Thermal Imaging Techniques to Survey and Monitor Animals in the Wild A Methodology Kirk J Havens Edward J Sharp AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier Academic Press is an imprint of Elsevier 125, London Wall, EC2Y 5AS, UK 525 B Street, Suite 1800, San Diego, CA 92101-4495, USA 225 Wyman Street, Waltham, MA 02451, USA The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, UK Copyright © 2016 Elsevier Inc All rights reserved No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein) Notices Knowledge and best practice in this field are constantly changing As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein ISBN: 978-0-12-803384-5 British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress For information on all Academic Press publications visit our website at http://store.elsevier.com/ Dedication To my wife, Karla, who only occasionally raised an eyebrow and rarely questioned the late night trips to “study wildlife.” To my son, Kade, who understands the wisdom in questioning everything and to my parents, Bill and Ginny, who gave me the childhood freedom to explore Kirk J Havens Preface Over the past few decades there has been a marked increase in areas of remote sensing, including thermal imaging, to study and count wildlife in their natural surroundings While much of the work with thermal imagers to date has been devoted to testing equipment during surveys, little advancement has actually been achieved This is primarily due to three basic problems: Early field studies were conducted with cryogenically cooled thermal imagers (photon detectors) with sensitivities an order of magnitude lower than those available today With few exceptions, the new and improved models of thermal imagers with superior sensitivities and resolution have not been used in the field because of the perceived difficulty in data acquisition and to some extent limited availability and cost The more recent fieldwork has been for the most part confined to the use of uncooled bolometric cameras that use thermal detectors as opposed to photon detectors A pervasive misunderstanding of what thermal imagers detect and record and what ultimately constitutes ideal conditions for conducting thermal imaging observations The promulgation of results that have erroneously compared survey data collected with thermal imaging equipment to that obtained with standard techniques such as spotlighting or visual surveys In this volume, we spend considerable effort reviewing the literature and pointing out fallacies that have been built upon as a result of these problems This book presents a methodology for maximizing the detectability of both vertebrates (homotherms and poikilotherms) and invertebrates during a census or survey when using proper thermal imaging techniques It also provides details for optimizing the performance of thermal cameras under a wide variety of field conditions It is intended to guide field biologists in the creation of a window of opportunity (a set of ideal conditions) for data gathering efforts In fact, when thermal imaging cameras are used properly, under ideal conditions, detectivity approaching 100% can be achieved Recent attempts of researchers and field biologists to use thermal imagers to survey, census, and monitor wildlife have in most cases met with limited success and while there are a number of good books that treat the theory and applications of remote sensing and thermal imaging in significant detail for applications in land mapping, construction, manufacturing, building and vehicle inspections, surveillance, and medical procedures and analyses (Barrett xi xii Preface and Curtis, 1992; Budzier and Gerlach, 2011; Burney et al., 1988; Holst, 2000; Kaplan, 1999; Kozlowski and Kosonocky, 1995; Kruse et al., 1962; Vollmer and Mollmann, 2010; Williams, 2009; Wolfe and Kruse, 1995), they contain very little on how wildlife biologists should go about using this equipment in the field to survey and monitor wildlife This book provides detailed information on the theory and performance characteristics of thermal imaging cameras utilizing cooled quantum detectors as the sensitive element and also the popular uncooled microbolometric imagers introduced into the camera market in the past decades, which rely on thermal effects to generate an image In addition, there are numerous excellent texts devoted to survey design and statistical modeling to aid in the monitoring and determination of wildlife populations (Bookhout, 1996; Borchers et al., 2004; Buckland et al., 1993; Buckland et al., 2001; Caughley, 1977; Conroy and Carroll, 2009; Garton et al., 2012; Krebs, 1989; Pollock et al., 2004; Seber, 1982, 1986; Silvy, 2012; Thompson et al., 1998; Thompson, 2004; Williams et al., 2001), but they not include the treatment of thermal imaging capabilities to help achieve these tasks This book is being offered as a bridge between the two technologies and the teachings presented in these excellent volumes so that their combined strengths might be united to improve upon past efforts to assess animal populations and to monitor their behavior Even though there has been a technological disconnect since the earliest field experiments, there has still been a considerable amount of work carried out by biologists using thermal imagers to study and monitor wildlife These studies began in the late 1960s and early 1970s when cryogenically cooled thermal imagers using photon detectors were first used for surveys and field work (Croon et al., 1968; Parker and Driscoll, 1972) and this phenomena continued to grow as thermal imagers became more readily available to field biologists At the time, these early cameras were acknowledged as being only marginally sensitive for the task of aerial surveying The more recent introduction of the low-cost uncooled bolometric cameras generated a new wave of experimentation with thermal imagers in the field The sensitivity and range of bolometric cameras are limited due to the fact that they rely on a thermal process to generate an image So we see at the start that all thermal imagers are not the same and if they are used in the field they must be used to exploit the strengths of the particular imaging camera so that reliable data can be obtained There are appropriate uses for imagers utilizing photon detectors where high sensitivity and long ranges are characteristics making them suitable for surveying applications There are also applications suitable for imagers fitted with thermal detectors that have lower sensitivities and ranges Their advantages are their availability, cost, and that they are uncooled Field applications favoring bolometric cameras that not require long ranges or high sensitivity will also be addressed in this book The process of using thermal imagers as a tool to collect field data has been compared with other data collection techniques; however, in nearly all cases the thermal imager was not used correctly and perhaps was even inadequate for Preface  xiii the task This practice has led to a number of misconceptions about the basic use of a thermal imager and the correct interpretation of the results There is a big distinction between thermal imagers that utilize quantum detectors as the sensitive element and detectors that rely on thermal effects to generate an image The differences are enormous as far as fieldwork goes for censusing and surveying, particularly on a landscape scale Unfortunately, a text describing the use of 3–5 and 8–12 mm photon detectors for animal surveys