Location: Range Management ResearchTitle: Digital mapping of ecological land units using a nationally scalable modeling framework
|NAUMAN, TRAVIS - Us Geological Survey (USGS)|
|DUNIWAY, MICHAEL - Us Geological Survey (USGS)|
|TALBOT, CURTIS - Natural Resources Conservation Service (NRCS, USDA)|
|BROWN, JOEL - Natural Resources Conservation Service (NRCS, USDA)|
Submitted to: Soil Science Society of America Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/3/2019
Publication Date: 6/20/2019
Citation: Maynard, J.J., Nauman, T., Salley, S.W., Bestelmeyer, B.T., Duniway, M., Talbot, C.J., Brown, J.R. 2019. Digital mapping of ecological land units using a nationally scalable modeling framework. Soil Science Society of America Journal. 83:666-686. https://doi.org/10.2136/sssaj2018.09.0346.
Interpretive Summary: Ecological site descriptions and associated state-and-transition models have been formally adopted by U.S. Federal land management agencies as a national ecological land classificaiton system. This has resulted in a single system for defining, mapping, and monitoring both forests and rangelands; which provides guidance to land managers on how best to allocate managment efforts that maximize land productivity while minizing degradation. Ecological site concepts are developed and mapped across a wide range of spatial scales to address different management questions and concerns. However, current spatial representations of ecological sites occur through correlations to the Soil Survey Geographic Database (SSURGO) soil map unit (SMU) components (i.e. soil types), and thus the spatial accuracy of ecological site maps is determined by the accuracy of soil map delineations. Ongoing advancements in geospatial technologies are providing the tools needed to create high spatial resolution maps of ecological sites. This study presents a consistent, nationally scalable framework for predicting ecological sites at multiple spatial scales using a national point database, remotely sensed geospatial data layers, and machine learning algorithms. We tested our spatial modeling framework in two study areas that had high quality ecological site and SSURGO data for model building and validation. Results from this study demonstrate that our modeling framework can produce predictive ecological site maps that have a similar accuracy to SSURGO ecological site maps, but more specificity. An additional benefit of digital mapping techniques is that they provide the ability to predict ecological site distributions within areas currently unmapped in SSURGO. The approach outlined in this study provides a scalable methodology for creating data products capable of addressing a range of new and evolving land management concerns at a variety of spatial scales.
Technical Abstract: Ecological site descriptions (ESDs) and associated state-and-transition models (STMs) provide a nationally consistent classification and information system for defining ecological land units for management applications in the U.S. Current spatial representations of ESDs, however, occur via soil mapping and are therefore confined to the spatial resolution used to map soils within a survey area. Land management decisions occur across a range of spatial scales and therefore require ecological information that spans similar scales. Digital mapping provides an approach for optimizing the spatial scale of modeling products to best serve decision makers and have the greatest impact in addressing land management concerns. Here we present a spatial modeling framework for mapping ecological sites using machine learning algorithms, soil survey field observations, soil survey geographic databases, ecological site data, and a suite of remote sensing-based spatial covariates (e.g., hyper-temporal remote sensing, terrain attributes, climate data, land-cover, lithology). Based upon the theoretical association between ecological sites and landscape biophysical properties, we hypothesized that the spatial distribution of ecological sites could be predicted using readily available geospatial data. This modeling approach was tested at two study areas within the western U.S., representing 6.1 million ha on the Colorado Plateau and 7.5 million ha within the Chihuahuan Desert. Results show our approach was effective in mapping grouped ecological site classes (ESGs), with a 70% correct classification based on 1,405 point observations across 8 expertly-defined ESG classes in the Colorado Plateau and a 79% correct classification based on 2,589 point observations across 9 expertly-defined ESG classes in the Chihuahuan Desert. National coverage of the training and covariate data used in this study provides opportunities for a consistent national-scale mapping effort of ecological sites.