Corona orthorectification using ASTER horizontal and vertical reference data

The advantages of the use of Corona imagery as a glacier mapping tool over other satellite-based image datasets are threefold. Firstly, the Corona archive includes imagery acquired in the 1960s, extending the temporal availability of historic satellite imagery for regions included in the mission coverage, such as the Himalayas. Secondly, the Corona KH-4, KH-4A and KH-4B camera models acquired imagery in stereo, allowing image areas covered to be mapped three-dimensionally through the extraction of DEMs. Thirdly, Corona imagery is available to purchase at a relatively low

cost ($30 for an image strip covering ~2630 km2 _{(KH-4B)). Additionally, with ground}

resolutions as low as 1.8 m, Corona imagery offers an alternative to aerial

photography (Slama et al., 1980). Considering these factors, Corona imagery

represents a potentially valuable glacier mapping tool, particularly in regions for which topographic maps and aerial photography are limited, such as the Himalayas (Bhambri & Bolch, 2009).

141

The advantages of Corona as a mapping tool are countered by the difficulties faced correcting the large geometric distortions present within raw Corona imagery and subsequently transforming image strips into a projected coordinate system (Altmaier

& Kany, 2002).These transformation difficulties continue to limit the use of Corona as

a source of historic high resolution imagery (Galiatsatos et al., 2008). The central

methodological aim of this study was to develop a broadly replicable and low cost method of orthorectifying Corona imagery. This was achieved through the use of: (1) a non-metric camera frame model photogrammetry approach and (2) horizontal and vertical reference data from ortho-ASTER imagery and the freely available ASTER GDEM.

The orthorectification process applied here achieved geometric accuracies of ±7.5 m and ±16.7 m for the Corona imagery used at the GC and ND study sites, respectively. Subsequently, the aim of achieving Corona geometric accuracies within 1 pixel of the horizontal reference source (ortho-ASTER imagery: 15 m) was met (for GC) or closely met (for ND). The geometric accuracies obtained were also shown to match or better

those obtained for Corona imagery used for glacier mapping purposes by Bolch et al.

(2008), Schmidt & Nusser (2012) and Narama et al. (2009) ( <±15 m, ~±15 m, <±20 m

and <±30 m, respectively). Furthermore, the Corona accuracies achieved compared well with those reported for other geospatial datasets used for Himalayan glacier

mapping purposes (e.g. raw ASTER satellite imagery: <±15 m (Bolch et al., 2008); ALOS

satellite imagery: <±30 m (Narama et al., 2009); and topographic maps: <±15 m

(Salerno et al., 2008).

The methodology applied here to orthorectify Corona imagery differs from that used in previous studies by combining a non-metric camera photogrammetry approach with low cost ASTER reference data in a high mountainous environment. Previously used Corona geometric correction methodologies can be roughly divided into two approaches, (1) image transformation approaches and (2) alternative photogrammetry-based approaches. A summary of the studies that have applied these two approaches to Corona imagery, and the horizontal accuracies achieved, is

142

provided in chapter 2. By comparing the results of these studies with those achieved here, the following conclusions were made.

(1)For Corona-based glacier mapping studies that only cover small areas and do not require surface elevation information, the transformation approach offers a preferable choice over the comparatively complex non-metric camera frame model

approach used in this study. Bhambri et al. (2011), for example, showed that the

transformation approach can be applied to geometrically correct Corona imagery to an accuracy of <±15 m, using GCPs derived from ASTER imagery. However, the image transformation approach does not provide the photogrammetric framework needed to extract DEMs from Corona stereo pairs and requires a number of image pre- processing steps that hinder its use over large areas (such as creating individual glacier image subsets).

(2) Compared to the empirical photogrammetry approach applied here, rigorous photogrammetric approaches likely represent a better choice for orthorectifying Corona imagery. This conclusion agrees with those made by Casana & Cothren (2008)

and Galiatsatos et al. (2008), who refer to the accuracy of the rigorous approach when

estimating the orientation parameters of the Corona camera. However, as noted in chapter 2, the rigorous approach requires extensive camera model development time and has yet to be tested for Corona imagery acquired in high mountainous environments.

