Mapping Forest Characteristics at Fine Resolution across Large Landscapes of the Southeastern United States Using NAIP Imagery and FIA Field Plot Data

John Hogland,Paul Medley,Jason Drake,Nathaniel Anderson,Joseph St. Peter

doi:10.3390/ijgi7040140

Abstract

Accurate information is important for effective management of natural resources. In the field of forestry, field measurements of forest characteristics such as species composition, basal area, and stand density are used to inform and evaluate management activities. Quantifying these metrics accurately across large landscapes in a meaningful way is extremely important to facilitate informed decision-making. In this study, we present a remote sensing based methodology to estimate species composition, basal area and stand tree density for pine and hardwood tree species at the spatial resolution of a Forest Inventory Analysis (FIA) program plot (78 m by 70 m). Our methodology uses textural metrics derived at this spatial scale to relate plot summaries of forest characteristics to remotely sensed National Agricultural Imagery Program (NAIP) aerial imagery across broad extents. Our findings quantify strong relationships between NAIP imagery and FIA field data. On average, models of basal area and trees per acre accounted for 43% of the variation in the FIA data, while models identifying species composition had less than 15.2% error in predicted class probabilities. Moreover, these relationships can be used to spatially characterize the condition of forests at fine spatial resolutions across broad extents.

Highlights

Forest management is a complex, integrated process that combines multiple objectives to accomplish a predefined set of goals as they relate to forested lands [1]
We developed and deployed a remote sensing based approach that relates summarized U.S Forest Service Forest Inventory and Analysis (FIA) [8] field plot data to spectral and textural metrics derived from the United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) [21] imagery in four regions of the southeastern United States (US)
Spatial, statistical, and geographic information system (GIS) analyses performed over the course of the project, we developed a suite of spatial modeling tools that take advantage of Function Modeling (FM) [30,32] and parallel processing

Summary

Introduction

Forest management is a complex, integrated process that combines multiple objectives to accomplish a predefined set of goals as they relate to forested lands [1]. Since the United States National Forest Management Act of 1976, the federal definition of forest management has expanded well beyond timber management to include economic and social goals as components of management choices, the consideration of broader multiple use management challenges, and the need to quantitatively justify forest management plans and decisions [1] This expansion in scope fundamentally changed the values for which forests are managed, and how managers justify forest management decisions, emphasizing the need for effective, information-driven natural resource planning for diverse values in broad spatial, ecological, social, and economic contexts. To gain an understanding of the existing structure and composition of forests, practitioners have been implementing well-established mensuration techniques for over a century [4] These techniques can be described as aggregating a sample of field plots for a given forest stand or geographic area to estimate means and variation in forest characteristics within the sampled area (i.e., direct estimation). Much of the existing information is focused on forestlands managed for commercial timber, which tends to be the subject of more intensive, more frequent inventory than lands managed primarily for other values

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Conclusion