Change detection and controlling forest information using multi-temporal Landsat TM imagery.

A method was developed for relative radiometric calibration of single multitemporal Landsat TM image, several multitemporal images covering each others, and several multitemporal images covering different geographic locations. The radiometricly calibrated difference images were used for detecting rapid changes on forest stands. The nonparametric Kernel method was applied for change detection. The accuracy of the change detection was estimated by inspecting the image analysis results in field. The change classification was applied for controlling the quality of the continuously updated forest stand information. The aim was to ensure that all the manmade changes and any forest damages were correctly updated including the attribute and stand delineation information. The image analysis results were compared with the registered treatments and the stand information base. The stands with discrepancies between these two information sources were recommended to be field inspected.

Multi-spectral satellite image classification using Glowworm Swarm Optimization

This paper investigates a new Glowworm Swarm Optimization (GSO) clustering algorithm for hierarchical splitting and merging of automatic multi-spectral satellite image classification (land cover mapping problem). Amongst the multiple benefits and uses of remote sensing, one of the most important has been its use in solving the problem of land cover mapping. Image classification forms the core of the solution to the land cover mapping problem. No single classifier can prove to classify all the basic land cover classes of an urban region in a satisfactory manner. In unsupervised classification methods, the automatic generation of clusters to classify a huge database is not exploited to their full potential. The proposed methodology searches for the best possible number of clusters and its center using Glowworm Swarm Optimization (GSO). Using these clusters, we classify by merging based on parametric method (k-means technique). The performance of the proposed unsupervised classification technique is evaluated for Landsat 7 thematic mapper image. Results are evaluated in terms of the classification efficiency - individual, average and overall.

Hierarchical Clustering Algorithm for Land Cover Mapping Using Satellite Images

This paper presents hierarchical clustering algorithms for land cover mapping problem using multi-spectral satellite images. In unsupervised techniques, the automatic generation of number of clusters and its centers for a huge database is not exploited to their full potential. Hence, a hierarchical clustering algorithm that uses splitting and merging techniques is proposed. Initially, the splitting method is used to search for the best possible number of clusters and its centers using Mean Shift Clustering (MSC), Niche Particle Swarm Optimization (NPSO) and Glowworm Swarm Optimization (GSO). Using these clusters and its centers, the merging method is used to group the data points based on a parametric method (k-means algorithm). A performance comparison of the proposed hierarchical clustering algorithms (MSC, NPSO and GSO) is presented using two typical multi-spectral satellite images - Landsat 7 thematic mapper and QuickBird. From the results obtained, we conclude that the proposed GSO based hierarchical clustering algorithm is more accurate and robust.

Quasi-based hierarchical clustering for land cover mapping using satellite images

This paper presents an improved hierarchical clustering algorithm for land cover mapping problem using quasi-random distribution. Initially, Niche Particle Swarm Optimization (NPSO) with pseudo/quasi-random distribution is used for splitting the data into number of cluster centers by satisfying Bayesian Information Criteria (BIC). Themain objective is to search and locate the best possible number of cluster and its centers. NPSO which highly depends on the initial distribution of particles in search space is not been exploited to its full potential. In this study, we have compared more uniformly distributed quasi-random with pseudo-random distribution with NPSO for splitting data set. Here to generate quasi-random distribution, Faure method has been used. Performance of previously proposed methods namely K-means, Mean Shift Clustering (MSC) and NPSO with pseudo-random is compared with the proposed approach - NPSO with quasi distribution(Faure). These algorithms are used on synthetic data set and multi-spectral satellite image (Landsat 7 thematic mapper). From the result obtained we conclude that use of quasi-random sequence with NPSO for hierarchical clustering algorithm results in a more accurate data classification.

China's changing landscape during the 1990s: Large-scale land transformations estimated with satellite data

Land-cover changes in China are being powered by demand for food for its growing population and by the nation's transition from a largely rural society to one in which more than half of its people are expected to live in cities within two decades. Here we use an analysis of remotely sensed data gathered between 1990 and 2000, to map the magnitude and pattern of changes such as the conversion of grasslands and forests to croplands and the loss of croplands to urban expansion. With high-resolution ( 30 m) imagery from Landsat TM for the entire country, we show that between 1990 and 2000 the cropland area increased by 2.99 million hectares and urban areas increased by 0.82 million hectares. In northern China, large areas of woodlands, grasslands and wetlands were converted to croplands, while in southern China large areas of croplands were converted to urban areas. The land-cover products presented here give the Chinese government and international community, for the first time, an unambiguous understanding of the degree to which the nation's landscape is being altered. Documentation of these changes in a reliable and spatially explicit way forms the foundation for management of China's environment over the coming decades.

