134 results on '"Hengl, T."'
Search Results
2. Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution
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Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Jung, M., Herold, M., Duveiller, G., Weynants, M., Wheeler, I., Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Jung, M., Herold, M., Duveiller, G., Weynants, M., and Wheeler, I.
- Abstract
The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short–term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials. Monthly aggregated FAPAR time-series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8-day GLASS FAPAR V6 product for 2000–2021 and used to determine long-term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio-physical variables representing climate, terrain, landform, and vegetation cover, as well as several variables representing human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (extremely randomized trees, gradient descended trees and artificial neural network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs. potential FAPAR. The produced global maps of actual vs. potential FAPAR and long-term trends
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- 2024
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3. Can Sodium Thiosulfate Act as a Reversal Agent for Calcium Hydroxylapatite Filler? Results of a Preclinical Study
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Danysz W, Nowag B, Hengl T, Kreymerman P, Furne C, Madeuf E, Höennscheidt C, and Mraz Robinson D
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calcium hydroxylapatite ,sodium thiosulfate ,dispersion effect ,computer tomography ,histology ,3d camera ,Dermatology ,RL1-803 - Abstract
Wojciech Danysz,1 Bartosch Nowag,1 Thomas Hengl,1 Peter Kreymerman,2 Céline Furne,3 Elise Madeuf,4 Christoph Höennscheidt,1 Deanne Mraz Robinson5,6 1R&D, Merz Pharmaceuticals GmbH, Frankfurt am Main, Germany; 2Medical Affairs, Merz North America, Raleigh, NC, USA; 3NAMSA, Chasse sur Rhône, France; 4Preclinical Studies, Voxcan, Marcy L´Etoile, France; 5Modern Dermatology of Connecticut, Westport, CT, USA; 6Yale-New Haven Hospital, New Haven, CT, USACorrespondence: Wojciech DanyszMerz Pharmaceuticals GmbH, Eckenheimer Landstrasse 100, Frankfurt am Main 60318, GermanyEmail Wojciech.danysz@merz.deIntroduction: Calcium hydroxylapatite microspheres suspended in a gel carrier of sodium carboxymethylcellulose (CaHA; Radiesse®) has demonstrated safe and effective restoration of facial volume in clinical trials, as well as collagen biostimulation leading to skin quality improvement. The potential with CaHA, as with any filler, to produce overcorrection and subsequent complications has led to the search for a reversal agent. Sodium thiosulfate (STS) was proposed based on experience with it as a chelating agent to treat calciphylaxis. Previous pilot studies with small sample sizes have suggested its efficacy in the reduction of CaHA volume and nodule formation. The present study focuses on the verification of this effect using various readout methods in preclinical experiments.Methods: We use both in vitro (co-incubation of STS with CaHA) and in vivo (injections in farm pig) methods with readout techniques such as 3D camera analysis, micro-computed tomography ex vivo (μCT), computed tomography in vivo (CT), histopathology and scanning electron microscopy.Results: We did not obtain any indications of CaHA degradation by STS, either in vitro or in vivo. 3D-camera analysis also did not show any decreasing effect of STS on CaHA. However, histology, μCT ex vivo, and CT in vivo indicated a decrease of Radiesse amount/volume after STS treatment, which could be attributed to dispersion effect. It should be noted that necrosis and haemorrhages were observed after STS treatment.Discussion: Results suggest no indication of CaHA microspheres degradation with STS and that the STS mechanism of action on CaHA is consistent with a dispersion effect. Observed necrosis is a further obstacle in the use of STS.Keywords: calcium hydroxylapatite, sodium thiosulfate, dispersion effect, computed tomography, histology, 3D camera
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- 2020
4. Earth Observation and Machine Learning as the key technologies to track implementation of the Green Deal: 10 main takeaways
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Hengl, T., Ross, C., and Delconte, V.
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Earth Observation ,Copernicus ,Environmental information - Abstract
One of the key objectives of the largest government-run Earth Observation missions such as Copernicus Europe, Landsat and similar is to provide reference scientific information to enable monitoring of our changing environment across borders (and beyond visible light!). EO-based products are becoming crucial for spatial planning but also for the econometric assessment of the social benefits, taxation and political decisions in general. We have invited a number of keynote speakers and asked them to share their opinions on the general question “How can Earth Observation (EO) and Machine Learning technology help deliver the green (new) deal and what could potentially be a win-win-win situation?” By win-win-win here we imply: (1) environmental data users (businesses and organizations) win, (2) data producers win, and (3) society wins. Each of the talks was video recorded and is available here. These are the main takeaways., {"references":["Breidenbach, J., Ellison, D., Petersson, H., Korhonen, K. T., Henttonen, H. M., Wallerman, J., … & Næsset, E. (2022). Harvested area did not increase abruptly — how advancements in satellite-based mapping led to erroneous conclusions. Annals of forest science, 79(1), 1–9. https://doi.org/10.1186/s13595-022-01120-4","Ceccherini, G., Duveiller, G., Grassi, G., Lemoine, G., Avitabile, V., Pilli, R., Cescatti, A. (2020). Abrupt increase in harvested forest area over Europe after 2015. Nature 583(7814):72–77. https://doi.org/10.1038/s41586-020-2438-y","Ceccherini, G., Duveiller, G., Grassi, G., Lemoine, G., Avitabile, V., Pilli, R., & Cescatti, A. (2022). Potentials and limitations of NFIs and remote sensing in the assessment of harvest rates: a reply to Breidenbach et al. Annals of Forest Science, 79(1), 1–7. https://doi.org/10.1186/s13595-022-01150-y","Nabuurs, G. J., Harris, N., Sheil, D., Palahi, M., Chirici, G., Boissière, M., … & Valbuena, R. (2022). Glasgow forest declaration needs new modes of data ownership. Nature Climate Change, 12(5), 415–417. https://doi.org/10.1038/s41558-022-01343-3","Sabatini, F. M., Jiménez-Alfaro, B., Jandt, U., Chytrý, M., Field, R., Kessler, M., … & Bruelheide, H. (2022). Global patterns of vascular plant alpha diversity. Nature communications, 13(1), 4683. https://doi.org/10.1038/s41467-022-32063-z","Schramm, M., Pebesma, E., Milenković, M., Foresta, L., Dries, J., Jacob, A., … & Reiche, J. (2021). The openeo api–harmonising the use of earth observation cloud services using virtual data cube functionalities. Remote Sensing, 13(6), 1125. https://doi.org/10.3390/rs13061125","Simoes, R., Camara, G., Queiroz, G., Souza, F., Andrade, P. R., Santos, L., … & Ferreira, K. (2021). Satellite image time series analysis for big earth observation data. Remote Sensing, 13(13), 2428. https://doi.org/10.3390/rs13132428","Venter, Z. S., Barton, D. N., Chakraborty, T., Simensen, T., & Singh, G. (2022). Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover. Remote Sensing, 14(16), 4101. https://doi.org/10.3390/rs14164101","Wagemann, J., Siemen, S., Seeger, B., & Bendix, J. (2021). Users of open Big Earth data–An analysis of the current state. Computers & Geosciences, 157, 104916. https://doi.org/10.1016/j.cageo.2021.104916"]}
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- 2022
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5. Modeling Wind Erosion Events – Bridging the Gap Between Digital Soil Mapping and Digital Soil Risk Assessment
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Reuter, H.I., Lado, L. Rodriguez, Hengl, T., Montanarella, L., Hartemink, Alfred E., editor, McBratney, Alex B., editor, Boettinger, Janis L., editor, Howell, David W., editor, Moore, Amanda C., editor, and Kienast-Brown, Suzann, editor
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- 2010
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6. Mapping efficiency and information content
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Hengl, T., Nikolić, M., and MacMillan, R.A.
