Abstract
We tested the Moment Distance Index (MDI) in combination with texture features for the summer vegetation mapping in the eastern Pamir Mountains, Tajikistan using the 2014 Landsat OLI (Operational Land Imager) image. The five major classes identified were sparse vegetation, medium-dense vegetation, dense vegetation, barren land, and water bodies. By utilizing object features in a random forest (RF) classifier, the overall classification accuracy of the land cover maps were 92% using a set of variables including texture features and MDI, and 84% using a set of variables including texture but without MDI. A decrease of the Kappa statistics, from 0.89 to 0.79, was observed when MDI was removed from the set of predictor variables. McNemar’s test showed that the increase in the classification accuracy due to the addition of MDI was statistically significant (p < 0.05). The proposed method provides an effective way of discriminating sparse vegetation from barren land in an arid environment, such as the Pamir Mountains.
Highlights
Various remote sensing approaches have been devised to generate land-use/land-cover (LULC)maps with improved classification accuracy and with a relatively low production cost
A high value of the normalized variable indicates that the variable has a high contribution for the entire random forest (RF)
The input layers at the top of the list were predominantly based on spectral data, such as Normalized Difference Vegetation Index (NDVI) and Moment Distance Index (MDI), with the exemption of the Digital Elevation Model (DEM) that ranked within the top three
Summary
Various remote sensing approaches have been devised to generate land-use/land-cover (LULC)maps with improved classification accuracy and with a relatively low production cost. Efforts to find crucial variables for classifying digital images and produce accurate LULC maps have been an important component of remote sensing studies in the past two decades [1,2]. Classification schemes that only utilize the spectral variables derived from image pixels used to be the most popular go-to procedure for LULC, delineating for instance, water bodies [3,4,5], urban areas [6,7,8], and vegetation [9,10]. Apart from the spectral information, the spatial information or the relationship between neighboring pixels were explored through object-based image analysis (OBIA) [11,12]. The OBIA approach generally improves classification accuracy with respect to the traditional pixel-based approach [13,14,15,16,17,18]. OBIA is preferable since an object is represented in its true spatial landscape pattern instead of a squared classified pixel [19]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
