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Modern Applied Statistics With S

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). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods-random forest and gradient boosting and/or multinomial logistic regression-as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10-fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.

/pdf/soilgrids250m-global-gridded-soil-information-based-on-2xwvokp8f3.pdf

SoilGrids250m: Global gridded soil information based on machine learning

. The System for Automated Geoscientific Analyses (SAGA) is an open source geographic information system (GIS), mainly licensed under the GNU General Public License. Since its first release in 2004, SAGA has rapidly developed from a specialized tool for digital terrain analysis to a comprehensive and globally established GIS platform for scientific analysis and modeling. SAGA is coded in C++ in an object oriented design and runs under several operating systems including Windows and Linux. Key functional features of the modular software architecture comprise an application programming interface for the development and implementation of new geoscientific methods, a user friendly graphical user interface with many visualization options, a command line interpreter, and interfaces to interpreted languages like R and Python. The current version 2.1.4 offers more than 600 tools, which are implemented in dynamically loadable libraries or shared objects and represent the broad scopes of SAGA in numerous fields of geoscientific endeavor and beyond. In this paper, we inform about the system's architecture, functionality, and its current state of development and implementation. Furthermore, we highlight the wide spectrum of scientific applications of SAGA in a review of published studies, with special emphasis on the core application areas digital terrain analysis, geomorphology, soil science, climatology and meteorology, as well as remote sensing.

/pdf/system-for-automated-geoscientific-analyses-saga-v-2-1-4-5c1e2q9p7x.pdf

System for Automated Geoscientific Analyses (SAGA) v. 2.1.4

Human appropriation of land for agriculture has greatly altered the terrestrial carbon balance, creating a large but uncertain carbon debt in soils. Estimating the size and spatial distribution of soil organic carbon (SOC) loss due to land use and land cover change has been difficult but is a critical step in understanding whether SOC sequestration can be an effective climate mitigation strategy. In this study, a machine learning-based model was fitted using a global compilation of SOC data and the History Database of the Global Environment (HYDE) land use data in combination with climatic, landform and lithology covariates. Model results compared favorably with a global compilation of paired plot studies. Projection of this model onto a world without agriculture indicated a global carbon debt due to agriculture of 133 Pg C for the top 2 m of soil, with the rate of loss increasing dramatically in the past 200 years. The HYDE classes “grazing” and “cropland” contributed nearly equally to the loss of SOC. There were higher percent SOC losses on cropland but since more than twice as much land is grazed, slightly higher total losses were found from grazing land. Important spatial patterns of SOC loss were found: Hotspots of SOC loss coincided with some major cropping regions as well as semiarid grazing regions, while other major agricultural zones showed small losses and even net gains in SOC. This analysis has demonstrated that there are identifiable regions which can be targeted for SOC restoration efforts.

https://www.pnas.org/content/pnas/114/36/9575.full.pdf

Soil carbon debt of 12,000 years of human land use

Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but spatial location of points (geography) is often ignored in the modeling process. Spatial auto-correlation, especially if still existent in the cross-validation residuals, indicates that the predictions are maybe biased, and this is suboptimal. This paper presents a random forest for spatial predictions framework (RFsp) where buffer distances from observation points are used as explanatory variables, thus incorporating geographical proximity effects into the prediction process. The RFsp framework is illustrated with examples that use textbook datasets and apply spatial and spatio-temporal prediction to numeric, binary, categorical, multivariate and spatiotemporal variables. Performance of the RFsp framework is compared with the state-of-the-art kriging techniques using fivefold cross-validation with refitting. The results show that RFsp can obtain equally accurate and unbiased predictions as different versions of kriging. Advantages of using RFsp over kriging are that it needs no rigid statistical assumptions about the distribution and stationarity of the target variable, it is more flexible towards incorporating, combining and extending covariates of different types, and it possibly yields more informative maps characterizing the prediction error. RFsp appears to be especially attractive for building multivariate spatial prediction models that can be used as "knowledge engines" in various geoscience fields. Some disadvantages of RFsp are the exponentially growing computational intensity with increase of calibration data and covariates and the high sensitivity of predictions to input data quality. The key to the success of the RFsp framework might be the training data quality-especially quality of spatial sampling (to minimize extrapolation problems and any type of bias in data), and quality of model validation (to ensure that accuracy is not effected by overfitting). For many data sets, especially those with lower number of points and covariates and close-to-linear relationships, model-based geostatistics can still lead to more accurate predictions than RFsp.