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- 2013
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7. OEMC D2.1 Report 'Stakeholder Committee and Open- Earth-Monitor Design' workshop
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Milenkovic, M., Georgieva, I., Hengl, T., Ross, C., Meyer zu Erpen, N., Fritz, S., Milenkovic, M., Georgieva, I., Hengl, T., Ross, C., Meyer zu Erpen, N., and Fritz, S.
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This deliverable of the Open-Earth-Monitor project describes the approach taken to compile and categorize the project's stakeholders, and provides recommendations for the future stakeholder interactions based on the results of a survey from the OEMC design workshop, which took place during the project's kick-off meeting in July, 2022.
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- 2022
8. Spatial and Spatiotemporal Interpolation / Prediction using Ensemble Machine Learning
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Hengl, T., Parente, L., and Bonannella, C.
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ensemble Machine Learning ,OpenLandMap ,predictive mapping ,spatial interpolation - Abstract
This R tutorial explains step-by-step how to use Ensemble Machine Learning to generate predictions (maps) from 2D, 3D, 2D+T training (point) datasets. We show functionality to do automated benchmarking for spatial/spatiotemporal prediction problems, and for which we use primarily the mlr framework and spatial packages terra, rgdal and similar. In addition, we explain how to plot spatial/spatiotemporal prediction inputs and outputs, including how to do accuracy plots and predictograms. We focus engineering the predictive mapping around three main areas: (a) accuracy performance, (b) computing time, (c) robustness of the algorithms (sensitivity to noise, artifacts etc). Online version of the book is available at: https://opengeohub.github.io/spatial-prediction-eml/, Acknowledgement: CEF Telecom project 2018-EU-IA-0095. This project is co-financed by the by the European Union.
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- 2022
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9. Spatial sampling and resampling for Machine Learning
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Hengl, T., Parente, L., and Wheeler, I.
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sampling ,machine learning ,ensemble machine learning - Abstract
This R tutorial contains instructions on how to organize spatial sampling using R packages. It is organized in three main parts: (1) planning new surveys: i.e. starting from scratch, (2) implementing resampling: learning from existing point data, focusing on subsampling and Cross-Validation strategies, (3) planning additional sampling: sampling additional point data based on initial models, the running re-analysis and gradually improving models until the maximum possible accuracy is reached. We use sample datasets to demonstrate processing steps and provide interpretation and dicussion of the results. More chapters will be added in the future. To use most up-to-date copy please refer to: https://opengeohub.github.io/spatial-sampling-ml/.
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- 2022
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10. Restoration culture: what is land degradation, how to measure it, and what you can do to reverse some negative trends?
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Hengl, T., Wheeler, I., and McMillan, B.
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Land degradation is a systematic loss of function in terrestrial ecosystems: a serious drop in primary productivity, biomass and biodiversity. This means the land produces less than what it is able to at its natural capacity and even eventually deteriorates completely with little to no production at all. No plants. No animals. No water. Where this occurs extensively enough, it also changes the climate. Since the start of the industrial revolution 150 years ago, land degradation has been continuously accelerating at a frightening pace. But all is not lost! We now understand how land degradation can be reversed by systematic land conservation practices, scaling up of agroecological systems, landscape-scale rehydration and massive planting of grasses, shrubs and forests, by leaning into nature and designing and building resilient ecosystems. But where is the potential for (land) restoration the highest? And how can you get involved and help re-green, re-store and expand resilient terrestrial ecosystems?, Published in: https://opengeohub.medium.com/restoration-culture-what-is-land-degradation-how-to-measure-it-and-what-can-you-do-to-reverse-341e448994da
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- 2021
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11. An Open Compendium of Soil Datasets: Soil Observations and Measurements
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Hengl, T.
