Name: use of principal components for scaling up topographic models to map soil redistribution and soil organic carbon
Uploaded: 2018-10-16T14:00:00.000+00:00
Duration: 9 min 44 s
Description: Watch this Scientific Journal Video about Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon at JoVE.com

This research was supported by the USDA Natural Resources Conservation Service in association with the Wetland Component of the National Conservation Effects Assessment Project (NRCS 67-3A75-13-177).

1. Topographic Analyses
<ol>
	<li>Digital data preprocess
	<ol>
		<li>Collect LiDAR data from the GeoTREE LiDAR mapping project website. Select &#34;boundary type&#34; and &#34;region&#34; to zoom into a specific area. Draw a polygon to download LiDAR tiles for the selected study area.</li>
		<li>Convert the raw LiDAR data to a LAS file using the geographic information system (GIS) mapping tool.</li>
		<li>Generate DEMs with a 3-m spatial resolution using inverse distance weighted interpolation.</li>
		<li>Filter the 3-m DEMs twice with a 3-kernel low pass filter to reduce noises associate with local variation.</li>
	</ol>
	</li>
	<li>Topographic metric generation
	<ol>
		<li>To generate topographic metrics, first download the latest version of the System for Automated Geoscientific Analysis (SAGA)23. Click &#34;Import Raster&#34; in the Import/Export section to import the filtered 3-m DEMs into SAGA.</li>
		<li>Click the &#34;Slope, Aspect, Curvature&#34; module of SAGA with the default settings to generate the slope and curvature-related [profile curvature (P_Cur), plan curvature (Pl_Cur), and general curvature (G_Cur)] metrics using the filtered DEMs (Figure 1).</li>
		<li>Click the &#34;Flow Accumulation (Top-Down)&#34; module of SAGA and select &#34;Deterministic infinity&#34; as the method to generate flow accumulation (FA) metric using the filtered DEMs.</li>
		<li>Click the &#34;SAGA Topographic Openness&#34; module with the default settings to generate the positive openness (POP) metric using a filtered z-axis amplified image.</li>
		<li>Click the &#34;LS- factor (Field Based)&#34; module of SAGA with the default settings to generate the upslope slope (Upsl) and slope length factor (LS_FB) metrics using the filtered DEMs.</li>
		<li>Click the &#34;Flow Path Length&#34; module of SAGA with the default settings to generate the flow path length (FPL) metric using the filtered DEMs.</li>
		<li>Click the &#34;Downslope Distance Gradient&#34; module of SAGA with the default settings to generate the downslope index (DI) metric using the filtered DEMs.</li>
		<li>Click the &#34;SAGA Wetness Index&#34; module and select &#34;absolute catchment area&#34; as the Type of Area to generate the catchment area (CA) and topographic wetness index (TWI) metrics using the filtered DEMs.</li>
		<li>Click the &#34;Stream Power Index&#34; module of SAGA and select &#34;pseudo specific catchment area&#34; as the Area Conversion to generate the stream power index (SPI) metric using the filtered DEMs.</li>
		<li>Generate maximum elevation maps with multiple radiuses. Filter the maximum elevation maps twice through a 3-kernel low pass filter. Subtract the filtered 3-m DEM from the filtered maximum elevation maps to obtain a series of relief maps. Extract a series of relief variables to a number of locations.</li>
		<li>Perform principal component analysis (PCA) on the relief variables to convert the reliefs into topographic relief components. Select principal components that explain more than 90% variance of the relief dataset as the topographic relief metrics.</li>
	</ol>
	</li>
</ol>
2. Field Data Collection
<ol>
	<li>Field sampling
	<ol>
		<li>Select a number of cropland field locations that can adequately represent the landscape characteristics of the study area and several representative small-scale cropland fields that can be intensively sampled. 
		NOTE: The soil samples collected from the two cropland fields were used for model calibration. Soil samples collected from the entire study area were used for model validation.</li>
		<li>Upload all the sample location coordinates to a code-based geographic positioning system (GPS) and physically locate them in the fields.</li>
		<li>Collect 3 samples for each sampling location from the top 30 cm soil layer using a push probe (3.2 cm in diameter). 
		&#8203;NOTE: Soil samples from 30-50 cm layers were collected at sites where sediment deposition was expected. The volume of each sample was 241 cm3.</li>
		<li>Record geographic coordinate information of sampling locations using GPS.</li>
