National-scale Datasets Research Articles

BULL Database – Spanish Basin attributes for Unravelling Learning in Large-sample hydrology

We present a novel basin dataset for large-sample hydrological studies in Spain. BULL comprises data for 484 basins, combining hydrometeorological time series with several attributes related to geology, soil, topography, land cover, anthropogenic influence and hydroclimatology. Thus, we followed recommendations in the CARAVAN initiative for generating a truly open global hydrological dataset to collect these attributes. Several climatological data sources were used, and their data were validated by hydrological modelling. One of the main novelties of BULL compared to other national-scale datasets is the analysis of the hydrological alteration of the basins included in this dataset. This aspect is critical in countries such as Spain, which are characterised by rivers suffering from the highest levels of anthropisation. The BULL dataset is freely available at https://zenodo.org/records/10605646.

National-scale 10-m maps of cropland use intensity in China during 2018–2023

The amount of actively cultivated land in China is increasingly threatened by rapid urbanization and rural population aging. Quantifying the extent and changes of active cropland and cropping intensity is crucial to global food security. However, national-scale datasets for smallholder agriculture are limited in spatiotemporal continuity, resolution, and precision. In this paper, we present updated annual Cropland Use Intensity maps in China (China-CUI10m) with descriptions of the extent of fallow/abandoned, actively cropped fields and cropping intensity at a 10-m resolution in recent six years (2018–2023). The dataset is produced by robust algorithms with no requirements for regional adjustments or intensive training samples, which take full advantage of the Sentinel-1 (S1) SAR and Sentinel-2 (S2) MSI time series. The China-CUI10m maps have achieved high accuracy when compared to ground truth data (Overall accuracy = 90.88%) and statistical data (R2 > 0.94). This paper provides the recent trends in cropland abandonment and agricultural intensification in China, which contributes to facilitating geographic-targeted cropland use control policies towards sustainable intensification of smallholder agricultural systems in developing countries.

FOCA: a new quality-controlled database of floods and catchment descriptors in Italy

Abstract. Here we present FOCA (Italian FlOod and Catchment Atlas), the first systematic collection of data on Italian river catchments for which historical discharge time series are available. Hydrometric information, including the annual maximum peak discharge and average daily annual maximum discharge, is complemented by several geomorphological, climatological, extreme rainfall, land-cover and soil-related catchment attributes. All hydrological information derives from the most recently released datasets of discharge and rainfall measurements. To enhance the reproducibility and transferability of the analysis, this paper provides a description of all the raw data and the algorithms used to build the basin attribute dataset. We also describe the approaches adopted to solve problems encountered during the digital elevation model elaboration in areas characterized by a complex morphology. Details about the data quality-control procedure developed to detect and correct errors are also reported. One of the main novelties of FOCA with respect to other national-scale datasets is the inclusion of a rich set of geomorphological attributes and extreme rainfall features for a large set of basins covering a wide range of elevations and areas. Using this first nationwide data collection (available at https://doi.org/10.5281/zenodo.10446258, Claps et al., 2023), a wide range of environmental applications, with a particular focus on flood studies, can be undertaken within the Italian territory.

DISP-01. THE IMPACT OF COVID-19 PANDEMIC ON THE HEALTHCARE DELIVERY OF RACIAL/ETHNIC MINORITIES WITH BRAIN METASTASIS BY FACILITY TYPE

