Use of freely available datasets and machine learning methods in predicting deforestation
暂无分享,去创建一个
Marcus Gallagher | Marc Hockings | Helen Mayfield | Carl Smith | M. Gallagher | Carl S. Smith | M. Hockings | H. Mayfield
[1] Biswajeet Pradhan,et al. A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India) , 2016, Environ. Model. Softw..
[2] Daniel G. Brown,et al. A review of current calibration and validation practices in land-change modeling , 2016, Environ. Model. Softw..
[3] L. D. Estes,et al. A platform for crowdsourcing the creation of representative, accurate landcover maps , 2016, Environ. Model. Softw..
[4] Jing Deng,et al. Hybrid Probabilistic Wind Power Forecasting Using Temporally Local Gaussian Process , 2016, IEEE Transactions on Sustainable Energy.
[5] Gustau Camps-Valls,et al. Mapping Leaf Area Index With a Smartphone and Gaussian Processes , 2015, IEEE Geoscience and Remote Sensing Letters.
[6] Brian E. Robinson,et al. Does secure land tenure save forests? A meta-analysis of the relationship between land tenure and tropical deforestation , 2014 .
[7] Hichem Omrani,et al. Land use changes modelling using advanced methods: Cellular automata and artificial neural networks. The spatial and explicit representation of land cover dynamics at the cross-border region scale , 2014 .
[8] Bryan C. Pijanowski,et al. Comparing three global parametric and local non-parametric models to simulate land use change in diverse areas of the world , 2014, Environ. Model. Softw..
[9] Kurt McLaren,et al. Assessing deforestation and fragmentation in a tropical moist forest over 68 years; the impact of roads and legal protection in the Cockpit Country, Jamaica , 2014 .
[10] Zhanli Sun,et al. Analyzing the drivers of tree planting in Yunnan, China, with Bayesian networks , 2014 .
[11] Neil D. Burgess,et al. Deforestation in an African biodiversity hotspot: extent, variation and the effectiveness of protected areas , 2013 .
[12] Gregory P. Asner,et al. Mapping Recent Deforestation and Forest Disturbance in Northeastern Madagascar , 2013 .
[13] N. Kingston,et al. World Database on Protected Areas (WDPA) , 2013 .
[14] N. Mizoue,et al. Changes in Determinants of Deforestation and Forest Degradation in Popa Mountain Park, Central Myanmar , 2013, Environmental Management.
[15] Norman Fenton,et al. Risk Assessment and Decision Analysis with Bayesian Networks , 2012 .
[16] Luis Cayuela,et al. Evidence of Incipient Forest Transition in Southern Mexico , 2012, PloS one.
[17] Vasilios P. Papanastasis,et al. Land Use Changes , 2012 .
[18] Arika Ligmann-Zielinska,et al. Comparing two approaches to land use/cover change modeling and their implications for the assessment of biodiversity loss in a deciduous tropical forest , 2012, Environ. Model. Softw..
[19] R. Müller,et al. Spatiotemporal modeling of the expansion of mechanized agriculture in the Bolivian lowland forests , 2011 .
[20] Sasmita Sahoo,et al. Assessing the extent and causes of forest degradation in India: Where do we stand? , 2010 .
[21] Ralf Wieland,et al. Classification in conservation biology: A comparison of five machine-learning methods , 2010, Ecol. Informatics.
[22] Carl S. Smith,et al. Predicting a 'tree change' in Australia's tropical savannas: Combining different types of models to understand complex ecosystem behaviour , 2010 .
[23] Vincent Calcagno,et al. glmulti: An R Package for Easy Automated Model Selection with (Generalized) Linear Models , 2010 .
[24] M. Hudson,et al. Prioritizing key biodiversity areas in Madagascar by including data on human pressure and ecosystem services , 2010 .
[25] R. DeFries,et al. Deforestation driven by urban population growth and agricultural trade in the twenty-first century , 2010 .
[26] Carl E. Rasmussen,et al. Gaussian Processes for Machine Learning (GPML) Toolbox , 2010, J. Mach. Learn. Res..
[27] Simon Haykin,et al. Neural Networks and Learning Machines , 2010 .
[28] Markku Kanninen,et al. Evaluating whether protected areas reduce tropical deforestation in Sumatra , 2009 .
[29] Sebastian Bassi,et al. Python Language Reference , 2009 .
[30] Tonny J. Oyana,et al. Mapping and spatial uncertainty analysis of forest vegetation carbon by combining national forest inventory data and satellite images , 2009 .
[31] Haibo He,et al. Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.
[32] Edward A. Ellis,et al. Is community-based forest management more effective than protected areas?: A comparison of land use/land cover change in two neighboring study areas of the Central Yucatan Peninsula, Mexico. , 2008 .
[33] Jon C. Lovett,et al. Predicting tree distributions in an East African biodiversity hotspot: model selection, data bias and envelope uncertainty , 2008 .
[34] R. Real,et al. AUC: a misleading measure of the performance of predictive distribution models , 2008 .
[35] N. Dudley. Guidelines for applying protected area management categories , 2008 .
[36] Philippe Mayaux,et al. Using remote sensing to inform conservation status assessment: Estimates of recent deforestation rates on New Britain and the impacts upon endemic birds , 2008 .
[37] C. Bradshaw,et al. Global evidence that deforestation amplifies flood risk and severity in the developing world , 2007 .
[38] Laura Uusitalo,et al. Advantages and challenges of Bayesian networks in environmental modelling , 2007 .
[39] Hannes Isaak Reuter,et al. An evaluation of void‐filling interpolation methods for SRTM data , 2007, Int. J. Geogr. Inf. Sci..
[40] Omri Allouche,et al. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS) , 2006 .
[41] Ana M. Aguilera,et al. Using principal components for estimating logistic regression with high-dimensional multicollinear data , 2006, Comput. Stat. Data Anal..
[42] Can Ozan Tan,et al. Predictive models in ecology: Comparison of performances and assessment of applicability , 2005, Ecol. Informatics.
[43] Deepak K. Agarwal,et al. Tropical deforestation in Madagascar: analysis using hierarchical, spatially explicit, Bayesian regression models , 2005 .
[44] Jean-François Mas,et al. Assessing protected area effectiveness using surrounding (buffer) areas environmentally similar to the target area , 2005, Environmental monitoring and assessment.
[45] Jean-François Mas,et al. Modelling deforestation using GIS and artificial neural networks , 2004, Environ. Model. Softw..
[46] William J. McConnell,et al. Physical and social access to land: spatio-temporal patterns of agricultural expansion in Madagascar , 2004 .
[47] Ronald,et al. Learning representations by backpropagating errors , 2004 .
[48] Christina Gloeckner,et al. Modern Applied Statistics With S , 2003 .
[49] S. Bergen,et al. Predictors of deforestation in the Brazilian Amazon , 2002 .
[50] E. Dinerstein,et al. The Global 200: Priority ecoregions for global conservation , 2002 .
[51] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[52] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[53] G. Powell,et al. Terrestrial Ecoregions of the World: A New Map of Life on Earth , 2001 .
[54] Eric F. Lambin,et al. What drives tropical deforestation?: a meta-analysis of proximate and underlying causes of deforestation based on subnational case study evidence , 2001 .
[55] David Lindley,et al. Introduction to the Practice of Statistics , 1990, The Mathematical Gazette.
[56] H. J. Arnold. Introduction to the Practice of Statistics , 1990 .
[57] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.