DEVELOPMENT OF AN ALGORITHM TO ANALYZE CARTOGRAPHIC FEATURES EXTRACTION METHODS IN DIGITAL IMAGES

The algorithm creates a buffer area around the cartographic features of interest in one of the images and compare it with the other one. During the comparison, the algorithm calculates the number of equals and different points and uses it to calculate the statistical values of the analysis. One calculated statistical value is the correctness, which shows the user the percentage of points that were correctly extracted. Another one is the completeness that shows the percentage of points that really belong to the interest feature. And the third value shows the idea of quality obtained by the extraction method, since that in order to calculate the quality the algorithm uses the correctness and completeness previously calculated. All the performed tests using this algorithm were possible to use the statistical values calculated to represent quantitatively the quality obtained by the extraction method executed. So, it is possible to say that the developed algorithm can be used to analyze extraction methods of cartographic features of interest, since that the results obtained were promising.

Estimation of chlorophyll-a concentration and the trophic state of the Barra Bonita hydroelectric reservoir using oli/landsat-8 images

Reservoirs are artificial environments built by humans, and the impacts of these environments are not completely known. Retention time and high nutrient availability in the water increases the eutrophic level. Eutrophication is directly correlated to primary productivity by phytoplankton. These organisms have an important role in the environment. However, high concentrations of determined species can lead to public health problems. Species of cyanobacteria produce toxins that in determined concentrations can cause serious diseases in the liver and nervous system, which could lead to death. Phytoplankton has photoactive pigments that can be used to identify these toxins. Thus, remote sensing data is a viable alternative for mapping these pigments, and consequently, the trophic. Chlorophyll-a (Chl-a) is present in all phytoplankton species. Therefore, the aim of this work was to evaluate the performance of images of the sensor Operational Land Imager (OLI) onboard the Landsat-8 satellite in determining Chl-a concentrations and estimating the trophic level in a tropical reservoir. Empirical models were fitted using data from two field surveys conducted in May and October 2014 (Austral Autumn and Austral Spring, respectively). Models were applied in a temporal series of OLI images from May 2013 to October 2014. The estimated Chl-a concentration was used to classify the trophic level from a trophic state index that adopted the concentration of this pigment-like parameter. The models of Chl-a concentration showed reasonable results, but their performance was likely impaired by the atmospheric correction. Consequently, the trophic level classification also did not obtain better results.

A comparison for a multiscale study of structural lineaments in southern Brazil: LANDSAT-7 ETM+ and shaded relief images from SRTM3-DEM

This paper presents a comparison of descriptive statistics obtained for brittle structural lineaments extracted manually from LANDSAT images and shaded relief images from SRTM 3 DEM at 1:100, 000 and 1:500, 000 scales. The selected area is located in the southern of Brazil and comprises Precambrian rocks and stratigraphic units of the Paraná Basin. The application of this methodology shows that the visual interpretation depends on the kind of remote sensing image. The resulting descriptive statistics obtained for lineaments extracted from the images do not follow the same pattern according to the scale adopted. The main direction obtained for Proterozoic rocks using both image types at a 1:500, 000 scale are close to NS±10, whereas at a 1:100, 000 scale N45E was obtained for shaded relief images from SRTM 3 DEM and N10W for LANDSAT images. The Paleozoic sediments yielded the best results for the different images and scales (N50W). On the other hand, the Mesozoic igneous rocks showed greatest differences, the shaded relief images from SRTM 3 DEM images highlighting NE structures and the LANDSAT images highlighting NW structures. The accumulated frequency demonstrated high similarity between products for each image type no matter the scale, indicating that they can be used in multiscale studies. Conversely, major differences were found when comparing data obtained using shaded relief images from SRTM 3 DEM and Landsat images at a 1:100, 000 scale.

