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Hengl T.,ISRIC World Soil Information | MacMillan R.A.,ISRIC World Soil Information | Nikolic M.,University of Belgrade
International Journal of Applied Earth Observation and Geoinformation | Year: 2013

This paper proposes two compound measures of mapping quality to support objective comparison of spatial prediction techniques for geostatistical mapping: (1) mapping efficiency - defined as the costs per area per amount of variation explained by the model, and (2) information production efficiency - defined as the cost per byte of effective information produced. These were inspired by concepts of complexity from mathematics and physics. Complexity i.e. the total effective information is defined as bytes remaining after compression and after rounding up the numbers using half the mapping accuracy (effective precision). It is postulated that the mapping efficiency, for an area of given size and limited budget, is basically a function of inspection intensity and mapping accuracy. Both measures are illustrated using the Meuse and Ebergötzen case studies (gstat, plotKML packages). The results demonstrate that, for mapping organic matter (Meuse data set), there is a gain in the mapping efficiency when using regression-kriging versus ordinary kriging: mapping efficiency is 7% better and the information production efficiency about 25% better (3.99 vs 3.14 EUR B-1 for the GZIP compression algorithm). For mapping sand content (Ebergötzen data set), the mapping efficiency for both ordinary kriging and regression-kriging is about the same; the information production efficiency is 29% better for regression-kriging (37.1 vs 27.7 EUR B-1 for the GZIP compression algorithm). Information production efficiency is possibly a more robust measure of mapping quality than mapping efficiency because: (1) it is scale-independent, (2) it can be more easily related to the concept of effective information content, and (3) it accounts for the extrapolation effects. The limitation of deriving the information production efficiency is that both reliable estimate of the model uncertainty and the mapping accuracy is required. © 2012 Elsevier B.V.


Soils play a key role in providing a range of ecosystem services. Quality-assessed soil information, with quantified uncertainty levels, is needed to address a range of global issues. Traditional mapping methods, which recognize that soil classes are "important carriers of soil information", were used to prepare an updated harmonized dataset of derived soil properties for the world at a nominal resolution of 30 by 30 arc sec (WISE30sec). The map unit composition was determined using an overlay of the Harmonized World Soil Database, with minor corrections, and the Köppen-Geiger climate zones map as categorical co-variate. Property estimates for the respective component soil units were derived using taxonomy-based transfer rules that draw on a statistical analysis of some 21,000 soil profiles. Best estimates (mean ± standard deviation) for twenty soil properties were calculated for seven depth intervals (up to 2 m depth or less when thinner): organic carbon content, total nitrogen, C/N ratio, pH(H2O), CECsoil, CECclay, effective CEC, total exchangeable bases (TEB), base saturation, aluminium saturation, calcium carbonate content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity, particle size distribution (content of sand, silt and clay), proportion of coarse fragments (>2 mm), bulk density, and available water capacity (-33 to -1500 kPa); also the dominant soil drainage class. Coefficients of variation tend to be large. WISE30sec may be used for applications at a broad scale (<1:1 M) upon consideration of the underlying data lineage, generalizations, and the associated uncertainties. As an example, the database was used to calculate the global soil organic carbon (SOC) stock to 2 m depth. Some 30% (607 ± 87 Pg C) of this stock (2060 ± 215 Pg C) is held in the Northern Circumpolar Region, which is considered most sensitive to climate change. © 2016 Elsevier B.V.


Hengl T.,ISRIC World Soil Information | Roudier P.,Landcare Research | Beaudette D.,USDA NRCS | Pebesma E.,University of Munster
Journal of Statistical Software | Year: 2015

plotKML is an R package that provides methods for writing the most common R spatial classes into KML files. It builds up on the existing XML parsing functionality (XML package), and provides similar plotting functionality as the lattice package. Its main objective is to provide a simple interface to generate KML files with a small number of arguments, and allows users to visually explore spatio-temporal data available in R: points, polygons, gridded maps, trajectory-type data, vertical profiles, ground photographs, time series vector objects or raster images, along with the results of spatial analysis such as geostatistical mapping, spatial simulations of vector and gridded objects, optimized sampling designs, species distribution models and similar. A generic plotKML() function automatically determines the parsing order and visualizes data directly from R; lower level functions can be combined to allow for new user-created visualization templates. In comparison to other packages writing KML, plotKML seems to be more object oriented, it links more closely to the existing R classes for spatio-temporal data (sp, spacetime and raster packages) than the alternatives, and provides users with the possibility to create their own templates. © 2015, Journal of Statistical Software All rights received.


