Landslide inventory using image fusion techniques in Brazil

Marcelino, Emerson Vieira; Formaggio, Antônio Roberto; Maeda, Eduardo Eiji

doi:10.1016/j.jag.2009.01.003

Cited by 57 publications

(30 citation statements)

References 22 publications

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“…The analysis of optical imagery for landslide mapping can be performed through (i) visual interpretation of single and stereoscopic images ; (ii) image classification with semi-automated pixel-based methods (Borghuis et al, 2007;Marcelino et al, 2009); (iii) image classification with semi-automated object-oriented methods (Martha et al, 2010;Lu et al, 2011;Stumpf and Kerle, 2011); (iv) change detection techniques (Nichol and Wong, 2005;Weirich and Blesius, 2007;Tsai et al, 2010); and (v) correlation of optical images (Delacourt et al, 2007;Leprince et al, 2007;Debella-Gilo and Kääb, 2011).…”

Section: Tofani Et Al: Use Of Remote Sensing For Landslide Studiementioning

confidence: 99%

Technical Note: Use of remote sensing for landslide studies in Europe

Tofani

Segoni

Agostini

et al. 2013

Nat. Hazards Earth Syst. Sci.

130

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Abstract. Within the framework of FP7, an EU-funded SafeLand project, a questionnaire was prepared to collect information about the use of remote sensing for landslide study and to evaluate its actual application in landslide detection, mapping and monitoring. The questionnaire was designed using a Google form and was disseminated among end-users and researchers involved in landslide studies in Europe. In total, 49 answers from 17 different European countries were collected. The outcomes showed that landslide detection and mapping is mainly performed with aerial photos, often associated with optical and radar imagery. Concerning landslide monitoring, satellite radars prevail over the other types of data. Remote sensing is mainly used for detection/mapping and monitoring of slides, flows and lateral spreads with a preferably large scale of analysis (1:5000–1:25 000). All the compilers integrate remote sensing data with other thematic data, mainly geological maps, landslide inventory maps and DTMs and derived maps. According to the research and working experience of the compilers, remote sensing is generally considered to have a medium effectiveness/reliability for landslide studies. The results of the questionnaire can contribute to an overall sketch of the use of remote sensing in current landslide studies and show that remote sensing can be considered a powerful and well-established instrument for landslide mapping, monitoring and hazard analysis.

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Section: Tofani Et Al: Use Of Remote Sensing For Landslide Studiementioning

confidence: 99%

Technical Note: Use of remote sensing for landslide studies in Europe

Tofani

Segoni

Agostini

et al. 2013

Nat. Hazards Earth Syst. Sci.

130

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show abstract

“…For single-date images, the pre-classification approach relies upon the use of density slicing or spectral enhancement techniques for landslide detection (Domakinis et al, 2008;Marcelino et al, 2009;Parker, 2009). When multi-date images are available, image differencing can be used to detect landslide activities.…”

Section: Introductionmentioning

confidence: 99%

Using multi-temporal remote sensor imagery to detect earthquake-triggered landslides

Yang

Chen

2010

International Journal of Applied Earth Observation and Geoinfor

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a b s t r a c tLandslides are a major type of geohazards claiming thousands of casualties and billions of dollars in property damages every year. Catastrophic landslide activities are often triggered by some extreme events such as earthquakes, excessive precipitations, or volcanic eruptions. Quickly identifying the spatial distribution of landslides induced by these extreme events is crucial for coordinating rescue efforts and planning in situ investigations. In this study, we propose an automated method for detecting the spatial distribution of earthquake-triggered landslides by examining after-event vegetation changes. Central to this method is the use of pre-and post-event remote sensor images covering the same area. Geometric correction and radiometric normalization are performed before deriving a vegetation index from each image. Then, an image differencing procedure is applied to the two derived indices. With the resultant difference image, an initial landslide distribution map is generated by highlighting the pixels with a threshold percentage decrease in the brightness values as a direct result of the image subtraction. The threshold percentage value is interactively determined by using a visual interpretation method. The final landslide distribution map is produced after using a modal filter to suppress boundary errors in the initial map. This method has been implemented in a test site, approximately 30 km from the epicenter of the Sichuan earthquake (7.9 Ms) that struck on 12 May 2008. A pre-event Thematic Mapper image and a post-event Advanced Spaceborne Thermal Emission and Reflection Radiometer scene are used. The thematic accuracy assessment indicates that 90% of the landslides have correctly been mapped. Given the relatively simple procedures and the good mapping accuracy, the image processing and change detection method identified in this study seems to be promising from an operational perspective.

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“…Several authors dealt with the assessment and evaluation of fusion techniques; [14][15][16]. Figures 4(a) and (b) show the results of brovey and HSV fused images respectively.…”

Section: Fusion Results and Assessmentmentioning

confidence: 99%

Assessment and Evaluation of Band Ratios, Brovey and HSV Techniques for Lithologic Discrimination and Mapping Using Landsat ETM<sup>+</sup> and SPOT-5 Data

Madani¹

2014

IJG

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This study aims to assess and to evaluate band ratios, brovey and HSV (Hue-Saturation-Value) techniques for discrimination and mapping the basement rock units exposed at Wadi Bulghah area, Saudi Arabia using multispectral Landsat ETM + and SPOT-5 panchromatic data. FieldSpec instrument is utilized to collect the spectral data of diorite, marble, gossan and volcanics, the main rock units exposed at the study area. Spectral profile of diorite exhibits very distinguished absorption features around 2.20 μm and 2.35 μm wavelength regions. These absorption features lead to lowering the band ratio values within the band-7 wavelength region. Diorite intrusions appear to have grey and dark grey image signatures on 7/3 and 7/2 band ratio images respectively. On the false color composite ratio image (7/3:R; 7/2:G and 5/2:B), diorite, marble, gossan and volcanics have very dark brown, dark blue, white and yellowish brown image signatures respectively. Image fusion between previously mentioned FCC ratio image and high spatial resolution (5 meters) SPOT-5 panchromatic image is carried out by using brovey and HSV transformation methods. Visual and statistical assessment methods prove that HSV fused image yields best image interpretability results rather than brovey image. It improves the spatial resolution of the original FCC ratios image with acceptable spectral preservation.

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Landslide inventory using image fusion techniques in Brazil

Cited by 57 publications

References 22 publications

Technical Note: Use of remote sensing for landslide studies in Europe

Technical Note: Use of remote sensing for landslide studies in Europe

Using multi-temporal remote sensor imagery to detect earthquake-triggered landslides

Assessment and Evaluation of Band Ratios, Brovey and HSV Techniques for Lithologic Discrimination and Mapping Using Landsat ETM<sup>+</sup> and SPOT-5 Data

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Landslide inventory using image fusion techniques in Brazil

Cited by 57 publications

References 22 publications

Technical Note: Use of remote sensing for landslide studies in Europe

Technical Note: Use of remote sensing for landslide studies in Europe

Using multi-temporal remote sensor imagery to detect earthquake-triggered landslides

Assessment and Evaluation of Band Ratios, Brovey and HSV Techniques for Lithologic Discrimination and Mapping Using Landsat ETM&lt;sup&gt;+&lt;/sup&gt; and SPOT-5 Data

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Assessment and Evaluation of Band Ratios, Brovey and HSV Techniques for Lithologic Discrimination and Mapping Using Landsat ETM<sup>+</sup> and SPOT-5 Data