and field studies has not emerged This is probably due to the fact that 3–5 and 8–14 mm imagers were not widely used since the first field experiments These experiments used cryogenically cooled units typically borrowed from military installations These robust units are now becoming available at a reasonable cost and should see increased use by field biologists An excellent text describing the practical use of pyroelectric and bolometric imagers for a wide range of applications has been written (Vollmer and Mollmann, 2010) and a number of distinctions are pointed out between these imagers and those using photon detectors as the focal plane Past work using thermal imagers in the field has mainly been carried out so that comparisons could be made with other data gathering methods From the outset we see that comparing the results obtained with thermal imagers with that of data collected with other methods such as spotlighting and visual surveys must necessarily be skewed and these efforts, while commendable, not allow for a fair comparison of the data collection capability of the compared techniques Thermal cameras are suitable for surveys and counts throughout the 24-h diurnal cycle while other methods are not These studies by their nature and design mean that the results of data collected with a thermal imager will be compared with data collected using a method that was optimized for the conditions of the survey at hand For example, consider the comparison of data collected during a visual survey and the data collected via thermal imagery using the same temporal and spatial conditions Note that the survey must be conducted during daylight hours because the visual spotters need daylight to see the animals of interest Thermal cameras can also detect the animals of interest during daylight hours but there are concomitant conditions required for the optimization of the thermal survey if it is conducted during daylight hours These conditions can be met in a relatively easy manner but were not generally addressed during these past comparisons so the results reported were skewed and in some cases grossly inaccurate We review many of these comparisons and offer alternatives A variety of statistical methods, such as distance sampling and mark recapture, among others, were used for estimating the abundance of animal populations in these comparisons and the results of these studies were built upon by others We not treat these statistical methods here but point out that each of them has strengths and weaknesses (Borchers et al., 2004), depending on the species of the animal being surveyed All will benefit from data collection methods that produce a detectability (see Chapter 1) that approaches ∼100% The widespread dissemination of these results is the existing foundation that later work has been built upon and it has led to a confusing and widespread xiv Preface misunderstanding of the capabilities of thermal imaging as a powerful survey tool in these applications This distribution of erroneous or badly skewed information regarding the performance of thermal imaging for these tasks needs to be rectified and it is one of the major goals of this book to start that process The work of Romesburg (1981, p 293) pointed out the fallacies of building on unreliable knowledge: “Unreliable knowledge is the set of false ideas that are mistaken for knowledge If we let unreliable knowledge in, then others, accepting these false laws, will build new knowledge on a false foundation.” We still overlook important aspects of the scientific inquiry to gain reliable scientific knowledge All the statistical methods applied to data gathered in the field are better predictors when the count is completely random and the sample is large It is also known that the general methods used to count animals in the field during a survey are usually biased and yield animal counts less than what is actually there; however, in some cases there will be more counted than are actually there These statistical losses or gains are presumably accounted for in the statistical formulation being used The problems arise when the estimated parameters to account for losses or gains in populations, along with other parameters to account for such things as species mingling, group sizes, mortality rates, and sometimes double counting, are folded into the calculations Even though these parameters are often very good guesses, they all come with systematic and random errors attached and cannot predict valid outcomes except by chance (Romesburg, 1981, p 309) This is because the more parameters a model contains that are guesses the more they are amplified by their interaction with one another through the calculations, such that the resulting errors can be quite large at the output of the calculations It is essential for wildlife management and the preservation of healthy populations that we seek and promulgate reliable knowledge regarding the current status of animals in the wild Ratti and Garton (1996) advance the important realization put forth by Romesburg by showing that in order for wildlife research to be useful to wildlife managers and their varied programs, it must be founded on high-quality scientific investigations that are in turn based upon carefully designed experiments and methodologies Limitations to achieving the desired high quality and reliable knowledge must be identified and rectified We postulate that the single most important thing to at the present time to mitigate the unreliable knowledge stemming from skewed and distorted animal surveys and counts is to look very carefully at the detectability possible by different counting methodologies The components of science required for meaningful and reliable outcomes are mingled together in a relatively complex way Wildlife managers and field biologists must incorporate biology, chemistry, atmospheric science, physics, and climatology, as well as the behavioral ecology and physiology of the animals surveyed or studied All must be considered when forming a research plan for a species The best window of opportunity for collecting data must be determined based on the best science available To this end, a detailed methodology for using Preface  xv infrared thermal imaging to conduct animal surveys in the field and other studies requiring nondisruptive observation of wildlife in their natural surroundings is developed in this book We show that ∼100% detection can be achieved for surveys if the methodology is formulated to take full advantage of the infrared cameras used for observation and if it is coupled with the details of the behavioral ecology and physiology of the animals being surveyed or studied In this book we address the primary difficulty with surveying or censusing animals and demonstrate that it is not the sampling methodology (i.e., distance sampling, aerial transect sampling, quadrat sampling, etc.) or the statistical model being used on the collected data, but rather lies with the detectability that can be achieved with any particular sampling or data collecting technique This suggests that more work needs to be done on comparing factors that influence the detectability of a species of interest rather than the statistical methods to compensate for the inadequacies of over or undercounting There are many other details of a research plan that could grossly skew or render the resulting survey invalid (Thompson et al., 1998; Lancia et al., 1996; Krebs, 1989) but the visual observation (or other counting