Abstract OBJECTIVES Brain metastases (BM) are the most common form of adult brain cancer and one of the most devastating complications of cancer. We propose to characterize the effect of the pandemic on health care delivery for racial/ethnic underserved populations on BM care in general and stratifying by facility type. METHODS We utilized two national-scale datasets - the National Cancer Database (NCDB) and one of the largest COVID-19 real-time centralized data in the world - the National COVID Cohort Collaborative (N3C) database. Multivariable binary logistic regression models were performed to determine whether the pandemic (three-dimensional measures: pre-pandemic vs. during-pandemic, COVID-19 positivity, and density of COVID-19 prevalence) influence BM care delivery [including stereotactic radiosurgery (SRS), chemotherapy, and immunotherapy], especially for race/ethnicity minorities and patients receiving treatments at non-academic centers (NACs). RESULTS Compared to pre-pandemic years, the proportions of receiving BM care significantly decreased across all racial/ethnic groups during the pandemic using two nationwide datasets (NCDB: N=5238; N3C: N=13106), but non-white patients were still significantly less likely to undergo SRS than non-Hispanic whites (NHW) in N3C (total: aOR-0.76, p< 0.001; Black: aOR-0.78, p< 0.001, Hispanic: aOR-0.75, p=0.013; APAA: aOR-0.62, p=0.002) and NCDB (Black: aOR-0.78, p=0.006). Similarly, the odds of receiving chemotherapy for race/ethnicity minorities BM patients were significantly lower than NHW in N3C (Black: aOR-0.71, Hispanic: aOR-0.63, APAA: aOR-0.46, all p< 0.001) and NCDB (Black: aOR-0.86, p=0.028). Furthermore, such inequality in SRS receipt by race/ethnicity was exacerbated during the pandemic at NACs over academic centers (ACs) (esp. Hispanic vs. NHW at NACs: aOR-0.58, p=0.004; Hispanic vs. NHW at ACs: aOR-0.88, p=0.380) in NCDB. CONCLUSION Our study suggests that BM healthcare delivery has been significantly reduced in general during the pandemic, but racial/ethnic disparities in BM care and inequality in healthcare access by facility type still exist and have been even exacerbated.

Developing a framework to improve global estimates of conservation area coverage

AbstractArea-based conservation is a widely used approach for maintaining biodiversity, and there are ongoing discussions over what is an appropriate global conservation area coverage target. To inform such debates, it is necessary to know the extent and ecological representativeness of the current conservation area network, but this is hampered by gaps in existing global datasets. In particular, although data on privately and community-governed protected areas and other effective area-based conservation measures are often available at the national level, it can take many years to incorporate these into official datasets. This suggests a complementary approach is needed based on selecting a sample of countries and using their national-scale datasets to produce more accurate metrics. However, every country added to the sample increases the costs of data collection, collation and analysis. To address this, here we present a data collection framework underpinned by a spatial prioritization algorithm, which identifies a minimum set of countries that are also representative of 10 factors that influence conservation area establishment and biodiversity patterns. We then illustrate this approach by identifying a representative set of sampling units that cover 10% of the terrestrial realm, which included areas in only 25 countries. In contrast, selecting 10% of the terrestrial realm at random included areas across a mean of 162 countries. These sampling units could be the focus of future data collation on different types of conservation area. Analysing these data could produce more rapid and accurate estimates of global conservation area coverage and ecological representativeness, complementing existing international reporting systems.

Comparison and Assessment of Different Land Cover Datasets on the Cropland in Northeast China

The provision of precise and dependable information regarding the extent and distribution of cropland is imperative for the evaluation of food security, agricultural planning, and resource management. Cropland is an important component of land cover type and is offered in multiple existing global/regional land cover products. However, global-scale accuracy evaluation may not be representative of class-specific or local-area accuracy, such as in Northeast China, which is an important grain-producing region of China and has various types of cultivated land (e.g., wheat, rice) and diverse terrains. It poses a great challenge in generating precise cropland classification by automated mapping. Thus, it is indispensable to evaluate the accuracy and reliability of these various land cover datasets before using them. In this study, we collected thirteen sets of global or national-scale land cover datasets. Through the visual interpretation of high-resolution images, ground “truth” samples were collected to evaluate the data accuracy across Northeast China. The overall accuracy (OA) evaluation results in Phase-2020 show that CLCD has the highest value with 0.914, followed by GlobeLand30 (0.906), GLC_FCS30 (0.902), and Esri (0.896) for cropland classification in Northeast China. CGLS-LC100 has the lowest OA (0.710). For the commission and omission errors of six datasets in Phase-2020, CGLS-LC100 has an obvious overestimation (larger commission error), while the two national-scale datasets (CLCD and CLUDs) perform relatively better. In terms of spatial consistency, high spatial agreement among the nine Phase-2015 datasets or in the six Phase-2020 datasets could be discovered in traditional agricultural regions like the Sanjiang–Songnen–Liaohe Plain, and low agreement is found in the transition areas of mountains (hills) and plains with the mixed landscape of forest (grassland) and farmland. In the aspect of comparison pairwise data, CLCD is in good agreement with GLC_FCS30, GlobeLand30, and Esri, while CGLS-LC100 is in the poorest agreement with any other dataset. The comparison and evaluation results are expected to provide a reference on which aspects and to what extent these land cover products may be consistent and guide the cropland data product selection for Northeast China.

