Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data

Remote Sens. 2015, 7(12), 16091-16107; doi:10.3390/rs71215820

Author

Qingting Li ^1,* , Cuizhen Wang ²

, Bing Zhang ¹

 and Linlin Lu ¹

¹

Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China

²

Department of Geography, University of South Carolina, 709 Bull Street, Columbia, SC 29208, USA

^*

Author to whom correspondence should be addressed. 

Academic Editors: Ioannis Gitas and Prasad S. Thenkabail

Received: 2 October 2015 / Revised: 12 November 2015 / Accepted: 19 November 2015 / Published: 2 December 2015

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Abstract 

Cropland mapping via remote sensing can provide crucial information for agri-ecological studies. Time series of remote sensing imagery is particularly useful for agricultural land classification. This study investigated the synergistic use of feature selection, Object-Based Image Analysis (OBIA) segmentation and decision tree classification for cropland mapping using a finer temporal-resolution Landsat-MODIS Enhanced time series in 2007. The enhanced time series extracted 26 layers of Normalized Difference Vegetation Index (NDVI) and five NDVI Time Series Indices (TSI) in a subset of agricultural land of Southwest Missouri. A feature selection procedure using the Stepwise Discriminant Analysis (SDA) was performed, and 10 optimal features were selected as input data for OBIA segmentation, with an optimal scale parameter obtained by quantification assessment of topological and geometric object differences. Using the segmented metrics in a decision tree classifier, an overall classification accuracy of 90.87% was achieved. Our study highlights the advantage of OBIA segmentation and classification in reducing noise from in-field heterogeneity and spectral variation. The crop classification map produced at 30 m resolution provides spatial distributions of annual and perennial crops, which are valuable for agricultural monitoring and environmental assessment studies. View Full-Text

Keywords: object-based;  feature selection;  decision tree;  satellite time series;  crop classification

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