Fishery dynamics of the California market squid (Loligo opalescens), as measured by satellite remote sensing

Novel data on the spatial and temporal distribution of fishing effort and population abundance are presented for the market squid fishery (Loligo opalescens) in the Southern California Bight, 1992−2000. Fishing effort was measured by the detection of boat lights by the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS). Visual confirmation of fishing vessels by nocturnal aerial surveys indicated that lights detected by satellites are reliable indicators of fishing effort. Overall, fishing activity was concentrated off the following Channel Islands: Santa Rosa, Santa Cruz, Anacapa, and Santa Catalina. Fishing activity occurred at depths of 100 m or less. Landings, effort, and squid abundance (measured as landings per unit of effort, LPUE) markedly declined during the 1997−98 El Niño; landings and LPUE increased afterwards. Within a fishing season, the location of fishing activity shifted from the northern shores of Santa Rosa and Santa Cruz Islands in October, the typical starting date for squid fishing in the Bight, to the southern shores by March, the typical end of the squid season. Light detection by satellites offers a source of fine-scale spatial and temporal data on fishing effort for the market squid fishery off California, and these data can be integrated with environmental data and fishing logbook data in the development of a management plan.

Application of remote sensing to marine fisheries: alternative approaches

A discussion is presented on the applications of remote sensing to fisheries. The measurement of temperature, wind stress, and ocean colour using remote sensing techniques is considered.

Remote sensing of the oceans from space with special reference to marine resources

A number of ocean science fields have profitted, either directly or indirectly from satellite remote sensing, including physical, biological and geological oceanography. User oriented applications include fishing, shipping, offshore drilling and mining, coastal engineering and coastal hydrology. Following a brief account of the technology involved, areas in oceanography benefitting from satellite information are detailed. Examples are given of satellite data applications to marine resources.

Visual pattern recognition models for remote sensing of civil infrastructure

As-built models have been proven useful in many project-related applications, such as progress monitoring and quality control. However, they are not widely produced in most projects because a lot of effort is still necessary to manually convert remote sensing data from photogrammetry or laser scanning to an as-built model. In order to automate the generation of as-built models, the first and fundamental step is to automatically recognize infrastructure-related elements from the remote sensing data. This paper outlines a framework for creating visual pattern recognition models that can automate the recognition of infrastructure-related elements based on their visual features. The framework starts with identifying the visual characteristics of infrastructure element types and numerically representing them using image analysis tools. The derived representations, along with their relative topology, are then used to form element visual pattern recognition (VPR) models. So far, the VPR models of four infrastructure-related elements have been created using the framework. The high recognition performance of these models validates the effectiveness of the framework in recognizing infrastructure-related elements.

Visual Pattern Recognition Models for Remote Sensing of Civil Infrastructure

As-built models have been proven useful in many project-related applications, such as progress monitoring and quality control. However, they are not widely produced in most projects because a lot of effort is still necessary to manually convert remote sensing data from photogrammetry or laser scanning to an as-built model. In order to automate the generation of as-built models, the first and fundamental step is to automatically recognize infrastructure-related elements from the remote sensing data. This paper outlines a framework for creating visual pattern recognition models that can automate the recognition of infrastructure-related elements based on their visual features. The framework starts with identifying the visual characteristics of infrastructure element types and numerically representing them using image analysis tools. The derived representations, along with their relative topology, are then used to form element visual pattern recognition (VPR) models. So far, the VPR models of four infrastructure-related elements have been created using the framework. The high recognition performance of these models validates the effectiveness of the framework in recognizing infrastructure-related elements.

Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing

The aim of this paper is to show that Dempster-Shafer evidence theory may be successfully applied to unsupervised classification in multisource remote sensing. Dempster-Shafer formulation allows for consideration of unions of classes, and to represent both imprecision and uncertainty, through the definition of belief and plausibility functions. These two functions, derived from mass function, are generally chosen in a supervised way. In this paper, the authors describe an unsupervised method, based on the comparison of monosource classification results, to select the classes necessary for Dempster-Shafer evidence combination and to define their mass functions. Data fusion is then performed, discarding invalid clusters (e.g. corresponding to conflicting information) thank to an iterative process. Unsupervised multisource classification algorithm is applied to MAC-Europe'91 multisensor airborne campaign data collected over the Orgeval French site. Classification results using different combinations of sensors (TMS and AirSAR) or wavelengths (L- and C-bands) are compared. Performance of data fusion is evaluated in terms of identification of land cover types. The best results are obtained when all three data sets are used. Furthermore, some other combinations of data are tried, and their ability to discriminate between the different land cover types is quantified