Research Papers

Regional-scale grassland classification using moderate-resolution imaging spectrometer datasets based on multistep unsupervised classification and indices suitability analysis

[+] Author Affiliations
Xuemei Yang

Lanzhou University, Institute of Glaciology and Ecogeography, College of Earth and Environmental Sciences, Lanzhou 730000, China

Taibao Yang

Lanzhou University, Institute of Glaciology and Ecogeography, College of Earth and Environmental Sciences, Lanzhou 730000, China

Qin Ji, Yi He

Lanzhou University, Institute of Glaciology and Ecogeography, College of Earth and Environmental Sciences, Lanzhou 730000, China

Mihretab G. Ghebrezgabher

Lanzhou University, Institute of Glaciology and Ecogeography, College of Earth and Environmental Sciences, Lanzhou 730000, China

Eritrea Institute of Technology, College of Education, Mai-Nefhi 12676, Eritrea

J. Appl. Remote Sens. 8(1), 083548 (Oct 01, 2014). doi:10.1117/1.JRS.8.083548
History: Received April 28, 2014; Revised September 5, 2014; Accepted September 8, 2014
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Abstract.  This study used the normalized difference vegetation index (NDVI), in conjunction with other ancillary indices, a digital elevation model (DEM), and the multistep unsupervised classification method to classify grassland in Gansu Province and the Qilian Mountains in China. The results showed that the overall accuracy of vegetation type reached 88.79% and that of grassland coverage level reached 87.23%. The ancillary indices suitability analysis revealed that meadow was distributed mainly in zones where the normalized difference moisture index (NDMI) varied between 0.64 and 0.4, whereas for steppe, it varied between 0.55 and 0.32. Grassland with a different coverage level was mainly distributed in zones where the normalized difference soil index (NDSI) varied between 0.20 and 0.25. To demonstrate the usability of these two indices, the maximum values of NDVI, NDMI, and NDSI and the DEM were used in the decision tree classification method for grassland. The results achieved relatively high kappa coefficients of 77.09% for vegetation type and 65.29% for grassland coverage level. Based on these results, it can be concluded that it is rational to apply the multistep unsupervised classification method and the selected indices for regional-scale grassland identification when a priori information is scarce, expensive, or unsuitable.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Topics

Vegetation

Citation

Xuemei Yang ; Taibao Yang ; Qin Ji ; Yi He and Mihretab G. Ghebrezgabher
"Regional-scale grassland classification using moderate-resolution imaging spectrometer datasets based on multistep unsupervised classification and indices suitability analysis", J. Appl. Remote Sens. 8(1), 083548 (Oct 01, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083548


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