Academic literature on the topic 'Pest monitoring'
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Journal articles on the topic "Pest monitoring"
Jeger, M. J., J. M. Waller, A. Johanson, and S. R. Gowen. "Monitoring in banana pest management." Crop Protection 15, no. 4 (June 1996): 391–97. http://dx.doi.org/10.1016/0261-2194(96)00011-7.
Full textFEDOR, PETER, JAROMÍR VAŇHARA, JOSEF HAVEL, IGOR MALENOVSKÝ, and IAN SPELLERBERG. "Artificial intelligence in pest insect monitoring." Systematic Entomology 34, no. 2 (January 31, 2009): 398–400. http://dx.doi.org/10.1111/j.1365-3113.2008.00461.x.
Full textCardoso, Bruno, Catarina Silva, Joana Costa, and Bernardete Ribeiro. "Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network." Applied Sciences 12, no. 18 (September 19, 2022): 9397. http://dx.doi.org/10.3390/app12189397.
Full textPachkin, A. A., O. Yu Kremneva, R. Yu Danilov, and A. V. Ponomarev. "Vegetable Pest Monitoring Using Insect Trap Lights." Machinery and Equipment for Rural Area, no. 10 (November 8, 2021): 28–32. http://dx.doi.org/10.33267/2072-9642-2021-10-28-32.
Full textBinns, Michael R., Jan P. Nyrop, and Wopke Van Der Werf. "Monitoring Pest Abundance by Cascading Density Classification." American Entomologist 42, no. 2 (1996): 113–21. http://dx.doi.org/10.1093/ae/42.2.113.
Full textBerlinger, M. J., Beke Lok-Van Dijk, R. Dahan, S. Lebiush-Mordechai, and R. A. J. Taylor. "Indicator Plants for Monitoring Pest Population Growth." Annals of the Entomological Society of America 89, no. 5 (September 1, 1996): 611–22. http://dx.doi.org/10.1093/aesa/89.5.611.
Full textMei, L., Z. G. Guan, H. J. Zhou, J. Lv, Z. R. Zhu, J. A. Cheng, F. J. Chen, C. Löfstedt, S. Svanberg, and G. Somesfalean. "Agricultural pest monitoring using fluorescence lidar techniques." Applied Physics B 106, no. 3 (November 12, 2011): 733–40. http://dx.doi.org/10.1007/s00340-011-4785-8.
Full textWang, Mei, Xin Ju Li, Yan Yan Lu, and Shu Li Guo. "Tobacco Pest Monitoring Feasibility Analysis Based on RS." Advanced Materials Research 217-218 (March 2011): 1516–19. http://dx.doi.org/10.4028/www.scientific.net/amr.217-218.1516.
Full textKo, Alexander E. "Urban Entomology Highlights From 2020—Monitoring and Control of Urban Pests." Journal of Medical Entomology 58, no. 5 (August 3, 2021): 2012–15. http://dx.doi.org/10.1093/jme/tjab119.
Full textRano, Saifatul Hossain, Mansura Afroz, and Md Mamunur Rahman. "APPLICATION OF GIS ON MONITORING AGRICULTURAL INSECT PESTS: A REVIEW." Reviews In Food and Agriculture 3, no. 1 (January 5, 2022): 19–23. http://dx.doi.org/10.26480/rfna.01.2022.19.23.
Full textDissertations / Theses on the topic "Pest monitoring"
Obeng-Ofori, Daniels. "Monitoring of stored product beetles." Thesis, University of Cambridge, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.317834.
Full textJeffcote, Richard Grant. "An Investigation into the Monitoring of Pest Control Devices using Wireless Communication." Thesis, University of Canterbury. Engineering Management, 2013. http://hdl.handle.net/10092/7457.
Full textPalumbo, John, David Kerns, Clay Mullis, and Francisco Reyes. "Implementation of a Pest Monitoring Network for Vegetable Growers in Yuma County." College of Agriculture, University of Arizona (Tucson, AZ), 1999. http://hdl.handle.net/10150/219985.