A national-scale mineral potential assessment for carbonatite-related rare earth element mineral systems in Australia

The production of rare earth elements is critical for the transition to a low carbon economy. Carbonatites (>50% carbonate minerals) are one of the most significant sources of rare earth elements (REEs), both domestically within Australia, as well as globally. Given the strategic importance of critical minerals, including REEs, for the Australian national economy, a mineral potential assessment has been undertaken to evaluate the prospectivity for carbonatite-related REE (CREE) mineralisation in Australia. CREE deposits form as the result of lithospheric- to deposit-scale processes that are spatially and temporally coincident.Building on previous research into the formation of carbonatites and their related REE mineralisation, a mineral system model has been developed that incorporates four components: (1) source of metals, fluids, and ligands, (2) energy sources and fluid flow drivers, (3) fluid flow pathways and lithospheric architecture, and (4) ore deposition. This study demonstrates how national-scale datasets and a mineral systems-based approach can be used to map the mineral potential for CREE mineral systems in Australia.Using statistical analysis to guide the feature engineering and map weightings, a weighted index overlay method has been used to generate national-scale mineral potential maps that reduce the exploration search space for CREE mineral systems by up to ∼90%. In addition to highlighting regions with known carbonatites and CREE mineralisation, the mineral potential assessment also indicates high potential in parts of Australia that have no previously identified carbonatites or CREE deposits.

Land market concentration, developers’ pricing decisions, and class monopoly rent in urban China

Drawing on Harvey’s assertion on class monopoly rent, this paper investigates developers’ monopolist pricing decisions in urban China, with an additional focus on the heterogeneity between state-owned enterprises (SOEs) and private developers. By contextualizing Harvey to China’s urban political economy, we find that when developers maximize profits and local governments maximize land revenues, their individual activities forge a collective to create “island-like” markets that lead to class monopoly rents, and that local governments seek to realize social goals primarily through the operation of SOEs, making SOE developers act differently. We specify a set of hypotheses on the relationship between private developers’ monopolist pricing and their power in local secondary land markets and SOEs’ imparity. We organize a city-level, national-scale dataset and use a fixed effects model and the instrumental variable approach to test our hypotheses. This research has implications for China’s land and housing policies, and suggests that a deeper examination of the concept of class monopoly rent would facilitate a better understanding of contemporary neoliberal cities.

Machine Learning Models Based on a National-Scale Cohort Identify Patients at High Risk for Prolonged Lengths of Stay Following Primary Total Hip Arthroplasty

BackgroundExisting machine learning models that predicted prolonged lengths of stay (LOS) following primary total hip arthroplasty (THA) were limited by the small training volume and exclusion of important patient factors. This study aimed to develop machine learning models using a national-scale data set and examine their performance in predicting prolonged LOS following THA. MethodsA total of 246,265 THAs were analyzed from a large database. Prolonged LOS was defined as exceeding the 75th percentile of all LOSs in the cohort. Candidate predictors of prolonged LOS were selected by recursive feature elimination and used to construct four machine learning models—artificial neural network, random forest, histogram-based gradient boosting, and k-nearest neighbor. The model performance was assessed by discrimination, calibration, and utility. ResultsAll models exhibited excellent performance in discrimination (area under the receiver operating characteristic curve [AUC] = 0.72 to 0.74) and calibration (slope: 0.83 to 1.18, intercept: −0.01 to 0.11, Brier score: 0.185 to 0.192) during both training and testing sessions. The artificial neural network was the best performer with an AUC of 0.73, calibration slope of 0.99, calibration intercept of −0.01, and Brier score of 0.185. All models showed great utility by producing higher net benefits than the default treatment strategies in the decision curve analyses. Age, laboratory tests, and surgical variables were the strongest predictors of prolonged LOS. ConclusionThe excellent prediction performance of machine learning models demonstrated their capacity to identify patients prone to prolonged LOS. Many factors contributing to prolonged LOS can be optimized to minimize hospital stay for high-risk patients.

