Quantitative Spatial Upscaling of Categorical Data in the Context of Landscape Ecology: A New Scaling Algorithm

[1]  Brigitte Poulin,et al.  Wetland monitoring using classification trees and SPOT-5 seasonal time series , 2010 .

[2]  Liquan Zhang,et al.  Multi-seasonal spectral characteristics analysis of coastal salt marsh vegetation in Shanghai, China , 2006 .

[3]  C. Brodley,et al.  Decision tree classification of land cover from remotely sensed data , 1997 .

[4]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[5]  M. Friedl,et al.  Land cover mapping in support of LAI and FPAR retrievals from EOS-MODIS and MISR: Classification methods and sensitivities to errors , 2003 .

[6]  Thomas G. Dietterich,et al.  Map Misclassification Can Cause Large Errors in Landscape Pattern Indices: Examples from Habitat Fragmentation , 2006, Ecosystems.

[7]  Remote Sensing Supported Vegetation Detection in the Hole-in-the-Donut Restoration Areas Report, September 2014 , 2014 .

[8]  Claus Weihs,et al.  klaR Analyzing German Business Cycles , 2005, Data Analysis and Decision Support.

[9]  P. Atkinson,et al.  Introduction Neural networks in remote sensing , 1997 .

[10]  Eric C. Brown de Colstoun,et al.  Improving global scale land cover classifications with multi-directional POLDER data and a decision tree classifier , 2006 .

[11]  Max Kuhn,et al.  caret: Classification and Regression Training , 2015 .

[12]  T. Sakamoto,et al.  A crop phenology detection method using time-series MODIS data , 2005 .

[13]  V. Engel.  Landscapes and Hydrology of the Predrainage Everglades , 2012, Wetlands.

[14]  Stacy L. Ozesmi,et al.  Satellite remote sensing of wetlands , 2002, Wetlands Ecology and Management.

[15]  Roger Bivand,et al.  Bindings for the Geospatial Data Abstraction Library , 2015 .

[16]  Jacob Cohen,et al.  Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. , 1968 .

[17]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Giles M. Foody,et al.  A relative evaluation of multiclass image classification by support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[19]  D. Gann,et al.  Detecting Calcareous Periphyton Mats in the Greater Everglades Using Passive Remote Sensing Methods , 2015 .

[20]  W. Kitchens,et al.  Multi-state succession in wetlands: a novel use of state and transition models. , 2009, Ecology.

[21]  B. Datt,et al.  On the relationship between training sample size and data dimensionality: Monte Carlo analysis of broadband multi-temporal classification , 2005 .

[22]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[23]  Guangxing Wang,et al.  A Methodology for Spatial Uncertainty Analysis Of Remote Sensing and GIS Products , 2005 .

[24]  Raimon Tolosana-Delgado,et al.  "compositions": A unified R package to analyze compositional data , 2008, Comput. Geosci..

[25]  Yegang Wu,et al.  SOUTH FLORIDA: THE REALITY OF CHANGE AND THE PROSPECTS FOR SUSTAINABILITY: The design of ecological landscape models for Everglades restoration , 2001 .

[26]  Marie Weiss,et al.  Mapping Biophysical Variables From Solar and Thermal Infrared Remote Sensing: Focus on Agricultural Landscapes With Spatial Heterogeneity , 2014, IEEE Geoscience and Remote Sensing Letters.

[27]  Yasushi Yamaguchi,et al.  Scaling of land surface temperature using satellite data: A case examination on ASTER and MODIS products over a heterogeneous terrain area , 2006 .

[28]  J. Welker,et al.  Modeling the effect of photosynthetic vegetation properties on the NDVI--LAI relationship. , 2006, Ecology.

[29]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[30]  Antoine Guisan,et al.  Predictive habitat distribution models in ecology , 2000 .

[31]  A. Jakomulska,et al.  Uncertainty in Land Cover Mapping from Remotely Sensed Data Using Textural Algorithm and Artificial Neural Networks , 2006 .

[32]  Francesca Bovolo,et al.  A support vector domain method for change detection in multitemporal images , 2010, Pattern Recognit. Lett..

[33]  D. Gann,et al.  Evaluating High-Resolution Aerial Photography Acquired by Unmanned Aerial Systems for Use in Mapping Everglades Wetland Plant Associations , 2013 .

[34]  Consulting Services to Determine the Effectiveness of Vegetation Classification Using WorldView 2 Satellite Data for the Greater Everglades , 2012 .

[35]  Rahul Khanna,et al.  Support Vector Machines for Classification , 2015 .

[36]  Mariana Belgiu,et al.  Random forest in remote sensing: A review of applications and future directions , 2016 .