Full textMalek, Robert Nehme. "Novel Monitoring and Biological Control of Invasive Insect Pests." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/257781.
Full textMalek, Robert Nehme. "Novel Monitoring and Biological Control of Invasive Insect Pests." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/257781.
Full textBöckmann, Elias [Verfasser]. "Combined monitoring of pest and beneficial insects with sticky traps, as basis for decision making in greenhouse pest control : a proof of concept study / Elias Böckmann." Hannover : Technische Informationsbibliothek und Universitätsbibliothek Hannover (TIB), 2015. http://d-nb.info/1076059481/34.
Full textFernandes, Winnie Cezario. "Thrips on roses: identification, monitoring and chemical control." Universidade Federal do CearÃ, 2015. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=14048.
Full textThe growth in the production of ornamental plants is increasingly significant in Brazil and in the Northeast region, but the occurrence of pests is shown as a limiting factor. To minimize losses, adequate control measures should be employed. Accordingly, the correct identification of pests, population monitoring and studies on managements should be performed. The objective of this study was to identify thrips species in rose, characterize and quantify the damage loss caused by arthropod pests in the production of roses in Serra da Ibiapaba; to assess the fluctuation of thrips species in ten cultivars of rose, at different stages of flower development and monitoring systems, and; evaluate the efficiency of pesticides on Frankliniella spp. The experiments were conducted at the Company âReijers ProduÃÃo de Rosasâ, SÃo Benedito, Cearà State, âLagoa Jussaraâ in planting roses in greenhouses. Three species of thrips have been identified: Frankliniella schultzei (Trybom, 1910), F. occidentalis (Pergande, 1895) and Caliothrips phaseoli (Pergande, 1825) (Thysanoptera: Thripidae) with the largest recorded infestations for F. occidentalis and F. schultzei in phenological phases of roses, especially in flowering. The injury caused by thrips in floral cut roses button affected the quality invalidating them for marketing. There was no difference between the sampling periods (morning and afternoon) and sampling (tray beat and direct view of the floral button) to the ten cultivars of roses, so the choice of the time and method must be reconciled with practicality and cost. The insecticides demonstrated ability to cause mortality of thrips in extreme conditions, within completely enclosed structures (flower buds).
O crescimento na produÃÃo de plantas ornamentais à cada vez mais significativo no Brasil e na regiÃo Nordeste do paÃs, porÃm a ocorrÃncia de pragas mostra-se como fator limitante. Para minimizar as perdas, medidas adequadas de controle devem ser empregadas. Nesse sentido, a identificaÃÃo correta das pragas, seu monitoramento populacional e estudos sobre manejos devem ser realizados. O objetivo deste estudo foi identificar espÃcies de tripes em roseira, caracterizar danos e quantificar as perdas ocasionadas pelo artrÃpode-praga na produÃÃo de rosas na Serra da Ibiapaba; avaliar a flutuaÃÃo populacional das espÃcies de tripes em dez cultivares de roseira, em diferentes fases do desenvolvimento floral e sistemas de monitoramento, e; avaliar a eficiÃncia de produtos fitossanitÃrios sobre Frankliniella spp. Os experimentos foram conduzidos na Empresa Reijers ProduÃÃo de Rosas, Unidade SÃo Benedito/CE, Fazenda Lagoa Jussara, em plantio de roseiras sob cultivo protegido. Foram identificadas trÃs espÃcies de tripes: Frankliniella schultzei (Trybom, 1910), F. occidentalis (Pergande, 1895) e Caliothrips phaseoli (Pergande, 1825) (Thysanoptera: Thripidae) sendo as maiores infestaÃÃes registradas para F. occidentalis e F. schultzei nas diferentes fases fenolÃgicas das roseiras, especialmente na floraÃÃo. As injÃrias causadas pelos tripes no botÃo floral de rosas de corte afetaram aqualidade inviabilizando-as para a comercializaÃÃo. NÃo houve diferenÃa estatÃstica entre os perÃodos de amostragem (manhà e tarde) e os mÃtodos de amostragem (batida de bandeja e visualizaÃÃo direta do botÃo floral) para as dez cultivares de roseiras, assim a escolha do horÃrio e do mÃtodo devem ser conciliadascom praticidade e custo.Os inseticidas demonstraram capacidade de causar mortalidade de tripes em condiÃÃes extremas, ou seja, dentro de estruturas completamente fechadas (botÃes florais).
