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.
CBI will use the San Diego Monitoring and Management Program’s Inspect and Manage rare plant monitoring protocol to document rare plant species population size, spatial extent, and threats and will make management recommendations based on the identified threats. The Cooperative Agreement has the potential to extend through 2023 and supports several objectives listed in the Naval Base Point Loma and Naval Base Coronado’s Integrated Natural Resources Plan.
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.
