Automated reconstruction of tree and canopy structure for modeling the internal canopy radiation regime

Understanding canopy radiation regimes is critical to successfully modeling vegetation growth and function. For instance, the vertical distribution of photosynthetically active radiation (PAR) affects vegetation growth, informative upon carbon and energy cycling. Availing upon advances in information capture and computing power, geometrically explicit modeling of forest structure becomes increasingly possible. A primary challenge however is acquiring the forest mensuration data required to parameterize these models and the related automation of modeling forest structure. In this research, to address these issues we employ a novel and automated approach that capitalizes upon the rich information afforded by ground-based laser scanning technology. The method is implemented in two steps: in the first step, geometric explicit models of canopy structure are created from the ground-based laser scanning data. These geometric explicit models are used to simulate the vertical range to first hit. In the second step, we derive canopy gap probability from full waveform laser scanning data which have been used in a number of studies for characterization of radiation transmission (Jupp et al., 2009; Yang et al., 2010) and do not require any geometric explicit modeling. The radiative consistency of the geometric explicit models from step 1 is validated against the gap probabilities of step 2. The results show a strong relationship between the radiative transmission properties of the geometric models and canopy gap probabilities at plot level (R = 0.91 to 0.97), while the geometric models suggest the additional benefit to serve as a bridge in scaling between shoot level and canopy level radiation.Keywords: Laser scanning, Explicit geometric, Ray tracing, Canopy structure, Modeling, Photosynthetically active radiatio

A Comparison of Foliage Profiles in the Sierra National Forest Obtained with a Full-Waveform Under-Canopy EVI Lidar System with the Foliage Profiles Obtained with an Airborne Full-Waveform LVIS Lidar System

Foliage profiles retrieved froma scanning, terrestrial, near-infrared (1064 nm), full-waveformlidar, the Echidna Validation Instrument (EVI), agree well with those obtained from an airborne, near-infrared, full-waveform, large footprint lidar, the Lidar Vegetation Imaging Sensor (LVIS). We conducted trials at 5 plots within a conifer stand at Sierra National Forest in August, 2008. Foliage profiles retrieved from these two lidar systems are closely correlated (e.g., r = 0.987 at 100 mhorizontal distances) at large spatial coverage while they differ significantly at small spatial coverage, indicating the apparent scanning perspective effect on foliage profile retrievals. Alsowe noted the obvious effects of local topography on foliage profile retrievals, particularly on the topmost height retrievals. With a fine spatial resolution and a small beam size, terrestrial lidar systems complement the strengths of the airborne lidars by making a detailed characterization of the crowns from a small field site, and thereby serving as a validation tool and providing localized tuning information for future airborne and spaceborne lidar missions

Chronic intra-uterine Ureaplasma parvum infection induces injury of the enteric nervous system in ovine fetuses

Background: Chorioamnionitis, inflammation of the fetal membranes during pregnancy, is often caused by intra-amniotic (IA) infection with single or multiple microbes. Chorioamnionitis can be either acute or chronic, and is associated with adverse postnatal outcomes of the intestine, including necrotizing enterocolitis (NEC). Neonates with NEC have structural and functional damage to the intestinal mucosa and the enteric nervous system (ENS), with loss of enteric neurons and glial cells. Yet, the impact of acute, chronic or repetitive antenatal inflammatory stimuli on the development of the intestinal mucosa and ENS has not been studied. The aim of this study is therefore to investigate the effect of acute, chronic and repetitive microbial exposure on the intestinal mucosa, submucosa and ENS in premature lambs. Materials and Methods: A sheep model of pregnancy was used in which the ileal mucosa, submucosa and ENS were assessed following IA exposure to lipopolysaccharide (LPS) for 2 or 7 days (acute), Ureaplasma parvum (UP) for 42 days (chronic) or repetitive microbial exposure (42 days UP with 2 or 7 days LPS). Results: IA LPS exposure for 7 days or IA UP exposure for 42 days caused intestinal injury and inflammation in the mucosal and submucosal layer of the gut. Repetitive microbial exposure did not further aggravate injury of the terminal ileum. Chronic IA UP exposure caused significant structural ENS alterations characterized by loss of PGP9.5 and S100β immunoreactivity whereas these changes were not found after re-exposure of chronic UP-exposed fetuses to LPS for 2 or 7 days. Conclusion: The in utero loss of PGP9.5 and S100β immunoreactivity following chronic UP exposure corresponds with intestinal changes in neonates with NEC, and may therefore form a novel mechanistic explanation for the association of chorioamnionitis and NEC