McCormack, Kevin. "Enhancing the monitoring and trapping of protected crop pests by incorporating LED technology into existing traps." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/22837.
Full textSchmid, Ryan B. "Hessian fly, Mayetiola destructor (Diptera: Cecidomyiidae), smart-trap design and deployment strategies." Diss., Kansas State University, 2018. http://hdl.handle.net/2097/38763.
Full textDepartment of Entomology
Brian P. McCornack
Timely enactment of insect pest management and incursion mitigation protocols requires development of time-sensitive monitoring approaches. Numerous passive monitoring methods exist (e.g., insect traps), which offer an efficient solution to monitoring for pests across large geographic regions. However, given the number of different monitoring tools, from specific (e.g., pheromone lures) to general (e.g., sticky cards), there is a need to develop protocols for deploying methods to effectively and efficiently monitor for a multitude of potential pests. The non-random movement of the Hessian fly, Mayetiola destructor (Say) (Diptera: Cecidomyiidae), toward several visual, chemical, and tactile cues, makes it a suitable study organism to examine new sensor technologies and deployment strategies that can be tailored for monitoring specific pests. Therefore, the objective was to understand Hessian fly behavior toward new sensor technologies (i.e., light emitting diodes (LEDs) and laser displays) to develop monitoring and deployment strategies. A series of laboratory experiments and trials were conducted to understand how the Hessian fly reacts to the technologies and how environmental factors may affect the insect’s response. Hessian fly pupae distribution within commercial wheat fields was also analyzed to determine deployment of monitoring strategies. Laboratory experiments demonstrated Hessian fly attraction to green spectrum (502 and 525 nm) light (LEDs), that response increased with light intensity (16 W/m2), and that they responded in the presence of wheat odor and the Hessian fly female sex-pheromone, but, response was reduced under ambient light. These laboratory experiments can be used to build a more targeted approach for Hessian fly monitoring by utilizing the appropriate light wavelength and intensity with pheromone and wheat odor to attract both sexes, and mitigating exposure to ambient light. Together this information suggested that light could be used with natural cues to increase attraction. Therefore, a light source (green laser display) was applied to a wheat microcosm, which resulted in greater oviposition in wheat covered by the laser display. Examination of Hessian fly pupal distribution within commercial wheat fields showed that proportion of wheat within a 1 km buffer of the field affected distribution between fields. This helps to inform deployment of monitoring strategies as it identified fields with a lower proportion of wheat within a 1 km buffer to be at higher risk Hessian fly infestation, and therefore monitoring efforts should be focused on those fields. Together this work demonstrates Hessian fly behavior toward new sensor technologies, how those technologies interact with environmental cues, and how environmental composition affects pupal distribution. Collectively this information will enable cheaper, more accurate and more efficient monitoring of this destructive pest.
Moore, Alana L. "Managing populations in the face of uncertainty : adaptive management, partial observability and the dynamic value of information /." Connect to thesis, 2008. http://repository.unimelb.edu.au/10187/3676.
Full textActive adaptive management has been increasingly advocated in natural resource management and conservation biology as a methodology for resolving key uncertainties about population dynamics and responses to management. However, when comparing management policies it is traditional to weigh future rewards geometrically (at a constant discount rate) which results in far-distant rewards making a negligible contribution to the total benefit. Under such a discounting scheme active adaptive management is rarely of much benefit, especially if learning is slow. In Chapter 2, we consider two proposed alternative forms of discounting for evaluating optimal policies for long term decisions which have a social component.
We demonstrate that discount functions which weigh future rewards more heavily result in more conservative harvesting strategies, but do not necessarily encourage active learning. Furthermore, the optimal management strategy is not equivalent to employing geometric discounting at a lower rate. If alternative discount functions are made mandatory in calculating optimal management policies for environmental management, then this will affect the structure of optimal management regimes and change when and how much we are willing to invest in learning.