		<li>Weigh the soil samples after drying them at 90 &#176;C for 48 h. Calculate soil density using information of total sample volumes at sampling locations and weights. Mix the three samples from the same location to get a composite soil sample.</li>
	</ol>
	</li>
	<li>Soil sample preparation
	<ol>
		<li>Sieve the composite soil samples with a 2-mm screen.</li>
		<li>Grind a 10 g subsample of the sieved soil to a very fine powder with a roller mill.</li>
	</ol>
	</li>
	<li>Soil sample analyses
	<ol>
		<li>Measure soil total carbon (C) content in roller milled samples through combustion on a CN elemental analyzer at a temperature of 1350 &#176;C. Estimate calcium carbonate C content by analyzing the remaining C after baking soil organic matter at a temperature of 420 &#176;C for 16 h in a furnace.</li>
		<li>Calculate SOC content (%) by subtracting calcium carbonate C content from total soil C content. Convert SOC content (%) to SOC density (kg m-2) using soil density.</li>
		<li>Put the bulk 2-mm sieved soil samples in Marinelli beakers and seal them. Measure 137Cs concentration of each sample through gamma-ray analysis using a spectroscopy system that receives inputs from three high purity coaxial germanium crystals (HpCN30% efficiency) into 8192-channel analyzers (see Table of Materials).</li>
		<li>Calibrate the system using an analytic mixed radionuclide standard11. Convert 137Cs concentration to 137Cs inventory using soil density.</li>
		<li>Calculate soil redistribution rate using 137Cs inventory by applying the Mass Balance Model II (MBMII) in a spreadsheet add-in program developed by Walling et al.24.</li>
	</ol>
	</li>
</ol>
3. Topography-Based Model Development
<ol>
	<li>Topographic principal component estimation
	<ol>
		<li>Extract the topographic metrics for sampling locations in the entire study area and the small-scale cropland fields.</li>
		<li>Standardize the topographic metrics of the sampling locations in the entire study area by using mean and standard deviation. Estimate the topographic metric loadings in each component based on the standardized topographic metrics using PCA with statistical software package. Collect the topographic metric loadings in each topographic principal component (TPC) and select the top TPCs that explain 90% variance of all metrics.</li>
		<li>Standardize the topographic metrics of the sampling locations in the small-scale cropland fields. Calculate the top TPCs for each location by sum of the standardized topographic metrics weighted by the corresponding loadings from the sampling locations in WCW.</li>
	</ol>
	</li>
	<li>Model calibration
	<ol>
		<li>Perform stepwise ordinary least square regression (SOLSR) to develop topography-based SOLSRf models for SOC density and soil redistribution rates based on all topographic metrics at the small-scale cropland fields. Use Akaike information criterion (AIC) and leave-one-out cross-validation to select the optimal combination of topographic metrics for the best-fitted SOLSRf models.</li>
		<li>Check the collinearity among the topographic variables using the variance inflation factor (VIF). Remove the variables with the largest VIF (VIF &#8805; 7.525), and check VIF again. Remove the variables until the VIFs of all variables are &#60; 7.5. Perform SOLSR to develop topography-based SOLSRr models for SOC density and soil redistribution rates based on topographic metrics that were removed high collinearity variables. Use the AIC and leave-one-out cross-validation to select the optimal combination for the best-fitted SOLSRr models.</li>
		<li>Perform stepwise principal component regression (SPCR) to develop topography-based SPCR models for SOC density and soil redistribution rates based on the TPCs at the small-scale cropland fields. Use the AIC and leave-one-out cross-validation to select the optimal combination of TPCs for the best-fitted SPCR models.</li>
		<li>Calculate the adjusted coefficient of determination (Radj2), Nash-Sutcliffe efficiency (NSE), and ratio of the root mean square error to the standard deviation of measured data (RSR) to assess model efficiencies.</li>
	</ol>
	</li>
	<li>Model evaluation
	<ol>
		<li>Estimate SOC density and soil redistribution rates in the entire study area by applying the estimated models.</li>
		<li>Validate the developed model by comparing prediction with measured dataset of SOC density and soil redistribution rates in the entire study area. Evaluate the model performances using Radj2, NSE, and RSR values.</li>
	</ol>
	</li>
</ol>

The authors have nothing to disclose.

