@article {JRN55207, title = {Rating fire danger from the ground up}, journal = {GeoHealth}, volume = {100}, year = {2020}, month = {12/2019}, abstract = {Better soil moisture information with spatial and temporal resolutions relevant for assessing fuel bed conditions and wildfire probability offers promise for improving fire danger ratings. Wildfires consumed more than 3.5 million hectares in the United States in 2018 and federal fire suppression costs topped $3 billion USD. These fires took a severe toll on many communities, resulting in the destruction of more than 18,000 residences and the deaths of more than 100 people. Wildfire damages like these are a threat in many nations, and researchers from across the globe, and across multiple scientific disciplines, are working to improve fire danger rating systems to help protect natural resources and human health and safety. One new concept emerging as a valuable contribution to this effort is the integration of soil moisture information as a predictor of wildfire probability. Soil moisture, particularly within the root zone, is a key link between weather conditions, such as precipitation and temperature, and the characteristics of the live vegetative {\textquotedblleft}fuel bed{\textquotedblright}, such as fuel moisture and fuel loads. These dynamic vegetation characteristics strongly influence wildfire probability and can be challenging to model and monitor at relevant spatial and temporal scales using field data or optical remote sensing of fuel moisture (e.g., remote sensing live fuel moisture models contain relatively large margins of error that varies by vegetation type). Soil moisture monitoring capabilities, in contrast, have been steadily growing due to the development of in situ networks and dedicated satellites. While the relationships between soil moisture, fuel conditions, and wildfire occurrence have long been intuitively understood, the increasing availability of soil moisture information is creating significant opportunities to quantify these relationships and incorporate them into new or existing weather-based fuel moisture models and fire danger rating systems.}, doi = {https://doi.org/10.1029/2019EO137858}, url = {files/bibliography/20-015.pdf}, author = {Matthew Levi and Krueger, Erik and Snitker, Grant J and Ochsner, Tyson and Miguel L Villarreal and Emile H. Elias and Dannele E. Peck} } @article {JRN55005, title = {Digital soil mapping for predicting and managing fire in rangelands}, journal = {Fire Ecology}, year = {2018}, month = {12/2018}, pages = {1-12}, chapter = {1}, abstract = {

Background: Soil properties have important effects on fire occurrence and spread, but soils are often overlooked in
fire prediction models. Quantifying soil-fire linkages is limited by information in conventional soil maps, but digital
soil mapping products (e.g., detailed soil property maps) could improve both wildfire prediction models and post-fire
management decisions.
Results: Of our estimated 3.7 Mkm2 of rangeland in the continental US and Alaska, an average of 38 000 km2 burned
per year between 2008 and 2017. To highlight the role of soils in fire ecology, we present 1) a conceptual framework
explaining why soil information can be useful for fire models, 2) a comprehensive suite of literature examples that used
soil property information in traditional soil survey for predicting wildfire, and 3) specific examples of how more detailed
soil information can be applied for pre- and post-fire decisions.
Conclusions: Digital soil mapping can improve fire prediction models and inform post-fire management decisions

}, keywords = {digital soil mapping, fire effects, grasslands, shrublands, soil moisture, spatial modeling, wildfire}, doi = {doi.org/10.1186/s42408-018-0018-4}, url = {files/bibliography/19-006.pdf}, author = {Matthew Levi and Brandon T Bestelmeyer} } @conference {JRN54806, title = {Drawing a representative sample from the NCSS soil database: Building blocks for the national wind erosion network}, booktitle = {ASA-CSSA-SSSA Annual Meeting Abstracts}, year = {2017}, month = {10/2017}, publisher = {ASA-CSSA-SSSA Annual Meeting Abstracts}, organization = {ASA-CSSA-SSSA Annual Meeting Abstracts}, address = {Tampa, Florida}, abstract = {