The second part of this thesis is concerned with how to account for partial observability when calculating optimal management policies. We consider the problem of controlling an invasive pest species when only partial observations are available at each time step. In the model considered, the monitoring data available are binomial observations of a probability which is an index of the population size. We are again concerned with estimating a probability, however, in this model the probability is changing over time.
Before including partial observability explicitly, we consider a model in which perfect observations of the population are available at each time step (Chapter 3). It is intuitive that monitoring will be beneficial only if the management decision depends on the outcome. Hence, a necessary condition for monitoring to be worthwhile is that control polices which are specified in terms of the system state, out-perform simpler time-based control policies. Consequently, in addition to providing a benchmark against which we can compare the optimal management policy in the case of partial observations, analysing the perfect observation case also provides insight into when monitoring is likely to be most valuable.
In Chapters 4 and 5 we include partial observability by modelling the control problem as a partially observable Markov decision process (POMDP). We outline several tests which stem from a property of conservation of expected utility under monitoring, which aid in validating the model. We discuss the optimal management policy prescribed by the POMDP for a range of model scenarios, and use simulation to compare the POMDP management policy to several alternative policies, including controlling with perfect observations and no observations.
In Chapter 6 we propose an alternative model, developed in the spirit of a POMDP, that does not strictly satisfy the definition of a POMDP. We find that although the second model has some conceptually appealing attributes, it makes an undesirable implicit assumption about the underlying population dynamics.
Books on the topic "Pest monitoring"
United States. Animal and Plant Health Inspection Service. The Cooperative Agricultural Pest Survey: Detecting plant pests and weeds nationwide. [United States]: USDA APHIS, 2005.
Find full textUnited States. Forest Health Protection, ed. Russia & United States exotic pest monitoring program. [Washington, D.C.]: Forest Health Protection, USDA Forest Service, State and Private Forestry, 2003.
Find full textUnited States. Forest Health Protection., ed. Russia & United States exotic pest monitoring program. [Washington, D.C.]: Forest Health Protection, USDA Forest Service, State and Private Forestry, 2003.
Find full textUnited States. Forest Health Protection., ed. Russia & United States exotic pest monitoring program. [Washington, D.C.]: Forest Health Protection, USDA Forest Service, State and Private Forestry, 2003.
Find full textUnited States. Forest Health Protection, ed. Russia & United States exotic pest monitoring program. [Washington, D.C.]: Forest Health Protection, USDA Forest Service, State and Private Forestry, 2003.
Find full textUnited States. Forest Health Protection., ed. Russia & United States exotic pest monitoring program. [Washington, D.C.]: Forest Health Protection, USDA Forest Service, State and Private Forestry, 2003.
Find full textUnited States. Forest Health Protection, ed. Russia & United States exotic pest monitoring program. [Washington, D.C.]: Forest Health Protection, USDA Forest Service, State and Private Forestry, 2003.
Find full textUnited States. Animal and Plant Health Inspection Service. Plant Protection and Quarantine Programs., ed. Exotic pest detection manual. [Beltsville, Md.?]: APHIS Plant Protection and Quarantine, 1986.
Find full textUnited States. Animal and Plant Health Inspection Service. Pest survey notice. [Riverdale, MD?]: USDA, APHIS, 2009.
Find full textMontana. Dept. of Agriculture. Cooperative agricultural pest survey 2006 report. [Helena, Mont.]: Commodity Services Bureau, Montana Dept. of Agriculture, 2007.
Find full textBook chapters on the topic "Pest monitoring"
Howse, P. E., I. D. R. Stevens, and O. T. Jones. "Pest monitoring." In Insect Pheromones and their Use in Pest Management, 263–79. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-5344-7_9.
Full textDent, David, and Richard H. Binks. "Sampling, monitoring and forecasting." In Insect pest management, 12–38. Wallingford: CABI, 2020. http://dx.doi.org/10.1079/9781789241051.0012.
