Mapping Program Research Articles

An Inter-Method Comparison of Drones, Side-Scan Sonar, Airplanes, and Satellites Used for Eelgrass (Zostera marina) Mapping and Management

Remote sensing is heavily relied upon where eelgrass maps are needed for tracking trends, project siting and permitting, water quality assessments, and restoration planning. However, there is only a moderate degree of confidence in the accuracy of maps derived from remote sensing, thus risking inadequate resource protection. In this study, semi-synchronous drone, side-scan sonar, airplane, and satellite missions were conducted at five Massachusetts eelgrass meadows to assess each method’s edge-detection capability and mapping accuracy. To ground-truth the remote sensing surveys, SCUBA divers surveyed the meadow along transects perpendicular to shore to locate the last shoot (i.e., meadow’s edge) and sampled quadrat locations along the transect for percent cover, canopy height, and meadow patchiness. In addition, drop frame underwater camera surveys were conducted to assess the accuracy of each remote sensing survey. Eelgrass meadow delineations derived from each remote sensing method were compared to ground-truthing data to address the following study objectives: (1) determine if and how much eelgrass was missed during manual photointerpretation of the imagery from each remote sensing method, (2) assess map accuracy, as well as the effects of eelgrass percent cover, canopy height, and meadow patchiness on method performance, and (3) make management recommendations regarding the use of remote sensing data for eelgrass mapping. Results showed that all remote sensing methods were associated with the underestimation of eelgrass. At the shallow edge, mean edge detection error was lowest for drone imagery (11.2 m) and increased with decreasing image resolution, up to 38.5 m for satellite imagery. At the deep edge, mean edge detection error varied by survey method but ranged from 72 to 106 m. Maximum edge detection errors across all sites and depths for each survey method were 112.4 m, 121.4 m, 121.7 m, and 106.7 m for drone, sonar, airplane, and satellite data, respectively. The overall accuracy of eelgrass delineations across the survey methods ranged from 76–89% and corresponded with image resolution, where drones performed best, followed by sonar, airplanes, and satellites; however, there was a high degree of site variability. Accuracy at the shallow edge was greater than at the deep edge across all survey types except for satellite, where accuracy was the same at both depths. Accuracy was influenced by eelgrass percent cover, canopy height, and meadow patchiness. Low eelgrass density (i.e., 1–10% cover), patchy eelgrass (i.e., shoots or patches spaced > 5 m) and shorter canopy height (i.e., <21 cm) were associated with reduced accuracy across all methods; however, drones performed best across all scenarios. Management recommendations include applying regulatory buffers to eelgrass maps derived from remote sensing in order to protect meadow edge areas from human disturbances, the prioritization of using SCUBA and high-resolution platforms like drones and sonar for eelgrass mapping, and for existing mapping programs to allocate more resources to ground-truthing along meadow edges.

Proposed Method For Studying Radon Exhalation

Objective: The main objective was to test the closed radon exhalation methodology, with AlphaGuard radon detector, for different grain sizes of a granite rock sampled from central area of Belo Horizonte and propose experimental methods for its determination. Theoretical Framework: Natural radioactivity accounts for about 80% of the average annual radiation dose humans receive, primarily due to primordial radionuclides in the Earth’s crust. The World Health Organization (WHO) recommends national radon (Rn) exposure mapping programs based on geological studies due to the significant contribution of Rn gas to annual radiation doses. Studies indicate that granitic and metamorphic rocks, black shales, phosphate rocks, and some carbonate rocks are common radon sources. Rn is generated in soil and migrates through soil pores, entering buildings through cracks and openings. Rn transport is influenced by factors like local geology, humidity, and temperature. Method: The study tested radon exhalation from granite samples in center of Belo Horizonte using a proposed method with AlphaGuard in an exhalation circuit. Results and Discussion The studied method proved to be effective with particle sizes up to 16#. For very fine particle sizes, the air flow is hampered and therefore reduces the effectiveness of transporting the radon-filled gas. Research Implications: The implications of this research are discussed, providing insights into radon exhalation method in a closed circuit and its challenges. This study contributes to literature by studing exhalation rate is vital for estimating radiation risk from many kinds of materials and the indoor radon.

