Field spectroradiometer and simulated multispectral bands for discriminating invasive species from morphologically similar cohabitant plants

One of the challenges in fighting plant invasions is the inefficiency of identifying their distribution using field inventory techniques. Remote sensing has the potential to alleviate this problem effectively using spectral profiling for species discrimination. However, little is known about the capability of remote sensing in discriminating between shrubby invasive plants with narrow leaf structures and other cohabitants with similar ecological niche. The aims of this study were therefore to (1) assess the classification performance of field spectroradiometer data among three bushy and shruby plants (Artemesia afra, Asparagus laricinus, and Seriphium plumosum) from the coexistent plant species largely dominated by acacia and grass species, and (2) explore the performance of simulated spectral bands of five space-borne images (Landsat 8, Sentinel 2A, SPOT 6, Pleiades 1B, and WorldView-3). Two machine-learning classifiers (boosted trees classification and support vector machines) were used to classify raw hyperspectral (n = 688) and simulated multispectral wavelengths. Relatively high classification accuracies were obtained for the invasive species using the original hyperspectral bands for both classifiers (overall accuracy, OA = 83–97%). The simulated data resulted in higher accuracies for Landsat 8, Sentinel 2A, and WorldView-3 compared to those computed for bands simulated to SPOT 6 and Pleiades 1B data. These findings suggest the potential of remote-sensing techniques in the discrimination of different plant species with similar morphological characteristics occupying the same niche.     

Chlorine in submarine glasses from the Lau Basin: seawater contamination and constraints on the composition of slab-derived fluids

Measurements of chlorine concentrations in matrix glasses from 18 primitive (>6 wt% MgO) and eight evolved lavas from active spreading centers in the Lau Basin back-arc system provide insight into the processes which control chlorine concentrations in subduction-related magmas, and can be used to investigate chlorine enrichment related to fluids derived from the underlying subducted slab. Chlorine contents of the glasses are highly variable (0.008-0.835 wt%) and generally high with respect to uncontaminated mid-ocean ridge basalt. Chlorine contents are highest in fractionated lavas from propagating ridge tips and lowest in more primitive basaltic lavas. Two different styles of enrichment in chlorine (relative to other incompatible elements) are recognized. Glasses from the Central Lau Spreading and Eastern Lau Spreading Center typically have low Ba/Nb ratios indicating minimal input of slab-derived components, and high to very high ratios of chlorine relative to K₂O, H₂O, and TiO₂. This style of chlorine enrichment is highest in the most fractionated samples and is consistent with crustal assimilation of chlorine-rich altered ocean crust material. Data from the literature suggest that contamination by chlorine-rich seawater-derived components also characterizes the Woodlark Basin and North Fiji Basin back-arc systems. The second style of chlorine enrichment reflects input from slab-derived fluid(s) to the mantle wedge from the adjacent Tonga subduction zone. Basaltic glasses from the Valu Fa Ridge and Mangatolu Triple Junction show correlations between ratios of chlorine and K₂O, H₂O, and TiO₂ and indices of slab-derived fluid input such as Ba/Nb, Ba/Th and U/Th, consistent with chlorine in these lavas originating from a saline fluid added to the mantle wedge. Within the Valu Fa Ridge the measured range of chlorine contents equates to a chlorine flux of 224-1120 kg/m/yr to the back-arc crust. Using a simple melting model and additional data from other back-arc and arc sample suites we conclude that chlorine is a major component within the slab fluids that contribute to many arc and back-arc melting systems, and probably plays an important role in regulating trace element transport by slab fluids in the mantle wedge. For the back-arc suites we have examined the estimated Cl/H₂O and Cl/K₂O ratios in the slab fluid component correlate with proximity to the arc front, suggesting that progressive dehydration of the slab and/or re-equilibration and transport within the mantle wedge may influence the overall degree of chlorine enrichment within the slab fluid component. The degree of chlorine enrichment observed in most back-arc lavas also appears too great to be explained solely by melting of amphibole, phlogopite or apatite within the mantle source and suggests that chlorine must be present in another phase, possibly a chlorine-rich fluid.     

Holocene hydrological and vegetation changes in southern France inferred by the study of an alluvial travertine system (Saint-Guilhem-le-Désert, Hérault)

A geobotanical study of the travertine system of Saint-Guilhem-le-Désert (southern France) was carried out in order to reconstruct the local Holocene environment in a region where the postglacial vegetation history is poorly documented. The travertinisation process has started at ca. 8500 cal. BP, in a landscape dominated by Pinus sylvestris type (probably Pinus nigra sub sp. salzmannii). Around 7000 cal. BP, the travertine system recorded torrential events not evidenced at the regional scale, showing the particularity of the Verdus hydrological regime. More recently, ca. 5100 cal. BP, a lake or a marsh was filled within the Verdus plain, as attested to by sand and silt particles accumulated in the sequence. The present-day vegetation dominated by Quercus ilex, on south facing slopes, was most likely established between the Bronze Age and the Gallo-Roman period correlatively to the decline of Pinus nigra and deciduous Quercus, most probably under human influence.     

