Data on animal species were collected during wildlife census using Kilometric Abundance Index (KAI). Thus, geographical coordinates were registered using GPS and all information of the species observation such as habitat, location were also recorded on field sheets by team leaders. A total number of 659 data were recorded for animal census in 2014 in the Biosphere Reserve of W.

Drone-Assisted Wildlife Population Census Market Outlook

According to our latest research, the global Drone-Assisted Wildlife Population Census market size reached USD 512.6 million in 2024, driven by the rapid adoption of advanced drone technologies across conservation and wildlife management sectors. The market is projected to grow at a robust CAGR of 13.2% from 2025 to 2033, reaching an estimated USD 1,473.2 million by 2033. This impressive growth is fueled by increasing investments in ecological monitoring, stricter wildlife protection regulations, and the widespread integration of high-resolution imaging and AI-powered data analytics in wildlife research.

The primary growth factor for the Drone-Assisted Wildlife Population Census market is the urgent need for accurate, efficient, and minimally invasive wildlife monitoring methods. Traditional wildlife census techniques often involve manual surveys, which are time-consuming, expensive, and potentially disruptive to animal habitats. Drones, equipped with advanced imaging technologies such as thermal, multispectral, and LiDAR sensors, offer a transformative alternative. These aerial systems enable researchers and conservationists to conduct large-scale surveys over challenging terrains, collect high-resolution data, and monitor elusive or endangered species without direct human interference. As biodiversity conservation becomes a global priority, especially in the face of climate change and habitat loss, the demand for drone-assisted census solutions is expected to rise significantly.

Another significant driver is the evolution of regulatory frameworks and governmental support for wildlife conservation initiatives. Many countries are enacting policies that encourage the use of unmanned aerial vehicles (UAVs) in environmental monitoring, anti-poaching efforts, and habitat mapping. This regulatory backing not only legitimizes the use of drones in protected areas but also opens up funding opportunities for research institutes and conservation organizations. Furthermore, collaborations between government agencies, NGOs, and private technology providers are fostering innovation in drone hardware and software, making these solutions more accessible and cost-effective for end-users worldwide. The growing ecosystem of partnerships and supportive policies is a critical catalyst in expanding the market’s reach.

Technological advancements in drone platforms and imaging sensors are also reshaping the landscape of wildlife population census. The integration of AI-driven analytics, real-time data transmission, and cloud-based processing has dramatically improved the accuracy and efficiency of wildlife monitoring. Drones now offer extended flight durations, improved payload capacities, and enhanced obstacle avoidance, making them suitable for diverse ecological environments. The ability to process vast amounts of visual and thermal data using machine learning algorithms allows for automated species identification, population estimation, and behavioral analysis. These innovations not only enhance data quality but also reduce operational costs, further accelerating the adoption of drone-assisted census methods across multiple regions and applications.

From a regional perspective, North America and Europe are leading the market, supported by strong research infrastructure, proactive conservation policies, and substantial funding for environmental initiatives. The Asia Pacific region is emerging as a high-growth market, fueled by increasing awareness of biodiversity loss, expanding protected areas, and rapid technological adoption. Latin America and the Middle East & Africa, with their rich biodiversity and expansive wildlife reserves, are also witnessing growing investments in drone-based monitoring solutions. However, regional disparities in regulatory frameworks, technological access, and funding availability continue to influence market dynamics, shaping the competitive landscape and growth opportunities across different geographies.

Drone Type Analysis

These occurrence data were collected systematically during wildlife census within the W park and its huntings zones as well. All the animal observation geographical points were recorded using GPS by fauna guards. These data were directly registered on paper sheets on field and controlled by team leaders. A total number of 1581 records were made on mammal species in 2013 and 2015 (respectively 745 records in 2013 and 836 in 2015).