Emission reduction strategies and health: a systematic review on the tools and methods to assess co-benefits

ObjectiveThe objective of this study is to review the current literature on the health co-benefits of emission reduction strategies and the methods and tools available to assess them.DesignSystematic review conducted...

Inter-Sensor Level 1 Radiometric Comparisons Using Deep Convective Clouds

To evaluate the radiometric performance of top-of-atmosphere reflectance images, Deep Convective Clouds (DCCs) can be used as temporally, spatially and spectrally stable targets. The DCCs method has been developed more than 20 years ago and applied recently to Sentinel-2 and Sentinel-3 sensors. In this paper, among other developments, we built a new methodology upon those existing by using the bootstrap method and spectral band adjustment factors computed with the Hyper-Spectral Imager (HSI) from the Environmental Mapping and Analysis Program (EnMAP). This methodology is applied to the two Multi-Spectral Imager (MSI) instruments onboard Sentinel-2A and 2B, but also the two Operational Land Imager (OLI) instruments onboard Landsat 8 and 9, from visible wavelength at 442 nm to shortwave-infrared at 2200 nm, using images with a ground resolution spanning from 10 m to 60 m. The results demonstrate the good inter-calibration of MSI units A and B, which are within one percent of relative difference on average between January 2022 and June 2024 for all visible, near-infrared and shortwave-infrared bands, except for the band at 1375 nm for which saturation prevents the use of the method. Similarly, OLI and OLI-2 are found to have a relative difference on the same period lower than one percent for all 30 m resolution bands. Evaluation of the relative difference between the MSI sensors and the OLI sensors with the DCCs method gives values lower than three percent. Finally, these validation results are compared to those obtained with Pseudo-Invariant Calibration Sites (PICSs) over Libya-4: an agreement better than two percent is found between the DCCs and PICSs methods.

PIMSAB: A Processing-In-Memory System with Spatially-Aware Communication and Bit-Serial-Aware Computation

Bit-serial Processing-In-Memory (PIM) is an attractive paradigm for accelerator architectures, for parallel workloads such as Deep Learning (DL), because of its capability to achieve massive data parallelism at a low area overhead and provide orders-of-magnitude data movement savings by moving computational resources closer to the data. While many PIM architectures have been proposed, improvements are needed in communicating intermediate results to consumer kernels, for communication between tiles at scale, for reduction operations, and for efficiently performing bit-serial operations with constants. We present PIMSAB, a scalable architecture that provides a spatially aware communication network for efficient intra-tile and inter-tile data movement and provides efficient computation support for generally inefficient bit-serial compute patterns. Our architecture consists of a massive hierarchical array of compute-enabled SRAMs (CRAMs), which is codesigned with a compiler to achieve high utilization. The key novelties of our architecture are (1) in providing efficient support for spatially aware communication by providing local H-tree network for reductions, by adding explicit hardware for shuffling operands, and by deploying systolic broadcasting, as well as (2) by taking advantage of the divisible nature of bit-serial computations through adaptive precision and efficient handling of constant operations. These innovations are integrated into a tensor expressions-based programming framework (including a compiler for easy programmability) that enables simple programmer control of optimizations for mapping programs into massively parallel binaries for millions of PIM processing elements. When compared against a similarly provisioned modern Tensor Core GPU (NVIDIA A100), across common DL kernels and end-to-end DL networks (Resnet18 and BERT), PIMSAB outperforms the GPU by 4.80×, and reduces energy by 3.76×. We compare PIMSAB with similarly provisioned state-of-the-art SRAM PIM (Duality Cache) and DRAM PIM (SIMDRAM), and observe a speedup of 3.7× and 3.88×, respectively.

