A Study of Types of Sensors used in Remote Sensing

Of late, the science of Remote Sensing has been gaining a lot of interest and attention due to its wide variety of applications. Remotely sensed data can be used in various fields such as medicine, agriculture, engineering, weather forecasting, military tactics, disaster management etc. only to name a few. This article presents a study of the two categories of sensors namely optical and microwave which are used for remotely sensing the occurrence of disasters such as earthquakes, floods, landslides, avalanches, tropical cyclones and suspicious movements. The remotely sensed data acquired either through satellites or through ground based- synthetic aperture radar systems could be used to avert or mitigate a disaster or to perform a post-disaster analysis

Whales from space: Assessing the feasibility of using satellite imagery to monitor whales

By the mid-twentieth century, the majority of great whale species were threatened with extinction, following centuries of commercial whaling. Since the implementation of a moratorium on commercial whaling in 1985 by the International Whaling Commission, the recovery of whale population is being regularly assessed. Various methods are used to survey whale populations, though most are spatially limited and prevent remote areas from being studied. Satellites orbiting Earth can access most regions of the planet, offering a potential solution to surveying remote locations. With recent improvements in the spatial resolution of satellite imagery, it is now possible to detect wildlife from space, including whales. In this thesis, I aimed to further investigate the feasibility of very high resolution (VHR) satellite imagery as a tool to reliably monitor whales. The first objective was to describe, both visually and spectrally, how four morphologically distinct species appear in VHR satellite imagery. The second objective was to explore different ways to automatically detect whales in such imagery, as the current alternative is manual detection, which is time-consuming and impractical when monitoring large areas. With the third objective, I attempted to give some insights on how to estimate the maximum depth at which a whale can be detected in VHR satellite imagery, as this will be crucial to estimate whale abundance from space. This thesis shows that the four species targeted could be detected with varying degrees of accuracy, some contrasting better with their surroundings. Compared to manual detection, the automated systems trialled here took longer, were not as accurate, and were not transferable to other images, suggesting to focus future automation research on machine learning and the creation of a well-labelled database required to train and validate. The maximum depth of detection could be assessed only approximately using nautical charts. Other methods such as the installation of panels at various depths should be trialled, although it requires prior knowledge of the spectral reflectance of whales above the surface, which I tested on post-mortem samples of whale integument and proved unreliable. Such reflectance should be measured on free-swimming whale using unmanned aerial vehicles or small aircraft. Overall, this thesis shows that currently VHR satellite imagery can be a useful tool to assess the presence or absence of whales, encouraging further developments to make VHR satellite imagery a reliable method to monitor whale numbers.The MAVA Foundation (16035

Investigation of natural environment by space means. Geobotany, Geomorphology, soil sciences, agricultural lands, landscape study

Reports given by Soviet specialists at a meeting of Socialist countries on remote sensing of the earth using aerospace methods are presented

The Utility of Fine-Scale Remote Sensing Data for Modeling Habitat Characteristics and Breeding Bird Species Distributions in an Appalachian Mature Deciduous Forest.

