Fish traps in inland waters of north kerala

Result of the study on traditional traps in the inland waters of three northern districts viz, Kasargod, Kannur and Kozhikode in Kerala state during 2003-2004 is presented. Mainly six types of traps are found in operation. Chempally koode is a rectangular bamboo trap with" D" shape in cross section operated without bait in some rivers of Kannur and Kasargod. Bamboo screen barriers are almost completely replaced with durable HDPE net screen to make handling easy. Thottil vala is a unique aerial trap operated from the dam in Pazhassi reservoir during monsoon to catch big fishes jumping against flowing water.

Coleópteros Saproxílicos de los Bosques de Montaña en el Norte de la Comunidad de Madrid

La fauna saproxílica ha cobrado mucha relevancia en los últimos años. Por una parte, debido a los múltiples papeles que juega en la ecología de los bosques y por otra, por encontrarse muchas especies de ese grupo amenazadas como consecuencia de la intensificación de las actividades forestales. Se supone que los bosques de Europa meridional albergan una fauna saproxílica rica y variada. Sin embargo apenas se han realizado estudios que permitan conocer la composición de las biocenosis saproxílicas, así como el estatus y grado de amenaza a que está sometida cada especie. En esta tesis se han muestreado de forma sistemática las comunidades de coleópteros saproxílicos de cuatro montes del norte de la Comunidad de Madrid, muy diferentes a pesar de su cercanía: Dehesa Bonita de Somosierra, Hayedo de Montejo, Dehesa de Madarcos y Pinar de La Maleza. Para llevar a cabo la recogida de muestras se definió una estación de muestreo tipo, compuesta por cuatro trampas aéreas con cebo oloroso, dos trampas de ventana y una trampa de embudos. En los dos primeros montes mencionados se desplegaron seis estaciones de muestreo, por sólo tres en los otros dos. El primer objetivo de esta tesis fue conocer las especies de coleópteros que constituyen la fauna de los cuatro montes estudiados. Los muestreos sistemáticos reportaron la presencia de un total de 357 especies de coleópteros saproxílicos, siendo el Hayedo de Montejo el bosque con la diversidad más alta, 220 especies; le siguen la Dehesa de Madarcos con 116; el pinar de La Maleza con 115; y la Dehesa de Somosierra con 109, si bien la fauna de este ultimo bosque podría ser mucho más variada dado que la interferencia del ganado con algunos dispositivos de captura hizo que se perdiera parte del material allí recolectado. Se han encontrado nueve especies nuevas para la fauna de la Península Ibérica, y otras muchas desconocidas previamente en el centro peninsular. Un total de 50 especies se encuentran incluidas en la Lista Roja Europea de coleópteros saproxílicos. El segundo objetivo fue estimar la riqueza de fauna de coleópteros saproxílicos en cada bosque. Partiendo de los datos de los respectivo muestreos se calcularon diferentes estimadores, paramétricos y no paramétricos, y se elaboraron las curvas de rarefacción para cada bosque y para el conjunto. El bosque con más biodiversidad ha resultado ser el Hayedo de Montejo, que albergaría entre 254 y 332 especies. En el Pinar de la Maleza se encontrarían de 132 a 223; de 128 a 205 en la Dehesa de Somosierra; y entre 134 y 188 en la Dehesa de Madarcos. Para el conjunto del área se estimó la presencia de entre 411 y 512 especies. El tercer objetivo fue evaluar la influencia de algunos factores como la especie arbórea dominante y la cantidad de madera muerta en la riqueza y diversidad de coleópteros saproxílicos. El estudio se realizó en el Hayedo de Montejo, encontrando una alta correlación positiva entre cantidad y calidad de madera muerta, y diversidad y riqueza de especies de coleópteros saproxílicos. El cuarto objetivo fue evaluar la eficacia y complementariedad de los diferentes tipos de dispositivos de captura empleados en los muestreos. El más eficaz resultó ser la trampa de ventana, seguido por la trampa aérea con cebo oloroso, y finalmente la trampa de embudos. La mayor complementariedad se encontró entre trampas de ventana y aéreas con cebo oloroso. No obstante, si se quiere optimizar la exhaustividad del inventario no se debe prescindir de ninguno de los sistemas. En cualquier caso, puede afirmarse que la efectividad de los tres tipos de dispositivos de captura utilizados en los muestreos fue baja, pues para la gran mayoría de especies presentes se capturó un número de ejemplares