Full textGenizi, A., H. Frankel, J. Palti, R. Ausher, C. H. Blazquez, R. Hochberg, G. Edelbaum, Y. Sachs, and A. Dinoor. "Pest and Disease Monitoring and Management." In Advisory Work in Crop Pest and Disease Management, 57–131. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-70992-0_8.
Full textDhang, Partho, Philip Koehler, Roberto Pereira, and Daniel D. Dye, II. "Stored product pests." In Key questions in urban pest management: a study and revision guide, 100–107. Wallingford: CABI, 2022. http://dx.doi.org/10.1079/9781800620179.0013.
Full textLapchin, Laurent, and Dan Shtienberg. "Sampling and Monitoring Pests and Diseases." In Integrated Pest and Disease Management in Greenhouse Crops, 82–96. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/0-306-47585-5_7.
Full textMarchand, Geneviève, Philippe C. Nicot, Ramon Albajes, and Odile Carisse. "Epidemiology and Population Dynamics: Modelisation, Monitoring and Management." In Integrated Pest and Disease Management in Greenhouse Crops, 195–230. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-22304-5_7.
Full textRajashekara, S., S. S. Gayathri Devi, and M. G. Venkatesha. "Biotechnological Tools for Monitoring, Assessment, and Insect Pest Management in Agricultural Ecosystems." In Advances in Integrated Pest Management Technology, 315–90. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94949-5_14.
Full textEnnouri, Karim, Mohamed Ali Triki, and Abdelaziz Kallel. "Applications of Remote Sensing in Pest Monitoring and Crop Management." In Bioeconomy for Sustainable Development, 65–77. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9431-7_5.
Full textMandal, S. N., and Parimal Sinha. "Pest and Disease Monitoring and Surveillance for Ensuring Food Security." In Innovations in Agriculture for a Self-Reliant India, 293–304. London: CRC Press, 2021. http://dx.doi.org/10.1201/9781003245384-19.
Full textDwivedi, Mahaveer, Malik Hashmat Shadab, and V. R. Santosh. "Insect Pest Detection, Migration and Monitoring Using Radar and LiDAR Systems." In Innovative Pest Management Approaches for the 21st Century, 61–76. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0794-6_4.
Full textConference papers on the topic "Pest monitoring"
Xiaobo, Guo, and Zhang Dexian. "Big Data Mining in Granary Pest Monitoring." In 2015 Sixth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA). IEEE, 2015. http://dx.doi.org/10.1109/isdea.2015.56.
Full textBacal, Svetlana. "New contributions to the knowledge of honey bee (Apis Mellifera) pests." In Xth International Conference of Zoologists. Institute of Zoology, Republic of Moldova, 2021. http://dx.doi.org/10.53937/icz10.2021.24.
Full textDeepika, P., and S. Kaliraj. "A Survey on Pest and Disease Monitoring of Crops." In 2021 3rd International Conference on Signal Processing and Communication (ICPSC). IEEE, 2021. http://dx.doi.org/10.1109/icspc51351.2021.9451787.
Full textChu, Hongyu, Depei Zhang, Yanhua Shao, Zhiyuan Chang, Yuying Guo, and Ningning Zhang. "Using HOG Descriptors and UAV for Crop Pest Monitoring." In 2018 Chinese Automation Congress (CAC). IEEE, 2018. http://dx.doi.org/10.1109/cac.2018.8623234.
Full textGorban, Victor, Vladimir Todiras, Vasile Voineac, and Denis Savranschii. "Combaterea insectelor dăunătoare culturilor de seră prin atragerea şi exterminarea acestora cu ajutorul capcanei cu lumină." In VIIth International Scientific Conference “Genetics, Physiology and Plant Breeding”. Institute of Genetics, Physiology and Plant Protection, Republic of Moldova, 2021. http://dx.doi.org/10.53040/gppb7.2021.84.
Full textPulsifer, Drew P., Akhlesh Lakhtakia, Jayant Kumar, Thomas C. Baker, and Raúl J. Martín-Palma. "Toward pest control via mass production of realistic decoys of insects." In SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, edited by Akhlesh Lakhtakia. SPIE, 2012. http://dx.doi.org/10.1117/12.915924.
