Evaluating EO1-Hyperion capability for mapping conifer and broadleaved forests

Abstract The objective of the present study is the comparison of the combined use of Earth Observation-1 (EO-1) Hyperion Hyperspectral images with the Random Forest (RF), Support Vector Machines (SVM) and Multivariate Adaptive Regression Splines (MARS) classifiers for discriminating forest cover groups, namely broadleaved and coniferous forests. Statistics derived from classification confusion matrix were used to assess the accuracy of the derived thematic maps. We demonstrated that Hyperion data can be effectively used to obtain rapid and accurate large-scale mapping of main forest types (conifers-broadleaved). We also verified higher capability of Hyperion imagery with respect to Landsat data to such an end. Results demonstrate the ability of the three tested classification methods, with small improvements given by SVM in terms of overall accuracy and kappa statistic.

[1]  Stefano Pignatti,et al.  Evaluating Hyperion capability for land cover mapping in a fragmented ecosystem : Pollino National Park, Italy , 2009 .

[2]  George P. Petropoulos,et al.  Hyperion hyperspectral imagery analysis combined with machine learning classifiers for land use/cover mapping , 2012, Expert Syst. Appl..

[3]  C. Özkan,et al.  Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities , 2004 .

[4]  Qihao Weng,et al.  A survey of image classification methods and techniques for improving classification performance , 2007 .

[5]  Rick L. Lawrence,et al.  The AmericaView classification methods accuracy comparison project: A rigorous approach for model selection , 2015 .

[6]  Lorenzo Fattorini,et al.  Sampling strategies for estimating forest cover from remote sensing-based two-stage inventories , 2015, Forest Ecosystems.

[7]  Hitendra Padalia,et al.  Forest tree species discrimination in western Himalaya using EO-1 Hyperion , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[8]  Nicholas C. Coops,et al.  Characterizing temperate forest structural and spectral diversity with Hyperion EO-1 data , 2010 .

[9]  Taskin Kavzoglu,et al.  A kernel functions analysis for support vector machines for land cover classification , 2009, Int. J. Appl. Earth Obs. Geoinformation.

[10]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[11]  Piermaria Corona,et al.  Consolidating new paradigms in large-scale monitoring and assessment of forest ecosystems. , 2016, Environmental research.

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

[13]  Jie Wang,et al.  Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery , 2014, Remote. Sens..

[14]  Jungho Im,et al.  Support vector machines in remote sensing: A review , 2011 .

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

[16]  Anna Barbati,et al.  Evaluating the Effects of Environmental Changes on the Gross Primary Production of Italian Forests , 2009, Remote. Sens..

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

[18]  Kurt Hornik,et al.  Misc Functions of the Department of Statistics (e1071), TU Wien , 2014 .

[19]  Francisco Javier de Cos Juez,et al.  Artificial neural networks applied to cancer detection in a breast screening programme , 2010, Math. Comput. Model..

[20]  Dhaval Vyas,et al.  Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[21]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Rick L. Lawrence,et al.  Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis , 2004 .

[23]  M. Ashton,et al.  Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications , 2004 .

[24]  Tena I. Katsaounis,et al.  Analyzing Multivariate Data , 2004, Technometrics.

[25]  Lorenzo Bruzzone,et al.  The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas. , 2007 .

[26]  J. Freidman,et al.  Multivariate adaptive regression splines , 1991 .

[27]  Jonathan Cheung-Wai Chan,et al.  Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery , 2008 .

[28]  Jordi Cristóbal,et al.  Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity , 2013, Int. J. Appl. Earth Obs. Geoinformation.