Large-area Enhanced Forest Inventory modeled on 3-D Airborne Laser Scanning data

Project Title: Large-area Enhanced Forest Inventory modeled on 3-D Airborne Laser Scanning data
Program:
Northeast (308)

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Tags:
data-management (260), programming (5), parallelization (223)

Status: Complete
Project Leader
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Project Leader:
Daniel Hayes

Email: segee@maine.edu
Mobile Phone: 2075812212
Work Phone: 2075812212

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Project Information
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Project Description:
Maine’s economy depends heavily on its forest resource base: it accounts for over 6% of the total GDP and has an estimated total annual economic impact of $8-10 billion. The sound, scientifically-based management of the forest resource requires a significant investment in inventory programs. Current methods for monitoring the forest are limited as using traditional ground-based sampling techniques are expensive ($3 million annually spent in Maine alone), imprecise, not real-time, difficult, and spatially coarse. New capabilities for the aerial collection of high quality, detailed remotely sensed information on 3-D forest structure over large areas are providing inventory information more accurately, efficiently and at lower cost relative to traditional methods. Working with the large volumes of data and increasingly complex analytics requires computer science research and development designed to make progress on processing workflow solutions.

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New capabilities for the aerial collection of high quality, detailed remotely sensed information on 3-D forest structure over large areas are providing inventory information more accurately, efficiently and at lower cost relative to traditional field methods. Over the last decade, studies have demonstrated the capabilities of Airborne Laser Scanning (ALS) as a potential operational tool in forest inventory, providing accurate estimates of a wide range of commonly-used measurements. There is a fast-growing need for leveraging the growing collection of ALS data across Maine for usable and reliable Enhanced Forest Inventory (EFI) data products to support management and decision-making in the state’s forest industry. However, working with continually evolving sensor technology, large volumes of data and increasingly complex analytics requires research and development designed to make progress on workflow solutions and advance processing methodologies. 

Area-based extrapolation of EFI variables requires a statistical model that relates a sample of plot measurements to a set of explanatory covariates calculated from the 3-D profiles of gridded ALS data. This project will be carried out at the University of Maine as a collaboration between the Advanced Computing Group (ACG) and the School of Forest Resources. The software development will involve porting and rewriting the code for calculating ALS metrics, from old and unsupported code written for Windows, to code that will run in a parallel computing environment on the ACG’s computer cluster. Model development will make use of extensive plot-based inventory information gathered from existing sources previously measured by SFR investigator teams, our Maine-based forest landowner cooperators and other State and Federal forest inventory programs. The result will be the software tools and computing workflow to accurately and efficiently produce geospatial data deliverables of inventory metrics to be used by forest industry practitioners, agency decision-makers and other researchers.

Project Information Subsection
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Project Deliverables:
Software solutions for a low-cost, efficient workflow for ALS-based EFI processing, including code to calculate metrics and run models on geospatial, gridded data sets in a parallel computing environment.

Geospatial data layers and summary analysis products representing current forest inventory conditions across various EFI variables at high resolution for large areas of interest.


Project Deliverables:
This project involves porting several key pieces of windows code to a linux  (HPC) environment

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Student Facilitator Programming Skill Level: Practical applications
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Project Institution: University of Maine
Project Address:
5708 Barrows
Orono, Maine. 04469-5708

Anchor Institution: NE-University of Maine
Preferred Start Date: 06/01/2018
Start as soon as possible.: No
Project Urgency: Already behind3Start date is flexible
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Planned Publications (if any):
Inventory sampling strategies for calibrating Airborne Laser Scanning data in developing Enhanced Forest Inventory models

Regional-scale biomass and wood supply patterns mapped from large-area, remotely-sensed Enhanced Forest Inventory

What will the student learn?:
Student will learn about Microsoft Foundation Class (MFC) objects and how to write parallizable C code to perform equivalent functions.  The student will learn about software testing and validation and gain domain knowledge.

What will the mentee learn?:
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What will the Cyberteam program learn from this project?:
This project will benefit the Cyberteam project by helping a non-computer researcher use cyberinfrastructure more effectively to advance his research.

HPC resources needed to complete this project?:
Some

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Final Report
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What is the impact on the development of the principal discipline(s) of the project?:
This work has the potential to have a significant impact in the Forestry and Forest Management disciplines.  The use of airborne LIDAR for 3D imaging is likely to have important ramifications.

What is the impact on other disciplines?:
The refinement of the existing code, parallelization, and characterization are important in the areas of Computer Science and Computer engineering.

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Is there an impact on the development of human resources for research computing?:
The student on this project has the opportunity to work on a multidisciplinary team on a real-world project

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Is there an impact on information resources that form infrastructure?:
This project will help advance cutting edge research and better utilize information resources that form infrastructure.

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Is there an impact on society beyond science and technology?:
Better understanding of forest structures is likely to lead to better management and sustainability.

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Overall results:
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