Remote Monitoring Systems for Conservation of the Amazon Rainforest: A Systematic Review
Sistemas de monitoreo remoto para la conservación del bosque tropical húmedo amazónico: una revisión sistemática
Sistemas de monitorização remota para a conservação da floresta amazónica: Uma revisão sistemática
DOI:
https://doi.org/10.15446/ma.v16n1.114004Palabras clave:
Tropical rainforest, sensor, biodiversity protection (en)Bosque tropical, sensor, protección de la biodiversidad (es)
sensor, proteção da biodiversidade, Floresta tropical (pt)
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The Amazon rainforest plays a crucial role in stabilizing the global climate and is vital for preserving biodiversity, wildlife, and indigenous cultures. Despite efforts to protect the Amazon rainforest, deforestation, wildlife trafficking, mining, oil exploitation and other extractive industries continue to threaten the region. Remote Monitoring Systems (RMS) are necessary to effectively detect and analyze these threats. This systematic review of technological initiatives to protect natural resources demonstrates the importance of technology in conservation and the need for further research and development. This article identifies technologies used worldwide for conservation efforts and highlights the challenges in developing an RMS for the Amazon region, such as harsh environmental conditions and limited infrastructure. The timely detection of threats could help authorities take corrective actions and prevent further environmental effects on the Amazon rainforest.
El bosque tropical húmedo amazónico desempeña un papel crucial en la estabilización del clima mundial y es vital para preservar la biodiversidad, la vida salvaje y las culturas indígenas. A pesar de los esfuerzos por proteger la Amazonia; la deforestación, el tráfico de especies silvestres, la minería, la explotación petrolera y otras industrias extractivas siguen amenazando la región. Los sistemas de monitoreo remoto (RMS) son necesarios para detectar y vigilar eficazmente estas amenazas. Esta revisión sistemática de las iniciativas tecnológicas para proteger los recursos naturales demuestra la importancia de la tecnología en la conservación y la necesidad de seguir investigando y desarrollando en dichas tecnologías. En este artículo se identifican los tipos de tecnologías utilizadas en los distintos continentes y se destacan los retos que plantea el desarrollo de un RMS para la Amazonia, entre los que se incluyen las duras condiciones ambientales y las limitadas infraestructuras. La detección a tiempo de las amenazas podría ayudar a las autoridades a tomar medidas correctivas y evitar una mayor afectación medioambiental del bosque amazónico.
A floresta tropical amazónica desempenha um papel crucial na estabilização do clima global e é vital para a preservação da biodiversidade, da vida selvagem e das culturas indígenas. Apesar dos esforços para proteger a Amazónia, a desflorestação, o tráfico de animais selvagens, a mineração, a exploração petrolífera e outras indústrias extrativas continuam a ameaçar a região. São necessários sistemas de monitorização remota (RMS) para detectar e monitorizar eficazmente estas ameaças. Esta revisão sistemática das iniciativas tecnológicas para proteger os recursos naturais demonstra a importância da tecnologia na conservação e a necessidade de mais investigação e desenvolvimento. Este artigo identifica os tipos de tecnologias utilizadas em diferentes continentes e destaca os desafios do desenvolvimento de um RMS para a Amazónia, incluindo condições ambientais adversas e infraestruturas limitadas. A deteção precoce de ameaças pode ajudar as autoridades a tomar medidas corretivas e a evitar mais danos ambientais na floresta amazónica.