Thermodynamics of U(VI) sorption onto Shewanella putrefaciens

We have conducted acid–base potentiometric titrations and U(VI) sorption experiments using the Gram negative, facultatively anaerobic bacterium Shewanella putrefaciens. Results of reversed titration studies on live, inactive bacteria indicate that their pH-buffering properties result from the equilibrium ionization of three discrete populations of functional groups. Carboxyl (pK_a=5.16±0.04), phosphoryl (pK_a=7.22±0.15) and amine (pK_a=10.04±0.67) groups most likely represent these three resolvable functionalities, based on their pK_a values. Site densities for carboxyl, phosphoryl and amine groups on the bacterial surface were approximately 31.7 μmol sites/g bacteria (0.35±0.02 sites/nm²), 8.95 μmol/g (0.11±0.007 sites/nm²) and 38.0 μmol/g (0.42±0.008 sites/nm²), respectively, based on an estimated bacterial specific surface area of 55 m²/g. Sorption experiments showed that U(VI) can reversibly complex with the bacterial surface in the pH 2–8 interval, with maximum adsorption occurring at a pH of 5. Sorption is not strongly sensitive to ionic strength (NaCl) in the range 0.02–0.10 M. The pH and ionic strength dependence of U(VI) sorption onto S. putrefaciens is similar to that measured for metal-oxide surfaces and Gram positive bacteria, and appears to be similarly governed by competitive speciation constraints. Measured U(VI) sorption is accounted for by using two separate adsorption reactions forming the surface complexes >COO–UO₂⁺ and >PO₄H–UO₂(OH)₂. Using S. putrefaciens as a model organism for dissimilatory metal-reducing Gram negative anaerobes, our results extend the applicability of geochemical speciation models to include bacteria that are capable of reductively solubilizing or precipitating a wide variety of environmentally and geologically important metals and metallic species.     

Evaluation of machine learning models for rice dry biomass estimation and mapping using quad-source optical imagery

ABSTRACT

Several machine learning regression models have been advanced for the estimation of crop biophysical parameters with optical satellite imagery. However, literature on the comparative performances of such models is still limited in range and scope, especially under multiple data sources, despite the potential of multi-source imagery to improving crop monitoring in cloudy areas. To fill in this knowledge gap, this study explored the synergistic use of Landsat-8, Sentinel-2A, China’s environment and disaster monitoring and forecasting satellites (HJ-1 A and B) and Gaofen-1 (GF-1) data to evaluate four machine learning regression models that include Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Gradient Boosting Decision Tree (GBDT), for rice dry biomass estimation and mapping. Taking a major rice cultivation area in southeast China as case study during the 2016 and 2017 growing seasons, a cross-calibrated time series of the Enhanced Vegetation Index (EVI) was obtained from the quad-source optical imagery and on which the aforementioned models were applied, respectively. Results indicate that in the before rice heading scenario, the most accurate dry biomass estimates were obtained by the GBDT model (R² of 0.82 and RMSE of 191.8 g/m²) followed by the RF model (R² of 0.79 and RMSE of 197.8 g/m²). After heading, the k-NN model performed best (R² of 0.43 and RMSE of 452.1 g/m²) followed by the RF model (R² of 0.42 and RMSE of 464.7 g/m²). Whist the k-NN model performed least in the before heading scenario, SVM performed least in the after heading scenario. These findings may suggest that machine learning regression models based on an ensemble of decision trees (RF and GBDT) are more suitable for the estimation of rice dry biomass, at least with optical satellite imagery. Studies that would extend the evaluation of these machine learning models, to other parameters like leaf area index, and to microwave imagery, are hereby recommended.     

How well can we simulate complex hydro‐geomorphic process chains? The 2012 multi‐lake outburst flood in the Santa Cruz Valley (Cordillera Blanca,Perú)

Changing high‐mountain environments are characterized by destabilizing ice, rock or debris slopes connected to evolving glacial lakes. Such configurations may lead to potentially devastating sequences of mass movements (process chains or cascades). Computer simulations are supposed to assist in anticipating the possible consequences of such phenomena in order to reduce the losses. The present study explores the potential of the novel computational tool r.avaflow for simulating complex process chains. r.avaflow employs an enhanced version of the Pudasaini ( 2012 ) general two‐phase mass flow model, allowing consideration of the interactions between solid and fluid components of the flow. We back‐calculate an event that occurred in 2012 when a landslide from a moraine slope triggered a multi‐lake outburst flood in the Artizón and Santa Cruz valleys, Cordillera Blanca, Peru, involving four lakes and a substantial amount of entrained debris along the path. The documented and reconstructed flow patterns are reproduced in a largely satisfactory way in the sense of empirical adequacy. However, small variations in the uncertain parameters can fundamentally influence the behaviour of the process chain through threshold effects and positive feedbacks. Forward simulations of possible future cascading events will rely on more comprehensive case and parameter studies, but particularly on the development of appropriate strategies for decision‐making based on uncertain simulation results. © 2017 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.     

