3D LiDAR Scanning of Urban Forest Structure Using a Consumer Tablet

Forest measurements using conventional methods may not capture all the important information required to properly characterize forest structure. The objective of this study was to develop a low-cost alternative method for forest inventory measurements and characterization of forest structure using handheld LiDAR technology. Three-dimensional (3D) maps of trees were obtained using an iPad Pro with a LiDAR sensor. Freely-available software programs, including 3D Forest Software and CloudCompare software, were used to determine tree diameter at breast height (DBH) and distance between trees. The 3D point cloud data obtained from the iPad Pro LiDAR sensor was able to estimate tree DBH accurately, with a residual error of 2.4 cm in an urban forest stand and 1.9 cm in an actively managed experimental forest stand. Distances between trees also were accurately estimated, with mean residual errors of 0.21 m for urban forest, and 0.38 m for managed forest stand. This study demonstrates that it is possible to use a low-cost consumer tablet with a LiDAR sensor to accurately measure certain forest attributes, which could enable the crowdsourcing of urban and other forest tree DBH and density data because of its integration into existing Apple devices and ease of use

Spatiotemporal Analysis of Soil Quality Degradation and Emissions in the State of Iowa (USA)

The concept of soil quality (SQ) is defined as the soil\u27s capacity to function, which is commonly assessed at the field scale. Soil quality is composed of inherent (soil suitability) and dynamic (soil health, SH) SQ, which can also be analyzed using geospatial tools as a SQ continuum (SQC). This study proposes an innovative spatiotemporal analysis of SQ degradation and emissions from land developments using the state of Iowa (IA) in the United States of America (USA) as a case study. The SQ degradation was linked to anthropogenic soil (SD) and land degradation (LD) in the state. More than 88% of land in IA experienced anthropogenic LD primarily due to agriculture (93%). All six soil orders were subject to various degrees of anthropogenic LD: Entisols (75%), Inceptisols (94%), Histosols (59%), Alfisols (79%), Mollisols (93%), and Vertisols (98%). Soil and LD have primarily increased between 2001 and 2016. In addition to agricultural LD, there was also SD/LD caused by an increase in developments often through urbanization. All land developments in IA can be linked to damages to SQ, with 8385.9 km2 of developed area, causing midpoint total soil carbon (TSC) losses of 1.7 × 1011 kg of C and an associated midpoint of social cost of carbon dioxide emissions (SC-CO2) of $28.8B (where B = billion = 109, USD). More recently developed land area (398.5 km2) between 2001 and 2016 likely caused the midpoint loss of 8.0 × 109 kg of C and a corresponding midpoint of$1.3B in SC-CO2. New developments are often located near urban areas, for example, near the capital city of Des Moines, and other cities (Sioux City, Dubuque). Results of this study reveal several different kinds of SQ damage from developments: loss of potential for future C sequestration in soils, soil C loss, and “realized” soil C social costs (SC-CO2). The state of IA has very limited potential land (2.0% of the total state area) for nature-based solutions (NBS) to compensate for SD and LD. The results of this study can be used to support pending soil health-related legislation in IA and monitoring towards achieving the Sustainable Development Goals (SDGs) developed by the United Nations (UN) by providing a landscape-level perspective on LD to focus field-level initiatives to reduce soil loss and improve SQ. Future technological innovations will provide higher spatial and temporal remote sensing data that can be fused with field-level direct sensing to track SH and SQ changes

Climate Change Planning: Soil Carbon Regulating Ecosystem Services and Land Cover Change Analysis to Inform Disclosures for the State of Rhode Island, USA

The state of Rhode Island (RI) has established its greenhouse gas (GHG) emissions reduction goals, which require rapidly acquired and updatable science-based data to make these goals enforceable and effective. The combination of remote sensing and soil information data can estimate the past and model future GHG emissions because of conversion of “low disturbance” land covers (e.g., evergreen forest, hay/pasture) to “high disturbance” land covers (e.g., low-, medium-, and high-intensity developed land). These modeled future emissions can be used as a predevelopment potential GHG emissions information disclosure. This study demonstrates the rapid assessment of the value of regulating ecosystems services (ES) from soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TSC) stocks, based on the concept of the avoided social cost of carbon dioxide (CO2) emissions for RI by soil order and county using remote sensing and information from the State Soil Geographic (STATSGO) and Soil Survey Geographic Database (SSURGO) databases. Classified land cover data for 2001 and 2016 were downloaded from the Multi-Resolution Land Characteristics Consortium (MRLC) website. Obtained results provide accurate and quantitative spatio-temporal information about likely GHG emissions and show their patterns which are often associated with existing urban developments. These remote sensing tools could be used by the state of RI to both understand the nature of land cover change and likely GHG emissions from soil and to institute mandatory or voluntary predevelopment assessments and potential GHG emissions disclosures as a part of a climate mitigation policy