The SOLSRf models had slightly better performances than the SPCR models in calibration at the field scale. However, some of the topographic metrics, such as SPI and CA (r &#62; 0.80), are closely correlated with each other. The collinearity may add uncertainties to model predictions. Because of the multicollinearity among predictors, small changes in the input variables can significantly affect the model predictions41. Therefore, the SOLSRf models tended to be unstable and showed low efficiencies in simulations of SOC density and soil redistribution rate at the watershed scale. The SPCR models substantially outperformed the SOLSRf models in prediction of SOC distribution at the watershed scale. TPCs eliminate the multicollinearity by converting the fifteen topographic metrics into mutually independent (orthogonal) components. The conversion also uncovered underlying relationships among topographic metrics. As indicated by the high loadings (&#62; 0.35) of topographic metrics to the components, the TPC1, TPC2, TPC3, TPC6, and TPC7 were associated with runoff velocity, soil water content, runoff volume, flow divergence, and flow acceleration, respectively. Spatial patterns of soil redistribution rates and SOC distribution were highly correlated with soil water content and runoff divergence in the WCW, which is consistent with the study of Fox and Papanicolaou2, which demonstrated that eroded soil from upland could be impacted by flow divergence in a low-relief agricultural watershed.
Moreover, fewer predictor variables in the SPCR models than the SOLSRf and SOLSRr models reduced the risk of over-fitting the prediction models42,43. There were more than six variables in all the SOLSR models, which may increase the difficulty of data interpretation and induce high variance in model simulations41,44,45. This may account for the lower prediction efficiencies in WCW by the SOLSR models than by the SPCR models.
Topography- based SPCR models have advantages in simulating soil redistribution and associated SOC dynamics. First, topographic information can be easily derived from DEMs. Recent increased accessibility of the high spatial resolution LiDAR data can help improve the accuracy of DEM-derived landscape topography and benefit investigations in regions with limited field observations. Second, using a set of topographic metrics and statistical analyses, the topography-based models can efficiently quantify soil redistribution and SOC distribution patterns. Third, the application of principal component can effectively reduce biases associated with multicollinearity of topographic metrics and increase the stability of the stepwise regression models when applied to multiple spatial scales.
However, the SPCA models may be limited by variables during model development. Although application of the LiDAR data increased in ecological studies, the methods to derive useful topographic information have not yet been fully explored. In this study, the TWI and LsRe showed the highest correlations with SOC density and soil redistribution rates, respectively. However, additional topographic variables that are not considered may be equally or more important in explaining soil erosion and C dynamics. Additionally, other factors such as management practices, which may cause soil erosion variability, were not included in this study. For example, when tillage was parallel to the direction of maximum slope, soil erosion may double relative to the erosion in slantwise tillage turning soil upslope46. Therefore, different tillage practices may also be a reason for the reduced prediction efficiencies of the SPCR models.
The study is based on the paper published in Catena17. Instead of a mechanistic-based analysis of topographic influences on soil movement and soil properties as performed in the Catena paper, here we focused on the methods for quantifying topographic metrics and developing topography-based models. We discussed the feasibility and advantages of using topography-based models in studies of the spatial structure of soil properties. Meanwhile, we improved our models by updating algorithms of slope length factor and flow accumulation. The scale of slope length factor measurement was limited to field&#39;s area. Additionally, the deterministic infinity algorithm was used for flow accumulation generation. Compared with the method reported in Li et al.17 which generated flow accumulation with a deterministic eight-node algorithm, the infinity algorithm adopted in this study reduces loops in the flow direction angles and proved to be a better algorithm for low relief areas47.
In conclusion, our results demonstrate the feasibility of topography-based SPCR models in simulating SOC distribution and soil redistribution patterns in agriculture fields. As a cost-effective method to estimate SOC stocks and soil redistribution rates, it is applicable to sites with limited observational data and private lands lacking public access. In future studies, the prediction models could be improved with further refinement and availability of LiDAR data and inclusion of additional topographic metrics. The large-scale soil property maps that were developed based on the models will lead to further understanding of the mechanisms underlying the topographic impacts on soil movement in agricultural landscapes and the fate of SOC at the watershed and regional scales.