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soil samples ,soil profiles ,soil spectroscopy ,soil mapping - Abstract
This is a public compendium of global, regional, national and sub-national soil samples and/or soil profile datasets (points with Observations and Measurements of soil properties and characteristics). Datasets listed here, assuming compatible open license, are afterwards imported into the Global compilation of soil chemical and physical properties and soil classes and eventually used to create a better open soil information across countries. Please feel free to contribute entries. See GitHub repository for more detailed instructions. To see the most up-to-date version of this compendium please visit: https://opengeohub.github.io/SoilSamples/, https://opengeohub.github.io/SoilSamples/
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- 2021
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12. Comparing spatial prediction methods for soil property mapping in Brazil
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de Souza, E, primary, Hengl, T, additional, Kempen, B, additional, Heuvelink, G, additional, Filho, E, additional, and Schaefer, C, additional
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- 2014
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13. Worldgrids—a public repository of global soil covariates
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Reuter, H, primary and Hengl, T, additional
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- 2012
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14. Methods and data sources for spatial prediction of rainfall
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Hengl, T., primary, AghaKouchak, A., additional, and Percěc Tadić, M., additional
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- 2010
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15. Chapter 13 Geomorphometry in ILWIS
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Hengl, T., primary, Maathuis, B.H.P., additional, and Wang, L., additional
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- 2009
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16. Chapter 28 The Future of Geomorphometry
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Gessler, P., primary, Pike, R., additional, MacMillan, R.A., additional, Hengl, T., additional, and Reuter, H.I., additional
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- 2009
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17. Chapter 22 Applications in Geomorphology
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Evans, I.S., primary, Hengl, T., additional, and Gorsevski, P., additional
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- 2009
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18. Chapter 19 Geomorphometry — A Key to Landscape Mapping and Modelling
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Hengl, T., primary and MacMillan, R.A., additional
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- 2009
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19. Chapter 2 Mathematical and Digital Models of the Land Surface
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Hengl, T., primary and Evans, I.S., additional
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- 2009
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20. Chapter 20 Soil Mapping Applications
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Dobos, E., primary and Hengl, T., additional
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- 2009
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21. A global spatially explicit database of changes in island palaeo-area and archipelago configuration during the late Quaternary
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Norder, SJ, Baumgartner, JB, Borges, PAV, Hengl, T, Kissling, WD, van Loon, EE, Rijsdijk, KF, Norder, SJ, Baumgartner, JB, Borges, PAV, Hengl, T, Kissling, WD, van Loon, EE, and Rijsdijk, KF
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- 2018
22. Ghana SoilGrids; Compilation of Legacy Soil Data and the Production of Gridded Functional Soil Class and Property Maps
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Nketia, K.A., Leenaars, J.G.B., Hengl, T., Asamoah, E., Ruiperez Gonzalez, M., and Owusu Ansah, A.
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Life Science ,PE&RC ,ISRIC - World Soil Information - Published
- 2017
23. World soil information developing from global, continental and national initiatives
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Leenaars, J.G.B., Kwabena, Nketia A., Silatsa, F.B.T., Fening, J.O., Yemefack, M., Hengl, T., and Heuvelink, G.B.M.
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Bodemgeografie en Landschap ,Soil Geography and Landscape ,Life Science ,PE&RC ,ISRIC - World Soil Information - Abstract
Resulting from the GlobalSoilMap initiative and the Globally-integrated Africa Soil Information Service (AfSIS) project, soil property maps of the world were produced in 2014, following the maps of Sub-Saharan Africa produced in 2013. The two maps were fully compliant with the GlobalSoilMap specifications except for the spatial resolution of 1km. The primary soil data used as input for mapping were from the Africa Soil Profiles (AfSP) database and an early version of the World Soil Information Service (WoSIS) database, both compiled mainly from existing sources in collaboration with various partners in Africa and the world. The maps have more recently been updated to a resolution of 250m upon availability of additional data. These initiatives, using a top-down approach due to lack of budget to actively involve soil institutes worldwide, showed that state of the art baseline products can be developed cost-efficiently based on legacy soil data and can be updated when additional, possibly newly collected, data become available. As a result, several bottom-up initiatives in Africa have started similar work aiming to add value to national soil data holdings of mainly analogue and fragmented nature. In collaboration with ISRIC and facilitated by the global soil information facilities (GSIF), the Soil Research Institute in Ghana compiled and georeferenced data, collected during decades of soil studies applying standard procedures, for over 1000 soil profiles adding to the 400 in the AfSP. A similar successful initiative with the University of Dschang and the International Institute of Tropical Agriculture (IITA) in Cameroon compiled data for 1300 soil profiles, including 500 from the AfSP of which many could be georeferenced more accurately. Each of the countries developed a baseline version of the national SoilGrids, with and without using the global SoilGrids maps as covariate. These collaborative studies are good examples how national and global initiatives can strengthen each other as a feasible way forward to compile sufficient data, at low costs, to produce maps that are nationally and locally accurate, detailed and relevant as well as globally coherent, harmonised and complete. Such hybrid approach requires training of national experts and sufficient computational capacity to locally produce maps at high spatial resolution, i.e. 100m, and also requires global standards and mechanisms for sharing and merging of data and results.
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- 2017
24. 3D soil hydraulic database of Europe at 250 m resolution
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Toth, B., Weynants, M., Pasztor, L., Hengl, T., Toth, B., Weynants, M., Pasztor, L., and Hengl, T.
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Soil hydraulic properties are required in various modelling schemes. We propose a consistent spatial soil hydraulic database at 7 soil depths up to 2 m calculated for Europe based on SoilGrids250m and 1 km datasets and pedotransfer functions trained on the European Hydropedological Data Inventory. Saturated water content, water content at field capacity and wilting point, saturated hydraulic conductivity and Mualem-van Genuchten parameters for the description of the moisture retention, and unsaturated hydraulic conductivity curves have been predicted. The derived 3D soil hydraulic layers (EU-SoilHydroGrids ver1.0) can be used for environmental modelling purposes at catchment or continental scale in Europe. Currently, only EU-SoilHydroGrids provides information on the most frequently required soil hydraulic properties with full European coverage up to 2 m depth at 250 m resolution.
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- 2017
25. SoilGrids250m: Global gridded soil information based on machine learning
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Hengl, T., Mendes de Jesus, J.S., Heuvelink, G.B.M., Ruiperez Gonzalez, M., Kilibarda, Milan, Blagotic, Aleksandar, Wei, Shangguan, Wright, Marvin N., Geng, Xiaoyuan, Bauer-Marschallinger, Bernhard, Guevara, Mario Antonio, Vargas, Rodrigo, MacMillan, Robert A., Batjes, N.H., Leenaars, J.G.B., Carvalho Ribeiro, E.D., Wheeler, Ichsani, Mantel, S., Kempen, B., Hengl, T., Mendes de Jesus, J.S., Heuvelink, G.B.M., Ruiperez Gonzalez, M., Kilibarda, Milan, Blagotic, Aleksandar, Wei, Shangguan, Wright, Marvin N., Geng, Xiaoyuan, Bauer-Marschallinger, Bernhard, Guevara, Mario Antonio, Vargas, Rodrigo, MacMillan, Robert A., Batjes, N.H., Leenaars, J.G.B., Carvalho Ribeiro, E.D., Wheeler, Ichsani, Mantel, S., and Kempen, B.