methods) are well-known to be skewed by a number of factors and limit data collection to daylight hours or when the landscapes or transects are artificially illuminated It is also known that artificial illumination introduces behavioral modifications that can adversely influence the detectability and introduce bias (Focardi et al., 2001) There are various treatments proposed to deal with known biases They are adjustments to the calculations to deal with under- or overcounting animals during surveys resulting from biased detectability In this work, we will concentrate on the task of increasing detectability by eliminating bias in the data collection aspect of wildlife monitoring Because thermal imaging can be conducted at any time during the diurnal cycle and can be conducted from various aerial or ground-based viewing platforms, it offers a host of configurations to observe animals of interest while using their preferred habitat If performed correctly, the observations can be conducted from a distance that precludes disturbances to the animals under study, thus reducing the possibilities of skewing the counts or surveys caused by anthropogenic-produced behavioral changes or double counting Each variable introduced by some recognized uncertainty in the counting or observation techniques used must be accounted for and if it is done statistically the results become more and more questionable If an uncertainty in the counting technique can be fixed at the field level, the resulting counts are closer in line with the true situation because there is one less layer of data manipulation to perform due to under- or overcounting As noted earlier, there is already a significant amount of up-to-date information available on methods for treating collections of field data with various statistical formulations and appropriate assumptions These mathematical tools allow the evaluation of field data (if correctly collected) so that meaningful estimations of the abundance and/or the density of wildlife populations can be xvi Preface determined As a result, we not delve into these methods but rather focus on the details of establishing a technique for correctly collecting data and achieving the highest detectability possible when conducting field work Applications other than those dealing with wildlife will not be treated here unless we need to make a specific point about some aspect of the workings of a thermal imager or if the application would clarify some aspect of the proposed methodology Applications such as military, surveillance, police work, fire detection, manufacturing, and building inspection have been well-treated by others and can be found in the references mentioned earlier The results of many studies of animal behavior, thermoregulation, pathology, and physiology are also reviewed In order to appreciate the advantages that thermal imaging has to offer we must recognize that our eyes are sensors that are limited in a number of ways that limit their utility as effective detectors of wildlife in their preferred habitat Our eyes are confined to the visible region of the spectrum and at low-light levels they not collect enough data so that our brain is able to form images that are recognizable; however, there are a number of ways that we can easily extend their functional range for our applications For example, binoculars greatly enhance the probability of observing an object when faced with low-light levels and long viewing ranges If we can use various technologies and instrumentation to aid our vision by seeing in the dark and seeing at longer ranges, then we need to add these things to our set of observational tools In short we need to detect objects in order to count them and we need to see them in some fashion to detect them The acquisition of images in the infrared region of the spectrum can be provided by thermal imagers and as such serve as an aid to our overall visual capability By utilizing thermal imagers we can create images of very high contrast so that objects of interest are clear and distinct from their backgrounds, allowing us to extend our visual capability into the dark portion of the diurnal cycle Once this is accomplished, the brain can process the images that the eyes see In fact, in recent work at Cal Tech and UCLA, researchers found that individual nerve cells fired when subjects were shown photos of well-known personalities The same individual nerve cell would fire for many different photos of the same personality and a different single nerve cell would fire for many different photos of another personality Follow-up research suggests that relatively few neurons are involved in representing any given person, place, or concept, which makes the brain extremely efficient at storing and recalling information after receiving visual stimulation Without going into a detailed mathematical description of thermal imaging and the complex principles behind the operation of thermal imagers (thermal cameras) we instead introduce basic laws and principles that allow us to set the stage for data collection with thermal imagers However, field biologists need to have a basic understanding of the physics governing heat transfer processes in the environment (Monteith and Unsworth, 2008) and the effects of local meteorological changes on the performance of a thermal imager The proper use of a thermal imager requires a basic knowledge of how an imager works, why we Preface  xvii see what we see with a thermal imager, and how we can optimize those images for the tasks at hand Simple “point-and-shoot” infrared imagery for data collection will not work nor will using someone else’s “point-and-shoot” imagery in sophisticated statistical calculations What the imagery actually represents and how it was acquired must be known for it to be useful While the performance capability of uncooled thermal imagers has improved remarkably over the last decade and the cost of these cameras has become reasonable for most researchers, field biologists must understand how they work, how to use them, and what they are actually recording as imagery Unfortunately, for the most part, the rapid technological advancement and availability of thermal imagers has outpaced the knowledge and understanding required of the specialists using them in the field (Vollmer and Mollmann, 2010, p xv) This sad commentary regarding the use of thermal imagers stems, for the most part, from applications associated with monitoring inanimate objects in fixed backgrounds Our applications, as we have already pointed out, are much more difficult and complex so we need to be particularly careful and thorough in our understanding of a few basic principles regarding thermal imaging and wildlife ecology This book is about formulating a methodology to optimize a window of opportunity so that wildlife can be observed and studied in its natural habitat This requires that biologists and program managers get together and formulate a sound survey design, which assumes that they know the ecology of the species of interest plus all mitigating factors that could possibly distort the outcome of a thermal imaging survey The methodology presented here is logical and simple yet it demands a detailed understanding and incorporation of critically interlinked disciplines arising from biology, physics, micrometeorology, animal physiology, and common sense Thermal imaging is a technique that forms images from heat radiating from objects and their backgrounds, so much of the information contained in