density of cbd

December 15, 2021 By admin Off

Cannabidiol[CBD] is the main non-psychotropic component of the glandular hairs of Cannabis sativa. It displays a plethora of actions including anticonvulsive, sedative, hypnotic, antipsychotic, and anti-inflammatory and neuroprotective properties. It is a major phyto-cannabinoid, accounting for up to 40% of the Cannabis plant's extract, that binds to a wide variety of physiological targets of the endocannabinoid system within the body. Although the exact medical implications are currently being investigated, CBD has shown promise as a therapeutic and pharmaceutical drug target. In particular, CBD has shown promise as an analgesic, anticonvulsant, muscle relaxant, anxiolytic, antipsychotic and has shown neuroprotective, anti-inflammatory, and antioxidant activity, among other currently investigated uses [1, 2] . Cannabidiol was isolated from marijuana in the late 1930s, but only in the 1963 were its structure and stereochemistry first elucidated [3] . Early studies focusing on CBD pharmacology started in the 1970s, with the first relevant finding concerning its hypnotic and anticonvulsant properties, published in 1981 [4] . Since then, a large body of pharmacological effects has been demonstrated, both in preclinical and in clinical studies.

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Scott, J.H., and E.D. Reinhardt. 2005. Stereo photo guide for estimating canopy fuel characteristics in conifer stands. USDA Forest Service General Technical Report RMRS-GTR-145 . Fort Collins: USDA Forest Service, Rocky Mountain Research Station. https://doi.org/10.2737/RMRS-GTR-145.

Keane, R.E., E.D. Reinhardt, J. Scott, K. Gray, and J. Reardon. 2005. Estimating forest canopy bulk density using six indirect methods. Canadian Journal of Forest Research 35 (3): 724–739. https://doi.org/10.1139/x04-213.

Frelich, L.E., and P.B. Reich. 1995. Spatial patterns and succession in a Minnesota southern-boreal forest. Ecological Monographs 65 (3): 325–346. https://doi.org/10.2307/2937063.

Field estimates of CBD were also determined allometrically using plot-wise biophysical tree data and the FuelCalc routine (CBD FC ; kg m −3 ). As mentioned above, in the absence of species-specific allometry, FuelCalc makes use of allometry from structurally similar forest species to determine a plot’s CBD profile (Lutes 2020), from which we extracted the maximum CBD. In northern Minnesota, most of the conifer species found in the pilot study area lacked the necessary allometry to calculate CBD. Hence, FuelCalc substituted the following species: Abies lasiocarpa (Hook.) Nutt. for Abies balsamea , Pinus contorta (Dougl.) for Pinus banksiana , Pinus ponderosa (Dougl.) for Pinus resinosa , Picea engelmannii (Parry) for Picea glauca , and Pseudotsuga menziesii (Mirb.) Franco for Picea mariana .

Rollins, M.G. 2009. LANDFIRE: A nationally consistent vegetation, wildland fire, and fuel assessment. International Journal Wildland Fire 18 (3): 235–249. https://doi.org/10.1071/WF08088.

Conclusions.

Remote detection of coniferous ladder fuels (surface and lower canopy) in the Upper Midwest remains a challenge using either low-density lidar (Engelstad et al. 2019) or dual-polarity C- and L-band SAR satellite sensor data. The distribution and abundance of ladder fuels within the SNF landscape (Fig. 8) represent a substantial gap in knowledge that affects the efficiency of efforts focused on fuel reduction treatments to mitigate wildfire risk. Research on linkages between the abundance of understory biomass and combinations of overstory canopy type and percent cover (Messier et al. 1998; Légaré et al. 2002) begs the question of whether modeling the presence of ladder fuels may be possible in this region. We suspect that positive understory light-to-biomass relationships likely hold true regarding broadleaf biomass in this region. However, the results of our analyses do not support the possibility of modeling balsam fir ladder fuels via this mechanism due to its strong shade tolerance (Corace et al. 2012). Low-density lidar sensor data (i.e., average of 0.44 points m −2 ) have not yet facilitated sufficient characterization of such ladder fuels in this region (Engelstad et al. 2019) to enable sound management decisions. Resolution of this gap in fuel information will likely only be resolved once higher-density lidar data (e.g., 8 points m −2 ) are available on a more frequent basis—a project that is currently underway in Minnesota (MNGAC [Minnesota Geospatial Advisory Council] 2020).

Location of Minnesota, USA, pilot study area (black box) proximal to the Superior National Forest (SNF) and the Boundary Waters Canoe Area Wilderness (BWCAW) and Lake Superior. Here, we investigated the potential of mapping coniferous forest fuel density using satellite sensor data and ground data collected between 2015 and 2016.

