999 resultados para THEMATIC MAPPER IMAGES


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The present study investigates the spatial and spectral discrimination potential for grassland patches in the inner Turku Archipelago using Landsat Thematic Mapper satellite imagery. The spatial discrimination potential was computed through overlay analysis using official grassland parcel data and a hypothetical 30 m resolution satellite image capturing the site. It found that Landsat TM imagery’s ability to retrieve pure or near-pure pixels (90% purity or more) from grassland patches smaller than 1 hectare was limited to 13% success, compared to 52% success when upscaling the resolution to 10 x 10 m pixel size. Additionally, the perimeter/area patch metric is proposed as a predictor for the suitability of the spatial resolution of input imagery. Regression analysis showed that there is a strong negative correlation between a patch’s perimeter/area ratio and its pure pixel potential. The study goes on to characterise the spectral response and discrimination potential for the five main grassland types occurring in the study area: recreational grassland, traditional pasture, modern pasture, fodder production grassland and overgrown grassland. This was done through the construction of spectral response curves, a coincident spectral plot and a contingency matrix as well as by calculating the transformed divergence for the spectral signatures, all based on training samples from the TM imagery. Substantial differences in spectral discrimination potential between imagery from the beginning of the growing season and the middle of summer were found. This is because the spectral responses for these five grassland types converge as the peak of the growing season draws nearer. Recreational grassland shows a consistent discrimination advantage over other grassland types, whereas modern pasture is most easily confused. Traditional pasture land, perhaps the most biologically valuable grassland type, can be spectrally discriminated from other grassland types with satisfactory success rates provided early growing season imagery is used.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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"September 1991."

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ABSTRACT The analysis of changes in species composition and vegetation structure in chronosequences improves knowledge on the regeneration patterns following land abandonment in the Amazon. Here, the objective was to perform floristic-structural analysis in mature forests (with/without timber exploitation) and secondary successions (initial, intermediate and advanced vegetation regrowth) in the Tapajós region. The regrowth age and plot locations were determined using Landsat-5/Thematic Mapper images (1984-2012). For floristic analysis, we determined the sample sufficiency and the Shannon-Weaver (H'), Pielou evenness (J), Value of Importance (VI) and Fisher's alpha (α) indices. We applied the Non-metric Multidimensional Scaling (NMDS) for similarity ordination. For structural analysis, the diameter at the breast height (DBH), total tree height (Ht), basal area (BA) and the aboveground biomass (AGB) were obtained. We inspected the differences in floristic-structural attributes using Tukey and Kolmogorov-Smirnov tests. The results showed an increase in the H', J and α indices from initial regrowth to mature forests of the order of 47%, 33% and 91%, respectively. The advanced regrowth had more species in common with the intermediate stage than with the mature forest. Statistically significant differences between initial and intermediate stages (p<0.05) were observed for DBH, BA and Ht. The recovery of carbon stocks showed an AGB variation from 14.97 t ha-1 (initial regrowth) to 321.47 t ha-1 (mature forests). In addition to AGB, Ht was also important to discriminate the typologies.

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The structure of Brazilian savannah, named locally as “cerrado”, tends to change if the human pressures, such as pasture and intensive fire, are suppressed showing a densification of the physiognomies throughout the time. Vegetation Index acquired from remotely sensed data has been a proper way to study and monitoring large areas, and the Normalized Difference Vegetation Index (NDVI) is one of the most used for this purpose. The aim of this study was to assess the dynamic of structural changes in protected and non-protected areas of cerrado vegetation using NDVI. For this purpose, three cerrado fragments in the state of São Paulo, Brazil, were evaluated for a 26 year time span from 1985 and 2011, being two of them protected against anthropogenic interference. Landsat 5 –Thematic Mapper images were used and processed in ArcGIS. In the protected areas NDVI indicated that the vegetation followed the expected trend of changes for cerrado, with more open physiognomies tending to be denser throughout this period of 26 years, whereas in the non-protected fragment the NDVI evidences human pressure, showing lower phytomass in 2011. NDVI showed to be efficient in detecting and monitoring changes in cerrado vegetation structure, and can be useful to study both, the natural dynamics of cerrado vegetation and the anthropogenic interference in protected areas.