Mapping social capital across Wales (UK) using secondary data and spatial analysis

Social capital, a powerful community resource based on trust, relationships, norms, culture, values, networks and belonging, could shape the acceptance, cooperation, and involvement of citizens towards new policies or interventions. In past, connections of social capital have been studied in relation to human health, wellbeing, social and economic development. More recently, social capital has been studied with respect to human resilience and adaptation to climate change. We argue that social capital could also play a vital role in our efforts to reduce carbon footprint through behaviour change, a shift on shared local renewable energy resources, and adoption of low carbon technologies. In Wales (UK) there is no national scale dataset, reflecting its social capital landscape, that could be used for designing the right policies/interventions in this context, based on an expected level of trust, cooperation, and support within the communities. This paper is an effort to fill this data gap using secondary datasets. Firstly, a literature review is carried out to identify the indicators of social capital (cognitive and participatory). Secondary datasets have then been identified and acquired. Geospatial analysis has been carried out to produce the criterion maps for various indicators of social capital. Finally, Analytical Hierarchy Process is applied to generate a social capital map of Wales combining these indicators together. For validation of the produced data, social capital’s known correlations were tested with crime rates, income level and multiple deprivations.

Impact of timing to initiate adjuvant therapy on survival of elderly glioblastoma patients using the SEER-Medicare and national cancer databases

The optimal time to initiate adjuvant therapy (AT) in elderly patients with glioblastoma (GBM) remains unclear. We investigated the impact of timing to start AT on overall survival (OS) using two national-scale datasets covering elderly GBM populations in the United States. A total of 3159 and 8161 eligible elderly GBM patients were derived from the Surveillance, Epidemiology and End Results (SEER)—Medicare linked dataset (2004–2013) and the National Cancer Database (NCDB) (2004–2014), respectively. The intervals in days from the diagnosis to the initiation of AT were categorized based on two scenarios: Scenario I (quartiles), ≤ 15, 16–26, 27–37, and ≥ 38 days; Scenario II (median), < 27, and ≥ 27 days. The primary outcome was OS. We performed the Kaplan–Meier and Cox proportional hazards regression methods for survival analysis. A sensitivity analysis was performed using Propensity Score Matching (PSM) method to achieve well-balanced characteristics between early-timing and delayed-timing in Scenario II. Improved OS was observed among patients who underwent resection and initiated AT with either a modest delay (27–37 days) or a longer delay (≥ 38 days) compared to those who received AT immediately (≤ 15 days) from both the SEER-Medicare dataset [adjusted hazard ratio (aHR) 0.74, 95% CI 0.64–0.84, P < 0.001; and aHR 0.81, 95% CI 0.71–0.92, P = 0.002] and the NCDB (aHR 0.83, 95% CI 0.74–0.93, P = 0.001; and aHR 0.87, 95% CI 0.77–0.98, P = 0.017). The survival advantage is observed in delayed-timing group as well in Scenario II. For elderly patients who had biopsy only, improved OS was only detected in a longer delay (Scenario I: ≥ 38 days vs. ≤ 15 days) or the delayed-timing group (Scenario II: ≥ 27 days vs. < 27 days) in the NCDB while no survival difference was seen in SEER-Medicare population. For the best timing to start AT in elderly GBM patients, superior survivals were observed among those who had craniotomy and initiated AT with a modest (27–37 days) or longer delays (≥ 38 days) following diagnosis using both the SEER-Medicare and NCDB datasets (Scenario I). Such survival advantage was confirmed when categorizing delayed-timing vs. early-timing with the cut-off at 27 day in both datasets (Scenario II). The increased likelihood of receiving delayed AT (≥ 27 days) was significantly associated with tumor resection (STR/GTR), years of diagnosis after 2006, African American and Hispanics races, treatments at academic facilities, and being referred. There is no difference in timing of AT on survival among elderly GBM patients who had biopsy in the SEER-Medicare dataset. In conclusion, initiating AT with a modest delay (27–37 days) or a longer delay (≥ 38 days) after craniotomy may be the preferred timing in the elderly GBM population.

