MACHINE LEARNING FOR CLASSIFICATION OF AN ERODING SCARP SURFACE USING TERRESTRIAL PHOTOGRAMMETRY WITH NIR AND RGB IMAGERY

Abstract. Increasingly advanced and affordable close-range sensing techniques are employed by an ever-broadening range of users, with varying competence and experience. In this context a method was tested that uses photogrammetry and classification by machine learning to divide a point cloud into different surface type classes. The study site is a peat scarp 20 metres long in the actively eroding river bank of the Rotmoos valley near Obergurgl, Austria. Imagery from near-infra red (NIR) and conventional (RGB) sensors, georeferenced with coordinates of targets surveyed with a total station, was used to create a point cloud using structure from motion and dense image matching. NIR and RGB information were merged into a single point cloud and 18 geometric features were extracted using three different radii (0.02 m, 0.05 m and 0.1 m) totalling 58 variables on which to apply the machine learning classification. Segments representing six classes, dry grass, green grass, peat, rock, snow and target, were extracted from the point cloud and split into a training set and a testing set. A Random Forest machine learning model was trained using machine learning packages in the R-CRAN environment. The overall classification accuracy and Kappa Index were 98% and 97% respectively. Rock, snow and target classes had the highest producer and user accuracies. Dry and green grass had the highest omission (1.9% and 5.6% respectively) and commission errors (3.3% and 3.4% respectively). Analysis of feature importance revealed that the spectral descriptors (NIR, R, G, B) were by far the most important determinants followed by verticality at 0.1 m radius

Snow-avalanche boulder fans in Jotunheimen, southern Norway: Schmidt-hammer exposure-age dating, geomorphometrics, dynamics and evolution

Eleven snow-avalanche boulder fans were dated from two high-alpine sites in Jotunheimen using Schmidt-hammer exposure-age dating (SHD) and lichenometry. Average exposure ages of the surface boulders ranged from 2285 ± 725 to 7445 ± 1020 years and demonstrate the potential of SHD for dating active landforms and diachronous surfaces. Application of GIS-based morphometric analyses showed that the volume of rock material within 10 of the fans is accounted for by 16-68 % of the combined volume of their respective bedrock chutes and transport zones. It is inferred that the fans were deposited entirely within the Holocene, mainly within the early- to mid Holocene, by frequent avalanches carrying very small debris loads. Relatively small transport-zone volumes are consistent with avalanches of low erosivity. Excess chute volumes appear to represent subaerial erosion in the Younger Dryas and possibly earlier. Debris supply to the fans was likely enhanced by early-Holocene paraglacial processes following deglaciation, and by later permafrost degradation associated with the mid-Holocene Thermal Maximum. The latter, together with the youngest SHD age from one of the fans, may presage a similar increase in geomorphic activity in response to current warming trends

Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non‐forest ecosystems

P. 1-15Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1–10 ha−1. Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation models and satellite-derived biomass products that are essential to understand vulnerable and understudied non-forested ecosystems around the globe.S

Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.

BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700

Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non‐forest ecosystems

This is the final version. Available on open access from Wiley via the DOI in this recordData Availability Statement: The data collected for this publication, including aerial images, marker and plot coordinates and dry sample weights, as well as site and survey metadata, are available from the NERC Environmental Information Data Centre . Code for photogrammetric processing and statistical analysis is available at Zenodo Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1–10 ha−1. Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation models and satellite-derived biomass products that are essential to understand vulnerable and understudied non-forested ecosystems around the globe

Application of X-ray fluorescence spectrometry in assessment of environmental pollution

A conventional X-ray fluorescence (XRF) method as well as total reflection XRF have been applied to the analysis of various environmental materials. Some methodological changes in order to extend the applicability range and improve the accuracy of the XRF analysis are also discussed

Combining soil erosion modeling with connectivity analyses to assess lateral fine sediment input into agricultural streams