Development of Novel InDel Markers for Breeding Applications in Pomegranate (Punica granatum L.)

ABSTRACTThe present study aims to enrich the genetic marker resources that are amenable for future trait mapping and quality improvement programmes in pomegranate. We analysed 140 well‐characterized genes that belonged to seven important gene families involved in the growth and development of pomegranate. We retrieved 140 representative genes for seven gene families from four draft genomes of pomegranate, cvs. Tunisia, Dabenzi, Taishanhong and Bhagawa, using BLASTn homology search. A total of 245 InDel primers were designed based on the comparison among four pomegranate genomes for the representative gene sequences, focusing on a size difference of at least 8 base pairs (bp). One hundred forty‐eight (60.41%) markers were in silico verified through e‐PCR on the Tunisia genome and further confirmed on the other three genomes, and we identified 107 polymorphic markers with an average PIC value of 0.62. By using 148 InDel markers physically mapped on the genome, we found that Tunisia had the highest syntenic relations with the Bhagawa (100%), Dabenzi (97.97%) and Taishanhong (97.30%) genomes. Experimental validation of 54 InDel primers on six pomegranate genotypes identified a set of 37 (71.15%) polymorphic markers, 24 of which had PIC values of ≥ 0.48. An investigation on genetic diversity and phenotype correlations in 16 pomegranate genotypes using 16 InDel markers demonstrated the utility of these markers. The developed InDel markers are an excellent resource for diverse applications in pomegranate including genetic diversity, trait mapping, gene discovery and gene editing.

Improved snow property retrievals by solving for topography in the inversion of at-sensor radiance measurements

Abstract. Accurately modelling optical snow properties like snow albedo and specific surface area (SSA) are essential for monitoring the cryosphere in a changing climate and are parameters that inform hydrologic and climate models. These snow surface properties can be modelled from spaceborne imaging spectroscopy measurements but rely on digital elevation models (DEMs) of relatively coarse spatial scales (e.g. Copernicus at 30 m), which degrade accuracy due to errors in derived products such as slope and aspect. In addition, snow deposition and redistribution can change the apparent topography, and thereby static DEMs may not be considered coincident with the imaging spectroscopy dataset. Testing in three different snow climates (tundra, maritime, alpine), we established a new method that simultaneously solves snow, atmospheric, and terrain parameters, enabling a solution that is more unified across sensors and introduces fewer sources of uncertainty. We leveraged imaging spectroscopy data from Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and PRecursore IperSpettrale della Missione Applicativa (PRISMA) (collected within 1 h) to validate this method and showed a 25 % increase in performance for the radiance-based method over the static method when estimating SSA. This concept can be implemented in missions such as Surface Biology and Geology (SBG), the Environmental Mapping and Analysis Program (EnMap), and the Copernicus Hyperspectral Imaging Mission for the Environment (CHIME).

Mapping Leaf Mass Per Area and Equivalent Water Thickness from PRISMA and EnMAP

With the continued advancement of spaceborne hyperspectral sensors, hyperspectral remote sensing is evolving as an increasingly pivotal tool for high-precision global monitoring applications. Novel image spectroscopy data, e.g., the PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Environmental Mapping and Analysis Program (EnMAP), can rapidly and non-invasively capture subtle spectral information of terrestrial vegetation, facilitating the precise retrieval of the required vegetation parameters. As critical vegetation traits, Leaf Mass per Area (LMA) and Equivalent Water Thickness (EWT) hold significant importance for comprehending ecosystem functionality and the physiological status of plants. To address the demand for high-precision vegetation parameter datasets, a hybrid modeling approach was proposed in this study, integrating the radiative transfer model PROSAIL and neural network models to retrieve LMA and EWT from PRISMA and EnMAP images. To achieve this objective, canopy reflectance was simulated via PROSAIL, and the optimal band combinations for LMA and EWT were selected as inputs to train neural networks. The evaluation of the hybrid inversion models over field measurements showed that the RMSE values for the LMA and EWT were 4.11 mg·cm−2 and 9.08 mg·cm−2, respectively. The hybrid models were applied to PRISMA and EnMAP images, resulting in LMA and EWT maps displaying adequate spatial consistency, along with cross-validation results showing high accuracy (RMSELMA = 5.78 mg·cm−2, RMSEEWT = 6.84 mg·cm−2). The results demonstrated the hybrid inversion model’s universality and applicability, enabling the retrieval of vegetation parameters from image spectroscopy data and offering a valuable contribution to hyperspectral remote sensing for vegetation monitoring, though the availability of field measurement data remained a significant challenge.