Developing national wind erosion models for the continental United States requires a comprehensive spatial representation of continuous soil particle size distributions (PSD) for model input. While the current coverage of soil survey is nearly complete, the most detailed particle size classes have categorical of sand, silt, and clay. The National Cooperative Soil Survey (NCSS) database has laboratory data for over 21,000 pedons, but continuous PSDs are not available. Existing soil maps provide the link between NCSS pedon data enable the transfer of detailed laboratory data to spatial representations. Extracting a representative sample from the existing NCSS data for expensive, detailed PSD analysis requires the ability to capture maximum variance with the minimal number of samples. We performed a sensitivity analysis of sample selection to compare the effect of different soil and geographic features on representation of the larger database. A conditioned Latin Hypercube sample design was used to select samples based on their feature space and compared to simple random samples. Comparisons of extracted sample datasets showed little difference for \ bulk density, water content (field capacity, wilting point, plant available water) and sand fractions. Stronger differences emerged with fewer samples in the dataset. Spatial coverage of all samples effectively represented the geographic coverage of the continental US. This exercise provides insight for how distributions of the PSDs may vary by selection method and also illustrates the ability of different sample sizes for representing the larger database. Our work will inform future sample selection from existing soil databases for modeling at continental scales.

}, author = {Matthew Levi and Nicholas Webb} } @article {JRN54809, title = {Modified centroid for estimating sand, silt, and clay from soil texture class}, journal = {Soil Science Society of America Journal}, volume = {81}, year = {2017}, month = {06/2017}, pages = {578-588}, chapter = {578}, abstract = {

Models that require inputs of soil particle size commonly use soil texture class for input; however, texture classes do not represent the continuum of soil size fractions. Soil texture class and clay percentage are collected as a standard practice for many land management agencies (e.g., NRCS, BLM, FAO) and clay content is frequently estimated with acceptable accuracy ({\textpm}5\%). When clay and texture class is known, sand and silt can be constrained to a narrow range that may differ from the geometric centroid of a given texture class (gcent). I tested the concept of a modified centroid approach (mcent) using 75,736 soil samples from the National Cooperative Soil Survey pedon database that also had measured hydraulic soil properties. Comparisons were made using the Rosetta pedotransfer function (PTF) to test modeled values from gcent, mcent, and measured data in comparison to measured values of water content at field capacity ({\textquoteright}330) wilting point ({\textquoteright}15000,). The mcent approach produced a continuous distribution of values for sand and silt whereas gcent produced a stair-step pattern for each of the 12 texture classes. Rosetta underestimated water content. Unbiased root mean square error was smaller for mcent than gcent for seven and ten of the 12 soil texture classes for {\textquoteright}330 and {\textquoteright}15000, respectively. Results support the practice of collecting field estimates of clay percentage along with texture class to expand the use of soil profile data for both spatial and non-spatial modeling of soil processes that will advance our understanding of soil contributions to ecosystem function.

}, doi = {10.2136/sssaj2016.09.0301}, url = {files/bibliography/17-013.pdf}, author = {Matthew Levi} } @article {JRN54695, title = {Neighborhood size of training data influences soil map disaggregation}, journal = {Soil Science Society of America Journal}, volume = {81}, year = {2017}, month = {04/2017}, pages = {354-368}, abstract = {
Soil class mapping relies on the ability of sample locations to represent portions of the landscape with similar soil types; however, most digital soil mapping (DSM) approaches intersect sample locations with one raster pixel per covariate layer regardless of pixel size. This approach does not take into account the variability of covariate information adjacent to training data that represent the polypedon. My objective was to disaggregate a soil map in a semiarid Arizona rangeland (78,569 ha) by exploring different neighborhood sizes for extracting covariate data to points. Eight machine learning algorithms were compared to assess the influence of aggregating covariate data in neighborhood sizes between 0 {\textendash} 180 m radius and a multi-scale model. Kappa values of all models ranged between 0.24 and 0.44 and increased with buffer radius up to a radius of 150 m. Support vector machine and random forest algorithms performed best across all scales. The radial support vector machine model using 150 m aggregations of covariates had the highest kappa and produced a more generalized map compared to the best multi-resolution model (random forest) which resulted in a mix of general and detailed soil features. Evaluating a range of neighborhood sizes for aggregating covariate data provides a method of accounting for multi-scale processes important for predicting soil patterns without modifying pixel size of final maps. Incorporating polypedon concepts from traditional soil survey with DSM approaches can strengthen ties between them and optimize the extraction of landscape information for predicting soil properties.
}, doi = {10.2136.sssaj2016.08.0258}, url = {/files/bibliography/17-007.pdf}, author = {Matthew Levi} } @article {JRN54785, title = {Predictor variable resolution governs modeled soil types}, volume = {62}, number = {17}, year = {2017}, month = {06/2017}, chapter = {6}, address = {CSA News}, abstract = {