Referencias
Ahmad, S. F., and Singh, D. K. (2022). Automatic detection of tree cutting in forests using acoustic properties. Journal of King Saud University-Computer and Information Sciences, 34(3), 757–763. https://doi.org/10.1016/j.jksuci.2019.01.016
Al Nuaimi, M., Sallabi, F., and Shuaib, K. (2011). A survey of Wireless Multimedia Sensor Networks challenges and solutions. 2011 International Conference on Innovations in Information Technology, 191–196. https://doi.org/10.1109/INNOVATIONS.2011.5893815
Almalkawi, I. T., Guerrero Zapata, M., Al-Karaki, J. N., and Morillo-Pozo, J. (2010). Wireless Multimedia Sensor Networks: Current Trends and Future Directions. Sensors, 10(7), 6662–6717. https://doi.org/10.3390/s100706662
Amin, R., Davey, K., and Wacher, T. (2017). ZSL camera trap data management and analysis package - Additional Software Installation Instructions. https://www.zsl.org/conservation/how-we-work/conservation-technology/zsl-camera-trap-data-management-and-analysis
Ammari, H. M. (2019). Mission-Oriented Sensor Networks and Systems: Art and Science: Volume 1: Foundations (1st ed. 20). Springer International Publishing. https://doi.org/10.1007/978-3-319-91146-5
Arevalo, J. D. C., Calica, P. C., Celestino, B. A. D. R., Dimapunong, K. A., Lopez, D. J. D., and Austria, Y. D. (2020). Towards Real-Time Illegal Logging Monitoring: Gas-Powered Chainsaw Logging Detection System using K-Nearest Neighbors. 2020 IEEE 10th International Conference on System Engineering and Technology (ICSET), 156–160. https://doi.org/10.1109/ICSET51301.2020.9265375
Arunkumar, M., and Raj, B. P. (2023). Surveillance of Forest Areas and Detection of Unusual Exposures using Deep Learning. Proceedings - 7th International Conference on Computing Methodologies and Communication, ICCMC 2023, 145–150. https://doi.org/10.1109/ICCMC56507.2023.10083641
Bandaranayake, H., Mahamohottala, D., Wijekoon, W., Sandakelum, K., Gamage, N., and Rankothge, W. (2022). GreenSoal: Illegal Tree Logging Detection System Using IOT. 2022 International Conference on ICT for Smart Society (ICISS), 1–6. https://doi.org/10.1109/ICISS55894.2022.9915139
Bragilevsky, L., and Bajić, I. V. (2017). Deep learning for Amazon satellite image analysis. 2017 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), 1–5. https://doi.org/10.1109/PACRIM.2017.8121895
Brittain, T. C. D. P. (2018). Helping zoological society of London to create new anti-poaching technology. https://www.cambridge-design.com/news-and-articles/blog/cdp-teams-up-with-the-zoological-society-of-london
Camacho, L., Baquerizo, R., Palomino, J., and Zarzosa, M. (2017). Deployment of a Set of Camera Trap Networks for Wildlife Inventory in Western Amazon Rainforest. IEEE Sensors Journal, 17(23), 8000–8007. https://doi.org/10.1109/JSEN.2017.2760254
Ceriotti, M., Chini, M., Murphy, A. L., Picco, G. Pietro, Cagnacci, F., and Tolhurst, B. (2010). Motes in the Jungle: Lessons Learned from a Short-Term WSN Deployment in the Ecuador Cloud Forest (pp. 25–36). https://doi.org/10.1007/978-3-642-17520-6_3
Chen, Y. Y., and Liaw, J. J. (2017). A novel real-Time monitoring system for illegal logging events based on vibration and audio. In Proceedings - 2017 IEEE 8th International Conference on Awareness Science and Technology, iCAST 2017 (Vols. 2018-Janua, Issue iCAST). Chaoyang University of Technology Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICAwST.2017.8256503
Chhabra, P., Jain, T., Kalaskar, H., and Bhamare, V. (2021). IOT based anti-poaching sensor system for trees in forest. International Journal of Scientific Research and Engineering Trends, 7(Issue 4).
Cisco Systems. (2019). Connected Conservation. https://www.cisco.com/c/en/us/about/csr/impact/environmental-sustainability/%0Aconnected-conservation.html%0A
Collen, W. (2016). The Amazon and Agenda 2030. UNDP. United Nations Development Programme, 40.