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This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total).
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- 2017
26. WoSIS: providing standardised soil profile data for the world
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Batjes, N.H., Carvalho Ribeiro, E.D., van Oostrum, A.J.M., Leenaars, J.G.B., Hengl, T., Mendes de Jesus, J.S., Batjes, N.H., Carvalho Ribeiro, E.D., van Oostrum, A.J.M., Leenaars, J.G.B., Hengl, T., and Mendes de Jesus, J.S.
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The aim of the World Soil Information Service (WoSIS) is to serve quality-assessed, georeferenced soil data (point, polygon, and grid) to the international community upon their standardisation and harmonisation. So far, the focus has been on developing procedures for legacy point data with special attention to the selection of soil analytical and physical properties considered in the GlobalSoilMap specifications (e.g. organic carbon, soil pH, soil texture (sand, silt, and clay), coarse fragments ( < 2 mm), cation exchange capacity, electrical conductivity, bulk density, and water holding capacity). Profile data managed in WoSIS were contributed by a wide range of soil data providers; the data have been described, sampled, and analysed according to methods and standards in use in the originating countries. Hence, special attention was paid to measures for soil data quality and the standardisation of soil property definitions, soil property values, and soil analytical method descriptions. At the time of writing, the full WoSIS database contained some 118 400 unique “shared” soil profiles, of which some 96 000 are georeferenced within defined limits. In total, this corresponds with over 31 million soil records, of which some 20 % have so far been quality-assessed and standardised using the sequential procedure discussed in this paper. The number of measured data for each property varies between profiles and with depth, generally depending on the purpose of the initial studies. Overall, the data lineage strongly determined which data could be standardised with acceptable confidence in accord with WoSIS procedures, corresponding to over 4 million records for 94 441 profiles. The publicly available data – WoSIS snapshot of July 2016 – are persistently accessible from ISRIC WDC-Soils through doi:10.17027/isric-wdcsoils.20160003.
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- 2017
27. Soil legacy data rescue via GlobalSoilMap and other international and national initiatives
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Arrouays, D., Leenaars, J., Richer-de-Forges, A., Adhikari, K., Ballabio, C., Greve, M., Grundy, M., Guerrero, E., Hempel, J., Hengl, T., Heuvelink, G., Batjes, N., Carvalho, E., Hartemink, A., Hewitt, A., Hong, S., Krasilnikov, P., Lagacherie, P., Lelyk, G., Libohova, Z., Lilly, A., McBratney, A., McKenzie, N., Vasquez, G., Mulder, V., Minasny, B., Montanarella, L., Odeh, I., Padarian, J., Poggio, L., Roudier, P., Saby, N., Savin, I., Searle, R., Solbovoy, V., Thompson, J., Smith, S., Sulaeman, Y., Vintila, R., Viscarra Rossel, Raphael, Wilson, P., Zhang, G., Swerts, M., Oorts, K., Karklins, A., Feng, L., Ibelles Navarro, A., Levin, A., Laktionova, T., Dell'Acqua, M., Suvannang, N., Ruam, W., Prasad, J., Patil, N., Husnjak, S., Pásztor, L., Okx, J., Hallet, S., Keay, C., Farewell, T., Lilja, H., Juilleret, J., Marx, S., Takata, Y., Kazuyuki, Y., Mansuy, N., Panagos, P., Van Liedekerke, M., Skalsky, R., Sobocka, J., Kobza, J., Eftekhari, K., Alavipanah, S., Moussadek, R., Badraoui, M., Da Silva, M., Paterson, G., Gonçalves, M., Theocharopoulos, S., Yemefack, M., Tedou, S., Vrscaj, B., Grob, U., Kozák, J., Boruvka, L., Dobos, E., Taboada, M., Moretti, L., Rodriguez, D., Arrouays, D., Leenaars, J., Richer-de-Forges, A., Adhikari, K., Ballabio, C., Greve, M., Grundy, M., Guerrero, E., Hempel, J., Hengl, T., Heuvelink, G., Batjes, N., Carvalho, E., Hartemink, A., Hewitt, A., Hong, S., Krasilnikov, P., Lagacherie, P., Lelyk, G., Libohova, Z., Lilly, A., McBratney, A., McKenzie, N., Vasquez, G., Mulder, V., Minasny, B., Montanarella, L., Odeh, I., Padarian, J., Poggio, L., Roudier, P., Saby, N., Savin, I., Searle, R., Solbovoy, V., Thompson, J., Smith, S., Sulaeman, Y., Vintila, R., Viscarra Rossel, Raphael, Wilson, P., Zhang, G., Swerts, M., Oorts, K., Karklins, A., Feng, L., Ibelles Navarro, A., Levin, A., Laktionova, T., Dell'Acqua, M., Suvannang, N., Ruam, W., Prasad, J., Patil, N., Husnjak, S., Pásztor, L., Okx, J., Hallet, S., Keay, C., Farewell, T., Lilja, H., Juilleret, J., Marx, S., Takata, Y., Kazuyuki, Y., Mansuy, N., Panagos, P., Van Liedekerke, M., Skalsky, R., Sobocka, J., Kobza, J., Eftekhari, K., Alavipanah, S., Moussadek, R., Badraoui, M., Da Silva, M., Paterson, G., Gonçalves, M., Theocharopoulos, S., Yemefack, M., Tedou, S., Vrscaj, B., Grob, U., Kozák, J., Boruvka, L., Dobos, E., Taboada, M., Moretti, L., and Rodriguez, D.