Soil mapping identifies different soil types by compressing a unique suite of spatial patterns and processes across multiple spatial scales. It can be quite difficult to quantify spatial patterns of soil properties with remotely sensed predictor variables. More specifically, matching the right scale of a given predictor variable with a specific soil type remains a challenge in digital soil mapping. An example of how scaling predictor variables derived from remote sensing and topographic indices can affect classification accuracy of soil types in southeastern Arizona is presented in the March{\textendash}April 2017 issue of the Soil Science Society of America Journal. Support vector machine and random forest approaches produced the best results across a range of circular buffer windows for averaging predictors (0{\textendash}180 m) compared with other commonly used machine learning models. Classification accuracy increased with the size of averaging window up to 150 m; however, larger window resolution produced general soil maps that poorly identified soils with narrow, linear features. Incorporating multiscale predictors produced soil{\textendash}landscape features with a mix of general and detailed patterns.
These findings suggest that traditional soil survey concepts can inform new soil mapping efforts and optimize the extraction of landscape information to predict soil properties.

}, author = {Matthew Levi} } @article {JRN54604, title = {Biophysical influences on the spatial distribution of fire in the desert grassland region of the southwestern USA}, journal = {Landscape Ecology}, volume = {31}, year = {2016}, month = {05/2016}, type = {Peer Reviewed}, chapter = {2079}, abstract = {
Context: Fire is an important driver of ecological processes in semiarid systems and serves a vital role in shrub-grass interactions. In desert grasslands of the Southwestern US, the loss of fire has been implicated as a primary cause of shrub encroachment. Where fires can currently be re-introduced and managed given past state changes and recent restoration actions, however, is unknown and controversial. We predicted that climate, soil, and topographic variables could be used to predict fire distributions and map the potential for use of fire in management. We also predicted that under conditions of intermediate precipitation, soil-landscape properties would have a significant effect on fire ignition frequencies.
Methods: We characterized the spatial distribution of fire in the Chihuahuan Desert and Madrean Archipelago ecoregions and investigated the influence of soil properties compared to other commonly used biophysical variables using multi-model inference techniques.\ 
Results: Soil-landscape properties significantly influenced the spatial distribution of fire ignition points. Bottomland ecological sites (i.e., soil-landscape classes) experienced more fires than expected in contrast to ecological sites with coarse soil textures and high fragment content that experienced fewer fire ignitions than expected. Influences of mean annual precipitation, distance to road/rail, soil available water holding capacity and topographic variables varied between ecoregions and by political jurisdictions and differed for peak and nonpeak fire seasons.\ 
Conclusions: Understanding the spatiotemporal distribution of recent fires in desert grasslands is perhaps the most important factor for predicting future patterns of fire in response to climate change. While climate variables have proven useful for predicting fire patterns in most systems, the use of landscape units such as ecological sites presents an opportunity for incorporating fire-related ecological processes into management planning. The application of soil property information readily available in soil survey for predicting fire size and spatial distribution is an important addition to the toolbox for predicting and managing fire.
}, doi = {10.1007/s10980-016-0383-9}, url = {/files/bibliography/16-027.pdf}, author = {Matthew Levi and Brandon T Bestelmeyer} } @conference {JRN54515, title = {Connecting long-term monitoring data from vegetation plots and remote sensing in the Southwestern USA }, booktitle = {69th Society for Range Management Annual Meeting}, year = {2016}, month = {01/2016}, publisher = {Society for Range Mangement}, organization = {Society for Range Mangement}, address = {Corpus Christi, TX}, abstract = {