Determining the irrigated area under center - pivots systems in the southewestern of Buenos Aires Province (Argentina) employing satellite images

p.109-119

Deglaciation of the Caucasus Mountains, Russia/Georgia, in the 21st century observed with ASTER satellite imagery and aerial photograpy

Changes in the map area of 498 glaciers located on the Main Caucasus ridge (MCR) and on Mt. Elbrus in the Greater Caucasus Mountains (Russia and Georgia) were assessed using multispectral ASTER and panchromatic Landsat imagery with 15 m spatial resolution in 1999/2001 and 2010/2012. Changes in recession rates of glacier snouts between 1987–2001 and 2001–2010 were investigated using aerial photography and ASTER imagery for a sub-sample of 44 glaciers. In total, glacier area decreased by 4.7 ± 2.1% or 19.2 ± 8.7 km2 from 407.3 ± 5.4 km2 to 388.1 ± 5.2 km2. Glaciers located in the central and western MCR lost 13.4 ± 7.3 km2 (4.7 ± 2.5%) in total or 8.5 km2 (5.0 ± 2.4%) and 4.9 km2 (4.1 ± 2.7%) respectively. Glaciers on Mt. Elbrus, although located at higher elevations, lost 5.8 ± 1.4 km2 (4.9 ± 1.2%) of their total area. The recession rates of valley glacier termini increased between 1987–2000/01 and 2000/01–2010 (2000 for the western MCR and 2001 for the central MCR and Mt.~Elbrus) from 3.8 ± 0.8, 3.2 ± 0.9 and 8.3 ± 0.8 m yr−1 to 11.9 ± 1.1, 8.7 ± 1.1 and 14.1 ± 1.1 m yr−1 in the central and western MCR and on Mt. Elbrus respectively. The highest rate of increase in glacier termini retreat was registered on the southern slope of the central MCR where it has tripled. A positive trend in summer temperatures forced glacier recession, and strong positive temperature anomalies in 1998, 2006, and 2010 contributed to the enhanced loss of ice. An increase in accumulation season precipitation observed in the northern MCR since the mid-1980s has not compensated for the effects of summer warming while the negative precipitation anomalies, observed on the southern slope of the central MCR in the 1990s, resulted in stronger glacier wastage.

Analysis of tectonic-controlled fluvial morphology and sedimentary processes of the western Amazon Basin: an approach using satellite images and digital elevation model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Assessment of tropical forest degradation by selective logging and fire using Landsat imagery

Many studies have assessed the process of forest degradation in the Brazilian Amazon using remote sensing approaches to estimate the extent and impact by selective logging and forest fires on tropical rain forest. However, only a few have estimated the combined impacts of those anthropogenic activities. We conducted a detailed analysis of selective logging and forest fire impacts on natural forests in the southern Brazilian Amazon state of Mato Grosso, one of the key logging centers in the country. To achieve this goal a 13-year series of annual Landsat images (1992-2004) was used to test different remote sensing techniques for measuring the extent of selective logging and forest fires, and to estimate their impact and interaction with other land use types occurring in the study region. Forest canopy regeneration following these disturbances was also assessed. Field measurements and visual observations were conducted to validate remote sensing techniques. Our results indicated that the Modified Soil Adjusted Vegetation Index aerosol free (MSAVI(af)) is a reliable estimator of fractional coverage under both clear sky and under smoky conditions in this study region. During the period of analysis, selective logging was responsible for disturbing the largest proportion (31%) of natural forest in the study area, immediately followed by deforestation (29%). Altogether, forest disturbances by selective logging and forest fires affected approximately 40% of the study site area. Once disturbed by selective logging activities, forests became more susceptible to fire in the study site. However, our results showed that fires may also occur in undisturbed forests. This indicates that there are further factors that may increase forest fire susceptibility in the study area. Those factors need to be better understood. Although selective logging affected the largest amount of natural forest in the study period, 35% and 28% of the observed losses of forest canopy cover were due to forest fire and selective logging combined and to forest fire only, respectively. Moreover, forest areas degraded by selective logging and forest fire is an addition to outright deforestation estimates and has yet to be accounted for by land use and land cover change assessments in tropical regions. Assuming that this observed trend of land use and land cover conversion continues, we predict that there will be no undisturbed forests remaining by 2011 in this study site. Finally, we estimated that 70% of the total forest area disturbed by logging and fire had sufficiently recovered to become undetectable using satellite data in 2004. (C) 2010 Elsevier B.V. All rights reserved.

Estrutura térmica identificada por transectos móveis e canal termal do Landsat 7 em cidade tropical

This study aims to examine the thermal structure of the urban climate based on the interpretation of the satellite Landsat 7 (thermal channel) and measures for the area. It identifies how the production of urban climate develops based on an analysis of the structure of space forms and characteristics of land use and constructive materials in the generation of heat islands and their implications in environmental comfort in a tropical climate medium size city at Brazil. To check intra-urban air temperature, measures were carried out in mobile transects in the North-South and East-West routes. Thermal Channel data of Landsat-7, were converted to surface values. The results showed that the pattern of urbanization and characteristics of land use are responsible for the distribution of temperature generating heat islands in downtown and popular densely built neighborhoods. In those cases the highest indexes of social segregation added to higher temperature also provokes elevation in the number of illnesses and morbidity cases, mostly of respiratory diseases.

Análise temporal do uso do solo e comparação entre os índices de vegetação ndvi e savi no município de Santa Cruz do Rio Pardo - SP usando imagens landsat-5

This work aims to analyze the land use evolution in the city of Santa Cruz do Rio Pardo - SP through supervised classification of Landsat-5 TM satellite images according to the maximum likelihood (Maxlike), as well as verifying the mapping accuracy through Kappa index, comparing NDVI and SAVI vegetation indexes in different adjustment factors for the canopy substrate and determining the vegetal coverage percentage in all methods used on 2007, May 26 th; 2009, January 7 th and 2009, April 29 th. The Maxlike classification showed several spatial changes in land use over the study period. The most appropriated vegetation indexes were NDVI and SAVI - 0,25 factor, which showed similar values of vegetal coverage percentage, but discrepant from the inferred value for Maxlike classification.

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.