A Spatiotemporal Empowerment Framework for China’s National 3D Mapping Program 3dRGLM

Abstract. China has launched a national 3D mapping program to build 3D Realistic geospatial Landscape Model (3dRGLm) over the whole country, covering both cities and countryside. The 3dRGLM is a digital description and representation of the real 3D geospatial spaces, and is defined as a new generation geospatial data product with 3D structures, realistic scenes, and geospatial entities. Such a national or local 3dRGLm will set up a digital 3D realistic geospatial space which can facilitate the vital connection with the real geospatial space, and will serve as a new generation of geospatial information infrastructure for our society. With the acquisition of complex 3D spatial data continues to advance, managing and utilizing the massive volumes of 3D data to provide spatiotemporal empowerment applications has become a significant technological challenge. This paper analyzes the technical characteristics of 3d realistic geospatial landscape model, and then proposes the main content and basic structure of a 3d realistic geospatial landscape model database, as well as the spatiotemporal service empowerment methods based on the database. Finally, a case analysis is provided to serve as a reference for the construction and application of 3d realistic geospatial landscape.

The Lechaion Harbor and Settlement Land Project: Preliminary Report, 2016–2018

ABSTRACT: Lechaion Harbor was the primary port of ancient Corinth, serving as a major economic and commercial hub in the Mediterranean for ca. 1,300 years. The Lechaion Harbor and Settlement Land Project began investigations in 2016 as an integrated archaeological program of geophysical survey, mapping, GIS studies, scientific analyses, and excavation. Our initial three seasons of fieldwork have revealed the scale and size of the harbor’s settlement, the active use of the site by the 8th century bce and perhaps the Late Bronze Age, a significant early Roman basilica, and a large circular structure that may be a lighthouse; they have also provided a better understanding of the settlement’s general abandonment in the 6th century ce.

Mapping forest tree species and its biodiversity using EnMAP hyperspectral data along with Sentinel-2 temporal data: An approach of tree species classification and diversity indices

Forests play a crucial role in maintaining ecological balance and biodiversity, making the accurate mapping of tree species and assessment of biodiversity indices essential for informed management decisions. This study introduces an innovative methodology that integrates EnMAP (Environmental Mapping and Analysis Program) hyperspectral data with Sentinel-2 multitemporal data to classify tree species in the biodiverse landscapes of Kampinos National Park and its surrounding regions in Poland.We extract essential vegetation indices such as NDVI, NDMI, SAVI, and EVI from Sentinel-2 data to assess forest health and dynamics. The Sentinel-2 data is upscaled from 10 m to 30 m to align with EnMAP’s spatial resolution, followed by precise co-registration of the images using QGIS. Utilizing a rich dataset from the National Forest Inventory, we extract spectral signatures of nine distinct tree species from both data sources. We employ five machine learning algorithms—Support Vector Machines (SVM), Random Forest (RF), CatBoost (CAT), Gradient Boosting Classifier (GBC), and XGBoost (XGB)—to enhance classification accuracy.Through iterative experimentation with data reduction techniques and algorithm tuning, we achieve optimal performance across needle-leaved and broad-leaved species. The resulting tree species maps are validated through quantitative accuracy assessments against mixed-species polygons from the National Forest Inventory and ground truthing in the Kampinos National Park. Achieving an overall accuracy of 85% to 93%, our study demonstrates the efficacy of this integrated approach in tree species mapping. Furthermore, the tree species maps serve as a foundation for deriving key biodiversity indices—species richness, Shannon-Wiener Diversity Index, Simpson’s Diversity Index, and a composite Biodiversity Index—providing insights into spatial biodiversity patterns and informing targeted conservation strategies. This study exemplifies the potential of combining advanced remote sensing techniques with field validation to enhance our understanding of forest ecosystems and guide sustainable management practices.