Dynamics of Shifting Cultivation Landscapes in Northern Lao PDR Between 2000 and 2009 Based on an Analysis of MODIS Time Series and Landsat Images

The rotational nature of shifting cultivation poses several challenges to its detection by remote sensing. Consequently, there is a lack of spatial data on the dynamics of shifting cultivation landscapes on a regional, i.e. sub-national, or national level. We present an approach based on a time series of Landsat and MODIS data and landscape metrics to delineate the dynamics of shifting cultivation landscapes. Our results reveal that shifting cultivation is a land use system still widely and dynamically utilized in northern Laos. While there is an overall reduction in the areas dominated by shifting cultivation, some regions also show an expansion. A review of relevant reports and articles indicates that policies tend to lead to a reduction while market forces can result in both expansion and reduction. For a better understanding of the different factors affecting shifting cultivation landscapes in Laos, further research should focus on spatially explicit analyses.

Near-coastal circum-Antarctic iceberg size distributions determined from Synthetic Aperture Radar images

The Radarsat-1 Antarctic Mapping Project (RAMP) compiled a mosaic of Antarctica and the adjacent ocean zone from more than 3000 high-resolution Synthetic Aperture Radar (SAR) images acquired in September and October 1997. The mosaic with a pixel size of 100 m was used to determine iceberg size distributions around Antarctica, combining an automated detection with a visual control of all icebergs larger than 5 km**2 and correction of recognized false detections. For icebergs below 5 km**2 in size, the numbers of false detections and accuracies of size retrievals were analyzed for three test sites. Nearly 7000 icebergs with horizontal areas between 0.3 and 4717.7 km**2 were identified in a near-coastal zone of varying width between 20 and 300 km. The spatial distributions of icebergs around Antarctica were calculated for zonal segments of 20° angular width and related to the types of the calving fronts in the respective section. Results reveal that regional variations of the size distributions cannot be neglected. The highest ice mass accumulations were found at positions of giant icebergs (> 18.5 km) but also in front of ice shelves from which larger numbers of smaller icebergs calve almost continuously. Although the coastal oceanic zone covered by RAMP is too narrow compared to the spatial coverage needed for oceanographic research, this study nevertheless demonstrates the usefulness of SAR images for iceberg research and the need for repeated data acquisitions extending ocean-wards over distances of 500 km and more from the coast to monitor iceberg melt and disintegration and the related freshwater input into the ocean.

Circumpolar Scaling Layer for downscaling of Scatterometer derived soil moisture with links to geotiff images and NetCDF files (2005-01 to 2011-12)

This dataset provides scaling information applicable to satellite derived coarse resolution surface soil moisture datasets following the approach by Wagner et al. (2008). It is based on ENVISAT ASAR data and can be utilized to apply the Metop ASCAT dataset (25 km) for local studies as well as to assess the representativeness of in-situ measurement sites and thus their potential for upscaling. The approach based on temporal stability (Wagner et al. 2008) consists of the assessment of the validity of the coarse resolution datasets at medium resolution (1 km, product is the so called 'scaling layer').

Classification of Satellite Images by means of Fuzzy Rules generated by a Genetic Algorithm

The data acquired by Remote Sensing systems allow obtaining thematic maps of the earth's surface, by means of the registered image classification. This implies the identification and categorization of all pixels into land cover classes. Traditionally, methods based on statistical parameters have been widely used, although they show some disadvantages. Nevertheless, some authors indicate that those methods based on artificial intelligence, may be a good alternative. Thus, fuzzy classifiers, which are based on Fuzzy Logic, include additional information in the classification process through based-rule systems. In this work, we propose the use of a genetic algorithm (GA) to select the optimal and minimum set of fuzzy rules to classify remotely sensed images. Input information of GA has been obtained through the training space determined by two uncorrelated spectral bands (2D scatter diagrams), which has been irregularly divided by five linguistic terms defined in each band. The proposed methodology has been applied to Landsat-TM images and it has showed that this set of rules provides a higher accuracy level in the classification process

Drought estimation maps by means multidate landsat fused images

The purpose of this paper is to analyze the usefulness of traditional indexes, such as NDVI and NDWI along with a recently proposed index (NDDI) using merged data for multiple dates, with the aim of obtaining drought data to facilitate the analysis for government premises. In this study we have used Landsat 7 ETM+ data for the month of June (2001-2009), which merged to get bands with twice the resolution. The three previous indices were calculated from these new bands, getting in turn drought maps that can enhance the effectiveness of decision making.

Preliminary applications of Landsat images and aerial photography for determining land-use, geologic, and hydrologic characteristics : Yampa River basin, Colorado and Wyoming /

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