MULTI-DATE SENTINEL1 SAR IMAGE TEXTURES DISCRIMINATE PERENNIAL AGROFORESTS IN A TROPICAL FOREST-SAVANNAH TRANSITION LANDSCAPE
暂无分享,去创建一个
[1] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[2] Cheng Wang,et al. Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux , 2018 .
[3] Hannes Feilhauer,et al. Estimating Vegetation Cover from High-Resolution Satellite Data to Assess Grassland Degradation in the Georgian Caucasus , 2016 .
[4] E. Næsset,et al. Forest biomass change estimated from height change in interferometric SAR height models , 2014, Carbon Balance and Management.
[5] Christian Thiel,et al. Mapping CORINE Land Cover from Sentinel-1A SAR and SRTM Digital Elevation Model Data using Random Forests , 2015, Remote. Sens..
[6] Robert M. Hawlick. Statistical and Structural Approaches to Texture , 1979 .
[7] Olena Dubovyk,et al. A rule-based approach for crop identification using multi-temporal and multi-sensor phenological metrics , 2018 .
[8] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[9] Sang Joon Kim,et al. A Mathematical Theory of Communication , 2006 .
[10] Olena Dubovyk,et al. Mapping Mangroves Extents on the Red Sea Coastline in Egypt using Polarimetric SAR and High Resolution Optical Remote Sensing Data , 2018 .
[11] R. Ryan,et al. Remote Sensing Time Series Product Tool , 2006 .
[12] S. Weise,et al. Structure and composition of cocoa agroforests in the humid forest zone of Southern Cameroon , 2017, Agroforestry Systems.
[13] A. Skidmore,et al. Narrow band vegetation indices overcome the saturation problem in biomass estimation , 2004 .
[14] N. Silleos,et al. Vegetation Indices: Advances Made in Biomass Estimation and Vegetation Monitoring in the Last 30 Years , 2006 .
[15] J. Shotton,et al. Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning , 2011 .
[16] Mryka Hall-Beyer,et al. Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales , 2017 .
[17] Luis Alonso,et al. Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content , 2011, Sensors.
[18] L. Norgrove,et al. Carbon stocks in shaded Theobroma cacao farms and adjacent secondary forests of similar age in Cameroon , 2013 .
[19] A. Viña,et al. Comparison of different vegetation indices for the remote assessment of green leaf area index of crops , 2011 .
[20] D. Unwin. Geographical information systems and the problem of 'error and uncertainty' , 1995 .
[21] Bernard De Baets,et al. Random Forests as a tool for estimating uncertainty at pixel-level in SAR image classification , 2012, Int. J. Appl. Earth Obs. Geoinformation.
[22] Giorgos Mountrakis,et al. Effect of classifier selection, reference sample size, reference class distribution and scene heterogeneity in per-pixel classification accuracy using 26 Landsat sites , 2018 .