Next Article in Journal
Precise Measurement of Stem Diameter by Simulating the Path of Diameter Tape from Terrestrial Laser Scanning Data
Next Article in Special Issue
Long-Term Post-Disturbance Forest Recovery in the Greater Yellowstone Ecosystem Analyzed Using Landsat Time Series Stack
Previous Article in Journal
Hurricane Wind Speed Estimation Using WindSat 6 and 10 GHz Brightness Temperatures
Previous Article in Special Issue
Landsat Imagery Spectral Trajectories—Important Variables for Spatially Predicting the Risks of Bark Beetle Disturbance
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(9), 719; doi:10.3390/rs8090719

Mapping Forest Health Using Spectral and Textural Information Extracted from SPOT-5 Satellite Images

1,†,* , 2,†,* , 3
,
2
,
1
and
4
1
Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China
2
Institute of Forest Resource and Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
3
Survey & Planning Institute of State Forestry Administration, Beijing 100714, China
4
School of Natural Resources, West Virginia University, Morgantown, WV 26506, USA
*
Authors to whom correspondence should be addressed.
Academic Editors: Lars T. Waser, Clement Atzberger and Prasad S. Thenkabail
Received: 22 May 2016 / Revised: 18 August 2016 / Accepted: 18 August 2016 / Published: 31 August 2016
(This article belongs to the Special Issue Remote Sensing of Forest Health)
View Full-Text   |   Download PDF [8546 KB, uploaded 15 September 2016]   |  

Abstract

Forest health is an important variable that we need to monitor for forest management decision making. However, forest health is difficult to assess and monitor based merely on forest field surveys. In the present study, we first derived a comprehensive forest health indicator using 15 forest stand attributes extracted from forest inventory plots. Second, Pearson’s correlation analysis was performed to investigate the relationship between the forest health indicator and the spectral and textural measures extracted from SPOT-5 images. Third, all-subsets regression was performed to build the predictive model by including the statistically significant image-derived measures as independent variables. Finally, the developed model was evaluated using the coefficient of determination (R2) and the root mean square error (RMSE). Additionally, the produced model was further validated for its performance using the leave-one-out cross-validation approach. The results indicated that our produced model could provide reliable, fast and economic means to assess and monitor forest health. A thematic map of forest health was finally produced to support forest health management. View Full-Text
Keywords: forest health; spectral and textural measures; Pearson’s correlation analysis; all-subsets regression; forest health management forest health; spectral and textural measures; Pearson’s correlation analysis; all-subsets regression; forest health management
Figures

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 alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Meng, J.; Li, S.; Wang, W.; Liu, Q.; Xie, S.; Ma, W. Mapping Forest Health Using Spectral and Textural Information Extracted from SPOT-5 Satellite Images. Remote Sens. 2016, 8, 719.

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]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top