Evaluating the Performance of Multi-Class and Single-Class Classification Approaches for Mountain Agriculture Extraction Using Time-Series NDVI
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Supervised multi-class classification (MCC) approach is widely being used for regional-level land use–land cover (LULC) mapping and monitoring. However, it becomes inefficient if the end user wants to map only one particular class. Therefore, an improved single-class classification (SCC) approach is required for quick and reliable map production purpose. In this regard, the current study attempts to evaluate the performance of MCC and SCC approaches for extracting mountain agriculture area using time-series normalized differential vegetation index (NDVI). At first, samples of eight LULC classes were acquired using Google Earth image, and corresponding temporal signatures (TS) were extracted from time-series NDVI to perform classification using minimum distance to mean (MDM) and spectral angle mapper (i.e., multi-class SAM—MCSAM) under MCC approach. Secondly, under SCC approach, the TS of three agriculture classes (i.e., agriculture, mixed agriculture and plantation) were utilized as a reference to extract agriculture extent using Euclidean distance (ED) and SAM (i.e., single-class SAM—SCSAM) algorithms. The area of all four maps (i.e., MDM—19.77% of total geographical area (TGA), MCSAM—21.07% of TGA, ED—15.23% of TGA, SCSAM—13.85% of TGA) was compared with reference agriculture area (14.54% of TGA) of global land cover product, and SCC-based maps were found to have close agreement. Also, the class-wise detection accuracy was evaluated using random sample point-based error matrix which reveals the better performance of ED-based map than rest three maps in terms of overall accuracy and kappa coefficient.
KeywordsEuclidean distance Spectral angle mapper MODIS NDVI Time series Mountain agriculture
We would like to acknowledge NASA MODIS team and National Geomatics Center of China for making MODIS NDVI product and GLC-30 m data freely available.
- DES. (2015). Statistical abstract of Himachal Pradesh 2014–2015 (pp. 1–189). Shimla: Department of Economics and Statistics, The Government of Himachal Pradesh.Google Scholar
- Erasmi, S., Bothe, M., & Petta, R. A. (2006). Enhanced filtering of MODIS time series data for the analysis of desertification process in northeast Brazil. In Proceedings of the ISPRS/ITC-midterm symposium—remote sensing: From pixels to processes, Enschede, The Netherlands (Vol. 34, No. 30, pp. 8–11).Google Scholar
- Frazier, A. E. & Wang, L. (2011). Optimal Ranges to evaluate sub-pixel classifications for landscape metrics. In ASPRS 2011 annual conference, Milwaukee, Wisconsin (pp. 1–12).Google Scholar
- Husak, G. J., Marshall, M. T., Michaelsen, J., Pedreros, D., Funk, C., & Galu, G. (2008). Crop area estimation using high and medium resolution satellite imagery in areas with complex topography. Journal of Geophysical Research: Atmospheres, 113(D14112), 1–8.Google Scholar
- Jakubauskas, M. E., Legates, D. R., & Kastens, J. H. (2001). Harmonic analysis of time-series AVHRR NDVI data. Photogrammetric Engineering and Remote Sensing, 67(4), 461–470.Google Scholar
- Justice, C., & Becker-Reshef, I. (2007). Developing a strategy for global agricultural monitoring in the framework of the Group on Earth Observations (GEO) Workshop Report (p. 67). Rome: Group on Earth Observations.Google Scholar
- Tax, D. M. J. (2001). One-class classification: Concept-learning in the absence of counterexamples. Ph.D. thesis, Delft University of Technology.Google Scholar
- Vintrou, E., Desbrosse, A., Bégué, A., Traoré, S., Baron, C., & Seen, D. L. (2012). Crop area mapping in West Africa using landscape stratification of MODIS time series and comparison with existing global land products. International Journal of Applied Earth Observation and Geoinformation, 14(1), 83–93.CrossRefGoogle Scholar