Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis

[1]  O. Phillips,et al.  Individual tree crown delineation in a highly diverse tropical forest using very high resolution satellite images , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[2]  P. Brancalion,et al.  Genetic diversity of reintroduced tree populations in restoration plantations of the Brazilian Atlantic Forest , 2018 .

[3]  Marco Heurich,et al.  Tree species classification using plant functional traits from LiDAR and hyperspectral data , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[4]  Jean-Baptiste Féret,et al.  Retrieving structural and chemical properties of individual tree crowns in a highly diverse tropical forest with 3D radiative transfer modeling and imaging spectroscopy , 2018, Remote Sensing of Environment.

[5]  Andrew K. Skidmore,et al.  Connecting infrared spectra with plant traits to identify species : abstract + powerpoint , 2018 .

[6]  Weiqi Zhou,et al.  Mapping vegetation functional types in urban areas with WorldView-2 imagery: integrating object-based classification with phenology. , 2018 .

[7]  P. Couteron,et al.  Disturbance Regimes Drive The Diversity of Regional Floristic Pools Across Guianan Rainforest Landscapes , 2018, Scientific Reports.

[8]  Markus Immitzer,et al.  Estimating stand density, biomass and tree species from very high resolution stereo-imagery- towards an all-in-one sensor for forestry applications? , 2017 .

[9]  Stefan Hinz,et al.  Tree species classification using within crown localization of waveform LiDAR attributes , 2017 .

[10]  O. Mutanga,et al.  Mapping leaf nitrogen and carbon concentrations of intact and fragmented indigenous forest ecosystems using empirical modeling techniques and WorldView-2 data , 2017 .

[11]  Jingjing Zhou,et al.  The Effects of GLCM parameters on LAI estimation using texture values from Quickbird Satellite Imagery , 2017, Scientific Reports.

[12]  Onisimo Mutanga,et al.  Multi-phenology WorldView-2 imagery improves remote sensing of savannah tree species , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[13]  M. Cho,et al.  Improving the classification of six evergreen subtropical tree species with multi-season data from leaf spectra simulated to WorldView-2 and RapidEye , 2017 .

[14]  Michael A. Lefsky,et al.  Review of studies on tree species classification from remotely sensed data , 2016 .

[15]  Susan L. Ustin,et al.  Canopy structural attributes derived from AVIRIS imaging spectroscopy data in a mixed broadleaf/conifer forest , 2016 .

[16]  Y. Shimabukuro,et al.  Mapping tree species in tropical seasonal semi-deciduous forests with hyperspectral and multispectral data , 2016 .

[17]  Huili Gong,et al.  Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images , 2015, Remote. Sens..

[18]  Galal Omer,et al.  Performance of Support Vector Machines and Artificial Neural Network for Mapping Endangered Tree Species Using WorldView-2 Data in Dukuduku Forest, South Africa , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[19]  Moses Azong Cho,et al.  Assessing the utility WorldView-2 imagery for tree species mapping in South African subtropical humid forest and the conservation implications: Dukuduku forest patch as case study , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[20]  Lars T. Waser,et al.  Evaluating the Potential of WorldView-2 Data to Classify Tree Species and Different Levels of Ash Mortality , 2014, Remote. Sens..

[21]  William J. Emery,et al.  The Importance of Physical Quantities for the Analysis of Multitemporal and Multiangular Optical Very High Spatial Resolution Images , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[22]  B. Manly,et al.  Forest destructuring as revealed by the temporal dynamics of fundamental species - Case study of Santa Genebra Forest in Brazil , 2014 .

[23]  Gregory Asner,et al.  Tree Species Discrimination in Tropical Forests Using Airborne Imaging Spectroscopy , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Clement Atzberger,et al.  Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data , 2012, Remote. Sens..

[25]  P. Stevenson,et al.  The Genus Cecropia: A Biological Clock to Estimate the Age of Recently Disturbed Areas in the Neotropics , 2012, PloS one.

[26]  Baoxin Hu,et al.  Individual Urban Tree Species Classification Using Very High Spatial Resolution Airborne Multi-Spectral Imagery Using Longitudinal Profiles , 2012, Remote. Sens..

[27]  G. Arturo Sánchez-Azofeifa,et al.  The effect of seasonal spectral variation on species classification in the Panamanian tropical forest , 2012 .

[28]  Taghi M. Khoshgoftaar,et al.  RUSBoost: A Hybrid Approach to Alleviating Class Imbalance , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[29]  R. Kokaly,et al.  Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies , 2009 .

[30]  Jean Paul Metzger,et al.  The Brazilian Atlantic Forest: How much is left, and how is the remaining forest distributed? Implications for conservation , 2009 .

[31]  Liangpei Zhang,et al.  Evaluation of Morphological Texture Features for Mangrove Forest Mapping and Species Discrimination Using Multispectral IKONOS Imagery , 2009, IEEE Geoscience and Remote Sensing Letters.

[32]  Bruno Aiazzi,et al.  A Comparison Between Global and Context-Adaptive Pansharpening of Multispectral Images , 2009, IEEE Geoscience and Remote Sensing Letters.

[33]  Manfred Ehlers.  Multi-image Fusion in Remote Sensing: Spatial Enhancement vs. Spectral Characteristics Preservation , 2008, ISVC.

[34]  Lorenzo Bruzzone,et al.  Fusion of Hyperspectral and LIDAR Remote Sensing Data for Classification of Complex Forest Areas , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Nikos Koutsias,et al.  Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site , 2008 .

[36]  Frans Bongers,et al.  Architecture of 54 moist-forest tree species: traits, trade-offs, and functional groups. , 2006, Ecology.

[37]  D. Roberts,et al.  Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales , 2005 .

[38]  Nathalie Japkowicz,et al.  The class imbalance problem: A systematic study , 2002, Intell. Data Anal..

[39]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[40]  John A. Richards,et al.  Remote Sensing Digital Image Analysis: An Introduction , 1999 .

[41]  Margaret E. Gardner,et al.  Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models , 1998 .

[42]  G. Asner.  Biophysical and Biochemical Sources of Variability in Canopy Reflectance , 1998 .

[43]  Vladimir Cherkassky,et al.  Learning from Data: Concepts, Theory, and Methods , 1998 .

[44]  D. Doak,et al.  The Keystone-Species Concept in Ecology and ConservationManagement and policy must explicitly consider the complexity of interactions in natural systems , 1993 .

[45]  H. P. Veloso,et al.  Classificação da vegetação brasileira, adaptada a um sistema universal , 1991 .

[46]  E Theodorsson-Norheim,et al.  Kruskal-Wallis test: BASIC computer program to perform nonparametric one-way analysis of variance and multiple comparisons on ranks of several independent samples. , 1986, Computer methods and programs in biomedicine.

[47]  R.M. Haralick,et al.  Statistical and structural approaches to texture , 1979, Proceedings of the IEEE.

[48]  P. Tomlinson,et al.  Tropical Trees and Forests: An Architectural Analysis , 1978 .

[49]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[50]  T. Kailath.  The Divergence and Bhattacharyya Distance Measures in Signal Selection , 1967 .