realmente bajo. El bajo rendimiento de captura implica un bajo impacto sobre las poblaciones de las especies muestreadas, y esto supone una importante ventaja desde el punto de vista de la conservación. Finalmente, se dejan algunas recomendaciones de manejo a aplicar en cada uno de los montes con el fin de preservar o mejorar los hábitats utilizables por la fauna saproxílica que garanticen el mantenimiento y mejora de dichas comunidades. ABSTRACT The saproxylic fauna has become increasingly important in recent years. It has been due, on the one hand, to the multiple roles they play in the forest ecosystems and, on the other, because of the large proportion of endangered saproxylic species as a result of the intensification of forestry. It is generally assumed that southern Europe forests are home to a rich and diverse saproxylic fauna. However, there are hardly any studies leading to reveal the composition of saproxylic biocenosis, or the stage and extent of the threat each species is suffering. For the purpose of this thesis the communities of saproxylic beetles of four mountain forests in northern Comunidad de Madrid have been systematically sampled: Dehesa Bonita de Somosierra, Hayedo de Montejo, Dehesa de Madarcos and Pinar de La Maleza. They are very different from each other in spite of not being too far apart. In order to carry out sample collection, a standard sampling station was defined as follows: four smelly bait aerial traps, two window traps and one funnel trap. Six sampling stations were deployed in each of the first two forests mentioned above; put only three in each of the other two. The first aim of this thesis was to determine the composition of saproxylic beetles fauna inhabiting each of the four forests studied. Systematic sampling reported the presence of a total of 357 species of saproxylic beetles. Hayedo de Montejo, with 220 species, is the forest with the highest diversity, followed by Dehesa de Madarcos, 116; Pinar de La Maleza, 115, and Dehesa de Somosierra, 109. The fauna of the latter forest, however, could be much more varied, since cattle interference with some capture devices caused the loss of part of the material collected there. Nine new species in the fauna of the Iberian Peninsula were found, and many others previously unknown in the center of the Peninsula. A total of 41 of those species are included in the European Red List of saproxylic beetles. The second aim was to estimate the richness of saproxylic (beetle) fauna in each forest. From the data of the respective sampling, different parametric and nonparametric estimators were calculated, and rarefaction curves for each forest, as well as for the four of them together, were drawn. The most biodiverse forest turned out to be Hayedo de Montejo, which houses between 254 and 332 species. In Pinar de La Maleza, between 132 and 223 species were found; between 128 and 205 in Dehesa de Somosierra, and between 134 and 188 in Dehesa de Madarcos. The estimated diversity of species for the whole area ranges from 411 to 512. The third aim was to evaluate the influence of such factors as the dominant tree species and the amount of dead wood in the richness and diversity of saproxylic beetles. The study was conducted at Hayedo de Montejo, finding a high positive correlation between quantity and quality of coarse woody debris and diversity and richness of saproxylic beetle species. The fourth aim was to evaluate the effectiveness and complementarity of the different sampling methods used in this research work. The most effective proved to be the window trap, followed by the smelly bait aerial trap and the funnel trap, in that order. The greater complementarity was found between window and aerial traps. However, in order to optimize the completeness of the inventory, neither of the systems should be discarded. Nevertheless, the effectiveness of the three types of capture devices used in this piece of research was on the whole rather low, since for the vast majority of species, a significant low number of specimens were captured. Poor trapping performance implies a low impact on the populations of the sampled species, and this is an important advantage in terms of conservation. Finally, this thesis gives some recommendations with regard to the management of each of those four forests, leading to preserve and improve the habitats of the saproxylic wildlife and so ensure the maintenance and growth of their communities.