Colonna, J. G., Gatto, B., Santos, E. M. Dos, and Nakamura, E. F. (2016). A Framework for Chainsaw Detection Using One-Class Kernel and Wireless Acoustic Sensor Networks into the Amazon Rainforest. 34–36. https://doi.org/10.1109/mdm.2016.86
Coman, C.-M., Toma, B. C., Constantin, M.-A., and Florescu, A. (2022). Ground Level Lidar as a Contributing Indicator in an Environmental Protection Application. SSRN Electronic Journal, 12. https://doi.org/10.2139/ssrn.4096563
Comstock, J. M., Ackerman, T. P., and Mace, G. G. (2002). Ground-based lidar and radar remote sensing of tropical cirrus clouds at Nauru Island: Cloud statistics and radiative impacts. Journal of Geophysical Research Atmospheres, 107(23), 1–14. https://doi.org/10.1029/2002JD002203
Connected Conservation Foundation. (2022). Connected Conservation Foundation - Projects. https://connectedconservation.foundation/projects/
Costanza, R., de Groot, R., Sutton, P., van der Ploeg, S., Anderson, S. J., Kubiszewski, I., Farber, S., and Turner, R. K. (2014). Changes in the global value of ecosystem services. Global Environmental Change, 26, 152–158. https://doi.org/10.1016/j.gloenvcha.2014.04.002
Dampage, U., Bandaranayake, L., Wanasinghe, R., Kottahachchi, K., and Jayasanka, B. (2022). Forest fire detection system using wireless sensor networks and machine learning. Scientific Reports, 12(1), 46. https://doi.org/10.1038/s41598-021-03882-9
de Andrade, R. B., Mota, G. L., and da Costa, G. A. (2022). Deforestation Detection in the Amazon Using DeepLabv3+ Semantic Segmentation Model Variants. In Remote Sensing (Vol. 14, Issue 19). https://doi.org/10.3390/rs14194694
Dimention Data. (2019). Connected Conservation. Connected Conservation
Diniz, C. G., Souza, A. A. D. A., Santos, D. C., Dias, M. C., Luz, N. C. Da, Moraes, D. R. V. De, Maia, J. S. A., Gomes, A. R., Narvaes, I. D. S., Valeriano, D. M., Maurano, L. E. P., and Adami, M. (2015). DETER-B: The New Amazon Near Real-Time Deforestation Detection System. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(7), 3619–3628. https://doi.org/10.1109/JSTARS.2015.2437075
Doblas, J., Reis, M. S., Belluzzo, A. P., Quadros, C. B., Moraes, D. R. V, Almeida, C. A., Maurano, L. E. P., Carvalho, A. F. A., Sant’Anna, S. J. S., and Shimabukuro, Y. E. (2022). DETER-R: An Operational Near-Real Time Tropical Forest Disturbance Warning System Based on Sentinel-1 Time Series Analysis. Remote Sensing, 14(15). https://doi.org/10.3390/rs14153658
Dorfling, J., Siewert, S. B., Bruder, S., Aranzazu-Suescun, C., Rocha, K., Landon, P. D., Bondar, G., Pederson, T., Le, C., Mangar, R., Rawther, C., and Trahms, B. (2021). Satellite, Aerial, and Ground Sensor Fusion Experiment for Management of Elephants and Rhinos and Poaching Prevention. In AIAA SCITECH 2022 Forum. American Institute of Aeronautics and Astronautics. https://doi.org/doi:10.2514/6.2022-1270
Dubayah, R. O., and Drake, J. B. (2000). Lidar Remote Sensing for Forestry. Journal of Forestry, 98(6), 44–46. https://doi.org/https://doi.org/10.1093/jof/98.6.44
Erol-Kantarci, M., and Mouftah, H. T. (2011). Wireless multimedia sensor and actor networks for the next generation power grid. Ad Hoc Networks, 9(4), 542–551. https://doi.org/10.1016/j.adhoc.2010.08.005
Figueira, N. M., Belmonte, G. N., and de Freitas, E. P. (2020). C4ISR Systems applied to Amazonian Constraints. 2020 International Conference on Unmanned Aircraft Systems (ICUAS), 568–572. https://doi.org/10.1109/ICUAS48674.2020.9213958
Figueira, N. M., Freire, I. L., Trindade, O., and Simoes, E. (2015). Mission-oriented sensor arrays and UAVs-A case study on environmental monitoring. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(1), 305. https://doi.org/10.5194/isprsarchives-XL-1-W4-305-2015
Foundation Peace Parks. (2018). A safe space for rhinos. https://www.peaceparks.org/a-safe-space-for-rhinos/
Franke, J., Navratil, P., Keuck, V., Peterson, K., and Siegert, F. (2012). Monitoring fire and selective logging activities in tropical peat swamp forests. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(6), 1811–1820. https://doi.org/10.1109/JSTARS.2012.22
Gaiţă, A., Nicolae, G., Rădoi, A., and Burileanu, C. (2018). Chainsaw sound detection based on spectral Haar coeffluents. 2018 International Symposium ELMAR, 139–142. https://doi.org/10.23919/ELMAR.2018.8534594
Ghulam, A. (2014). Monitoring tropical forest degradation in Betampona Nature Reserve, Madagascar using multisource remote sensing data fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(12), 4960–4971. https://doi.org/10.1109/JSTARS.2014.2319314
Giannopoulos, N., Goumopoulos, C., and Kameas, A. (2009). Design Guidelines for Building a Wireless Sensor Network for Environmental Monitoring. 2009 13th Panhellenic Conference on Informatics, 148–152. https://doi.org/10.1109/PCI.2009.17
Gobernación de Amazonas. (2020). Plan de Desarrollo Departamental. Amazonas Progresando con Equidad 2020-2023. Gobernación de Amazonas, 1–470.
