Abstract
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.
Highlights
Forests are crucial ecosystems that provide critical habitats to plants and animals [1,2], affect the global carbon cycle through deforestation and reforestation [3], and offer livelihood to humankind [4]
It is noteworthy that we managed to improve the accuracy of the FNF classification for Vietnam using a combination of ScanSAR, Normalized Difference Vegetation Index (NDVI), and Shuttle Radar Topography Mission (SRTM) images
We showed that using multi-temporal PALSAR-2/ScanSAR images can create greater accuracies of FNF maps than using single-temporal PALSAR-2 mosaic images in the national scale
Summary
Forests are crucial ecosystems that provide critical habitats to plants and animals [1,2], affect the global carbon cycle through deforestation and reforestation [3], and offer livelihood to humankind [4]. It is possible to extract information about forests from global land-cover datasets such as the 1 km Global Land Cover Dataset for the year 2000 (GLC2000) [7], the 500 m MODIS Land Cover Type (MCD12Q1) [8], the 300 m MERIS Global Land Cover Service (GlobCover) [9], and the 30 m Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) [10]. These products focus on forests and on other land cover categories, and they are different in spatial resolutions. Further efforts are needed to improve the reliability of global forest maps for national reporting [14]
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