In addition to optical sensor bands from each date, we calculated three indices for use as predictors of burnable fuel density: normalized difference vegetation index (NDVI; Rouse et al. 1974), shortwave infrared to near-infrared ratio (SWIR:NIR; Vogelmann and Rock 1988), and SWIR to visible ratio (SVR; Wolter et al. 2008) (Table 2). We also included ten mapped estimates of forest basal area (total, conifer, broadleaf, and seven conifer species; Wolter and Townsend 2011) at 30-m spatial resolution to the pool of optical predictors. Thus, there were 61 initial optical sensor predictors and ten structure estimates as predictors available for calibrations with ground data.

Agee, J.K. 1996. The influence of forest structure on fire behavior. In Proceedings of the 17th annual forest vegetation management conference. University of California, Agriculture and Natural Resources, 16-18 January 1996, Redding, California , 52–68.

Rauste, Y. 2005. Multi-temporal JERS SAR data in boreal forest biomass mapping. Remote Sensing of Environment 97 (2): 263–275. https://doi.org/10.1016/j.rse.2005.05.002.

While CBD is a salient variable for modeling crown fire behavior, it is also one of the most difficult parameters to measure accurately in the field (Alexander 1988; Keane et al. 2005). This is because the vertical distribution of biomass varies by conifer species, crown position, and shade tolerance and from stand to stand (Brown 1978; Keane et al. 2002; Keane et al. 2006). Moreover, according to Keane et al. (2005), various vertical average measures of CBD likely underestimate effective CBD fuel conditions, noting that a few patches of fuel having substantially higher biomass values than the vertical canopy average will sustain fire spread. Therefore, they included the maximum CBD among all 1-m vertical layers as an important fuel variable to model, which we follow in this study. Equally vexing is the fact that key forest fuel properties (e.g., surface fuel, CBD, and canopy base height) are practically impossible to detect or distinguish via optical satellite sensors (e.g., Landsat-8 and Sentinel-2). Logical options suggested for remote detection of vertical canopy structures include lidar and radar (Keane et al. 2001; Keane et al. 2006).

Lutes, D.C. 2020. FuelCalc 1.7 Users Guide . Missoula: USDA Forest Service, Rocky Mountain Research Station, Fire Modeling Institute.

Skewness and kurtosis measures (both unitless) of frequency distribution shape for respective estimates of fuel density (labeled dots). Shape statistics for total fuel density (TFD) and canopy bulk density (CBD) based on weighted canopy gap fraction are denoted T and C, respectively, while numbers 1, 12, and 123 refer to zenith angle ranges (0 to 7 ° , 0 to 23 ° , and 0 to 38 ° , respectively) of the LAI-2200C instrument. Shape statistics for FuelCalc-based estimates of CBD are denoted as FC, while LANDFIRE CBD is denoted LF. Dashed lines indicate zones of relative normality for the respective shape metrics. Field data for these analyses were collected in 2015 and 2016 and combined with satellite sensor data to calibrate models for mapping fuel density across the Superior National Forest, Minnesota, USA.

Wolter, P.T., P.A. Townsend, and B.R. Sturtevant. 2009. Estimation of forest structural parameters using 5 and 10 meter SPOT-5 satellite data. Remote Sensing of Environment 113 (9): 2019–2036. https://doi.org/10.1016/j.rse.2009.05.009.

Kellndorfer, J., W. Walker, L. Pierce, C. Dobson, J.A. Fites, C. Hunsaker, J. Vona, and M. Clutter. 2004. Vegetation height estimation from shuttle radar topography mission and national elevation datasets. Remote Sensing of Environment 93 (3): 339–358. https://doi.org/10.1016/j.rse.2004.07.017.

Understory conifer biomass and overstory forest structure.

Synthetic aperture radar imagery. We acquired SAR backscatter amplitude imagery (Table 1) from two satellite sensors (Sentinel-1 and Palsar-1) to complement optical sensor data for calibrating fuel density models (TFD, SFD, and CBD). We downloaded Sentinel-1 dual-polarity (vertical send, vertical receive [VV]; vertical send, horizontal receive [VH]) C-band SAR interferometric wide (IW) imagery from 16 May 2016 from the European Space Agency’s Copernicus Open Access Hub website (https://scihub.copernicus.eu/dhus/#/home) as ground range detected images georeferenced and resampled to a common pixel spacing (10 m) in both range and azimuth. These SAR data captured pre-leaf flush conditions for deciduous tree species in the study area. Leaf-off Palsar-1 dual-polarization (HH, HV) L-band data from 6 November 2010 (no snow cover) and 22 December 2010 (38-cm snow cover) were downloaded from the Alaska Satellite Facility’s NASA distributed active archive center as high-resolution (12.5 m), terrain-corrected backscatter amplitude imagery (https://www.asf.alaska.edu/about/asf-daac/).

