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1Airborne laser scanning systems (LiDAR) are very well suited to the study of landscape and 2 vegetation structure over large extents. Spatially distributed measurements describing the 3D 3 character of landscape surfaces and vegetation architecture can be used to understand eco-4 geomorphic and ecohydrological processes, and this is particularly pertinent in peatlands given 5 the increasing recognition that these landscapes provide a variety of ecosystem services (water 6 provision, flood mitigation, carbon sequestration). In using LiDAR data for monitoring 7 peatlands, it is important to understand how well peatland surface structures (with fine length 8 scales) can be described. Our approach integrates two laser scanning technologies, namely 9Terrestrial Laser Scanning and airborne LiDAR surveys, to assess how effective airborne 10LiDAR is at measuring these fine scale microtopographic ecohydrological structures. By 11 combining airborne and terrestrial laser scanning, we demonstrate an improved spatial 12 understanding of the signal measured by the airborne LiDAR. Critically, results demonstrate 13 that LiDAR DSMs are subject to specific errors related to short-sward ecosystem structure, 14 causing the vegetation canopy height and surface-drainage network depth to be underestimated. 15TLS is shown to be effective at describing these structures over small extents, allowing the 16 information content and accuracy of airborne LiDAR to be understood and quantified more 17 appropriately. These findings have important implications for the appropriate degree of 18 confidence ecohydrologists can apply to such data when using them as a surrogate for field 19 measurements. They also illustrate the need to couple LiDAR data with ground validation data 20 in order to improve assessment of ecohydrological function in such landscapes. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 deliver robust measurements of both the short-sward canopy and/or the landscape surface? 43Spatially distributed information on both of these factors is needed because many temperate 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 (Hutton and Brazier 2012; Hinsley et al. 2002; Vierling 61 et al. 2008; Chassereau et al. 2011; Clawges et al. 2008; Horning et al. 2010). However, in 62 using these data it is important to note that the applicability of LiDAR is always constrained by 67Whilst we acknowledge that LiDAR datasets offer an as yet unparalleled ability to understand 68 landscape structure and function (Korpela et al. 2009; Vierling et al. 2008; Zimble et al. 2003; 69 Evans and Lindsay 2010; Rango et al. 2000), herein we stress the need to better quantify the 70 spa...

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This study identifies the major mineralized zones including supergene enrichment and hypogene enrichment in the Kahang Cu-Mo porphyry deposit which is located in Central Iran based on subsurface data and utilization of the concentration-volume (C-V) fractal model. Additionally, a correlation between results achieved from a C-V fractal model and geological models consisting of zonation, mineralography and alteration have been conducted in order to have an accurate recognition and modification of the main mineralized zones. Log-log plots indicate five geochemical populations for Cu and Mo in the deposit which means that mineralization commences with 0.075 % and 13 ppm for Cu and Mo (as the first thresholds) respectively. The main mineralization began for Cu 0.42 % and Mo 100 ppm and also enriched mineralization containing Cu 1.8 % and Mo 645 ppm which is located in the central part of the deposit. According to the C-V model, the main Cu-Mo mineralized zones occur in the hypogene zone, especially in the central, NW and NE parts of the Kahang deposit. The supergene enrichment zone derived via the C-V model is smaller than that in the geological model and is located in the central and eastern parts of the deposit. Results analysed by the C-V fractal model certify that the interpreted zones based on the fractal model are accurate. To certify this, a logratio matrix has been employed to validate the C-V fractal model for the Cu and Mo main mineralized zones.