this book is devoted to managing the interplay of the heat transfer processes of conduction, convection, and radiation between the objects of interest (animals) and their backgrounds to obtain the best thermal images We will see that creating this window of opportunity is not as restrictive as one might think Data can be collected from ground- or aerial-based platforms at any time during the diurnal cycle without compromising detectivity, disturbing the animals, or altering their behavior Even though the methodology used to obtain meaningful data brings together a wide range of criterion and requirements that must be met concomitantly, it boils down to creating a window of opportunity that will allow researchers to conduct surveys with near 100% detectability by properly using thermal imagers as a detection tool 340 References Wyatt, C.L., Trivedi, M.M., Anderson, D.R., 1980 Statistical evaluation of remotely sensed thermal data for deer census J Wildl Manag 44 (2), 397–402 Wyatt, C.L., Trivedi, M.M., Anderson, D.R., Pate, M.C., 1985 Measurement techniques for spectral characterization for remote sensing Program Eng Remote Sens 51 (2), 245–251 Zalewski, E.F., 1995 Radiometry and photometry In: Bass, M (Ed.), Handbook of Optics, Vol II, Devices, Measurements, and Properties McGraw-Hill, Inc, New York, pp 24.3–24.51 Subject Index A Absorbed radiant flux, 123 Absorbed solar radiation, 306 Absorption coefficients, 150 Absorption process, 301 Acoustic survey, 51 advantage, 51 Adaptive retina camera, 37 Aerial-based surveys, 10 Aerial counts, 21–26 Aerial LWIR thermal imaging, 214 Aerial moose surveys, 167 Aerial observation platform, 97 Aerial photographs, 30 Aerial surveys, 1, 132, 154, 185, 216, 253, 255, 277 contrast for, 26 techniques, 16, 201 accuracy of, 201 visible techniques for, 248 Aerial thermal imaging sampling, 212 Aerial vertical-looking infrared thermal imaging, 212 Aga Thermovision infrared imaging camera, 241 Agema model 782 LWIR scanning thermal imager, 223 Agema MWIR Thermovision 210 imager, 235 Agema Thermovision® 1000 (FLIR systems), 205 Airborne infrared thermal imagery, 132, 204, 257, 311 Aircraft-based thermal imaging techniques, 239 Air-to-air survey, 261 Airy disk, diameter, 114 Alternative method, 171 American white pelicans (Pelecanus erythrorhynchos), 41 Ammodramus nests, 236 AN/AAD-5 imaging system, 216 Angular dependencies, 155 effects on animal ecology, 249–272 individual thermal signatures, size and shape of, 252–253 survey area, spatial extent of, 253 survey geometry, 253–272 Angular effect, 273 Angular resolution, 112 Animal census of, 3, 106 detection and identification of, heat loss of, 75 infrared images of, 252 nonuniform distribution of, 185 populations, densities of, 33 species, listing of, 17 surveys, 105, 173 facts of, 105 thermal signatures of, 143 Animal ecology angular dependencies and effects, 249–272 individual thermal signatures, size and shape of, 252–253 survey area, spatial extent of, 253 survey geometry, 253–272 atmospheric effects, 300 absorption, 301 atmospheric transmission, 300 cloud cover, 302 patience, 305–307 precipitation, 303 radiation, 301 scattering, 301 solar loading, 303 water vapor, 302 wind, 304–305 automated detection, 307–312 automated detection of animals, 308–309 automated enumeration of animals, 309–310 image fusion, 311–312 large area surveys, 311 robotic vision, 310–311 thermal imaging and, 307–310 background clutter, 276–286 clutter from species mixing and bedding sites, 282–283 341 342 Subject Index Animal ecology (cont.) complete snow cover and thermal contrast, 281–282 longevity of signatures, 283–285 re-emitted solar radiation, 280 reflected solar radiation, 279–280 scattered radiation, 281 small-scale backgrounds, 285 Beaufort wind chart, 304 bobcat images, 299 complete diurnal cycle, relative S/N ratio for, 293 deer, thermal imagery of, 278, 279 diurnal cycle, 286 daily cyclic, 290, 291 diurnal effects and background, 286–289 homothermic animals and, 297–300 poikilothermic animals and, 296–297 solar loading, 289–296 texamples, 294–296 thermal shading, 289–296 emissivity, 272–276 apparent temperature difference, 274–276 plot of, 275 reflectivity and heat transfer (through the reflectivity), 273–274 energy budget of target species, 313–314 homothermic animals, 314 ground-to-air survey, geometrical relationships for, 260 helicopter showing geometrical arrangement, sketch of, 255 heron, thermal image of, 284 methodology, 246–247 imager specifications and use, 246–247 training, 247 red-cockaded woodpecker location of, 266 tree cavity, 269 photograph of, 264, 265 viewing geometry, 267 spherically-shaped passive (nonbiological) object, diurnal cycle for, 292 subtended footprint, 256 surveys, 248–249 introduction (factors influencing the detectivity), 248–249 thermal imagers, 245–268, 314 thermal images, 289 thermography, 312–314 thermoregulation, 312–314 unoccupied tree cavity, thermal signature of, 270 VFOV footprint, 256 viewing angle of camera, 250 viewing platforms, schematic showing geometry for, 261 Antelope (Antilocapra americana), 177 Apparent temperatures, 63, 84–89, 214, 274, 278 apparent temperature difference, 87–88 of background, 86–87 calculation of, 190 difference, 175, 294, 296 of object, 85–86, 275 radiative components, 85 vs viewing angle, 96–99 Arctic ground squirrel (Spermophilus parryii) burrows, 232 Array fabrication techniques, 121 Array of detectors, 254, 255 Arthropod vision system, 318 Artic foxes (Alopex lagopus), 71 thermal image of, 71 Artificial light, 43 AS350B2 helicopter, 201 Atmosphere effects on animal ecology, 300 absorption, 301 atmospheric transmission, 300 cloud cover, 302 patience, 305–307 precipitation, 303 radiation, 301 scattering, 301 solar loading, 303 water vapor, 302 wind, 304–305 Atmospheric attenuation conditions, 109 Atmospheric transmittance, 80, 107, 108, 140 as function, 107 Atmospheric windows, 121 long wavelength IR band (LWIR), 121 midwave IR band (MWIR), 121 Attenuation coefficient, 108 Attenuation mechanisms, 108 Automated all-optical image recognition system, 250 Automated detection, 307–312 automated detection of animals, 308–309 automated enumeration of animals, 309–310 image fusion, 311–312 large area surveys, 311 Subject Index robotic vision, 310–311 thermal imaging and, 307–310 Automated thermal video recording system, 309 Automatically-triggered cameras, 48 Automatic recognition system, 151 Avian species (Birds), 213–221 introduction, 213–215 reviews (avian species surveys), 215–221 B Background clutter, 251, 276–286 complete snow cover and thermal contrast, 281–282 longevity of signatures, 283–285 re-emitted solar radiation, 280 reflected solar radiation, 279–280 scattered radiation, 281 small-scale backgrounds, 285 from species mixing and bedding sites, 282–283 Background radiation, 81 Background temperature, 81–84, 191, 277 emitted power differential, 83 T3 dependence of, 83 Back-scattering, 302 Barn owls (Tyto alba), 96 radiative temperature of, 273 Barred owl (Strix varia), 42 Bats (Vespertilionidae), 42, 221–227 introduction, 221 reviews (bat surveys and observations), 221–227 study result, 225 Battery-powered LWIR handheld thermal imager, 187 Bayesian decision model, 307 Beaufort number, 304 Beaufort wind chart, 304 Beaver (Castor canadensis), 131 Beer–Lambert law, 108 Bell 212 helicopter, 237 Bias, 12 Big Cypress National Preserve, 59, 188 Binoculars, 38, 192 image intensifiers, 40 Bioinspired MWIR detector, 320 Bioinspired photonic devices, 317 Biological population, definition