Fernández-Alonso, J.M., L. Alberdi, J.G. Álvarez-González, J.A. Vega, L. Cañellas, and A.D. Ruiz-González. 2013. Canopy fuel characteristics in relation to crown fire potential in pine stands: Analysis, modelling and classification. European Journal of Forest Research 132 (2): 363–377. https://doi.org/10.1007/s10342-012-0680-z.

Thapa, B., P.T. Wolter, B.R. Sturtevant, and P.A. Townsend. 2020. Reconstructing past forest composition and abundance by using archived Landsat and national forest inventory data. International Journal of Remote Sensing 41 (10): 4022–4056. https://doi.org/10.1080/01431161.2019.1711245.

Keane, R.E., R.E. Burgan, and J.W. van Wagtendonk. 2001. Mapping wildland fuel for fire management across multiple scales: Integrating remote sensing, GIS, and biophysical modeling. International Journal of Wildland Fire 10 (4): 301–319. https://doi.org/10.1071/WF01028.

The 356-km 2 pilot study area falls within the Gunflint Ranger District of the Superior National Forest (SNF), Minnesota, USA, and is a mix of both managed and wilderness forest (Fig. 1). Forest cover is diverse (e.g., five conifer genera and seven broadleaf genera) and is considered transitional between the sub-boreal Great Lakes–St. Lawrence forests and boreal forest (Heinselman 1973; Baker 1989). Non-wilderness forest areas are intensively managed for wood fiber, which has resulted in a dominance of quaking aspen ( Populus tremuloides Michx.), paper birch ( Betula papyrifera Marsh), white spruce ( Picea glauca [Moench] Voss), and balsam fir forest associations (Frelich and Reich 1995; Wolter and White 2002). Wilderness areas have an extensive fire history that supports vast stands of pioneer forest dominated by jack pine ( Pinus banksiana Lamb.), as well as remnants of old-growth white pine and red pine ( Pinus strobus L. and P. resinosa Ait., respectively) (Heinselman 1973; Frelich and Reich 1995). However, early twentieth century fire suppression policies led to an increase in the dominance of shade-tolerant, fire-sensitive balsam fir on this landscape (Frelich and Reich 1995; Corace et al. 2012). Several wilderness and non-wilderness areas within the northwestern portion of the study perimeter experienced the effects of a severe downburst wind event in 1999 that caused substantial wind throw damage (Rich et al. 2007). Much of the downed course woody material from this wind event persists today amidst the regenerating forest. Throughout much of the study area, balsam fir exists largely in the understory below dominant and co-dominant canopy associates (Frelich and Reich 1995; Wolter and Townsend 2011). High flammability and understory canopy position make balsam fir an effective ladder fuel for crown fire propagation (Abbas et al. 2011). Other conifer species in this study area include eastern larch ( Larix laricina [Du Roi] K. Koch), northern white cedar ( Thuja occidentalis L.), and black spruce ( Picea mariana Mill.). The coniferous forest species within this landscape are the focus of this research, which we discuss in the methods below.

Van Wagner, C.E. 1977. Conditions for the start and spread of crownfire. Canadian Journal of Forest Research 7 (1): 23–24. https://doi.org/10.1139/x77-004.

Optical imagery. We acquired images from three optical satellite sensors (Table 1) for this study: five Landsat-8 Operational Land Imager (OLI) scenes, one SPOT-5 multispectral (XS) image, and one Sentinel-2 Multispectral Instrument (MSI) image. All OLI images were from the worldwide reference system (WRS-2) path 26 row 27 footprint and were downloaded from the US Geological Survey’s Earth Explorer portal (www.earthexplorer.org). Images came processed to surface reflectance, ortho-rectified, and geo-corrected as part of the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS; Vermote et al. 1997; Masek et al. 2006). A SPOT-5 winter image (with 53-cm snow ground cover) from 13 March 2013 was downloaded from the Earth Explorer portal (https://earthexplorer.usgs.gov/) under a temporary data purchase agreement between the US Geological Survey and SPOT Image Corporation (https://www.spotimage.com/). Finally, a MSI image from 7 May 2017 (pre-leaf flush) was downloaded from the Earth Explorer portal (https://earthexplorer.usgs.gov/) as an orthometrically and radiometrically corrected top of atmosphere reflectance product. The relative humidity recorded by the Seagull remote automated weather station (48.120536°, −90.838725°) at the time of MSI image acquisition (1030 Central Daylight Time) was.

Results.

Gray, K.L., and E.D. Reinhardt. 2003. Analysis of algorithms for predicting canopy fuel. In Proceedings of the Second International Wildland Fire Ecology and Fire Management Congress and Fifth Symposium on Fire and Forest Meteorology . Orlando: American Meteorological Society.

