On the uncertainty of stream networks derived from elevation data: the error propagation approach

DEM error propagation methodology is extended to the derivation of vector-based objects (stream networks) using geostatistical simulations. First, point sampled elevations are used to fit a variogram model. Next 100 DEM realizations are generated using conditional sequential Gaussian simulation; the stream network map is extracted for each of these realizations, and the collection of stream networks is analyzed to quantify the error propagation. At each grid cell, the probability of the occurrence of a stream and the propagated error are estimated. The method is illustrated using two small data sets: Baranja hill (30 m grid cell size; 16 512 pixels; 6367 sampled elevations), and Zlatibor (30 m grid cell size; 15 000 pixels; 2051 sampled elevations). All computations are run in the open source software for statistical computing R: package geoR is used to fit variogram; package gstat is used to run sequential Gaussian simulation; streams are extracted using the open source GIS SAGA via the RSAGA library. The resulting stream error map (Information entropy of a Bernoulli trial) clearly depicts areas where the extracted stream network is least precise – usually areas of low local relief and slightly convex (0–10 difference from the mean value). In both cases, significant parts of the study area (17.3% for Baranja Hill; 6.2% for Zlatibor) show high error (H>0.5) of locating streams. By correlating the propagated uncertainty of the derived stream network with various land surface parameters sampling of height measurements can be optimized so that delineated streams satisfy the required accuracy level. Such error propagation tool should become a standard functionality in any modern GIS. Remaining issue to be tackled is the computational burden of geostatistical simulations: this framework is at the moment limited to small data sets with several hundreds of points. Scripts and data sets used in this article are available on-line via the www.geomorphometry.org website and can be easily adopted/adjusted to any similar case study

Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

Spatial predictions of soil macro and micro-nutrient content across Sub-Saharan Africa at 250 m spatial resolution and for 0–30 cm depth interval are presented. Predictions were produced for 15 target nutrients: organic carbon (C) and total (organic) nitrogen (N), total phosphorus (P), and extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), aluminum (Al) and boron (B). Model training was performed using soil samples from ca. 59,000 locations (a compilation of soil samples from the AfSIS, EthioSIS, One Acre Fund, VitalSigns and legacy soil data) and an extensive stack of remote sensing covariates in addition to landform, lithologic and land cover maps. An ensemble model was then created for each nutrient from two machine learning algorithms

Spatial risk assessment of hydrological extremities : Inland excess water hazard, Szabolcs-Szatmár-Bereg Country, Hungary

Inland excess water hazard was regionalized and digitally mapped using auxiliary spatial environmental information for a county in Eastern Hungary. Quantified parameters representing the effect of soil, geology, groundwater, land use and hydrometeorology on the formulation of inland excess water were defined and spatially explicitly derived. The complex role of relief was characterized using multiple derivatives computed from a DEM. Legacy maps displaying inland excess water events were used as a reference dataset. Regression kriging was applied for spatial inference with the correlation between environmental factors and inundation determined using multiple linear regressions. A stochastic factor derived through kriging the residual was added to the regression results,thus producing the final inundation hazard map. This may be of use for numerous landrelated activities

Modelling sea level driven change of Macaronesian archipelago configurations since 120 kyr BP

The MacArthur and Wilson island biogeography theory relates species diversity on islands as the result of equilibrium between extinctions and colonization events which rates depend on island size and isolation. Although island size and isolation can be considered static on ecological timescales (<100 years) they are not static on longer time scales. Since the last million years sea levels fluctuate with a period of ca. 120 kyr between -120 m and up to +10 m MSL (Mean Sea Level). Due to these sea level changes islands have changed in size and ultimately may have drowned or emerged. The rate and degree of their drowning depends on island morphometry and the shape of the sea level change curve. We explore the effects of global sea level cycles on the configuration of archipelagos and volcanic islands of Macaronesia. The results indicate that the islands changed shape considerably during the last 120 kyr. Notably the period between 80 kyr and 15 kyr ago sea levels were at least 80 m lower than present and several islands now isolated were merged or were much larger than present. Recent shrinking of islands due to the sea level rise since the last glacial maximum period (20 kyr BP) led to more than 50% reductions in island size, significant loss of coastal habitat and a significant increase in isolation by the increase of distances between islands and island and continents. Island size reduction must have induced pressures especially on terrestrial insular ecosystems, inducing upward migrations and interspecies competitions, and probable extinctions. The splitting of merged islands must have led to separations of populations leading to gene flow losses for some biota. Present day islands are not representative for the mean island configurationsduring the last Myr but rather represent an anomaly. Islands at present are smallest and mostisolated and this configuration makes the insular biota even more vulnerable to human impact

The Land-Potential Knowledge System (LandPKS): mobile apps and collaboration for optimizing climate change investments

