A Density-Based Approach for Leaf Area Index Assessment in a Complex Forest Environment Using a Terrestrial Laser Scanner

Forests are an important part natural ecosystems, by for example providing food, fiber, habitat, and biodiversity, all of which contribute to stable natural systems. Assessing and modeling the structure and characteristics of forests, e.g., Leaf Area Index (LAI), volume, biomass, etc., can lead to a better understanding and management of these resources. In recent years, Terrestrial Laser Scanning (TLS) has been recognized as a tool that addresses many of the limitations of manual and traditional forest data collection methods. In this study, we propose a density-based approach for estimating the LAI in a structurally-complex forest environment, which contains variable and diverse structural attributes, e.g., non-circular stem forms, dense canopy and below-canopy vegetation cover, and a diverse species composition. In addition, 242 TLS scans were collected using a portable low-cost scanner, the Compact Biomass Lidar (CBL), in the Hawaii Volcanoes National Park (HAVO), Hawaii Island, USA. LAI also was measured for 242 plots in the site, using an AccuPAR LP-80 ceptometer. The first step after cleaning the point cloud involved detecting the higher forest canopy in the light detection and ranging (lidar) point clouds, using normal change rate assessment. We then estimated Leaf Area Density (LAD), using a voxel-based approach, and divided the canopy point cloud into five layers in the Z (vertical) direction. These five layers subsequently were divided into voxels in the X direction, where the size of these voxels were obtained based on inter-quartile analysis and the number of points in each voxel. We hypothesized that the intensity returned to the lidar system from woody materials, like branches, would be higher than from leaves, due to the liquid water absorption feature of the leaves and higher reflectance for woody material at the 905 nm laser wavelength. We also differentiated between foliar and woody materials using edge detection in the images from projected point clouds and evaluated the density of these regions to support our hypothesis. Density of points, or the number of points divided by the volume of a grid, in a 3D grid size of 0.1 m, was calculated for each of the voxels. The grid size was determined by investigating the size of the branches in the lower portion of the canopy. Subsequently, we fitted a Kernel Density Estimator (KDE) to these values, with the threshold set based on half of the area under the curve in each of the density distributions. All the grids with a density below the threshold were labeled as leaves, while those grids above the threshold were identified as non-leaves. Finally, we modeled LAI using the point densities derived from the TLS point clouds and the listed analysis steps. This model resulted in an R 2 value of 0.88. We also estimated the LAI directly from lidar data using the point densities and calculating LAD, which is defined as the total one-sided leaf area per unit volume. LAI can be obtained as the sum of the LAD values in all the voxels. The accuracy of LAI estimation was 90%, with an RMSE value of 0.31, and an average overestimation of 9% in TLS-derived LAI, when compared to field-measured LAI. Algorithm performance mainly was affected by the vegetation density and complexity of the canopy structures. It is worth noting that, since the LAI values cannot be considered spatially independent throughout all the plots in this site, we performed semivariogram analysis on the field-measured LAI data. This analysis showed that the LAI values can be assumed to be independent in plots that are at least 30 m apart. As a result, we divided the data into six subsets in which the plots were 30 m spaced. The R 2 values for these subsets, based on modeling of the field-measured LAI using leaf point density values, ranged between 0.84–0.96. The results bode well for using this method for efficient, automatic, and accurate/precise estimation of LAI values in complex forest environments, using a low-cost, rapid-scan TLS

Clinical evaluation of Corridor disease in Bos indicus (Boran) cattle naturally infected with buffalo-derived Theileria parva

Corridor disease (CD) is a fatal condition of cattle caused by buffalo-derived Theileria parva. Unlike the related condition, East Coast fever, which results from infection with cattle-derived T. parva, CD has not been extensively studied. We describe in detail the clinical and laboratory findings in cattle naturally infected with buffalo-derived T. parva. Forty-six cattle were exposed to buffalo-derived T. parva under field conditions at the Ol Pejeta Conservancy, Kenya, between 2013 and 2018. The first signs of disease observed in all animals were nasal discharge (mean day of onset was 9 days post-exposure), enlarged lymph nodes (10 days post-exposure), and pyrexia (13.7 days post-exposure). Coughing and labored breathing were observed in more than 50% of animals (14 days post-exposure). Less commonly observed signs, corneal edema (22%) and diarrhea (11%), were observed later in the disease progression (19 days post-exposure). All infections were considered clinically severe, and 42 animals succumbed to infection. The mean time to death across all studies was 18.4 days. The mean time from onset of clinical signs to death was 9 days and from pyrexia to death was 4.8 days, indicating a relatively short duration of clinical illness. There were significant relationships between days to death and the days to first temperature (chi2 = 4.00, p = 0.046), and days to peak temperature (chi2 = 25.81, p = 0.001), animals with earlier onset pyrexia died sooner. These clinical indicators may be useful for assessing the severity of disease in the future. All infections were confirmed by the presence of macroschizonts in lymph node biopsies (mean time to parasitosis was 11 days). Piroplasms were detected in the blood of two animals (4%) and 20 (43%) animals seroconverted. In this study, we demonstrate the successful approach to an experimental field study for CD in cattle. We also describe the clinical progression of CD in naturally infected cattle, including the onset and severity of clinical signs and pathology. Laboratory diagnoses based on examination of blood samples are unreliable, and alternatives may not be available to cattle keepers. The rapid development of CD requires recognition of the clinical signs, which may be useful for early diagnosis of the disease and effective intervention for affected animals

Inherited tolerance in cattle to the apicomplexan protozoan Theileria parva is associated with decreased proliferation of parasite-infected lymphocytes