Hengl T.,ISRIC World Soil Information | Heuvelink G.B.M.,Wageningen University | Tadic M.P.,Meteorological and Hydrological Service of Croatia | Pebesma E.J.,University of Munster
Theoretical and Applied Climatology | Year: 2012

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. © 2011 The Author(s).


The Carbon Benefits Project (CBP) is developing a standardized system for sustainable land management projects to measure, model and report changes in carbon stocks and greenhouse gas (GHG) emissions for use at varying scales. A global framework of soil organic carbon (SOC) stocks under native vegetation for application in data poor regions, using the simple assessment option of the CBP system, is presented. It considers default classes for climate and mineral soils as required for IPCC Tier 1 (empirical) level GHG inventories. Suitable soil profiles were extracted from an expanded version of the ISRIC-WISE database. Probable outliers within each climate-soil cluster were removed using a robust outlier-rejection procedure. Mean SOC stocks, to the IPCC reference depth of 30cm (SOC30), vary greatly within each cluster. Overall, present estimates of SOC30 are lower than those listed in the 2006 IPCC Guidelines (though not necessarily in the statistical sense) that drew on a smaller selection of profiles from a more limited geographic area. They represent globally averaged values of SOC stocks under native vegetation that may differ from country/region specific values. Finer criteria for defining climate zones and soil classes, and replacement of default reference stocks and stock change factors with region-specific values, will be necessary to reduce uncertainty. © 2011 Elsevier B.V.


Brevik E.C.,Dickinson State University | Hartemink A.E.,ISRIC World Soil Information
Catena | Year: 2010

Soils knowledge dates to the earliest known practice of agriculture about 11,000 BP. Civilizations all around the world showed various levels of soil knowledge by the 4th century AD, including irrigation, the use of terraces to control erosion, various ways of improving soil fertility, and ways to create productive artificial soils. Early soils knowledge was largely based on observations of nature; experiments to test theories were not conducted. Many famous scientists, for example, Francis Bacon, Robert Boyle, Charles Darwin, and Leonardo da Vinci worked on soils issues. Soil science did not become a true science, however, until the 19th century with the development of genetic soil science, led by Vasilii V. Dokuchaev. In the 20th century, soil science moved beyond its agricultural roots and soil information is now used in residential development, the planning of highways, building foundations, septic systems, wildlife management, environmental management, and many other applications in addition to agriculture. © 2010 Elsevier B.V.


Batlle-Bayer L.,ISRIC World Soil Information | Batjes N.H.,ISRIC World Soil Information | Bindraban P.S.,ISRIC World Soil Information
Agriculture, Ecosystems and Environment | Year: 2010

This paper reviews current knowledge on changes in carbon stocks upon land use conversion in the Brazilian Cerrado. First, we briefly characterize the savanna ecosystem and summarize the main published data on C stocks under natural conditions. The effects of increased land use pressure in the Cerrado and current uncertainties of estimations of changes in land cover and land use are reviewed next. Thereafter, we focus on soil organic carbon (SOC) dynamics due to changes in land use, particularly conversion to pastures and soybean-based cropping systems, and effects of management practices such as soil fertilization, crop rotations and tillage practices. Most studies considered here suggest that more intensive agriculture, which include no-till practices and the implementation of best or recommended management practices (RMP), reduces SOC losses after land use conversion from conventional tillage-based, monocropping systems; however, these studies focussed on the first 0.3 m of soil, or less, and seldom considered full carbon accounting. To better estimate possible global warming mitigation with agriculture in the Cerrado more comprehensive studies are needed that analyse fluxes of the biogenic greenhouse gases (GHG; CO2, N2O and CH4) to determine the net global warming potential (GWP). Follow up studies should include the application of an integrated modelling system, comprised of a Geographic Information System (GIS) linked to dynamic modelling tools, to analyse SOC dynamics and make projections for possible changes in net C flows in the Cerrado region upon defined changes in soil use and management. © 2010 Elsevier B.V. All rights reserved.


