Analyzing the Relationship Between Deforestation and Climate Change Using Satellite Data
DOI:
https://doi.org/10.54489/ijcim.v4i2.503Keywords:
Deforestation, Climate change, Satellite data, Remote sensing, Greenhouse gasAbstract
Deforestation and climate change are two interdependent factors lying at the foundation of global ecosystems and human societies. In this research, the link between climate change and deforestation is searched by employing high-resolution satellite data and advanced analytic methods. Deforestation rates are quantified, changes in land use are assessed, and the emission of greenhouse gases over time is monitored using satellite imagery and remote sensing data. Also, the study examines the thermal variations and precipitation patterns on a regional and global scale as well as carbon recruitment capabilities in the affected areas. Moreover, this research paper makes a thorough analysis of how deforestation activities cause global climate changes by integrating different metrics derived from satellite data and climate models. The results underline the importance of forest conservation in the fight against climate change and put forward practical proposals for the development of policies and land use sustainable practices. The ultimate objective of this study is to create a better understanding of the complex interactions between climate change and deforestation, thus creating a basis for addressing in a data-driven way such challenges as the ones that affect our planet on a global level.
References
P. Choudhary, P. Shukla, and M. Muthamilarasan, “Genetic enhancement of climate-resilient traits in small millets: A review,” 2023. doi: 10.1016/j.heliyon.2023.e14502. DOI: https://doi.org/10.1016/j.heliyon.2023.e14502
G. KAÇMAZ AKKURT, “Vulnerability to Climate Change,” in Sustainability, Conservation and Ecology in Spatial Planning and Design: New approaches, solutions, applications, 2023. doi: 10.4018/978-1-5225-0803-8.ch066. DOI: https://doi.org/10.4018/978-1-5225-0803-8.ch066
M. Gomathy and K. Kalaiselvi, “Climate change and its impact on agriculture,” in Advanced Technologies for Smart Agriculture, 2023. doi: 10.55126/ijzab.2024.v09.i04.004. DOI: https://doi.org/10.1201/9781032628745-12
United Nations, “Take urgent action to combat climate change and its impacts,” https://sdgs.un.org/goals/goal13.
Y. Guo et al., “Integrated phenology and climate in rice yields prediction using machine learning methods,” Ecol Indic, vol. 120, 2021, doi: 10.1016/j.ecolind.2020.106935. DOI: https://doi.org/10.1016/j.ecolind.2020.106935
T. Hasegawa et al., “A global dataset for the projected impacts of climate change on four major crops,” Sci Data, vol. 9, no. 1, 2022, doi: 10.1038/s41597-022-01150-7. DOI: https://doi.org/10.1038/s41597-022-01150-7
R. Z. Abramoff, P. Ciais, P. Zhu, T. Hasegawa, H. Wakatsuki, and D. Makowski, “Adaptation Strategies Strongly Reduce the Future Impacts of Climate Change on Simulated Crop Yields,” Earths Future, vol. 11, no. 4, 2023, doi: 10.1029/2022EF003190. DOI: https://doi.org/10.1029/2022EF003190
A. Crane-Droesch, “Machine learning methods for crop yield prediction and climate change impact assessment in agriculture,” Environmental Research Letters, vol. 13, no. 11, 2018, doi: 10.1088/1748-9326/aae159. DOI: https://doi.org/10.1088/1748-9326/aae159
B. S. Sidhu, Z. Mehrabi, N. Ramankutty, and M. Kandlikar, “How can machine learning help in understanding the impact of climate change on crop yields?,” Environmental Research Letters, vol. 18, no. 2, 2023, doi: 10.1088/1748-9326/acb164. DOI: https://doi.org/10.1088/1748-9326/acb164
S. A. Gyamerah, C. Asare, D. Mintah, B. Appiah, and F. A. Kayode, “Exploring the optimal climate conditions for a maximum maize production in Ghana: Implications for food security,” Smart Agricultural Technology, vol. 6, 2023, doi: 10.1016/j.atech.2023.100370. DOI: https://doi.org/10.1016/j.atech.2023.100370
G. Ravindiran et al., “Impact of air pollutants on climate change and prediction of air quality index using machine learning models,” Environ Res, vol. 239, 2023, doi: 10.1016/j.envres.2023.117354. DOI: https://doi.org/10.1016/j.envres.2023.117354
L. Li et al., “Integrating machine learning and environmental variables to constrain uncertainty in crop yield change projections under climate change,” European Journal of Agronomy, vol. 149, 2023, doi: 10.1016/j.eja.2023.126917. DOI: https://doi.org/10.1016/j.eja.2023.126917
T. Hu et al., “Crop yield prediction via explainable AI and interpretable machine learning: Dangers of black box models for evaluating climate change impacts on crop yield,” Agric For Meteorol, vol. 336, 2023, doi: 10.1016/j.agrformet.2023.109458. DOI: https://doi.org/10.1016/j.agrformet.2023.109458
T. V. Ramachandra, T. Mondal, and B. Setturu, “Relative performance evaluation of machine learning algorithms for land use classification using multispectral moderate resolution data,” SN Appl Sci, vol. 5, no. 10, 2023, doi: 10.1007/s42452-023-05496-4. DOI: https://doi.org/10.1007/s42452-023-05496-4
A. Antonelli, K. L. Dhanjal-Adams, and D. Silvestro, “Integrating machine learning, remote sensing and citizen science to create an early warning system for biodiversity,” Plants People Planet, vol. 5, no. 3, 2023, doi: 10.1002/ppp3.10337.
