Remote working: survey of attitudes to eHealth of doctors and nurses in rural general practices in the United Kingdom.

BACKGROUND: Health professionals in rural primary care could gain more from eHealth initiatives than their urban counterparts, yet little is known about eHealth in geographically isolated areas of the UK. OBJECTIVE: To elicit current use of, and attitudes towards eHealth of professionals in primary care in remote areas of Scotland. METHODS: In 2002, a questionnaire was sent to all general practitioners (n=154) in Scotland's 82 inducement practices, and to 67 nurses. Outcome measures included reported experience of computer use; access to, and experience of eHealth and quality of that experience; views of the potential usefulness of eHealth and perceived barriers to the uptake of eHealth. RESULTS: Response rate was 87%. Ninety-five percent of respondents had used either the Internet or email. The proportions of respondents who reported access to ISDN line, scanner, digital camera, and videoconferencing unit were 71%, 48%, 40% and 36%, respectively. Use of eHealth was lower among nurses than GPs. Aspects of experience that were rated positively were 'clinical usefulness', 'functioning of equipment' and 'ease of use of equipment' (76%, 74%, and 74%, respectively). The most important barriers were 'lack of suitable training' (55%), 'high cost of buying telemedicine equipment' (54%), and 'increase in GP/nurse workload' (43%). Professionals were concerned about the impact of tele-consulting on patient privacy and on the consultation itself. CONCLUSIONS: Although primary healthcare professionals recognize the general benefits of eHealth, uptake is low. By acknowledging barriers to the uptake of eHealth in geographically isolated settings, broader policies on its implementation in primary care may be informed

Sentinel-1 Shadows Used to Quantify Canopy Loss from Selective Logging in Gabon

Selective logging is a major cause of forest degradation in the tropics, but its precise scale, location and timing are not known as wide-area, automated remote sensing methods are not yet available at this scale. This limits the abilities of governments to police illegal logging, or monitor (and thus receive payments for) reductions in degradation. Sentinel-1, a C-band Synthetic Aperture Radar satellite mission with a 12-day repeat time across the tropics, is a promising tool for this due to the known appearance of shadows in images where canopy trees are removed. However, previous work has relied on optical satellite data for calibration and validation, which has inherent uncertainties, leaving unanswered questions about the minimum magnitude and area of canopy loss this method can detect. Here, we use a novel bi-temporal LiDAR dataset in a forest degradation experiment in Gabon to show that canopy gaps as small as 0.02 ha (two 10 m × 10 m pixels) can be detected by Sentinel-1. The accuracy of our algorithm was highest when using a timeseries of 50 images over 20 months and no multilooking. With these parameters, canopy gaps in our study site were detected with a false alarm rate of 6.2%, a missed detection rate of 12.2%, and were assigned disturbance dates that were a good qualitative match to logging records. The presence of geolocation errors and false alarms makes this method unsuitable for confirming individual disturbances. However, we found a linear relationship (r2=0.74) between the area of detected Sentinel-1 shadow and LiDAR-based canopy loss at a scale of 1 hectare. By applying our method to three years’ worth of imagery over Gabon, we produce the first national scale map of small-magnitude canopy cover loss. We estimate a total gross canopy cover loss of 0.31 Mha, or 1.3% of Gabon’s forested area, which is a far larger area of change than shown in currently available forest loss alert systems using Landsat (0.022 Mha) and Sentinel-1 (0.019 Mha). Our results, which are made accessible through Google Earth Engine, suggest that this approach could be used to quantify the magnitude and timing of degradation more widely across tropical forests

An Effective Method for InSAR Mapping of Tropical Forest Degradation in Hilly Areas

Current satellite remote sensing methods struggle to detect and map forest degradation, which is a critical issue as it is likely a major and growing source of carbon emissions and biodiveristy loss. TanDEM-X InSAR phase height is a promising variable for measuring forest disturbances, as it is closely related to the mean canopy height, and thus should decrease if canopy trees are removed. However, previous research has focused on relatively flat terrains, despite the fact that much of the world's remaining tropical forests are found in hilly areas, and this inevitably introduces artifacts in sideways imaging systems. In this paper, we find a relationship between InSAR phase height and aboveground biomass change in four selectively logged plots in a hilly region of central Gabon. We show that minimising multilooking prior to the calculation of InSAR phase height on a pixel-by-pixel basis. This shows that TanDEM-X InSAR can measure the magnitude of degradation, and that topographic effects can be mitigated if data from multiple SAR viewing geometries are available

First Evidence of Peat Domes in the Congo Basin using LiDAR from a fixed-wing drone

This work was funded by CongoPeat, a NERC Large Grant (NE/R016860/1) to S.L.L. and E.T.A.M. (UAV data collection, I.J.D. time), a NERC Open CASE Studentship to S.L.L., I.L. and G.D. and a Phillip Leverhulme Prize to S.L.L. (peat depths).The world’s most extensive tropical peatlands occur in the Cuvette Centrale depression in the Congo Basin, which stores 30.6 petagrams of carbon (95% CI, 6.3–46.8). Improving our understanding of the genesis, development and functioning of these under-studied peatlands requires knowledge of their topography and, in particular, whether the peat surface is domed, as this implies a rain-fed system. Here we use a laser altimeter mounted on an unmanned airborne vehicle (UAV) to measure peat surface elevation along two transects at the edges of a peatland, in the northern Republic of Congo, to centimetre accuracy and compare the results with an analysis of nearby satellite LiDAR data (ICESat and ICESat-2). The LiDAR elevations on both transects show an upward slope from the peatland edge, suggesting a surface elevation peak of around 1.8 m over ~20 km. While modest, this domed shape is consistent with the peatland being rainfed. In-situ peat depth measurements and our LiDAR results indicate that this peatland likely formed at least 10,000 years BP in a large shallow basin ~40 km wide and ~3 m deep.Publisher PDFPeer reviewe