Soil redistribution (erosion and deposition) exerts significant impacts on soil organic carbon (SOC) stocks and dynamics. Increasing efforts have been devoted to investigating how SOC is detached, transported, and deposited over the landscape1,2,3. Carbon (C) sequestration and SOC distribution are influenced by gravity-driven soil movement induced by water erosion4,5,6. In cultivated fields, soil translocation by tillage is another important process contributing to C redistribution7,8,9. Tillage erosion causes a considerable net downslope movement of soil particles and leads to a within-field soil variation10. Both water and tillage erosion are significantly affected by landscape topography, which determines the locations of erosional and depositional sites11. Therefore, effective soil erosion regulation and C dynamic investigation in agricultural lands calls for a better understanding of topographic controls on soil erosion and movements.
Several studies have investigated the impacts of topography on soil redistribution and associated SOC dynamics9,12,13,14,15,16,17. Van der Perk et al.12 reported that topographic factors explained 43% of variability in soil redistribution. Rezaei and Gilkes13 found higher SOC in soils on a shady aspect, due to lower temperatures and less evaporation when compared to other aspects in rangelands. Topography may have more significant impacts on soil redistribution in agricultural lands with traditional tillage treatment than those with minimum tillage, due to the interactions between landforms and tillage practices9. However, these findings were primarily derived from field observations, which present difficulties in investigating soil properties at a broader spatial scale. There is a pressing need to develop new strategies to effectively understand spatial patterns of soil properties at watershed and regional scales.
The objective of this study is to develop efficient models to simulate soil redistribution and SOC distribution. Topography-based models using topographic metrics as predictors have been developed to quantify soil erosion and deposition processes. Compared with empirical- or process-based erosion models that employed discrete field samplings to simulate soil erosion18,19, topography-based models could be developed based on topographic information derived from digital elevation models (DEMs) with high resolutions. This approach allows for continuous soil property simulations at the watershed or regional scale. In the past several decades, accuracy of topographic information has substantially improved, with increasing availability of high resolution remotely sensed data. Although previous studies have employed topography-based models to simulate soil properties12,20,21,22, most of these investigations used a single topographic metric or single category of topographic metrics (local, non-local, or combined topographic metrics), which may not have sufficiently explored topographic impacts on soil microbial activity. Therefore, to gain a better understanding of topography controls on soil erosion and C dynamics, we examined a comprehensive set of topographic metrics including local, non-local, and combined topographic metrics and developed multi-variable topography-based models to simulate soil property dynamics. Applications of these models are expected to provide scientific support for better soil erosion control and agricultural land management.
Topographic metrics are generally categorized into one of three categories: a) local topographic metrics, b) non-local topographic metrics, or c) combined topographic metrics. Local topographic metrics refer to local features of one point on the land surface. Non-local topographic metrics refer to the relative locations of selected points. Combined topographic metrics integrate local and non-local topographic metrics. A set of topographic metrics affecting soil erosion and deposition were used in this study to investigate the topographic controls on soil movement and C stocks (Table 1). Specifically, we used four local topographic metrics [slope, profile curvature (P_Cur), plan curvature (Pl_Cur), general curvature (G_Cur)], seven non-local topographic metrics [flow accumulation (FA), topographic relief, positive openness (POP), upslope slope (UpSl), flow path length (FPL), downslope index (DI), catchment area (CA)], and three combined topographic metrics [topographic wetness index (TWI), stream power index (SPI), and slope length factor (LS)].

<table><tbody><tr><td>Light Detection and Ranging (LiDAR) data&amp;nbsp;</td><td></td><td>http://www.geotree.uni.edu/lidar/</td><td>Collected from the GeoTREE LiDAR mapping project&amp;nbsp;</td></tr><tr><td>LECO CNS 2000 elemental analyzer&amp;nbsp;</td><td>LECO Corp., St. Joseph, MI</td><td></td><td></td></tr><tr><td>Canberra Genie-2000 Spectroscopy System</td><td>CANBERRA Industries</td><td></td><td></td></tr><tr><td>Geographic positioning system</td><td>Trimble&amp;nbsp;</td><td>RTK 4700 GPS</td><td></td></tr><tr><td>ArcGIS</td><td>ESRI, Redlands, CA</td><td>10.2.2</td><td></td></tr><tr><td>Statistical Analysis System&amp;nbsp;</td><td>SAS Institute Inc</td><td></td><td></td></tr><tr><td>System for Automated Geoscientific Analysis&amp;nbsp;</td><td>University of G&amp;ouml;ttingen, Germany&amp;nbsp;</td><td>v. 2.2.5, http://www.saga-gis.org/</td><td>GNU General Public License</td></tr></tbody></table>