of, 11 Bird-wind turbine collisions, 218 Black-and-white video camera, 45 Blackberry (Rubus allegheniensis), 55 Blackbodies, 272 343 Blackbody radiation curves, 78, 132, 178 Blocking and shading effects, 88 Blue crab (Callinectes sapidus), 52 Blue morpho butterfly (Morpho peleides), 318 thermal response of, 320 Bobcats (Lynx rufus), 48 images, 299 Boiga irregularis, 40 Bolometric imagers, 139 Bolometric MWIR Thermovision 210 thermal imager, 233 Bolometric systems, 102 Brazilian free-tailed bats (Tadariada brasiliensis), 226, 309 complex colony dynamics of, 226 Brown bats (Eptesicus fuscus), 221 C Calibration models, 204 California bighorn sheep (Ovis canadensis californiana), 197 Camera DSLR See Digital single lens reflex (DSLR) cameras features, 134–137 performance characteristics for, sensor, 44 technology, 121 Canada geese (Branta canadensis), 30, 72 Canopy closure, 182 Capacitive devices, 102 Capelin (Mallotus villosus), 51 Carbon dioxide (CO2), 106 Carnivores abundance of, 31 noninvasive survey methods for, 31 Catfish (Ictalurus punctatus), 41 Cessna fixed-wing aircraft, 58 Chatten method, 215 Classification process, 310 Clinical thermography implications for, 96 Closed mark–recapture design, 203 Complete diurnal cycle, relative S/N ratio for, 293 Complete snow cover, 26–30 Computer vision analysis, 227 Conduction, 64 in animals, 70–71 rate of, 64 Convection, 64–65 in animals, 71 344 Subject Index Convective transfer processes, 99 Conventional censusing methods, 243 Conventional IR imagers, 110 Correction factor, 30 Correlation, 161 Counter/camera system strengths and weaknesses of, 47 Counting methods, 14–33 direct methods, 14–31 indirect methods, 31–33 Counting techniques, 202 Covariate analysis, 209 Cow thermal image of small herd, including adults and calves, Cross-correlation function, 250 Cryogenically cooled Inframetrics® 525 LWIR imager, 222 Cryogenically cooled thermal imager, 192 Current technology, 124 D Data acquisition, 228 photographic implementations of, 44 Data analysis, 201 Data collection methods, 244 Data-gathering nights, 49 Data processing techniques, 207, 307, 311 Dead coral (Oculina varicose), 51 Deer (Odocoileus virginianus clavium), 27, 42, 48, 173 thermal imagery of, 278, 279 Deformable mirror, 37 Dense forest canopy, 29 Density of animals, 31 estimator, 185 Department of Defense (DOD), Detectability models, 5, 19, 28 advantage of, 28 Detection algorithm, 223 curves, 213 probability, 209 rates, 147 systems, 108 Detector array technology, 121 Detector elements, 115 Digital cameras See also Camera sensitivity of, 44 Digital photography, 44 Digital processing, 223 Digital single lens reflex (DSLR) cameras, 45, 46 Direct correlation, 21 Direct counting methods, 14–31 complete counts, 15–18 incomplete counts, 18–31 aerial counts, 21–26 complete snow cover, 26–30 indices, 19–21 photographic techniques, 30–31 “Direct viewing” devices, 37 Discrimination curve, 163 Display system, 58 Distance sampling techniques, 187, 212 Diurnal cycle, 157, 171, 173, 220, 286, 291, 293 aspects of, 286 daily cyclic, 290, 291 diurnal effects and background, 286–289 homothermic animals and, 297–300 poikilothermic animals and, 296–297 solar loading, 289–296 texamples, 294–296 thermal shading, 289–296 time of, 297 visible images in field, 297 daytime conditions, 297 dusk conditions, 297 nighttime conditions, 297 predawn conditions, 297 Diurnal radiant temperature variation, 287 Doppler radars, 217 networks of, 50 Double-count surveys, 202 Double-count technique, 202 Double-sampling approach, 19 Down-looking angles, 107 Drones, 315–317 platform technology, 315 E Edge enhancement processes, 250 Edwin S George Reserve (ESGR), 174, 208 visual count on, 208 Effective radiant temperature (ERT), 177 plots of, 177 Electrical conductivity, 125 Electrochromic materials, 99 Electromagnetic energy, 77 Electro-optic/infrared multispectral systems, 311 Elevation angles, 271 Subject Index Elk (Cervus elaphus), 13, 29 activity patterns of, 287 populations, 28 Emergence cycle, 224 Emissivity, 77, 87, 89–99, 272–276 aerial observation platform, 97 angular dependence of, 95 apparent temperature difference, 274–276 vs viewing angle, 96–99 compensation, 127 definition of, 89 dependence of, 95 introduction, 89–92 plot for, 275 cross-section of quadrant, 95 quality of surface, 92–94 reflectivity and heat transfer (through the reflectivity), 273–274 shape of object, 95–96 thermal image of man taken in coastal Virginia, 91 man wearing eyeglasses and holding a cold beverage, 92 MWIR camera, 93 of river otter, 93, 94 value of, 98 viewing angle, 94–95 Emittance, 77 Emus (Dromaeus novaehollandiae), 23 Endangered Species Act of 1973, Endangered species, manage and recover, Energy budget of target species, 313–314 homothermic animals, 314 Energy conservation, 76 Energy distribution, 103, 143 Energy exchange, 64 Energy-intensive/destructive marking techniques, 48 Enhanced visual sensing, 36–39 broadest sense of, 36 Evaporation, 68 of sweat, 72 Evapotranspiration, definition of, 67 Exit pupil, 37 diameter of, 37 Extinction coefficient, 108 F Ferroelectric crystals, 123 Field data, Field methods, 31 345 Field-of-view (FOV), 37, 95, 138, 152, 266 Fill factor, 112 FIR thermal sensor systems, 234 Fixed-beam radar, 213 Fixed wing aircrafts, 23, 25, 181, 315 flying, 16 Fixed-wing Cessna 337G aircraft, 186 Fixed wing planes, 25 Fixed-wing/rotary-wing aircraft platform, 317 Fixed-wing thermal surveys, 208 Flew fixed-wing surveys, 186 Flight protocol, 188, 197 Florida panther (Puma concolor coryi), 288 Big Cypress National Preserve, 283 National Wildlife Refuge, 58, 188, 283 photograph of, 61 searching with radio telemetry, 60 Flying circles, 271 Flying fixed wing aircrafts, 25 Focal length, 116 Focal-plane-arrays, 112, 113, 116, 123, 254 Focal plane fabrication process, 125 Forward looking infrared (FLIR) systems, 133, 185, 190, 205 calibration, 181 crew, 197 devices, 237 features, 206 flights, 200 MilCAM-Recon®, 114 Model 1000A® thermal imager, 231 P100 Nightsight®, 189 Prism DS MWIR thermal infrared imager, 191 SC8000 HS® Series MWIR camera, 115 Series 2000F® LWIR camera, 200 surveys, 195, 205 ThermaCAM® HS-324, 209 ThermaCAM P65HS® LWIR thermal imager, 238 ThermaCAM PM 575® thermal imager, 236 ThermaCAM® SC640, 136 video, 237 Forward-looking infrared thermal imager, 197 Fourier equation, 64 FPA microbolometer detector, 210 FPA technology, 110 Free-ranging snowshoe hare (Lepus americanus), 233 Fromozov-Malyshev-Perelshin (FMP) formula, 31 346 Subject Index G Gallium aluminum arsenide (GaAlAs), 125 Gallium arsenide (GaAs), 125 Geat-horned owl (Bubo virginianus), 234 General-use field cameras, advantages and disadvantages of, 46 Genetic sampling, 205 GEN IIPLUS image intensifier tube, 189 Geometrical factors, 94 GIS data layers, 204 Global positioning systems (GPS), 55 radio-collars, 56 tracking, of animals, 56 Gopherus agassizzi, 28 Gray bat (Myotis grisescens), 221 Gray squirrel (Sciurus carolinensis), 153 Gray wolf (Canis lupus), 90 Grazing angle, 94, 96, 155 Great blue heron (Ardea herodias) thermal image of, 214 Ground-based survey technique, 22, 145, 187, 212, 248 Ground-based thermal imaging techniques, 213 Ground imaging surveys, 212 Ground-to-air survey, geometrical relationships for, 260 Ground truth experiments, 182 Ground-truthing surveys, 22 Gyrostabilization, 188 H Habitat fragmentation, Handheld devices, 262 Handheld thermal imagers, 212, 231, 238 Harvesting numbers, Heat-generated images, 143 Heat losses, 90 Heat-shielding, 242 Heat