Next Article in Journal
InSAR-Constrained Interseismic Deformation and Potential Seismogenic Asperities on the Altyn Tagh Fault at 91.5–95°E, Northern Tibetan Plateau
Previous Article in Journal
Mapping and Characterizing Thermal Dilation of Civil Infrastructures with Multi-Temporal X-Band Synthetic Aperture Radar Interferometry
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2018, 10(6), 942; https://doi.org/10.3390/rs10060942

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

1
VTT Technical Research Centre of Finland Ltd., P.O. Box 1000, VTT FI-02044, Finland
2
Arbonaut Ltd., Kaislakatu 2, Joensuu FI-80130, Finland
3
El Colegio de la Frontera Sur, Av. Rancho Polígono 2-A, Parque Industrial Lerma, Campeche, Campeche CP 24500, Mexico
4
Colegio de Postgraduados, Km 36.5 Carretera México-Texcoco, Texcoco 56230, Mexico
*
Author to whom correspondence should be addressed.
Received: 9 May 2018 / Revised: 4 June 2018 / Accepted: 11 June 2018 / Published: 14 June 2018
View Full-Text   |   Download PDF [8789 KB, uploaded 14 June 2018]   |  

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. View Full-Text
Keywords: REDD; land cover classification; Landsat; RapidEye; ALOS PALSAR; Envisat ASAR REDD; land cover classification; Landsat; RapidEye; ALOS PALSAR; Envisat ASAR
Figures

Graphical abstract

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

Share & Cite This Article

MDPI and ACS Style

Sirro, L.; Häme, T.; Rauste, Y.; Kilpi, J.; Hämäläinen, J.; Gunia, K.; de Jong, B.; Paz Pellat, F. Potential of Different Optical and SAR Data in Forest and Land Cover Classification to Support REDD+ MRV. Remote Sens. 2018, 10, 942.

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