Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory

Andrew J Lister,Ronald Mcroberts,Greg C Liknes,Linda S Heath,Karen Schleeweis,Tracey Frescino,Demetrios Gatziolis,Todd A Schroeder,James Westfall,Hans Andersen,B Tyler Wilson,Mark Nelson,Gretchen G Moisen,Rachel Riemann,Sean Healey

doi:10.3390/f11121364

Abstract

Globally, forests are a crucial natural resource, and their sound management is critical for human and ecosystem health and well-being. Efforts to manage forests depend upon reliable data on the status of and trends in forest resources. When these data come from well-designed natural resource monitoring (NRM) systems, decision makers can make science-informed decisions. National forest inventories (NFIs) are a cornerstone of NRM systems, but require capacity and skills to implement. Efficiencies can be gained by incorporating auxiliary information derived from remote sensing (RS) into ground-based forest inventories. However, it can be difficult for countries embarking on NFI development to choose among the various RS integration options, and to develop a harmonized vision of how NFI and RS data can work together to meet monitoring needs. The NFI of the United States, which has been conducted by the USDA Forest Service’s (USFS) Forest Inventory and Analysis (FIA) program for nearly a century, uses RS technology extensively. Here we review the history of the use of RS in FIA, beginning with general background on NFI, FIA, and sampling statistics, followed by a description of the evolution of RS technology usage, beginning with paper aerial photography and ending with present day applications and future directions. The goal of this review is to offer FIA’s experience with NFI-RS integration as a case study for other countries wishing to improve the efficiency of their NFI programs.

Highlights

It is not intended to be an exhaustive listing of remote sensing (RS) activities in Forest Inventory and Analysis (FIA); rather, it focuses on key work that is based on either FIA data or institutional knowledge generated by the program
Digital imagery from a variety of satellites allowed for improvements; we focus the discussion on research activities and operational products from Advanced Very High Resolution Radiometer (AVHRR), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS)
This case study reveals that FIA has created an environment where the competition of ideas and a culture of collegiality has led to creative thought and improvements in program efficiency, as well as a strong foundation of institutional knowledge that serves as a platform from which future research will advance

Summary

Introduction

Value of National Forest Inventory (NFI) Data: Management, Research, Policy Decisions. Many nations consider natural resource monitoring (NRM) systems to be critical sources of information for making decisions on natural resource management, planning, and policy. Additional expressions of precision include variances and standard errors of estimates, margins of error, sampling errors, or uncertainty, all of which serve as metrics that a data user will consider when making judgments or taking actions based on FIA estimates. These precision metrics are, fundamental barometers of the usefulness of FIA data and estimates. The primary factors that affect precision, confidence interval widths and, the efficiency of the estimation, are the sampling design, the sample size, the statistical estimator, and the mode of inference of which we consider two, design-based inference, and model-based inference [33]

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