Mapping forest cover and forest cover change with airborne S-band radar

Ningthoujam, Ramesh K.; Tansey, Kevin; Balzter, Heiko; Morrison, Keith; Johnson, Sarah C.M.; Gerard, France; George, Charles; Burbidge, Geoff; Doody, Sam; Veck, Nick; Llewellyn, Gary M.; Blythe, Thomas. 2016 Mapping forest cover and forest cover change with airborne S-band radar [in special issue: Remote sensing of vegetation structure and dynamics] Remote Sensing, 8 (7), 577. 21, pp. https://doi.org/10.3390/rs8070577

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Official URL: http://dx.doi.org/10.3390/rs8070577

Abstract/Summary

Assessments of forest cover, forest carbon stocks and carbon emissions from deforestation and degradation are increasingly important components of sustainable resource management, for combating biodiversity loss and in climate mitigation policies. Satellite remote sensing provides the only means for mapping global forest cover regularly. However, forest classification with optical data is limited by its insensitivity to three-dimensional canopy structure and cloud cover obscuring many forest regions. Synthetic Aperture Radar (SAR) sensors are increasingly being used to mitigate these problems, mainly in the L-, C- and X-band domains of the electromagnetic spectrum. S-band has not been systematically studied for this purpose. In anticipation of the British built NovaSAR-S satellite mission, this study evaluates the benefits of polarimetric S-band SAR for forest characterisation. The Michigan Microwave Canopy Scattering (MIMICS-I) radiative transfer model is utilised to understand the scattering mechanisms in forest canopies at S-band. The MIMICS-I model reveals strong S-band backscatter sensitivity to the forest canopy in comparison to soil characteristics across all polarisations and incidence angles. Airborne S-band SAR imagery over the temperate mixed forest of Savernake Forest in southern England is analysed for its information content. Based on the modelling results, S-band HH- and VV-polarisation radar backscatter and the Radar Forest Degradation Index (RFDI) are used in a forest/non-forest Maximum Likelihood classification at a spatial resolution of 6 m (70% overall accuracy, κ = 0.41) and 20 m (63% overall accuracy, κ = 0.27). The conclusion is that S-band SAR such as from NovaSAR-S is likely to be suitable for monitoring forest cover and its changes.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.3390/rs8070577
UKCEH and CEH Sections/Science Areas:	Reynard
ISSN:	2072-4292
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	remote sensing, SAR, S-band SAR, forest/non-forest, forest cover change, MIMICS-I model, Savernake Forest
NORA Subject Terms:	Ecology and Environment Electronics, Engineering and Technology
Date made live:	03 Aug 2016 13:47 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/514130

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.3390/rs8070577

UKCEH and CEH Sections/Science Areas:

Reynard

ISSN:

2072-4292

Additional Information. Not used in RCUK Gateway to Research.:

Open Access paper - full text available via Official URL link.

Additional Keywords:

remote sensing, SAR, S-band SAR, forest/non-forest, forest cover change, MIMICS-I model, Savernake Forest