This report evaluates the impact that administrative and ecological constraints might have on the amount of forest biomass that could be extracted for energy use in the Southeastern U.S. Using available spatial datasets, we quantified and mapped how the application of various “conservation value screens” would change previous estimates of available standing forest biomass (Blackard et al. 2008). These value screens included protected areas managed for conservation values, USDA Forest Service and Bureau of Land Management (BLM) lands, steep slopes, designated critical habitat for federally-listed threatened and endangered species, inventoried roadless areas, old-growth forests, wetlands, hydrographic (lake, stream, and coastline) buffers, and locations of threatened and endangered species (G1-G3, S1-S3).

Two alternative combinations of values were examined: in Alternative 1, all areas within value screens, including all Forest Service and BLM lands, were excluded from biomass development. In Alternative 2, Forest Service and BLM lands not afforded extra protection by such designations as wilderness or research natural areas were assumed available for biomass extraction; all other values continued to be excluded from extraction. In both alternatives, biomass located within the Wildland-Urban Interface (WUI) was assumed available for extraction regardless of conservation value screens.

The analysis was conducted at 100-m x 100-m resolution. Summary statistics were derived at three scales – entire study area, 13 states, and 24 World Wildlife Fund (WWF) ecoregions. Results were also summarized and mapped for all 1,342 counties.

Finally, we compared hydrologic datasets at two different scales (1:24,000 and 1:100,000) at multiple sample areas in the study area to evaluate how hydrologic scale might affect the delineation of riparian reserves and resulting estimates of biomass availability.

CBI will develop and apply a forest management decision-support system (DSS) for forest resilience planning in the southern Sierra Nevada that integrates the latest science on how vegetation, terrain, climate, and weather interact to influence fire risks and forest resilience. The interdisciplinary team led by CBI includes ecological modelers, forest ecologists, fire scientists, physicists, and statisticians. The core of the DSS will be a Forest Resilience Model built using EEMS (Ecosystem Evaluation Modeling System; Sheehan and Gough 2016). The DSS will be tested, refined, and applied to resilience planning in that portion of the modeling region of greatest concern to the interagency Sequoia Regional Partnership, which is working to restore ecologically resilient conditions in and near Sequoia National Forest and Sequoia-Kings Canyon National Park.

The resilience model evaluates forest resilience to fire, drought, and other factors based on landscape conditions. The DSS will allow managers to simulate fuel-reduction treatments, evaluate their effects on a range of risks and resources (e.g., fire, sequoias, fisher habitat), project the impacts into the future, and assess levels of uncertainty.  The DSS and component models will help managers understand how, in concert with terrain and weather, vegetation structure influences fire behavior and forest resilience. Importantly, the DSS will for the first time consider how fire-atmosphere coupling affects fire in models to support forest planning. This will apply how vegetation structure influences fire via both fuel arrangements and air flows, and thus more accurately reflect the full picture of how vegetation treatments may affect fire and fire effects on the landscape.

The DSS will be further refined and applied to resilience planning by the Sequoia Regional Partnership, whose primary focus is reducing fire risks to giant sequoia groves, fishers, and human communities.  

External Team members include: Joe Werne (NorthWest Research Associates NWRA), Christopher Wikle (Department of Statistics, University of Missouri) and David Marvin (SALO Science).   

Map of project study area.

Conservation Biology Institute is supporting the Spatial Informatics Group – Natural Assets Laboratory (SIG-NAL) on a multi-year Regional Wildlife Mitigation Program (RWMP) in Santa Barbara county that is funded by the National Fish and Wildlife Foundation. Specifically, CBI in partnership with SIG-NAL will develop and propose a fire-resistant buffer or “greenbelt” area in strategic locations within the program area to create wildfire resilient green space, working lands, and habitats. Program outcomes will also provide numerous co-benefits that support watershed and coastal ecological health using a suite of tools including:

The RWMP is designed to assess hazard, exposure and vulnerability and equitably reduce wildfire hazard across the Santa Barbara front country. The program goals are to decrease the risk of wildfire impacts to structures and infrastructure, promote wildfire resilient green space, working lands, and habitats, and develop community capacity to adapt and recover from the shocks of natural disasters. The Program is divided into three primary Resilience Domains: the Landscape Resilience Domain, the Built Environment Resilience Domain, and the Community Resilient Domain. Each domain will work collaboratively to foster resilience and build adaptive capacity that will allow the community to prepare, respond and recover from the shock of large wildfires. More details can be found at this link: https://rwmpsantabarbara.org/.

As the Sierra Nevada town of Paradise rebuilds after the devastating Camp Fire of 2018, the community has an opportunity to incorporate strategies to increase its resilience to fire and climate change, enhance the safety and well-being of its residents, and at the same time care for the surrounding natural areas that make it a beautiful place to live.

