Relative radiometric normalisation of multitemporal Landsat data-a comparison of different approaches

Over, M.; Schottker, B.; Braun, Matthias; Menz, Gunter

doi:10.1109/igarss.2003.1294874

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…The reliability of the radiometric correction was confirmed using an inter-calibration algorithm between the NDVI images over a test area of the study site. Among the numerous radiometric normalization approaches specific to Landsat data (Elvidge et al 1995, Jensen 1996, Yuan & Elvidge 1996, Yang & Lo 2000, Callahan 2001, Over et al 2003, Mateos et al 2010, we selected the intercalibration algorithm, defined as no-change (NC) regression normalization, which has already been applied over areas characterized by very complex landscape patterns (Simoniello et al 2008). Comparisons of results between the dark object subtraction model and the inter-calibration algorithm of NC regression normalization showed no significant differences in change detection over forest areas between the two methods.…”

Section: Image Pre-processingmentioning

confidence: 99%

Landsat TM imagery and NDVI differencing to detect vegetation change: assessing natural forest expansion in Basilicata, southern Italy

Mancino¹,

Nolè²,

Ripullone³

et al. 2014

iForest

109

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© iForest -Biogeosciences and Forestry IntroductionAssessment of natural forest expansion represents a crucial issue to elucidate several processes, including biogeochemical cycles, atmospheric composition related to climate change, and forest carbon uptake, as well as socio-economic processes and issues. Anthropogenic and naturally induced land cover changes affect spatial and temporal distribution and availability of environmental resources, and alter ecosystem composition and productivity. Globally, these processes can be considered the primary catalysts for change in biogeochemical cycling, atmospheric composition, and climate (Pielke 2005, Metz et al. 2007, Turner et al. 2007). Forest land-use and land-cover change (LU-LCC) were recognised as key issues in greenhouse gas removal/emission processes as specified by the Good Practices Guidance for Land Use, Land Use Change, and Forestry (GPG-LULUCF) during the Intergovernmental Panel on Climate Change (IPCC) established at the Kyoto Protocol (Penman et al. 2003). Observation and assessment of forest cover changes are crucial to elucidate the complexities inherent in feedback processes between forest distribution and human activities in sustainable forest development, natural resource management, biodiversity conservation, ecosystem functioning, and biogeochemical cycling (IPCC 2007). In Mediterranean regions, natural forest expansion is primarily related to the abandonment of agricultural practices and cattle-raising in marginal areas representing the principal change in Italy's Mediterranean rural landscape over the past five decades (Piussi 2005). These processes generally vary in terms of the vegetation successional series and time scale, however the expansion dynamics are shared, beginning with an initial phase of spontaneous shrub dominance, followed by tree colonisation (Biondi et al. 2006).In recent decades, satellite-based high-resolution observations with multispectral scanners provided the scientific community with consistent data to implement detailed thematic mapping for local and regional scale land classification (Friedl et al. 2002, 2010, Lu & Weng 2007, Giri 2012. The near infrared wavebands on the Landsat Thematic Mapper (TM) facilitates advanced land classification analyses based on differences in spectral reflectance of different land cover types. In particular, specific foliar reflectance, pigment absorptions, and foliar moisture wavelength ranges represent the basis of vegetation class analyses. Furthermore, the availability of such land cover data at different spatial and temporal scales promotes the development and implementation of vegetation change detection techniques, which furthers our understanding on vegetation and ecosystem dynamics (Cohen & Fiorella 1998, Coppin et al. 2004, Lu et al. 2004, Martinez & Gilabert 2009).In forest ecosystems, land cover change dynamic detection based on visual and statistical approaches represents a challenge to the scientific community due to the difficulties in remotely sensed image acquisition err...

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Section: Image Pre-processingmentioning

confidence: 99%

Landsat TM imagery and NDVI differencing to detect vegetation change: assessing natural forest expansion in Basilicata, southern Italy

Mancino¹,

Nolè²,

Ripullone³

et al. 2014

iForest

109

View full text Add to dashboard Cite

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“…The number of PIFs used in radiometric correction studies vary from a few dozens (Eckhart et al 1990;Schroeder et al, 2006) to several hundreds (Over et al, 2003;Janzen et al, 2006;Galiatsatos et al, 2007). In this study, only four types of invariant features covering at least 2x2 pixels and providing a number of independent replicates sufficient for statistical analyses were available on all images (Fig.…”

Section: Pseudo-invariant Features (Pifs)mentioning

confidence: 99%

Radiometric Normalization of SPOT-5 Scenes

Davranche¹,

Lefebvre²,

Poulin³

2009

photogramm eng remote sensing

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“…In that context the block-wide radiometric normalization is often called 'radiometric aerial triangulation' because of various similarities to a geometric aerial triangulation. Surveys on radiometric normalization approaches can be found in, e.g., Yang and Lo (2000), Over et al (2003), Hong (2007), Gehrke (2010) and Pros et al (2013), which discuss and, in parts, compare approaches for histogram adaptation, retrieval and evaluation of radiometric tie points ('invariant features') as well as computational aspects.…”

Section: Recent Developments In Radiometric Normalizationmentioning

confidence: 99%

Radiometric Normalization of Large Airborne Image Data Sets Acquired by Different Sensor Types

Gehrke

Beshah

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Generating seamless mosaics of aerial images is a particularly challenging task when the mosaic comprises a large number of images, collected over longer periods of time and with different sensors under varying imaging conditions. Such large mosaics typically consist of very heterogeneous image data, both spatially (different terrain types and atmosphere) and temporally (unstable atmospheric properties and even changes in land coverage). We present a new radiometric normalization or, respectively, radiometric aerial triangulation approach that takes advantage of our knowledge about each sensor's properties. The current implementation supports medium and large format airborne imaging sensors of the Leica Geosystems family, namely the ADS line-scanner as well as DMC and RCD frame sensors. A hierarchical modellingwith parameters for the overall mosaic, the sensor type, different flight sessions, strips and individual images -allows for adaptation to each sensor's geometric and radiometric properties. Additional parameters at different hierarchy levels can compensate radiometric differences of various origins to compensate for shortcomings of the preceding radiometric sensor calibration as well as BRDF and atmospheric corrections. The final, relative normalization is based on radiometric tie points in overlapping images, absolute radiometric control points and image statistics. It is computed in a global least squares adjustment for the entire mosaic by altering each image's histogram using a location-dependent mathematical model. This model involves contrast and brightness corrections at radiometric fix points with bilinear interpolation for corrections in-between. The distribution of the radiometry fixes is adaptive to each image and generally increases with image size, hence enabling optimal local adaptation even for very long image strips as typically captured by a line-scanner sensor. The normalization approach is implemented in HxMap software. It has been successfully applied to large sets of heterogeneous imagery, including the adjustment of original sensor images prior to quality control and further processing as well as radiometric adjustment for ortho-image mosaic generation.

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Relative radiometric normalisation of multitemporal Landsat data-a comparison of different approaches

Cited by 8 publications

References 6 publications

Landsat TM imagery and NDVI differencing to detect vegetation change: assessing natural forest expansion in Basilicata, southern Italy

Landsat TM imagery and NDVI differencing to detect vegetation change: assessing natural forest expansion in Basilicata, southern Italy

Radiometric Normalization of SPOT-5 Scenes

Radiometric Normalization of Large Airborne Image Data Sets Acquired by Different Sensor Types

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