Soil erosion causes severe on- and off-site effects, including loss of organic matter, reductions in soil depth, sedimentation of reservoirs, eutrophication of water bodies, and clogging and smothering of spawning habitats. The involved sediment source-mobilization-delivery process is complex in space and time, depending on a multiplicity of factors that determine lateral sediment connectivity in catchment systems. Shortcomings of soil erosion models and connectivity approaches call for methodical improvement when it comes to assess lateral sediment connectivity in agricultural catchments. This study aims to (i) apply and evaluate different approaches, i.e., Index of Connectivity (IC), the Geospatial Interface forWater Erosion Prediction Project (GeoWEPP) soil erosion model, field mapping and (ii) test a connectivity-adapted version of GeoWEPP (i.e., "GeoWEPP-C") in the context of detecting hot-spots for soil erosion and lateral fine sediment entry points to the drainage network in a medium-sized (138 km2) agricultural catchment in Austria, further discussing their applicability in sediment management in agricultural catchments. The results revealed that (a) GeoWEPP is able to detect sub-catchments with high amount of soil erosion/sediment yield that represent manageable units in the context of soil erosion research and catchment management; (b) the combination of GeoWEPP modeling and field-based connectivity mapping is suitable for the delineation of lateral (i.e., field to stream) fine sediment connectivity hotspots; (c) the IC is a useful tool for a rapid Geographic Information System (GIS)-based assessment of structural connectivity. However, the IC showed significant limitations for agricultural catchments and functional aspects of connectivity; (d) the process-based GeoWEPP-C model can be seen as a methodical improvement when it comes to the assessment of lateral sediment connectivity in agricultural catchments.</p

Schmidt-hammer exposure-age dating (SHD) of sorted stripes on Juvflye, Jotunheimen (central South Norway): Morphodynamic and palaeoclimatic implications

Measurements with an electronic Schmidt-hammer (RockSchmidt) were conducted on 23 sites of sorted stripes (periglacial patterned ground) on Juvflye, Jotunheimen (central South Norway). All were located above the current lower limit of alpine permafrost. Performing Schmidt-hammer exposure-age dating (SHD) based on application of a new local age-calibration equation for RRock-values yielded SHD-ages between 7975 ± 370 and 6660 ± 355 years ago, which are closely comparable to results obtained previously from sorted circles at the same location. The age estimates are interpreted as ‘composite’ ages indicative of upfreezing of boulders, lateral sorting, and subsequent stabilisation. Formation of patterned ground essentially ceased with the onset of the regional Holocene Thermal Maximum (HTM). Neither sorted stripe sites at higher altitude, continuously underlain by permafrost during the entire Holocene, nor those at lower altitudes affected by re-aggradation of permafrost in the late Holocene show signs of significant recent morphodynamic activity. Likely explanations for early- to mid-Holocene stabilisation include (1) substantial changes of soil moisture conditions and related thermodynamics within the active layer affecting frost action, (2) loss of fine-grained substrate matrix from the coarse stripes and hence reduced frost susceptibility, and (3) exhaustion of supply of boulders from the fines-dominated areas. Whereas the sorted stripe data set as a whole did not reproduce the altitudinal gradient characteristic of sorted circles on Juvflye, the strength of the relationship between sorted stripe mean RRock-values and altitude increased with declining slope gradient. Although interpretation of SHD-ages for patterned ground remains challenging, this successful application of the electronic Schmidt-hammer, with its increased efficiency and technical improvements over the mechanical Schmidt-hammer, offers considerable potential for future SHD-studies in both morphodynamic and palaeoclimatic contexts

Machine learning for classification of an eroding scarp surface using terrestrial photogrammetry with nir and rgb imagery

Increasingly advanced and affordable close-range sensing techniques are employed by an ever-broadening range of users, with varying competence and experience. In this context a method was tested that uses photogrammetry and classification by machine learning to divide a point cloud into different surface type classes. The study site is a peat scarp 20 metres long in the actively eroding river bank of the Rotmoos valley near Obergurgl, Austria. Imagery from near-infra red (NIR) and conventional (RGB) sensors, georeferenced with coordinates of targets surveyed with a total station, was used to create a point cloud using structure from motion and dense image matching. NIR and RGB information were merged into a single point cloud and 18 geometric features were extracted using three different radii (0.02 m, 0.05 m and 0.1 m) totalling 58 variables on which to apply the machine learning classification. Segments representing six classes, dry grass, green grass, peat, rock, snow and target, were extracted from the point cloud and split into a training set and a testing set. A Random Forest machine learning model was trained using machine learning packages in the R-CRAN environment. The overall classification accuracy and Kappa Index were 98% and 97% respectively. Rock, snow and target classes had the highest producer and user accuracies. Dry and green grass had the highest omission (1.9% and 5.6% respectively) and commission errors (3.3% and 3.4% respectively). Analysis of feature importance revealed that the spectral descriptors (NIR, R, G, B) were by far the most important determinants followed by verticality at 0.1 m radius