USDA · USDA-146 · record R1444

Forest Mapping using G-LiHT airborne data in interior Alaska

Department of Agriculture · Natural Resources and Environment

Overview

Development stage: PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: 2014-06-01 (day precision)Source: 6/1/2014
Withheld from public reporting: Not reported

Mission

Topic area: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

This project supports the first comprehensive forest inventory of interior Alaska in the modern era. Machine learning techniques will be used to predict various forest attributes (volume, biomass, composition).

Expected benefits

Use of multi-sensor airborne data will improve the accuracy and precision of forest inventory estimates over interior Alaska, improving the quality and reliability of products provided to the public.

System outputs

Maps and tabular estimates of inventory attributes over all forested regions of interior Alaska.

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Not reported
Custom-developed code: Yes
Public source code: Not reported

Sourcing

How it was built: Developed in-houseSource: b) Developed in-house
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: Not reported
Authorization to Operate: No
Demographic features: k) None of the above
Training and evaluation data: Airborne remote sensing data and Forest Inventory and Analysis field inventory data.
Federal Data Catalog: Not reported