CBI and the The Nature Conservancy helped Paradise seize this opportunity when the Paradise Recreation and Park District asked us to help them explore community design principles that could provide all of these benefits. The CBI team created geographic models of “Wildfire Risk Reduction Buffers” between the structures and the surrounding wildlands to reduce exposure of homes to wildfire risks. These buffers, which can be made up of parklands, orchards, and other low fire-risk land uses, can be managed to provide many benefits, including buffering homes from ignition, providing safe-haven refuges for residents to escape from fire, strategically-placed staging areas for fire-fighters, recreational access to open space, and protecting natural habitat from the effects of an encroaching urban landscape.

The team combined spatial data about the landscape with local knowledge to prioritize locations for fire risk-reduction and analyzed ignition risks and co-benefits with and without the buffers. The resulting maps illustrate the potential for local partnerships to make a real difference in the town’s future. Through innovative thinking about the role of land use planning, the community of Paradise is changing its approach to living with fire and providing a model for fire-prone communities everywhere.

The Conservation Biology Institute’s recent work with the Deschutes Trails Coalition (DTC) and the Deschutes National Forest focuses on designing a Trails Assessment and Planning Tool for Deschutes County. We have developed a blueprint for the design, in collaboration with the U.S. Forest Service and the DTC. In this new phase of the project, funded by the U.S. Forest Service, CBI will partner with the DTC to build a prototype of the trails decision-support tool and sustainability model for Deschutes County. Then we will scale up and customize this prototype to meet the requirements of the U.S. Forest Service and its partners in the states of Oregon and Washington. The Trail Assessment and Planning Tool design includes creating a preliminary version of a sustainability framework that incorporates an interactive spatially-explicit model, addressing the physical, environmental, social, and economic aspects of sustainability. The model is powered by CBI’s Environmental Evaluation Modelling System (EEMS), allowing for its collaborative development with a diverse group of stakeholders, to create a transparent framework for local, regional, and national organizations to answer important questions relevant to trails planning and management.

Proxy Falls, Oregon

Michael Riffle / Flickr

CBI worked closely with the Natural Resource Defense Council (NRDC) to integrate relevant spatial datasets to map areas of high value from the standpoint of carbon storage and sequestration, terrestrial ecological value, and aquatic value in support of several NRDC programs, including their 30X30 campaign to protect 30% of nature in the nation by 2030. Click here to learn more about the 30×30 initiative.

Using CBI’s online modeling software called Environmental Evaluation Modeling System (or EEMS), team members were able to construct, review, and modify the models in a rigorous and highly transparent fashion from their individual remote locations. The resulting “living” models can then be used alone or together and in combination with other spatial data (e.g., existing protected areas) to add further context and insight using Data Basin. Data Basin and EEMS were effectively used to help guide NRDC’s important conservation mission.

CBI is working in collaboration with Oregon State University, the University of Minnesota, and the University of Colorado Boulder on a project designed to help improve our understanding of post-fire community resilience. Central to the project will be the development of an RFID sensor network which will help support post-fire assessments of water infrastructure damage. These sensors will be deployed across a community’s freshwater pipeline network and will transmit data (e.g., temperature reached, pipeline material, exposure duration) that will help determine whether or not toxins have started leaching from the pipes into the freshwater supply. The current phase of the project focuses on Santa Rosa and Paradise in California where the 2017 Tubbs Fire and the 2018 Camp Fire caused damage to the water distribution systems, resulting in contaminated water within the system. 

To help support this effort, CBI has developed a web application called the Wildfire Vulnerability Explorer, which can be accessed at https://wildfirevulnerability.eemsonline.org. This application allows users to explore a set of spatially explicit models developed for Santa Rosa and Paradise that identify areas likely to be vulnerable to water contamination exposure following a large-scale fire event. Estimates of vulnerability are based on three primary factors: the probability of water contamination, socioeconomic sensitivity, and adaptive capacity. The Wildfire Vulnerability Explorer brings this information together in an interactive map in order to help officials with both pre-fire planning and post-fire prioritization of recovery efforts – by identifying communities that are the most vulnerable to water contamination exposure, efforts can be taken to better plan for and direct resources to those areas.  

Additional information about the project is available on the OSU project page at the link below:     

Sensor Technology for Improved Wildland Urban Interface (WUI) Fire Resilience  

Funding for the project was provided by the Alfred P. Sloan Foundation.

Photo courtesy of NASA (https://earthobservatory.nasa.gov/images/144225/camp-fire-rages-in-california): Camp Fire in Paradise, California, which is one of the project study areas.

