L-band Synthetic Aperture Radar imagery performs better than optical datasets at retrieving woody fractional cover in deciduous,dry savannahs |
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| Institution: | 1. Department of Forest Resource Management, 2424 Main Mall, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, Canada;2. Department of Renewable Resources, Faculty of Agricultural, Life, and Environmental Sciences, University of Alberta, Edmonton, Alberta, T6G 2H1, Canada;3. Forest Management Branch, Forestry Division, Alberta Agriculture and Forestry, 9920-108 Street NW, Edmonton, Alberta, T5K 2M4, Canada;1. Department of Geosciences and Natural Resource Management, University of Copenhagen, 1350 Copenhagen, Denmark;2. Geosciences Environnement Toulouse (GET), Observatoire Midi-Pyrénées, UMR 5563 (CNRS/UPS/IRD/CNES), 14 Avenue Edouard Belin, 31400 Toulouse, France;3. CREAF, Cerdanyola del Vallès, 08193, Catalonia, Spain;4. Centre de Suivi Ecologique, BP, 15532 Dakar-Fann, Senegal;5. Science Domain 6, ICRAF (World Agroforestry Center), 00100, Nairobi, Kenya |
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| Abstract: | Woody canopy cover (CC) is the simplest two dimensional metric for assessing the presence of the woody component in savannahs, but detailed validated maps are not currently available in southern African savannahs. A number of international EO programs (including in savannah landscapes) advocate and use optical LandSAT imagery for regional to country-wide mapping of woody canopy cover. However, previous research has shown that L-band Synthetic Aperture Radar (SAR) provides good performance at retrieving woody canopy cover in southern African savannahs. This study’s objective was to evaluate, compare and use in combination L-band ALOS PALSAR and LandSAT-5 TM, in a Random Forest environment, to assess the benefits of using LandSAT compared to ALOS PALSAR. Additional objectives saw the testing of LandSAT-5 image seasonality, spectral vegetation indices and image textures for improved CC modelling. Results showed that LandSAT-5 imagery acquired in the summer and autumn seasons yielded the highest single season modelling accuracies (R2 between 0.47 and 0.65), depending on the year but the combination of multi-seasonal images yielded higher accuracies (R2 between 0.57 and 0.72). The derivation of spectral vegetation indices and image textures and their combinations with optical reflectance bands provided minimal improvement with no optical-only result exceeding the winter SAR L-band backscatter alone results (R2 of ∼0.8). The integration of seasonally appropriate LandSAT-5 image reflectance and L-band HH and HV backscatter data does provide a significant improvement for CC modelling at the higher end of the model performance (R2 between 0.83 and 0.88), but we conclude that L-band only based CC modelling be recommended for South African regions. |
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| Keywords: | Woody canopy cover SAR LandSAT-5 Textures Spectral vegetation indices Random Forest |
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