Image data fusion for the remote sensing of freshwater environments Salman Ashraf a, * , Lars Brabyn a , Brendan J. Hicks b a Geography Programme, School of Social Sciences, The University of Waikato, Private Bag 3105, Hamilton 3240, New Zealand b Centre for Biodiversity and Ecology Research, The University of Waikato, Private Bag 3105, Hamilton 3240, New Zealand Keywords: Freshwater environment Data fusion New Zealand QuickBird Subtractive resolution merge abstract Remote sensing based mapping of diverse and heterogeneous freshwater environments requires high- resolution images. Data fusion is a useful technique for producing a high-resolution multispectral image from the merging of a high-resolution panchromatic image with a low-resolution multispectral image. Given the increasing availability of images from different satellite sensors that have different spectral and spatial resolutions, data fusion techniques that combine the strengths of different images will be increasingly important to Geography for land-cover mapping. Different data fusion methods however, add spectral and spatial distortions to the resultant data depending on the geographical context; therefore a careful selection of the fusion method is required. This paper compares a technique called subtractive resolution merge, which has not previously been formally tested, with conventional techniques such as Brovey transformation, principal component substitution, local mean and variance matching, and optimised high pass filter addition. Data fusion techniques are grouped into spectral and spatial centric methods. Subtractive resolution merge belongs to a new class of data fusion techniques that uses a mix of both spatial and spectral centric approaches. The different data fusion techniques were applied to a QuickBird image of a semi-aquatic freshwater environment in New Zealand. The results were compared both qualitatively and quantitatively using spectral and spatial error metrics. This research concludes that subtractive resolution merge performed better than all the other techniques and will be a valuable technique for enhancing images for freshwater land-cover mapping. Ó 2011 Elsevier Ltd. All rights reserved. Introduction The role of remote sensing has been pivotal for accurately mapping land cover and monitoring environmental changes in different habitats. This has been demonstrated by Melendez-Pastor, Navarro-Pedreño, Gómez, and Koch (2010) who used remote sensing to compare wetlands inside and outside a protected park using Landsat-5 TM and Landsat-7 ETMþ. Remote sensing is often combined with standard Geographical data such as elevation to extract detailed features such as hedgerows (Tansey, Chambers, Anstee, Denniss, & Lamb, 2009) and other agricultural features that are an important part of landscape character. In developing countries, remote sensing is particularly valuable because it is a cost effective mapping tool and these countries often have very few base maps (Shalaby & Tateishi, 2007). A common technique used in enhancing images for land-cover mapping is to sharpen multi- spectral bands with panchromatic images. Mallinis, Emmanoloudis, Giannakopoulos, Maris, and Koutsias (2011) used such a technique prior to classifying land-cover/land-use changes in the Nestos Delta, Greece. Remote sensing is rapidly advancing with the increasing avail- ability of satellite images, and improved image enhancement and analysis techniques. Many remote satellites do not capture both high spatial and spectral images at the same time due to their technical limitations. Instead, dual images are often captured; one is a high (spatial) resolution panchromatic image (HRPI), which is good for identifying spatial details, and the other is a low (spatial) resolution multispectral image (LRMI), which is suitable for detecting features based on their spectral properties. Examples of these dual resolution satellites are; Landsat-7, SPOT 1-5, EO-1, IKONOS, QuickBird-2, WorldView-2, GeoEye-1 and FormoSat. There is considerable benefit from integrating HRPI and LRMI to produce a high-resolution multispectral image (HRMI) for further image analysis. This process is commonly labelled data fusion, pan sharpening, or resolution merging (de Béthune, Muller, & Binard, 1998; Wang, Ziou, Armenakis, Li, & Li, 2005), and is a common image enhancement process used in many land-cover mapping applications (FoxIII, Garrett, Heasty, & Torres, 2002; Mallinis et al., 2011; Midwood & Chow-Fraser, 2010; Munechika, Warnick, Sal- vaggio, & Schott, 1993). * Corresponding author. Tel.: þ64 7 838 4466x8314; fax: þ44 7 838 4633. E-mail address: msa14@waikato.ac.nz (S. Ashraf). Contents lists available at ScienceDirect Applied Geography journal homepage: www.elsevier.com/locate/apgeog 0143-6228/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.apgeog.2011.07.010 Applied Geography 32 (2012) 619e628