Potential of Different Optical and SAR Data in Forest and Land Cover Classification to Support REDD+ MRV

Laura Sirro, Tuomas Häme, Yrjö Rauste, Jorma Kilpi, Jarno Hämäläinen, Katja Gunia, Bernardus de Jong, Fernando Paz Pellat

    Research output: Contribution to journalArticleScientificpeer-review

    8 Citations (Scopus)

    Abstract

    The applicability of optical and synthetic aperture radar (SAR) data for land cover classification to support REDD+ (Reducing Emissions from Deforestation and Forest Degradation) MRV (measuring, reporting and verification) services was tested on a tropical to sub-tropical test site. The 100 km by 100 km test site was situated in the State of Chiapas in Mexico. Land cover classifications were computed using RapidEye and Landsat TM optical satellite images and ALOS PALSAR L-band and Envisat ASAR C-band images. Identical sample plot data from Kompsat-2 imagery of one-metre spatial resolution were used for the accuracy assessment. The overall accuracy for forest and non-forest classification varied between 95% for the RapidEye classification and 74% for the Envisat ASAR classification. For more detailed land cover classification, the accuracies varied between 89% and 70%, respectively. A combination of Landsat TM and ALOS PALSAR data sets provided only 1% improvement in the overall accuracy. The biases were small in most classifications, varying from practically zero for the Landsat TM based classification to a 7% overestimation of forest area in the Envisat ASAR classification. Considering the pros and cons of the data types, we recommend optical data of 10 m spatial resolution as the primary data source for REDD MRV purposes. The results with L-band SAR data were nearly as accurate as the optical data but considering the present maturity of the imaging systems and image analysis methods, the L-band SAR is recommended as a secondary data source. The C-band SAR clearly has poorer potential than the L-band but it is applicable in stratification for a statistical sampling when other image types are unavailable.
    Original languageEnglish
    Article number942
    Number of pages26
    JournalRemote Sensing
    Volume10
    Issue number6
    Publication statusPublished - 1 Jun 2018
    MoE publication typeA1 Journal article-refereed

    Keywords

    • REDD
    • land cover classification
    • Landsat
    • RapidEye
    • ALOS PALSAR
    • Envisat ASAR

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