NOTE: A more current version of the Protected Areas Database of the United States (PAD-US) is available: PAD-US 3.0 https://doi.org/10.5066/P9Q9LQ4B. The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme (https://communities.geoplatform.gov/ngda-cadastre/). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using over twenty-five attributes and five feature classes representing the U.S. protected areas network in separate feature classes: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. Five additional feature classes include various combinations of the primary layers (for example, Combined_Fee_Easement) to support data management, queries, web mapping services, and analyses. This PAD-US Version 2.1 dataset includes a variety of updates and new data from the previous Version 2.0 dataset (USGS, 2018 https://doi.org/10.5066/P955KPLE ), achieving the primary goal to "Complete the PAD-US Inventory by 2020" (https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-vision) by addressing known data gaps with newly available data. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in PAD-US, along with continued improvements and regular maintenance of the federal theme. Completing the PAD-US Inventory: 1) Integration of over 75,000 city parks in all 50 States (and the District of Columbia) from The Trust for Public Land's (TPL) ParkServe data development initiative (https://parkserve.tpl.org/) added nearly 2.7 million acres of protected area and significantly reduced the primary known data gap in previous PAD-US versions (local government lands). 2) First-time integration of the Census American Indian/Alaskan Native Areas (AIA) dataset (https://www2.census.gov/geo/tiger/TIGER2019/AIANNH) representing the boundaries for federally recognized American Indian reservations and off-reservation trust lands across the nation (as of January 1, 2020, as reported by the federally recognized tribal governments through the Census Bureau's Boundary and Annexation Survey) addressed another major PAD-US data gap. 3) Aggregation of nearly 5,000 protected areas owned by local land trusts in 13 states, aggregated by Ducks Unlimited through data calls for easements to update the National Conservation Easement Database (https://www.conservationeasement.us/), increased PAD-US protected areas by over 350,000 acres. Maintaining regular Federal updates: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/); 2) Complete National Marine Protected Areas (MPA) update: from the National Oceanic and Atmospheric Administration (NOAA) MPA Inventory, including conservation measure ('GAP Status Code', 'IUCN Category') review by NOAA; Other changes: 1) PAD-US field name change - The "Public Access" field name changed from 'Access' to 'Pub_Access' to avoid unintended scripting errors associated with the script command 'access'. 2) Additional field - The "Feature Class" (FeatClass) field was added to all layers within PAD-US 2.1 (only included in the "Combined" layers of PAD-US 2.0 to describe which feature class data originated from). 3) Categorical GAP Status Code default changes - National Monuments are categorically assigned GAP Status Code = 2 (previously GAP 3), in the absence of other information, to better represent biodiversity protection restrictions associated with the designation. The Bureau of Land Management Areas of Environmental Concern (ACECs) are categorically assigned GAP Status Code = 3 (previously GAP 2) as the areas are administratively protected, not permanent. More information is available upon request. 4) Agency Name (FWS) geodatabase domain description changed to U.S. Fish and Wildlife Service (previously U.S. Fish & Wildlife Service). 5) Select areas in the provisional PAD-US 2.1 Proclamation feature class were removed following a consultation with the data-steward (Census Bureau). Tribal designated statistical areas are purely a geographic area for providing Census statistics with no land base. Most affected areas are relatively small; however, 4,341,120 acres and 37 records were removed in total. Contact Mason Croft (masoncroft@boisestate) for more information about how to identify these records. For more information regarding the PAD-US dataset please visit, https://usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the Online PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual .

Staff conduct an annual census of the elk in the enclosure at San Luis National Wildlife Refuge. The data includes the number of elk and the composition of the group. This data and metadata were submitted by California Department of Fish and Wildlife (CDFW) Staff though the Data Management Plan (DMP) framework with the id: DMP000494. For more information, please visit https://wildlife.ca.gov/Data/Sci-Data.

Drone-Assisted Wildlife Population Census Market Outlook

According to our latest research, the global drone-assisted wildlife population census market size reached USD 1.62 billion in 2024. Supported by a robust compound annual growth rate (CAGR) of 13.8%, the market is forecasted to attain USD 4.42 billion by 2033. This remarkable growth is primarily driven by the increasing adoption of advanced drone technologies for ecological monitoring and wildlife conservation, as well as the rising need for accurate, non-invasive, and scalable population census solutions in diverse ecosystems worldwide. The market's trajectory reflects an era where technological innovation is transforming traditional wildlife management and conservation practices.

