Caracterização de cicatrizes de deslizamentos por processamento de dados TM Landsat em Caraguatatuba - SP

Sestini, Marcelo Francisco; Florenzano, Teresa Gallotti

doi:10.5327/s1519-874x2004000200005

Cited by 7 publications

(11 citation statements)

References 11 publications

(13 reference statements)

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“…6 confirms that these bands, when used together, are quite capable of differentiating soil from vegetation due to the high contrast existing between the two targets in the correspondent intervals from these bands in the electromagnetic spectrum (Bowker et al, 1985;Sestini and Florenzano, 2004).…”

Section: Band Selectionsupporting

confidence: 57%

“…The RMSE found in the satellite images registration were 23.53 and 36.14 m, for the X and Y directions, respectively, while the total and Sestini and Florenzano (2004). Silva (1988), that is, the excessive concentration of points in a given area and the absence in others is the main source of geometric distortions in the application of geometric correction methods.…”

Section: Spatial Assessmentmentioning

confidence: 98%

“…Marcelino et al (2003) also found similar results in landslides areas at Sierra General, in the state of Santa Catarina, pointing bands 3 and 4 as the most appropriated to the identification of landslides. Sestini and Florenzano (2004) mention that the ETM+/Landsat composition 473 is also appropriated in the classification of landslide scars, giving that a great contrast can be observed between areas of exposed soil adjacent to vegetation areas. Moreover, Florenzano (1993) highlights the ability of the composition TM 453 in emphasizing the morphology, the wetlands and drainage network.…”

Section: Band Selectionmentioning

confidence: 98%

“…Therefore, based on the prior researches performed by Florenzano (1993), Sestini (1999), Marcelino et al (2003) and Sestini and Florenzano (2004), the band 3 (Red 630-690 nm), band 4 (NIR 760-900 nm), and band 5 (MIR 1550-1750 nm) of the ETM+ sensor were selected to identify landscape scars for the Caraguatatuba inventory. Table 4 shows that all six fusion techniques under assessment performed with correlations above 80%.…”

Section: Band Selectionmentioning

confidence: 99%

See 3 more Smart Citations

Landslide inventory using image fusion techniques in Brazil

Marcelino

Formaggio

Maeda

2009

International Journal of Applied Earth Observation and Geoinfor

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Section: Band Selectionsupporting

confidence: 57%

Section: Spatial Assessmentmentioning

confidence: 98%

Section: Band Selectionmentioning

confidence: 98%

Section: Band Selectionmentioning

confidence: 99%

See 2 more Smart Citations

Landslide inventory using image fusion techniques in Brazil

Marcelino

Formaggio

Maeda

2009

International Journal of Applied Earth Observation and Geoinfor

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“…Photo‐interpretation, geometric and radiometric corrections of satellite imagery, and contrast enhancement for visual interpretation are techniques commonly implemented, as evidenced in the works of Sestini and Florenzano (2004), who applied image transforms like Landsat TM band ratios and principal component analysis before visual interpretation of landslides; Marcelino et al (2003) working with image transformations (e.g., RGB to IHS, principal components analysis and wavelet fusion) of Landsat TM data to discriminate landslide scars; and Ochoa Tejeda and Parrot (2007) using spectral indices from IKONOS imagery and a DTM for automated detection of landslide traces.…”

Section: Water‐induced Erosionmentioning

confidence: 99%

Remote Sensing of Land Degradation: Experiences from Latin America and the Caribbean

Metternicht

Zinck²,

Blanco

et al. 2010

J of Env Quality

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Land degradation caused by deforestation, overgrazing, and inappropriate irrigation practices affects about 16% of Latin America and the Caribbean (LAC). This paper addresses issues related to the application of remote sensing technologies for the identification and mapping of land degradation features, with special attention to the LAC region. The contribution of remote sensing to mapping land degradation is analyzed from the compilation of a large set of research papers published between the 1980s and 2009, dealing with water and wind erosion, salinization, and changes of vegetation cover. The analysis undertaken found that Landsat series (MSS, TM, ETM+) are the most commonly used data source (49% of the papers report their use), followed by aerial photographs (39%), and microwave sensing (ERS, JERS-1, Radarsat) (27%). About 43% of the works analyzed use multi-scale, multi-sensor, multi-spectral approaches for mapping degraded areas, with a combination of visual interpretation and advanced image processing techniques. The use of more expensive hyperspectral and/or very high spatial resolution sensors like AVIRIS, Hyperion, SPOT-5, and IKONOS tends to be limited to small surface areas. The key issue of indicators that can directly or indirectly help recognize land degradation features in the visible, infrared, and microwave regions of the electromagnetic spectrum are discussed. Factors considered when selecting indicators for establishing land degradation baselines include, among others, the mapping scale, the spectral characteristics of the sensors, and the time of image acquisition. The validation methods used to assess the accuracy of maps produced with satellite data are discussed as well.

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