[37]  Flávio Jorge Ponzoni,et al.  Calibration of a Species-Specific Spectral Vegetation Index for Leaf Area Index (LAI) Monitoring: Example with MODIS Reflectance Time-Series on Eucalyptus Plantations , 2012, Remote. Sens..

[38]  D. Lu.  The potential and challenge of remote sensing‐based biomass estimation , 2006 .

[39]  Daniel Gann,et al.  Using Bi-Seasonal WorldView-2 Multi-Spectral Data and Supervised Random Forest Classification to Map Coastal Plant Communities in Everglades National Park , 2018, Sensors.

[40]  John W. Jones,et al.  An approach to regional wetland digital elevation model development using a differential global positioning system and a custom-built helicopter-based surveying system , 2012 .

[41]  A. Melesse,et al.  Land use and climate change impacts on the hydrology of the upper Mara River Basin, Kenya: results of a modeling study to support better resource management , 2011 .

[42]  Keqi Zhang,et al.  Remote sensing of seasonal changes and disturbances in mangrove forest: a case study from South Florida , 2016 .

[43]  D. Gann,et al.  Quantitative Comparison of Plant Community Hydrology Using Large-Extent, Long-Term Data , 2015, Wetlands.

[44]  Giles M. Foody,et al.  An evaluation of some factors affecting the accuracy of classification by an artificial neural network , 1997 .

[45]  Zoltan Szantoi,et al.  Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features , 2015, Environmental Monitoring and Assessment.

[46]  V. Radeloff,et al.  Phenological differences in Tasseled Cap indices improve deciduous forest classification , 2002 .

[47]  B. Chandrasekaran,et al.  On dimensionality and sample size in statistical pattern classification , 1971, Pattern Recognit..

[48]  Anil K. Jain,et al.  39 Dimensionality and sample size considerations in pattern recognition practice , 1982, Classification, Pattern Recognition and Reduction of Dimensionality.

[49]  D. Gann,et al.  Vegetation Trends in Indicator Regions of Everglades National Park , 2015 .

[50]  P. Atkinson,et al.  Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture , 2012 .

[51]  J. Dungan,et al.  Generating global Leaf Area Index from Landsat: Algorithm formulation and demonstration , 2012 .

[52]  Giles M. Foody,et al.  The effect of training set size and composition on artificial neural network classification , 1995 .

[53]  P. C. Smits,et al.  QUALITY ASSESSMENT OF IMAGE CLASSIFICATION ALGORITHMS FOR LAND-COVER MAPPING , 1999 .

[54]  Yang Shao,et al.  Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points , 2012 .

[55]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[56]  Marie Laure Delignette-Muller,et al.  A Toolbox for Nonlinear Regression in R: The Package nlstools , 2015 .

[57]  Mahesh Pal,et al.  Random forest classifier for remote sensing classification , 2005 .

[58]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[59]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[60]  Edit J. Kaminsky,et al.  Neural network classification of remote-sensing data , 1995 .

[61]  John W. Jones.  Remote Sensing of Vegetation Pattern and Condition to Monitor Changes in Everglades Biogeochemistry , 2011 .

[62]  S. Running,et al.  MODIS Leaf Area Index (LAI) And Fraction Of Photosynthetically Active Radiation Absorbed By Vegetation (FPAR) Product , 1999 .

[63]  V. Engel,et al.  Recent and Historic Drivers of Landscape Change in the Everglades Ridge, Slough, and Tree Island Mosaic , 2011 .

[64]  Zhong Lu,et al.  Monitoring Everglades freshwater marsh water level using L-band synthetic aperture radar backscatter , 2014 .

[65]  Phaedon C. Kyriakidis,et al.  A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model predictions , 2001, Environmental and Ecological Statistics.

[66]  Janet Franklin,et al.  Mapping land-cover modifications over large areas: A comparison of machine learning algorithms , 2008 .

[67]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[68]  Donald L. DeAngelis,et al.  Spatially Explicit Modeling in Ecology: A Review , 2016, Ecosystems.

[69]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[70]  A. Melesse,et al.  A Modeling Approach to Determine the Impacts of Land Use and Climate Change Scenarios on the Water Flux of the Upper Mara River , 2010 .

[71]  Bing Zhang,et al.  A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information , 2014 .

[72]  Thomas G. Dietterich.  Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.

[73]  G. Foody.  Classification accuracy comparison: hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority , 2009 .

[74]  J. L. Hodges,et al.  Estimates of Location Based on Rank Tests , 1963 .

[75]  David G. Rossiter,et al.  Technical Note: Statistical methods for accuracy assesment of classified thematic maps , 2004 .