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)
Wind energy developed in federal ocean waters off California’s coastline is poised to play an important role in diversifying the portfolio of resources that will help California achieve its 100% renewable and zero-carbon energy goals. Since 2016, the state has coordinated with other governmental partners, including the BOEM-California Renewable Energy Intergovernmental Task Force, to identify areas in federal waters off the state’s coast suitable for potential offshore wind energy development. To support this effort, the Conservation Biology Institute (CBI) is using data from the California Offshore Wind Energy Gateway to produce a robust set of spatial models, designed to synthesize information to help stakeholders and decision-makers assess the suitability of offshore wind energy development in federal waters off the coast of California. These models, created using the Environmental Evaluation Modeling System (EEMS), provide a transparent and data-driven means for assessing a range of considerations at a given location, such as existing energy potential, deployment feasibility, ocean uses, fisheries, and marine life occurrence. Together, these models can be used to inform planning processes for offshore wind energy development to maximize renewable power generation and to avoid or minimize impacts to existing ocean uses and the environment.
The California Offshore Wind Energy Modeling Platform, powered by EEMS Online technology, provides an interface where stakeholders and decision-makers can interact with and explore the models and their data sources to help support decision-making processes.
The project’s technical report, executive summary, and presentation slides are available under “Project Files”, on the right side of this page. A California Energy Commission webinar recording with a project overview can be found here.
Conservation Biology Institute specializes in harnessing the power of spatial data for conservation planning and decision-making. We create tools in close collaboration with state agencies that help them achieve their missions. Recently we’ve had the opportunity to work with the California Department of Food and Agriculture (CDFA).
The CDFA Healthy Soils Program promotes the development of healthy soils on California’s farmlands and ranch lands by providing financial incentives to California growers and ranchers to implement agricultural management practices that sequester carbon, reduce atmospheric GHGs and improve soil health.
Conservation Biology Institute created the CDFA Healthy Soils Program tool, an online tool to streamline the submission process for proposals to the Healthy Soils Program. This tool, a custom module of RePlan, allows a grant recipient to locate and map proposed conservation practices, view and select from recommended species for planting, and conform with multiple project eligibility requirements. All project components are then summarized in a proposal report for upload to the CDFA Healthy Soils Program project submission website.
*Find the tool here: https://sitecheck.opr.ca.gov/
CBI developed the tool for the Governor’s Office of Planning and Research (OPR) in coordination with the Department of Housing and Community Development. The tool was developed in partnership with OPR and is based on public input from partners through interviews, presentations, and workshops. Site Check is an innovative mapping tool that allows users to see if selected parcels may qualify for an existing streamlining option for housing development. The free tool allows users to map various CEQA definitions and filter parcels based on planning, transportation, and environmental criteria. Site Check is a good first step for developers and public agencies considering how California Environmental Quality Act (CEQA) may apply to a housing project. Check out the free tool here Site Check.
This tool is focused on the CEQA provisions that cover a variety of housing types. The Legislature has also created specialized provisions for specific types of projects, including affordable housing, agricultural employee housing, and motel-to-supportive housing conversions.
CBI updated the UI for the tool in 2023 and updated data, including the regional below-average Vehicle Miles Traveled (VMT), 15% below regional average VMT, parcel data, Specific Plans. Check these layers out in the tool or download them from Data Basin.
If you have any questions about Site Check, please feel free to contact Brianne Masukawa, brianne.masukawa@opr.ca.gov.
Conservation Biology Institute (CBI) and American Farmland Trust (AFT), the organization behind the national movement No Farms No Food®, continue to build off their previous work on the San Joaquin Land and Water Strategy. With the support from a Conservation Innovation Grant by the USDA Natural Resources Conservation Service, this phase of work focused on identifying priority areas for groundwater recharge in Madera and Stanislaus counties as well as the greater San Joaquin Valley.
CBI and AFT have compiled informative datasets related to soils, crop types, water infrastructure, and conservation areas in the San Joaquin Valley. These will be located in a San Joaquin Valley Gateway Gallery and mapping tool (with an associated manual) to aid in identifying optimal projects with the greatest potential to increase infiltration and conserve water. These datasets will provide a broad context that will allow for multiple benefits where possible such as locating conservation easements near currently protected areas in the productive farmland regions of Madera and Stanislaus counties, California.
The Project Prioritization Tool (PPT) is a conservation decision-making tool to increase the adoption of water infiltration practices, improve groundwater recharge, and protect agricultural land in the San Joaquin Valley (SJV). This PPT manual was developed by AFT and the Conservation Biology Institute (CBI) based on the data analysis of the San Joaquin Land and Water Strategy report.
CBI is collaborating with the United States Department of Agriculture (USDA) and the Farm Service Agency to produce an online application depicting ecological and economic features across Bottomland Hardwood Forest Conservation Reserve Program lands in the state of Mississippi. The Conservation Reserve Program (CRP) pays landowners to maintain these Bottomland Hardwood Forests providing important ecological benefits such as removing nitrogen and phosphorus from water, storing flood waters and reducing downstream flooding, trapping sediment, and promoting carbon sequestration. These benefits provided by the CRP are in addition to the restoration and enhancement of wetlands and wildlife habitat. The key ecological and economic features across these CRP lands will be estimated using remote sensing satellite imagery from the Sentinel satellite platform and machine learning modeling using a random forest approach. Additionally CBI staff will be conducting on the ground assessments of the ecological metrics during the 2019 field season.
By providing an online platform that provides metrics on these CRP lands, the USDA Farm Service Agency will be able to better monitor and evaluate existing acres of Bottomland Hardwood Forests that are part of the Conservation Reserve Program. This project is a pilot to determine the utility of the online platform and remote sensing methods, which if proven useful can be expanded to all regions where the CRP restores and enhances Bottomland Hardwood Forests.
The Random Forest modelling process was used to estimate various forest biometric measurements like biomass, density, height, etc., for CRP lands in Mississippi. These were also converted to economic values using standard procedures. We used Forest Inventory and Assessment (FIA) as training data and used field samples to augment the validation of the modelling process. Predicated outputs collected All these outputs were spatialized and incorporated into a customized tool for the USDA Conservation Reserve program.
The CRP tool allows USDA staff, land owners, and third-party organizations to view pertinent spatial information and guide decision making in relation to the status of CRP farms in Mississippi state. This tool allows one to summarize, filter and compare CRP farm tracts across counties and watersheds. You can also download reports in either of two appropriate formats (PDF or CSV). We have also included three different base layers, and relevant contextual data layers that you can view in relation to the CRP farm tracts. You will need permission from USDA to use the tool, but it is available at www.crptool.org. Anyone can view the design of the tool at https://www.sketch.com/s/feba6e2a-ff3e-4c3c-8d2d-9ea4f6bdc896.
CBI initially developed predictive maps of tree height, tree density, biomass, basal area, and forest type using Random Forest machine learning models. Numerous satellite-derived indices from the European Space Agency’s (ESA) Sentinel-1 and Sentinel-2 sensors, in addition to soils and topography data, were used as predictor inputs. We then refined these predictive models, focusing primarily on biomass improvements, by implementing new methods for processing Sentinel-1 imagery on the cloud computing platform Google Earth Engine (GEE); significantly updating model code; and incorporating preliminary data products derived from NASA’s spaceborne LiDAR mission – the Global Ecosystem Dynamics Investigation (GEDI). We refined the GEDI LiDAR-derived data products and included them in our models, and overall accuracy for the four forest regression models ranged from 57% to 91%. The Biomass model saw the greatest improvement in accuracy with the R2 increasing by 8%, from 49% to 57%. The Basal Area and Tree Height models both had minor 1-2% increases in accuracy, while the Tree Density model had no improvement. The Forest Type classification model had a negligible improvement in overall accuracy, however, the Elm/Ash/Cottonwood class increased in accuracy by ~6%, from 64% to 70%.