Codon usage comparison of novel genes in clinical isolates of Haemophilus influenzae

A similarity statistic for codon usage was developed and used to compare novel gene sequences found in clinical isolates of Haemophilus influenzae with a reference set of 80 prokaryotic, eukaryotic and viral genomes. These analyses were performed to obtain an indication as to whether individual genes were Haemophilus-like in nature, or if they probably had more recently entered the H.influenzae gene pool via horizontal gene transfer from other species. The average and SD values were calculated for the similarity statistics from a study of the set of all genes in the H.influenzae Rd reference genome that encoded proteins of 100 amino acids or longer. Approximately 80% of Rd genes gave a statistic indicating that they were most like other Rd genes. Genes displaying codon usage statistics >1 SD above this range were either considered part of the highly expressed group of H.influenzae genes, or were considered of foreign origin. An alternative determinant for identifying genes of foreign origin was when the similarity statistics produced a value that was much closer to a non-H.influenzae reference organism than to any of the Haemophilus species contained in the reference set. Approximately 65% of the novel sequences identified in the H.influenzae clinical isolates displayed codon usages most similar to Haemophilus sp. The remaining novel sequences produced similarity statistics closer to one of the other reference genomes thereby suggesting that these sequences may have entered the H.influenzae gene pool more recently via horizontal transfer

Evaluation of Riparian Tree Cover and Shading in the Chauga River Watershed Using LiDAR and Deep Learning Land Cover Classification

River systems face negative impacts from development and removal of riparian vegetation that provide critical shading in the face of climate change. This study used supervised deep learning to accurately classify the land cover, including shading, of the Chauga River watershed, located in Oconee County, South Carolina, for 2011 and 2019. The study examined the land cover differences along the Chauga River and its tributaries, inside and outside the Sumter National Forest. LiDAR data were incorporated in solar radiation calculations for the Chauga River inside and outside the National Forest. The deep learning classifications produced land cover maps with high overall accuracy (97.09% for 2011; 97.58% for 2019). The most significant difference in land cover was in tree cover in the 50 m buffer of the tributaries inside the National Forest compared to the tributaries outside the National Forest (2011: 95.39% vs. 81.84%, 2019: 92.86% vs. 82.06%). The solar radiation calculations also confirmed a difference between the area inside and outside the National Forest, with the mean temperature being greater outside the protected area (outside: 455.845 WH/m2; inside: 416,770 WH/m2). This study suggests that anthropogenic influence in the Chauga River watershed is greater in the areas outside the Sumter National Forest, which could cause damage to the river ecosystem if left unchecked in the future as development pressures increase. This study demonstrates the accurate application of deep learning for high-resolution classification of river shading combined with the use of LiDAR data to estimate solar radiation reaching the Chauga River. Techniques to monitor riparian zones and shading at high spatial resolutions are critical for the mitigation of the negative impacts of warming climates on aquatic ecosystems

Bacteria in Construction Site Sediment Basins

2010 S.C. Water Resources Conference - Science and Policy Challenges for a Sustainable Futur

Characterization and modeling of the Haemophilus influenzae core and supragenomes based on the complete genomic sequences of Rd and 12 clinical nontypeable strains

The genomes of 9 non-typeable H. influenzae clinical isolates were sequenced and compared with a reference strain, allowing the characterisation and modelling of the core-and supra genomes of this organism

Diagnostic Informatics—The Role of Digital Health in Diagnostic Stewardship and the Achievement of Excellence, Safety, and Value

Diagnostic investigations (pathology laboratory and medical imaging) aim to: increase certainty of the presence or absence of disease by supporting the process of differential diagnosis; support clinical management; and monitor a patient's trajectory (e. g., disease progression or response to treatment). Digital health can be defined as the collection, storage, retrieval, transmission, and utilization of data, information, and knowledge to support healthcare. Digital health has become an essential component of the diagnostic process, helping to facilitate the accuracy and timeliness of information transfer and enhance the effectiveness of decision-making processes. Digital health is also important to diagnostic stewardship, which involves coordinated guidance and interventions to ensure the appropriate utilization of diagnostic tests for therapeutic decision-making. Diagnostic stewardship and informatics are thus important in efforts to establish shared decision-making. This is because they contribute to the establishment of shared information platforms (enabling patients to read, comment on, and share in decisions about their care) based on timely and meaningful communication. This paper will outline key diagnostic informatics and stewardship initiatives across three interrelated fields: (1) diagnostic error and the establishment of outcomes-based diagnostic research; (2) the safety and effectiveness of test result management and follow-up; and (3) digitally enhanced decision support systems