Multi-date Sentinel1 SAR image textures discriminate perennial agroforests in a tropical forest-savannah transition landscape
| Autor: | R. R. De Wulf, F. Van Coillie, Frederick N. Numbisi |
|---|---|
| Přispěvatelé: | Jutzi, B., Weinmann, M., Hinz, S. |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Synthetic aperture radar
lcsh:Applied optics. Photonics 010504 meteorology & atmospheric sciences 0211 other engineering and technologies 02 engineering and technology Land cover 01 natural sciences lcsh:Technology GLCM textures REDD+ Strategy law.invention law Radar imaging Perennial Agroforestry Mapping Radar Image resolution 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing Pixel lcsh:T lcsh:TA1501-1820 Class discrimination Random forest lcsh:TA1-2040 Earth and Environmental Sciences Congo Basin Rainforest Environmental science Sentinel1 SAR Random Forest Algorithm lcsh:Engineering (General). Civil engineering (General) |
| Zdroj: | ISPRS TC I mid-term symposium : innovative sensing : from sensors to methods and applications The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-1, Pp 339-346 (2018) |
| ISSN: | 1682-1750 2194-9034 |
| Popis: | Synthetic Aperture Radar (SAR) provides consistent information on target land features; especially in tropical conditions that restrain penetration of optical imaging sensors. Because radar response signal is influenced by geometric and di-electrical properties of surface features’, the different land cover may appear similar in radar images. For discriminating perennial cocoa agroforestry land cover, we compare a multi-spectral optical image from RapidEye, acquired in the dry season, and multi-seasonal C-band SAR of Sentinel 1: A final set of 10 (out of 50) images that represent six dry and four wet seasons from 2015 to 2017. We ran eight RF models for different input band combinations; multi-spectral reflectance, vegetation indices, co-(VV) and cross-(VH) polarised SAR intensity and Grey Level Co-occurrence Matrix (GLCM) texture measures. Following a pixel-based image analysis, we evaluated accuracy metrics and uncertainty Shannon entropy. The model comprising co- and cross-polarised texture bands had the highest accuracy of 88.07 % (95 % CI: 85.52–90.31) and kappa of 85.37; and the low class uncertainty for perennial agroforests and transition forests. The optical image had low classification uncertainty for the entire image; but, it performed better in discriminating non-vegetated areas. The measured uncertainty provides reliable validation for comparing class discrimination from different image resolution. The GLCM texture measures that are crucial in delineating vegetation cover differed for the season and polarization of SAR image. Given the high accuracies of mapping, our approach has value for landscape monitoring; and, an improved valuation of agroforestry contribution to REDD+ strategies in the Congo basin sub-region. |
| Databáze: | OpenAIRE |
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