Random Forest as a generic framework for predictive modeling of spatial and spatio-temporal variables

Keymessage The “NFI 2012 environmental data base climate” is part of the environmental database of the German National Forest Inventory. It contains climate information for 26,450 inventory points generated from gridded daily climate data for 1961–2100 at a spatial resolution of 250 m. Grids are based on DWD-Observations and REMO EURO-CORDEX climate projections. Access to the databases is provided via the URL: https://doi.org/10.3220/DATA/20180823-102429. Associated metadata are available at https://agroenvgeo.data.inra.fr/geonetwork/srv/fre/ catalog.search#/metadata/d0789030-c94e-4883-8d38-2a7332c98673.

/pdf/temporal-and-spatial-high-resolution-climate-data-from-1961-4nosf15v73.pdf

Temporal and spatial high-resolution climate data from 1961 to 2100 for the German National Forest Inventory (NFI)

The Okavango catchment in southern Africa is subject to environmental as well as socio-economic transformation processes such as population growth and climate change. The degradation of soil and vegetation by deforestation and overgrazing is one of the downsides of this development, reducing the capacity of the land to provide ecosystem functions and services. In this study, climate simulations are brought together with secondary socioeconomic, pedologic and remote-sensing data in a GIS-based assessment of the factors commonly associated with land degradation risk. A high resolution overview is provided for decision-makers and stakeholders in the region by identifying priority intervention areas where a long-term decline in ecosystem function and land productivity is most likely to occur. The approach combines 19 risk factors into seven individual ratings for topography, landcover, soil, demography, infrastructure, livestock pressure and climate. These ratings are then weighted and combined into an integrated degradation risk index (DRI). The results show that the land degradation risk is quite heterogeneously distributed in the study area and caused by different factors. Three hot-spots are identified and compared, one of which is in the far northwestern part of the catchment, one around the local center Rundu and one on the outskirts of the Okavango Delta. We conclude that the approach is suitable to give an overview on degradation risk in the study area, although the classification process is a crucial procedure that should be standardized for further research. Copyright © 2015 John Wiley & Sons, Ltd.

Spatial Assessment of Land Degradation Risk for the Okavango River Catchment, Southern Africa

/pdf/regionalization-of-baseline-climatologies-and-time-series-or25ml3439.pdf

Regionalization of Baseline Climatologies and Time Series for the Okavango Catchment

A reliable quantification of total inorganic nitrogen (TIN) deposition to forests is required for the evaluation of ecological effects of TIN inputs to forests and to monitor the success of clean-air policy. As direct measurements are scarce, different modeling approaches have been developed to estimate TIN deposition to forests. Three common methods are the (i) “canopy budget model,” (ii) “inferential method,” and (iii) “emission based estimates” using a chemical transport model. Previous studies have reported considerable and site-specific differences between these methods, complicating the interpretation of results. We use data from more than 100 German intensive forest monitoring sites over a period of 16 years for a cross-comparison of these approaches. Non-linear mixed-effect models were applied to evaluate how factors like meteorology, terrain and stand characteristics affect discrepancies between the model approaches. Taking into account the uncertainties in deposition estimates, there is a good agreement between the
canopy budget and the inferential method when using semi-empirical correction factors for deposition velocity. Wet deposition estimates of the emission based approach were in good agreement with wet-only corrected bulk open field deposition measurements used by the other two approaches. High precipitation amounts partly explained remaining differences in wet deposition. Larger discrepancies were observed when dry deposition estimates are compared between the emissions based approach and the other two approaches, which appear to be related to a combination of meteorological conditions and tree species effects.

/pdf/comparison-of-methods-for-the-estimation-of-total-inorganic-5cla1gug9j.pdf

Jan Wehberg

Papers

System for Automated Geoscientific Analyses (SAGA) v. 2.1.4

Temporal and spatial high-resolution climate data from 1961 to 2100 for the German National Forest Inventory (NFI)

Spatial Assessment of Land Degradation Risk for the Okavango River Catchment, Southern Africa

Regionalization of Baseline Climatologies and Time Series for the Okavango Catchment

Comparison of Methods for the Estimation of Total Inorganic Nitrogen Deposition to Forests in Germany