use of principal components for scaling up topographic models to map soil redistribution and soil organic carbon

Landscape topography is a critical factor affecting soil formation and plays an important role in determining soil properties on the earth surface, as it regulates the gravity-driven soil movement induced by runoff and tillage activities. The recent application of Light Detection and Ranging (LiDAR) data holds promise for generating high spatial resolution topographic metrics that can be used to investigate soil property variability. In this study, fifteen topographic metrics derived from LiDAR data were used to investigate topographic impacts on redistribution of soil and spatial distribution of soil organic carbon (SOC). Specifically, we explored the use of topographic principal components (TPCs) for characterizing topography metrics and stepwise principal component regression (SPCR) to develop topography-based soil erosion and SOC models at site and watershed scales. Performance of SPCR models was evaluated against stepwise ordinary least square regression (SOLSR) models. Results showed that SPCR models outperformed SOLSR models in predicting soil redistribution rates and SOC density at different spatial scales. Use of TPCs removes potential collinearity between individual input variables, and dimensionality reduction by principal component analysis (PCA) diminishes the risk of overfitting the prediction models. This study proposes a new approach for modeling soil redistribution across various spatial scales. For one application, access to private lands is often limited, and the need to extrapolate findings from representative study sites to larger settings that include private lands can be important.

Watch this Scientific Journal Video about Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon at JoVE.com

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Landscape processes are critical components of soil formation and play important roles in determining soil properties and spatial structure in landscapes. We propose a new approach using stepwise principal component regression to predict soil redistribution and soil organic carbon across various spatial scales.

Landscape topography is a critical factor affecting soil formation and plays an important role in determining soil properties on the earth surface, as it ...

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10.1K Views.  University of Maryland. Landscape processes are critical components of soil formation and play important roles in determining soil properties and spatial structure in landscapes. We propose a new approach using stepwise principal component regression to predict soil redistribution and soil organic carbon across various spatial scales.

Video: Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Assessment of Labile Organic Carbon in Soil Using Sequential Fumigation Incubation Procedures

Management practices and environmental changes can alter soil nutrient and carbon cycling. Soil labile organic carbon, a readily decomposable C pool, is highly sensitive to disturbance. It is also the primary substrate for soil microorganisms, which is fundamental to nutrient cycling. Due to these attributes, labile organic carbon (LOC) has been identified as an indicator parameter for soil health. Quantifying the turnover rate of LOC also aids in understanding changes in soil nutrient cycling processes. A sequential fumigation incubation method has been developed to estimate soil LOC and potential C turnover rate. The method requires fumigating soil samples and quantifying CO2-C respired during a 10 day incubation period over a series of fumigation-incubation cycles. Labile organic C and potential C turnover rate are then extrapolated from accumulated CO2 with a negative exponential model. Procedures for conducting this method are described.

Labile organic carbon (LOC) and the potential carbon turnover rate are sensitive indicators of changes in soil nutrient cycling processes. Details are provided for a method based on fumigating and incubating soil in a series of cycles and using the CO2 accumulated during the incubation periods to estimate these parameters.

Management practices and environmental changes can alter soil nutrient and carbon cycling. Soil labile organic carbon, a readily decomposable C pool, is ...

Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils

Recent soils research has shown that important chemical soil characteristics can change in less than a decade, often the result of broad environmental changes. Repeated sampling to monitor these changes in forest soils is a relatively new practice that is not well documented in the literature and has only recently been broadly embraced by the scientific community. The objective of this protocol is therefore to synthesize the latest information on methods of soil resampling in a format that can be used to design and implement a soil monitoring program. Successful monitoring of forest soils requires that a study unit be defined within an area of forested land that can be characterized with replicate sampling locations. A resampling interval of 5 years is recommended, but if monitoring is done to evaluate a specific environmental driver, the rate of change expected in that driver should be taken into consideration. Here, we show that the sampling of the profile can be done by horizon where boundaries can be clearly identified and horizons are sufficiently thick to remove soil without contamination from horizons above or below. Otherwise, sampling can be done by depth interval. Archiving of sample for future reanalysis is a key step in avoiding analytical bias and providing the opportunity for additional analyses as new questions arise.

Repeated soil sampling has recently been shown to be an effective way to monitor forest soil change over years and decades. To support its use, a protocol is presented that synthesizes the latest information on soil resampling methods to aid in the design and implementation of successful soil monitoring programs.

Recent soils research has shown that important chemical soil characteristics can change in less than a decade, often the result of broad environmental ...

Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management

Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes in elevation are related to changes in near-surface soil carbonate (CaCO3) profiles. The objective is to describe a simple conceptual model and detailed protocol for repeatable field and laboratory measurements of these quantities. Here, accurate elevation is measured using a ground-based differential global positioning system (GPS); other data acquisition methods could be applied to the same basic method. Soil samples are collected from prescribed depth intervals and analyzed in the lab using an efficient and precise modified pressure-calcimeter method for quantitative analysis of inorganic carbon concentration. Standard statistical methods are applied to point data, and representative results show significant correlations between changes in soil surface layer CaCO3 and changes in elevation consistent with the conceptual model; CaCO3 generally decreased in depositional areas and increased in erosional areas. Maps are derived from point measurements of elevation and soil CaCO3 to aid analyses. A map of erosional and depositional patterns at the study site, a rain-fed winter wheat field cropped in alternating wheat-fallow strips, shows the interacting effects of water and wind erosion affected by management and topography. Alternative sampling methods and depth intervals are discussed and recommended for future work relating soil erosion and deposition to soil CaCO3.

Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes in elevation are related to changes in near-surface soil carbonates. Repeatable methods for field and laboratory measurements of these quantities and data analysis methods are described here.

Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes ...

Sampling Soils in a Heterogeneous Research Plot

Soils are highly heterogeneous. In general, the number of soil samples required for soil research has always been determined arbitrarily and the associated accuracy is unknown. Here, we present a detailed protocol for efficient and clustered soil sampling in a research plot and, relying on a pilot sampling using this design, for demonstrating soil spatial heterogeneity and informing reasonable sample sizes and associated accuracy for future study. The protocol mainly comprises four steps: sampling design, field collection, soil analysis, and geostatistical analysis. The step-by-step procedure is modified according to former publications. Two examples will be presented to demonstrate contrasting spatial distributions of soil organic carbon (SOC) and soil microbial biomass carbon (MBC) under different management practices. In addition, we present a strategy to determine the sample size requirement (SSR) given a certain level of accuracy based on the plot-level coefficient of variation (CV). The field sampling protocol and the quantitative determination of the sample size will assist researchers in seeking feasible sampling strategies to meet research needs and resources' availability.

The traditional soil-sampling procedure determines the number of soil samples arbitrarily. Here, we provide a simple yet efficient clustered soil-sampling design to demonstrate soil spatial heterogeneity and quantitatively determine the number of soil samples required and the associated sampling accuracy.

Soils are highly heterogeneous. In general, the number of soil samples required for soil research has always been determined arbitrarily and the ...

Combined Size and Density Fractionation of Soils for Investigations of Organo-Mineral Interactions