Optimal mission path planning (MPP) for an air sampling unmanned aerial system

This paper presents advanced optimization techniques for Mission Path Planning (MPP) of a UAS fitted with a spore trap to detect and monitor spores and plant pathogens. The UAV MPP aims to optimise the mission path planning search and monitoring of spores and plant pathogens that may allow the agricultural sector to be more competitive and more reliable. The UAV will be fitted with an air sampling or spore trap to detect and monitor spores and plant pathogens in remote areas not accessible to current stationary monitor methods. The optimal paths are computed using a Multi-Objective Evolutionary Algorithms (MOEAs). Two types of multi-objective optimisers are compared; the MOEA Non-dominated Sorting Genetic Algorithms II (NSGA-II) and Hybrid Game are implemented to produce a set of optimal collision-free trajectories in three-dimensional environment. The trajectories on a three-dimension terrain, which are generated off-line, are collision-free and are represented by using Bézier spline curves from start position to target and then target to start position or different position with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of coupling a Hybrid-Game strategy to a MOEA for MPP tasks. The reduction of numerical cost is an important point as the faster the algorithm converges the better the algorithms is for an off-line design and for future on-line decisions of the UAV.

Development of an autonomous unmanned aerial system to collect time-stamped samples from the atmosphere and localize potential pathogen sources

This paper presents the hardware development and testing of a new concept for air sampling via the integration of a prototype spore trap onboard an unmanned aerial system (UAS).We propose the integration of a prototype spore trap onboard a UAS to allow multiple capture of spores of pathogens in single remote locations at high or low altitude, otherwise not possible with stationary sampling devices.We also demonstrate the capability of this system for the capture of multiple time-stamped samples during a single mission.Wind tunnel testing was followed by simulation, and flight testing was conducted to measure and quantify the spread during simulated airborne air sampling operations. During autonomous operations, the onboard autopilot commands the servo to rotate the sampling device to a new indexed location once the UAS vehicle reaches the predefined waypoint or set of waypoints (which represents the region of interest). Time-stamped UAS data are continuously logged during the flight to assist with analysis of the particles collected. Testing and validation of the autopilot and spore trap integration, functionality, and performance is described. These tools may enhance the ability to detect new incursions of spores

The utility of aerial pan-trapping for assessing insect pollinators across vertical strata

Insect pollinators provide a critical ecosystem service by pollinating many wild flowers and crops. It is therefore essential to be able to effectively survey and monitor pollinator communities across a range of habitats, and in particular, sample the often stratified parts of the habitats where insects are found. To date, a wide array of sampling methods have been used to collect insect pollinators, but no single method has been used effectively to sample across habitat types and throughout the spatial structure of habitats. Here we present a method of ‘aerial pan-trapping’ that allows insect pollinators to be sampled across the vertical strata from the canopy of forests to agro-ecosystems. We surveyed and compared the species richness and abundance of a wide range of insect pollinators in agricultural, secondary regenerating forest and primary forest habitats in Ghana to evaluate the usefulness of this approach. In addition to confirming the efficacy of the method at heights of up to 30 metres and the effects of trap color on catch, we found greatest insect abundance in agricultural land and higher bee abundance and species richness in undisturbed forest compared to secondary forest.

Automatic Detection of Marine Mammal from Aerial Imagery

The following technical report describes the approach and algorithm used to detect marine mammals from aerial imagery taken from manned/unmanned platform. The aim is to automate the process of counting the population of dugongs and other mammals. We have developed and algorithm that automatically presents to a user a number of possible candidates of these mammals. We tested the algorithm in two distinct datasets taken from different altitudes. Analysis and discussion is presented in regards with the complexity of the input datasets, the detection performance.

Enhancing digital road map with lane details extracted from large-scale stereo aerial imagery using object-oriented image analysis

Precise, up-to-date and increasingly detailed road maps are crucial for various advanced road applications, such as lane-level vehicle navigation, and advanced driver assistant systems. With the very high resolution (VHR) imagery from digital airborne sources, it will greatly facilitate the data acquisition, data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lane information from aerial images with employment of the object-oriented image analysis method. Our proposed algorithm starts with constructing the DSM and true orthophotos from the stereo images. The road lane details are detected using an object-oriented rule based image classification approach. Due to the affection of other objects with similar spectral and geometrical attributes, the extracted road lanes are filtered with the road surface obtained by a progressive two-class decision classifier. The generated road network is evaluated using the datasets provided by Queensland department of Main Roads. The evaluation shows completeness values that range between 76% and 98% and correctness values that range between 82% and 97%.

