The objective of this Statement of Need (SON) is to advance development, validation, and implementation of data fusion in the service of natural infrastructure monitoring and decision-making on Department of War (DoW) lands and waters. Statistical data fusion employs multiple data sources, which can support more complete, decision-relevant understanding than can be obtained from any single data source alone. Potential data sources for monitoring changing natural infrastructure conditions include field observations, established and emerging remote sensing technologies, reanalysis products, and management records. Individual data sources inevitably differ in spatial and temporal scales, sampling and measurement processes, coverage, biases, error structures, and uncertainties. These differences must be explicitly characterized and reconciled throughout the fusion process to avoid compounding errors and producing misleading results.

This SON specifically seeks to promote innovative approaches to addressing data fusion challenges, particularly for transitioning dual purpose products or approaches to installation applications. 

Proposals should address one or more of the following specific challenges by developing generalizable and broadly applicable approaches to:

• Integrate data from advanced or emerging monitoring technologies with those from existing or historical monitoring approaches, including use of contemporary observations to improve inferences from legacy monitoring records;
• Combine data from multiple advanced or emerging monitoring technologies; 
• Develop effective statistical approaches that reconcile contrasting spatial and/or temporal scales and sensitivities among data sources;
• Fuse spatiotemporal data, incorporating temporal dynamics across space;
• Characterize and propagate error and uncertainty through the fusion process.

Successful proposals are expected to focus on monitoring variables of specific interest and relevance to resource management on DoW installations. Proposals should leverage publicly available data sets, including new and existing earth observation platforms. In the development or application of any data fusion product to installation applications, proposals should account for the specific sampling/sensing properties of the respective data sources (e.g., how and why collected; what the data represent; their spatial and temporal precision and sensitivity; characterization of key uncertainties across data sources). Proposers are encouraged to include subject-matter experts on the investigative team who are familiar with the natural hazard or resource management contexts to which the data fusion will be applied. Successful proposals will evaluate the performance, efficiency, and limitations of data-fusion approaches relative to single-source approaches, including differences in accuracy, precision, spatial coverage, uncertainty, cost, and labor requirements.

Investigators are expected to work closely with natural resource or installation range management communities in project design and execution.

Consideration will also be given to limited-scope proposals that broadly advance the capacity for promising innovative data-fusion capabilities, including development of relevant, computationally efficient algorithms, and efficient approaches for working with natural infrastructure datasets of large size and high dimensionality.

DoW installation managers rely on precise, accurate, and up-to-date monitoring of natural resources, including ecosystem structure and function, habitat availability, and occurrence and abundance of numerous species (including threatened, endangered, and at-risk species). Monitoring of these and other environmental variables can be labor-intensive and laden with sampling gaps and other uncertainties. Data fusion, in which multiple data sources are co-analyzed systematically, offer potential increases in precision, accuracy, and areal coverage with simultaneous reductions in overall monitoring costs. Improvements in monitoring capacity at reduced cost will enhance DoW installation managers’ ability to make robust and timely decisions that will support training, testing, and other mission-related operations on DoW lands and waters.

Monitoring of changing natural infrastructure for hazard assessments, ecosystem properties, environmental variables, species habitat and mission relevant environmental conditions is vitally important to provide baselines, detect changes, inform forecasting models, guide decisions, and evaluate outcomes of management practices. Although monitoring is critical to effective natural-resource management, it is among the most costly and labor-intensive components. Many new and emerging technologies (e.g., satellite-based remote sensing, uncrewed aircraft system [UAS] remote sensing, DNA bar-coding, environmental DNA, bioacoustics, camera traps, AI-assisted image analysis) are providing cost-efficiencies, higher monitoring sensitivities, and greater temporal and/or spatial coverage. However, all monitoring tools and approaches, whether based on traditional observations and measurements or on advanced technologies, are characterized by uncertainties and blind spots. Multiple data sources involving simultaneous or overlapping observations can be used together to provide mutual strengthening of inferences and clarification of uncertainties. In some cases, advanced-technology sources are most effective when used in tandem with traditional observational methods. In addition, as new technologies replace or complement historical approaches, resource managers and other users need to ensure continuity and consistency of monitoring observations between old and new approaches.

Well developed for some DoW applications, data fusion is the formal integration of multiple data sources to improve accuracy, consistency, efficiency, and utility beyond what is possible using any individual data source. Development and application of data-fusion approaches is proceeding rapidly across multiple fields in the environmental sciences and beyond. Data fusion is highly context-dependent, typically requiring detailed consideration of the specific sampling and sensing properties of the respective data sources, the purposes for which they were developed, and the intended application of the data-fusion products. However, general approaches, algorithms, and computational solutions can often be adapted from one setting or discipline to another, and dual puporse applications to installation challenges are needed. The widespread proliferation of data fusion in multiple disciplines and contexts provides abundant opportunities for innovation in monitoring of natural infrastructure on or adjacent to installations.

Development, validation, and application of generalizable approaches and solutions for data fusion in natural hazards or resource management will result in more effective use of new and emerging technologies, linking them with antecedent monitoring approaches, and yielding improved quality and utility of monitoring together with cost efficiencies. Interdisciplinary teams of remote sensing experts, environmental scientists, natural hazard experts, statisticians, computational scientists, and modelers will advance the science and practice of data fusion. These teams will be particularly effective if they include DoW staff, end user communities, and/or other resource experts with experience in monitoring implementation or in utilization of monitoring products.

The cost and time to meet the requirements of this SON are at the discretion of the proposer. Two options are available: 

Standard Proposals: These proposals describe a complete research effort. The proposer should incorporate the appropriate time, schedule, and cost requirements to accomplish the scope of work proposed. SERDP projects normally run from two to five years in length and vary considerably in cost consistent with the scope of the effort. It is expected that most proposals will fall into this category. 

Limited Scope Proposals: Proposers with innovative approaches to the SON that entail high technical risk or have minimal supporting data may submit a Limited Scope Proposal for funding up to $350,000 and approximately one year in duration. Such proposals may be eligible for follow-on funding if they result in a successful initial project. The objective of these proposals should be to acquire the data necessary to demonstrate proof-of-concept or reduction of risk that will lead to development of a future Standard Proposal. Proposers should submit Limited Scope Proposals in accordance with the SERDP Core Solicitation instructions and deadlines.

Mr. J. Kevin Hiers 

SERDP & ESTCP Program Manager 

Resource Management and Resilience 

Email: john.k.hiers.civ@mail.mil