One of the primary growth factors propelling the drone-assisted wildlife population census market is the urgent requirement for precise and efficient wildlife monitoring methods. Traditional census techniques, such as ground surveys and manned aerial counts, are often labor-intensive, time-consuming, and prone to human error or disturbance of wildlife habitats. Drones, equipped with advanced imaging and sensor technologies, offer a non-intrusive alternative that significantly enhances data accuracy and operational efficiency. Their ability to cover vast and inaccessible terrains, such as dense forests, wetlands, and rugged mountainous regions, enables conservationists and researchers to collect real-time, high-resolution data on species population, distribution, and behavior. This technological edge, coupled with the growing emphasis on biodiversity conservation and regulatory mandates for wildlife protection, is fueling market expansion.

Another key driver is the integration of cutting-edge technologies into drone platforms, including thermal imaging, LiDAR, multispectral imaging, and high-definition video capabilities. These advancements have revolutionized the way wildlife populations are assessed, enabling the detection of animals even in challenging environments or under dense vegetation cover. For example, thermal imaging can identify nocturnal or camouflaged species, while LiDAR mapping provides detailed topographical data for habitat analysis. The convergence of artificial intelligence and machine learning algorithms with drone systems further enhances data processing and analytics, automating species identification and population estimation. This technological synergy is attracting significant investments from governmental agencies, conservation organizations, and research institutes, fostering a dynamic ecosystem for market growth.

Furthermore, the increasing prevalence of poaching and illegal wildlife trade has heightened the demand for drone-assisted surveillance and anti-poaching operations. Drones offer a proactive tool for monitoring protected areas, detecting suspicious activities, and responding rapidly to threats, thereby supporting law enforcement and conservation efforts. The versatility of drone applications, from population monitoring and habitat mapping to behavioral studies and real-time anti-poaching interventions, underscores their critical role in modern wildlife management strategies. As global awareness regarding wildlife preservation intensifies, and as funding for conservation initiatives rises, the adoption of drone-assisted census solutions is expected to accelerate, reinforcing the market's upward trajectory.

From a regional perspective, North America currently dominates the drone-assisted wildlife population census market, owing to its advanced technological infrastructure, well-established regulatory frameworks, and strong presence of leading drone manufacturers and service providers. Europe follows closely, driven by progressive conservation policies and significant investments in biodiversity research. The Asia Pacific region is projected to witness the highest CAGR during the forecast period, fueled by expanding conservation programs, governmental initiatives, and increasing awareness about the ecological and economic importance of wildlife monitoring. Emerging markets in Latin America and the Middle East & Africa are also showing promising growth, supported by international collaborations and the adoption of drone technologies in critical biodiversity hotspots.

Drone Type Analysis

The drone type segment is pivotal in shaping the market dynamics of the drone-assisted wildlife populat

The official State census period for common terns was June 1-10. The survey was conducted on June 4 by Biologist Healey, Biotech Springfield, and Maintenance Lundblad in the Boston Whaler from 10:30 am to 1:30 pm with high tide peaking at 12:00 pm

description: The main objective of this internship project was to approximately determine the population of beaver ( castor canadensis) in the Seneca division of the Erie National Wildlife Refuge (E.N.W.R.). Determining the population of beaver will give a better idea as to whether the beaver are enhancing their environment or creating additional environmental p-roblems and provide- a basis for implementation of a management program. Another objective of this internship project was to improve the method of censusing beaver described in a previous internship project by Malagise. The results from this internship project show the total number of beaver in the Seneca division of the E.N.W.R. to be 204 +/- 59. The uncertainty value for the total number of beaver is so high because of the difficulty in measuring the caches, dams, and lodges mentioned in the discussion. The size of the caches, dams and lodges, as well as other signs of beaver activity, were taken into consideration when compiling the results for this project. Trapping area B in the Sugar Lake division was not included in this data because of a miscommunication.; abstract: The main objective of this internship project was to approximately determine the population of beaver ( castor canadensis) in the Seneca division of the Erie National Wildlife Refuge (E.N.W.R.). Determining the population of beaver will give a better idea as to whether the beaver are enhancing their environment or creating additional environmental p-roblems and provide- a basis for implementation of a management program. Another objective of this internship project was to improve the method of censusing beaver described in a previous internship project by Malagise. The results from this internship project show the total number of beaver in the Seneca division of the E.N.W.R. to be 204 +/- 59. The uncertainty value for the total number of beaver is so high because of the difficulty in measuring the caches, dams, and lodges mentioned in the discussion. The size of the caches, dams and lodges, as well as other signs of beaver activity, were taken into consideration when compiling the results for this project. Trapping area B in the Sugar Lake division was not included in this data because of a miscommunication.

