• DocumentCode
    3447499
  • Title

    A novel approach to assess and monitor forests for REDD

  • Author

    Heli Lu ; Guifang Liu ; Zhong Huang ; Wenlong Jin

  • Author_Institution
    Key Lab. of Geospatial Technol. for the Middle & Lower Yellow River Regions, Kaifeng, China
  • fYear
    2013
  • fDate
    20-22 June 2013
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Reducing emissions from deforestation and forest degradation, plus conservation of forest carbon stocks, sustainable management of forests, and enhancement of forest carbon stocks, is a set of steps designed to use market and financial incentives in order to reduce the emissions of greenhouse gases. The paper addresses the role of satellite remote sensing technologies as a tool for monitoring, assessment, reporting and verification of carbon credits. In particular, image fusion techniques were used to assess and monitor forests based on moderate resolution images of MODIS and TM data. The result showed that satellite image fusion could provide more spatial details and better spectral information compared with the original image and thus prove to be an excellent tool for monitoring carbon storage change for REDD.
  • Keywords
    air pollution; carbon capture and storage; feature extraction; geophysical image processing; geophysical techniques; image fusion; remote sensing; vegetation; MODIS data; REDD; TM data; carbon credit verification; carbon storage change; emission reduction; financial incentive; forest carbon stocks; forest degradation; forest monitoring; forest sustainable management; greenhouse gas emissions; image fusion techniques; market incentive; moderate resolution images; satellite image fusion; satellite remote sensing technologies; Accuracy; Image fusion; MODIS; Principal component analysis; Remote sensing; Satellites; Sensors; REDD; data fusion; deforestation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoinformatics (GEOINFORMATICS), 2013 21st International Conference on
  • Conference_Location
    Kaifeng
  • ISSN
    2161-024X
  • Type

    conf

  • DOI
    10.1109/Geoinformatics.2013.6626185
  • Filename
    6626185