Understanding vegetation response to changing climate patterns is an important element of rangeland management and supports the use and development of ecological site descriptions. Monitoring of rangeland conditions with remote sensing can be misleading if ground measurements are not used to interpret changes in vegetation indices. We used a 25 year time series of Landsat normalized difference vegetation index (NDVI) to evaluate vegetation dynamics in the Malpai Borderlands Area of Arizona and New Mexico(~324,000 ha). The NDVI time series was decomposed into trend and seasonal variability to better understand spatial and temporal changes in vegetation dynamics across the region. Long-term vegetation monitoring plots and soil profile descriptions provided ground-based information to interpret patterns of NDVI. Monthly PRISM climate data were also used to interpret NDVI and vegetation dynamics. We compared changes in percent cover of plant functional types on a five year interval to the decomposed time series between measurements to elucidate causes of NDVI variability. Management practices across the time period are largely unknown; hence our interpretations reflect variable climate patterns and the effect of fires in the region. Preliminary results indicate a decrease in perennial grass cover in 2003 following a period of extreme regional drought . Perennial grass cover at most plots rebounded to pre-drought conditions or more perennial grass cover by 2008; however, some did not. We will present a detailed analysis of NDVI trends across the region and directly link changes in measured cover at monitoring locations to changes in NDVI and precipitation. Connecting NDVI time series data to point locations of measured cover values will advance interpretations of remotely sensed imagery for broad-scale assessments of rangeland condition and facilitate improved ecological site interpretation.

}, author = {Matthew Levi and Brandon T Bestelmeyer} } @conference {JRN54516, title = {Fire in desert grassland region of the southwestern USA: where and why}, booktitle = {69th Society for Range Management Annual Meeting}, year = {2016}, month = {01/2016}, publisher = {Society for Range Mangement}, organization = {Society for Range Mangement}, address = {Corpus Christi, TX}, abstract = {

Fire is an important driver of ecological processes in semiarid systems and serves a vital role in shrub-grass interactions. In desert grasslands of the Southwestern US, the loss of fire has been implicated as a primary cause of shrub encroachment. Where fires can currently be re-introduced and managed given past state changes and recent restoration actions, however, is unknown and controversial. Biophysical variables that influence fuel load and quality are commonly employed to model fire distributions; however, soil-landscape properties like those captured by ecological site descriptions are rarely employed to model fire probability. We characterized the spatial distribution of fire in the Chihuahuan Desert and Madrean Archipelago ecoregions and investigated the influence of soil properties and ecological site groups compared to other commonly used biophysical variables using multi-model inference techniques.\  Soil-landscape properties significantly influenced the spatial distribution of fire ignitions. Bottomland ecological sites (i.e., soil-landscape classes) experienced more fires than expected in contrast to ecological sites with coarse soil textures and high fragment content that experienced fewer fire ignitions than expected. Influences of mean annual precipitation, distance to road/rail, soil available water holding capacity (AWHC) and topographic variables varied between ecoregions and by political jurisdictions and differed for peak and nonpeak fire seasons. AWHC explained more variability of fire ignitions in the Madrean Archipelago compared to the Chihuahuan Desert.\  Understanding the spatiotemporal distribution of recent fires in desert grasslands is needed to\  manage fire and predict responses to climate change. While climate variables have proven useful for predicting fire patterns in most systems, the use of landscape units such as ecological sites presents an opportunity to improve predictions at management scales. The application of soil property information readily available in soil survey is an important addition to the toolbox for predicting and managing fire.

}, author = {Matthew Levi and Brandon T Bestelmeyer} } @conference {JRN54521, title = {Spatial probability models of fire in the desert grasslands of the southwestern USA}, booktitle = {6th Natural History of the Gila Symposium}, year = {2016}, month = {02/2016}, address = {Silver City, NM}, abstract = {

Fire is an important driver of ecological processes in semiarid environments; however, the role of fire in desert grasslands of the Southwestern US is controversial and the regional fire distribution is largely unknown. We characterized the spatial distribution of fire in the desert grassland region of the southwestern United States, which includes a large portion of the Upper Gila River watershed. Our objective was to investigate the influence of soil properties and ecological site groups compared to other commonly used biophysical variables for modeling fire probability. Soil-landscape properties significantly influenced the spatial distribution of fire ignitions. Bottomland ecological sites (i.e., soil-landscape classes) experienced more fires than expected in contrast to those with coarse soil textures and high rock fragment content that experienced fewer fire ignitions than expected. Influences of mean annual precipitation, distance to road/rail, soil available water holding capacity (AWHC) and topographic variables varied between ecoregions and political jurisdictions and by fire season. AWHC explained more variability of fire ignitions in the Madrean Archipelago compared to the Chihuahuan Desert. Understanding the spatiotemporal distribution of recent fires in desert grasslands is needed to manage fire and predict responses to changing climate.

