Monitoring animal behaviour and environmental interactions : GPS collars and satellite remote sensing within a satellite remote sensing context

Remote monitoring of animal behaviour in the environment can assist in managing both the animal and its environmental impact. GPS collars which record animal locations with high temporal frequency allow researchers to monitor both animal behaviour and interactions with the environment. These ground-based sensors can be combined with remotely-sensed satellite images to understand animal-landscape interactions. The key to combining these technologies is communication methods such as wireless sensor networks (WSNs). We explore this concept using a case-study from an extensive cattle enterprise in northern Australia and demonstrate the potential for combining GPS collars and satellite images in a WSN to monitor behavioural preferences and social behaviour of cattle.

Monitoring animal behaviour and environmental interactions using wireless sensor networks, GPS collars and satellite remote sensing

Remote monitoring of animal behaviour in the environment can assist in managing both the animal and its environmental impact. GPS collars which record animal locations with high temporal frequency allow researchers to monitor both animal behaviour and interactions with the environment. These ground-based sensors can be combined with remotely-sensed satellite images to understand animal-landscape interactions. The key to combining these technologies is communication methods such as wireless sensor networks (WSNs). We explore this concept using a case-study from an extensive cattle enterprise in northern Australia and demonstrate the potential for combining GPS collars and satellite images in a WSN to monitor behavioural preferences and social behaviour of cattle.

Design and Simulation of a Solar Powered UAV for Remote Sensing Tasks Technical Report

This research deals with the development of a Solar-Powered UAV designed for remote sensing, in particular to the development of the autopilot sub-system and path planning. The design of the Solar-Powered UAS followed a systems engineering methodology, by first defining system architecture, and selecting each subsystem. Validation tests and integration of autopilot is performed, in order to evaluate the performances of each subsystem and to obtain a global operational system for data collection missions. The flight tests planning and simulation results are also explored in order to verify the mission capabilities using an autopilot on a UAS. The important aspect of this research is to develop a Solar-Powered UAS for the purpose of data collection and video monitoring, especially data and images from the ground; transmit to the GS (Ground Station), segment the collected data, and afterwards analyze it with a Matlab code.

Remote sensing applications for cotton.

This paper compares classified normalized difference vegetation index images of cotton crops derived from both low and high resolution satellite imagery to determine the most accurate and feasible option for Australian cotton growers. It also demonstrates a rapid automated processing and internet delivery system for distributing satellite SPOT-2 imagery. Also provided is the profile of two case studies conducted in the Darling Towns demonstrating the potential benefit of adopting this technology for improving in-season agronomic crop assessments and therefore enable improved management decisions to be made.

Remote sensing of boreal land cover: estimation of forest attributes and extent

Remote sensing provides methods to infer land cover information over large geographical areas at a variety of spatial and temporal resolutions. Land cover is input data for a range of environmental models and information on land cover dynamics is required for monitoring the implications of global change. Such data are also essential in support of environmental management and policymaking. Boreal forests are a key component of the global climate and a major sink of carbon. The northern latitudes are expected to experience a disproportionate and rapid warming, which can have a major impact on vegetation at forest limits. This thesis examines the use of optical remote sensing for estimating aboveground biomass, leaf area index (LAI), tree cover and tree height in the boreal forests and tundra taiga transition zone in Finland. The continuous fields of forest attributes are required, for example, to improve the mapping of forest extent. The thesis focus on studying the feasibility of satellite data at multiple spatial resolutions, assessing the potential of multispectral, -angular and -temporal information, and provides regional evaluation for global land cover data. Preprocessed ASTER, MISR and MODIS products are the principal satellite data. The reference data consist of field measurements, forest inventory data and fine resolution land cover maps. Fine resolution studies demonstrate how statistical relationships between biomass and satellite data are relatively strong in single species and low biomass mountain birch forests in comparison to higher biomass coniferous stands. The combination of forest stand data and fine resolution ASTER images provides a method for biomass estimation using medium resolution MODIS data. The multiangular data improve the accuracy of land cover mapping in the sparsely forested tundra taiga transition zone, particularly in mires. Similarly, multitemporal data improve the accuracy of coarse resolution tree cover estimates in comparison to single date data. Furthermore, the peak of the growing season is not necessarily the optimal time for land cover mapping in the northern boreal regions. The evaluated coarse resolution land cover data sets have considerable shortcomings in northernmost Finland and should be used with caution in similar regions. The quantitative reference data and upscaling methods for integrating multiresolution data are required for calibration of statistical models and evaluation of land cover data sets. The preprocessed image products have potential for wider use as they can considerably reduce the time and effort used for data processing.

