Pixel-based crop classification in Peru from Landsat 7 ETM+ images using a Random Forest model

Crop classification within large agricultural regions is challenging owing to the presence of crops with similar phenological variation and intra-class variability. The development of efficient and simple classification methods is needed for more accurate mapping, monitoring, and analysis of land-use categories. Multi-seasonal aggregated statistical variables of Tasseled-Cap (TC) bands (brightness (B), greenness (G), and wetness (W)) obtained from the Landsat 7 Enhanced Thematic Mapper Plus satellite (Landsat 7 ETM+) covering cropped areas in the catchments of the Ica and Grande Rivers of Peru were evaluated to assess the performance of random forest (RF) classifiers in identifying crop type. The effects of various TC band combinations on the classification results were also examined. Seventeen crops (asparagus, cotton, grape, maize, mango, and so on) were included. Overall accuracy and kappa coefficient analyses showed that the three-band combination of B–G–W, using multi-seasonal data, led to more accurate classification than did other combinations, yielding values of 86% and 0.81, respectively. The results indicate that employing aggregated statistical variables of TC bands in conjunction with RF classification techniques by using freely available multi-temporal satellite image data is not only a useful but also more economical and computationally efficient method for crop classification than the current one.

[1]  Giles M. Foody,et al.  Supervised image classification by MLP and RBF neural networks with and without an exhaustively defined set of classes , 2004 .

[2]  Chenghai Yang,et al.  Original paper: Evaluating high resolution SPOT 5 satellite imagery for crop identification , 2011 .

[3]  V. Simonneaux,et al.  The use of high‐resolution image time series for crop classification and evapotranspiration estimate over an irrigated area in central Morocco , 2008 .

[4]  Y. L. Everingham,et al.  Advanced satellite imagery to classify sugarcane crop characteristics , 2007, Agronomy for Sustainable Development.

[5]  Mohammad Ali Ghorbani,et al.  Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks , 2010, Comput. Geosci..

[6]  Christopher Conrad,et al.  Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data , 2010, Remote. Sens..

[7]  J. Pereira,et al.  Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest , 2012 .

[8]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[9]  E. Crist.  A TM Tasseled Cap equivalent transformation for reflectance factor data , 1985 .

[10]  D. R. Cutler,et al.  Utah State University From the SelectedWorks of , 2017 .

[11]  S. Panigrahy,et al.  Mapping of crop rotation using multidate Indian Remote Sensing Satellite digital data , 1997 .

[12]  Giles M. Foody,et al.  Crop classification by support vector machine with intelligently selected training data for an operational application , 2008 .

[13]  G. Vieilledent,et al.  Estimating deforestation in tropical humid and dry forests in Madagascar from 2000 to 2010 using multi-date Landsat satellite images and the random forests classifier , 2013 .

[14]  Björn Waske,et al.  Classifier ensembles for land cover mapping using multitemporal SAR imagery , 2009 .

[15]  Simon D. Jones,et al.  The Performance of Random Forests in an Operational Setting for Large Area Sclerophyll Forest Classification , 2013, Remote. Sens..

[16]  C. Lippitt,et al.  Mapping Selective Logging in Mixed Deciduous Forest: A Comparison of Machine Learning Algorithms , 2008 .

[17]  B. Wardlow,et al.  Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains , 2007 .

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

[19]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[20]  Richard R. Irish,et al.  Landsat 7 automatic cloud cover assessment , 2000, SPIE Defense + Commercial Sensing.

[21]  Ruiliang Pu,et al.  Penalized discriminant analysis of in situ hyperspectral data for conifer species recognition , 1999, IEEE Trans. Geosci. Remote. Sens..

[22]  Jennifer A. Miller,et al.  Contextual land-cover classification: incorporating spatial dependence in land-cover classification models using random forests and the Getis statistic , 2010 .

[23]  R. Tibshirani,et al.  Penalized Discriminant Analysis , 1995 .

[24]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[25]  D. Bargiel,et al.  Capabilities of high resolution satellite radar for the detection of semi-natural habitat structures and grasslands in agricultural landscapes , 2013, Ecol. Informatics.

[26]  Mario Chica-Olmo,et al.  An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .

[27]  M. Pal,et al.  Random forests for land cover classification , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[28]  Julie B. Odenweller,et al.  Crop identification using Landsat temporal-spectral profiles , 1984 .

[29]  E. Witkowski,et al.  Classification of the indigenous forests of Mpumalanga Province, South Africa , 2014 .

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

[31]  Saskia Foerster,et al.  Crop type mapping using spectral-temporal profiles and phenological information , 2012 .

[32]  Johannes R. Sveinsson,et al.  Random Forests for land cover classification , 2006, Pattern Recognit. Lett..

[33]  P. Gong,et al.  Conifer species recognition: Effects of data transformation , 2001 .

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

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

[36]  Meiling Liu,et al.  Multivariable integration method for estimating sea surface salinity in coastal waters from in situ data and remotely sensed data using random forest algorithm , 2015, Comput. Geosci..

[37]  Guoping Qiu,et al.  Automatic habitat classification using image analysis and random forest , 2014, Ecol. Informatics.

[38]  Joydeep Ghosh,et al.  Investigation of the random forest framework for classification of hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[39]  Gonzalo Pajares,et al.  Support Vector Machines for crop/weeds identification in maize fields , 2012, Expert Syst. Appl..