Data on animal species were collected during wildlife census by fauna guards and ecological staff on linear transects and the transects dedicated for KAI estimation. The geographical coordinates were systematically registered using GPS and all the ecological information and signs of threats on wildlife were recorded on field sheets by team leaders during the observations. A total number of 4295 occurrence data on animal species were recorded

The objective of this study was to determine an approximate population number for beaver (castor canadensis) on the Sugar Lake division of the Erie Wildlife Refuge. A secondary objective is to demonstrate, through my findings in research and in the field, the most feasible method of censusing the beaver at the E.N.W.R. The results of this study appear in Table #1 as number of caches, lodges, and an estimation of the beaver population for each of the trapping areas as designated by the E.N.W.R. on Figure #1. The population estimate for the portions of the Refuge other than area B is 270 +/- 80 beaver. By my judgement the population is more likely around 300 beaver.

Technological and methodological advances have facilitated the use of genetic data to infer census population size (Nc) in natural populations, particularly where traditional mark-and-recapture is challenging. The effective number of breeders (Nb) describes how many adults effectively contribute to a cohort and is often correlated with Nc. Predicting Nc from Nb or vice-versa in species with overlapping generations has important implications for conservation by permitting (i) estimation of the more difficult to quantify variable and (ii) inferences of Nb/Nc relationships in related species lacking data. We quantitatively synthesized Nb/Nc relationships in three salmonid fishes where sufficient data has recently accumulated. Mixed-effects models were analyzed in which each variable was included as a dependent variable or predictor term (Nb from Nc and vice versa). Species-dependent Nb/Nc slope estimates were significantly positive in two of three species; variation in species slopes were ...

This three-part survey was conducted in 1980 by the Census Bureau for the Department of the Interior to examine the fishing, hunting, and wildlife-associated activities of United States civilians. Part 1, File FH3, contains information on the kinds of hunting and fishing done. Variables include the state, wildlife region, or foreign country in which the activities occurred, the number of trips taken, duration of trips, distance traveled from home, the average catch or yield, and number of hours per day hunted or fished. Additional information pertains to expenditures for hunting- and fishing-related activities and membership in national or local conservation or wildlife-related organizations. Part 2, File FH4, includes data on wildlife observation, photography, and feeding. Data furnished include type of site visited, type of area (local, state, or federal), kinds of wildlife observed, and expenses for food, lodging, transportation, and fees. Additional information is available on kinds of wildlife present, types and amounts of feed provided, and equipment costs. Part 3, File FH2, supplies information on respondents who participated in fishing, hunting, or nonconsumptive wildlife-associated activities (i.e., wildlife observation, photography, and feeding).

The Eastern Canada (ECA) Flocks data set consists of manually annotated Images from the Common Eider (COEI, Somateria mollissima) Winter Survey and the Greater Snow Geese (GSGO, Anser caerulescens atlanticus) Spring Survey. The images were taken in Eastern Canada using fixed-wing aircraft and manually annotated with ImageJ's Cell counter plugins. We selected and annotated the ECA Flocks images in order to test the precision of the CountEm flock size estimation method. ECA Flocks includes 179 COEI and 99 GSGO single flock images. We cut each image manually to a rectangle that excluded large parts of the image with no birds. Both versions (original and cut) of each image are available in the data set. We manually annotated 637,555 (124,309 COEI and 514,235 GSGO) bird positions in the cut images from both surveys. Each bird has an associated "Type" which refers to species and/or sex. Sex identification was only possible for adult common eiders since females and immature males are brown birds whereas adult males have mainly white plumage. 64,484 male and 58,029 females were identified in the COEI images, as well as 1796 birds of other species. 504,891 Snow Geese and 9344 birds of other species were labeled in the GSGO images. A .csv file including all annotated bird positions and types is available for each image. The COEI and GSGO photos of the ECA Flocks data set were taken in the years 2006 and 2018 and 2016-2018 respectively. We selected these photos in order to include images with different quality and resolution. COEI and GSGO flock sizes range from 6 to 4,154 and from 43 to 36, 241 respectively. There is high variability in light conditions, backgrounds, number and spatial arrangement of birds across the images. The data set is therefore potentially useful to test the precision of methods for analyzing imagery to estimate the abundance of animals by directly detecting, identifying and counting individuals.