Predictive GAM seabed maps can account for defined and fuzzy boundaries to improve accuracy in a Scottish sea loch seascape

Marine seabed mapping is an important element in marine spatial and conservation planning. Recent large scale mapping programmes have greatly increased our knowledge of the seafloor, yet at finer resolutions, large gaps remain. Loch Eriboll, Scotland, is an area of conservation interest with a diverse marine environment supporting habitats and species of conservation importance. Here we test and present strategies for a predictive seabed substrata map for Loch Eriboll using drop down Stereo Baited Remote Underwater Video (SBRUV) imagery collected as part of systematic underwater survey of the Loch. A total of 216 SBRUV deployments were made across the study site in depths of 3 m–117 m, with six seabed classes identified using an adaptation of the EUNIS (European Nature Information System) hierarchical habitat classification scheme. Four statistical learning approaches were tested, we found, Generalised Additive Models (GAMs) provided the optimal balance between over- and underfitted predictions. We demonstrate the creation of a predictive substratum habitat map covering 63 km2 of seabed which predicts the probability of presence and relative proportion of substratum types. Our method enables naturally occurring edges between habitat patches to be described well, increasing the accuracy of mapping habitat boundaries when compared to categorical approaches. The predictions allow for both defined boundaries such as those between sand and rock and fuzzy boundaries seen among fine mixed sediments to exist in the same model structure. We demonstrate that SBRUV imagery can be used to generate cost effective, fine scale predictive substrata maps that can inform marine planning. The modelling procedure presented has the potential for a wide adoption by marine stakeholders and could be used to establish baselines for long term monitoring of benthic habitats and further research such as animal distribution and movement work which require detailed benthic maps.

QTL mapping to identify loci and candidate genes associated with freezing tolerance trait in Camelina sativa

Lack of freezing tolerance is a major constraint for the production of agronomically important Brassica species, particularly in the Northern Great Plains (NGP) of the United States and Canada. However, within the Brassicaceae family, winter germplasm of camelina have shown excellent freezing tolerance and overwinter potential in the NGPs. Differences in freezing tolerance between a winter variety (Joelle) and a spring variety (C046) of camelina appear to be controlled by a small number of dominant or co-dominant genes. To unravel the genetic mechanisms for the differences in freezing tolerance, 254 Recombinant Inbred Lines (RILs) were developed using reciprocal crosses between these two camelina varieties. The RIL population was phenotyped at the F7 stage for freezing tolerance under controlled conditions and genotyped by whole-genome skim sequencing. A one-way ANOVA test revealed a significant (P < 0.001) difference exists among the RILs for freezing tolerance. A significant and strong correlation (r = 0.60, P < 0.001) was also observed between freezing tolerance and flowering time, indicating that regulation of flowering time might also influence freezing tolerance in camelina. A de novo linkage map was constructed using 4507 SNP markers covering a total of 1208.5 cM map distance with an average of 0.3 cM distances between the markers, which formed 20 linkage groups representing the 20 chromosomes (Chr) of C. sativa. The QTL analyses using three different programs revealed significant loci on Chr 8, 11, 13, 16 and 18 with LOD threshold value of over 3.5 for freezing tolerance. The QTL peaks with the greatest LOD values of 20.7 and 26.8 were observed at Chr 8 and Chr 13 and accounted for 18.3 % and 25.3 % of the phenotypic variation respectively. A total of 3369 annotated camelina genes were identified within ±50 Kb from the consensus QTL intervals generated from the output of the three mapping programs. Among them, 125 were transcription factors including twelve MIKC_MADS on Chr 8, 11, 13, 16 and 18 and two that annotate as the floral regulators FLOWERING LOCUS C (FLC) on Chr 8 and 13, an orthologue of MADS AFFECTING FLOWERING 3 and 4 (MAF4 and MAF3) of arabidopsis on Chr18, and an orthologue of SHORT VEGETATIVE PHASE (SVP) on Chr 16. Although many of the candidate genes identified near the freezing tolerance QTLs have previously been associated with flowering time, further studies are needed to help unravel how these genes impact freezing tolerance mechanisms and improve freezing tolerance in camelina and other Brassica crop species.

