SBIR Phase I: Climate analysis at scale through mapping biomass, water restoration, and soil carbon sequestration

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to scale nature-based climate mitigation. The Phase 1 project will create and manage comprehensive data sets and models representing the current and proposed future state of ecosystem health as measured by aboveground biomass growth in forests, the health and carbon sequestration in soils, and the restoration of groundwater in arid or otherwise degraded environments through the restoration of hydrological function. Through its open source and community-oriented approach, the project intends to be a valuable resource for organizations mitigating loss of biodiversity, soil health, forest cover, and groundwater due to extractive land use practices. The information provided by this technology will be useful for landowners, governmental authorities, researchers, and financial institutions. The project will utilize recent advances in remote sensing, cloud compute infrastructure, and machine learning to predict the highest leverage land-based interventions for managing and enhancing the hydrological cycle, protecting forests and increasing reforestation efforts, and building soil health and resiliency through soil organic carbon sequestration. The highest impact water interventions will be determined using random forest algorithms improved through the use of soil models that include porosity, rates of runoff, and texture. The forest-based interventions for conserving and increasing above-ground biomass will be based on historic land use change and disturbance regimes as monitored through satellite imagery and coupled with potential sequestration rates achieved through restoration and improved forest management. Soil health and carbon sequestration modeling will be accomplished using historic soil sample data over an 80 year period and the associated environmental covariates in addition to research into rates of soil sequestration by land management type. This will enable the training of a random forest-based model to predict the highest impact land use interventions to enhance or restore soil organic carbon. These models will be combined to create an interactive land viewer. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.