description: Adaptation of Kirtland's Warbler Recovery Team census protocol as applied to Seney National Wildlife Refuge and Kirtland's Warbler Wildlife Management Area; abstract: Adaptation of Kirtland's Warbler Recovery Team census protocol as applied to Seney National Wildlife Refuge and Kirtland's Warbler Wildlife Management Area

This dataset provides summary statistics of multiple sage-grouse microhabitat characteristics of the Great Basin. These data support the following publication: Coates, P.S., Brussee, B.E., Ricca, M.A., Dudko, J.E., Prochazka, B.G., Espinosa, S.P., Casazza, M.L., and Delehanty, D.J., 2017, Greater sage-grouse (Centrocercus urophasianus) nesting and brood-rearing microhabitat in Nevada and California—Spatial variation in selection and survival patterns: U.S. Geological Survey Open-File Report 2017-1087, 79 p., https://doi.org/10.3133/ofr20171087.

This file contains census data on salt marsh birds using 100 m radius counting circles for the Parker River National Wildlife Refuge from 2001-2009 using 100 m radius counting circles

This narrative report for Sabine NWR outlines Refuge accomplishments from August to October 1940. Topics include equipment, Civilian Conservation Corps (CCC) organization, developments, waterfowl, grazing, fur harvesting, law enforcement, weather, and public relations. Photographs are attached.

We developed a model for analyzing multi-year demographic data for long-lived animals and used data from a population of Agassiz’s desert tortoise (Gopherus agassizii) at the Desert Tortoise Research Natural Area in the western Mojave Desert of California, USA, as a case study. The study area was 7.77 square kilometers and included two locations: inside and outside the fenced boundary. The wildlife-permeable, protective fence was designed to prevent entry from vehicle users and sheep grazing. We collected mark-recapture data from 1,123 tortoises during 7 annual surveys consisting of two censuses each over a 34-year period. We used a Bayesian modeling framework to develop a multistate Jolly-Seber model because of its ability to handle unobserved (latent) states and modified this model to incorporate the additional data from non-survey years. For this model we incorporated 3 size-age states (juvenile, immature, adult), sex (female, male), two location states (inside and outside the fenced boundary) and 3 survival states (not-yet-entered, entered/alive, and dead/removed). We calculated population densities and estimated probabilities of growth of the tortoises from one size-age state to a larger size-age state, survival after 1 year and 5 years, and detection. Our results show a declining population with low estimates for survival after 1 year and 5 years. The probability for tortoises to move from outside to inside the boundary fence was greater than for tortoises to move from inside the fence to outside. The probability for detecting tortoises differed by size-age state and was lowest for the smallest tortoises and highest for the adult tortoises. The framework for the model can be used to analyze other animal populations where vital rates are expected to vary depending on multiple individual states. The model was incorporated into the manuscript that included several other databases for publication in Wildlife Monographs in 2020 by Berry et al.

The National Survey of Fishing, Hunting, and Wildlife-Associated Recreation (FHWAR) is a series conducted by the Census Bureau for the United States Department of the Interior Fish and Wildlife Service. This collection contains information regarding fishing, hunting, and other wildlife-associated activities for 1991. The survey is conducted every 5 years and includes 3 waves. Wave 1 is household-based and consists of a screener with the possibility of detailed interviews asking about a person's hunting, fishing or wildlife-watching activities and the likelihood that they will hunt, fish or watch wildlife. Wave 2 and Wave 3 are person-based, detailed interviews in which respondents were selected for the sample based on data collected from the screener in the first wave. The Sportsmen and Wildlife-Watching surveys for Wave 2 and Wave 3 gathered specific information about respondents' recreational participation including species hunted, fished, and watched; the state in which these activities occurred; number of trips taken; days of participation; and expenditures for food, lodging, transportation, and equipment. The questions asked throughout the 3 waves have been organized by topic into 3 datasets. The three datasets, (1) Screener, (2) Hunting and Fishing, and (3) Nonconsumptive, may contain responses from people surveyed during multiple waves. Demographic variables include sex, age, race, marital status and parental relations, education level, household income, state of residence, and type of residential area (e.g., urban or rural).

The dataset includes information on population size, demographics, and hunter harvest statistics for managed wildlife species over time.