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Remote sensing, as a direct adjunct to field, lithologic and structural mapping, and more recently, GIS have played an important role in the study of mineralized areas. A review on the application of remote sensing in mineral resource mapping is attempted here. It involves understanding the application of remote sensing in lithologic, structural and alteration mapping. Remote sensing becomes an important tool for locating mineral deposits, in its own right, when the primary and secondary processes of mineralization result in the formation of spectral anomalies. Reconnaissance lithologic mapping is usually the first step of mineral resource mapping. This is complimented with structural mapping, as mineral deposits usually occur along or adjacent to geologic structures, and alteration mapping, as mineral deposits are commonly associated with hydrothermal alteration of the surrounding rocks. In addition to these, understanding the use of hyperspectral remote sensing is crucial as hyperspectral data can help identify and thematically map regions of exploration interest by using the distinct absorption features of most minerals. Finally coming to the exploration stage, GIS forms the perfect tool in integrating and analyzing various georeferenced geoscience data in selecting the best sites of mineral deposits or rather good candidates for further exploration.

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Using Landsat imagery, forest canopy density (FCD) estimated with the FCD Mapper®, was correlated with predominant height (PDH, measured as the average height of the tallest 50 trees per hectare) for 20 field plots measured in native forest at Noosa Heads, south-east Queensland, Australia. A corresponding image was used to calculate FCD in Leyte Island, the Philippines and was validated on the ground for accuracy. The FCD Mapper was produced for the International Tropical Timber Organisation and estimates FCD as an index of canopy density using reflectance characteristics of Landsat Enhanced Thematic (ETM) Mapper images. The FCD Mapper is a ‘semi-expert’ computer program which uses interactive screens to allow the operator to make decisions concerning the classification of land into bare soil, grass and forest. At Noosa, a positive strong nonlinear relationship (r2 = 0.86) was found between FCD and PDH for 15 field plots with variable PDH but complete canopy closure. An additional five field plots were measured in forest with a broken canopy and the software assessed these plots as having a much lower FCD than forest with canopy closure. FCD estimates for forest and agricultural land in the island of Leyte and subsequent field validation showed that at appropriate settings, the FCD Mapper differentiated between tropical rainforest and banana or coconut plantation. These findings suggest that in forests with a closed canopy this remote sensing technique has promise for forest inventory and productivity assessment. The findings also suggest that the software has promise for discriminating between native forest with a complete canopy and forest which has a broken canopy, such as coconut or banana plantation.

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The reflectance signatures of plantation pine canopy and understorey components were measured using a spectro-radiometer. The aim was to establish whether differences observed in the reflectance signature of stressed and unstressed pine needles were consistent with observed differences in the reflectance of multispectral Landsat Thematic Mapper (TM) images of healthy and stressed forest. Because overall scene reflectance includes the contribution of each scene component, needle reflectance may not be representative of canopy reflectance. In this investigation, a limited dataset of reflectance signatures from stressed and unstressed needles confirmed the negative relationship between pine needle health and reflectance which was observed in visible red wavelengths. However, the reflectance contribution from bushes, pine needle litter and bare soil tended to reinforce this relationship suggesting that in this instance, overall scene reflectance is comprised of the proportional reflectance of each scene component. In near infrared wavelengths, differences between healthy and stressed needle reflectance suggested a strong positive relationship between reflectance and tree health. For Landsat TM images, previous research had only observed a weak positive relationship between stand health and near infrared reflectance in these pine canopies. This suggests that for multispectral Landsat TM images, reflectance of near infrared light from pine canopies may be affected by other factors which may include the scattering of light within canopies. These results are seen as promising for the use of hyperspectral images to detect stand health, provided that pixel reflectance is not influenced by other scene components.

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This paper describes the development and applications of a super-resolution method, known as Super-Resolution Variable-Pixel Linear Reconstruction. The algorithm works combining different lower resolution images in order to obtain, as a result, a higher resolution image. We show that it can make significant spatial resolution improvements to satellite images of the Earth¿s surface allowing recognition of objects with size approaching the limiting spatial resolution of the lower resolution images. The algorithm is based on the Variable-Pixel Linear Reconstruction algorithm developed by Fruchter and Hook, a well-known method in astronomy but never used for Earth remote sensing purposes. The algorithm preserves photometry, can weight input images according to the statistical significance of each pixel, and removes the effect of geometric distortion on both image shape and photometry. In this paper, we describe its development for remote sensing purposes, show the usefulness of the algorithm working with images as different to the astronomical images as the remote sensing ones, and show applications to: 1) a set of simulated multispectral images obtained from a real Quickbird image; and 2) a set of multispectral real Landsat Enhanced Thematic Mapper Plus (ETM+) images. These examples show that the algorithm provides a substantial improvement in limiting spatial resolution for both simulated and real data sets without significantly altering the multispectral content of the input low-resolution images, without amplifying the noise, and with very few artifacts.