Full textMaric, Milan, Irena Orovic, and Srdjan Stankovic. "Compressive sensing based image processing in trapview pest monitoring system." In 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). IEEE, 2016. http://dx.doi.org/10.1109/mipro.2016.7522197.
Full textTimlin, Jerilyn, Thomas Reichardt, Danae Maes, Cameron Kunstadt, Christopher Katinas, Tyler Hipple, Todd Lane, et al. "Real-time monitoring of algal pond productivity and pest presence." In Proposed for presentation at the Algal Biomass, Biofuels & Bioproducts held June 14-16, 2021. US DOE, 2021. http://dx.doi.org/10.2172/1873286.
Full textBuklagin, D. S. "OILSEED MOISTURE MONITORING DEVICES." In INNOVATIVE TECHNOLOGIES IN SCIENCE AND EDUCATION. DSTU-Print, 2020. http://dx.doi.org/10.23947/itno.2020.481-486.
Full textFrolov, A. N., I. V. Grushevaya, A. G. Kononchuk, T. A. Ryabchinskaya, V. B. Kolesnikov, and Tóth Miklós. "Evaluation of the effectiveness of the European corn borer monitoring using bisexual lure based on tests results in the Kuban and the Central Black Earth Zone of Russia." In CURRENT STATE, PROBLEMS AND PROSPECTS OF THE DEVELOPMENT OF AGRARIAN SCIENCE. Federal State Budget Scientific Institution “Research Institute of Agriculture of Crimea”, 2020. http://dx.doi.org/10.33952/2542-0720-2020-5-9-10-51.
Full textReports on the topic "Pest monitoring"
Fatzinger, Carl W., H. David Muse, Thomas Miller, and Helen T. Bhattacharyya. Estimating Cone and Seed Production and Monitoring Pest Damage in Southern Pine Seed Orchards. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station, 1988. http://dx.doi.org/10.2737/se-rp-271.
Full textYoung, Craig. Problematic plant monitoring in Hopewell Culture National Historical Park: 2008–2019. Edited by Tani Hubbard. National Park Service, July 2021. http://dx.doi.org/10.36967/nrr-2286658.
Full textYoung, Craig. Problematic plant monitoring in Lincoln Boyhood National Memorial: 2006–2019. Edited by Tani Hubbard. National Park Service, July 2021. http://dx.doi.org/10.36967/nrr-2286660.
Full textYoung, Craig. Problematic plant monitoring in George Washington Carver National Monument: 2006–2020. Edited by Tani Hubbard. National Park Service, June 2022. http://dx.doi.org/10.36967/nrr-2293655.
Full textYoung, Craig. Problematic plant monitoring in Arkansas Post National Memorial: 2006–2019. Edited by Tani Hubbard. National Park Service, July 2021. http://dx.doi.org/10.36967/nrr-2286657.
Full textYoung, Craig. Problematic plant monitoring in Wilson's Creek National Battlefield: 2006–2020. Edited by Tani Hubbard. National Park Service, June 2022. http://dx.doi.org/10.36967/nrr-2293658.
Full textYoung, Craig. Problematic plant monitoring in Pea Ridge National Military Park: 2006–2021. Edited by Tani Hubbard. National Park Service, June 2022. http://dx.doi.org/10.36967/nrr-2293656.
Full textVelázquez López, Noé. Working Paper PUEAA No. 7. Development of a farm robot (Voltan). Universidad Nacional Autónoma de México, Programa Universitario de Estudios sobre Asia y África, 2022. http://dx.doi.org/10.22201/pueaa.005r.2022.
Full textBoyle, Maxwell, and Elizabeth Rico. Terrestrial vegetation monitoring at Fort Pulaski National Monument: 2019 data summary. National Park Service, December 2021. http://dx.doi.org/10.36967/nrds-2288716.
Full textBoyle, M., and Elizabeth Rico. Terrestrial vegetation monitoring at Fort Matanzas National Monument: 2019 data summary. National Park Service, May 2022. http://dx.doi.org/10.36967/nrds-2293409.
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