FREE: Open-Access Geographic Data for the Argali Habitat in the Southeastern Tajik Pamirs

Open-Access Geographic Data for the Argali Habitat in the Southeastern Tajik Pamirs
by Eric Ariel L. Salas, Raul Valdez, and Kenneth G. Boykin

Abstract
Seven Geographic Information System (GIS) layers comprise this dataset intended for understanding the Marco Polo argali habitat in the southeastern Tajikistan Pamirs (37°33′ N, 74°09′ E). Extensive remote sensing habitat data processing and field data analysis of the Marco Polo sheep study area have yielded these layers that are now available online to download and for use by other researchers interested in studying the argali patterns and habitat suitability in the southeastern Tajik Pamirs. It is important to note that the layers were generated using a 30-m Landsat ETM image and field data from 2012.
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SAS Code For Discriminant Analysis For Two Groups

Multivariate Statistics: SAS program code for Discriminant Analysis for Two Groups. SAS software has a procedure “PROC DISCRIM” for performing Discriminant Analysis. The procedure can create a linear discriminant function for a given dataset. The linear discriminant function can be used to classify new observations to one of the two available populations.

Discriminant analysis is sometimes know as classification analysis. It can be used to build rules that can classify new observations into two or more labeled classes. The emphasis is on deriving a rule that can be used to optimally assign new observations to the labeled classes.
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Multispectral and Texture Feature Application in Image-Object Analysis of Summer Vegetation in Eastern Tajikistan Pamirs

Multispectral and Texture Feature Application in Image-Object Analysis of Summer Vegetation in Eastern Tajikistan Pamirs
by Eric Ariel L. Salas, Kenneth G. Boykin and Raul Valdez

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. Read On…