LUE estimation of a forest. In order to evaluate this, directional hyperspectral reflectance measurements were collected continuously throughout the daytime periods using an automated spectroradiometer in conjunction with tower-based eddy covariance fluxes and environmental measurements at a coastal conifer forest in British Columbia, Canada throughout the 2006 growing season. A parameter calculated as the PRI(570) difference (dPRI(570)) between shaded versus sunlit canopy foliage sectors showed a strong correlation to tower-based LUE. The seasonal pattern for this correlation produced a dramatic change from high negative (r ~ ?0.80) values in the springtime and early fall to high positive values (r ~ 0.80) during the summer months, which could represent the seasonality of physiological characteristics and environmental factors. Although the PRI(570) successfully tracked canopy LUE, one or both of its green bands (~531 and 570 nm) used to calculate the PRI are unavailable on most existing and planned near-term satellites. Therefore, we examined the potential to use 24 other spectral indexes for LUE monitoring that might be correlated to PRI, and thereby a substitute for it. We also continued our previous investigations into the influence of illumination conditions on the observed PRI(570) and other indexes. Among the 24 indexes examined, three PRI indexes using different reference bands (488, 551 and 705 nm) showed high correlations to the traditional PRI(570), especially PRI(551) and PRI(705). This indicates three additional PRI variations for LUE monitoring if the traditional reference band at 570 nm is not available but the 531 nm band is available. Five other indexes also yielded high correlations to PRI(570): Dmax and DM705, two indexes calculated from derivative reflectance spectra; a simple ratio of reflectance values at 685 nm and 655 nm (SR685_655); and a double-peak optical index (DPI). The diurnal and seasonal dynamics of these eight indexes and PRI(570) were explored. All of these indexes except DPI expressed linear dependence on available sunlight and more strongly expressed diurnal dynamics in April than in August during summer drought. The differences for shaded versus sunlit canopy foliage sectors were also calculated for the eight indexes, and their correlations to canopy LUE across the season were examined. The performances were similar for the most successful and seasonally stable indexes: dPRI(551), dPRI(705) and dPRI(570). The other five indexes showed good correlation to LUE in some but not all the months, and the months with high correlations varied among them
Remote sensing technologies for enhancing forest inventories: a review
No full textForest inventory and management requirements are changing rapidly in the context of an increasingly complex set of economic, environmental, and social policy objectives. Advanced remote sensing technologies provide data to assist in addressing these escalating information needs and to support the subsequent development and parameterization of models for an even broader range of information needs. This special issue contains papers that use a variety of remote sensing technologies to derive forest inventory or inventory-related information. Herein, we review the potential of 4 advanced remote sensing technologies, which we posit as having the greatest potential to influence forest inventories designed to characterize forest resource information for strategic, tactical, and operational planning: airborne laser scanning (ALS), terrestrial laser scanning (TLS), digital aerial photogrammetry (DAP), and high spatial resolution (HSR)/very high spatial resolution (VHSR) satellite optical imagery. ALS, in particular, has proven to be a transformative technology, offering forest inventories the required spatial detail and accuracy across large areas and a diverse range of forest types. The coupling of DAP with ALS technologies will likely have the greatest impact on forest inventory practices in the next decade, providing capacity for a broader suite of attributes, as well as for monitoring growth over time
Analysis on the use of multiple returns LiDAR data for the estimation of tree stems volume
No full textIntegrating Landsat pixel composites and change metrics with lidar plots to predictively map forest structure and aboveground biomass in Saskatchewan, Canada
