Search Results - "Heuvelink, Gerard"

Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables by Hengl, Tomislav, Nussbaum, Madlene, Wright, Marvin N, Heuvelink, Gerard B M, Gräler, Benedikt

“…Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but spatial location of points (geography) is often…”
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SoilGrids250m: Global gridded soil information based on machine learning by Hengl, Tomislav, Mendes de Jesus, Jorge, Heuvelink, Gerard B M, Ruiperez Gonzalez, Maria, Kilibarda, Milan, Blagotić, Aleksandar, Shangguan, Wei, Wright, Marvin N, Geng, Xiaoyuan, Bauer-Marschallinger, Bernhard, Guevara, Mario Antonio, Vargas, Rodrigo, MacMillan, Robert A, Batjes, Niels H, Leenaars, Johan G B, Ribeiro, Eloi, Wheeler, Ichsani, Mantel, Stephan, Kempen, Bas

“…This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June…”
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Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions by Hengl, Tomislav, Heuvelink, Gerard B M, Kempen, Bas, Leenaars, Johan G B, Walsh, Markus G, Shepherd, Keith D, Sila, Andrew, MacMillan, Robert A, Mendes de Jesus, Jorge, Tamene, Lulseged, Tondoh, Jérôme E

“…80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year…”
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SoilGrids1km--global soil information based on automated mapping by Hengl, Tomislav, de Jesus, Jorge Mendes, MacMillan, Robert A, Batjes, Niels H, Heuvelink, Gerard B M, Ribeiro, Eloi, Samuel-Rosa, Alessandro, Kempen, Bas, Leenaars, Johan G B, Walsh, Markus G, Gonzalez, Maria Ruiperez

“…Soils are widely recognized as a non-renewable natural resource and as biophysical carbon sinks. As such, there is a growing requirement for global soil…”
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Random Forest Spatial Interpolation by Sekulić, Aleksandar, Kilibarda, Milan, Heuvelink, Gerard B.M., Nikolić, Mladen, Bajat, Branislav

“…For many decades, kriging and deterministic interpolation techniques, such as inverse distance weighting and nearest neighbour interpolation, have been the…”
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Comparing the prediction performance, uncertainty quantification and extrapolation potential of regression kriging and random forest while accounting for soil measurement errors by Takoutsing, Bertin, Heuvelink, Gerard B.M.

“…•Measurement errors were incorporated in regression kriging and random forest.•Kriging predictions were more accurate than random forest predictions.•Random…”
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Validation of uncertainty predictions in digital soil mapping by Schmidinger, Jonas, Heuvelink, Gerard B.M.

“…•Uncertainty predictions in digital soil mapping are not optimally validated.•The prediction interval coverage probability cannot account for one-sided…”
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Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning by Hengl, Tomislav, Leenaars, Johan G. B., Shepherd, Keith D., Walsh, Markus G., Heuvelink, Gerard B. M., Mamo, Tekalign, Tilahun, Helina, Berkhout, Ezra, Cooper, Matthew, Fegraus, Eric, Wheeler, Ichsani, Kwabena, Nketia A.

“…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…”
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Soil resources and element stocks in drylands to face global issues by Plaza, César, Zaccone, Claudio, Sawicka, Kasia, Méndez, Ana M., Tarquis, Ana, Gascó, Gabriel, Heuvelink, Gerard B. M., Schuur, Edward A. G., Maestre, Fernando T.

“…Drylands (hyperarid, arid, semiarid, and dry subhumid ecosystems) cover almost half of Earth’s land surface and are highly vulnerable to environmental…”
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SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty by Poggio, Laura, de Sousa, Luis M., Batjes, Niels H., Heuvelink, Gerard B. M., Kempen, Bas, Ribeiro, Eloi, Rossiter, David

“…SoilGrids produces maps of soil properties for the entire globe at medium spatial resolution (250 m cell size) using state-of-the-art machine learning methods…”
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Mapping rootable depth and root zone plant-available water holding capacity of the soil of sub-Saharan Africa by Leenaars, Johan G.B., Claessens, Lieven, Heuvelink, Gerard B.M., Hengl, Tom, Ruiperez González, Maria, van Bussel, Lenny G.J., Guilpart, Nicolas, Yang, Haishun, Cassman, Kenneth G.

“…In rainfed crop production, root zone plant-available water holding capacity (RZ-PAWHC) of the soil has a large influence on crop growth and the yield response…”
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Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging by Jin, Yan, Ge, Yong, Wang, Jianghao, Chen, Yuehong, Heuvelink, Gerard B. M., Atkinson, Peter M.

“…Soil moisture (SM) plays an important role in the land surface energy balance and water cycle. Microwave remote sensing has been applied widely to estimate SM…”
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Propagation of positional error in 3D GIS: estimation of the solar irradiation of building roofs by Biljecki, Filip, Heuvelink, Gerard B.M., Ledoux, Hugo, Stoter, Jantien

“…While error propagation in GIS is a topic that has received a lot of attention, it has not been researched with 3D GIS data. We extend error propagation to 3D…”
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Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images by Hengl, Tomislav, Heuvelink, Gerard B. M., Perčec Tadić, Melita, Pebesma, Edzer J.

“…A computational framework to generate daily temperature maps using time-series of publicly available MODIS MOD11A2 product Land Surface Temperature (LST)…”
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Recent insights on uncertainties present in integrated catchment water quality modelling by Tscheikner-Gratl, Franz, Bellos, Vasilis, Schellart, Alma, Moreno-Rodenas, Antonio, Muthusamy, Manoranjan, Langeveld, Jeroen, Clemens, Francois, Benedetti, Lorenzo, Rico-Ramirez, Miguel Angel, de Carvalho, Rita Fernandes, Breuer, Lutz, Shucksmith, James, Heuvelink, Gerard B.M., Tait, Simon

“…This paper aims to stimulate discussion based on the experiences derived from the QUICS project (Quantifying Uncertainty in Integrated Catchment Studies)…”
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Spatial cross-validation is not the right way to evaluate map accuracy by Wadoux, Alexandre M.J.-C., Heuvelink, Gerard B.M., de Bruin, Sytze, Brus, Dick J.

“…For decades scientists have produced maps of biological, ecological and environmental variables. These studies commonly evaluate the map accuracy through…”
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Estimating soil organic carbon stock change at multiple scales using machine learning and multivariate geostatistics by Szatmári, Gábor, Pásztor, László, Heuvelink, Gerard B.M.

“…•Use of geostatistics is indispensable when spatial aggregation with quantified uncertainty is targeted.•Spatial aggregation decreases uncertainty and supports…”
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Multivariate random forest for digital soil mapping by van der Westhuizen, Stephan, Heuvelink, Gerard B.M., Hofmeyr, David P.

“…In digital soil mapping (DSM), soil maps are usually produced in a univariate manner, that is, each soil map is produced independently and therefore, when…”
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Efficiency Comparison of Conventional and Digital Soil Mapping for Updating Soil Maps by Kempen, Bas, Brus, Dick J, Stoorvogel, Jetse J, Heuvelink, Gerard B.M, Vries, Folkert de

“…This study compared the efficiency of geostatistical digital soil mapping (DSM) with conventional soil mapping (CSM) for updating soil class and property maps…”
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Tier 4 maps of soil pH at 25 m resolution for the Netherlands by Helfenstein, Anatol, Mulder, Vera L., Heuvelink, Gerard B.M., Okx, Joop P.

“…•Tier 4 GlobalSoilMap products that include spatially explicit accuracy thresholds.•Assessment of map accuracy using various statistical validation strategies,…”
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