Use of LANDSAT 8 images for depth and water quality assessment of el Guájaro Reservoir, Colombia

The aim of this study was to evaluate the viability of using Landsat 8 spectral images to estimate water quality parameters and depth in El Guájaro Reservoir. On February and March 2015, two samplings were carried out in the reservoir, coinciding with the Landsat 8 images. Turbidity, dissolved oxygen, electrical conductivity, pH and depth were evaluated. Through multiple regression analysis between measured water quality parameters and the reflectance of the pixels corresponding to the sampling stations, statistical models with determination coefficients between 0.6249 and 0.9300 were generated. Results indicate that from a small number of measured parameters we can generate reliable models to estimate the spatial variation of turbidity, dissolved oxygen, pH and depth, as well the temporal variation of electrical conductivity, so models generated from Landsat 8 can be used as a tool to facilitate the environmental, economic and social management of the reservoir

Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification

The European Space Agency Sentinel-2 satellites provide multispectral images with pixel sizes down to 10 m. This high resolution allows for fast and frequent detection, classification and discrimination of various objects in the sea, which is relevant in general and specifically for the vast Arctic environment. We analyze several sets of multispectral image data from Denmark and Greenland fall and winter, and describe a supervised search and classification algorithm based on physical parameters that successfully finds and classifies all objects in the sea with reflectance above a threshold. It discriminates between objects like ships, islands, wakes, and icebergs, ice floes, and clouds with accuracy better than 90%. Pan-sharpening the infrared bands leads to classification and discrimination of ice floes and clouds better than 95%. For complex images with abundant ice floes or clouds, however, the false alarm rate dominates for small non-sailing boats

Identificação de embarcações em imagens aerotransportadas de radar de abertura sintética (R-99 SAR) na área marítima do Brasil

O objetivo deste trabalho foi identificar embarcações em imagens de radar obtidas pela aeronave R-99 da Força Aérea Brasileira. Dados de amplitude, obtidas na banda L e nas polarizações HH, HV, VH e VV da região de Porto de Tubarão, ES, foram processados por meio de diferentes tipos de realces, filtros, classificadores e transformadores espectrais. As imagens com maior potencial para identificar embarcações foram ainda analisadas para diferenciar embarcações militares de mercantes, considerando-se os cinco elementos de interpretação (forma, tamanho, sombra, tonalidade e fatores associados, isto é, o contexto em que as embarcações se encontram nas imagens) e as cinco fases de interpretação de imagens (detecção, reconhecimento, análise, dedução e classificação). A combinação de processamentos mais favoráveis foi o realce com contraste 50-200, seguido de filtro abertura ou erosão e classificador SVM (Support Vector Machine) ou transformação SCI (Synthetic Color Image). Foi possível discriminar embarcações nas fases de detecção e reconhecimento, enquanto a diferenciação entre embarcações mercantes e militares foi obtida nas fases de análise e dedução. No nível de classificação, não foi possível definir o tipo de embarcação militar (e.g., fragata ou contratorpedeiro) ou o tipo de embarcação mercante (e.g., petroleiro ou graneleiro)

Space-based Global Maritime Surveillance. Part I: Satellite Technologies

Maritime surveillance (MS) is crucial for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since the early days of seafaring, MS has been a critical task for providing security in human coexistence. Several generations of sensors providing detailed maritime information have become available for large offshore areas in real time: maritime radar sensors in the 1950s and the automatic identification system (AIS) in the 1990s among them. However, ground-based maritime radars and AIS data do not always provide a comprehensive and seamless coverage of the entire maritime space. Therefore, the exploitation of space-based sensor technologies installed on satellites orbiting around the Earth, such as satellite AIS data, synthetic aperture radar, optical sensors, and global navigation satellite systems reflectometry, becomes crucial for MS and to complement the existing terrestrial technologies. In the first part of this work, we provide an overview of the main available space-based sensors technologies and present the advantages and limitations of each technology in the scope of MS. The second part, related to artificial intelligence, signal processing and data fusion techniques, is provided in a companion paper, titled: "Space-based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques" [1].Comment: This paper has been submitted to IEEE Aerospace and Electronic Systems Magazin

Hurricane Induced Land and Vegetation Changes in the Breton Sound Estuary and Chandeleur Islands Using Landsat 5 TM

