Estimating the Above-Ground Biomass in Miombo Savanna Woodlands (Mozambique, East Africa) Using L-Band Synthetic Aperture Radar Data
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João Manuel de Brito Carreiras | Maria J. P. de Vasconcelos | Joana B. Melo | J. Carreiras | M. Vasconcelos | J. Melo
[1] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[2] James H. Torrie,et al. Principles and procedures of statistics: a biometrical approach (2nd ed) , 1980 .
[3] Manabu Watanabe,et al. ALOS PALSAR: A Pathfinder Mission for Global-Scale Monitoring of the Environment , 2007, IEEE Transactions on Geoscience and Remote Sensing.
[4] Sandra A. Brown,et al. Monitoring and estimating tropical forest carbon stocks: making REDD a reality , 2007 .
[5] Casey M. Ryan,et al. Carbon sequestration and biodiversity of re-growing miombo woodlands in Mozambique , 2008 .
[6] J. V. Soares,et al. Distribution of aboveground live biomass in the Amazon basin , 2007 .
[7] Richard E. Lewis,et al. At the heart of REDD+: a role for local people in monitoring forests? , 2011 .
[8] I. Woodhouse,et al. Quantifying small‐scale deforestation and forest degradation in African woodlands using radar imagery , 2012 .
[9] Leo Breiman,et al. Classification and Regression Trees , 1984 .
[10] Trevor Hastie,et al. The Elements of Statistical Learning , 2001 .
[11] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[12] Bruce M. Campbell,et al. Beyond Copenhagen: REDD+, agriculture, adaptation strategies and poverty , 2009 .
[13] Damien Sulla-Menashe,et al. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .
[14] John A. Richards,et al. Remote Sensing with Imaging Radar , 2009 .
[15] J. Carreiras,et al. Assessing the extent of agriculture/pasture and secondary succession forest in the Brazilian Legal Amazon using SPOT VEGETATION data , 2006 .
[16] Birger Solberg,et al. Estimation of biomass and volume in Miombo Woodland at Kitulangalo Forest Reserve, Tanzania , 1994 .
[17] Ha Henny Romijn. Land clearing and greenhouse gas emissions from Jatropha biofuels on African Miombo Woodlands , 2011 .
[18] Casey M. Ryan,et al. Above‐ and Belowground Carbon Stocks in a Miombo Woodland Landscape of Mozambique , 2011 .
[19] S. Popescu,et al. Satellite lidar vs. small footprint airborne lidar: Comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level , 2011 .
[20] I. Woodhouse. Introduction to Microwave Remote Sensing , 2005 .
[21] R. Houghton,et al. Characterizing 3D vegetation structure from space: Mission requirements , 2011 .
[22] David A. Landgrebe,et al. A survey of decision tree classifier methodology , 1991, IEEE Trans. Syst. Man Cybern..
[23] L. Montanarella,et al. Estimating forest soil bulk density using boosted regression modelling , 2010 .
[24] L. Breiman. Arcing classifier (with discussion and a rejoinder by the author) , 1998 .
[25] J. Friedman. Stochastic gradient boosting , 2002 .
[26] C. Brodley,et al. Decision tree classification of land cover from remotely sensed data , 1997 .
[27] J. Chambers,et al. Tree allometry and improved estimation of carbon stocks and balance in tropical forests , 2005, Oecologia.
[28] Eric Bauer,et al. An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.
[29] N. H. Ravindranath,et al. 2006 IPCC Guidelines for National Greenhouse Gas Inventories , 2006 .
[30] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[31] A. Marshall,et al. Carbon storage, structure and composition of miombo woodlands in Tanzania's Eastern Arc Mountains , 2011 .
[32] Masanobu Shimada,et al. An Evaluation of the ALOS PALSAR L-Band Backscatter—Above Ground Biomass Relationship Queensland, Australia: Impacts of Surface Moisture Condition and Vegetation Structure , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[33] Maxim Neumann,et al. Assessing Performance of L- and P-Band Polarimetric Interferometric SAR Data in Estimating Boreal Forest Above-Ground Biomass , 2012, IEEE Transactions on Geoscience and Remote Sensing.
[34] Erich Meier,et al. Rigorous Derivation of Backscattering Coefficient. , 1994 .