The partitioning behavior of silver in a vapor-brine-rhyolite melt assemblage

The partitioning of silver in a sulfur-free rhyolite melt-vapor-brine assemblage has been quantified at 800 °C, pressures of 100 and 140 MPa and f_O2≈NNO (nickel-nickel oxide). Silver solubility (±2σ) in rhyolite increases 5-fold from 105 ± 21 to 675 ± 98 μg/g as pressure increases from 100 to 140 MPa. Nernst-type partition coefficients describing the mass transfer of silver at 100 MPa between vapor and melt, brine and melt and vapor and brine are 32 ± 30, 1151 ± 238 and 0.026 ± 0.004, respectively. At 140 MPa, values for for vapor and melt, brine and melt, and vapor and brine are 32 ± 10, 413 ± 172 and 0.06 ± 0.03, respectively. Apparent equilibrium constant values (±2σ) describing the exchange of silver and sodium between vapor and melt, , at 100 and 140 MPa are 105 ± 68 and 14 ± 6. The average values (±2σ) for silver and sodium exchange between brine and melt, , at 100 and 140 MPa are 313 ± 288 and 65 ± 12. These data indicate that the mass transfer of silver from rhyolite melt to an exsolved volatile phase(s) is enhanced at 100 MPa relative to 140 MPa, suggesting that decompression increases the silver ore-generative potential of an evolving silicate magma. Model calculations using the new data suggest that the evolution of low-density, aqueous fluid (i.e., vapor) may be responsible for the the silver tonnage of many porphyry-type and perhaps epithermal-type ore deposits. For example, Halter et al. (Halter W. E., Pettke T. and Heinrich C. A. (2002) The origin of Cu/Au ratios in porphyry-type ore deposits. Science296, 1842-1844) used detailed silicate and sulfide melt inclusion and vapor and brine fluid inclusions analyses to estimate a melt volume on the order of 15 km³ to satisfy the copper budget at the Bajo de la Alumbrera copper-, gold-, silver-ore deposit. Using their melt volume estimate with the data presented here, model calculations for a 15-km³ felsic melt, saturated with pyrrhotite and magnetite, suggest that a low-salinity magmatic vapor may scavenge on the order of 7 × 10¹² g of silver from the melt. This quantity of silver exceeds the discovered 2 × 10⁹ g of Ag at Alumbrera. Calculated tonnages for numerous other deposits yield similar results. The excess silver in the vapor, remaining after porphyry formation, is then available to precipitate at lower PTconditions in the stratigraphically higher epithermal environment. These data suggest that silver, and perhaps other ore metals, in the porphyry-epithermal continuum may be derived solely from the time-integrated flux of dominantly low-salinity vapor exsolved from a series of sequential magma batches.     

Testing the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods

In this study, the strength and reliability of internal accuracy estimate built in random forest (RF) ensemble classifier was evaluated. Specifically, we compared the reliability of the internal validation methods of RF with independent data-sets of different splitting options for defoliation classification. Furthermore, we set out to statistically validate the best independent split option for image classification using RF and multispectral Rapideye imagery. Results show that the internal accuracy measure yields comparable results with those derived from an independent test data-set. More important, it was observed that the errors produced by the internal validation methods of RF were relatively stable as statistically shown by the lower confidence interval obtained as compared to the independent test data. Results also showed that the 70–30% split option had the lowest mean standard errors (0.2351) and hence highest accuracy when compared to the other split options. The study confirms the reliability and stability of the internal bootstrapping estimate of accuracy built within the random forest algorithm.     

A conceptual model of supra‐glacial lake formation on debris‐covered glaciers based on GPR facies analysis

Supra‐glacial lakes and ponds can create hotspots of mass loss on debris‐covered glaciers. While much research has been directed at understanding lateral lake expansion, little is known about the rates or processes governing lake deepening. To a large degree, this knowledge gap persists due to sparse observations of lake beds. Here we report on the novel use of ground penetrating radar (GPR) surveys to simultaneously collect supra‐glacial lake bathymetry and bottom composition data from Spillway Lake (surface area of 2.4 × 10⁵ m²; volume of 9.5 × 10⁴ m³), which is located in the terminus region of the Ngozumpa Glacier in the Khumbu region of the Nepal Himalaya. We identified two GPR bottom signals corresponding to two sedimentary facies of (1) sub‐horizontal layered fine sediment drape and (2) coarse blocky diamict. We provide an understanding of the changes in subaqueous debris distribution that occur through stages of lake expansion by combining the GPR results with in situ observations of shoreline deposits matching the interpreted facies. From this, we present an updated conceptual model of supra‐glacial lake evolution, with the addition of data on the evolving debris environment, showing how dominant depositional processes can change as lakes evolve from perched lakes to multi‐basin base‐level lakes and finally onto large moraine‐dammed lakes. Throughout lake evolution, processes such as shoreline steepening, lakebed collapse into voids and conduit interception, subaerial and subaqueous calving and rapid areal expansion alter the spatial distribution and makeup of lakebed debris and sediments forcing a number of positive and negative feedbacks on lake expansion. Copyright © 2016 John Wiley & Sons, Ltd.