This study focuses on hurricane-induced changes in land and vegetation primarily in two study areas, the Breton Sound Estuary and the Chandeleur Islands, southeast of New Orleans, Louisiana. Breton Sound Estuary consists of the Caernarvon Diversion, a fresh water diversion of the Mississippi River that supplies this region with managed pulses of fresh water and sediments. The Chandeleur Islands are a chain of barrier islands that are uninhabited and transgressive in nature. A sequence of hurricanes in the past two decades has greatly altered both areas significantly. Satellite data were analyzed for a period of 24 years (1987-2011) of Breton Sound Estuary region and for 14 years (1997-2010) of the Chandeleur Islands. Landsat 5 Thematic Mapper data were used to classify and analyze changes using ERDAS IMAGINE 9.3 software. Images were classified into land and water classes using a hybrid classification technique that is unlike the techniques used in the past. Quantitative spatial analyses of the extent of land loss, vegetation changes and beach loss/gain over time were performed. Three change detection techniques were used in this research, which include post-classification spatial intersection, Change Vector Analysis (CVA) and image differencing. Maximum land loss in the Breton Sound Estuary region was due to Hurricane Katrina in 2005 when 196 km2 of land was converted to water from November 2004 to October 2005. Marsh area loss in the 24-year time series coincided with the overall land area loss. An increase in marsh area was detected in three segments of the time series i.e. 1987 to 1991, 1992 (after Hurricane Andrew) to 2003 (before Hurricane Ivan) and 2006 (after Hurricane Katrina) to 2010 indicating some recovery between hurricane years. At the Chandeleur Islands, most of the land loss over the past decade was due to four major hurricanes since 1997; Hurricane Georges in 1998, Hurricane Ivan in 2004, Hurricane Katrina in 2005 and Hurricane Gustav in 2008. The most significant hurricane that impacted these islands was Hurricane Georges in 1998 that resulted in a land loss of 76.5% measured from 1997. The land area increase after the impact of Hurricane Gustav in 2008 to 2011 was very low ranging from 0 km2 to 2 km2. Shoreline change detection results indicated that the barrier islands moved westward (landward), a maximum of 1.7 km in the southern section. Seven kilometres of the linear coastline was lost in the northern tip and 15 km in the southern tip. The change detection analysis and the shoreline change analysis indicated that the southern section of these islands has undergone greater damage due to erosion than the northern section

Performance of LANDSAT TM in ship detection in turbid waters

The visible and near infrared bands of Landsat have limitations for detecting ships in turbid water. The potential of TM middle infrared bands for ship detection has so far not been investigated. This study analyzed the performance of the six Landsat TM visible and infrared bands for detecting dredging ships in the turbid waters of the Poyang Lake, China. A colour composite of principal components analysis (PCA) components 3, 2 and 1 of a TM image was used to randomly select 81 dredging ships. The reflectance contrast between ships and adjacent water was calculated for each ship. A z-score and related p-value were used to assess the ship detection performance of the six Landsat TM bands. The reflectance contrast was related to water turbidity to analyze how water turbidity affected the capability of ship identification. The results revealed that the TM middle infrared bands 5 and 7 better discriminated vessels from surrounding waters than the visible and near infrared bands 1-4. A significant relation between reflectance contrast and water turbidity in bands 1-4 could explain the limitations of bands 1¿4; while water turbidity has no a significant relation to the reflectance contrast of bands 5 and 7. This explains why bands 5 and 7 detect ships better than bands 1-

Performance of Landsat TM in ship detection in turbid waters

The visible and near infrared bands of Landsat have limitations for detecting ships in turbid water. The potential of TM middle infrared bands for ship detection has so far not been investigated. This study analyzed the performance of the six Landsat TM visible and infrared bands for detecting dredging ships in the turbid waters of the Poyang Lake, China. A colour composite of principal components analysis (PCA) components 3, 2 and 1 of a TM image was used to randomly select 81 dredging ships. The reflectance contrast between ships and adjacent water was calculated for each ship. A z-score and related p-value were used to assess the ship detection performance of the six Landsat TM bands. The reflectance contrast was related to water turbidity to analyze how water turbidity affected the capability of ship identification. The results revealed that the TM middle infrared bands 5 and 7 better discriminated vessels from surrounding waters than the visible and near infrared bands 1-4. A significant relation between reflectance contrast and water turbidity in bands 1-4 could explain the limitations of bands 1¿4; while water turbidity has no a significant relation to the reflectance contrast of bands 5 and 7. This explains why bands 5 and 7 detect ships better than bands 1-