Next Article in Journal
Large-Scale, Multi-Temporal Remote Sensing of Palaeo-River Networks: A Case Study from Northwest India and its Implications for the Indus Civilisation
Previous Article in Journal
Continued Reforestation and Urban Expansion in the New Century of a Tropical Island in the Caribbean
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(7), 736; doi:10.3390/rs9070736

A Burned Area Mapping Algorithm for Chinese FengYun-3 MERSI Satellite Data

1
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Hainan Key Laboratory of Earth Observation, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Sanya 572029, China
*
Author to whom correspondence should be addressed.
Received: 12 June 2017 / Revised: 8 July 2017 / Accepted: 12 July 2017 / Published: 16 July 2017
View Full-Text   |   Download PDF [4551 KB, uploaded 19 July 2017]   |  

Abstract

Biomass burning is a worldwide phenomenon, which emits large amounts of carbon into the atmosphere and strongly influences the environment. Burned area is an important parameter in modeling the impacts of biomass burning on the climate and ecosystem. The Medium Resolution Spectral Imager (MERSI) onboard FengYun-3C (FY-3C) has shown great potential for burned area mapping research, but there is still a lack of relevant studies and applications. This paper describes an automated burned area mapping algorithm that was developed using daily MERSI data. The algorithm employs time-series analysis and multi-temporal 1000-m resolution data to obtain seed pixels. To identify the burned pixels automatically, region growing and Support Vector Machine) methods have been used together with 250-m resolution data. The algorithm was tested by applying it in two experimental areas, and the accuracy of the results was evaluated by comparing them to reference burned area maps, which were interpreted manually using Landsat 8 OLI data and the MODIS MCD64A1 burned area product. The results demonstrated that the proposed algorithm was able to improve the burned area mapping accuracy at the two study sites. View Full-Text
Keywords: image classification; remote sensing; burned area; FengYun-3C Medium Resolution Spectral Imager (FY-3C MESRI) image classification; remote sensing; burned area; FengYun-3C Medium Resolution Spectral Imager (FY-3C MESRI)
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).

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

Shan, T.; Wang, C.; Chen, F.; Wu, Q.; Li, B.; Yu, B.; Shirazi, Z.; Lin, Z.; Wu, W. A Burned Area Mapping Algorithm for Chinese FengYun-3 MERSI Satellite Data. Remote Sens. 2017, 9, 736.

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