Towards an automatic road lane marks extraction based on ISODATA segmentation and shadow detection from large-scale aerial images

The automatic extraction of road features from remote sensed images has been a topic of great interest within the photogrammetric and remote sensing communities for over 3 decades. Although various techniques have been reported in the literature, it is still challenging to efficiently extract the road details with the increasing of image resolution as well as the requirement for accurate and up-to-date road data. In this paper, we will focus on the automatic detection of road lane markings, which are crucial for many applications, including lane level navigation and lane departure warning. The approach consists of four steps: i) data preprocessing, ii) image segmentation and road surface detection, iii) road lane marking extraction based on the generated road surface, and iv) testing and system evaluation. The proposed approach utilized the unsupervised ISODATA image segmentation algorithm, which segments the image into vegetation regions, and road surface based only on the Cb component of YCbCr color space. A shadow detection method based on YCbCr color space is also employed to detect and recover the shadows from the road surface casted by the vehicles and trees. Finally, the lane marking features are detected from the road surface using the histogram clustering. The experiments of applying the proposed method to the aerial imagery dataset of Gympie, Queensland demonstrate the efficiency of the approach.

Automated road pavement marking detection from high resolution aerial images based on Multi-resolution image analysis and anisotropic Gaussian filtering

Road features extraction from remote sensed imagery has been a long-term topic of great interest within the photogrammetry and remote sensing communities for over three decades. The majority of the early work only focused on linear feature detection approaches, with restrictive assumption on image resolution and road appearance. The widely available of high resolution digital aerial images makes it possible to extract sub-road features, e.g. road pavement markings. In this paper, we will focus on the automatic extraction of road lane markings, which are required by various lane-based vehicle applications, such as, autonomous vehicle navigation, and lane departure warning. The proposed approach consists of three phases: i) road centerline extraction from low resolution image, ii) road surface detection in the original image, and iii) pavement marking extraction on the generated road surface. The proposed method was tested on the aerial imagery dataset of the Bruce Highway, Queensland, and the results demonstrate the efficiency of our approach.

Multi-objective mission flight planning in civil unmanned aerial systems

Unmanned Aerial Vehicles (UAVs) are emerging as an ideal platform for a wide range of civil applications such as disaster monitoring, atmospheric observation and outback delivery. However, the operation of UAVs is currently restricted to specially segregated regions of airspace outside of the National Airspace System (NAS). Mission Flight Planning (MFP) is an integral part of UAV operation that addresses some of the requirements (such as safety and the rules of the air) of integrating UAVs in the NAS. Automated MFP is a key enabler for a number of UAV operating scenarios as it aids in increasing the level of onboard autonomy. For example, onboard MFP is required to ensure continued conformance with the NAS integration requirements when there is an outage in the communications link. MFP is a motion planning task concerned with finding a path between a designated start waypoint and goal waypoint. This path is described with a sequence of 4 Dimensional (4D) waypoints (three spatial and one time dimension) or equivalently with a sequence of trajectory segments (or tracks). It is necessary to consider the time dimension as the UAV operates in a dynamic environment. Existing methods for generic motion planning, UAV motion planning and general vehicle motion planning cannot adequately address the requirements of MFP. The flight plan needs to optimise for multiple decision objectives including mission safety objectives, the rules of the air and mission efficiency objectives. Online (in-flight) replanning capability is needed as the UAV operates in a large, dynamic and uncertain outdoor environment. This thesis derives a multi-objective 4D search algorithm entitled Multi- Step A* (MSA*) based on the seminal A* search algorithm. MSA* is proven to find the optimal (least cost) path given a variable successor operator (which enables arbitrary track angle and track velocity resolution). Furthermore, it is shown to be of comparable complexity to multi-objective, vector neighbourhood based A* (Vector A*, an extension of A*). A variable successor operator enables the imposition of a multi-resolution lattice structure on the search space (which results in fewer search nodes). Unlike cell decomposition based methods, soundness is guaranteed with multi-resolution MSA*. MSA* is demonstrated through Monte Carlo simulations to be computationally efficient. It is shown that multi-resolution, lattice based MSA* finds paths of equivalent cost (less than 0.5% difference) to Vector A* (the benchmark) in a third of the computation time (on average). This is the first contribution of the research. The second contribution is the discovery of the additive consistency property for planning with multiple decision objectives. Additive consistency ensures that the planner is not biased (which results in a suboptimal path) by ensuring that the cost of traversing a track using one step equals that of traversing the same track using multiple steps. MSA* mitigates uncertainty through online replanning, Multi-Criteria Decision Making (MCDM) and tolerance. Each trajectory segment is modeled with a cell sequence that completely encloses the trajectory segment. The tolerance, measured as the minimum distance between the track and cell boundaries, is the third major contribution. Even though MSA* is demonstrated for UAV MFP, it is extensible to other 4D vehicle motion planning applications. Finally, the research proposes a self-scheduling replanning architecture for MFP. This architecture replicates the decision strategies of human experts to meet the time constraints of online replanning. Based on a feedback loop, the proposed architecture switches between fast, near-optimal planning and optimal planning to minimise the need for hold manoeuvres. The derived MFP framework is original and shown, through extensive verification and validation, to satisfy the requirements of UAV MFP. As MFP is an enabling factor for operation of UAVs in the NAS, the presented work is both original and significant.

