Paper Info
Title
Developing Forest Cover Composites through a Combination of Landsat-8 Optical and Sentinel-1 SAR Data for the Visualization and Extraction of Forested Areas.
Abstract
Mapping the distribution of forested areas and monitoring their spatio-temporal changes are necessary for the conservation and management of forests. This paper presents two new image composites for the visualization and extraction of forest cover. By exploiting the Landsat-8 satellite-based multi-temporal and multi-spectral reflectance datasets, the Forest Cover Composite (FCC) was designed in this research. The FCC is an RGB (red, green, blue) color composite made up of short-wave infrared reflectance and green reflectance, specially selected from the day when the Normalized Difference Vegetation Index (NDVI) is at a maximum, as the red and blue bands, respectively. The annual mean NDVI values are used as the green band. The FCC is designed in such a way that the forested areas appear greener than other vegetation types, such as grasses and shrubs. On the other hand, the croplands and barren lands are usually seen as red and water/snow is seen as blue. However, forests may not necessarily be greener than other perennial vegetation. To cope with this problem, an Enhanced Forest Cover Composite (EFCC) was designed by combining the annual median backscattering intensity of the VH (vertical transmit, horizontal receive) polarization data from the Sentinel-1 satellite with the green term of the FCC to suppress the green component (mean NDVI values) of the FCC over the non-forested vegetative areas. The performances of the FCC and EFCC were evaluated for the discrimination and classification of forested areas all over Japan with the support of reference data. The FCC and EFCC provided promising results, and the high-resolution forest map newly produced in the research provided better accuracy than the extant MODIS (Moderate Resolution Imaging Spectroradiometer) Land Cover Type product (MCD12Q1) in Japan. The composite images proposed in the research are expected to improve forest monitoring activities in other regions as well.
Year
DOI
Venue
2018
10.3390/jimaging4090105
JOURNAL OF IMAGING
Keywords
Field
DocType
forests,multi-spectral,Landsat-8,Sentinel-1,backscattering,composite images,neural networks
Vegetation,Satellite,Moderate-resolution imaging spectroradiometer,Composite material,Backscatter,Remote sensing,Normalized Difference Vegetation Index,RGB color model,Land cover,Mathematics,Snow
Journal
Volume
Issue
ISSN
4
9
2313-433X
Citations 
PageRank 
References 
0
0.34
3
Authors
3
Name
Order
Citations
PageRank
Ram C. Sharma122.03
Keitarou Hara2315.55
Ryutaro Tateishi37813.21