Correlação espacial do índice de vegetação (NDVI) de imagem Landsat/ETM+ com atributos do solo

The precision agriculture technologies such as the spatial variability of soil attributes have been widely studied mostly with sugarcane. Among these technologies have been recently highlighted the use of the vegetation index derived from remote sensing products, such as powerful tools indicating the development of vegetation. This study aimed to analyze the spatial variability of clay content, pH and phosphorus in an Oxisol in an area with sugarcane production, and correlate with the Normalized Difference Vegetation Index (NDVI). The georeferenced grid was created for the soil properties (clay, phosphorus and pH) and generated the maps of spatial variability. For these same sites were calculated the NDVI, in addition to mapping of this ratio, the evaluation of the spatial correlation between this and other studied properties. The clay and phosphorus content showed positive spatial correlation with the NDVI, while no spatial correlation was observed between NDVI and pH. The satellite images from the sensor ETM + Landsat were used to correlate to NDVI to observe the spatial variability of the studied attributes.

Estudo de ilhas de calor no município de Piratininga/SP, por meio de dados orbitais do landsat 5 sensor tm

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Avaliação dos aspectos texturais na imagem Landsat como subsídio à compartimentação fisiográfica dos municípios de Peruíbe e Itanhaém - SP

Pós-graduação em Geociências e Meio Ambiente - IGCE

Cyanobacteria detection in Guarapiranga Reservoir (São Paulo State, Brazil) using Landsat TM and ETM+ images

Algae bloom is one of the major consequences of the eutrophication of aquatic systems, including algae capable of producing toxic substances. Among these are several species of cyanobacteria, also known as blue-green algae, that have the capacity to adapt themselves to changes in the water column. Thus, the horizontal distribution of cyanobacteria harmful algae blooms (CHABs) is essential, not only to the environment, but also for public health. The use of remote sensing techniques for mapping CHABs has been explored by means of bio-optical modeling of phycocyanin (PC), a unique inland waters cyanobacteria pigment. However, due to the small number of sensors with a spectral band of the PC absorption feature, it is difficult to develop semi-analytical models. This study evaluated the use of an empirical model to identify CHABs using TM and ETM+ sensors aboard Landsat 5 and 7 satellites. Five images were acquired for applying the model. Besides the images, data was also collected in the Guarapiranga Reservoir, in São Paulo Metropolitan Region, regarding the cyanobacteria cell count (cells/mL), which was used as an indicator of CHABs biomass. When model values were analyzed excluding calibration factors for temperate lakes, they showed a medium correlation (R²=0.81, p=0.036), while when the factors were included the model showed a high correlation (R²=0.96, p=0.003) to the cyanobacteria cell count. The empirical model analyzed proved useful as an important tool for policy makers, since it provided information regarding the horizontal distribution of CHABs which could not be acquired from traditional monitoring techniques.

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.

Utilização de imagem de satélite Landsat no mapeamento da ocupação da terra e seus conflitos de uso em APPs na bacia hidrográfica do ribeirão Paraíso - São Manuel (SP)

The objectives of this study were to evaluate land use and occupation of Permanent Preservation Areas (PPA) as well as its use conflicts by TM (Thematic Mapper) image of the 2010 Landsat-5 satellite, according to the Forest Code. For that purpose, Geographic Information Systems in the Ribeirão Paraíso watershed, São Manuel, SP were used. The combination of Remote Sensing and Geographic Information System technologies allowed representation of spatial distribution of the landscape and data integration in the diagnosis of geographic interest. The 2010 mapping showed 12 use categories, and the sugar cane crop had the largest land occupation, 48.25% of the area. The areas of permanent preservation amounted to 925.74 ha, which is an ideal value based on the Brazilian legislation. Mapping of land use conflicts showed intensive anthropic actions going 80.13% forward on PPAs, with only 19.87% remaining forests, which highlights their negative impact and illegal situations in these areas.

