Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection

Du, Yi; Teillet, Philippe; Cihlar, J.

doi:10.1016/s0034-4257(02)00029-9

Cited by 330 publications

(241 citation statements)

References 10 publications

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“…Landsat images were processed in two steps: (1) radiometric calibration and (2) atmospheric correction; these steps are the two types of corrections that are commonly employed to normalize remotely sensed images (Coppin, Jonckheere, Nackaerts, Muys, & Lambin, 2004;Du, Teillet, & Cihlar, 2002;Kaufman, 1998;Roy et al, 2016;Schott, 1997;Song, Woodcock, Seto, Lenney, & Macomber, 2001). This method of data acquisition and data extraction has been used in numerous studies that focused on mapping different phenomena or on particular properties that occur or are revealed on the Earth's surface, such as mining activities (Cutaia, Massacci, & Roselli, 2004;Lobo, Costa, & Novo, 2015), water quality parameters and temperature (Bonansea et al, 2015;Ding & Elmore, 2015;Tebbs, Remedios, & Harper, 2013), water surface extent and dynamics (Mueller et al, 2016) and mapping major flood events (Bhatt et al, 2016;Cutaia et al, 2004;Sakai et al, 2015;Sandholt et al, 2003;Thomas et al, 2015;Tulbure et al, 2016;Wang, 2004;Yang et al, 2015).…”

Section: Remote Sensing Image Processingmentioning

confidence: 99%

Historic flood events in NE Romania (post-1990)

et al. 2017

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Using open-source satellite imagery like Landsat TM, ETM+ and Sentinel 2 can lead to accurate cartographic products. We mapped flood events from Siret and Prut river basins in the last 30 years based on the availability of Landsat data archive. In this area were recorded historical values in flow rates for the entire Romanian territory: 4650 m³/s on the Siret River in 2005 -the maximum value ever recorded for Romania; 4240 m³/s on the Prut in 2008 -second maximum value recorded for Romania. The most powerful floods that took place in Romania in the last years were in

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Section: Remote Sensing Image Processingmentioning

confidence: 99%

Historic flood events in NE Romania (post-1990)

et al. 2017

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“…The objective was to minimize errors in vegetation index calculation and change detection analysis as described in the next session. The procedure adopted for radiometric correction includes: (1) converting image digital numbers to apparent reflectance (Anderson and Milton, 2005;Gomez et al, 2005;Li and Niu, 2006); and (2) radiometric normalization (Canty et al, 2004;Du et al, 2002). In this study, the reflective radiance, R r , of the Formosat-2 images was converted from the original digital numbers (DN) and provided sensor calibration factors (gain and offset) as:…”

Section: Radiometric Correctionmentioning

confidence: 99%

“…After converting all image data into apparent reflectance, radiometric normalization was further applied to images covering the same area. The normalization is based on pseudoinvariant features that can be selected interactively (Schott et al, 1988) or according to statistic characteristics (Du et al, 2002) calculated from the images. In this research, instead of processing only the study area, the radiometric corrections were carried out over the complete images.…”

Section: Radiometric Correctionmentioning

confidence: 99%

Post-disaster assessment of landslides in southern Taiwan after 2009 Typhoon Morakot using remote sensing and spatial analysis

Tsai

Hwang

Chen

et al. 2010

Nat. Hazards Earth Syst. Sci.

105

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Abstract. On 8 August 2009, the extreme rainfall of Typhoon Morakot triggered enormous landslides in mountainous regions of southern Taiwan, causing catastrophic infrastructure and property damages and human casualties. A comprehensive evaluation of the landslides is essential for the post-disaster reconstruction and should be helpful for future hazard mitigation. This paper presents a systematic approach to utilize multi-temporal satellite images and other geo-spatial data for the post-disaster assessment of landslides on a regional scale. Rigorous orthorectification and radiometric correction procedures were applied to the satellite images. Landslides were identified with NDVI filtering, change detection analysis and interactive post-analysis editing to produce an accurate landslide map. Spatial analysis was performed to obtain statistical characteristics of the identified landslides and their relationship with topographical factors. A total of 9333 landslides (22 590 ha) was detected from change detection analysis of satellite images. Most of the detected landslides are smaller than 10 ha. Less than 5% of them are larger than 10 ha but together they constitute more than 45% of the total landslide area. Spatial analysis of the detected landslides indicates that most of them have average elevations between 500 m to 2000 m and with average slope gradients between 20 • and 40 • . In addition, a particularly devastating landslide whose debris flow destroyed a riverside village was examined in depth for detailed investigation. The volume of this slide is estimated to be more than 2.6 million m 3 with an average depth of 40 m.

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“…First, all remotely sensed images are subject to geometric distortion (Du et al 2002, Guienko 2004) and, while this may be relatively inconsequential for single-date analysis where general spatial patterns are sought, it can be a major difficulty for multi-temporal analysis where images are compared on a spatial basis. Therefore, all investigations involving multi-temporal imagery require accurate geometric co-registration to register multiple images to each other or to a common coordinate system (Coppin and Bauer 1996, Eugenio and Marques 2003, Siqueira et al 2004).…”

Section: Temporal Variabilitymentioning

confidence: 99%

“…Clearly, any such inconsistencies between images may create difficulties for multi-temporal analysis, and corrections are often necessary prior to temporal comparison (Song et al 2001, Liang et al 2002, Richter and Schlapfer 2002, Steven et al 2003. Rather than performing absolute corrections to generate true reflectance values, multi-temporal studies often use image matching or normalization, whereby discrepancies between images are simply averaged out (Du et al 2002, Canty et al 2004, Nelson et al 2005. However, there are certain circumstances where multi-temporal analysis may not require any radiometric, atmospheric or illumination corrections.…”

Section: Temporal Variabilitymentioning

confidence: 99%

On scales and dynamics in observing the environment

Aplin

2006

International Journal of Remote Sensing

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Natural and anthropogenic processes at the Earth's surface operate at a range of spatial and temporal scales. Different scales of observation are required to match the spatial scales of the processes under observation. At the same time, the temporal sampling rate of the observing systems must be reconciled with the dynamics of the processes observed. Bringing together these issues requires insight, innovation and, inevitably, compromise. This paper reviews spatial and temporal considerations in remote sensing and introduces the papers in this Special Issue on 'Scales and Dynamics in Observing the Environment'. The review comprises three main sections. The first section focuses on spatial variability in remote sensing, while the second section focuses on temporal variability in remote sensing. The third section links these two issues, focusing on the interplay of space and time in remote sensing. The review is primarily theoretical, explaining spatial and temporal properties of remote sensing and remotely sensed phenomena. Where appropriate, however, practical examples are included to demonstrate how remote sensing is used in environmental applications. Following the review, the papers included in the Special Issue are introduced, outlining their significance in the context of 'Scales and Dynamics in Observing the Environment'.

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Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection

Cited by 330 publications

References 10 publications

Historic flood events in NE Romania (post-1990)

Historic flood events in NE Romania (post-1990)

Post-disaster assessment of landslides in southern Taiwan after 2009 Typhoon Morakot using remote sensing and spatial analysis

On scales and dynamics in observing the environment

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