}, author = {Matthew Levi and Brandon T Bestelmeyer} } @conference {JRN54616, title = {Utilizing soil polypedons to improve model performance for digital soil mapping}, booktitle = {Soil Science Society of America Meeting}, year = {2016}, month = {11/2016}, publisher = {Soil Science Society of America}, organization = {Soil Science Society of America}, address = {Phoenix, AZ}, abstract = {
Most digital soil mapping approaches that use point data to develop relationships with covariate data intersect sample locations with one raster pixel regardless of pixel size. Resulting models are subject to spurious values in covariate data which may limit model performance. An alternative approach is to aggregate covariate data for discernable soil bodies of similar soils, called polypedons, as training data for sample locations. Our objective was to explore the utility of different aggregation schemes of covariate data for soil mapping efforts and disaggregation of existing soil map units. Two study areas in semiarid rangelands of southeastern Arizona and southwestern New Mexico were the focus of this work. We present three examples that represent recent developments in the use of polypedon concepts for spatial predictions of soil components: 1) the effect of aggregating covariate data with a circular buffer around training data, 2) hand digitizing of polypedons to bolster sample size and overcome limitations of class imbalance for model development within a soil survey update, and 3) object-based image analysis for a data-driven approach of model development with polypedon units. A comparison of eight machine learning algorithms showed that a circular buffer size of 150 meters around training data had the best model performance (Kappa = 0.43) in the Arizona watershed. Radial support vector machine and random forest models performed best. In the New Mexico example, random forest models had with a Kappa of 0.16 for sampled points that improved to 0.95 with an additional 50 polypedon points for each class. Image segmentation of covariate data in the Arizona watershed using eCognition generated polypedons for model development without hand digitizing. Using polypedons as training data will improve spatial predictions of soil properties by maximizing information provided by covariate data and linking concepts of traditional soil survey and digital soil mapping.\ 
}, author = {Matthew Levi and Brandon T Bestelmeyer and David A. White II} } @article {JRN54514, title = {Application of spatial pedotransfer functions to understand soil modulation of vegetation response to climate}, journal = {Vadose Zone Journal}, volume = {14}, year = {2015}, month = {09/2015}, pages = {1-14}, chapter = {1}, abstract = {

A fundamental knowledge gap in understanding land-atmosphere interactions is accurate, high resolution spatial representation of soil physical and hydraulic properties. We present a novel approach to predict hydraulic soil parameters by combining digital soil mapping techniques with pedotransfer functions and demonstrate that simple derived quantities are related to observed spatial patterns in ecosystem production during the North American Monsoon. Landsat reflectance and elevation data were used to predict physical soil properties at a 5 m spatial resolution for a semiarid landscape of 6,265 ha using regression kriging. Resulting soil property maps were applied to the Rosetta pedotransfer function to predict saturated hydraulic conductivity and water retention parameters from which approximate water residence times were derived. Estimated idealized residence time for water lost to the deeper vadose zone and evapotranspiration corresponded to vegetation response. Antecedent precipitation was more important for explaining the relationships between modeled soil properties and vegetation response than the amount of monsoon precipitation. Increased spring precipitation prior to the monsoon produced stronger negative correlations with hydraulic conductivity and stronger positive correlations with plant available water. Modeled water residence times explained the patterns of vegetation and landscape morphology validating our approach as a method of producing functional spatial pedotransfer functions. Linking digital soil mapping with Rosetta led to predictions of hydraulic soil properties that were more closely related to vegetation dynamics compared to data available in the SSURGO soil database.

}, doi = {10.2136/vzj2014.09.0126}, url = {/files/bibliography/15-022.pdf}, author = {Matthew Levi and Schaap, Marcel and Craig Rasmussen} } @conference {JRN54410, title = {Considerations for applying digital soil mapping to ecological sites}, booktitle = {2015 National Cooperative Soil Survey National Conference}, year = {2015}, month = {06/2015}, address = {Duluth, MN}, abstract = {