I. McCallum et al., “Crowd-Driven Deep Learning Tracks Amazon Deforestation,” Remote Sens (Basel), vol. 15, no. 21, 2023, doi: 10.3390/rs15215204. DOI: https://doi.org/10.3390/rs15215204
R. D. D. Altarez, A. Apan, and T. Maraseni, “Deep learning U-Net classification of Sentinel-1 and 2 fusions effectively demarcates tropical montane forest’s deforestation,” Remote Sens Appl, vol. 29, 2023, doi: 10.1016/j.rsase.2022.100887. DOI: https://doi.org/10.1016/j.rsase.2022.100887
C. Duku and L. Hein, “Assessing the impacts of past and ongoing deforestation on rainfall patterns in South America,” Glob Chang Biol, vol. 29, no. 18, 2023, doi: 10.1111/gcb.16856. DOI: https://doi.org/10.1111/gcb.16856
V. Picanço Rodrigues and M. A. Leonel Caetano, “The impacts of political activity on fires and deforestation in the Brazilian Amazon rainforest: An analysis of social media and satellite data,” Heliyon, vol. 9, no. 12, 2023, doi: 10.1016/j.heliyon.2023.e22670. DOI: https://doi.org/10.1016/j.heliyon.2023.e22670
J. Pisl, L. H. Hughes, M. Ruswurm, and D. Tuia, “Classification of Tropical Deforestation Drivers with Machine Learning and Satellite Image Time Series,” in International Geoscience and Remote Sensing Symposium (IGARSS), 2023. doi: 10.1109/IGARSS52108.2023.10281472. DOI: https://doi.org/10.1109/IGARSS52108.2023.10281472
Y. G. Yuh, W. Tracz, H. D. Matthews, and S. E. Turner, “Application of machine learning approaches for land cover monitoring in northern Cameroon,” Ecol Inform, vol. 74, 2023, doi: 10.1016/j.ecoinf.2022.101955. DOI: https://doi.org/10.1016/j.ecoinf.2022.101955
A. Antonelli, K. L. Dhanjal-Adams, and D. Silvestro, “Integrating machine learning, remote sensing and citizen science to create an early warning system for biodiversity,” Plants People Planet, vol. 5, no. 3, 2023, doi: 10.1002/ppp3.10337. DOI: https://doi.org/10.1002/ppp3.10337
B. F. Frimpong, A. Koranteng, T. Atta-Darkwa, O. F. Junior, and T. Zawiła-Niedźwiecki, “Land Cover Changes Utilising Landsat Satellite Imageries for the Kumasi Metropolis and Its Adjoining Municipalities in Ghana (1986–2022),” Sensors, vol. 23, no. 5, 2023, doi: 10.3390/s23052644. DOI: https://doi.org/10.3390/s23052644
T. Sboui, S. Saidi, and A. Lakti, “A Machine-Learning-Based Approach to Predict Deforestation Related to Oil Palm: Conceptual Framework and Experimental Evaluation,” Applied Sciences (Switzerland), vol. 13, no. 3, 2023, doi: 10.3390/app13031772. DOI: https://doi.org/10.3390/app13031772
A. Daiyoub, P. Gelabert, S. Saura-Mas, and C. Vega-Garcia, “War and Deforestation: Using Remote Sensing and Machine Learning to Identify the War-Induced Deforestation in Syria 2010–2019,” Land (Basel), vol. 12, no. 8, 2023, doi: 10.3390/land12081509. DOI: https://doi.org/10.3390/land12081509
F. R. da Silva et al., “Machine learning application to assess deforestation and wildfire levels in protected areas with tourism management,” J Nat Conserv, vol. 74, 2023, doi: 10.1016/j.jnc.2023.126435. DOI: https://doi.org/10.1016/j.jnc.2023.126435
E. W. Butt, J. C. A. Baker, F. G. Silva Bezerra, C. von Randow, A. P. D. Aguiar, and D. V. Spracklen, “Amazon deforestation causes strong regional warming,” Proc Natl Acad Sci U S A, vol. 120, no. 45, 2023, doi: 10.1073/pnas.2309123120. DOI: https://doi.org/10.1073/pnas.2309123120