[35] Ariel E. Lugo,et al. Biomass Estimation Methods for Tropical Forests with Applications to Forest Inventory Data , 1989, Forest Science.
[36] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[37] E. Chidumayo,et al. Miombo Ecology and Management: An Introduction , 1997 .
[38] J. Carreiras,et al. Understanding the relationship between aboveground biomass and ALOS PALSAR data in the forests of Guinea-Bissau (West Africa) , 2012 .
[39] J. Carreiras,et al. Greenhouse gas emissions from shifting cultivation in the tropics, including uncertainty and sensitivity analysis , 2011 .
[40] J Elith,et al. A working guide to boosted regression trees. , 2008, The Journal of animal ecology.
[41] S. Goetz,et al. Mapping and monitoring carbon stocks with satellite observations: a comparison of methods , 2009, Carbon balance and management.
[42] Greg Ridgeway,et al. Generalized Boosted Models: A guide to the gbm package , 2006 .
[43] S. Goetz,et al. Reply to Comment on ‘A first map of tropical Africa’s above-ground biomass derived from satellite imagery’ , 2008, Environmental Research Letters.
[44] Michael G. Wing,et al. Airborne Light Detection and Ranging (LiDAR) for Individual Tree Stem Location, Height, and Biomass Measurements , 2011, Remote. Sens..
[45] L. Breiman. Arcing Classifiers , 1998 .
[46] B. Campbell. The miombo in transition: woodlands and welfare in Africa. , 1996 .
[47] S. Goetz,et al. Importance of biomass in the global carbon cycle , 2009 .
[48] Glenn De ' ath. BOOSTED TREES FOR ECOLOGICAL MODELING AND PREDICTION , 2007 .
[49] C. Kroeze. N2O from animal waste. Methodology according to IPCC Guidelines for National Greenhouse Gas Inventories. , 1997 .
[50] Corinne Le Quéré,et al. Trends in the sources and sinks of carbon dioxide , 2009 .
[51] M. Herold,et al. Capacity development in national forest monitoring: Experiences and progress for REDD+ , 2012 .
[52] F. Ulaby,et al. Handbook of radar scattering statistics for terrain , 1989 .
[53] Niall P. Hanan,et al. Woody cover in African savannas: the role of resources, fire and herbivory , 2008 .
[54] W. Cohen,et al. Estimates of forest canopy height and aboveground biomass using ICESat , 2005 .
[55] K. Tully,et al. Untangling a Decline in Tropical Forest Resilience: Constraints on the Sustainability of Shifting Cultivation Across the Globe , 2010 .
[56] J. Chambers,et al. Regional ecosystem structure and function: ecological insights from remote sensing of tropical forests. , 2007, Trends in ecology & evolution.
[57] Sassan Saatchi,et al. Comment on ‘A first map of tropical Africa’s above-ground biomass derived from satellite imagery’ , 2011 .
[58] Raymond J. Mooney,et al. Combining Bias and Variance Reduction Techniques for Regression Trees , 2005, ECML.
[59] Sassan Saatchi,et al. Estimation of Forest Fuel Load From Radar Remote Sensing , 2007, IEEE Transactions on Geoscience and Remote Sensing.
[60] K. Annan. Center for International Forestry Research Center for International , 2001 .
[61] R. DeFries,et al. Classification trees: an alternative to traditional land cover classifiers , 1996 .
[62] P. R. Bevington,et al. Data Reduction and Error Analysis for the Physical Sciences , 1969 .
[63] S. Quegan,et al. Understanding Synthetic Aperture Radar Images , 1998 .
[64] M. Herold,et al. A step-wise framework for setting REDD+ forest reference emission levels and forest reference levels , 2012 .
[65] Niklaus E. Zimmermann,et al. Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods , 2006 .
[66] R. B. Jackson,et al. A Large and Persistent Carbon Sink in the World’s Forests , 2011, Science.
[67] W. Salas,et al. Benchmark map of forest carbon stocks in tropical regions across three continents , 2011, Proceedings of the National Academy of Sciences.
[68] J. Glenday. Carbon storage and emissions offset potential in an African dry forest, the Arabuko-Sokoke Forest, Kenya , 2008, Environmental monitoring and assessment.