High-resolution circa-2020 map of urban lakes in China

Urban lakes provide important ecological services to local communities, such as flood mitigation, biodiversity, and recreation. With rapid urbanization, urban lakes are significantly affected by socio-economic development and urgently need attention. Yet there is still a lack of datasets that include tiny urban lakes on a global or national scale. This study aims to produce a high-resolution circa-2020 map of urban lakes (≥0.001 km2) in China. The 10-m-resolution Sentinel-2 imagery and a simple but robust water extraction method was used to generate waterbodies. The accuracy of this national-scale dataset was evaluated by comparing it with manually sampled urban units, with the average accuracy of 81.85% in area and 93.35% in count. The database totally inventories 1.11 × 106 urban lakes in China, with a net area of ~2.13 × 103 km2. Overall, the spatial distribution of urban lakes in China showed strongly heterogeneous characteristics. This dataset will enhance our understanding of the distribution pattern of China’s urban lakes and contribute to better ecological and environmental management as well as sustainable urban development planning.

Public Transit Equity Analysis at Metropolitan and Local Scales: A Focus on Nine Large Cities in the US

Recent studies on transit service through an equity lens have captured broad trends from the literature and national-level data or analyzed disaggregate data at the local level. This study integrates these methods by employing a geostatistical analysis of new transit access and income data compilations from the Environmental Protection Agency. By using a national data set, this study demonstrates a method for income-based transit equity analysis and provides results spanning nine large auto-oriented cities in the US. Results demonstrate variability among cities’ transit services to low-income populations, with differing results when viewed at the regional and local levels. Regional-level analysis of transit service hides significant variation through spatial averaging, whereas the new data employed in this study demonstrates a block-group scale equity analysis that can be used on a national-scale data set. The methods used can be adapted for evaluation of transit and other modes’ transportation service in areas to evaluate equity at the regional level and at the neighborhood scale while controlling for spatial autocorrelation. Transit service equity planning can be enhanced by employing local Moran’s I to improve local analysis.

Analytical modelling of soil porosity and bulk density across the soil organic matter and land-use continuum

The thin layer of soil at the earth’s surface supports life, storing water and nutrients for plant uptake. These processes occur in the soil pore space, often half the soil volume, but our understanding of how this volume responds to environmental change is poor. Convention, has been to predict soil porosity, or its reciprocal bulk density (BD), from soil texture using pedotransfer functions (PTFs). A texture based approach, invariant to environmental change, prevents feedback from land use or climate change to soil porosity. Moreover, PTFs are often limited to mineral soils with < 20% soil organic matter (SOM) content. Here, we develop an analytical model to predict soil porosity, or BD, as a function of SOM. We test it on two comprehensive, methodologically consistent, temperate national-scale topsoil data sets (0–15 cm) (Wales, n = 1385; Great Britain, n = 2570). The purpose of the approach is to generate an analytical function suitable for predicting soil porosity change with SOM content, while providing insight into the main grain-scale factors determining the porosity emergence. The newly developed function covering the entire SOM gradient allows for impacts of land use, management or climate change to feedback on soil porosity or bulk density through decadal dynamic changes in SOM.

Nitrogen fertilizer consumption and nitrous oxide emissions associated with ethanol production – A national-scale comparison between Brazilian sugarcane and corn in the United States