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Propôs-se, neste trabalho, estimar dados de albedo à superfície terrestre usando-se o sensor Thematic Mapper (TM) do satélite LANDSAT 5 e compará-lo com dados de duas estações agrometeorológicas localizadas em região de Cerrado e a outra em cultivo da cana-de-açúcar. A região de estudo está localizada no município de Santa Rita do Passa Quatro, SP, Brasil. Para a realização do estudo obtiveram-se seis imagens orbitais do satélite Landsat 5 sensores TM, na órbita 220 e ponto 75, nas datas de 22/02, 11/04, 29/05, 01/08, 17/08 e 21/11, todas do ano de 2005, a que correspondem os dias juliano de 53, 101, 149, 213, 229 e 325, respectivamente. As correções geométricas para as imagens foram realizadas e geradas as cartas de albedo. O algoritmo SEBAL estimou satisfatoriamente os valores de albedo de superfícies sobre áreas de cerrado e de cana-de-açúcar, na região de Santa Rita do Passa Quatro, SP, consistentes com observações realizadas do albedo à superfície.

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The applicability of image calibration to like-values in mapping water quality parameters from multitemporal images is explored, Six sets of water samples were collected at satellite overpasses over Moreton Bay, Brisbane, Australia. Analysis of these samples reveals that waters in this shallow bay are mostly TSS-dominated, even though they are occasionally dominated by chlorophyll as well. Three of the images were calibrated to a reference image based on invariant targets. Predictive models constructed from the reference image were applied to estimating total suspended sediment (TSS) and Secchi depth from another image at a discrepancy of around 35 percent. Application of the predictive model for TSS concentration to another image acquired at a time of different water types resulted in a discrepancy of 152 percent. Therefore, image calibration to like-values could be used to reliably map certain water quality parameters from multitemporal TM images so long as the water type under study remains unchanged. This method is limited in that the mapped results could be rather inaccurate if the water type under study has changed considerably. Thus, the approach needs to be refined in shallow water from multitemporal satellite imagery.

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The majority of the world's population now resides in urban environments and information on the internal composition and dynamics of these environments is essential to enable preservation of certain standards of living. Remotely sensed data, especially the global coverage of moderate spatial resolution satellites such as Landsat, Indian Resource Satellite and Systeme Pour I'Observation de la Terre (SPOT), offer a highly useful data source for mapping the composition of these cities and examining their changes over time. The utility and range of applications for remotely sensed data in urban environments could be improved with a more appropriate conceptual model relating urban environments to the sampling resolutions of imaging sensors and processing routines. Hence, the aim of this work was to take the Vegetation-Impervious surface-Soil (VIS) model of urban composition and match it with the most appropriate image processing methodology to deliver information on VIS composition for urban environments. Several approaches were evaluated for mapping the urban composition of Brisbane city (south-cast Queensland, Australia) using Landsat 5 Thematic Mapper data and 1:5000 aerial photographs. The methods evaluated were: image classification; interpretation of aerial photographs; and constrained linear mixture analysis. Over 900 reference sample points on four transects were extracted from the aerial photographs and used as a basis to check output of the classification and mixture analysis. Distinctive zonations of VIS related to urban composition were found in the per-pixel classification and aggregated air-photo interpretation; however, significant spectral confusion also resulted between classes. In contrast, the VIS fraction images produced from the mixture analysis enabled distinctive densities of commercial, industrial and residential zones within the city to be clearly defined, based on their relative amount of vegetation cover. The soil fraction image served as an index for areas being (re)developed. The logical match of a low (L)-resolution, spectral mixture analysis approach with the moderate spatial resolution image data, ensured the processing model matched the spectrally heterogeneous nature of the urban environments at the scale of Landsat Thematic Mapper data.

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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

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Urbanization has caused significant environmental impacts, replacing natural surfaces by buildings, decreasing green vegetated areas, soil sealing and atmospheric pollution which contribute to increase the land surface temperature in such areas. Thus, this study aimed to analyze the influence of urbanization on land surface temperature (Ts) in Recife city - Pernambuco (PE), in Brazil, using the Thematic Mapper (TM) sensor images from Landsat 5 satellite. To perform the study, images of August 4, 1998 and September 6, 2010 were obtained and processed to generate Ts thematic maps of Recife-PE and of two districts of this city (Curado and Casa Amarela), in order to analyze the transformation dynamics that has occurred in the area. Through the profile produced for the study area, a spatial and temporal increase of the Ts surface was noticeable in the suburb-downtown direction: 6°C of difference between these areas. The Casa Amarela district, with high urban concentration, presented the highest Ts values observed (>27°C).