Hirschmugl, M., Deutscher, J., Sobe, C., Bouvet, A., Mermoz, S., and Schardt, M. (2020). Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping. Remote Sensing, 12(4). https://doi.org/10.3390/rs12040727
Iniewski, K. (2017). Optical, Acoustic, Magnetic, and Mechanical Sensor Technologies. CRC Press. https://doi.org/10.1201/b11487
Ishitha, S., Nagaraju, S., Mohan, H. A., Harshitha, M., Gowda, G. R., and Jeevan, N. (2021). IoT based Anti-poaching and Fire Alarm System for Forest. 2021 IEEE Mysore Sub Section International Conference, MysuruCon 2021, 711–715. https://doi.org/10.1109/MysuruCon52639.2021.9641539
Jaramillo, A. (2013). Diseño de una propuesta para el "monitoreo de la dinámica en áreas protegidas (Parque Nacional Yasuní) a través de un sistema de sensores remotos. 41.
Jubjainai, P., Pathomwong, S., Siripujaka, P., Chiengmai, N., Chaiboot, A., and Wardkein, P. (2020). Chainsaw location finding based on travelling of sound wave in air and ground. IOP Conference Series: Earth and Environmental Science, 467(1), 12065. https://doi.org/10.1088/1755-1315/467/1/012065
Kalhara, P. G., Jayasinghearachchi, V. D., Dias, A., Ratnayake, V. C., Jayawardena, C., and Kuruwitaarachchi, N. (2017). TreeSpirit: Illegal logging detection and alerting system using audio identification over an IoT network. 2017 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), 1–7. https://doi.org/10.1109/SKIMA.2017.8294127
Kameswararao, E. V, Jaya Shankar, M., Sai Lokesh, T. V, and Terence, E. (2023). IoT Based Anti Poaching of Trees and Protection of Forest. In J. Hemanth, D. Pelusi, and J. I.-Z. Chen (Eds.), Intelligent Cyber Physical Systems and Internet of Things (pp. 45–55). Springer International Publishing. https://doi.org/10.1007/978-3-031-18497-0_4
Kamminga, J., Ayele, E., Meratnia, N., and Havinga, P. (2018). Poaching detection technologies-A survey. Sensors (Switzerland), 18(5). https://doi.org/10.3390/s18051474
Kays, R., Kranstauber, B., Jansen, P., Carbone, C., Rowcliffe, M., Fountain, T., and Tilak, S. (2009). Camera traps as sensor networks for monitoring animal communities. Proceedings - Conference on Local Computer Networks, LCN, October, 811–818. https://doi.org/10.1109/LCN.2009.5355046
Krasovskii, A., Maus, V., Yowargana, P., Pietsch, S., and Rautiainen, M. (2018). Monitoring deforestation in rainforests using satellite data: A pilot study from Kalimantan, Indonesia. Forests, 9(7), 389. https://doi.org/10.3390/f9070389
Kumar, M. A., Krishnan, R. S., Manimegalai, P., Gayathry, G., Mahanathiya, N., and Abinaya, G. (2022). Solar Operated IoT based Smart System to Monitor Illegal Logging. 2022 7th International Conference on Communication and Electronics Systems (ICCES), 455–461. https://doi.org/10.1109/ICCES54183.2022.9835935
Lim, K., Treitz, P., Wulder, M., and Flood, M. (2003). LiDAR remote sensing of forest structure. 1, 88–106. https://doi.org/10.1191/0309133303pp360ra
Ministerio del Medio Ambiente, República de Colombia. (2002). Resolución 0764 de 2002 “Por la cual se reserva, alindera y declara el Parque Nacional Natural Río Puré”.