- Abstract
Legacy soil data have been produced over 70 years in nearly all countries of the world. Unfortunately, data, information and knowledge are still currently fragmented and at risk of getting lost if they remain in a paper format. To process this legacy data into consistent, spatially explicit and continuous global soil information, data are being rescued and compiled into databases. Thousands of soil survey reports and maps have been scanned and made available online. The soil profile data reported by these data sources have been captured and compiled into databases. The total number of soil profiles rescued in the selected countries is about 800,000. Currently, data for 117, 000 profiles are compiled and harmonized according to GlobalSoilMap specifications in a world level database (WoSIS). The results presented at the country level are likely to be an underestimate. The majority of soil data is still not rescued and this effort should be pursued. The data have been used to produce soil property maps. We discuss the pro and cons of top-down and bottom-up approaches to produce such maps and we stress their complementarity. We give examples of success stories. The first global soil property maps using rescued data were produced by a top-down approach and were released at a limited resolution of 1 km in 2014, followed by an update at a resolution of 250 m in 2017. By the end of 2020, we aim to deliver the first worldwide product that fully meets the GlobalSoilMap specifications.
- Published
- 2017
28. Uncertainty quantification of interpolated maps derived from observations with different accuracy levels
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Heuvelink, G. B. M., Brus, D., Hengl, T., Kempen, B., Johan G.B. Leenaars, and Ruiperez-Gonzalez, M.
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Kriging ,Soil ,Soil, Water and Land Use ,Measurement error ,Africa ,Geostatistics ,PE&RC ,ISRIC - World Soil Information ,Regression ,Bodem, Water en Landgebruik ,Interpolation - Abstract
Most practical applications of spatial interpolation ignore that some measurements may be more accurate than others. As a result all measurements are treated equally important, while it is intuitively clear that more accurate measurements should carry more weight than less accurate measurements. Geostatistics provides the tools to perform spatial interpolation using measurements with different accuracy levels. In this short paper we use these tools to explore the sensitivity of interpolated maps to differences in measurement accuracy for a case study on mapping topsoil clay content in Namibia using kriging with external drift (KED). We also compare the kriging variance maps and show how incorporation of different measurement accuracy levels influences estimation of the KED model parameters.
- Published
- 2016
29. SoilGrids en SoilInfo App: wereldbodeminformatie op je mobiel
- Author
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Hengl, T. and Heuvelink, G.B.M.
- Subjects
PE&RC ,app ,ISRIC - World Soil Information - Published
- 2016
30. Advancing spatio-temporal analysis of ecological data: Examples in R
- Author
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Hengl, T., van Loon, E., Sierdsema, H., Bouten, W., Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L., and Computational Geo-Ecology (IBED, FNWI)
- Subjects
Computer science ,business.industry ,Spatio-Temporal Analysis ,Statistical model ,computer.software_genre ,Machine learning ,Scripting language ,Data quality ,Virtual Laboratory ,Artificial intelligence ,Data mining ,business ,Spatial analysis ,computer ,Level of detail - Abstract
The article reviews main principles of running geo-computations in ecology, as illustrated with case studies from the EcoGRID and FlySafe projects, and emphasizes the advantages of using R computing environment as the most attractive programming/scripting environment. Three case studies (including R code) of interest to ecological applications are described: (a) analysis of GPS trajectory data for two gull-birds species; (b) species distribution mapping in space and time for a bird species (sedge warbler; EcoGRID project); and (c) change detection using time-series of maps. The case studies demonstrate that R , together with its numerous packages for spatial and geostatistical analysis, is a well-suited tool to produce quality outputs (maps, statistical models) of interest in Geo-Ecology. Moreover, due to the recent implementation of the maptools and sp packages, such outputs can be easily exported to popular geographical browsers such as Google Earth and similar. The key computational challenges for Computational Geo-Ecology recognized were: (1) solving the problem of input data quality (filtering techniques), (2) solving the problem of computing with large data sets, (3) improving the over-simplistic statistical models, and (4) producing outputs of increasingly higher level of detail.
- Published
- 2008
31. Islands as model systems in ecology and evolution: Prospects fifty years after MacArthur-Wilson
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Warren, B.H. Simberloff, D. Ricklefs, R.E. Aguilée, R. Condamine, F.L. Gravel, D. Morlon, H. Mouquet, N. Rosindell, J. Casquet, J. Conti, E. Cornuault, J. Fernández-Palacios, J.M. Hengl, T. Norder, S.J. Rijsdijk, K.F. Sanmartín, I. Strasberg, D. Triantis, K.A. Valente, L.M. Whittaker, R.J. Gillespie, R.G. Emerson, B.C. Thébaud, C.
- Abstract
The study of islands as model systems has played an important role in the development of evolutionary and ecological theory. The 50th anniversary of MacArthur and Wilson's (December 1963) article, 'An equilibrium theory of insular zoogeography', was a recent milestone for this theme. Since 1963, island systems have provided new insights into the formation of ecological communities. Here, building on such developments, we highlight prospects for research on islands to improve our understanding of the ecology and evolution of communities in general. Throughout, we emphasise how attributes of islands combine to provide unusual research opportunities, the implications of which stretch far beyond islands. Molecular tools and increasing data acquisition now permit re-assessment of some fundamental issues that interested MacArthur and Wilson. These include the formation of ecological networks, species abundance distributions, and the contribution of evolution to community assembly. We also extend our prospects to other fields of ecology and evolution - understanding ecosystem functioning, speciation and diversification - frequently employing assets of oceanic islands in inferring the geographic area within which evolution has occurred, and potential barriers to gene flow. Although island-based theory is continually being enriched, incorporating non-equilibrium dynamics is identified as a major challenge for the future. © 2014 John Wiley & Sons Ltd/CNRS.
- Published
- 2015
32. Root zone plant-available water holding capacity of the Sub-Saharan Africa soil, version 1.0. : Gridded functional soil information (dataset RZ-PAWHC SSA v. 1.0)
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Leenaars, J.G.B., Hengl, T., Ruiperez Gonzalez, M., Mendes de Jesus, J.S., Heuvelink, G.B.M., Wolf, J., van Bussel, L.G.J., Claessens, H., Yang, H., and Cassman, K.G.
