Next Article in Journal
A Wireless Sensor Network Framework for Real-Time Monitoring of Height and Volume Variations on Sandy Beaches and Dunes
Next Article in Special Issue
New Geospatial Approaches for Efficiently Mapping Forest Biomass Logistics at High Resolution over Large Areas
Previous Article in Journal
Collecting Typhoon Disaster Information from Twitter Based on Query Expansion
Previous Article in Special Issue
Fine Resolution Probabilistic Land Cover Classification of Landscapes in the Southeastern United States
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessFeature PaperArticle
ISPRS Int. J. Geo-Inf. 2018, 7(4), 140; https://doi.org/10.3390/ijgi7040140

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

1
Rocky Mountain Research Station, U.S. Forest Service, Missoula, MT 59801, USA
2
College of Forestry and Conservation, University of Montana, Missoula, MT 59801, USA
3
U.S. Forest Service, Tallahassee, FL 32321, USA
*
Author to whom correspondence should be addressed.
Received: 15 February 2018 / Revised: 28 March 2018 / Accepted: 29 March 2018 / Published: 3 April 2018
Full-Text   |   PDF [28786 KB, uploaded 3 May 2018]   |  

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. View Full-Text
Keywords: NAIP; FIA; remote sensing; forest measurements NAIP; FIA; remote sensing; forest measurements
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Hogland, J.; Anderson, N.; St. Peter, J.; Drake, J.; Medley, P. Mapping Forest Characteristics at Fine Resolution across Large Landscapes of the Southeastern United States Using NAIP Imagery and FIA Field Plot Data. ISPRS Int. J. Geo-Inf. 2018, 7, 140.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top