Construction of a REDD Monitoring System Using GIS and RS Techniques

This study aimed to construct a MRV system for the REDD regions. The system was based on the GIS and Remote Sensing techniques, and it developed a method which could detect the land cover change area as well as evaluate its accuracy. GIS, the Remote Sensing technique and the statistical modelling technique were merged, and then we monitored areas of deforestation among the REDD regions. This study compared deforestation from administrative information (GIS deforestation1) with deforestation (RS deforestation) extracted from satellite imagery by vegetation indices (NDVI, NBR, NDWI). The highest extraction accuracy that applies filtering to NDVI with a threshold of 1.5 showed reliable accuracy 35.47% with a k-value of 0.20. However, one of the reasons for the accuracy error was due to the difference between land-use change and land-cover change. The actual rate of land-cover change deforestation was 32% on the administrative information. The other reason was a 7.52% error extraction of the forest management...

[1]  C. Elvidge,et al.  NALC Land Cover Change Detection Pilot Study , 1998 .

[2]  Estimation of non-CO 2 Greenhouse Gases Emissions from Biomass Burning in the Samcheok Large-Fire Area Using Landsat TM Imagery , 2008 .

[3]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[4]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[5]  C. Woodcock,et al.  Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? , 2001 .

[6]  Cheol-Min Kim,et al.  Monitoring of Forest Burnt Area using Multi-temporal Landsat TM and ETM+ Data , 2004 .

[7]  Marvin E. Bauer,et al.  Processing of multitemporal Landsat TM imagery to optimize extraction of forest cover change features , 1994, IEEE Trans. Geosci. Remote. Sens..

[8]  K. Rutchey,et al.  Inland Wetland Change Detection in the Everglades Water Conservation Area 2A Using a Time Series of Normalized Remotely Sensed Data , 1995 .

[9]  A. Angelsen,et al.  Do Trees Grow on Money ? The implications of deforestation research for policies to promote REDD , 2007 .

[10]  Satoshi Tsuyuki,et al.  Detection of burned peat swamp forest in a heterogeneous tropical landscape: A case study of the Klias Peninsula, Sabah, Malaysia , 2007 .

[11]  Tal Svoray,et al.  The use of remote sensing and GIS for spatio-temporal analysis of the physiological state of a semi-arid forest with respect to drought years , 2005 .

[12]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[13]  Siamak Khorram,et al.  Correspondence analysis for detecting land cover change , 2006 .

[14]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[15]  Development of a prediction probability model to monitor ARD areas. , 2007 .

[16]  R. Congalton.  A Quantitative Method to Test for Consistency and Correctness in Photointerpretation , 1983 .