MyJournals Home  

RSS FeedsRemote Sensing, Vol. 11, Pages 2412: JAXA Annual Forest Cover Maps for Vietnam during 2015-2018 Using ALOS-2/PALSAR-2 and Auxiliary Data (Remote Sensing)

 
 

17 october 2019 22:02:39

 
Remote Sensing, Vol. 11, Pages 2412: JAXA Annual Forest Cover Maps for Vietnam during 2015-2018 Using ALOS-2/PALSAR-2 and Auxiliary Data (Remote Sensing)
 


Monitoring the temporal changes of forests is important for sustainable forest management. In this study, we investigated the potential of using multi-temporal synthetic aperture radar (SAR) images for mapping annual change in forest cover at a national scale. We assessed the robustness of using multi-temporal Phased Array L-band Synthetic Aperture Radar-2/Scanning Synthetic Aperture Radar (PALSAR-2/ScanSAR) mosaic images for forest mapping by comparison with single-temporal PALSAR-2 mosaic images for three test sites in North, Central, and Southern Vietnam. We then used a combination of multi-temporal PALSAR-2/ScanSAR images, multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) images, and Shuttle Radar Topography Mission (SRTM) images to map annual forest cover for mainland Vietnam during 2015–2018. Average overall accuracies of our forest/non-forest (FNF) maps (86.6% ± 3.1%) were greater than recent maps of Japan Aerospace Exploration Agency (JAXA, (77.5% ± 3.2%)) and European Space Agency (ESA, (85.4% ± 1.6%)). Our estimates of mainland Vietnam’s forest area were close to that of the Vietnamese government. A comparison of the spatial distribution of forest estimated from JAXA and ESA FNF maps showed that our FNF map in 2015 agreed relatively well with the ESA map, with 77% of pixels being consistent. This study demonstrates the merit of using multi-temporal PALSAR-2/ScanSAR images for annual forest mapping at a national scale.


 
243 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 2406: Spatial and Seasonal Patterns in Vegetation Growth-Limiting Factors over Europe (Remote Sensing)
Remote Sensing, Vol. 11, Pages 2413: Project-Based Learning Applied to Unmanned Aerial Systems and Remote Sensing (Remote Sensing)
 
 
blog comments powered by Disqus


MyJournals.org
The latest issues of all your favorite science journals on one page

Username:
Password:

Register | Retrieve

Search:

Physics


Copyright © 2008 - 2024 Indigonet Services B.V.. Contact: Tim Hulsen. Read here our privacy notice.
Other websites of Indigonet Services B.V.: Nieuws Vacatures News Tweets Nachrichten