Water quality estimation by optical remote sensing in boreal lakes

In lake-rich regions, the gathering of information about water quality is challenging because only a small proportion of the lakes can be assessed each year by conventional methods. One of the techniques for improving the spatial and temporal representativeness of lake monitoring is remote sensing from satellites and aircrafts. The experimental material included detailed optical measurements in 11 lakes, air- and spaceborne remote sensing measurements with concurrent field sampling, automatic raft measurements and a national dataset of routine water quality measurements from over 1100 lakes. The analyses of the spatially high-resolution airborne remote sensing data from eutrophic and mesotrophic lakes showed that one or a few discrete water quality observations using conventional monitoring can yield a clear over- or underestimation of the overall water quality in a lake. The use of TM-type satellite instruments in addition to routine monitoring results substantially increases the number of lakes for which water quality information can be obtained. The preliminary results indicated that coloured dissolved organic matter (CDOM) can be estimated with TM-type satellite instruments, which could possibly be utilised as an aid in estimating the role of lakes in global carbon budgets. Based on the results of reflectance modelling and experimental data, MERIS satellite instrument has optimal or near-optimal channels for the estimation of turbidity, chlorophyll a and CDOM in Finnish lakes. MERIS images with a 300 m spatial resolution can provide water quality information in different parts of large and medium-sized lakes, and in filling in the gaps resulting from conventional monitoring. Algorithms that would not require simultaneous field data for algorithm training would increase the amount of remote sensing-based information available for lake monitoring. The MERIS Boreal Lakes processor, trained with the optical data and concentration ranges provided by this study, enabled turbidity estimations with good accuracy without the need for algorithm correction with field measurements, while chlorophyll a and CDOM estimations require further development of the processor. The accuracy of interpreting chlorophyll a via semi empirical algorithms can be improved by classifying lakes prior to interpretation according to their CDOM level and trophic status. Optical modelling indicated that the spectral diffuse attenuation coefficient can be estimated with reasonable accuracy from the measured water quality concentrations. This provides more detailed information on light attenuation from routine monitoring measurements than is available through the Secchi disk transparency. The results of this study improve the interpretation of lake water quality by remote sensing and encourage the use of remote sensing in lake monitoring.

Fusion of multi resolution remote sensing data for urban sprawl analysis

Urban population is growing at around 2.3 percent per annum in India. This is leading to urbanisation and often fuelling the dispersed development in the outskirts of urban and village centres with impacts such as loss of agricultural land, open space, and ecologically sensitive habitats. This type of upsurge is very much prevalent and persistent in most places, often inferred as sprawl. The direct implication of such urban sprawl is the change in land use and land cover of the region and lack of basic amenities, since planners are unable to visualise this type of growth patterns. This growth is normally left out in all government surveys (even in national population census), as this cannot be grouped under either urban or rural centre. The investigation of patterns of growth is very crucial from regional planning point of view to provide basic amenities in the region. The growth patterns of urban sprawl can be analysed and understood with the availability of temporal multi-sensor, multi-resolution spatial data. In order to optimise these spectral and spatial resolutions, image fusion techniques are required. This aids in integrating a lower spatial resolution multispectral (MSS) image (for example, IKONOS MSS bands of 4m spatial resolution) with a higher spatial resolution panchromatic (PAN) image (IKONOS PAN band of 1m spatial resolution) based on a simple spectral preservation fusion technique - the Smoothing Filter-based Intensity Modulation (SFIM). Spatial details are modulated to a co-registered lower resolution MSS image without altering its spectral properties and contrast by using a ratio between a higher resolution image and its low pass filtered (smoothing filter) image. The visual evaluation and statistical analysis confirms that SFIM is a superior fusion technique for improving spatial detail of MSS images with the preservation of spectral properties.

