MAPPING OF BLUE CARBON ECOSYSTEMS: EFFECT OF PROXIMITY, ACTIVITY TYPES AND FREQUENCY OF VISITS IN THE ACCURACY OF PARTICIPATORY MAPS

Interest in blue carbon has drastically increased in recent years, particularly in improving the coastal resource carbon storage estimates and the development of methodology for identifying and monitoring such resources. In coastal resource mapping, participatory mapping techniques have the potential to provide a level of granularity and detail by taking advantage of local knowledge. In this work, we aim to evaluate the agreement between blue carbon ecosystem status obtained from participatory mapping versus the ones discriminated from satellite images, as well as assess how “relative proximity” to landmarks affects the accuracy. Results showed that the accuracy of mapped mangrove extents, evaluated as intersection-over-union, is positively correlated with frequency of visits. It is also found that maps generated by participants who have jobs or activities that nurture awareness about mangroves and seagrasses tend to agree better with remotely-sensed maps. The participants were even able to identify small patches of mangroves and seagrasses that are not present in the classified satellite images. While our initial work explores factors that impact the consistency between these sources, there is a need to establish a baseline for which both sources of information are evaluated against; and define relevant accuracy metrics. Our final goal is to systematically combine these two sources of information for an accurate holistic coastal resource map

QUALITY ASSESSMENT AND CONTROL OF OUTPUTS OF A NATIONWIDE AGRICULTURAL LAND COVER MAPPING PROGRAM USING LIDAR: PHIL-LIDAR 2 PARMAP EXPERIENCE

The Agricultural Resources Extraction from LiDAR Surveys (PARMAP) project component of the Nationwide Detailed Resources Assessment using LiDAR (Phil-LiDAR 2) Program aims to produce detailed agricultural maps using LiDAR. Agricultural land cover at crop level was classified through object based image analysis using Support Vector Machine as classifier and LiDAR derivatives from point cloud (2 points per sq.m.) and orthophoto (0.5-meter resolution) as inputs. An accuracy of at least 90 %, assessed using validation points from the field and through image interpretation, was required before proceeding to post-processing and map lay-out. Knowledge sharing and capacity development facilitated by the University of the Philippines Diliman (UPD) enabled partner universities across the Philippines to produce outputs for their assigned region. Considering output layers were generated by multiple teams working on different landscape complexities with some degree of data quality variability, quality checking is crucial to ensure accuracy standards were met. UPD PARMap devised a centralized and end-to-end scheme divided into four steps – land classification, GIS post-processing, schema application, and map lay-out. At each step, a block is reviewed and, subsequently, either approved or returned with documentation on required revisions. Turnaround time of review is at least one block (area ranging from 10 to 580 sq. km.) per day. For coastal municipalities, an additional integration process to incorporate mapped coastal features was applied. Common problems observed during quality checking include misclassifications, gaps between features, incomplete attributes and missing map elements. Some issues are particular to specific blocks such as problematic LiDAR derivatives. UPD addressed these problems through discussion and mentoring visits to partner universities. As of March 2017, a total of 336 municipal agricultural maps have been turned-over to various stakeholders. For the remaining months of the program, an additional 360 maps are expected to be distributed

Outpatient COVID-19 convalescent plasma recipient antibody thresholds correlated to reduced hospitalizations within a randomized trial

BACKGROUND COVID-19 convalescent plasma (CCP) viral specific antibody levels that translate into recipient post-transfusion antibody levels sufficient to prevent disease progression is not defined.METHODS This secondary analysis correlated donor and recipient antibody levels to hospitalization risk among unvaccinated, seronegative CCP recipients within the outpatient, double blind, randomized clinical trial that compared CCP to control plasma. The majority of COVID-19 CCP arm hospitalizations (15/17, 88%) occurred in this unvaccinated, seronegative subgroup. A functional cutoff to delineate recipient high versus low post-transfusion antibody levels was established by two methods: 1) analyzing virus neutralization-equivalent anti-Spike-receptor-binding-domain immunoglobulin G (anti-S-RBD IgG) responses in donors or 2) receiver operated curve (ROC) analysis.RESULTS SARS-CoV-2 anti-S-RBD IgG antibody was volume diluted 21.3 fold into post-transfusion seronegative recipients from matched donor units. Viral specific antibody delivered approximated 1.2 mg. The high antibody recipients transfused early (symptom onset within 5 days) had no hospitalizations. A CCP recipient analysis for antibody thresholds correlated to reduced hospitalizations found a statistical significant association between early transfusion and high antibodies versus all other CCP recipients (or control plasma) with antibody cutoffs established by both methods-donor-based virus neutralization cutoff in post-transfusion recipients: (0/85; 0% versus 15/276; 5.6%) p=0.03 or ROC based cutoff: (0/94; 0% versus 15/267; 5.4%) p=0.01.CONCLUSION In unvaccinated, seronegative CCP recipients, early transfusion of plasma units in the upper 30% of study donors antibody levels reduced outpatient hospitalizations. High antibody level plasma units, given early, should be reserved for therapeutic use. Trial registration: NCT04373460 FUNDING Defense Health Agency and others