- Subjects
Plant Production Systems ,Plantaardige Productiesystemen ,Life Science ,PE&RC ,ISRIC - World Soil Information - Published
- 2015
33. The Land-Potential Knowledge System (LandPKS): mobile apps and collaboration for optimizing climate change investments
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Herrick, J. E., Beh, A., Barrios, E., Bouvier, I., Coetzee, M., Dent, D., Elias, E., Hengl, T., Karl, J. W., Liniger, H., Matuszak, J., Neff, J. C., Ndungu, L. W., Obersteiner, M., Shepherd, K. D., Urama, K.C., van den Bosch, R., Webb, N. P., Herrick, J. E., Beh, A., Barrios, E., Bouvier, I., Coetzee, M., Dent, D., Elias, E., Hengl, T., Karl, J. W., Liniger, H., Matuszak, J., Neff, J. C., Ndungu, L. W., Obersteiner, M., Shepherd, K. D., Urama, K.C., van den Bosch, R., and Webb, N. P.
- Abstract
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.
- Published
- 2016
- Full Text
- View/download PDF
34. SoilGrids en SoilInfo App: wereldbodeminformatie op je mobiel : bodeminformatie, waar ook ter wereld, vrij beschikbaar
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Hengl, T., Heuvelink, G., Hengl, T., and Heuvelink, G.
- Abstract
We zijn er al helemaal aan gewend. Informatie over bijvoorbeeld het weer is vrij beschikbaar op het Internet, of via een smartphone-app. Ruimtelijke informatie over de bodem echter is vaak slecht toegankelijk of lastig te begrijpen. Het SoilGrids systeem maakt informatie over bodems van de wereld, zowel bodemsoorten als hun eigenschappen met diepte, vrij beschikbaar als bijvoorbeeld administratieve gegevens in OpenStreetMap.
- Published
- 2016
35. Land-Surface parameters and objects in hydrology
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Hengl, T, Reuter, H I, Hengl, T ( T ), Reuter, H I ( H I ), Gruber, S, Peckham, S, Hengl, T, Reuter, H I, Hengl, T ( T ), Reuter, H I ( H I ), Gruber, S, and Peckham, S
- Published
- 2008
36. Modelling mass movements and landslide susceptibility
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Hengl, T, Reuter, H I, Hengl, T ( T ), Reuter, H I ( H I ), Gruber, S, Huggel, C, Pike, R, Hengl, T, Reuter, H I, Hengl, T ( T ), Reuter, H I ( H I ), Gruber, S, Huggel, C, and Pike, R
- Published
- 2008
37. Comparing spatial prediction methods for soil property mapping in Brazil: a case study for the Rio Doce Basin
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Souza, E., Hengl, T., Kempen, B., Heuvelink, G. B. M., Elpidio Inácio Fernandes Filho, and Schaefer, C. E. G. R.
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Life Science ,PE&RC ,ISRIC - World Soil Information - Published
- 2014
38. Modelling sea level driven change of Macaronesian archipelago configurations since 120 kyr BP
- Author
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Rijsdijk, K.F., Hengl, T., Norder, S.J., Ávila, S.P., Fernández-Palácios, J.M., Fernández-Palacios, J.M., De Nascimento, L., Hernández, J.C., Clemente, S., González, A., Díaz-González, J.P., and Computational Geo-Ecology (IBED, FNWI)
- Abstract
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 (
- Published
- 2014
39. Towards improved soil information for studies of global sustainability
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Batjes, N.H., Hengl, T., Mendes de Jesus, J.S., Heuvelink, G.B.M., Carvalho Ribeiro, E.D., Kempen, B., Leenaars, J.G.B., and Ruiperez Gonzalez, M.
- Subjects
Bodemgeografie en Landschap ,Soil Geography and Landscape ,Life Science ,ICSU World Data Centre for Soils ,ISRIC - World Soil Information - Published
- 2014
40. Development of GIS methods to assess glaciers response to climatic fluctuations: a Minimal Model approach
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Jasiewicz, J, Zwoliński, Z, Mitasova, H, Hengl, T, Strigaro, D, Moretti, M, Mattavelli, M, DE AMICIS, M, Maggi, V, Provenzale, A, STRIGARO, DANIELE, MORETTI, MASSIMILIANO, MATTAVELLI, MATTEO, DE AMICIS, MATTIA GIOVANNI MARIA, MAGGI, VALTER, Provenzale, A., Jasiewicz, J, Zwoliński, Z, Mitasova, H, Hengl, T, Strigaro, D, Moretti, M, Mattavelli, M, DE AMICIS, M, Maggi, V, Provenzale, A, STRIGARO, DANIELE, MORETTI, MASSIMILIANO, MATTAVELLI, MATTEO, DE AMICIS, MATTIA GIOVANNI MARIA, MAGGI, VALTER, and Provenzale, A.
- Abstract
Theoretical work on glacier dynamics led to the construction of mathematical models for estimating glacier response to different climate change scenarios [2]. The aim of this work is to include a simple version of such models (the so-called Minimal Glacier Models [6]) within a GIS framework, to better understand, evaluate and reproduce the glacier response to climate fluctuations. Then, in this work three sections have been included: (I) the formulation of the Minimal Glacier Models, evaluating physical laws and numeric resolution; (II) the description of a GIS algorithm to calibrate and to validate the simulated results; (III) the application of GRASS – GIS module to obtain a spatial representation of glacier retreat
- Published
- 2015
41. Soil property maps of Africa at 250 m
- Author
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Hengl, T. and Hengl, T.
- Abstract
Over the period 2008–2014, the AfSIS project has compiled two soil profile data sets: the Africa Soil Profiles (legacy) database [Leenaars, 2014] and the AfSIS Sentinel Site (new soil samples) database [Vagen et al, 2010], jointly consisting of ca. 28 thousand sampling locations. Using these soil point observations and an extensive collection of global (SoilGrids1km) and local (African continent) environmental covariates, ISRIC - World Soil Information, in collaboration with The Earth Institute, Columbia University, World Agroforestry Centre, Nairobi and the International Center for Tropical Agriculture (CIAT), have produced (February 2015) predictions of soil properties — organic carbon, pH, sand, silt and clay fractions, coarse fragments, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content, and exchangeable bases (Ca, K, Mg, Na) — for the whole African continent at 250 m spatial resolution at two or six standard soil depths. The predictions are obtained using an automated mapping framework (3D regression-kriging based on random forests). Compressed GeoTiffs of the soil property maps, together with all metadata, input data sets and the R functions used to generate the maps, are available for download from here. A web-mapping interface to the maps is available via: http://af.soilgrids.org/. Read more about how were these maps made.
