Next Article in Journal
Snow Depth Variations Estimated from GPS-Reflectometry: A Case Study in Alaska from L2P SNR Data
Next Article in Special Issue
Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images
Previous Article in Journal
Correction: Pimont, F. et al. Estimating Leaf Bulk Density Distribution in a Tree Canopy Using Terrestrial LiDAR and a Straightforward Calibration Procedure. Remote Sens. 2015, 7(6), 7995-8018
Previous Article in Special Issue
Tropical Forests of Réunion Island Classified from Airborne Full-Waveform LiDAR Measurements
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(1), 62; doi:10.3390/rs8010062

Quantifying Multi-Decadal Change of Planted Forest Cover Using Airborne LiDAR and Landsat Imagery

1
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences (CAS), Beijing 100101, China
2
Department of Environmental Sciences, Policy & Management, University of California, Berkeley, CA 94720, USA
3
Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China
4
Joint Center for Global Change Studies, Beijing 100875, China
5
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Sangram Ganguly, Compton Tucker, Nicolas Baghdadi and Prasad S. Thenkabail
Received: 27 November 2015 / Revised: 5 January 2016 / Accepted: 8 January 2016 / Published: 14 January 2016
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
View Full-Text   |   Download PDF [4045 KB, uploaded 18 January 2016]   |  

Abstract

Continuous monitoring of forest cover condition is key to understanding the carbon dynamics of forest ecosystems. This paper addresses how to integrate single-year airborne LiDAR and time-series Landsat imagery to derive forest cover change information. LiDAR data were used to extract forest cover at the sub-pixel level of Landsat for a single year, and the Landtrendr algorithm was applied to Landsat spectral data to explore the temporal information of forest cover change. Four different approaches were employed to model the relationship between forest cover and Landsat spectral data. The result shows incorporating the historic information using the temporal trajectory fitting process could infuse the model with better prediction power. Random forest modeling performs the best for quantitative forest cover estimation. Temporal trajectory fitting with random forest model shows the best agreement with validation data (R2 = 0.82 and RMSE = 5.19%). We applied our approach to Youyu county in Shanxi province of China, as part of the Three North Shelter Forest Program, to map multi-decadal forest cover dynamics. With the availability of global time-series Landsat imagery and affordable airborne LiDAR data, the approach we developed has the potential to derive large-scale forest cover dynamics. View Full-Text
Keywords: forest inventory; forest monitoring; Three-North Shelter Forest Program; afforestation; time-series forest inventory; forest monitoring; Three-North Shelter Forest Program; afforestation; time-series
Figures

Figure 1

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).

Supplementary material

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

Wang, X.; Huang, H.; Gong, P.; Biging, G.S.; Xin, Q.; Chen, Y.; Yang, J.; Liu, C. Quantifying Multi-Decadal Change of Planted Forest Cover Using Airborne LiDAR and Landsat Imagery. Remote Sens. 2016, 8, 62.

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