A novel antigen biomarker for detection of high-level of Loa loa microfilaremia.

Loiasis is a disease caused by the nematode Loa loa. Serious adverse events sometimes occur in people with heavy L. loa microfilaremia after ivermectin treatment. In regions of Central Africa where loiasis is endemic, this significantly impedes global elimination programs for lymphatic filariasis and onchocerciasis that use mass distribution of ivermectin. Improved diagnostic tests to identify individuals at increased risk of serious adverse events could facilitate efforts to eliminate lymphatic filariasis and onchocerciasis in this region. We previously identified the L. loa protein Ll-Bhp-1 in loiasis patient sera. Here, we further characterize Ll-Bhp-1 and report development of an antigen capture ELISA to detect this antigen. This assay detected Ll-Bhp-1 in 74 of 116 (63.8%) loiasis patient sera. Ll-Bhp-1 levels were significantly correlated with L. loa microfilarial counts, and the sensitivity of the assay was highest for samples from people with high counts, (94% and 100% in people with ≥20,000 and ≥50,000 microfilaria per milliliter of blood, respectively). The antigen was not detected in 112 sera from people with other filarial infections, or in 34 control sera from the USA. This Ll-Bhp-1 antigen assay is specific for loiasis, and highly sensitive for identifying people with high L. loa microfilarial counts who are at increased risk for serious adverse events after ivermectin treatment. L. loa antigen detection has the potential to facilitate loiasis mapping efforts and programs to eliminate lymphatic filariasis and onchocerciasis in Central Africa.

Assessment of the spaceborne EnMAP hyperspectral data for alteration mineral mapping: A case study of the Reko Diq porphyry Cu[sbnd]Au deposit, Pakistan

For over four decades, spaceborne multispectral data have played a crucial role in supporting mineral exploration and geologic mapping. The spaceborne multispectral datasets, however, have a restricted number of bands with coarse spectral resolution and, thus are very limited in mineral mapping. The advent of high-quality spaceborne imaging spectroscopic data like the Environmental Mapping and Analysis Program (EnMAP), has bridged this gap initiating a new era in global hyperspectral mineral mapping. The EnMAP satellite, operational since November 2022, covers the spectral range of 420 and 2450 nm in 224 bands, offering a spatial resolution of 30 m and a mean spectral sampling distance of 8.1 and 12.5 nm in the visible-near infrared and shortwave infrared regions, respectively. In this paper, we demonstrate the enhanced mapping capabilities of EnMAP using datasets acquired over the Reko Diq mining district, a cluster of Miocene porphyries located in Pakistan's Chagai Belt hosting an undeveloped world-class porphyry Cu-Au ± Mo deposit. The EnMAP's Level 2A data product was processed using the polynomial fitting technique to characterize the diagnostic absorption features of the alteration minerals in the Reko Diq porphyry system. This involved retrieving the minimum wavelength, depth, width, and asymmetry parameters for key absorption features and employing them interactively for mineral characterization. A diverse array of minerals were successfully mapped over the study area and validated by ground spectroscopy. This includes abundance/composition maps for white micas, chlorite, epidote, calcite, kaolinite, gypsum, jarosite, and ferric and ferrous iron minerals. The minimum wavelength of white mica was found to vary between 2195 and 2210 nm, with the shorter wavelengths (Al-rich) white mica occurring proximal to the known mineralized zones. The potassic alteration cores and the outer propylitic zones were identified by the ferrous iron and chlorite-epidote-calcite mineral maps, respectively. This study demonstrated the superiority of EnMAP hyperspectral data in delineating the alteration mineralogy and zonation pattern of porphyry copper systems. This capability can potentially contribute to the exploration of new deposits in exposed terrains worldwide.