transfer mechanisms, 4, 63–73, 279, 284, 285, 303 animals, 70 background, 63–64 conduction, 64 in animals, 70–71 convection, 64–65 in animals, 71 house cat, thermal images of, 70 nuances of, 63 phase changes, 66–67 in animals, 72–73 evaporation/condensation, 67–70 porous soil, cross-sectional segment of, 67 radiation, 65–66 in animals, 71–72 Helicopters advantages of, 25, 197 aerial survey, 22, 60 over fixed wing planes advantages of, 25 showing geometrical arrangement, sketch of, 255 vs fixed-wing planes, 197 Hemispherical electronic eye camera system, 317 Heron, thermal image of, 284 Herring (Clupea harengus harengus), 51 Hierarchical modeling framework, 311 High-altitude survey, 181 High-performance thermal imaging cameras, 121 High-powered maneuverable helicopter, 25 High-resolution video monitor, 198 Homothermic animals, 70, 71, 303, 305 Horizon, 315–320 drones, 315–317 miniaturized thermal cameras, 317–320 quadcopter drone, 316 Horizontal field-of-view (HFOV), 116 Horse (Equus caballus), 249 Hot spots, 117 House cat, thermal images of, 70 Human eye, visual impairment of, 36 I Identification process, 145 I2 devices See Image intensifiers Image analysis methods, 226 Image blur, 44 Image fusion process, 160 Image intensifiers (I2 devices), 38–44, 49, 221 applications of, 39 available models of, 43 coupling of, 41 drawbacks to, 40 for feeding ecology of prairie ducks, 40 lens, 42 performance of, 39, 61 Imager selection, 121–141 camera features, 134–137 introduction, 121–122 IR imager, selection, 127–134 application requirements, 127–129 image, 132 MWIR vs LWIR, 132–134 Subject Index performance specifications, listing of, 136 wavelength selection, 129–132 photon detector relative spectral response of, 126 solar radiation, effect of, 131 spatial resolution and NETD comparison of, 135 thermal detectors relative spectral response of, 126 vs photon detectors, 122–127 thermal imaging cameras, 125 typical MWIR camera, 138–141 accessories, 140–141 checklist of, 141 small portable thermal imaging system, photograph of, 139 verifying performance, 137–138 Imager selection process, 127 Imager sensitivity, 246 Imagery, recording consequence of, 193 Incident flux, 76 Incident planar wavefront, 37 Incident radiation flux, 123 Index methods, 19 Index population, 31 Indiana bat (Myotis sodalis), 221 Indices, 19–21 Indirect viewing devices, 38 Indium antimonide (InSb) detector, 111, 125, 132, 176, 178, 215 Industrial-sized wind energy, 219 Infracam® thermal imager, 188 Model-A handheld thermal imager, 138 Inframetrics Infracam® thermal imager, 114, 259 Inframetrics IRTV-445L®, 217 Infrared (IR) cameras, 103, 130, 140, 143, 148 operators of, 186 Infrared (IR) detectors, 110, 121, 123 focal plane arrays (FPAs), 102 sensitivity of, 179 types of photon/quantum detectors, 101 thermal detectors, 101 Infrared (IR) imagers, 102, 105, 131, 132, 172 performance, 172 selection, 127–134 application requirements, 127–129 MWIR vs LWIR, 132–134 performance specifications, listing of, 136 wavelength selection, 129–132 spatial resolution of, 111 versatility of, 172 347 Infrared imaging, 221 cameras, 313 Infrared (IR) imaging technology, 113 use of, 187 Infrared (IR) radiation, 90, 121, 276 detecting materials, 124 Infrared scanners, performance of, 177 Infrared sensitive video system, 41 Infrared (IR) sensor, 167 Infrared signals, 276 Infrared spectrum, 148 Infrared (IR) thermal imagers, 105, 134 availability, 174 Infrared thermal imaging cameras, performance of, 246 systems, use of, 313 Infrared thermography digital analysis of, 30 Infrared-triggered cameras, 48 comparison of, 48 Inspection technique, 242 Instantaneous field-of-view (IFOV), 111, 113, 138 spatial resolution of, 179 Insulator-metal transition (IMT), 99 Integrated resonant absorber elements, 124 Integrated sensor technologies, 311 Invertebrates (arthropoda), 239–240 introduction, 239–240 reviews (invertebrate observations), 240–243 ISO, 44 Isothermal anatomical surface, 96 J Japanese honeybee (Apis cerana japonica), 240 JENOPTIK imager, 209 Johnson criterion, 163 K Kirchhoff’s law, 76, 84, 94, 155, 272 equation, 148 L Land infrared line-scan imagery of, Large-particle scattering See Mie scattering Large-scale field survey, 188 Laser in situ keratomileusis (LASIK), 36 Light level sensor, 43 348 Subject Index Light radiation, 65 Lightweight multipropeller drones, 316 Line-scanning imagers, 78 Line transect estimators, 22 Line transect sampling systems, 177 Local physiography, 24 Long wavelength IR band (LWIR) detectors, 124, 133 Long wavelength IR band (LWIR) imagers, 79, 106 bands relative to atmospheric windows, 79 Long wavelength IR band (LWIR) infrared scanner, 203 Long wavelength IR band (LWIR) Raytheon Palm IR 250 Digital®, 203 Long wavelength IR band (LWIR) thermal imager, 217, 231 Louisiana black bear (Ursus americanus luteolus), 43, 55 Low-contrast grayish-green images, 40 Low-light-level cameras, 43–46 Low-resolution detector, 174 M Mammals, 172–213 introduction, 172–173 mammalian reviews, 173–213 Mark-recapture model, 23, 48 mathematics of, 22 Mark–resight surveys, 193 McFadden National Wildlife Refuge, Texas, 217 Mercury cadmium telluride (HgCdTe) detector, 115, 125, 132, 176, 178, 215 Merlin MID® MWIR thermal imagers, 223 Metabolic energy production, 300 Metabolic processess, 175, 280 Metabolic rate scales, 314 Methodology for animal ecology, 246–247 imager specifications and use, 246–247 training, 247 Mexican free-tailed bats, behavior of, 41 Microbolometers, 123–124 detector element in, 123 Microbolometric uncooled imagers, 262 Microchannel plate impinges, 39 Micro-FLIR WF-160DS® airborne surveillance system, 194 Mid-wave IR band (MWIR), 79, 106 Agema Thermovision 210 thermal imager, 233 bands, 80 relative to atmospheric windows, 79 detectors, 124, 133, 215, 317, 320 imagers, 97, 114 systems, 106, 216 thermal imagers, 226 thermal imaging camera, 285 vs LWIR, 132–134 Mie scattering, 301 Miniaturized thermal cameras, 317–320 blue morpho butterfly detector, 318–320 hemispherical drone optics, 317–318 Minimum detectable temperature difference (MDTD), 106, 117, 123, 165 Minimum resolvable temperature differences (MRTDs), 106, 117, 122, 138, 160, 175 frequencies, 163 Mitsubishi IR-M500 NIR-MWIR thermal imager, 212 Mixed-wood forests, 186 Moderate resolution atmospheric transmission (MODTRAN) computer model, 128 Modern thermal imaging cameras, 102 Moonwatch method, 49 potential and limitations of, 217 Moose (Alces alces), 305 wintering areas, 186 Motion detector/heat sensor trigger, 43 Mule deer (Odocoileus hemionus), 16, 25, 151, 177 spectral signatures for, 282 Multiple observer methods, 18 Multistage pattern recognition algorithm, 151 N Narrow field-of-view (NFOV) optics, 115 National Aeronautics and Space Administration, 315 Plum Brook Station (PBS), 189 National Research Council, 219 Natural nocturnal lighting scenarios, 43 Near-infrared radiation, 39 Nesting insects, 239 Nests (dens, tree cavities, lairs, burrows), 227–239 introduction, 227–229 nest imagery, examples of, 229–230 reviews of, 230–239 Night-flying birds, 223 Night-vision device See also Image intensifier performance of, 222 Subject Index Night vision devices (NVDs), 39 Night-vision goggles (NVG), 189 use of, 190 Night vision technology, 43 Night vision thermal imaging systems, 161 Nocturnal distance sampling surveys, 211 tool for, 193 Nocturnal flights, 207 Nocturnal line transect sampling, 210 surveys, 211 Noise equivalent power (NEP), 110 Noise equivalent temperature difference (NETD), 84, 101, 106, 