Variation of Snowline and Mass Balance of Glaciers of Warwan and Bhut Basins of Western Himalaya Using Remote Sensing Technique

Glaciers are natural reservoirs of fresh water in frozen state and sensitive indicators of climate change. Among all the mountainous glaciated regions, glaciers of Himalayas form one of the largest concentrations of ice outside the Polar Regions. Almost all the major rivers of northern India originate from these glaciers and sustain perennial flow. Therefore, in view of the importance and role of the glaciers in sustaining the life on the Earth, monitoring the health of glaciers is necessary. Glacier's health is monitored in two ways (i) by mapping the change in extent of glaciers (ii) by finding variation in the annual mass balance. This paper has been discussed the later approach for monitoring the health of glaciers of Warwan and Bhut basins. Mass balance of glaciers of these two basins was determined based on the extraction of snow line at the end of ablation season. A series of satellite images of AWiFS sensor were analysed for extraction of snowline on the glaciers for the period of 2005, 2006 and 2007. The snow line at the end of ablation season is used to compute accumulation area ratio (AAR = Accumulation area/Glacier area) for each glacier of basins. An approach based on relationship of AAR to specific mass balance (computed in field) for glaciers of Basapa basin was employed in the present study. Mean of specific mass balance of individual glacier for the year 2005, 2006 and 2007 of Warwan basin was found to be -ve 0.19 m, -ve 0.27 m and -ve 0.2 m respectively. It is 0.05 m, -ve 0.11 m and -ve 0.19 m for Bhut basin. The analysis suggests a loss of 4.3 and 0.83 kmA(3) of glacier in the monitoring period of 3 years for Warwan and Bhut basins respectively. The overall results suggest that the glaciers of Warwan basin and Bhut basins have suffered more loss of ice than gain in the monitoring period of 3 years.

Remote sensing study of snowline altitude at the end of melting season, Chandra-Bhaga basin, Himachal Pradesh, 1980-2007

Glaciers have a direct relation with climate change. The equilibrium line altitude (ELA) is the most useful parameter to study the effect of climate change on glaciers. The ELA is a theoretical snowline at which the glacier mass balance is zero. Snowline altitude (SLA) at the end of melting season is generally regarded as the ELA. Glaciers of Chandra-Bhaga basin in Lahaul-Spiti district of Himachal Pradesh were chosen to study the ELA, using satellite images from 1980 to 2007. A total of 19 glaciers from the Chandra-Bhaga basin were identified and selected to carry out the study of ELA variation over 27 years. This study reveals that the mean SLA of the sub-basin has increased from 5009 +/- 61m to 5401 +/- 21m from 1980 to 2007. The study is in agreement with the reported increase in the temperature and decrease in winter snowfall of North-West Himalaya in the last century.

Light Reflectance Characteristics and Remote Sensing of Waterlettuce

Waterlettuce ( Pistia stratiotes L.) is a free-floating exotic aquatic weed that often invades and clogs waterways in the southeastern United States. A study was conducted to evaluate the potential of using remote sensing technology to distinguish infestations of waterlettuce in Texas waterways. Field reflectance measurements showed that waterlettuce had higher visible green reflectance than associated plant species. Waterlettuce could be detected in both aerial color- infrared (CIR) photography and videography where it had light pink to pinkish-white image tonal responses. Computer analysis of CIR photographic and videographic images had overall accuracy assessments of 86% and 84%, respectively. (PDF contains 6 pages.)

