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Hyper-Temporal C-Band SAR for Baseline Woody Structural Assessments in Deciduous Savannas.

, , , , , and . Remote. Sens., 8 (8): 661 (2016)

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Mapping and Monitoring Fractional Woody Vegetation Cover in the Arid Savannas of Namibia Using LiDAR Training Data, Machine Learning, and ALOS PALSAR Data., , , , , and . Remote. Sens., 11 (22): 2633 (2019)Evaluating the Seasonality of Remote Sensing Indicators of System State for Eucalyptus Grandis Growing on Different Site Qualities., , , and . IGARSS (3), page 487-490. IEEE, (2008)Early Detection of Myrtle Rust on Pōhutukawa Using Indices Derived from Hyperspectral and Thermal Imagery., , , , , , , , , and 3 other author(s). Remote. Sens., 16 (6): 1050 (March 2024)Woody cover assessments in a Southern African savanna, using hyper-temporal C-band ASAR-WS data., , , , , and . IGARSS, page 1148-1151. IEEE, (2014)Detection of Burned Areas in Southern African Savannahs Using Time Series of C-Band Sentinel-1 Data., , , , and . IGARSS, page 5337-5339. IEEE, (2018)L-band Synthetic Aperture Radar imagery performs better than optical datasets at retrieving woody fractional cover in deciduous, dry savannahs., , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2016)The assessment of data mining algorithms for modelling Savannah Woody cover using multi-frequency (X-, C- and L-band) synthetic aperture radar (SAR) datasets., , , , , , and . IGARSS, page 1049-1052. IEEE, (2014)Hyper-Temporal C-Band SAR for Baseline Woody Structural Assessments in Deciduous Savannas., , , , , and . Remote. Sens., 8 (8): 661 (2016)Indirect Estimation of Structural Parameters in South African Forests Using MISR-HR and LiDAR Remote Sensing Data., , , , , , and . Remote. Sens., 10 (10): 1537 (2018)Estimating South African Maize Biomass Using Integrated High-Resolution UAV and Sentinel 1 and 2 Datasets., , , , and . IGARSS, page 1594-1596. IEEE, (2021)