Globally, Brazil and United States (US) have the large-scale programs of biofuels production. Among other parameters, the feasibility and sustainability of petroleum-based fuels by biofuels replacement depend upon the proper nitrogen (N) fertilizers usage as well as the associated nitrous oxide (N2O) emissions along the production chain. The efficiency of N use also depends on the choice of biofuel crop. Previous studies indicated that sugarcane (Saccharum spp.) requires less N than corn (Zea mays L), thus, the hypothesis tested was that Brazilian sugarcane-based ethanol requires less synthetic N inputs per unit produced than corn-based ethanol in the US. Consequently, ethanol production in Brazil from sugarcane has lower N2O emissions associated with N fertilization than ethanol produced in the US from corn. Here, we reviewed national-scale dataset on sugarcane and corn cultivation in Brazil and US respectively, in order to compare temporal changes in the cultivated area for both crops (devoted for ethanol production), N-fertilizers consumption, and associated emission of N2O in the period 2000–2020. The same parameters were forecasted for the upcoming decade. For both periods, there were calculated two indexes: the N-fertilizer consumption index (the quotient of N-fertilizer input by ethanol production) and the N2O emission index (the quotient of N-fertilizer input by N2O emission). In the period 2000–2020, the devoted area for sugarcane and corn cultivation increased around 43 and 9%, respectively, while the N rates recorded in 2020 for sugarcane in Brazil and corn in the US averaged, respectively, 78 and 167 kg ha−1 N. Moreover, within the 2000–2020 period, the average yield was 74 Mg ha−1 for sugarcane (stalks) in Brazil and 9.6 Mg ha−1 for corn (grain) in the US, which resulted in ethanol yield (in area basis) of 6009 and 4003 L ha−1, respectively. Thus, the N-fertilizer consumption index was much lower for sugarcane-based ethanol than for corn-based ethanol (0.009 versus 0.04 kg N L−1). Through the Intergovernmental Panel on Climate Change (IPCC) emission factor (Tier 1), we estimated that N2O emission is 4.6 times lower for sugarcane-based ethanol than for corn-based ethanol (94 versus 433 mg N2O per L−1). By 2030, ethanol production might reach 45 and 58 billion L in Brazil and within the US, respectively, leading to 0.45 and 2.07 million t of N-fertilizer consumption per year−1 for sugarcane and corn respectively. The outcomes herein endorse the sustainability of sugarcane as a feedstock for ethanol production and may guide stakeholders to establish public policies to enable sugarcane-based ethanol production as one alternative to tackle global warming and climate changes.

Towards a National-Scale Dataset of Geotechnical and Hydrological Soil Parameters for Shallow Landslide Modeling

One of the main constraints in assessing shallow landslide hazards through physically based models is the need to characterize the geotechnical parameters of the involved materials. Indeed, the quantity and quality of input data are closely related to the reliability of the results of every model used, therefore data acquisition is a critical and time-consuming step in every research activity. In this perspective, we reviewed all official certificates of tests performed through 30 years at the Geotechnics Laboratory of the Earth Science Department (University of Firenze, Firenze, Italy), compiling a dataset in which 380 points are accurately geolocated and provide information about one or more geotechnical parameters used in slope stability modeling. All tests performed in the past (in the framework of previous research programs, agreements of cooperation, or to support didactic activities) were gathered, homogenized, digitalized, and geotagged. The dataset is based on both on-site tests and laboratory tests, it accounts for 40 attributes, among which 13 are descriptive (e.g., lithology or location) and 27 may be of direct interest in slope stability modeling as input parameters. The dataset is made openly available and can be useful for scientists or practitioners committed to landslide modeling.

Mapping the potential for Payments for Ecosystem Services schemes to improve water quality in agricultural catchments: A multi-criteria approach based on the supply and demand concept

Payments for Ecosystem Services (PES) schemes are an increasingly popular form of catchment management for improving surface water and groundwater quality. In these schemes, downstream water users who are impacted by agricultural diffuse pollution incentivise upstream farmers to adopt better practices. However, this type of scheme will not be successful in all situations, in part, due to a lack of potential for agriculture to improve the suuply of good water quality and/or a lack in demand from downstream users for good water quality. As such, this study aims to present a flexible approach to mapping the potential for PES schemes to improve water quality in agricultural catchments. The approach is based on multi-criteria analysis, with supply and demand as key criteria. It uses expert judgement or current guidance on PES to select supply and demand sub-criteria, expert judgement to weight all criteria through pairwise comparisons and readily available, national datasets to indicate criteria. Once indicator data are normalized, it combines them in a weighted sums analysis and presents results spatially at the national scale, all within a geographical information system. The approach can easily be applied to the country or region of interest by using locally relevant criteria, expert judgement and data. For example, when applied to the situation for river waterbodies in England, supply sub-criteria were the contribution of agriculture to loads of the major pollutants (nitrogen, phosphorus and sediments) and demand sub-criteria were the different downstream water users present (water companies and, tourist and local recreational users). Experts assigned equal weight to supply and demand criteria and the highest weights to sediments and water companies for sub-criteria, respectively. When national scale datasets to indicate these criteria were combined in a weighted sums analysis, it was possible to identify areas of high potential for PES. This would hopefully motivate more detailed research at the individual catchment level into the constraints in linking supply and demand. Three case-study schemes were also examined to show how some of these constraints are being identified and overcome. As such, the approach forms the first tier in a two-tier framework for establishing PES schemes to improve water quality in agricultural catchments.