Riociguat treatment in patients with chronic thromboembolic pulmonary hypertension: Final safety data from the EXPERT registry

Objective: The soluble guanylate cyclase stimulator riociguat is approved for the treatment of adult patients with pulmonary arterial hypertension (PAH) and inoperable or persistent/recurrent chronic thromboembolic pulmonary hypertension (CTEPH) following Phase

Using Bayesian multitemporal classification to monitor tropical forest cover changes in Kalimantan, Indonesia

Significant areas of native forest in Kalimantan, on the island of Borneo, have been cleared for the expansion of plantations of oil palm and rubber. In this study multisource remote sensing was used to develop a time series of land cover maps that distinguish native forest from plantations. Using a study area in east Kalimantan, Landsat images were combined with either ALOS PALSAR or Sentinel-1 images to map four land cover classes (native forest, oil palm plantation, rubber plantation, non-forest). Bayesian multitemporal classification was applied to increase map accuracy and maps were validated using a confusion matrix; final map overall accuracy was >90%. Over 18 years from 2000 to 2018 nearly half the native forests in the study area were converted to either non-forest or plantations of either rubber or oil palm, with the highest losses between 2015 and 2016. Trending upwards from 2008 large areas of degraded or cleared forests, mapped as non-forest, were converted to oil palm plantation. Conversion of native forests to plantation mainly occurred in lowland and wetland forest, while significant forest regrowth was detected in degraded peatland. These maps will help Indonesia with strategies and policies for balancing economic growth and conservation

Developing Multi-Source Indices to Discriminate between Native Tropical Forests, Oil Palm and Rubber Plantations in Indonesia

Over the last 18 years, Indonesia has experienced significant deforestation due to the expansion of oil palm and rubber plantations. Accurate land cover maps are essential for policymakers to track and manage land change to support sustainable forest management and investment decisions. An automatic digital processing (ADP) method is currently used to develop land cover change maps for Indonesia, based on optical imaging (Landsat). Such maps produce only forest and non-forest classes, and often oil palm and rubber plantations are misclassified as native forests. To improve accuracy of these land cover maps, this study developed oil palm and rubber plantation discrimination indices using the integration of Landsat-8 and synthetic aperture radar Sentinel-1 images. Sentinel-1 VH and VV difference (>7.5 dB) and VH backscatter intensity were used to discriminate oil palm plantations. A combination of Landsat-8 NDVI, NDMI with Sentinel-1 VV and VH were used to discriminate rubber plantations. The improved map produced four land cover classes: native forest, oil palm plantation, rubber plantation, and non-forest. High-resolution SPOT 6/7 imagery and ground truth data were used for validation of the new classified maps. The map had an overall accuracy of 92%; producer’s accuracy for all classes was higher than 90%, except for rubber (65%), and user’s accuracy was over 80% for all classes. These results demonstrate that indices developed from a combination of optical and radar images can improve our ability to discriminate between native forest and oil palm and rubber plantations in the tropics. The new mapping method will help to support Indonesia’s national forest monitoring system and inform monitoring of plantation expansion

Automated reconstruction of tree and canopy structure for modeling the internal canopy radiation regime

Understanding canopy radiation regimes is critical to successfully modeling vegetation growth and function. For instance, the vertical distribution of photosynthetically active radiation (PAR) affects vegetation growth, informative upon carbon and energy cycling. Availing upon advances in information capture and computing power, geometrically explicit modeling of forest structure becomes increasingly possible. A primary challenge however is acquiring the forest mensuration data required to parameterize these models and the related automation of modeling forest structure. In this research, to address these issues we employ a novel and automated approach that capitalizes upon the rich information afforded by ground-based laser scanning technology. The method is implemented in two steps: in the first step, geometric explicit models of canopy structure are created from the ground-based laser scanning data. These geometric explicit models are used to simulate the vertical range to first hit. In the second step, we derive canopy gap probability from full waveform laser scanning data which have been used in a number of studies for characterization of radiation transmission (Jupp et al., 2009; Yang et al., 2010) and do not require any geometric explicit modeling. The radiative consistency of the geometric explicit models from step 1 is validated against the gap probabilities of step 2. The results show a strong relationship between the radiative transmission properties of the geometric models and canopy gap probabilities at plot level (R = 0.91 to 0.97), while the geometric models suggest the additional benefit to serve as a bridge in scaling between shoot level and canopy level radiation. ?? 2013 Elsevier Inc