- Published
- 2015
42. Mapping Soil Properties of Africa at 250 m resolution: random forest significantly improve current predictions
- Author
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Hengl, T., Heuvelink, G.B.M., Kempen, B., Leenaars, J.G.B., Walsh, M.G., Shepherd, K.D., Sila, A., Macmillan, R.A., Mendes de Jesus, J.S., Tamene, L., Tondoh, J.E., Hengl, T., Heuvelink, G.B.M., Kempen, B., Leenaars, J.G.B., Walsh, M.G., Shepherd, K.D., Sila, A., Macmillan, R.A., Mendes de Jesus, J.S., Tamene, L., and Tondoh, J.E.
- Abstract
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 due to unsustainable soil management practices. This is partially the result of insufficient use of soil management knowledge. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS) project was established in 2008. Over the period 2008–2014, the AfSIS project compiled two point data sets: the Africa Soil Profiles (legacy) database and the AfSIS Sentinel Site database. These data sets contain over 28 thousand sampling locations and represent the most comprehensive soil sample data sets of the African continent to date. Utilizing these point data sets in combination with a large number of covariates, we have generated a series of spatial predictions of soil properties relevant to the agricultural management—organic carbon, pH, sand, silt and clay fractions, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content and exchangeable bases (Ca, K, Mg, Na). We specifically investigate differences between two predictive approaches: random forests and linear regression. Results of 5-fold cross-validation demonstrate that the random forests algorithm consistently outperforms the linear regression algorithm, with average decreases of 15–75% in Root Mean Squared Error (RMSE) across soil properties and depths. Fitting and running random forests models takes an order of magnitude more time and the modelling success is sensitive to artifacts in the input data, but as long as quality-controlled point data are provided, an increase in soil mapping accuracy can be expected. Results also indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols) help improve continental scale soil property mapping, and are among the most important predictors. This indicates a promising potential for transferring pedological knowled
- Published
- 2015
43. Spatial and spatio-temporal modeling of meteorological and climatic variables using Open Source software
- Author
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Hengl, T. (Tom), primary, Pebesma, E. (Edzer), additional, and Hijmans, R. (Robert) J., additional
- Published
- 2015
- Full Text
- View/download PDF
44. Sampling desigh optimization for space-time kriging
- Author
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Heuvelink, G.B.M., Griffith, D.A., Hengl, T., and Melles, S.J.
- Subjects
Bodemgeografie en Landschap ,Sampling design optimization ,Upper austria ,Soil Geography and Landscape ,Land Dynamics ,ICSU World Data Centre for Soils ,Spatial simulated annealing ,PE&RC ,Space-Time universal kriging ,ISRIC - World Soil Information ,Leerstoelgroep Landdynamiek - Published
- 2012
45. Semi-automated identification and extraction of geomorphological features using digital elevation data
- Author
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Seijmonsbergen, A.C., Hengl, T., Anders, N.S., Smith, M.J., Paron, P., Griffiths, J.S., and Computational Geo-Ecology (IBED, FNWI)
- Subjects
Identification (information) ,Lidar ,Digital mapping ,Geomorphometry ,Pixel ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Statistical model ,Segmentation ,Digital elevation model ,Remote sensing - Abstract
Geomorphological maps that are automatically extracted from digital elevation data are gradually replacing classical geomorphological maps. Commonly, digital mapping projects are based upon statistical techniques, object-based protocols or both. In addition to digital elevation data, expert knowledge can still be used to calibrate feature extraction algorithms. Such hybrid expert/statistical-based methods translate land surface parameters into areal extents of geomorphological features in an automated, reproducible manner, which increases spatial detail of final products and speeds up map production. The development of efficient statistical methods for the extraction of geomorphological features is today promoted by high-resolution digital elevation data from light detection and ranging (LiDAR) technology. In this chapter, case studies from the Netherlands (very low relief) and Austrian Alps (high relief) are presented to illustrate how statistical-based and object-based supervised classification can be used for the semi-automated identification and extraction of geomorphological features using high-resolution LiDAR digital elevation models (DEMs). In the first case study, multinomial logistic regression is used to increase the detail of a classic geomorphological map. Medial axes of the manually delineated polygons are used to locate the training pixels and to build the statistical model, which is then implemented over the whole area of interest. In the second case study, object-based segmentation of slope and topographic openness extracted from LiDAR data are used for rule-based mapping of geomorphological features. Both studies confirm that LiDAR DEMs can be used to increase detail of existing geomorphological maps. In addition, semi-automated techniques provide a more objective framework for geomorphological mapping.
- Published
- 2011
46. The unofficial guide for authors : from research design to publication
- Author
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Hengl, T., Gould, M., and Gerritsma, W.
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Library Research & Education Support ,writing skills ,schrijven ,publishing ,schrijfvaardigheden ,scientific research ,ICSU World Data Centre for Soils ,wetenschappelijk onderzoek ,writing ,ISRIC - World Soil Information ,publiceren - Abstract
Most scientific journals provide guidelines for authors - how to format references and prepare artwork, how many copies of the paper to submit and to which address. However, most official guidelines say little about how you should design and produce your paper and the chances that it will be accepted. This book provides a comprehensive but focused guide to producing scientific information - from research design to publication. It provides practical tips and answers to some of the most frequently asked questions: Why do we publish in the first place? What is OA publishing and why bother about it? What is the h-index? What is a Journal Impact Factor and does it matter? How can I increase my research production efficiency? Why should I use OS software tools for academic work? How can I produce graphics that will impress? How can I brainstorm good titles? How can I select a suitable journal and where can I find out more about it? How can I get into the reviewers' heads? This is an Open Access publication.