Recent advancements in the spatial prediction of soil properties are not currently being fully utilized for ecological studies. Linking digital soil mapping (DSM) with ecological sites (ES) has the potential to better land management decisions by improving spatial resolution and precision as well as quantifying uncertainty of environmental information. Current spatial representation of ES largely depends on soil survey data. Any discrepancies in soil survey data are carried forward into ES inventories and interpretations. Recent advances in DSM technologies have focused on identifying unique soil components (disaggregating soil map units) and mapping continuous soil properties and associated uncertainty to improve soil data products. Soil surveys often do not discriminate properties important to ecosystem variation because of mapping limitations, yet subtle soil variations within map units or components may have important consequences for vegetation and ecological dynamics. Detailed maps of soil properties linked to important ecological variables could improve explanations of ecosystem patterns and dynamics. A recent workshop was held at the Jornada Experimental Range to bring together NCSS partners experienced with ES and DSM to initiate integration of these efforts. The application of DSM for ecological site development and mapping could help attain goals of the NRCS, BLM and USFS to produce ES products for improved land stewardship. We present a vision of how coupling DSM and ecological site descriptions could change the way we produce and consume ecological information.

}, author = {Matthew Levi and Travis Nauman and Brandon T Bestelmeyer and Joel R. Brown and Colby Brungard and Libohova, Zamir and Danielle Duni and J. Johansen} } @conference {JRN54405, title = {Digital soil mapping as a tool for quantifying state-and-transition models}, booktitle = {68th Annual Meeting of Society for Range Management}, year = {2015}, month = {02/2015}, publisher = {Sociiety for Range Management}, organization = {Sociiety for Range Management}, address = {Sacramento, CA}, abstract = {

Ecological sites and associated state-and-transition models (STMs) are rapidly becoming important land management tools in rangeland systems in the US and around the world. Descriptions of states and transitions are largely developed from expert knowledge and generally accepted species and community level responses to external drivers. A major limitation of current STMs is the lack of quantitative data on vegetation dynamics to describe state transitions. Detailed soil property characterization is also needed to better understand soil-landscape controls of state transitions resulting from land use and climate drivers. We used spatiotemporal patterns of normalized difference vegetation index (NDVI) response to precipitation and soil properties in southeastern Arizona to determine how hydraulic soil properties mediate attributes of vegetation dynamics at a landscape level. Pixels in a time series of NDVI derived from Landsat reflectance data were clustered according to types of vegetation dynamics for each pixel-time series combination. Clustered time series data were linked to a high-resolution digital soil map of physical soil properties related to soil moisture dynamics to help explain vegetation response patterns. Monthly PRISM climate data was used to interpret vegetation dynamics. Linking high-resolution soil property maps with vegetation dynamics at broad scales can be used to quantify the interactions between soil properties and land use/climate drivers on vegetation. This process can also be used for forecasting climate change effects on vegetation at relatively fine spatial scales.

}, author = {Matthew Levi and Brandon T Bestelmeyer and Joel R. Brown} } @article {JRN54004, title = {Covariate selection with iterative principal component analysis for predicting physical soil properties}, journal = {Geoderma}, volume = {219-220}, year = {2014}, month = {05/2014}, pages = {46-57}, chapter = {46}, abstract = {

Local and regional soil data can be improved by coupling new digital soil mapping techniques with high resolution remote sensing products to quantify both spatial and absolute variation of soil properties. The objective of this research was to advance data-driven digital soil mapping techniques for the prediction of soil physical properties at high spatial resolution using auxiliary data in a semiarid ecosystem in southeastern Arizona, USA. An iterative principal component analysis (iPCA) data reduction routine of reflectance and elevation covariate layers was combined with a conditioned Latin Hypercube field sample design to effectively capture the variability of soil properties across the 6250\ ha study area. We sampled 52 field sites by genetic horizon to a 30\ cm depth and determined particle size distribution, percent coarse fragments, Munsell color, and loss on ignition. Comparison of prediction models of surface soil horizons using ordinary kriging and regression kriging indicated that ordinary kriging had greater predictive power; however, regression kriging using principal components of covariate data more effectively captured the spatial patterns of soil property{\textendash}landscape relationships. Percent silt and soil redness rating had the smallest normalized mean square error and the largest correlation between observed and predicted values, whereas soil coarse fragments were the most difficult to predict. This research demonstrates the efficacy of coupling data reduction, sample design, and geostatistical techniques for effective spatial prediction of soil physical properties in a semiarid ecosystem. The approach applied here is flexible and data-driven, allows incorporation of wide variety of numerically continuous covariates, and provides accurate quantitative prediction of individual soil properties for improved land management decisions and ecosystem and hydrologic models.