Análise do potencial do sensor OLI (Operational Land Imager) a bordo do Landsat-8 em estimar a concentração de sólidos totais em suspensão no reservatório de Barra Bonita

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

STRUCTURAL CHARACTERIZATION OF CANOPIES OF Eucalyptus spp. USING RADIOMETRIC DATA FROM TM/Landsat 5

Empirical approaches and, more recently, physical approaches, have grounded the establishment of logical connections between radiometric variables derived from remote data and biophysical variables derived from vegetation cover. This study was aimed at evaluating correlations of dendrometric and density data from canopies of Eucalyptus spp., as collected in Capao Bonito forest unit, with radiometric data from imagery acquired by the TM/Landsat-5 sensor on two orbital passages over the study site (dates close to field data collection). Results indicate that stronger correlations were identified between crown dimensions and canopy height with near-infrared spectral band data (rho(s)4), irrespective of the satellite passage date. Estimates of spatial distribution of dendrometric data and canopy density (D) using spectral characterization were consistent with the spatial distribution of tree ages during the study period. Statistical tests were applied to evaluate performance disparities of empirical models depending on which date data were acquired. Results indicated a significant difference between models based on distinct data acquisition dates.

Table 1: Glacier measurements from air photos 1969 and landsat images 1972 and 1973

The extent of snow cover at the end of the ablation season on glaciers in the Tyrolean Alps in 1972 and 1973 was determined from Landsat-1 Multispectral Scanner (MSS) images. For snovv mapping the MSS-images with a ground resolution of 80 meters were enlarged to a scale of 1: 100.000 by photographic methods. Different appearance of snow cover in the 4 MSS-channels is discussed in connection with ground truth control. The accuracy of snow and ice mapping from Landsat images was checked on 15 glaciers with an area from 1 to 10 km2 by aerial photography and/or ground truth control. These comparisons imply the usefulness of Landsat images for snow mapping on glaciers of a few square kilometers. The altitude of the equilibrium line was determined from Landsat images for 53 glaciers in the Tyrolean Alps. The regional differences in the equilibrium line altitude correspond to the regional precipitation patterns. The equilibrium line was identical with the snow line at the end of the budget year 1971/1972; therefore it was possible to determine the equilibrium line from satellite images. For 1968/69 the equilibrium line was mapped from aerial photographs for several glaciers. In 1972/73 mass balance was strongly negative and the equilibrimn line was within the firn area of the glaciers. Therefore it was not possible to distinguish between accumulation and ablation areas from the Landsat images of September 1973; however, snow and ice areas could be olearly differentiated. The ratios of accumulation area 01' snow area to the total area of the glaciers were determineel from satellite images and aerial photography separately for aelvancing anel for retreating glaciers and were relateel to the mass balance. In the budget years 1968/69 and 1972/73 with negative mass balance the accumulation area ratios of the advancing glacien; were olearly different from the ratios of the retreating glaciers, in 1971/72 with positive 01' balanced mass budget the differences between advancing and retreating glaciers were not significant.

Landsat derived percent vegetation cover of Israel (as of 2003)

Wildfires are part of the Mediterranean ecosystem, however, in Israel all wildfires are human caused, either intentionally or un-intentionally. In this study we aimed to develop and test a new method for mapping fire scars from MODIS imagery, to examine the temporal and spatial patterns of wildfires in Israel in the 2000s and to examine the factors controlling Israel's wildfire regime. To map the fires we used two 'off-the-shelf' MODIS fire products as our basis-the 1 km MODIS Collection 5 fire hotspots, the 500 m MCD45A1 burnt areas-and we created a new set of fire scar maps from the 250 m MOD13Q1 product. We carried out a cross comparison of the three MODIS based wildfire scar maps and evaluated them independently against the wild fire scars mapped from 30 m Landsat TM imagery. To examine the factors controlling wildfires we used GIS layers of rainfall, land use, and a Landsat-based national vegetation map. Wildfires occurred in areas where annual rainfall was above 250 mm, mostly in areas with herbaceous vegetation. Wildfire frequency was especially high in the Golan Heights and in the foothills of the Judean mountains, and a high correspondence was found between military training zones and the spatial distribution of fire scars. The use of MODIS satellite images enabled us to map wildfires at a national scale due to the high temporal resolution of the sensor. Our MOD13Q1 based mapping of fire scars adequately mapped large (>1 km**2) fires with accuracies above 80%. Such large fires account for a large proportion of all fires, and pose the greatest threats. This database can aid managers in determining wildfire risks in space and in time.

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

Satellite earth resources data : a users assistance publication /

"Module U-3."

Overview of conferences with non-Federal users on U.S. operational land remote sensing satellite program /

"April 24, 1980."

Land Satellite Project, National Aeronautics and Space Administration : study /

"LANDSAT is a NASA experimental project ... "