You can get more details in this publication. Degagne, R., Pizzino, D., Friedrich, H, Gough, M., Joseph, G., Iovanna, R., Smith, C. and Strittholt, J. 2022. Mississippi CRP Forest Remote Sensing with Preliminary Global Ecosystem and Dynamics (GEDI) Mission Derived Data Products. CBI Technical Report 2022-1. 40 pp. (10.6084/m9.figshare.19142147)
Wildfires are becoming larger and more severe, causing negative impacts on our natural ecosystems. These post-fire impacts include invasions of exotic plants and an increase in the risk of mudslides, erosion, and siltation of important stream habitat. It is a daunting challenge for resource managers to determine where restoration can do the most good.
In this pilot project, CBI worked with the Santa Barbara Botanic Garden to develop and test online software coupled with a citizen-science process that helps resource managers identify the best candidate locations for restoration in the aftermath of the Thomas and Whittier Fires in Santa Barbara County.
Spatial data about the landscape are combined in a model that provides a preliminary prioritization map, which is then refined using field observations of professionals and community scientists in the Spring following a fire. The sites can then be visited in person to make the final decision about where to focus restoration efforts to maximize the return on the investment.
Figure 1. A flow chart displaying the steps involved in prioritizing areas for post-fire restoration
The community scientists used iNaturalist to record their observations and assist in identifications. You can see the more than 5,000 observations collected so far on iNaturalist here!
How You Can Learn More :
Watch this short video about the project.
View the model with prioritized restoration locations here.
Explore the restoration prioritization map together with other important data layers and photographs in Data Basin.
Read more technical details in this article.
Email john.gallo@consbio.org to be notified of community science opportunities when they arise.
In this project, CBI is a member of and also providing support to the Fort Bragg Headlands Consortium, with a mission to to help achieve environmentally sound restoration and development solutions that will improve the quality of life and economy for current and future residents of our Coast.
CBI’s major contribution to the Consortium is to develop and maintain the Fort Bragg Headlands web-mapping Gallery. Powered by Data Basin, this web-mapping Gallery is a feature-rich platform for local citizens, stakeholders and decision-makers to access publicly available spatial data (e.g. maps of streams, wetlands, hazards, soils, geology, etc.). This is timely, as Fort Bragg is currently making a significant Local Coastal Program (LCP) Amendment. Currently zoned Forest Light Industrial, these zoning changes will make the third of the city that was once a lumber mill into prime real estate zoned for profitable development. Fort Bragg is also figuring out how to deal with the remaining wetlands out there that still contain hazardous materials despite a first round of clean-up by the landowner. Providing easy access to data and maps will facilitate decisions that conserve the landscapes and biodiversity of the Fort Bragg region while bringing much needed development and jobs to sustain current and future generations. You can donate directly to this cause here.
Wildfires are a natural part of California ecosystems and play an important role in maintaining ecological structure and function. However, different fire regimes in the state have been altered due to past management practices, climate change, invasive species, and population growth and urban expansion. Given the potential for conflicts between fire management and conserving biodiversity and ecological functioning, solutions are needed to balance ecosystem health with human welfare and community safety.
CBI is partnering with Dr. Jon Keeley (USGS) and an international team of landscape ecologists, biologists, geographers and economists to study the factors that control wildfire activity in southern California, which experiences the largest number of housing losses to wildfire in the U.S. This work focuses on the role of different ignition sources, climate patterns, vegetation change, and housing location and arrangement in altering fire patterns and contributing to housing loss at the wildland-urban interface. Other topics of research include the effectiveness and effects of fuel treatments and prescribed fire in controlling large fire behavior; the role of homeowner practices, such as minimizing vegetation around homes and upgrading building construction materials to prevent house loss; and the relative importance of land planning decisions that could best minimize housing loss while preventing negative impacts to biodiversity.
While the research takes place primarily in southern California, the findings are applicable to other fire-prone non-forested ecosystems such as the Great Basin and the other Mediterranean-climate ecosystems across the world.
The results of this research are shared with management agencies like the National Park Service and U.S. Forest Service, in addition to local and state planners and policy makers, to identify the best strategies to increase community safety while minimizing effects on natural ecosystems.
nflicts between fire management and biodiversity conservation