Minasny B.,University of Sydney | McBratney Alex.B.,University of Sydney | Hartemink A.E.,ISRIC World Soil Information
Geoderma | Year: 2010

This paper discusses the study of taxonomic distance and pedodiversity by (1) deriving taxonomic distances for the World Reference Base for Soil Resources (WRB), (2) calculating pedodiversity indices at the global scale using the soil map of the world at a scale 1:25M, and (3) comparing traditional diversity measures which are based on abundance of soil individuals to measures that are based on taxonomic distance. Based on dominant identifiers in the WRB soil groups, taxonomic distances were derived between the soil groups and plotted in feature space. Using this information the soil's mean taxonomic distance for the world was calculated. The mean taxonomic distance combines the abundance and taxonomic relationship between soil groups and appears to be a useful index of pedodiversity. There is a good relation between mean taxonomic distance and climate or soil classes; areas with extreme temperatures and precipitation have the lowest pedodiversity. It was observed that areas with more detailed soil mapping units exhibit the largest pedodiversity and it was concluded that the measure of pedodiversity depends amongst others on the detail of the soil survey in an area. © 2009 Elsevier B.V. All rights reserved.


Hartemink A.E.,ISRIC World Soil Information
Journal of Tropical Forest Science | Year: 2010

Piper aduncum is a shrub native to Central America. It is found in most Central and South American countries and also in the Caribbean and southern Florida (USA). In Asia and the Pacific, P. aduncum occurs in Indonesia, Malaysia, Philippines, Papua New Guinea, Solomon Islands, Vanuatu, Fiji, Micronesia, American Samoa, Niue, the Marianas, Tonga, Samoa, the Cook Islands, Palau and Hawaii (USA). Piper aduncum arrived in Papua New Guinea before the mid-1930s. From the 1970s, it started to dominate the secondary fallow vegetation in many parts of the humid lowlands. It invaded grassland areas and also appeared in the highlands up to 2100 m asl. The seeds are dispersed by birds, bats and wind, as well as by logging equipment and in some localities, by migrating people. The combination of its vigorous generative characteristics (small and abundant seeds), high growth rate and the accidental or intentional spreading has resulted in its presence in most provinces of Papua New Guinea. In the 1990s, awareness of the spread of P. aduncum grew and there was a corresponding increase in research interest from a range of disciplines, e.g. pharmacology, agronomy, quarantine, forestry and taxonomy. The invasion of P. aduncum has affected the farming system and livelihood of many rural people. Future research should focus on mapping its extent, and studying its agronomic, socio-economic and ecological effects, particularly its effect on biodiversity.


Large areas in the Upper Tana river catchment, Kenya, have been over-exploited, resulting in soil erosion, nutrient depletion and loss of soil organic matter (SOM). This study focuses on sections of the catchment earmarked as being most promising for implementing Green Water Credits, an incentive mechanism to help farmers invest in land and soil management activities that affect all fresh water resources at source. Such management practices can also help restore SOM levels towards their natural level. Opportunities to increase soil organic carbon (SOC) stocks, for two broadly defined land use types (croplands and plantation crops, with moderate input levels), are calculated using a simple empirical model, using three scenarios for the proportion of suitable land that may be treated with these practices (low=40percent, medium=60percent, high=80percent). For the medium scenario, corresponding to implementation on ~348000ha in the basin, the eco-technologically possible SOC gains are estimated at 4·8 to 9·3×106tonnes (Mg) CO2 over the next 20years. Assuming a conservative price of US$10 per tonne CO2-equivalent on the carbon offset market, this would correspond to ~US$48-93 million over a 20-year period of sustained green water management. This would imply a projected (potential) payment of some US$7-13ha-1 to farmers annually; this sum would be in addition to incentives that are being put in place for implementing green water management practices and also in addition to the benefits that farmers would realize from the impact on production of these practices themselves. © 2012 John Wiley & Sons, Ltd.

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