Integration and Analysis of Multi-Modal Geospatial Secondary Data to Inform Management of at-Risk Archaeological Sites

Climate change poses an imminent physical risk to cultural heritage sites and their surrounding landscape through intensifying environmental processes such as damaging wetting and drying cycles that disrupt archaeological preservation conditions, and soil erosion which threatens to expose deposits and alter the archaeological context of sites. In the face of such threats, geospatial techniques such as GIS, remote sensing, and spatial modelling have proved invaluable tools for archaeological research and cultural heritage monitoring. This paper presents the application of secondary multi-source and multi-temporal geospatial data within a processing framework to provide a comprehensive assessment of geophysical risk to the Roman fort of Magna, Carvoran, UK. An investigation into the ancient hydraulic system at Magna was carried out with analysis of vegetation change over time, and spatio-temporal analysis of soil erosion risk at the site. Due to COVID-19 restrictions in place at the time of this study, these analyses were conducted using only secondary data with the aim to guide further archaeological research, and management and monitoring strategies for the stakeholders involved. Results guided inferences about the ancient hydraulic system, providing insights regarding how to better manage the site at Magna in the future. Analysis of soil erosion allowed the identification of hot spot areas, indicating a future increase in rates of erosion at Magna and suggesting a seasonal period of higher risk of degradation to the site. Results have proven that freely available multi-purpose national-scale datasets are sufficient to create meaningful insights into archaeological sites where physical access to the site is inhibited. This infers the potential to carry out preliminary risk assessment to inform future site management practices.

Annual and 16-Day Rangeland Production Estimates for the Western United States

Rangeland production is a foundational ecosystem service and resource on which livestock, wildlife, and people depend. Capitalizing on recent advancements in the use of remote sensing data across rangelands, we provide estimates of herbaceous rangeland production from 1986 to 2019 at 16-d and annual time steps and 30-m resolution across the western United States. A factorial comparison of this dataset and three national scale datasets is presented, and we highlight a multiple-lines-of-evidence approach when using production estimates in decision making. Herbaceous aboveground biomass at this scale and resolution provides critical information applicable for management and decision making, particularly in the face of annual grass invasion and woody encroachment of rangeland systems. These readily available data remove analytical and technological barriers allowing immediate utilization for monitoring and management.

Threats of illegal, unregulated, and unreported fishing to biodiversity and food security in the Republic of the Congo.

Illegal, unregulated, and unreported (IUU) fishing poses a major threat to effective management of marine resources, affecting biodiversity and communities dependent on these coastal resources. Spatiotemporal patterns of industrial fisheries in developing countries are often poorly understood, and global efforts to describe spatial patterns of fishing vessel activity are currently based on automatic identification system (AIS) data. However, AIS is often not a legal requirement on fishing vessels, likely resulting in underestimates of the scale and distribution of legal and illegal fishing activity, which could have significant ramifications for targeted enforcement efforts and the management of fisheries resources. To help address this knowledge gap, we analyzed 3 years of vessel monitoring system (VMS) data in partnership with the national fisheries department in the Republic of the Congo to describe the behavior of national and distant-water industrial fleets operating in these waters. We found that the spatial footprint of the industrial fisheries fleet encompassed over one-quarter of the Exclusive Economic Zone. On average, 73% of fishing activity took place on the continental shelf (waters shallower than 200 m). Our findings highlight that VMS is not acting as a deterrent or being effectively used as a proactive management tool. As much as 33% (13% on average) of fishing effort occurred in prohibited areas set aside to protect biodiversity, including artisanal fisheries resources, and the distant-water fleet responsible for as much as 84% of this illegal activity. Given the growth in industrial and distant-water fleets across the region, as well as low levels of management and enforcement, these findings highlight that there is an urgent need for the global community to help strengthen regional and national capacity to analyze national scale data sets if efforts to combat IUU fishing are to be effective.