Extraction of road lanes from high-resolution stereo aerial imagery based on maximum likelihood segmentation and texture enhancement

Accurate road lane information is crucial for advanced vehicle navigation and safety applications. With the increasing of very high resolution (VHR) imagery of astonishing quality provided by digital airborne sources, it will greatly facilitate the data acquisition and also significantly reduce the cost of data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lanes from aerial images with employment of the image analysis procedures. This algorithm starts with constructing the (Digital Surface Model) DSM and true orthophotos from the stereo images. Next, a maximum likelihood clustering algorithm is used to separate road from other ground objects. After the detection of road surface, the road traffic and lane lines are further detected using texture enhancement and morphological operations. Finally, the generated road network is evaluated to test the performance of the proposed approach, in which the datasets provided by Queensland department of Main Roads are used. The experiment result proves the effectiveness of our approach.

Unmanned Aerial Vehicles UAVs attitude, height, motion estimation and control using visual systems

This paper presents an implementation of an aircraft pose and motion estimator using visual systems as the principal sensor for controlling an Unmanned Aerial Vehicle (UAV) or as a redundant system for an Inertial Measure Unit (IMU) and gyros sensors. First, we explore the applications of the unified theory for central catadioptric cameras for attitude and heading estimation, explaining how the skyline is projected on the catadioptric image and how it is segmented and used to calculate the UAV’s attitude. Then we use appearance images to obtain a visual compass, and we calculate the relative rotation and heading of the aerial vehicle. Additionally, we show the use of a stereo system to calculate the aircraft height and to measure the UAV’s motion. Finally, we present a visual tracking system based on Fuzzy controllers working in both a UAV and a camera pan and tilt platform. Every part is tested using the UAV COLIBRI platform to validate the different approaches, which include comparison of the estimated data with the inertial values measured onboard the helicopter platform and the validation of the tracking schemes on real flights.

Evaluation of aerial remote sensing techniques for vegetation management in power line corridors

The following paper presents an evaluation of airborne sensors for use in vegetation management in powerline corridors. Three integral stages in the management process are addressed including, the detection of trees, relative positioning with respect to the nearest powerline and vegetation height estimation. Image data, including multi-spectral and high resolution, are analyzed along with LiDAR data captured from fixed wing aircraft. Ground truth data is then used to establish the accuracy and reliability of each sensor thus providing a quantitative comparison of sensor options. Tree detection was achieved through crown delineation using a Pulse-Coupled Neural Network (PCNN) and morphologic reconstruction applied to multi-spectral imagery. Through testing it was shown to achieve a detection rate of 96%, while the accuracy in segmenting groups of trees and single trees correctly was shown to be 75%. Relative positioning using LiDAR achieved a RMSE of 1.4m and 2.1m for cross track distance and along track position respectively, while Direct Georeferencing achieved RMSE of 3.1m in both instances. The estimation of pole and tree heights measured with LiDAR had a RMSE of 0.4m and 0.9m respectively, while Stereo Matching achieved 1.5m and 2.9m. Overall a small number of poles were missed with detection rates of 98% and 95% for LiDAR and Stereo Matching.