Moutinho, S. (2021). First Brazilian-made satellite watches the Amazon. Science, 371(6533), 975. https://doi.org/10.1126/science.371.6533.975
Mporas, I., Perikos, I., Kelefouras, V., and Paraskevas, M. (2020). Illegal logging detection based on acoustic surveillance of forest. Applied Sciences (Switzerland), 10(20), 1–12. https://doi.org/10.3390/app10207379
Mujetahid, A., Nursaputra, M., and Soma, A. S. (2023). Monitoring Illegal Logging Using Google Earth Engine in Sulawesi Selatan Tropical Forest, Indonesia. Forests, 14(3), 652. https://doi.org/10.3390/f14030652
Mutiara, G. A., Herman, N. S., and Mohd, O. (2020). Using long-range wireless sensor network to track the illegal cutting log. Applied Sciences, 10(19), 6992. https://doi.org/10.3390/app10196992
Mutiara, G. A., Suryana, N., and Mohd, O. (2020). Multiple sensor on clustering wireless sensor network to tackle illegal cutting. International Journal on Advanced Science, Engineering and Information Technology, 1, 164–170. https://doi.org/10.18517/ijaseit.10.1.8849
Neme, L. (2018). National Gegraphic, New Alarm System May Stop Poachers In Their Tracks. https://relay.nationalgeographic.com/%0Aproxy/distribution/public/amp/2018/04/wildlife-watch-alarm-system-poachers-south-africa?__twitter_impression=true%0A
Oancea, C. D., Coman, C. M., and Toma, B. C. (2022). Evaluation of the Use of LiDAR Type Systems in Environmental Protection. 1–5. https://doi.org/10.1109/aqtr55203.2022.9801922
Oliveira, A., and Figueira, N. (2021). An Application of Command, Control, Computing, Communication, Intelligence, Surveillance and Reconnaissance Systems in the Protection of the Amazon Rainforest. 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), 1–5. https://doi.org/10.1109/ICECET52533.2021.9698538
Ovienmhada, D. I. (2015). Iridium helps monitor and protect rare species around the globe.
Painter, L., Alencar, A., Bennett, A., Bynoe, P., Guio, C., Murmis, M. R., Paez, B., Robison, D., von Hildebrand, M., Ochoa-Herrera, V., and Leite Lucas, I. (2022). Capítulo 26: Objetivos de Desarrollo Sostenible (ODS) y la Amazonía. Informe de Evaluación de Amazonía 2021. https://doi.org/10.55161/yikx6472
Plotkin, M. J. (2020). The Amazon: what everyone needs to know. Oxford University Press, USA. https://doi.org/10.1093/wentk/9780190668297.001.0001
Prasetyo, D. C., Mutiara, G. A., and Handayani, R. (2018). Chainsaw Sound and Vibration Detector System for Illegal Logging. Proceedings - 2018 International Conference on Control, Electronics, Renewable Energy and Communications, ICCEREC 2018, 93–98. https://doi.org/10.1109/ICCEREC.2018.8712091
Rainforest Connection. (2023). Guardian Platform. https://rfcx.org/guardian
Redowan, M., Phinn, S. R., Roelfsema, C. M., and Aziz, A. A. (2022). Modeling forest cover dynamics in Bangladesh using multilayer perceptron neural network with Markov chain. Journal of Applied Remote Sensing, 16(3), 34502. https://doi.org/10.1117/1.JRS.16.034502
Reiche, J., Hamunyela, E., Verbesselt, J., Hoekman, D., and Herold, M. (2018). Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2. Remote Sensing of Environment, 204, 147–161. https://doi.org/https://doi.org/10.1016/j.rse.2017.10.034
Reutech Radar Systems. (2017). Reutech’s RSR 904 radar system used in anti-poaching initiatives in the Kruger Park. http://www.reutechradar.com/news-events/latest-news/96-reutech-s-rsr-904-radar-system-used-in-anti-poaching-initiatives-in-the-kruger-park
Rozali, S. (2021). Quantifying forest disturbance using LiDAR data and time series Landsat images / Syaza Rozali.
Schwarz, B. (2010). Mapping the world in 3D. Nature Photonics, 4(July), 429–430. https://doi.org/https://doi.org/10.1038/nphoton.2010.148
Seccombe, S. (2019). Instant Detect 2.0 emerges. Wildlabs.Net. https://www.wildlabs.net/resources/case-studies/instant-detect-20-emerges
Sharma, G. (2018). Acoustic signal classification for deforestation monitoring: tree cutting problem. Journal of Computer Science & Systems Biology, 11, 178–184. https://doi.org/10.4172/jcsb.1000269
Shimabukuro, Y. E., Arai, E., Duarte, V., Jorge, A., Santos, E. G. dos, Gasparini, K. A. C., and Dutra, A. C. (2019). Monitoring deforestation and forest degradation using multi-temporal fraction images derived from Landsat sensor data in the Brazilian Amazon. International Journal of Remote Sensing, 40(14), 5475–5496. https://doi.org/10.1080/01431161.2019.1579943
Sierra Praeli, Y. (2018). Proyecto Providence: monitoreo en tiempo real que integra imágenes y sonidos de la Amazonía. https://es.mongabay.com/2018/05/proyecto-providence-monitoreo-amazonia/
Silva, C. A., Guerrisi, G., Frate, F. Del, and Sano, E. E. (2022). Near-real time deforestation detection in the Brazilian Amazon with Sentinel-1 and neural networks. European Journal of Remote Sensing, 55(1), 129–149. https://doi.org/10.1080/22797254.2021.2025154
Singh, R., Kumar, R., Gehlot, A., and Akram, S. V. (2023). Internet of Wild Things with the Integration of Vision Technology and LoRa Network. March. https://doi.org/10.14445/23488379/IJEEE-V10I3P101
Sivasankari, N., and Mounika, S. C. (2020). Implementation of Wireless Sensor Networks to Prevent Deforestation Using Node MCU. Intelligent Systems and Computer Technology, 37, 45. https://doi.org/10.3233/4PC200117
Skolnik, M. I. (2001). Introduction to Radar Systems. McGraw-Hill.