In 2006, the Micronesia Challenge began as a commitment by the Republic of Palau, Guam, the Commonwealth of the Northern Mariana Islands, the Federated State of Micronesia, and the Republic of the Marshall Islands to preserve the marine and terrestrial resources crucial to the survival of the Pacific traditions, cultures, and livelihoods. The overall goal of the initial Micronesia Challenge was to effectively conserve at least 30% of the near-shore marine resources and 20% of the terrestrial resources across Micronesia by 2020.

During the 24th Micronesia Island Forum in 2019, the regional leadership recognized the success of the first 15 years of the Micronesia Challenge and endorsed the new Micronesia Challenge 2030 goals to effectively manage 50% of the marine resources and 30% of terrestrial resources by 2030.

In 2016, the USFS Forest Inventory and Analysis (FIA) team, regional partners and CBI developed the Micronesia Challenge Regional Terrestrial Monitoring Initiative tool (mcterrestrialmeasures.org) to allow users to visualize the spatial data from the Micronesia Challenge monitoring effort by regional framework indicator(s) that measure the status of managed conservation areas set aside under the program. The first version of the tool included forest data collected between 2003 and 2018 and determined the status and trends in forest area, forest health, understory vegetation, biomass, and carbon storage.

In this new phase of work, the Terrestrial Measure Initiative tool will be updated with the most recent data and information. The team also plans to develop a webinar presentation to communicate with local stakeholders and others about the tool and the ongoing success of the Micronesia Challenge.

The USDA Conservation Reserve Program (CRP) works with farmers and landowners to implement conservation management practices on enrolled lands, with paid contracts ranging from 10 to 15 years in length. The CRP Grasslands practices target restoration of agricultural grassland systems by augmenting native vegetation for pollinators, providing habitat for grassland plants and animals, increasing biodiversity, reducing soil erosion, and improving water quality. The USDA’s CRP has been successful in improving the conservation value of millions of acres of farmlands; however, the program currently lacks spatially explicit information on land cover and vegetation within CRP-enrolled tracts. 

In partnership with the USDA FSA program, the Conservation Biology Institute (CBI) used a combination of remote sensing and machine learning algorithms deployed on the innovative cloud-computing platform, Google Earth Engine, to map grassland characteristics. We used a rich suite of enviro-climatic data, multiple sources of satellite imagery, and Random Forest modeling techniques to predict land cover for study areas in Washington, Colorado, and Kansas, where CRP Grasslands holdings are most prevalent. We used machine learning to create predictive maps of vegetation type by leveraging an extensive set of satellite-derived variables, environmental layers, and federal survey data (from BLM’s AIM and USDA NRCS’s NRI programs). Our initial investigation utilized Landsat 8 satellite data to model vegetation cover across the Washington study area and then scaled up to the Colorado-Kansas study area. The Washington study site was selected for further model enhancements and an in-depth comparison of Landsat 8, Sentinel-2, and MODIS satellite imagery, to evaluate differences in model development and performance among sensor types. We generated vegetation cover predictions for the year 2019 using Random Forest classification models. Classified outputs for the five vegetation cover models – annual grass, perennial grass, annual forb, perennial forb, and bare soil – were post-processed to exclude water and urban land cover and areas that were not relevant for mapping grasslands.

Mapped outputs showing vegetation percent cover predictions from our pilot project have been integrated into CBI’s CRP online decision support tool. This online tool offers functionality for managers and landowners to view, filter, compare and summarize geospatial information relevant for assessing CRP tracts in the study areas. You will need permission from USDA to use the tool, but it is available at https://crptool.org/. Anyone can view the design of the tool at USDA CRPtool.

You can read more details in the following publication.

Degagne, Rebecca; Pizzino, Declan; Friedrich, Hannah; Gough, Mike; Joseph, Gladwin; Strittholt, James; et al. (2022): Mapping Conservation Reserve Program Grasslands in Washington, Colorado, and Kansas with Remote Sensing and Machine Learning. figshare. Journal contribution. https://doi.org/10.6084/m9.figshare.19141853.v1 

The Conservation Biology Institute and the Deschutes Trails Coalition (DTC) are in the process of developing a web-based system to assist the DTC in sustainably managing multi-use trails in Deschutes County. The collaborative process of creating a framework to support DTC’s decision making relies on modeling Environmental, Economic, Physical, and Social Sustainability of recreation activities and trails.

*Images provided by Danielle MacBain at the Deschutes Trails Coalition.

*The DTC Dashboard will include a form-based system to give users the ability to enter or modify information in the trails database.

*Mockup of the DTC Dashboard (Query Tools on the Manage Tab)