111, 117, 122, 130, 138, 165, 175, 294 comparison of, 135 Noninvasive assessment method, 242 Nonuniformity correction (NUC), 115 O Object-detection system See Radar (radio detection and ranging) Observability, Ocular lens, 39 Ohio farmland habitat, 15 Operator’s panning technique, 205 Opossum (Didelphis virginiana), 42 Optical phenomena, 300 Optical processor, 250 Optical pyrometer, 76 Optical radiation, 75–88 apparent temperature, 84–88 apparent temperature difference, 87–88 of background, 86–87 of object, 85–86 radiative components, 85 atmospheric transmittance, 80 background temperature, 81–84 emitted power differential, 83 t3 dependence of, 83 Kirchhoff’s law, 76 MWIR and LWIR bands relative to atmospheric windows, 79 Planck radiation law, 77–81 power spectral distribution, radiated from blackbody source, 78 radiative components of, 82 Stefan–Boltzmann law, 76–77 Optimum allocation procedures, 25 Orb weaving spiders (Argiope aurantia), 241, 303 P 349 Pacific walrus (Odobenus rosmarus divergens) signatures, 203, 311 Panning technique, 155 Passive signage, 58 Passive thermal imaging, 50, 128, 217 Peaking loading time, 294 “Peep” camera, 45 Peltier devices, 124 Penned deer morphological characteristics of, 181 Performance parameters, 105–116 range, 116–117 signal-to-noise ratio, 108–109 spatial resolution, 111–116 spectral response, 106–107 thermal resolution, 117–118 thermal sensitivity, 110–111 Phase changes, 66–67 in animals, 72–73 evaporation/condensation, 67–70 Photodiode arrays, 139 Photographic techniques, 30–31 Photon detectors, 118, 124–127 relative spectral response of, 126 spectral response of, 126 Photovoltaic/photoconductive device, 125 Pine (Pinus taeda), 250, 265 Pixels, 121 angular subtense, 112 Planck’s radiation law, 77–81 blackbody radiation law, 77 Planning process, 13 Platinum silicide (PtSi), 125 focal plane arrays, 118 Poikilothermic animals, 296 Poikilotherms, temperature of, 73 Polar bears (Ursus maritimus), 72, 90, 176 Polar plot, cross-section of quadrant, 95 Political population, definition of, 11 Populations density, 3, 19 surveys, 4, 286, 287 types of, 11 biological population, 11 political population, 11 research population, 11 Porous soil, cross-sectional segment of, 67 Possum (Trichosurus vulpecula) populations, 133, 190 Power spectral distribution radiated from blackbody source, 78 350 Subject Index Predator®, 315 Prevailing processes, 280 Proportionality constant, 64 Puma concolor coryi, 31, 188 Pyroelectric imagers, 139 Pyroelectric vidicons, 102 Q Quadcopter drone, 316 Quality of surface, 92–94 Quantitative population, 13 Quantum detectors, 125, 139 Quantum efficiency, 104, 125 Quantum well infrared photodetector (QWIP) detector, 125 Quasi-thermal equilibrium approaches, 228 R Raccoons (Procyon lotor), 229 Radar (radio detection and ranging), 49–53, 217 electromagnetic waves, 49 Radiant energy, 93 Radiant power difference, 84 Radiation, 65–66 in animals, 71–72 laws, 77 spatial distribution of, 155 Radiative components, 82 Radiative processes, 229 Radio-collared animals, 31 Radio-collared Florida panther, thermal image of, 60 Radio-tagged juvenile cougars, 57 Radio telemetry systems, 54–61 Radio-tracking, 55 advantages of, 55 of animals, 60 Range, 116–117 Rayleigh scattering, 108 Raytheon Thermal-Eye 250D® LWIR handheld thermal imager, 212 Real-time automated censusing system, 309 Real time observations, 45 Recording equipment, 104 Red-cockaded woodpecker (Picoides borealis), 45, 262–264 clusters of, 266 location of, 266 translocation of, 263 tree cavity, 263, 268, 269 to monitoring, 45 photograph of, 264, 265 scanning for, 266 viewing geometry, 267 Red deer (Cervus elaphus), 181 Red-filtered light source, 41 Red squirrel (Tamiasciurus hudsonicus), 233 Reflected ambient radiation, 143, 276 Reflected solar radiation, 273, 279 Reflective band electro-optic sensor, 160 Reflectivity, 94 Refractive and laser eye surgery, 36 Relative index methods, 19 Remote cameras, 46 usefulness of, 46 Remote sensing, 35–62 definitions of, 35 devices, 75 enhanced visual, 36–39 form of, 35 image intensifiers (I2 devices), 39–44 image intensifiers/thermal imagers, 61–62 introduction, 35–36 low light level cameras, 44–46 radars and sonars, 49–53 radiotelemetry systems, 54–61 technique See Thermal imaging thermal imaging, 53–54 trip cameras, 46–49 Remote trip cameras, 46 Reproductive processes, control of, 10 Research population, definition of, 11 Residual forest canopy, 202 Retro-reflector, 37 Ringed seal (Phoca hispida), 231 River otters (Lutra canadensis), 92 Road-based surveys, 201, 213 Roe deer fawns (Capreolus capreolus), 308 Rotary-winged aircraft, 188, 283 Rule of thumb, 98 S Sagebrush (Artemisia spp.) steppe (SBS), 25 Saltmarsh cordgrass (Spartina alterniflora), 241 Sampling analysis techniques, 18 counting methods, 185 method, 187 plots, selection and placement of, 19 Subject Index Sand Lake National Wildlife Refuge, 184 Saw palmetto (Serenoa repens), 288 Scaling law, 314 Scanning systems, 115 Scanning techniques, 249 Scanning thermal imager, 189 Scattered ambient radiation, 103 Scattering of radiation, 271 Sea otter (Enhydra lutris), 28 Semiconductors, 125 Sensors active, 310 operators, 194 passive, 310 SGS habitat, 25 Shading effects, 53, 270, 303 Shape of object, 95–96 Sheer volume, Shielding effects, 128 Side-imaging sonar techniques, 52 Sightability, Sighting probabilities, 24 Sighting techniques, 192 Signal processing techniques, 104 Signal-to-noise (S/N) ratio, 44, 87, 108–110, 116, 292, 301 threshold for, 292 Signatures See Thermal signatures Silicon integrated circuit fabrication technology, 125 Silicon photodiodes, 318 Single focal plane array, 160 Single lens reflex, 46 Skip-minute method, 222 Snooperscope, 38 Snow-covered landscape, 23 Snowshoe hare (Lepus americanus), 90 Solar absorption, 150 Solar energy, 175 Solar irradiance, 67 Solar loading, 289, 293 angular exposure on, 249 effects of, 294, 298, 303 function of, 294 Solar radiation, 130, 184, 238, 241, 294, 303 direction and intensity of, 150 effect of, 131 Solar reflectance, 150 Solar reflections, effects of, 280 Solid-state coolers See Peltier devices Sonar (sound navigation and ranging), 49–53 acoustic waves, 49 351 detectors, 51 image, 52 Sony® zoom lens color television camera, 186 South Dakota, hunters of, Spatial distribution, 145, 154, 312 Spatial domain, 144 Spatial frequency, 118, 164 Spatial light modulator (SLM), 250 Spatial resolution, 111–116, 178, 253, 258 comparison of, 135 detector-limited resolution, 112–114 optics-limited resolution, 114–116 Spectral response, 106–107 Spherically-shaped passive (nonbiological) object, diurnal cycle for, 292 Spotlighting techniques, 178 Spotlight surveys, 192 State-of-the-art infrared imager, 105, 140 Stefan–Boltzmann equation, 78 Stefan–Boltzmann law, 76–77, 89, 272 Stirling cycle cooler, 102, 134 Subtended footprint, 256 Surface imperfections, 92 Surveillance applications, 101, 129, 136 systems, 165 Survey geometry, 253–272 air-to-air, 261 air-to-ground, 254–259 atmospheric transmission, 271 diurnal cycle, angular dependence on, 271–272 ground-to-air, 259–261 ground-to-ground, 262 signature dependence on viewing angles, 262–266 thermal shadows, 270–271 tree cavity analysis, 266–270 Surveying, 192, 196 Surveys, 248–249 introduction (factors influencing the detectivity), 248–249 plot, 14 protocol, 29 techniques, T Target species ecology of, 13 Target transfer probability function (TTPF), 162 352 Subject Index Television imaging devices, 250 Temperature fluctuations, 297 