}, keywords = {data reduction, digital soil mapping, landsat, regression kriging, spatial variability, terrain analysis}, doi = {10.1016/j.geoderma.2013.12.013}, url = {/files/bibliography/14-026.pdf}, author = {Matthew Levi and Craig Rasmussen} } @conference {JRN54411, title = {Disaggregation of soil map units for improved ecological site mapping in rangelands}, booktitle = {Agronomy Society of America, Crop Science Society of America, Soil Science Society of America Meetings 2014}, year = {2014}, month = {11/2014}, publisher = {ASA, CSSA, SSSA}, organization = {ASA, CSSA, SSSA}, address = {Long Beach, CA}, abstract = {

Rangeland soils are often mapped with soil map units consisting of associations, complexes, and undifferentiated groups composed of varied soil components. Because different components may be related to different ecological sites, the unmapped heterogeneity within map units limits the potential uses of ecological sites for planning and assessment. Advances in digital soil mapping (DSM) techniques coupled with readily available environmental covariate layers present an opportunity to identify unique soil components that can provide a more detailed spatial representation of ecological sites. Our objectives were to utilize environmental covariate data and DSM techniques to identify individual soil components and ecological sites in a semiarid landscape of southeastern Arizona (~72,000 ha). We used a time-series of Landsat reflectance, high-resolution orthoimagery and a digital elevation model to derive indices reflecting soil, vegetation and topographic attributes. An unsupervised classification of the environmental covariates was performed with a maximum likelihood classification of isodata clusters to create unique landscape units. Field data from initial soil survey efforts were digitized and supplemented with additional targeted sampling locations to attribute landscape classes. Classification of integrated environmental covariate layers separated existing soil map unit boundaries and different soil components. Classified soil landscape units also captured information in detailed maps of ecological states illustrating the utility of linking DSM with ecological sites and states to provide rapid characterization of the landscape for improving rangeland management. Incorporating multiple imagery dates in the classification provided a means to overcome problems of management differences on the ground (e.g., fence lines) to facilitate more realistic separation of soil properties. Disaggregating soil map units into more explicit soil components presents a unique opportunity for developing a hierarchical soil map scalable to desired objectives that can overcome many of the problems associated with multi-component maps.

}, author = {Matthew Levi and Brandon T Bestelmeyer and Joel R. Brown} } @conference {JRN54123, title = {Disaggregation of soil map units for improved ecological site mapping in rangelands}, booktitle = {Agronomy Society of America, Crop Science Society of America, Soil Science Society of America Meeting}, year = {2014}, month = {11/2014}, publisher = {ASA CSSA, SSSA}, organization = {ASA CSSA, SSSA}, address = {Long Beach, CA}, abstract = {

Rangeland soils are often mapped with soil map units consisting of associations, complexes, and undifferentiated groups composed of varied soil components. Because different components may be related to different ecological sites, the unmapped heterogeneity within map units limits the potential uses of ecological sites for planning and assessment. Advances in digital soil mapping (DSM) techniques coupled with readily available environmental covariate layers present an opportunity to identify unique soil components that can provide a more detailed spatial representation of ecological sites. Our objectives were to utilize environmental covariate data and DSM techniques to identify individual soil components and ecological sites in a semiarid landscape of southeastern Arizona (~72,000 ha). We used a time-series of Landsat reflectance, high-resolution orthoimagery and a digital elevation model to derive indices reflecting soil, vegetation and topographic attributes. An unsupervised classification of the environmental covariates was performed with a maximum likelihood classification of isodata clusters to create unique landscape units. Field data from initial soil survey efforts were digitized and supplemented with additional targeted sampling locations to attribute landscape classes. Classification of integrated environmental covariate layers separated existing soil map unit boundaries and different soil components. Classified soil landscape units also captured information in detailed maps of ecological states illustrating the utility of linking DSM with ecological sites and states to provide rapid characterization of the landscape for improving rangeland management. Incorporating multiple imagery dates in the classification provided a means to overcome problems of management differences on the ground (e.g., fence lines) to facilitate more realistic separation of soil properties. Disaggregating soil map units into more explicit soil components presents a unique opportunity for developing a hierarchical soil map scalable to desired objectives that can overcome many of the problems associated with multi-component maps.

}, author = {Matthew Levi and Brandon T Bestelmeyer and Joel R. Brown} }