Theileria parva is the causative agent of East Coast fever and Corridor disease, which are fatal, economically important diseases of cattle in eastern, central and southern Africa. Improved methods of control of the diseases are urgently required. The parasite transforms host lymphocytes, resulting in a rapid, clonal expansion of infected cells. Resistance to the disease has long been reported in cattle from T. parva-endemic areas. We reveal here that first- and second-generation descendants of a single Bos indicus bull survived severe challenge with T. parva, (overall survival rate 57.3% compared to 8.7% for unrelated animals) in a series of five field studies. Tolerant cattle displayed a delayed and less severe parasitosis and febrile response than unrelated animals. The in vitro proliferation of cells from surviving cattle was much reduced compared to those from animals that succumbed to infection. Additionally, some pro-inflammatory cytokines such as IL1β, IL6, TNFα or TGFβ which are usually strongly expressed in susceptible animals and are known to regulate cell growth or motility, remain low in tolerant animals. This correlates with the reduced proliferation and less severe clinical reactions observed in tolerant cattle. The results show for the first time that the inherited tolerance to T. parva is associated with decreased proliferation of infected lymphocytes. The results are discussed in terms of whether the reduced proliferation is the result of a perturbation of the transformation mechanism induced in infected cells or is due to an innate immune response present in the tolerant cattle

Multi-channel open tubular traps for headspace sampling, gas chromatographic fraction collection and olfactory assessment of milk volatiles

A headspace sampling method is described for concentrating milk volatiles onto a multi-channel open tubular silicone rubber trap (MCT) for thermal desorption into a GC-FID. Sections of the chromatographic profile, single peaks or combinations of compounds are recaptured with secondary MCTs during a subsequent run. The recaptured aroma is released in a controlled manner by heating the MCT in a portable heating device. An aroma release window of several minutes allows up to six people the opportunity to sniff each aroma fraction more than once. Olfactory results suggest that a synergistic combination of 2-heptanone and 2-nonanone could be responsible for a pungent cheese, sour milk-like aroma. MCTs containing single components or fractions can be desorbed into a GC–MS for compound identification.The authors thank Sasol for the financial support

A Density-Based Approach for Leaf Area Index Assessment in a Complex Forest Environment Using a Terrestrial Laser Scanner

Forests are an important part natural ecosystems, by for example providing food, fiber, habitat, and biodiversity, all of which contribute to stable natural systems. Assessing and modeling the structure and characteristics of forests, e.g., Leaf Area Index (LAI), volume, biomass, etc., can lead to a better understanding and management of these resources. In recent years, Terrestrial Laser Scanning (TLS) has been recognized as a tool that addresses many of the limitations of manual and traditional forest data collection methods. In this study, we propose a density-based approach for estimating the LAI in a structurally-complex forest environment, which contains variable and diverse structural attributes, e.g., non-circular stem forms, dense canopy and below-canopy vegetation cover, and a diverse species composition. In addition, 242 TLS scans were collected using a portable low-cost scanner, the Compact Biomass Lidar (CBL), in the Hawaii Volcanoes National Park (HAVO), Hawaii Island, USA. LAI also was measured for 242 plots in the site, using an AccuPAR LP-80 ceptometer. The first step after cleaning the point cloud involved detecting the higher forest canopy in the light detection and ranging (lidar) point clouds, using normal change rate assessment. We then estimated Leaf Area Density (LAD), using a voxel-based approach, and divided the canopy point cloud into five layers in the Z (vertical) direction. These five layers subsequently were divided into voxels in the X direction, where the size of these voxels were obtained based on inter-quartile analysis and the number of points in each voxel. We hypothesized that the intensity returned to the lidar system from woody materials, like branches, would be higher than from leaves, due to the liquid water absorption feature of the leaves and higher reflectance for woody material at the 905 nm laser wavelength. We also differentiated between foliar and woody materials using edge detection in the images from projected point clouds and evaluated the density of these regions to support our hypothesis. Density of points, or the number of points divided by the volume of a grid, in a 3D grid size of 0.1 m, was calculated for each of the voxels. The grid size was determined by investigating the size of the branches in the lower portion of the canopy. Subsequently, we fitted a Kernel Density Estimator (KDE) to these values, with the threshold set based on half of the area under the curve in each of the density distributions. All the grids with a density below the threshold were labeled as leaves, while those grids above the threshold were identified as non-leaves. Finally, we modeled LAI using the point densities derived from the TLS point clouds and the listed analysis steps. This model resulted in an R 2 value of 0.88. We also estimated the LAI directly from lidar data using the point densities and calculating LAD, which is defined as the total one-sided leaf area per unit volume. LAI can be obtained as the sum of the LAD values in all the voxels. The accuracy of LAI estimation was 90%, with an RMSE value of 0.31, and an average overestimation of 9 % in TLS-derived LAI, when compared to field-measured LAI. Algorithm performance mainly was affected by the vegetation density and complexity of the canopy structures. It is worth noting that, since the LAI values cannot be considered spatially independent throughout all the plots in this site, we performed semivariogram analysis on the field-measured LAI data. This analysis showed that the LAI values can be assumed to be independent in plots that are at least 30 m apart. As a result, we divided the data into six subsets in which the plots were 30 m spaced. The R 2 values for these subsets, based on modeling of the field-measured LAI using leaf point density values, ranged between 0.84–0.96. The results bode well for using this method for efficient, automatic, and accurate/precise estimation of LAI values in complex forest environments, using a low-cost, rapid-scan TLS

SAT1 BEAST phylogeographic file

BEAST data and priors for running a discrete trait model for geographic transmission of FMD SAT1 in East Afric

SAT1 Host BEAST File

BEAST data and priors for running a discrete trait model for host species transmission of FMD SAT1 in East Afric

SAT2 Phylogeography BEAST file

Contains data and prior for running a discrete trait model for phylogeography of FMD SAT2 in east Afric