Next Article in Journal
Exploiting Differential Vegetation Phenology for Satellite-Based Mapping of Semiarid Grass Vegetation in the Southwestern United States and Northern Mexico
Next Article in Special Issue
Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data
Previous Article in Journal
Investigation of Snow Cover Effects and Attenuation Correction of Gamma Ray in Aerial Radiation Monitoring
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(11), 891; doi:10.3390/rs8110891

The Potential of Forest Biomass Inversion Based on Vegetation Indices Using Multi-Angle CHRIS/PROBA Data

1
Harbin Institute of Technology, School of Electronics Information Engineering, Harbin 150001, China
2
Department of Surveying Engineering, Heilongjiang Institute of Technology, Harbin 150040, China
3
Department of Geography, Hunter College of CUNY, New York, NY 10065, USA
4
Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
5
Department of Geography, University of Maryland, College Park, MD 20742, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Lalit Kumar, Onisimo Mutanga and Prasad S. Thenkabail
Received: 27 August 2016 / Revised: 17 October 2016 / Accepted: 19 October 2016 / Published: 28 October 2016
(This article belongs to the Special Issue Remote Sensing of Above Ground Biomass)
View Full-Text   |   Download PDF [8686 KB, uploaded 28 October 2016]   |  

Abstract

Multi-angle remote sensing can either be regarded as an added source of uncertainty for variable retrieval, or as a source of additional information, which enhances variable retrieval compared to traditional single-angle observation. However, the magnitude of these angular and band effects for forest structure parameters is difficult to quantify. We used the Discrete Anisotropic Radiative Transfer (DART) model and the Zelig model to simulate the forest canopy Bidirectional Reflectance Distribution Factor (BRDF) in order to build a look-up table, and eight vegetation indices were used to assess the relationship between BRDF and forest biomass in order to find the sensitive angles and bands. Further, the European Space Agency (ESA) mission, Compact High Resolution Imaging Spectrometer onboard the Project for On-board Autonomy (CHRIS-PROBA) and field sample measurements, were selected to test the angular and band effects on forest biomass retrieval. The results showed that the off-nadir vegetation indices could predict the forest biomass more accurately than the nadir. Additionally, we found that the viewing angle effect is more important, but the band effect could not be ignored, and the sensitive angles for extracting forest biomass are greater viewing angles, especially around the hot and dark spot directions. This work highlighted the combination of angles and bands, and found a new index based on the traditional vegetation index, Atmospherically Resistant Vegetation Index (ARVI), which is calculated by combining sensitive angles and sensitive bands, such as blue band 490 nm/−55°, green band 530 nm/55°, and the red band 697 nm/55°, and the new index was tested to improve the accuracy of forest biomass retrieval. This is a step forward in multi-angle remote sensing applications for mining the hidden relationship between BRDF and forest structure information, in order to increase the utilization efficiency of remote sensing data. View Full-Text
Keywords: multi-angle remote sensing; forest structure information; vegetation indices; forest biomass; Bidirectional Reflectance Distribution Factor multi-angle remote sensing; forest structure information; vegetation indices; forest biomass; Bidirectional Reflectance Distribution Factor
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

Wang, Q.; Pang, Y.; Li, Z.; Sun, G.; Chen, E.; Ni-Meister, W. The Potential of Forest Biomass Inversion Based on Vegetation Indices Using Multi-Angle CHRIS/PROBA Data. Remote Sens. 2016, 8, 891.

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