Resource management of dynamic coastal environments using synoptic measures from remote sensing

In addition to providing vital ecological services, coastal areas of North Carolina provide prized areas for habitation, recreation, and commercial fisheries. However, from a management perspective, the coasts of North Carolina are highly variable and complex. In-water constituents such as nutrients, suspended sediments, and chlorophyll a concentration can vary significantly over a broad spectrum of time and space scales. Rapid growth and land-use change continue to exert pressure on coastal lands. Coastal environments are also very vulnerable to short-term (e.g., hurricanes) and long-term (e.g., sea-level rise) natural changes that can result in significant loss of life, economic loss, or changes in coastal ecosystem functioning. Hence, the dynamic nature, effects of human-induced change over time, and vulnerability of coastal areas make it difficult to effectively monitor and manage these important state and national resources using traditional data collection technologies such as discrete monitoring stations and field surveys. In general, these approaches provide only a sparse network of data over limited time and space scales and generally are expensive and labor-intensive. Products derived from spectral images obtained by remote sensing instruments provide a unique vantage point from which to examine the dynamic nature of coastal environments. A primary advantage of remote sensing is that the altitude of observation provides a large-scale synoptic view relative to traditional field measurements. Equally important, the use of remote sensing for a broad range of research and environmental applications is now common due to major advances in data availability, data transfer, and computer technologies. To facilitate the widespread use of remote sensing products in North Carolina, the UNC Coastal Studies Institute (UNC-CSI) is developing the capability to acquire, process, and analyze remotely sensed data from several remote sensing instruments. In particular, UNC-CSI is developing regional remote sensing algorithms to examine the mobilization, transport, transformation, and fate of materials between coupled terrestrial and coastal ocean systems. To illustrate this work, we present the basic principles of remote sensing of coastal waters in the context of deriving information that supports efficient and effective management of coastal resources. (PDF contains 4 pages)

Semantic Discriminant mapping for classification and browsing of remote sensing textures and objects

We present a new approach based on Discriminant Analysis to map a high dimensional image feature space onto a subspace which has the following advantages: 1. each dimension corresponds to a semantic likelihood, 2. an efficient and simple multiclass classifier is proposed and 3. it is low dimensional. This mapping is learnt from a given set of labeled images with a class groundtruth. In the new space a classifier is naturally derived which performs as well as a linear SVM. We will show that projecting images in this new space provides a database browsing tool which is meaningful to the user. Results are presented on a remote sensing database with eight classes, made available online. The output semantic space is a low dimensional feature space which opens perspectives for other recognition tasks. © 2005 IEEE.

Remote sensing of ocean waves by along-track interferometric synthetic aperture radar

A new wave retrieval method for the Along-Track Interferometric Synthetic Aperture Radar (AT-InSAR) phase image is presented. The new algorithm, named parametric retrieval algorithm (PRA), uses the full nonlinear mapping relations. It differs from previous retrieval algorithms in that it does not require a priori information about the sea state or the wind vector from scatterometer data. Instead, it combines the observed AT-InSAR phase spectrum and assumed wind vector to estimate the wind sea spectrum. The method has been validated using several C-band and X-band HH-polarized AT-InSAR observations collocated with spectral buoy measurements. In this paper, X-band and C-band HH-polarized AT-InSAR phase images of ocean waves are first used to study AT-InSAR wave imaging fidelity. The resulting phase spectra are quantitatively compared with forward-mapped in situ directional wave spectra collocated with the AT-InSAR observations. Subsequently, we combine the parametric retrieval algorithm (PRA) with X-band and C-band HH-polarized AT-InSAR phase images to retrieve ocean wave spectra. The results show that the ocean wavelengths, wave directions, and significant wave heights estimated from the retrieved ocean wave spectra are in agreement with the buoy measurements.