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 recently worked with the Pacific Marine & Estuarine Fish Habitat Partnership (PMEP) to update the West Coast Estuaries Explorer, a tool designed to engage a broad range of users with detailed information about estuaries along the U.S. West Coast. The first version of this tool was developed in partnership with PMEP and the North Pacific Landscape Conservation Cooperative. The partnership between CBI and PMEP continues with support from NOAA and the Pacific States Marine Fisheries Commission (PSMFC). The Estuaries Explorer got several performance and design updates to make it easier to use and more visually engaging. In addition to the latest available information for estuary boundaries and biological habitats, the Explorer now includes aerial images for each of the estuaries in Washington, Oregon, and California. Later this year, CBI and PMEP will incorporate additional information on the location of eelgrass habitat and areas of tidal wetland loss. PSMFC has taken over long-term hosting of this exciting tool.
Conservation Biology Institute is a partner in a new $1 million grant from a new interdisciplinary NSF program to foster building an “open knowledge network.” The inspiration for this type of network comes from Tim Berners-Lee’s (best known founder of the World-wide Web) vision for the “semantic web,” which applies tags with relationships to information on the Internet, allowing computers to do basic reasoning for improving search results and answering questions. Apple’s Siri, Amazon’s Alexa, and Google’s Assistant all use these technologies.
Dr. John Gallo co-wrote the proposal and leads CBI’s participation in the team of 13 researchers and practitioners from 10 other institutions. The team is focused on improving access and contributions to tools for analyzing geographic data called spatial decision support systems. “The proliferation of online mapping technologies has greatly increased access to and utility of these kinds of tools, and a logical next step is increasing our ability to find the appropriate data and tools for your problem and link these together for more complex analyses,” says Principal Investigator Sean Gordon of Portland State University. Through engaging stakeholders in three applied case studies (the management of wildland fire, water quality, and biodiversity conservation), the interdisciplinary project team will develop and test participatory and automated methods for finding and sharing decision-relevant information using semantic web technologies.
The new NSF Convergence Accelerator program is named for its focus on bringing together interdisciplinary teams to address one of NSF’s 10 big ideas, specifically “Harnessing the Data Revolution“, also known as building an Open Knowledge Network. Eighteen other of these phase 1 grants were made, covering areas from molecular manufacturing to tracking potentially disruptive solar phenomena. The “accelerator” part comes from the short time frame. “The application required a 3-week turn around, which is very quick for a NSF grant,” Gordon said. “Our success was largely due to having formed the Spatial Decision Support Consortium, a professional networking group four years ago, so we had ideas and people ready to go.” Each phase 1 project is eligible to submit a phase 2 proposal for up to $5 million by next March, and the process will include giving a short “pitch” talk to a panel of experts and potential funders, much like a venture capital approach.
*Learn more about this ongoing project here.
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
Environmental scientists and decision-makers often employ mapping and modeling to address a wide range of complex environmental challenges. Barriers practitioners face include long data processing times, lack of access to robust and up-to-date datasets, and complex programming languages and libraries.
GEODE (the Global Environmental Online Decision Engine) is a web-based mapping and modeling system currently under development at the Conservation Biology Institute. The goal of the GEODE project is to put the power of Google Earth Engine into the hands of environmental scientists, decision makers, and land managers – no programming experience required.
GEODE is a complete spatial decision support modeling system that can be used to help answer complex management questions and provide critical insight into the challenging environmental problems that threaten biodiversity and the planet’s fragile ecosystems. Users will be able to publish, share, edit, and modify GEODE models so results can be applied to environmental issues anywhere across the globe.
Empowered by GEODE, users benefit from:
- The ability to work with significantly larger datasets than current capacity allows.
- Very fast turn-around time for analyses.
- Plug and play library development for geoprocessing, statistical, and modeling functions.
- A programming free model building environment.
- A collaborative environment for teams of researchers and managers to work together, building, sharing, editing, and exploring models.
- Automatic model updates as new datasets become available.
By coupling the modeling framework of GEODE with the power of Google Earth Engine, anyone able to use a simple interface will have access the power Google Earth Engine has to offer – and that is a lot!
Conservation Biology Institute is working in partnership with Riverside County Habitat Conservation Agency, Bureau of Land Management, U.S. Fish and Wildlife Service, San Diego Zoo Wildlife Alliance Academy, and others to develop and implement a rangewide conservation plan for the Stephens’ kangaroo rat (Dipodomys stephensi, SKR), a tiny rodent native to Southern California’s shrinking grassland habitats.