Massive investments in climate change mitigation and adaptation are projected during coming decades. Many of these investments will seek to modify how land is managed. The return on both types of investments can be increased through an understanding of land potential: the potential of the land to support primary production and ecosystem services, and its resilience. A Land-Potential Knowledge System (LandPKS) is being developed and implemented to provide individual users with point-based estimates of land potential based on the integration of simple, geo-tagged user inputs with cloud-based information and knowledge. This system will rely on mobile phones for knowledge and information exchange, and use cloud computing to integrate, interpret, and access relevant knowledge and information, including local knowledge about land with similar potential. The system will initially provide management options based on long-term land potential, which depends on climate, topography, and relatively static soil properties, such as soil texture, depth, and mineralogy. Future modules will provide more specific management information based on the status of relatively dynamic soil properties such as organic matter and nutrient content, and of weather. The paper includes a discussion of how this system can be used to help distinguish between meteorological and edaphic drought

Geodiversity assessment of Paraná state (Brazil): an innovative approach

Geodiversity is considered as the natural range of geological, geomorphological, and soil features, including their assemblages, relationships, properties, interpretations, and systems. A method developed for the quantitative assessment of geodiversity was applied to Parana ́ , a Brazilian state with an area of about 200,000 km2. The method is based on the overlay of a grid over different maps at scales ranging from 1/500,000 to 1/650,000, with the final Geodiversity Index the sum of five partial indexes calculated on a 25 9 25 km grid. The partial indexes represent the main components of geodi- versity, including geology (stratigraphy and lithology), geomorphology, paleontology, and soils. The fifth partial index covers mineral occurrences of geodiversity, such precious stones and metals, energy and industrial minerals, mineral waters, and springs. The Geodiversity Index takes the form of an isoline map that can be used as a tool in land-use planning, particularly in identifying priority areas for conservation, management, and use of natural resources at the state level.The Portuguese authors express their gratitude for the financial support given by the Fundacao para a Ciencia e a Tecnologia to the Centro de Geologia da Universidade do Porto, which partially supports this research. The Brazilian author expresses his gratitude for the financial support given by the CNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico) (Process Number 200074/2011-3)

Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images

A computational framework to generate daily temperature maps using time-series of publicly available MODIS MOD11A2 product Land Surface Temperature (LST) images (1 km resolution; 8-day composites) is illustrated using temperature measurements from the national network of meteorological stations (159) in Croatia. The input data set contains 57,282 ground measurements of daily temperature for the year 2008. Temperature was modeled as a function of latitude, longitude, distance from the sea, elevation, time, insolation, and the MODIS LST images. The original rasters were first converted to principal components to reduce noise and filter missing pixels in the LST images. The residual were next analyzed for spatio-temporal auto-correlation; sum-metric separable variograms were fitted to account for zonal and geometric space-time anisotropy. The final predictions were generated for time-slices of a 3D space-time cube, constructed in the R environment for statistical computing. The results show that the space-time regression model can explain a significant part of the variation in station-data (84%). MODIS LST 8-day (cloud-free) images are unbiased estimator of the daily temperature, but with relatively low precision (±4.1°C); however their added value is that they systematically improve detection of local changes in land surface temperature due to local meteorological conditions and/or active heat sources (urban areas, land cover classes). The results of 10–fold cross-validation show that use of spatio-temporal regression-kriging and incorporation of time-series of remote sensing images leads to significantly more accurate maps of temperature than if plain spatial techniques were used. The average (global) accuracy of mapping temperature was ±2.4°C. The regression-kriging explained 91% of variability in daily temperatures, compared to 44% for ordinary kriging. Further software advancement—interactive space-time variogram exploration and automated retrieval, resampling and filtering of MODIS images—are anticipated

Enhancing quantum efficiency of thin-film silicon solar cells by Pareto optimality

We present a composite design methodology for the simulation and optimization of the solar cell performance. Our method is based on the synergy of different computational techniques and it is especially designed for the thin-film cell technology. In particular, we aim to efficiently simulate light trapping and plasmonic effects to enhance the light harvesting of the cell. The methodology is based on the sequential application of a hierarchy of approaches: (a) full Maxwell simulations are applied to derive the photon’s scattering probability in systems presenting textured interfaces; (b) calibrated Photonic Monte Carlo is used in junction with the scattering matrices method to evaluate coherent and scattered photon absorption in the full cell architectures; (c) the results of these advanced optical simulations are used as the pair generation terms in model implemented in an effective Technology Computer Aided Design tool for the derivation of the cell performance; (d) the models are investigated by qualitative and quantitative sensitivity analysis algorithms, to evaluate the importance of the design parameters considered on the models output and to get a first order descriptions of the objective space; (e) sensitivity analysis results are used to guide and simplify the optimization of the model achieved through both Single Objective Optimization (in order to fully maximize devices efficiency) and Multi Objective Optimization (in order to balance efficiency and cost); (f) Local, Global and “Glocal” robustness of optimal solutions found by the optimization algorithms are statistically evaluated; (g) data-based Identifiability Analysis is used to study the relationship between parameters. The results obtained show a noteworthy improvement with respect to the quantum efficiency of the reference cell demonstrating that the methodology presented is suitable for effective optimization of solar cell devices