Combined size and density fractionation (CSDF) is a method used to physically separate soil into fractions differing in particle size and mineralogy. CSDF relies on sequential density separation and sedimentation steps to isolate (1) the free light fraction (uncomplexed organic matter), (2) the occluded light fraction (uncomplexed organic matter trapped in soil aggregates) and (3) a variable number of heavy fractions (soil minerals and their associated organic matter) differing in composition. Provided that the parameters of the CSDF (dispersion energy, density cut-offs, sedimentation time) are properly selected, the method yields heavy fractions of relatively homogeneous mineral composition. Each of these fractions is expected to have a different complexing ability towards organic matter, rendering this a useful method to isolate and study the nature of organo-mineral interactions. Combining density and particle size separation brings an improved resolution compared to simple size or density fractionation methods, allowing the separation of heavy components according to both mineralogy and size (related to surface area) criteria. As is the case for all physical fractionation methods, it may be considered as less disruptive or aggressive than chemically-based extraction methods. However, CSDF is a time-consuming method and furthermore, the quantity of material obtained in some fractions can be limiting for subsequent analysis. Following CSDF, the fractions may be analyzed for mineralogical composition, soil organic carbon concentration and organic matter chemistry. The method provides quantitative information about organic carbon distribution within a soil sample and brings light to the sorptive capacity of the different, naturally-occurring mineral phases, thus providing mechanistic information about the preferential nature of organo-mineral interactions in soils (i.e., which minerals, what type of organic matter).

Combined size and density fractionation (CSDF) is a method to physically separate soil into fractions differing in texture (particle size) and mineralogy (density). The purpose is to isolate fractions with different reactivities towards soil organic matter (SOM), in order to better understand organo-mineral interactions and SOM dynamics.

Combined size and density fractionation (CSDF) is a method used to physically separate soil into fractions differing in particle size and mineralogy. CSDF ...

Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

We describe a method for two-dimensional (2D) visualization and quantification of the distribution of labile (i.e., reversibly adsorbed) inorganic nutrient (e.g., P, Fe, Mn) and contaminant (e.g., As, Cd, Pb) solute species in the soil adjacent to plant roots (the &#8216;rhizosphere&#8217;) at sub-millimeter (~100 &#181;m) spatial resolution. The method combines sink-based solute sampling by the diffusive gradients in thin films (DGT) technique with spatially resolved chemical analysis by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The DGT technique is based on thin hydrogels with homogeneously distributed analyte-selective binding phases. The variety of available binding phases allows for the preparation of different DGT gel types following simple gel fabrication procedures. For DGT gel deployment in the rhizosphere, plants are grown in flat, transparent growth containers (rhizotrons), which enable minimal invasive access to a soil-grown root system. After a pre-growth period, DGT gels are applied to selected regions of interest for in situ solute sampling in the rhizosphere. Afterwards, DGT gels are retrieved and prepared for subsequent chemical analysis of the bound solutes using LA-ICP-MS line-scan imaging. Application of internal normalization using 13C and external calibration using matrix-matched gel standards further allows for the quantification of the 2D solute fluxes. This method is unique in its capability to generate quantitative, sub-mm scale 2D images of multi-element solute fluxes in soil-plant environments, exceeding the achievable spatial resolution of other methods for measuring solute gradients in the rhizosphere substantially. We present the application and evaluation of the method for imaging multiple cationic and anionic solute species in the rhizosphere of terrestrial plants and highlight the possibility of combining this method with complementary solute imaging techniques.

This protocol presents a workflow for sub-mm 2D visualization of multiple labile inorganic nutrient and contaminant solute species using diffusive gradients in thin films (DGT) combined with mass spectrometry imaging. Solute sampling and high-resolution chemical analysis are described in detail for quantitative mapping of solutes in the rhizosphere of terrestrial plants.

We describe a method for two-dimensional (2D) visualization and quantification of the distribution of labile (i.e., reversibly adsorbed) inorganic ...

Monitoring Pedogenic Inorganic Carbon Accumulation Due to Weathering of Amended Silicate Minerals in Agricultural Soils.

The present study aims to demonstrate a systematic procedure for monitoring inorganic carbon induced by enhanced weathering of comminuted rocks in agricultural soils. To this end, the core soil samples taken at different depth (including 0-15 cm, 15-30 cm, and 30-60 cm profiles) are collected from an agriculture field, the topsoil of which has already been enriched with an alkaline earth metal silicate containing mineral (such as wollastonite). After transporting to the laboratory, the soil samples are air-dried and sieved. Then, the inorganic carbon content of the samples is determined by a volumetric method called calcimetry. The representative results presented herein showed five folded increments of inorganic carbon content in the soils amended with the Ca-silicate compared to control soils. This compositional change was accompanied by more than 1 unit of pH increase in the amended soils, implying high dissolution of the silicate. Mineralogical and morphological analyses, as well as elemental composition, further corroborate the increase in the inorganic carbon content of silicate-amended soils. The sampling and analysis methods presented in this study can be adopted by researchers and professionals looking to trace pedogenic inorganic carbon changes in soils and subsoils, including those amended with other suitable silicate rocks such as basalt and olivine. These methods can also be exploited as tools for verifying soil inorganic carbon sequestration by private and governmental entities to certify and award carbon credits.

