A forward/backward principal component analysis of Landsat-7 ETM+ data to enhance the spectral signal of burnt surfaces

Koutsias, Nikos; Mallinis, Giorgos; Karteris, M.

doi:10.1016/j.isprsjprs.2008.06.004

Cited by 40 publications

(20 citation statements)

References 53 publications

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“…This permits the capture of most natural and managed disturbance events at regional and global scales (Wilson & Sader 2002;Tucker et al 2004;Koutsias et al 2009;Kennedy et al 2012). In future studies, the monitoring of postfire forest recovery can also be conducted based on these images, as well as with a combination of data from other sensors, including Quickbird, MODIS and SAR (Synthetic Aperture Radar), etc.…”

Section: Discussionmentioning

confidence: 99%

“…It is intended for geographical mapping of the relative amounts of different vegetation components, which can then be interpreted in terms of ecosystem conditions (Carlson & Ripley 1997;Koutsias et al 2009;Sharma et al 2013). Here, we selected two VIs, the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI).…”

Section: Calculation Of the Four Indicesmentioning

confidence: 99%

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Mapping a burned forest area from Landsat TM data by multiple methods

Chen

Moriya

Sakai

et al. 2014

Geomatics, Natural Hazards and Risk

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Forest fire is one of the dominant disturbances in boreal forests. It is the primary process responsible for organizing the physical and biological attributes of the boreal biome, shaping landscape diversity and influencing biogeochemical cycles. The Greater Hinggan Mountain of China is rich in forest resources while suffers from a high incidence of forest fires simultaneously. In this study, focusing on the most serious forest fire in the history of P. R. China which occurred in this region, we made use of two Landsat-5 TM (Thematic Mapper) images, and proposed to map the overall burned area and burned forest area by multiple methods. During the mapping, the fire perimeter, as well as rivers, roads and urban areas were first extracted and masked visually, and then four indices of Normalized Difference Vegetation Index, Enhanced Vegetation Index, Vegetation Fractional Cover and Disturbance Index were calculated. For each index, the optimal threshold for separating burned from unburned forest area was determined using their histograms. For comparison, threshold segmentation using single-band reflectance was performed, in addition to a Maximum Likelihood Classifier (MLC) based supervised classification of all features and forest area alone; their accuracies were also evaluated and analysed. Among all the methods compared here, mapping by EVI threshold segmentation proved to be optimal by the comparisons of overall accuracy (99.78%) and the kappa coefficient (0.9946). Finally, the calculated burned area and burned forest area were compared with the values from official statistics. Compared with the classical methods used to report official statistics on burned areas, the remote sensing-based mapping is more objective and efficient, less labour-and time-consuming, and more repeatable.

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Section: Discussionmentioning

confidence: 99%

Section: Calculation Of the Four Indicesmentioning

confidence: 99%

Mapping a burned forest area from Landsat TM data by multiple methods

Chen

Moriya

Sakai

et al. 2014

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

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“…The variance among the input variables is calculated and the principal components assigned in a descending order as given by an Eigen matrix. Koutsias, Mallinis, and Karteris (2009) successfully applied PCA, image enhancement tool to improve the spectral signal of burnt surfaces while, Meng, Cieszewski, and Madden (2009) developed a geostatistical technique to map pine basal area after comparing the performance of PCs (1,2,3), NDVI, band combination 432 and 543 of Landsat data.…”

Section: Application Of Digital Image Processing In Lulc Classificationmentioning

confidence: 99%

Knowledge based multi-source, time series classification: A case study of central region of Kenya

Mwaniki

Möller

2015

Applied Geography

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“…In the relevant bibliography, apart from studies on the spectral information of satellite sensor images (Koutsias, Mallinis, and Karteris 2009), which are often unable to discriminate objects due to spectral overlapping, research includes image processing techniques that also make use of spatial information. For example, an autocovariate resulting from neighbouring estimates of the predicted probabilities estimated by the multiple logistic regression was successfully incorporated into modelling to improve the spatial delineation of burned surfaces (Koutsias 2003).…”

Section: Introductionmentioning

confidence: 99%

Comparing the spectral signal of burned surfaces between Landsat 7 ETM+ and Landsat 8 OLI sensors

Koutsias

Pleniou

2015

International Journal of Remote Sensing

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There is a long history of the use of Landsat data in burned land mapping mainly due to certain characteristics of the Landsat imagery including the spatial, spectral, and temporal data resolution, the low cost (Landsat data are now freely available), and the existence of an almost 35-year historical archive (excluding Landsat 1-3). Landsat 8 (Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS)) was launched on 11 February 2013 and it captures data in three new bands along with two additional thermal bands. However, is the spectral signal of burned surfaces in satellite remotesensing data of Landsat series consistent and robust enough to allow the successful application of the techniques developed so far for Landsat 8? In this article, we compare the spectral signal of burned surfaces between Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 OLI sensors using five case studies that correspond to five large fire events in different biophysical environments in Greece, for which both Landsat 7 ETM+ and Landsat 8 OLI data were available. From the comparative analysis using histogram data plots of burned (post-fire image) and vegetated (pre-fire image) areas, spectral signature plots and separability indices of certain land-cover types, estimated using the same sampling areas over both satellite images, a general consistency was observed between the two sensors. Slight differences between the sensors were attributed to differences in the acquisition dates and were related to the type of vegetation rather than the sensors used to record the satellite images. Neither sensor provided improved discrimination over the other.

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A forward/backward principal component analysis of Landsat-7 ETM+ data to enhance the spectral signal of burnt surfaces

Cited by 40 publications

References 53 publications

Mapping a burned forest area from Landsat TM data by multiple methods

Mapping a burned forest area from Landsat TM data by multiple methods

Knowledge based multi-source, time series classification: A case study of central region of Kenya

Comparing the spectral signal of burned surfaces between Landsat 7 ETM+ and Landsat 8 OLI sensors

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