No full textForest inventory and monitoring programs are needed to provide timely, spatially complete (i.e. mapped), and verifiable information to support forest management, policy formulation, and reporting obligations. Satellite images, in particular data from the Landsat Thematic Mapper and Enhanced Thematic Mapper (TM/ETM +) sensors, are often integrated with field plots from forest inventory programs, leveraging the complete spatial coverage of imagery with detailed ecological information from a sample of plots to spatially model forest conditions and resources. However, in remote and unmanaged areas such as Canada's northern forests, financial and logistic constraints can severely limit the availability of inventory plot data. Additionally, Landsat spectral information has known limitations for characterizing vertical vegetation structure and biomass; while clouds, snow, and short growing seasons can limit development of large area image mosaics that are spectrally and phenologically consistent across space and time. In this study we predict and map forest structure and aboveground biomass over 37 million ha of forestland in Saskatchewan, Canada. We utilize lidar plots—observations of forest structure collected from airborne discrete-return lidar transects acquired in 2010—as a surrogate for traditional field and photo plots. Mapped explanatory data included Tasseled Cap indices and multi-temporal change metrics derived from Landsat TM/ETM + pixel-based image composites. Maps of forest structure and total aboveground biomass were created using a Random Forest (RF) implementation of Nearest Neighbor (NN) imputation. The imputation model had moderate to high plot-level accuracy across all forest attributes (R2 values of 0.42–0.69), as well as reasonable attribute predictions and error estimates (for example, canopy cover above 2 m on validation plots averaged 35.77%, with an RMSE of 13.45%, while unsystematic and systematic agreement coefficients (ACuns and ACsys) had values of 0.63 and 0.97 respectively). Additionally, forest attributes displayed consistent trends in relation to the time since and magnitude of wildfires, indicating model predictions captured the dominant ecological patterns and processes in these forests. Acknowledging methodological and conceptual challenges based upon the use of lidar plots from transects, this study demonstrates that using lidar plots and pixel compositing in imputation mapping can provide forest inventory and monitoring information for regions lacking ongoing or up-to-date field data collection programs
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Decreasing Net Primary Production in forest and shrub vegetation across southwest Australia
No full textMonitoring changes in the terrestrial carbon cycle and vegetation health can only be undertaken over large areas and on a regular basis using ecological indicators derived from satellite-based sensors. Climate conditions in Mediterranean ecosystems have undergone, and are projected to undergo, significant change in the future with marked impacts on forest and shrubland vegetation. In the southwest of Australia (SWAU), endemic tree species have experienced significant declines in health and mortality since the early 1990s primarily due to these climatic changes. In this paper we examine trends in Net Primary Production (NPP) from 2000 to 2011 as an indicator of productivity and health condition of the woody vegetation across the SWAU region. To do so, we examine NPP estimates derived from satellite imagery and climate data to answer the questions: (1) what is the extent and rate of change in NPP for the SWAU region over the study period, and (2) how important is fire as a contributing factor in the observed trends? Our results suggest that, similar to the global trend in Mediterranean ecosystems, between 2000 and 2011, overall NPP declined across the study region, with the majority of declines occurring in the ecological transition zone between trees and shrubs. Twenty-six percent of the 37,042 square kilometre of woody vegetation that showed a declining NPP trend, was affected by fire. The overall rate of NPP decline for the region was estimated to be -0.38 megaton C per year since 2000, indicating a reduction in the capacity of the region to act as a carbon sink. Under climate change projections, the observed decline trends are likely to continue and our results suggest that the carbon storage potential in this region is gradually decreasing following an ecological shift from tall tree-dominated to lower shrub-dominated vegetation
lwiniwar/roadCNN: Release for publication
No full text<p>This is the release accompanying the paper</p>
<p>Winiwarter, L., Coops, N.C., Bastyr, A., Roussel, J.-R., Zhao, D., Lamb, C.T., and Ford, A.T., "Extraction of Forest Road Information from CubeSat Imagery using CNNs". Submitted to <em>Remote Sensing</em>.</p>
Satellite data: Beyond sharing Earth observations
No full textTo improve the accuracy of products derived from shared satellite observations of Earth (see M.A. Wulder and N.C. Coops [[Nature]] 513, 30-31; 2014), governments and research institutes also need to share calibration and validation data. Such data are measured on the ground or interpreted from high-resolution satellite imagery
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