- Published
- 2011
47. Optimizing object-based image analysis for semi-automated geomorphological mapping
- Author
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Anders, N., Smith, M., Seijmonsbergen, H., Bouten, W., Hengl, T., Evans, I.S., Wilson, J.P., Gould, M., and Computational Geo-Ecology (IBED, FNWI)
- Abstract
Object-Based Image Analysis (OBIA) is considered a useful tool for analyzing high-resolution digital terrain data. In the past, both segmentation and classification parameters were optimized manually by trial and error. We propose a method to automatically optimize classification parameters for increasing the accuracy and efficiency of OBIA for semi-automated geomorphological mapping. We test our method by semi-automatically extracting three geomorphological ‘feature types’ (river terrace, gypsum sink holes, and fluvial incision) from a 1m Digital Terrain Model (DTM) of an alpine area in Vorarlberg, Austria. Segmentation parameters were optimized for each specific geomorphological ‘feature type’, by comparing frequency distribution matrices of training samples and automatically generated image objects. Subsequently image objects are iteratively classified with varying classification settings. The best classification scores and corresponding segmentation and classification settings are summarized in a library of feature signatures for stratified feature extraction. Our results show that through optimization, a limited number of classifiers can be used to accurately classify geomorphological features in complex terrain. This allows classification schemes to be standardized for automated and effective analysis of high-resolution terrain data. In addition, by automating mapping procedures, this research increases the efficiency of geomorphological research. Further research will include the classification of the remaining geomorphological ‘feature types’ to create a full-covered geomorphological map, and the application of the feature signature library to other areas.
- Published
- 2011
48. SoilGrids1km— global soil information based on automated mapping
- Author
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Hengl, T., Mendes de Jesus, J.S., Macmillan, R.A., Batjes, N.H., Heuvelink, G.B.M., Carvalho Ribeiro, E.D., Samuel Rosa, A., Kempen, B., Leenaars, J.G.B., Walsh, M.G., Ruiperez Gonzalez, M., Hengl, T., Mendes de Jesus, J.S., Macmillan, R.A., Batjes, N.H., Heuvelink, G.B.M., Carvalho Ribeiro, E.D., Samuel Rosa, A., Kempen, B., Leenaars, J.G.B., Walsh, M.G., and Ruiperez Gonzalez, M.
- Abstract
Background 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 information. Although several global soil information systems already exist, these tend to suffer from inconsistencies and limited spatial detail. Methodology/Principal Findings We present SoilGrids1km — a global 3D soil information system at 1 km resolution — containing spatial predictions for a selection of soil properties (at six standard depths): soil organic carbon (g kg-1), soil pH, sand, silt and clay fractions (%), bulk density (kg m-3), cation-exchange capacity (cmol+/kg), coarse fragments (%), soil organic carbon stock (t ha-1), depth to bedrock (cm), World Reference Base soil groups, and USDA Soil Taxonomy suborders. Our predictions are based on global spatial prediction models which we fitted, per soil variable, using a compilation of major international soil profile databases (ca. 110,000 soil profiles), and a selection of ca. 75 global environmental covariates representing soil forming factors. Results of regression modeling indicate that the most useful covariates for modeling soils at the global scale are climatic and biomass indices (based on MODIS images), lithology, and taxonomic mapping units derived from conventional soil survey (Harmonized World Soil Database). Prediction accuracies assessed using 5–fold cross-validation were between 23–51%. Conclusions/Significance SoilGrids1km provide an initial set of examples of soil spatial data for input into global models at a resolution and consistency not previously available. Some of the main limitations of the current version of SoilGrids1km are: (1) weak relationships between soil properties/classes and explanatory variables due to scale mismatches, (2) difficulty to obtain covariates that capture soil forming factors, (3) low sampling density and spatial clustering of soil profile locations. However, as the SoilGrids system is highly aut
- Published
- 2014
49. Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution
- Author
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Kilibarda, M., Hengl, T., Heuvelink, G.B.M., Graler, B., Pebesma, E., Tadic, M.P., Bajat, B., Kilibarda, M., Hengl, T., Heuvelink, G.B.M., Graler, B., Pebesma, E., Tadic, M.P., and Bajat, B.
- Abstract
Combined Global Surface Summary of Day and European Climate Assessment and Dataset daily meteorological data sets (around 9000 stations) were used to build spatio-temporal geostatistical models and predict daily air temperature at ground resolution of 1km for the global land mass. Predictions in space and time were made for the mean, maximum, and minimum temperatures using spatio-temporal regression-kriging with a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) 8 day images, topographic layers (digital elevation model and topographic wetness index), and a geometric temperature trend as covariates. The accuracy of predicting daily temperatures was assessed using leave-one-out cross validation. To account for geographical point clustering of station data and get a more representative cross-validation accuracy, predicted values were aggregated to blocks of land of size 500x500km. Results show that the average accuracy for predicting mean, maximum, and minimum daily temperatures is root-mean-square error (RMSE) =2 degrees C for areas densely covered with stations and between 2 degrees C and 4 degrees C for areas with lower station density. The lowest prediction accuracy was observed at high altitudes (>1000m) and in Antarctica with an RMSE around 6 degrees C. The model and predictions were built for the year 2011 only, but the same methodology could be extended for the whole range of the MODIS land surface temperature images (2001 to today), i.e., to produce global archives of daily temperatures (a next-generation repository) and to feed various global environmental models. Key Points Global spatio-temporal regression-kriging daily temperature interpolation Fitting of global spatio-temporal models for the mean, maximum, and minimum temperatures Time series of MODIS 8 day images as explanatory variables in regression part
- Published
- 2014
50. SoilGrids: a system for automated global soil mapping
- Author
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Hengl, T., Mendes de Jesus, J.S., Carvalho Ribeiro, E.D., Batjes, N.H., Heuvelink, G.B.M., Kempen, B., Ruiperez Gonzalez, M., Leenaars, J.G.B., Caspari, T.M., Samuel Rosa, A., Reuter, H.I., Macmillan, R.A., Hengl, T., Mendes de Jesus, J.S., Carvalho Ribeiro, E.D., Batjes, N.H., Heuvelink, G.B.M., Kempen, B., Ruiperez Gonzalez, M., Leenaars, J.G.B., Caspari, T.M., Samuel Rosa, A., Reuter, H.I., and Macmillan, R.A.
- Abstract
SoilGrids is a collection of updatable soil property and class maps of the world produced using state-of-the-art model-based statistical methods: 3D regression with splines for continuous soil properties and multinomial logistic regression for soil classes.
- Published
- 2014
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