New Tools for Modern Land Management Decisions
In an era of rapid land use changes and shifting climates, it is imperative that land managers and policymakers have actionable and current information available for decision processes. In this work, we seek to meet these needs through new data products and decision support tools built on digital soil mapping, new vegetation cover maps, agency inventory and monitoring data sets, and cutting-edge analytical frameworks. By building on large databases of field observations, available remote sensing, and cloud computing, we are able to create new, decision-relevant information more quickly.
Ecological Site Group maps for the Upper Colorado River Basin
What determines the effectiveness of Pinyon-Juniper clearing treatments?
Background & Importance
An opportunity for achieving this is through analysis of past land management and actions. Here, we apply a technique from the econometric literature that can account for these unplanned actions, called the synthetic control, to assess landscape change and treatment effectiveness.
It is also important to connect existing and new scientific information to lands where decisions are being made. As an extension of our soil mapping work, we and partners from the Natural Resources Conservation Service (NRCS) and Bureau of Land Management (BLM) are mapping broad land units that share similar land potential & ecological dynamics and then developing synthesis information for the mapped units, including decision support tools.
Our current focus for these efforts is the Upper Colorado River Basin and fire fuels management, sage grouse habitats, and energy development.
Soil Property and Class Maps of the Conterminous United States at 100-Meter Spatial Resolution
30m Resolution soil maps for the Colorado River Basin
Below are other science projects associated with this research.
Below are USGS data associated with this research.
Below are publications associated with this research.
POLARIS: A 30-meter probabilistic soil series map of the contiguous United States
Machine learning for predicting soil classes in three semi-arid landscapes
Below are the partners associated with this research.
In an era of rapid land use changes and shifting climates, it is imperative that land managers and policymakers have actionable and current information available for decision processes. In this work, we seek to meet these needs through new data products and decision support tools built on digital soil mapping, new vegetation cover maps, agency inventory and monitoring data sets, and cutting-edge analytical frameworks. By building on large databases of field observations, available remote sensing, and cloud computing, we are able to create new, decision-relevant information more quickly.
Ecological Site Group maps for the Upper Colorado River Basin
What determines the effectiveness of Pinyon-Juniper clearing treatments?
Background & Importance
An opportunity for achieving this is through analysis of past land management and actions. Here, we apply a technique from the econometric literature that can account for these unplanned actions, called the synthetic control, to assess landscape change and treatment effectiveness.
It is also important to connect existing and new scientific information to lands where decisions are being made. As an extension of our soil mapping work, we and partners from the Natural Resources Conservation Service (NRCS) and Bureau of Land Management (BLM) are mapping broad land units that share similar land potential & ecological dynamics and then developing synthesis information for the mapped units, including decision support tools.
Our current focus for these efforts is the Upper Colorado River Basin and fire fuels management, sage grouse habitats, and energy development.
Soil Property and Class Maps of the Conterminous United States at 100-Meter Spatial Resolution
30m Resolution soil maps for the Colorado River Basin
Below are other science projects associated with this research.
Below are USGS data associated with this research.
Below are publications associated with this research.
POLARIS: A 30-meter probabilistic soil series map of the contiguous United States
Machine learning for predicting soil classes in three semi-arid landscapes
Below are the partners associated with this research.