Skolnik, M. I. (2008). Radar Handbook, Third Edition. McGraw-Hill Education.
South Africa’s Council for Scientific and Industrial Research (CSIR). (2016). Launch of postcode meerkat surveillance system in Kruger National Park. https://www.csir.co.za/launch-postcode-meerkat-surveillance-system-kruger-national-park-0
Spracklen, D. V, and Garcia-Carreras, L. (2015). The impact of Amazonian deforestation on Amazon basin rainfall. Geophysical Research Letters, 42(21), 9546–9552. https://doi.org/10.1002/2015GL066063
Srisuphab, A., Kaakkurivaara, N., Silapachote, P., Tangkit, K., Meunpong, P., and Sunetnanta, T. (2020). Illegal Logging Listeners Using IoT Networks. 2020 IEEE REGION 10 CONFERENCE (TENCON), 1277–1282. https://doi.org/10.1109/TENCON50793.2020.9293935
Torres, J., Cortés, D., Portela, D., Triana, A., Cano, C., and Varón, M. (2023). Radar based monitoring system to protect the Colombian Amazon Rainforest. 2023 IEEE International Humanitarian Technology Conference, IHTC 2023. https://doi.org/10.1109/IHTC58960.2023.10508862
United Nations. (2023a). Goal 13: Take urgent action to combat climate change and its impacts (Sustainable Development Goals). https://www.un.org/sustainabledevelopment/climate-change/
United Nations. (2023b). Goal 14: Conserve and sustainably use the oceans, seas and marine resources (Sustainable Development Goals). https://www.un.org/sustainabledevelopment/oceans/
United Nations. (2023c). Goal 15: Sustainably manage forests, combat desertification, halt and reverse land degradation, halt biodiversity loss (Sustainable Development Goals). https://www.un.org/sustainabledevelopment/biodiversity/
United Nations. (2023d). Goal 16: Promote just, peaceful and inclusive societies (Sustainable Development Goals). https://www.un.org/sustainabledevelopment/peace-justice/
United Nations. (2023e). Sustainable Development Goals. https://www.un.org/sustainabledevelopment/development-agenda/
Webster, J. G. (1999). The Measurement, Instrumentation, and Sensors: Handbook. CRC Press. https://books.google.com.co/books?id=b7UuZzf9ivIC
Wireless Innovation. (2016). instant detect – monitoring wildlife through satellite imagery.
Wyniawskyj, N. S., Napiorkowska, M., Petit, D., Podder, P., and Marti, P. (2019). Forest Monitoring in Guatemala Using Satellite Imagery and Deep Learning. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 6598–6601. https://doi.org/10.1109/IGARSS.2019.8899782
Yadav, K. (2020). Design and implementation IOT based forest monitoring and illegal tree cutting prevention system. I-Manager’s Journal on Electronics Engineering, 11(1), 16. https://doi.org/10.26634/jele.11.1.18295
Yang, W. (2022). Monitoring of Land-Use/Land-Cover Change in the Peruvian Andes-Amazon based on Spatiotemporal Fusion of Multiple Satellite Data. AGU Fall Meeting Abstracts, 2022, B41A-05.
Zoological Society of London. (2013). Instant Detect. Https://Www.Zsl.Org/Conservation. http://www.zsl.org/conservation-initiatives/conservation-technology/instant-detect
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Derechos de autor 2025 Jorge Andrés Torres Cepeda, Nicolas Ospina Mendivelso, Valentina María Díaz Palacios , Diana Patricia Cortés Nava, Cristian Andrés Triana Infante, Christian Camilo Cano Vásquez, Daniel Aristizábal Corredor, Gloria Margarita Varón Durán

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