Reliably Mapping Low-intensity Forest Disturbance Using Satellite Radar Data

In the last decades tropical forests have experienced increased fragmentation due to a global growing demand for agricultural and forest commodities. Satellite remote sensing offers a valuable tool for monitoring forest loss, thanks to the global coverage and the temporal consistency of the acquisitions. In tropical regions, C-band Synthetic Aperture Radar (SAR) data from the Sentinel-1 mission provides cloud-free and open imagery on a 6- or 12-day repeat cycle, offering the unique opportunity to monitor forest disturbances in a timely and continuous manner. Despite recent advances, mapping subtle forest losses, such as those due to small-scale and irregular selective logging, remains problematic. A Cumulative Sum (CuSum) approach has been recently proposed for forest monitoring applications, with preliminary studies showing promising results. Unfortunately, the lack of accurate in-situ measurements of tropical forest loss has prevented a full validation of this approach, especially in the case of low-intensity logging. In this study, we used high-quality field measurements from the tropical Forest Degradation Experiment (FODEX), combining unoccupied aerial vehicle (UAV) LiDAR, Terrestrial Laser Scanning (TLS), and field-inventoried data of forest structural change collected in two logging concessions in Gabon and Peru. The CuSum algorithm was applied to VV-polarized Sentinel-1 ground range detected (GRD) time series to monitor a range of canopy loss events, from individual tree extraction to forest clear cuts. We developed a single change metric using the maximum of the CuSum distribution, retrieving location, time, and magnitude of the disturbance events. A comparison of the CuSum algorithm with the LiDAR reference map resulted in a 78% success rate for the test site in Gabon and 65% success rate for the test site in Peru, for disturbances as small as 0.01 ha in size and for canopy height losses as fine as 10 m. A correlation between the change metric and above ground biomass (AGB) change was found with R2 = 0.95, and R2 = 0.83 for canopy height loss. From the regression model we directly estimated local AGB loss maps for the year 2020, at 1 ha scale and in percentages of AGB loss. Comparison with the Global Forest Watch (GFW) Tree Cover Loss (TCL) product showed a 61% overlap between the two maps when considering only deforested pixels, with 504 ha of deforestation detected by CuSum vs. 348 ha detected by GFW. Low intensity disturbances captured by the CuSum method were largely undetected by GFW and by the SAR-based Radar for Detecting Deforestation (RADD) Alert System. The results of this study confirm this approach as a simple and reproducible change detection method for monitoring and quantifying fine-scale to high intensity forest disturbances, even in the case of multi-storied and high biomass forests

The role of quantitative cross-case analysis in understanding tropical smallholder farmers’ adaptive capacity to climate shocks

Climate shocks are predicted to increase in magnitude and frequency as the climate changes, notably impacting poor and vulnerable communities across the Tropics. The urgency to better understand and improve communities' resilience is reflected in international agreements such as the Paris Agreement and the multiplication of adaptation research and action programs. In turn, the need for collecting and communicating evidence on the climate resilience of communities has increasingly drawn questions concerning how to assess resilience. While empirical case studies are often used to delve into the context-specific nature of resilience, synthesizing results is essential to produce generalizable findings at the scale at which policies are designed. Yet datasets, methods and modalities that enable cross-case analyses that draw from individual local studies are still rare in climate resilience literature. We use empirical case studies on the impacts of El Niño on smallholder households from five countries to test the application of quantitative data aggregation for policy recommendation. We standardized data into an aggregated dataset to explore how key demographic factors affected the impact of climate shocks, modeled as crop loss. We find that while cross-study results partially align with the findings from the individual projects and with theory, several challenges associated with quantitative aggregation remain when examining complex, contextual and multi-dimensional concepts such as resilience. We conclude that future exercises synthesizing cross-site empirical evidence in climate resilience could accelerate research to policy impact by using mixed methods, focusing on specific landscapes or regional scales, and facilitating research through the use of shared frameworks and learning exercises

Structural diversity and tree density drives variation in the biodiversity-ecosystem function relationship of woodlands and savannas

Positive biodiversity-ecosystem function relationships (BEFRs) have been widely documented, but it is unclear if BEFRs should be expected in disturbance-driven systems. Disturbance may limit competition and niche differentiation, which are frequently posited to underlie BEFRs. We provide the first exploration of the relationship between tree species diversity and biomass, one measure of ecosystem function, across southern African woodlands and savannas, an ecological system rife with disturbance from fire, herbivores and humans. We used >1000 vegetation plots distributed across 10 southern African countries, and structural equation modelling, to determine the relationship between tree species diversity and aboveground woody biomass, accounting for interacting effects of resource availability, disturbance by fire, tree stem density and vegetation type. We found positive effects of tree species diversity on aboveground biomass, operating via increased structural diversity. The observed BEFR was highly dependent on organismal density, with a minimum threshold of c. 180 mature stems ha-1. We found that water availability mainly affects biomass indirectly, via increasing species diversity. The study underlines the close association between tree diversity, ecosystem structure, environment and function in highly disturbed savannas and woodlands. We suggest that tree diversity is an under-appreciated determinant of wooded ecosystem structure and function