Paediatric oncology physiotherapy in Africa: International Society of Paediatric Oncology (SIOP) Global Mapping Programme.

Childhood cancer treatment in Africa has a dramatically increasing patient population resulting in greater rehabilitation needs. The International Society of Paediatric Oncology (SIOP) mapped childhood cancer services in Africa including access to physiotherapy. Irrespective of income classification, just over two-thirds of countries in Africa reported having access to physiotherapy services in paediatric oncology sites. There is a lack of knowledge about African childhood physiotherapy services. Research is needed to understand the rehabilitation needs of these children/adolescents and how to meet their needs in a globally equitable and sustainable way.

International Society of Paediatric Oncology (SIOP) Global Mapping Program: Analysis of healthcare centers in countries of the Latin American Society of Pediatric Oncology (SLAOP).

The International Society of Paediatric Oncology Society Global Mapping Program aims to describe the local pediatric oncology capacities. Here, we report the data from Latin America. A 10-question survey was distributed among chairs of pediatric oncology services. Centers were classified according to patient volume into high- (HVC; 100 or more new cases per year), medium- (MVC; 31-99 cases), and low-volume centers (LVC; 30 cases or less), respectively. National referral centers (NRC) were identified. Total 307 centers in 20 countries were identified (271 responded), and 264 responses were evaluable, accounting for 78% of the expected cases (21,359 cases per year). Seventy-seven percent of patients are treated in public centers, including additional support by civil society organizations. We found that 66% of the patients are treated in 70 centers of excellence, including 21 NRC. There was a median of one pediatric oncologist every 21 newly diagnosed patients (44 for NRC), and in 84% of the centers, nurses rotated to other services. A palliative care team was lacking in 25% of the centers. LVC with public funding have significantly lower probability of having a palliative care team or trained pediatric oncology surgeons. Psychosocial, pharmacy, and nutrition services were available in more than 93% of the centers. No radiotherapy facility was available on campus in nine of 21 NRC. Most children with cancer in Latin America are treated in public HVC. There is a scarcity of pediatric oncologists, specialized nurses and surgeons, and palliative care teams, especially in centers with public funding.

Optimizing BenMAP health impact assessment with meteorological factor driven machine learning models

This study aims to address accuracy challenges in assessing air pollution health impacts using Environmental Benefits Mapping and Analysis Program (BenMap), caused by limited meteorological factor data and missing pollutant data. By employing data increment strategies and multiple machine learning models, this research explores the effects of data volume, time steps, and meteorological factors on model prediction performance using several years of data from Tianjin City as an example. The findings indicate that increasing training data volume enhances the performance of Random Forest Regressor (RF) and Decision Tree Regressor (DT) models, especially for predicting CO, NO2, and PM2.5. The optimal prediction time step varies by pollutant, with the DT model achieving the highest R2 value (0.99) for CO and O3. Combining multiple meteorological factors, such as atmospheric pressure, relative humidity, and dew point temperature, significantly improves model accuracy. When using three meteorological factors, the model achieves an R2 of 0.99 for predicting CO, NO2, PM10, PM2.5, and SO2. Health impact assessments using BenMap demonstrated that the predicted all-cause mortality and specific disease mortalities were highly consistent with actual values, confirming the model's accuracy in assessing health impacts from air pollution. For instance, the predicted and actual all-cause mortality for PM2.5 were both 3120; for cardiovascular disease, both were 1560; and for respiratory disease, both were 780. To validate its generalizability, this method was applied to Chengdu, China, using several years of data for training and prediction of PM2.5, CO, NO2, O3, PM10, and SO2, incorporating atmospheric pressure, relative humidity, and dew point temperature. The model maintained excellent performance, confirming its broad applicability. Overall, we conclude that the machine learning and BenMap-based methods show high accuracy and reliability in predicting air pollutant concentrations and health impacts, providing a valuable reference for air pollution assessment.

