Abstract
The integration between vegetation data, human disturbance factors, and geo-spatial data (Digital Elevation Model (DEM) and image data) is a particular challenge for vegetation mapping in mountainous areas. The present study aimed to incorporate the relationships between species distribution (or vegetation spatial distribution pattern) and topography and human disturbance factors with remote sensing data, to improve the accuracy of mountain vegetation maps. Two different mountainous areas located in Lancang (Mekong) watershed served as study sites. An Artificial Neural Network (ANN) architecture classification was used as image classification protocol. In addition, canonical correspondence analysis (CCA) ordination was applied to address the relationships between topography and human disturbance factors with the spatial distribution of vegetation patterns. We used ordinary kriging at unobserved locations to predict the CCA scores. The CCA ordination results showed that the vegetation spatial distribution patterns are strongly affected by topography and human disturbance factors. The overall accuracy of vegetation classification was significantly improved by incorporating DEM or four CCA axes as additional channels in both the northern and southern study areas. However, there was no significant difference between using DEM or four CCA axes as extra channels in the northern steep mountainous areas because of a strong redundancy between CCA axes and DEM data. In the southern lower mountainous areas, the accuracy was significantly higher using four CCA axes as extra bands, compared to using DEM as an extra band. In the southern study area, the variance of vegetation data explained by human disturbance factors was larger than the variance explained by topographic attributes.
Highlights
Remote sensing of vegetation in rugged and mountainous areas is severely affected by topographic effects [1,2]
From the Monte Carlo permutation test, except for planiform curvature (PLF) and cost distance to streams (CDS), all topographic and human disturbance variables were significantly correlated with the vegetation and non-vegetation spatial distribution pattern data in the northern study areas (Table 8)
In the southern study area, topographic position index (TPI), Slope planiform curvature (PLF), and slope profile curvature (PRF) were not significantly correlated with the vegetation data (Table 8)
Summary
Remote sensing of vegetation in rugged and mountainous areas is severely affected by topographic effects [1,2]. To improve image classification in mountainous terrain, DEM data have been used in four different ways: To reduce the topographic effect by topographic normalization techniques [1,11,12,13,14,15,16,17,18,19,20,21]; As an additional ―channel‖ increasing forest map accuracy [9,22,23,24,25]; Combined with expert knowledge or a decision tree enhancing classification accuracy [26,27,28]; Integrating the prior probability of the relationship between elevation and vegetation distributions improving image classification [29,30,31,32,33,34]
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