Predictive Mapping of Dwarf Shrub Vegetation in an Arid High Mountain Ecosystem Using Remote Sensing and Random Forests

Kim Vanselow,Cyrus Samimi

doi:10.3390/rs6076709

Kim Vanselow, Cyrus Samimi

Open Access

https://doi.org/10.3390/rs6076709

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Abstract

In many arid mountains, dwarf shrubs represent the most important fodder and firewood resources; therefore, they are intensely used. For the Eastern Pamirs (Tajikistan), they are assumed to be overused. However, empirical evidence on this issue is lacking. We aim to provide a method capable of mapping vegetation in this mountain desert. We used random forest models based on remote sensing data (RapidEye, ASTER GDEM) and 359 plots to predictively map total vegetative cover and the distribution of the most important firewood plants, K. ceratoides and A. leucotricha. These species were mapped as present in 33.8% of the study area (accuracy 90.6%). The total cover of the dwarf shrub communities ranged from 0.5% to 51% (per pixel). Areas with very low cover were limited to the vicinity of roads and settlements. The model could explain 80.2% of the total variance. The most important predictor across the models was MSAVI2 (a spectral vegetation index particularly invented for low-cover areas). We conclude that the combination of statistical models and remote sensing data worked well to map vegetation in an arid mountainous environment. With this approach, we were able to provide tangible data on dwarf shrub resources in the Eastern Pamirs and to relativize previous reports about their extensive depletion.

Highlights

Common pool resources play an important role in the sustainable development of many high mountain regions, as they provide crucial ecosystem goods and services
Despite the wide application of remote sensing data, arid regions and high mountains are underrepresented in studies, especially those focusing on more detailed aspects, such as species composition or species distribution [4,5,6,7,8]
It can be stated that the application of the presented mapping approach led to suitable and reproducible vegetation maps in an arid, mountainous environment, such as the Eastern Pamirs

Summary

Introduction

Common pool resources play an important role in the sustainable development of many high mountain regions, as they provide crucial ecosystem goods and services. Field-based generation of maps in high mountain regions is very time-consuming and costly for the usually large and often difficult-to-access areas. In this study, we generate maps of common pool resources using remote sensing data that could provide a practical and economical alternative [2,3]. Despite the wide application of remote sensing data, arid regions and high mountains are underrepresented in studies, especially those focusing on more detailed aspects, such as species composition or species distribution [4,5,6,7,8]. If single species are derived from remote sensing data, often hyperspectral data are used [9]

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