Despite the advantages of image transformation and rigorous photogrammetry approaches, it is the author’s belief that, through combining the benefits of photogrammetry (image orthorectification and DEM extraction) with time and cost efficient image processing steps, the methods applied in this study offer a good alternative for studies requiring Corona orthorectification. By using DGPS survey data, other variants of the empirically-based non-metric camera frame model approach have achieved better horizontal accuracies than those presented here (e.g. <±6 m,

Altmaier & Kany (2002) and Schmidt et al. (2001)) and, where possible, these

143

shown that Corona imagery acquired over high mountainous terrain can be corrected to a good degree of accuracy without the use of high resolution satellite imagery (such

as Galiatsatos et al. (2008) and Casana & Cothren (2008)), topographic maps (such as,

Namara et al. (2009)) or DGPS surveys.

With regards to the glaciological aims of this study, the accuracy of the orthorecitifed Corona imagery produced was such that in most places glaciated features closely matched those visible in ortho-ASTER imagery, allowing planimetric comparisons to be made. Furthermore, the close agreement between the glacier area and length changes observed in this study, for the GC and ND study sites, and those obtained by

previous studies (e.g. Bhambri et al. (2008) and Dobhal and Mehta (2010)) supports

the planimetric accuracy of the orthorecitified Corona images produced. However, despite the general success of the Corona orthorectification procedure, some areas of image distortion were still evident, resulting in spatial inconsistencies when compared to the ortho-ASTER imagery. With the non-metric camera frame model approach applied in this study, the quality of the triangulation process is largely determined by the sample size and distribution of GCPs (Altmaier & Kany, 2002; Galiatsatos, 2004). Here, a number of Corona image strips used lacked sufficient GCPs. Furthermore, GCPs were often located in clusters and transects. These two distribution features have been found to be particularly detrimental to the quality of the triangulation process (Mather, 1999).

GCP sampling in this study was hindered by two main factors. The first factor concerned a range of image quality issues, affecting both the Corona and ASTER datasets, including cloud/snow cover, terrain shadowing, poor image contrast (Corona imagery) and areas of image skewing (ortho-ASTER imagery). Image skewing is a quality issue specific to ortho-ASTER imagery and represents a limitation of the use of this dataset. Hence, freely available Landsat satellite imagery which undergo more

rigorous geometric calibrations (Storey et al., 2006) could have provided an

alternative source of low cost horizontal reference information. However, the higher geometric quality of Landsat imagery, compared to ortho-ASTER imagery, is offset by their higher resolution (30-79 m).

144

Spatial resolution of the ASTER horizontal and vertical reference sources represented the second GCP sampling constraint. Ideally, when registering one geospatial dataset to another, the spatial resolution of the master dataset should be similar to the slave dataset (Mather, 1999; Kääb, 2005a.; Bhambri & Bolch, 2009). Here, the spatial resolution of the horizontal (ortho-ASTER imagery: 15 m) and vertical (ASTER GDEM data: 30 m) reference sources was relatively coarse compared to the 1.8 m resolution Corona imagery. Due to this, it was sometimes difficult to locate corresponding GCPs. As a result, GCPs were often located in non-vegetated image areas (clustering around glaciers), or along valley bottoms utilising river junction features (forming linear GCP transects), reducing the quality of the geometric GCP network. The use of ASTER reference data, although cost-effective, therefore placed a limitation on the Corona correction process.

As an alternative to the approach used here, Bolch et al. (2008) successfully

orthorectified Corona imagery (~±15 m), acquired over the Nepalese Himalaya, using the RSG ERDAS Imagine module. Including a photogrammetric model specifically designed for the Corona camera (camera parameter inputs are not specified), RSG was similarly able to utilise ortho-ASTER derived GCPs to correct Corona imagery. However, as an independently developed ERDAS Imagine add-on, the purchasing of the RSG software would represent an additional project expense. Nevertheless, a comparative test between the RSG and non-metric camera frame model approaches for correcting Corona imagery would be an interesting future area of research.