The SKR Rangewide Management and Monitoring Plan, developed in collaboration with species managers, researchers, and land owners, complements existing management plans rather than replaces them, recognizing that each location has unique management priorities. Coordinating across local conservation efforts will facilitate collaborative conservation action across the species’ entire range.
The SKR Plan recommends management actions to improve habitat and ameliorate threats from human activities and climate change and provides a standardized monitoring protocol to track the species’ population status and trends. CBI has developed a customized field data collection application using ArcGIS Field Maps, and our SKR data management team supports the field monitoring effort and ensures long-term integrity of the data in partnership with USFWS’ Ecosphere Program.
This work builds upon a habitat suitability model developed in 2019 by Conservation Biology Institute using Sentinel-2 satellite imagery and being updated in time to support the 2024 monitoring season. These updatable landscape-scale habitat maps are the foundation for statistically-defensible monitoring and play a key role in planning coordinated conservation of the species.
This work is funded by the U.S. Bureau of Land Management. For more information about this effort, please contact Wayne Spencer at wdspencer@consbio.org or Brian Shomo at bshomo@wrcog.us.
Please see the SKR Rangewide Management and Monitoring Website for The SKR Rangewide Management and Monitoring Plan and Protocol, as well as other documents, maps, and data from this project.
Stephens’ kangaroo rat (Dipodomys stephensi, SKR). Photo by Moose Peterson.
Cachuma Resource Conservation District (RCD) is working in partnership with Conservation Biology Institute, LegacyWorks, and Sharyn Main Consulting on the Regional Priority Plan to Reduce Wildfire Risk and Improve Forest Health in Santa Barbara County (RPP), which is a flagship project of the Santa Barbara County Conservation Blueprint. Funded by the California Coastal Conservancy, the RPP helps with the planning, mapping, and prioritization of projects that will proactively address wildfire threat in Santa Barbara County, as well as improve forest and habitat health.
The RPP is a multi-prong collaboration, which focuses on public and private land in the wildland/urban interface (WUI). CBI is the lead for a component of the project, to develop a sophisticated decision-support mapping tool to not only predict areas of high fire-risk on a landscape scale, but also help the community prioritize where fire risk mitigation projects should occur. The tool will serve as a community resource within the Santa Barbara County Conservation Blueprint and should improve communication, network building, and support community priorities through a regional approach to fire resilience and habitat health. The project team is talking with the many agencies and stakeholders involved, and facilitating the collaborative decision-making process.
Destructive wildfires are sudden, extreme events: In a matter of hours, both social and ecological communities are transformed by the loss of homes and lives, and change in natural vegetation. After such an event, residents take stock of their transformed landscape and environment, deciding to remain, rebuild, or move, while ecological communities restructure and regrow. These combined social and ecological responses to wildfire may present a ‘hot moment’ or ‘window of opportunity’ where governments, communities, and residents can take action to reduce the future exposure to disaster.
An interdisciplinary team of researchers, convened by CBI’s Dr. Alexandra Syphard, Dr. Miranda Mockrin from Northern Research Station, USDA Forest Service and Dr. Van Butsic, from the Department of Environmental Science, Policy, & Management at University of California, Berkeley, are examining the question “Do wildfires lead to transformative adaptation, reducing future wildfire risk or do they lead to entrenchment, as residents and institutions re-create hazard-prone environments?”
To examine this question, they will review national data of post-fire housing change (rebuilding, sales, new development, land subdivision) and investigate how social and ecological settings and impacts, as well as event characteristics, influence subsequent housing and ecological trends. They will also determine, at the household scale, how changes in housing patterns relate to the post-wildfire ecological setting and socioeconomic characteristics, determining adaptation or entrenchment.
Wildfire is an important ecological process in California, where a diversity of fire regimes shape the structure and composition of plant and animal communities. Fire regimes are changing beyond their historical range of variability, however, due to several factors, including past fire management, invasive species, land use change, and climate change. These changes not only threaten the integrity and diversity of biological communities, but affect human communities, too, as residential losses to wildfire have skyrocketed in the last several decades. Two of these drivers, land use and climate, are expected to change dramatically in the coming century, raising substantial concern about their effects on fire regimes and subsequent impacts to human communities and biological diversity.
CBI has partnered with University of California, Berkeley to develop and implement a scenario-based integrated modeling framework to quantify the relative importance of climatic and land use factors on current and future projected fire patterns and risk of structure loss for three study areas in California. Select research questions driving this project include:
1) How do patterns of fire activity vary by land use change and climate?
2) How does structure loss vary by land use and climate change?
3) How do these relationships varyby geographic region?
4) Given these relationships, how are large fires and associated structure risk likely to change in the future?
Results of the assembled model output will be distributed to appropriate stakeholders and Data Basin will host the mapped output data.