Terrain, features of, Terrestrial and marine wind farms environmental consequences of, 219 ThermaCAM PM 545 FLIR, 211 Thermal cameras, 158, 171, 210, 218 advantages of, 124 sensitivity of, 81 Thermal coefficient of resistance (TCR), 123 Thermal conductivity, 67, 287 Thermal contrast, 151, 294, 305, 307 detectability of, 188 measurements, 194 Thermal detectors, 123–124, 126 microbolometers, 123–124 relative spectral response of, 126 vs photon detectors, 122–127 Thermal energy, 143, 283 distribution of, 105 fluxes, 54 sources of, 307 spatial distribution of, 103 Thermal equilibrium, 81, 280 Thermal image, 103–105 man taken in coastal Virginia, 91 man wearing eyeglasses and holding a cold beverage, 92 MWIR camera, 93 of river otter, 93, 94 Thermal imagers, 12, 46, 61–62, 65, 66, 75, 84, 89, 90, 96, 101, 107, 113, 118, 160, 171, 213, 226, 245–314 current status and availability of, manufacturers of, 140 3–5 micron, 103 operation of, 246 performance characteristics of, 134 parameters of, 246 placement of, 227 quality of, 104 and system considerations, 101–118 atmospheric attenuation conditions, 109 atmospheric transmittance as function, 107 brief history, 101–105 thermal image, 103–105 focal plane array, 112 instantaneous field-of-view, 113 performance parameters, 105–116 range, 116–117 signal-to-noise ratio, 108–109 spatial resolution, 111–116 spectral response, 106–107 thermal resolution, 117–118 thermal sensitivity, 110–111 spatial resolution, 113 thermal image of house cat in forested setting, 104 uses of, 121, 316 weight, 316 Thermal imagery, 58, 62, 145, 183, 207, 223, 225, 278 endorsement of, 178 loss of energy in, 270 Thermal imaging surveys, 16, 175, 183, 190, 194, 195, 244 Thermal imaging systems, 7, 53–54, 80, 101, 102, 106, 124, 129, 148, 208, 242, 289 advantages of, 220, 281 applications, 80 applications and experiments, 171–244 background, 171–172 in background clutter, 191 band, 79 cameras, 39, 103, 121, 125, 249, 274 advantages of, 286 performance of, 140 parameters, 75 use of, 225, 272 concluding remarks, 243 counts, 16 of flock of small birds, 214 of great blue heron, 215 group of mallards, 215 of herons roosting in tree, 216 of house cat in forested setting, 104 of large hollow log, 230 literature reviews, 172–243 Avian species (Birds), 213–221 bats, 221–227 invertebrates (arthropoda), 239–240 mammals, 172–213 nests (dens, tree cavities, lairs, burrows), 227–239 quality of, 148 role of, 211 of striped skunk after leaving its den, 230 use of, 8, 173 Thermal index, 204 Thermal inertia, 287, 289, 291, 303 of dry soils, 67 Thermal infrared (TIR) data, 53 application of, 53 spatial resolution of, 54 Subject Index Thermal infrared imaging techniques, 198, 219, 220, 227, 310 sensing, 185, 202 survey, 189 video, 224 Thermal loading, 279 Thermal monitoring program, 218 Thermal phenomenon, 105 Thermal processes, 63 Thermal radiation, 99 Thermal resolution, 117–118 Thermal scanning methods, 180 Thermal sensitivity, 110–111, 115, 118, 294 Thermal shading, 289 time response for, 270 Thermal shielding, 91 Thermal signals, 276 Thermal signatures, 66, 305 of animal, 153 associated with deer, 59 of bedding sites, 283 black bear, thermal images of, 158 of deer (O virginianus), 166, 174 thermal image of, 147, 158 emissivity of animals, 149 grouping of, 152 house cat, thermal image of, 145 identification, 199 image quality, 145–148 signature saturation, 145–148 introduction, 143–145 for mammal, 167 probability of discrimination, 162 function multiplier, 162 properties of, 143–169 radiation, spectral distribution of, 150 spatial domain, 152–156 grouping, 156 intensity, 154–155 shape, 154 size, 152–154 viewing angles, 155–156 spectral domain, 148–152 surveillance, 160–169 application, 165–169 discrimination levels, 161–165 temporal domain, 156–157 habitat, 157 motion, 157 thermal contrast, 164 vertical and horizontal MRTD frequencies geometric average of, 163 353 visibility bias, 157–160 yellow-crowned night heron, thermal image of, 146 of young moose (A alces), 168 Thermal stabilization, 277 Thermal surveys, 208 Thermal uniformity, 64 Thermodynamics, first law of, 63 Thermoelectric (TE) device, 123 Thermoelectric devices, 134 Thermographers, 139, 146, 153, 169, 247, 270, 314 Thermographic temperature measurement, 96 Thermography, 312–314 Thermoregulatory processes, 70, 121, 305, 312–314 Thermovision 210®, 232 Tiger (Panthera tigris), 46 Time index methods, 19 Time resolutions, 139 Tongan fruit bat (Pteropus tonganus), 41 Transect surveys, use of, 60 Transmittance curve, 108 Triangulation, 56 Trip cameras, 46–49 Tripod-mounted handheld IR imager, 243 Tripod-mounted Inframetrics InfraCam imager, 294 True natural constant, 76 Tunable hemispherical imaging system, 318 Turkey (Meleagris gallopavo) night-time thermal image of, 61, 62 photograph of, 62 population, 47 Typical MWIR camera, 138–141 accessories, 140–141 checklist of, 141 small portable thermal imaging system, photograph of, 139 U Uncooled bolometer, 308 Underwater seismography, 49 Unmanned aerial systems, 315 Unmanned aerial vehicles (UAVs), 315 based detection, 308 Unoccupied tree cavity, 265 thermal signature of, 270 US Fish and Wildlife Service, 354 V Subject Index Vanadium dioxide (VO2), 99 Vanadium oxide (VOx), 123 microbolometer, 160 Vaporization latent heat of, 68 Vegetative obscuration, 252 Vertical field-of-view (VFOV), 116 footprint, 256 Vertical-looking infrared imagery technique, 207 Vertically-pointed radar and thermal imager (VERTRAD/TI), 50, 217 Vertically-pointed stationary radar beam, 50 Vespa mandarinia japonica, 240 Video peak store (VPS), 50, 217 Viewing angles, 94–95, 97, 98, 155, 225, 253, 254, 272 of camera, 250 Viewing platforms, schematic showing geometry for, 261 Virginia Marine Resources Commission, 52 Visibility bias phenomenon, 12, 21, 29, 158 in aerial surveys, 30 Visible aerial photography, 53 Vision correction surgery, 36 types of, 36 LASIK See Laser in situ keratomileusis (LASIK) Visual aerial surveys, 29, 175, 207 techniques, effectiveness of, 195 Visual counting methods, 222 Visual high-resolution photography, 311 Visual monitoring of nocturnal activity, 219 Visual observation techniques, 186 Visual surveys, 180, 208, 306 W Walrus (Odobenus rosmarus), 180 Warning system, 58 Wavefront technology, 36 WesCam DS® infrared sensor, 186 White-tailed deer density, 20 eye-shine from tapeta of, 20 Wide field-of-view (WFOV), 115 Wien displacement law, 78 Wien’s law, 149 Wi-Fi signals, 317 Wildlife habitats, 57 Wildlife managers, 2, 14, 18 Wildlife monitoring, 19 Wildlife population density, monitoring, 13 robust program for, 13 Wildlife survey applications, 315 Wildlife telemetry, 55 Willow ptarmigan (Lagopus lagopus), 90 Wind energy development, 219 Window of opportunity, 247 Wind turbines, 224 Woodchuck (Marmota monax), 97 Woodcock (Scolopax rusticola), 231 Woodland caribou (Rangifer tarandus caribou), 12 population estimation for, 205 Z Zero thermal loading, 228 ... results of thermal imaging experiments for Thermal Imaging Techniques to Survey and Monitor Animals in the Wild monitoring and counting wildlife as described in the literature Most of these efforts... for the use of thermal imaging techniques in the inventorying and monitoring of a broad range of animals (both homothermic and poikilothermic), including threatened and endangered species These... 18 Thermal Imaging Techniques to Survey and Monitor Animals in the Wild with thermal imagers of inferior performance by today’s standards (relatively poor spatial and thermal resolution) They