Construction of a REDD Monitoring System Using GIS and RS Techniques

Abstract

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 area. Finally, considering the deforestation of the changed land-cover region (GIS deforestation) and RS deforestation, the highest accuracy was observed at the NDVI threshold value interval of 2 and showed an extraction rate and k̂ index of 61.2% and 0.23, respectively. In comparison with the case of applying GIS deforestation I, it showed 36.7% and 0.03 improvements. The MRV monitoring system based on the GIS and Remote Sensing techniques would enable the monitoring of larger size areas regularly and with high reliability.

Leg-amplitude differentiation guided by haptic and visual feedback to detect alterations in motor flexibility due to Total Knee Replacement

“…Peak detection was checked per subject and if necessary the threshold value for peak detection was manually adjusted. The time (in seconds) in between two high impact positive peaks was calculated (ST) (Lee & Lee, 2002).…”

Exact laser beam positioning for measurement of vegetation vitality

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