Comparison of terrestrial and airborne LiDAR in describing stand structure of a thinned lodgepole pine forest

Airborne LiDAR (ALS) has been widely used for measuring canopy structure, but much of the woody components of the canopy are not directly visible with this system. Terrestrial LiDAR (TLS) data may help fill this gap by helping to understand the relationship between above- and below-canopy architecture. In this study, we report on the potential for combining TLS and ALS, thereby focusing on forest inventory and wood quality?related characteristics (such as number and dimension of branches). Our results show that both TLS and ALS were able to describe stand height using the top 10% of LiDAR returns at a high level of precision; however, TLS measurements were negatively biased by approximately 1 m (R 2 = 0.96 and 0.86 for ALS and TLS, respectively; P < 0.05). The distribution of foliage measured by ALS and TLS was strongly related to basal area (R 2 = 0.63 and 0.91 for ALS and TLS, respectively) and stand density (R 2 = 0.89 and 0.72 for ALS and TLS, respectively). Tree-level attributes were more accurately described by TLS (R 2 = 0.63) compared with ALS (R 2 = 0.37) for crown depth and a similar result applied to dbh with R 2 = 0.63 for TLS versus R 2 = 0.43 for ALS

Assessment of standing wood and fiber quality using ground and airborne laser scanning: A review

Accurate information on the wood-quality characteristics of standing timber and logs is needed to optimize the forest production value chain and to assess the potential of forest resources to meet other services. Physical and chemical characteristics of wood vary with both tree and site characteristics. At the tree scale, crown development, stem shape and taper, branch size and branch location, knot size, type and placement, and age all influence wood properties. More broadly, at the stand level, stocking density, moisture, nutrient availability, climate, competition, disturbance, and stand age have also been identified as key determinants of wood quality. Such information is often captured in polygon based forest inventory data. Other terrain-related spatial information, such as elevation, slope and aspect, can improve assessments of site conditions and limitations upon plant growth which impact wood quality. Light Detection And Ranging (LiDAR) is an emerging technology, which directly measures the three-dimensional structure of forest canopies using ground or airborne laser instruments, and can provide highly accurate information on individual-tree and stand-level forest structure. In this paper, we explore the potential of LiDAR and other geospatial information sources to model and predict wood quality based on individual-tree and stand structural metrics. We identify a number of key wood quality attributes (i.e., basic wood density, cell perimeter, cell coarseness, fiber length, and microfibril angle) and demonstrate links between these properties and forest structure and site attributes. Finally, the potential for using LiDAR in combination with other geospatial information sources to predict wood quality in standing timber is discussed. ?? 2011 Elsevier B.V

Estimating canopy structure of Douglas-fir forest stands from discrete-return LiDAR

Variations in vertical and horizontal forest structure are often difficult to quantify as field-based methods are labour intensive and passive optical remote sensing techniques are limited in their capacity to distinguish structural changes occurring below the top of the canopy. In this study the capacity of small footprint (0.19 cm), discrete return, densely spaced (0.7 hits/m?2), multiple return, Light Detection and Ranging (LiDAR) technology, to measure foliage height and to estimate several stand and canopy structure attributes is investigated. The study focused on six Douglas-fir [Pseudotsuga menziesii spp. menziesii (Mirb.) Franco] and western hemlock [Tsuga heterophylla (Raf.) Sarg.] stands located on the east coast of Vancouver Island, British Columbia, Canada, with each stand representing a different structural stage of stand development for forests within this biogeoclimatic zone. Tree height, crown dimensions, cover, and vertical foliage distributions were measured in 20 m × 20 m plots and correlated to the LiDAR data. Foliage profiles were then fitted, using the Weibull probability density function, to the field measured crown dimensions, vertical foliage density distributions and the LiDAR data at each plot. A modified canopy volume approach, based on methods developed for full waveform LiDAR observations, was developed and used to examine the vertical and horizontal variation in stand structure. Results indicate that measured stand attributes such as mean stand height, and basal area were significantly correlated with LiDAR estimates (r 2 = 0.85, P < 0.001, SE = 1.8 m and r 2 = 0.65, P < 0.05, SE = 14.8 m2 ha?1, respectively). Significant relationships were also found between the LiDAR data and the field estimated vertical foliage profiles indicating that models of vertical foliage distribution may be robust and transferable between both field and LiDAR datasets. This study demonstrates that small footprint, discrete return, LiDAR observations can provide quantitative information on stand and tree height, as well as information on foliage profiles, which can be successfully modelled, providing detailed descriptions of canopy structure