Efficient methods for the calculation of CBD from indirect measurement of canopy gap fraction (CGF) and leaf area index (LAI) have been tested under Western forest conditions. Keane et al. (2005) described and tested six indirect ground-based methods to measure CGF (AccuPAR ceptometer, CID digital plant canopy imager, hemispherical photography, LI-COR LAI-2000, point sampling, and spherical densiometer). Of these methods, the LAI-2000 instrument (LI-COR Biosciences Inc., Lincoln, NE, USA) measures transmittance of diffuse sky radiation through a vegetation canopy via separate optical sensors oriented at five zenith angles (7, 23, 38, 58, and 68 ° ). With this instrument, CGF and LAI are modeled in three dimensions using radiation attenuation rates at the different zenith angles (Norman and Welles 1983; Perry et al. 1988). In the Keane et al. (2005) study, iterative, incremental, destructive sampling of the coniferous forest canopy (25% basal area removal per iteration) was performed to produce field estimates of CBD across a gradient of crown biomass conditions. Concurrently, they collected six intervening measurements of CGF (listed above) before and after each destructive removal of the forest basal area. Results were used to build a suite of regression models to relate CGF estimates to their direct field measurements of burnable CBD. One of their models (CID digital plant canopy imager; R 2 = 0.94, root mean squared error [RMSE] = 0.01 kg m −3 ) required the use of a tree height parameter, while the remaining models did not. Of the remaining methods tested, the summed weighted average of LAI-2000 sensor data using the top three zenith angles (7, 23, and 38 ° ) showed the strongest relationship to the maximum burnable CBD (foliage and small branches <3 mm diameter) in the vertical profile for Western coniferous forests ( R 2 = 0.70, RMSE = 0.03 kg m −3 ). Equation 1 shows the Keane et al. (2005) weighted transformation of CGF for estimating maximum burnable CBD (CBD, kg m −3 ) across LAI-2000 zenith angles Ɵ i :

Stenberg, P., S. Linder, H. Smolander, and J. Flower-Ellis. 1994. Performance of the LAI-2000 plant canopy analyzer in estimating leaf area index of some Scots pine stands. Tree Physiology 14 (7-8-9): 981–995. https://doi.org/10.1093/treephys/14.7-8-9.981.

Peter T. Wolter & Jacob J. Olbrich.

Example photos of conifer ladder fuels (primarily balsam fir, Abies balsamea [L.] Mill.) taken by Patricia J. Johnson in 2016 within the Gunflint Ranger District of the Superior National Forest, Minnesota, USA, under a mixed conifer–broadleaf overstory and b pure broadleaf overstory. One goal of this research was to determine the impact of coniferous surface and lower canopy fuels on satellite-based detection and mapping of canopy bulk density (CBD; kg m −3 ) in this region using indirect measurements of CBD as ground truth.

Fuel density model calibration. Using the xPLS routine, respective field estimates of TFD and CBD were each analyzed independently against the candidate group of 77 optical and SAR image predictors to calibrate parsimonious models (fewest predictors) for estimating fuel density metrics (i.e., TFD 1 , TFD 12 , TFD 123 , CBD 1 , CBD 12 , CBD 123 , and CBD FC ). We also used the three respective differences between TFD and CBD to build models for surface fuel density (SFD 1 , SFD 12 , and SFD 123 ). Given the procedural differences in deriving fuel density estimates (i.e., CGF and FuelCalc), we chose to perform separate xPLS regression sequences for each response variable. In each instance, final model calibrations were assessed based on results of leave-one-out cross-validation (PRESS), Akaike’s information criterion (AIC; Akaike 1973), Bayesian information criterion (BIC; Schwartz 1978), and other standard metrics (Adj R 2 and RMSE). Relationships between respective field estimates of fuel density were evaluated using Pearson correlation plots (Müller and Büttner 1994). For canopy fuels, we used each final set of image predictors for mapping the respective estimates of CBD across the SNF study area.

Sader, S.A. 1987. Forest biomass, canopy structure, and species composition relationships with multipolarization L-band synthetic aperture radar data. Photogrammetric Engineering & Remote Sensing 53 (2): 193–202.

For TFD models, two complementary explanations for deviations from unity with ground-based estimates arise. First, based largely on failure of all SFD model calibrations, we suspect that contributions of smaller, understory conifer saplings to estimates of TFD were not effectively detected by either optical or SAR satellite sensors, in spite of the relative retention difference among SAR predictors observed for TFB and CBD calibrations (Table 4). As such, TFD model calibrations between ground and satellite sensor data remained possible because some portion of canopy fuel structures were visible to SAR sensors and, to a lesser degree, by optical sensors, which we posit was not the case for SFD. Second, with respect to ground-level CGF measurements, we suspect that TFD is biased by live, coniferous surface and lower canopy fuels (e.g., saplings) when present. If true, this may preclude field estimation of SFD (i.e., TFD minus CBD) via CGF metrics, which comports with prior research (Gower and Norman 1991; Fassnacht et al. 1994).