The verification method described here is adaptable for monitoring pedogenic inorganic carbon sequestration in various agricultural soils amended with alkaline earth metal silicate-containing rocks, such as wollastonite, basalt, and olivine. This type of validation is essential for carbon credit programs, which can benefit farmers that sequester carbon in their fields.

The present study aims to demonstrate a systematic procedure for monitoring inorganic carbon induced by enhanced weathering of comminuted rocks in ...

Utilizing Soil Density Fractionation to Separate Distinct Soil Carbon Pools

Soil organic matter (SOM) is a complicated mixture of different compounds that span the range from free, partially degraded plant components to more microbially altered compounds held in the soil aggregates to highly processed microbial by-products with strong associations with reactive soil minerals. Soil scientists have struggled to find ways to separate soil into fractions that are easily measurable and useful for soil carbon (C) modeling. Fractionating soil based on density is increasingly being used, and it is easy to perform and yields C pools based on the degree of association between the SOM and different minerals; thus, soil density fractionation can help to characterize the SOM and identify SOM stabilization mechanisms. However, the reported soil density fractionation protocols vary significantly, making the results from different studies and ecosystems hard to compare. Here, we describe a robust density fractionation procedure that separates particulate and mineral-associated organic matter and explain the benefits and drawbacks of separating soil into two, three, or more density fractions. Such fractions often differ in their chemical and mineral composition, turnover time, and degree of microbial processing, as well as the degree of mineral stabilization.

Soil density fractionation separates soil organic matter into distinct pools with differing stabilization mechanisms, chemistries, and turnover times. Sodium polytungstate solutions with specific densities allow the separation of free particulate organic matter and mineral-associated organic matter, resulting in organic matter fractions suitable for describing the soil response to management and climate change.

Soil organic matter (SOM) is a complicated mixture of different compounds that span the range from free, partially degraded plant components to more ...

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Digital imaging and processing technology have evolved to facilitate the expansion of large-area imaging surveys, which increase our capacity to study the status, trends, and dynamics of organisms living in subtidal habitats. By creating photo-realistic digital twins for ex situ analyses, these approaches allow small field teams to collect substantially more data than was previously possible. Here we present a four-step, large-area imaging survey pipeline and analysis methodology, including image collection, model construction, ecological analysis, and data curation, that has been developed and refined through experimentation over the past decade. Each step described has a consistent focus on the unique value of the original source imagery. While types of data extracted from large-area image surveys are vast, we include here workflows to extract ecological data for structural complexity, community composition, and demographic analyses valuable for monitoring and hypothesis-driven efforts. We additionally include recommendations for metadata standards, which complement the collection of large-area imaging data and support archival efforts facilitating transparency and collaboration between research groups.

This protocol covers a four-step large-area imaging survey methodology used to extract metrics of structural complexity, community composition, and population demographics for coral reef communities. The quality of imagery collected and integrated access to the source imagery are prioritized within each step of the protocol.

Digital imaging and processing technology have evolved to facilitate the expansion of large-area imaging surveys, which increase our capacity to study the ...

Using Topographic Maps to Generate Topographic Profiles

Source: Laboratory of <a href="https://www.jove.com/author/Alan_Lester">Alan Lester</a> - University of Colorado Boulder
Topographic maps are &#34;plan-view&#34; representations of Earth&#39;s three-dimensional surface. They are a standard type of map-view that provides an overhead, or aerial, perspective.
Among the defining features of a topographic map are the contour lines that indicate locations of constant elevation. The elevation interval between the contour lines is dependent on the level of detail provided by the map and the kind of topography present. For example, regions with significant topographic variation might require contour lines separated by 40-100 ft., whereas generally flat-lying regions with little topographic variation might have more broadly separated 10-20 ft. contours.
To an experienced user of such maps, the patterns made by the topographic lines are representative of various landform patterns, such as ridges, valleys, hills, and plateaus.