Full mission evaluation of EnMAP water leaving reflectance products using three atmospheric correction processors.

This study presents what we believe is the first extensive assessment of the water reflectance products from the German hyperspectral Environmental Mapping and Analysis Program (EnMAP). We evaluate EnMAP's standard normalized water leaving reflectance [ρW]N over 17 water sites in the first two years of the mission. The EnMAP [ρW]N standard product is generated by a dedicated water atmospheric correction (AC) called the Modular Inversion Program (MIP). The quality of the [ρW]N retrievals was assessed using in situ hyperspectral measurements and Aerosol Robotic Network - Ocean Colour (AERONET-OC) multispectral measurements. The results showed very good agreement between in situ hyperspectral match-ups and EnMAP [ρW]N, with an underestimation of EnMAP of -17.37% (bias, β) and an error (ϵ) of 23.75% at 418 - 797 nm. Two other AC processors were also investigated: the polynomial based algorithm applied to MERIS (Polymer) and the atmospheric correction for OLI lite (Acolite). The intercomparison exercise between the three AC methods applied to EnMAP data using the hyperspectral match-up dataset showed better statistical metrics for MIP (ϵ=23%,β=-17.37%) compared to Polymer (ϵ=42.20%,β=-2.43%) and Acolite (ϵ=97%,β=97%). The superior performance of MIP was further confirmed by the validation results obtained with the multispectral match-up dataset; MIP retrievals show good agreement with in situ measurements at the majority of study sites. Conversely, Polymer and Acolite retrievals tended to overestimate, especially in clearer waters as the Lampedusa study site.

Linking trends of habitat types and plant species using repeated habitat mapping data

AbstractAimsTrends in the extent of habitat types and species frequencies might not follow a simple pattern. However, data that are able to link those trends are scarce. Here, we use data from a repeated habitat mapping program to test consistency between habitat type and species change.LocationFederal state of Baden‐Württemberg, Germany.MethodsWe derived temporal trends over several decades concerning: (a) the extent of all protected habitat types across the state, (b) plant species across the state, and (c) plant species within habitat types. We tested the consistency between trends in the extent of every protected habitat type with trends of those species that prefer that particular habitat type, as well as with mean trends of all species that occur within that particular habitat type.ResultsWe found that, on average, most protected habitat types decreased in area, with the exception of forests, which showed positive trends. Species analysis showed positive trends for species preferring the habitat types scrubs, copses and field hedges, as well as bog, carr, swamp and alluvial forests. By contrast, negative trends were found for species preferring the habitat types heaths and semi‐natural grasslands, meadows and pastures. While non‐native species increased, species that were considered endangered decreased. A species’ trend across Baden‐Württemberg mostly followed the trend of the species’ preferred habitat type. However, within some habitat types, the mean species’ trend did not follow the trend of those habitat types’ extent. Decreasing species within habitat types were especially those that preferred each particular habitat type.ConclusionsOur results point to an ongoing turnover of floristic composition, and thus to changes in habitat quality within habitat types. This implies that future monitoring programs should track temporal trends of both habitat types and the species occurring in these habitat types as a measure of habitat quality, because they can show diverging trends.