In this study, I tested the potential for remote sensing data with a high spatial resolution to model breeding forest bird species and their habitat at a fine spatial scale. The research took place on ridgetops in a large, relatively contiguous Appalachian mature deciduous forest in northwestern WV, USA. The remote sensing data sources were a leaf-on QuickBird satellite image (0.6-m panchromatic and 2.4-m multispectral) and a 3-m digital elevation model (DEM). For the first part of the study, I extracted spectral and textural measures from the satellite image and terrain information from the DEM. I then used these data to analyze avian community survey and habitat data collected at circular plots (n = 68) distributed across the ridgetops. The primary results of this analysis indicated that the satellite image provided information about trends in forest composition and structure across the study site, and further that a relatively simple plot-level measure of image texture (the panchromatic pixel standard deviation calculated at plot radii of 50 and 100 m) was a useful proxy of environmental heterogeneity for predicting the distributions of certain forest canopy gap-dependent bird species. For the second part of the study, I analyzed the habitat and remote sensing data at a finer spatial scale to develop remote sensing-based indices of forest structure and composition. These indices provided further insight into local variation in forest characteristics (e.g., in relation to topographic aspect) on the ridgetops. I also tested these indices, the DEM, and anthropogenic forest edge for modeling the breeding territory distributions of three focal species (Cerulean Warbler, Setophaga cerulea; Hooded Warbler, S. citrina; and Ovenbird, Seiurus aurocapilla) mapped over ~11 km of ridgetop transects. These models indicated the importance of local influences of terrain (e.g., east-facing aspects for Cerulean and Hooded Warbler, west-facing aspects for Ovenbird, and knolls for Cerulean Warbler), and forest edges (positive for Cerulean Warbler and negative for Ovenbird) on their distributions. Among the remotely-sensed indices, the index of forest structural complexity was primarily useful as a strong predictor of the distribution of the canopy gap-dependent Hooded Warbler. For the third and final part of the study, I used the locations of singing males of the three focal species collected across a greater extent of the site (~28 km of ridgetop transects) in point pattern analyses that incorporated the remote sensing data and the potential for intraspecific interactions (attraction and repulsion) between neighboring individuals. The results of these analyses supported that intraspecific interactions in addition to environmental influences as indicated by the remote sensing data explained the species’ fine-scale distribution patterns. While the individuals of all three species exhibited regular spacing over short distances that was consistent with competition for territorial space, Cerulean Warbler individuals exhibited more clustering than could be statistically accounted for by the remote sensing data, suggesting the importance of conspecific attraction in its distribution. In summary, my findings supported the potential application of fine-scale remote sensing data for purposes such as complementing coarse-scale environmental data (e.g., land cover maps) in predicting forest breeding bird species distributions, and for comparative analyses of the local spatial distributions of these species. The capacity for remote sensing data to provide useful environmental information at a fine spatial scale is likely to improve as the technology continues to develop

Automatic Archeological Feature Extraction from Satellite VHR Images

Abstract Archaeological applications need a methodological approach on a variable scale able to satisfy the intra-site (excavation) and the inter-site (survey, environmental research). The increased availability of high resolution and micro-scale data has substantially favoured archaeological applications and the consequent use of GIS platforms for reconstruction of archaeological landscapes based on remotely sensed data. Feature extraction of multispectral remotely sensing image is an important task before any further processing. High resolution remote sensing data, especially panchromatic, is an important input for the analysis of various types of image characteristics; it plays an important role in the visual systems for recognition and interpretation of given data. The methods proposed rely on an object-oriented approach based on a theory for the analysis of spatial structures called mathematical morphology. The term ‘‘morphology’’ stems from the fact that it aims at analysing object shapes and forms. It is mathematical in the sense that the analysis is based on the set theory, integral geometry, and lattice algebra. Mathematical morphology has proven to be a powerful image analysis technique; two-dimensional grey tone images are seen as three-dimensional sets by associating each image pixel with an elevation proportional to its intensity level. An object of known shape and size, called the structuring element, is then used to investigate the morphology of the input set. This is achieved by positioning the origin of the structuring element to every possible position of the space and testing, for each position, whether the structuring element either is included or has a nonempty intersection with the studied set. The shape and size of the structuring element must be selected according to the morphology of the searched image structures. Other two feature extraction techniques were used, eCognition and ENVI module SW, in order to compare the results. These techniques were applied to different archaeological sites in Turkmenistan (Nisa) and in Iraq (Babylon); a further change detection analysis was applied to the Babylon site using two HR images as a pre–post second gulf war. We had different results or outputs, taking into consideration the fact that the operative scale of sensed data determines the final result of the elaboration and the output of the information quality, because each of them was sensitive to specific shapes in each input image, we had mapped linear and nonlinear objects, updating archaeological cartography, automatic change detection analysis for the Babylon site. The discussion of these techniques has the objective to provide the archaeological team with new instruments for the orientation and the planning of a remote sensing application. & 2009 Elsevier Ltd. All rights reserved

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing