Analysis of environmental variables and deforestation in the amazon using logistical regression models.
Arc of deforestation
MODIS
PRODES
Remote sensing
Journal
Environmental monitoring and assessment
ISSN: 1573-2959
Titre abrégé: Environ Monit Assess
Pays: Netherlands
ID NLM: 8508350
Informations de publication
Date de publication:
09 Sep 2024
09 Sep 2024
Historique:
received:
04
05
2024
accepted:
31
08
2024
medline:
10
9
2024
pubmed:
10
9
2024
entrez:
9
9
2024
Statut:
epublish
Résumé
In this study, we applied a multivariate logistic regression model to identify deforested areas and evaluate the current effects on environmental variables in the Brazilian state of Rondônia, located in the southwestern Amazon region using data from the MODIS/Terra sensor. The variables albedo, temperature, evapotranspiration, vegetation index, and gross primary productivity were analyzed from 2000 to 2022, with surface type data from the PRODES project as the dependent variable. The accuracy of the models was evaluated by the parameters area under the curve (AUC), pseudo R
Identifiants
pubmed: 39251519
doi: 10.1007/s10661-024-13086-z
pii: 10.1007/s10661-024-13086-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
911Subventions
Organisme : Conselho Nacional de Desenvolvimento Científico e Tecnológico
ID : 001
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
Références
Abreu, M. N. S., Siqueira, A. L., & Caiaffa, W. T. (2009). Regressão logística ordinal em estudos epidemiológicos. Revista Saúde Pública, 43, 183–194. https://doi.org/10.1590/S0034-89102009000100025
doi: 10.1590/S0034-89102009000100025
Alkama, R., & Cescatti, A. (2016). Biophysical climate impacts of recent changes in global forest cover. Science, 351, 600–604. https://doi.org/10.1126/science.aac8083
doi: 10.1126/science.aac8083
Alvares, C. A., Stape, J. L., Sentelhas, P. C., Gonçlaves, J. L. M., & Sparovek, G. (2013). Koppen‟s climate classification map for Brazil. Meteorologische Zeitschrift, 22, 711–728. https://doi.org/10.1127/0941-2948/2013/0507
doi: 10.1127/0941-2948/2013/0507
Alves, L. M., Marengo, J. A., Fu, R., & Bombardi, R. J. (2017). Sensitivity of Amazon regional climate to deforestation. American Journal of Climate Change, 6(1), 75–98. https://doi.org/10.4236/ajcc.2017.61005
doi: 10.4236/ajcc.2017.61005
Amelung, T. (1993). Tropical deforestation as an international economic problem. In: Giersch, H. (eds) Economic progress and environmental concerns. A Publications of the Egon-Sohmen-Foundation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-78074-5_10
Arekhi, S. (2011). Modeling spatial pattern of deforestation using GIS and logistic regression: A case study of northern Ilam forests, Ilam province, Iran. African Journal of Biotechnology, 10, 6236–6249. https://doi.org/10.5897/AJB11.1122
doi: 10.5897/AJB11.1122
Assunção, J., Gandour, C., & Rocha, R. (2015). Deforestation slowdown in the Brazilian Amazon: Prices or policies? Environment and Development Economics, 20, 697–722. https://doi.org/10.1017/S1355770X15000078
doi: 10.1017/S1355770X15000078
Ayalew, L., & Yamagishi, H. (2005). The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, 65, 15–31. https://doi.org/10.1016/j.geomorph.2004.06.010
doi: 10.1016/j.geomorph.2004.06.010
Barlow, J., Berenguer, E., Carmenta, R., & França, F. (2019). Clarifying Amazonia’s burning crisis. Global Change Biology, 26, 319–321. https://doi.org/10.1111/gcb.14872
doi: 10.1111/gcb.14872
Bavaghar, M. P. (2015). Deforestation modeling using logistic regression and GIS. Journal of Forest Science, 61, 193–199. https://doi.org/10.17221/78/2014-JFS
doi: 10.17221/78/2014-JFS
Bax, V., Francesconi, F., & Quinteroc, M. (2016). Spatial modeling of deforestation processes in the Central Peruvian Amazon. Journal for Nature Conservation, 29, 79–88. https://doi.org/10.1016/j.jnc.2015.12.002
doi: 10.1016/j.jnc.2015.12.002
Biswas, S., Lasko, K. D., & Vadrevu, K. P. (2015). Fire disturbance in tropical forests of Myanmar—Analysis using MODIS satellite datasets. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 2273–2281. https://doi.org/10.1109/JSTARS.2015.2423681
doi: 10.1109/JSTARS.2015.2423681
Bragagnolo, L., da Silva, R. V., & Grzybowski, J. M. V. (2021). Amazon forest cover change mapping based on semantic segmentation by U-Nets. Ecological Informatics, 62, 1–12. https://doi.org/10.1016/j.ecoinf.2021.101279
doi: 10.1016/j.ecoinf.2021.101279
Brancalion, P. H., Broadbent, E. N., De-Miguel, S., Cardil, A., Rosa, M. R., Almeida, C. T., et al. (2020). Emerging threats linking tropical deforestation and the COVID-19 pandemic. Perspectives in Ecology and Conservation, 18(4), 243–246. https://doi.org/10.1016/j.pecon.2020.09.006
doi: 10.1016/j.pecon.2020.09.006
Brown, F., Santos, G. P., Pires, F. F., & Costa, C. B. (2011). Brazil: Drought and fire response in the Amazon. World Resources Report Case Study: Washington DC, (1), 1–11. Retrieved March 18, 2021, from https://silo.tips/download/brazil-drought-and-fire-response-in-the-amazon
Bui, D. T., Tuan, T. A., Klempe, H., Pradhan, B., & Revhaug, I. (2016). Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13(2), 361–378. https://doi.org/10.1007/s10346-015-0557-6
doi: 10.1007/s10346-015-0557-6
Cabral, A. I. R., Saito, C., Pereira, H., & Laques, A. E. (2018). Deforestation pattern dynamics in protected areas of the Brazilian Legal Amazon using remote sensing data. Applied Geography, 100, 101–115. https://doi.org/10.1016/j.apgeog.2018.10.003
doi: 10.1016/j.apgeog.2018.10.003
Cabral Júnior, J. B., Silva, C. M. S., de Almeida, H. A., et al. (2019). Detecting linear trend of reference evapotranspiration in irrigated farming areas in Brazil’s semiarid region. Theoretical and Applied Climatology, 138, 215–225. https://doi.org/10.1007/s00704-019-02816-w
doi: 10.1007/s00704-019-02816-w
Cabral Júnior, J. B., Silva, H. J. F., & Reis, J. S. (2022). Características da Cobertura do Solo em Anos de Contrastes Climáticos (chuvoso e seco) no Oeste da Amazônia, Rio Branco-Acre, Utilizando Sensoriamento Remoto. Revista Brasileira de Geografia Física, 15, 2704–2714. https://doi.org/10.26848/rbgf.v15.6.p2704-2714
doi: 10.26848/rbgf.v15.6.p2704-2714
Calef, M. P., McGuire, A. D., Epstein, H. E., Rupp, T. S., & Shugart, H. H. (2005). Analysis of vegetation distribution in Interior Alaska and sensitivity to climate change using a logistic regression approach. Journal of Biogeography, 32(5), 863–878. https://doi.org/10.1111/j.1365-2699.2004.01185.x
doi: 10.1111/j.1365-2699.2004.01185.x
Camarinha-Neto, G. F., Cohen, J. C. P., Dias-Júnior, C. Q., Sörgel, M., Cattanio, J. H., Araújo, A., Wolff, S., Kuhn, P. A. F., Souza, R. A. F., Rizzo, L. V., & Artaxo, P. (2021). The friagem event in the central Amazon and its influence on micrometeorological variables and atmospheric chemistry. Atmospheric Chemistry and Physics, 21, 339–356. https://doi.org/10.5194/acp-21-339-2021
doi: 10.5194/acp-21-339-2021
Campos, M. S., Adami, M., & Araújo, A. C. (2021). Análise do Albedo de Superfície da Palma de Óleo e Diferentes Usos e Coberturas do Solo no Leste da Amazônia. Revista Brasileira de Meteorologia, 36, 15–21. https://doi.org/10.1590/0102-77863540070
doi: 10.1590/0102-77863540070
Cardille, J. A., & Foley, J. A. (2003). Agricultural land-use change in Brazilian Amazonia between 1980 and 1995: Evidence from integrated satellite and census data. Remote Sensing of Environment, 87, 551–562. https://doi.org/10.1016/j.rse.2002.09.001
doi: 10.1016/j.rse.2002.09.001
Chatfield, C. (1995). Model uncertainty, data mining and statistical inference. Journal of the Royal Statistical Society, 158, 419–466. https://doi.org/10.2307/2983440
doi: 10.2307/2983440
Culf, A. D., Esteves, J. L., Marques Filho, A. O., & Rocha, H. R. (1996). Radiation, temperature and humidity over forest and pasture in Amazonia. In J. H. C. Gash, C. A. Nobre, J. Roberts, & R. L. Victoria (Eds.), Amazonian deforestation and climate (pp. 175–191). John Wiley.
da Rocha, H. R., Manzi, A. O., Cabral, O. M., et al. (2009). Patterns of water and heat flux across a biome gradient from tropical forest to savanna in Brazil. Journal of Geophysical Research, 114(G00B12), 1–8. https://doi.org/10.1029/2007JG000640
doi: 10.1029/2007JG000640
da Silva, H. J. F., Santos, M. S., Cabral Júnior, J. B., & Spyrides, M. H. C. (2016). Modeling of reference evapotranspiration by multiple linear regression. Journal of Hyperspectral Remote Sensing, 6(1), 44–58. https://doi.org/10.5935/2237-2202.20160005
doi: 10.5935/2237-2202.20160005
da Silva, H. J. F., Gonçalves, W. A., & Bezerra, B. G. (2019). Comparative analyzes and use of evapotranspiration obtained through remote sensing to identify deforested areas in the Amazon. International Journal of Applied Earth Observation and Geoinformation, 78, 163–174. https://doi.org/10.1016/j.jag.2019.01.015
doi: 10.1016/j.jag.2019.01.015
da Silva, H. J. F., Gonçalves, W. A., Bezerra, B. G., Santos e Silva, C. M., Oliveira, CPd., & Mutti, P. R. (2022). Analysis of the influence of deforestation on the microphysical parameters of clouds in the Amazon. Remote Sensing, 14(21), 5353. https://doi.org/10.3390/rs14215353
doi: 10.3390/rs14215353
Dai, F. C., & Lee, C. F. (2002). Landslide characteristics and slope instability modeling using GIS Lantau Island, Hong Kong. Geomorphology, 42, 213–238. https://doi.org/10.1016/S0169-555X(01)00087-3
doi: 10.1016/S0169-555X(01)00087-3
Delgado, R. C., Pereira, M. G., Teodoro, P. E., Santos, G. L., Carvalho, D. C., Magistrali, I. C., & Vilanova, R. S. (2018). Seasonality of gross primary production in the Atlantic Forest of Brazil. Global Ecology and Conservation, 14, 1–12. https://doi.org/10.1016/j.gecco.2018.e00392
doi: 10.1016/j.gecco.2018.e00392
Didan, K. (2015). MOD13A3 MODIS/Terra vegetation Indices Monthly L3 Global 1km SIN Grid V006. distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD13A3.006
Dlamini, W. M. (2016). Analysis of deforestation patterns and drivers in Swaziland using efficient Bayesian multivariate classifiers. Modeling Earth Systems and Environment, 2, 1–14. https://doi.org/10.1007/s40808-016-0231-6
doi: 10.1007/s40808-016-0231-6
dos Santos Silva, F. D., da Costa, C. P. W., dos Santos Franco, V., Gomes, H. B., da Silva, M. C. L., dos Santos Vanderlei, M. H. G., Costa, R. L., da Rocha Júnior, R. L., Cabral Júnior, J. B., dos Reis, J. S., et al. (2023). Intercomparison of different sources of precipitation data in the Brazilian Legal Amazon. Climate, 11(12), 241. https://doi.org/10.3390/cli11120241
doi: 10.3390/cli11120241
Dreiseitl, S., & Machado, L. O. (2002). Logistic regression and artificial neural network classification models: A methodology review. Journal of Biomedical Informatics, 35, 352–359. https://doi.org/10.1016/S1532-0464(03)00034-0
doi: 10.1016/S1532-0464(03)00034-0
Dung, P. T., Chuc, M. D., Thanh, N. T. N., Hung, B. Q., & Chung, D. M. (2018). Comparison of resampling methods on different remote sensing images for Vietnam’s urban classification. Research and Development on Information and Communication Technology, 12(15), 8–20. https://doi.org/10.32913/rd-ict.vol2.no15.663
doi: 10.32913/rd-ict.vol2.no15.663
Ekström, M., Esseen, P. A., Westerlund, B., Grafström, A., Jonsson, B. G., & Ståhl, G. (2018). Logistic regression for clustered data from environmental monitoring program. Ecological Informatics, 43, 165–173. https://doi.org/10.1016/j.ecoinf.2017.10.006
doi: 10.1016/j.ecoinf.2017.10.006
Escobar, H. (2019). Brazilian president attacks deforestation data. Science, 365, 419–419. https://doi.org/10.1126/science.365.6452.419
doi: 10.1126/science.365.6452.419
Evans, S. (2021, October 5). Which countries are historically responsible for climate change?. Carbon Brief: clear on climate. Retrieved January 7, 2023, from https://www.carbonbrief.org/analysis-which-countries-are-historically-responsible-for-climate-change
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27, 861–874. https://doi.org/10.1016/j.patrec.2005.10.010
doi: 10.1016/j.patrec.2005.10.010
Fearnside, P. M. (2006). Desmatamento na Amazônia: Dinâmica, impactos e controle. Acta Amazonica, 36(3), 395–400. https://doi.org/10.1590/S0044-59672006000300018
doi: 10.1590/S0044-59672006000300018
Fearnside, P. M. (2005). Deforestation in Brazilian Amazonia: History, rates, and consequences. Conservation Biology, 19(3), 680–688. http://www.jstor.org/stable/3591054
Fernandes, A. A. T., Figueiredo-Filho, D. B., Rocha, E. C., & Nascimento. (2020). Read this paper if you want to learn logistic regression. Revista de Sociologia e Politica, 8(74), 1–19. https://doi.org/10.1590/1678-987320287406en
doi: 10.1590/1678-987320287406en
Ferrante, L., & Fearnside, P. M. (2019). Brazil’s new president and ‘ruralists’ threaten Amazonia’s environment, traditional peoples and the global climate. Environmental Conservation, 46, 261–263. https://doi.org/10.1017/S0376892919000213
doi: 10.1017/S0376892919000213
Ferreira, R. R., Mutti, P., Mendes, K. R., Campos, S., Marques, T. V., Oliveira, C. P., Gonçalves, W., Mota, J., Difante, G., Urbano, S. A., Fernandes, L., Bezerra, B. G., & Santos e Silva, C. M. (2020). An assessment of the MOD17A2 gross primary production product in the Caatinga biome, Brazil. International Journal of Remote Sensing., 42, 1275–1291. https://doi.org/10.1080/01431161.2020.1826063
doi: 10.1080/01431161.2020.1826063
Fohrer, N., Haverkamp, S., Eckhardt, K., & Frede, H. G. (2001). Hydrologic response to land use changes on the catchment scale. Physics and Chemistry of the Earth, 26, 577–582. https://doi.org/10.1016/S1464-1909(01)00052-1
doi: 10.1016/S1464-1909(01)00052-1
Fox, J., & Weisberg, S. (2018). An R companion to applied regression (3ª). Sage Publications.
Fox, J. C., Ades, P. K., & Bi, H. (2001). Stochastic structure and individual-tree growth models. Forest Ecology and Management, 154(1–2), 261–276. https://doi.org/10.1016/S0378-1127(00)00632-0
doi: 10.1016/S0378-1127(00)00632-0
Franca, R. R. (2015). Climatologia das chuvas em Rondônia – período 1981–2011. GEOgrafias, 11(1), 44–58. https://doi.org/10.35699/2237-549X..13392
doi: 10.35699/2237-549X..13392
Freitas, S. R., Mello, M. C. S., & Cruz, C. B. M. (2005). Relationships between forest structure and vegetation indices in Atlantic Rainforest. Forest Ecology and Management, 218, 353–362. https://doi.org/10.1016/j.foreco.2005.08.036
doi: 10.1016/j.foreco.2005.08.036
Galvão, L. S., Santos, J. R., Roberts, D. A., Breunig, F. M., Toomey, M., & Moura, Y. M. (2011). On intra-annual EVI variability in the dry season of tropical forest: A case study with MODIS and hyperspectral data. Remote Sensing of Environment, 115, 2350–2359. https://doi.org/10.1016/j.rse.2011.04.035
doi: 10.1016/j.rse.2011.04.035
Geoghegan, J., Villar, S. C., Klepeis, P., et al. (2001). Modeling tropical deforestation in the southern Yucatán peninsular region: Comparing survey and satellite data. Agriculture, Ecosystems and Environment, 85(1–3), 25–46. https://doi.org/10.1016/S0167-8809(01)00201-8
doi: 10.1016/S0167-8809(01)00201-8
Gholami, H., Mohamadifara, A., Sorooshianb, A., & Jansend, J. D. (2020). Machine-learning algorithms for predicting land susceptibility to dust emissions: The case of the Jazmurian Basin, Iran. Atmospheric Pollution Research, 11(8), 1303–1315. https://doi.org/10.1016/j.apr.2020.05.009
doi: 10.1016/j.apr.2020.05.009
Giam, X. (2017). Global biodiversity loss from tropical deforestation. Proceedings of the National Academy of Sciences, 114, 5775–5777. https://doi.org/10.1073/pnas.1706264114
doi: 10.1073/pnas.1706264114
Gregoire, T. G., Schabenberger, O., & Barret, J. P. (1995). Linear modelling of irregularly spaced, unblanaced, longitudinal data from permanent-plot measurements. Canadian Journal of Forest Research, 25(10), 237–256. https://doi.org/10.1139/x95-017
doi: 10.1139/x95-017
Griffiths, P., Jakimow, B., & Hostert, P. (2018). Reconstructing long term annual deforestation dynamics in Pará and Mato Grosso using the Landsat archive. Remote Sensing of Environment, 216, 497–513. https://doi.org/10.1016/j.rse.2018.07.010
doi: 10.1016/j.rse.2018.07.010
Grings, F., Roitberg, E., & Barraza, V. (2020). EVI time-series breakpoint detection using convolutional networks for online deforestation monitoring in Chaco forest. IEEE Transactions on Geoscience and Remote Sensing, 58(2), 1303–1312. https://doi.org/10.1109/TGRS.2019.2945719
doi: 10.1109/TGRS.2019.2945719
Hartkamp, A. D., de Beurs, K., Stein, A., & White, J. W. (1999). Interpolation techniques for climate variables. Geographic Information Systems, 99-01, 1–24. https://original-ufdc.uflib.ufl.edu/UF00077518/00001/1j
Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). John Wiley & Sons.
doi: 10.1002/0471722146
Hosmer, D. W., Hosmer, T., Cessie, S. L., & Lemeshow, S. (1997). A comparison of goodness-of-fit tests for the logistic regression model. Statistics in Medicine, 16, 965–980. https://doi.org/10.1002/(SICI)1097-0258(19970515)16:9%3c965::AID-SIM509%3e3.0.CO;2-O
doi: 10.1002/(SICI)1097-0258(19970515)16:9<965::AID-SIM509>3.0.CO;2-O
Huete, A. R., Didan, K., Shimabukuro, Y. E., Ratana, P., Saleska, S. R., Hutyra, L. R., Yang, W., Nemani, R. R., & Myneni, R. (2006). Amazon rainforests green-up with sunlight in dry season. Geophysical Research Letters, 33, 1–4. https://doi.org/10.1029/2005GL025583
doi: 10.1029/2005GL025583
IBGE. (2022). Instituto Brasileiro de Geografia e Estatística. IBGE Estados. Rondônia. Retrieved July 14, 2024, from https://www.ibge.gov.br/cidades-e-estados/ro.html
INPE. (2024). Instituto Nacional de Pesquisas Espaciais – INPE, PRODES - Programa de desmatamento da Amazônia – Monitoramento da floresta amazônica por satélite. Retrieved Marcy 20, 2024, from http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes
James, G., Witten. D., Hastie, T., & Tibshirani, R. (2014). An introduction to statistical learning: With applications in R. Springer Publishing Company, Incorporated (1st ed., pp. 99–204). https://www.stat.berkeley.edu/users/rabbee/s154/ISLR_First_Printing.pdf
Japkowicz, N., & Stephen, S. (2002). The class imbalance problem: A systematic stud. Intelligent Data Analysis, 6(5), 429–449. https://dl.acm.org/doi/10.5555/1293951.1293954
Jiang, X., Li, G., Lu, D., Moran, E., & Batistella, M. (2020). Modeling forest aboveground carbon density in the Brazilian Amazon with Integration of MODIS and Airborne LiDAR Data. Remote Sensing, 12(20), 1–25. https://doi.org/10.3390/rs12203330
Khan, S. H., He, X., Porikli, F., & Bennamoun, M. (2017). Forest change detection in incomplete satellite images with deep neural networks. IEEE Transactions on Geoscience and Remote Sensing, 55, 5407–5423. https://doi.org/10.1109/TGRS.2017.2707528
doi: 10.1109/TGRS.2017.2707528
Kumar, R., Nandy, S., Agarwal, R., & Kushwaha, S. P. S. (2014). Forest cover dynamics analysis and prediction modeling using logistic regression model. Ecological Indicators, 45, 444–455. https://doi.org/10.1016/j.ecolind.2014.05.003
doi: 10.1016/j.ecolind.2014.05.003
Kummer, D. M., & Turner, B. L. (1994). The human causes of deforestation in Southeast Asia. BioScience, 44, 323–328. https://doi.org/10.2307/1312382
doi: 10.2307/1312382
Latimer, A. M., Wu, S., Gelfand, A. E., & Jr Silander, J. A. (2006). Building statistical models to analyze species distributions. Ecological Applications, 16(1), 33–50. https://doi.org/10.1890/04-0609
doi: 10.1890/04-0609
Leite-Filho, A. T., Soares-Filho, B. S., Davis, J. L., Abrahão, G. M., & Börner, J. (2021). Deforestation reduces rainfall and agricultural revenues in the Brazilian Amazon. Nature Communicatons, 12(2591), 1–7. https://doi.org/10.1038/s41467-021-22840-7
doi: 10.1038/s41467-021-22840-7
Li, J., & Heap, A. D. (2014). Spatial interpolation methods applied in the environmental sciences: A review. Environmental Modelling & Software, 53, 173–189. https://doi.org/10.1016/j.envsoft.2013.12.008
doi: 10.1016/j.envsoft.2013.12.008
Lindsey, R., & Dlugokencky, E. (2024, April 9). Climate change: Atmospheric carbon dioxide. Retrieved July 15, 2024, from https://www.climate.gov/news-features/understanding-climate/climate-change-atmospheric-carbon-dioxide
Lohmann, M., Santos, L. J. C., & Cumico, C. (2016). Avaliação de modelos regressivo logístico e baseado em rede neural para previsão da probabilidade de ocorrência de alagamentos em Curitiba - PR. Revista Brasileira de Geografia Física, 9, 2247–2263. https://doi.org/10.5935/1984-2295.20160160
doi: 10.5935/1984-2295.20160160
Ludeke, A. K., Maggio, R. C., & Reid, L. M. (1990). An analysis of antropogenic deforestation using logistic regression and GIS. Journal of Environmental Management, 31, 247–259. https://doi.org/10.1016/S0301-4797(05)80038-6
doi: 10.1016/S0301-4797(05)80038-6
Marengo, J. A. (2006). On the hydrological cycle of the Amazon basin: A historical review and current state-of-the-art. Revista Brasileira de Meteorologia, 21, 1–19. https://doi.org/10.1590/0102-778620140049
doi: 10.1590/0102-778620140049
Marengo, J. A., Nobre, C. A., & Culf, A. D. (1997). Climatic impacts of “Friagens” in forested and deforested areas of the Amazon basin. Journal of Applied Meteorology, 36, 1553–1556. https://doi.org/10.1175/1520-0450(1997)036%3c1553:CIOFIF%3e2.0.CO;2
doi: 10.1175/1520-0450(1997)036<1553:CIOFIF>2.0.CO;2
McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. CRC Press. Second Edition, USA. https://doi.org/10.1201/9780429029608
McFadden, D. (1987). Regression-based specification tests for the multinomial logit model. Journal of Econometrics, 4(1–2), 63–82. https://doi.org/10.1016/0304-4076(87)90067-4
doi: 10.1016/0304-4076(87)90067-4
McRoberts, R., & Walters, B. (2012). Statistical inference for remote sensing-based estimates of net deforestation. Remote Sensing of Environment, 124, 394–401. https://doi.org/10.1016/j.rse.2012.05.011
doi: 10.1016/j.rse.2012.05.011
Miranda-Aragón, L., Treviño-Garza, E. J., Jiménez-Pérez, Aguirre-Calderón, O. A., González-Tagle, M. A., Pompa-García, M., & Aguirre-Salado, C. A. (2012). Modeling susceptibility to deforestation of remaining ecosystems in North Central Mexico with logistic regression. Journal of Forestry Research, 23, 345–354. https://doi.org/10.1007/s11676-012-0230-z
doi: 10.1007/s11676-012-0230-z
Moore, N., & Messina, J. (2010). A landscape and climate data logistic model of tsetse distribution in Kenya. PLoS ONE, 5, 1–10. https://doi.org/10.1371/journal.pone.0011809
doi: 10.1371/journal.pone.0011809
Nandy, S., Kushwaha, S. P. S., & Mukhopadhyay, S. (2007). Monitoring the Chilla-Motichur wildlife corridor using geospatial tools. Journal for Nature Conservation, 15(4), 237–244. https://doi.org/10.1016/j.jnc.2007.03.003
doi: 10.1016/j.jnc.2007.03.003
Nobre, C. A., Gash, J. H. C., Roberts, J. M., & Victoria, R. L. (1996). Conclusions from ABRACOS. In J. H. C. Gash, C. A. Nobre, J. Roberts, & R. L. Victoria (Eds.), Amazonian deforestation and climate. John Wiley.
Oliveira, G., & Moraes, E. C. (2013). Validação do balanço de radiação obtido a partir de dados MODIS/TERRA na Amazônia com medidas de superfície do LBA. Revista Acta Amazonica, 43, 353–364. https://doi.org/10.1590/S0044-59672013000300011
doi: 10.1590/S0044-59672013000300011
Orriols, A., & Mansilla, E. B. (2005). The class imbalance problem in learning classifier systems: A preliminary study. Proc. 7th Annu. Workshop Genet. Evol. Comput., 74–78. https://doi.org/10.1145/1102256.1102271
Overmars, K. P., de Koning, G. H. J., & Veldkamp, A. (2003). Spatial autocorrelation in multi-scale land use models. Ecological Modelling, 164(2–3), 257–270. https://doi.org/10.1016/S0304-3800(03)00070-X
doi: 10.1016/S0304-3800(03)00070-X
Pereira da Silva, S. D., Santos, S. B., Pereira, P. C. G., Melo, M. R. S., & Eugenio, F. C. (2021). Landscape analysis in a municipality in the arc of deforestation of the Brazilian Amazon rainforest. Ecological Engineering, 173, 106417. https://doi.org/10.1016/j.ecoleng.2021.106417
doi: 10.1016/j.ecoleng.2021.106417
Petrie, M. D., Brunsell, N. A., Vargas, R., Collins, S. L., Flanagan, L. B., Hanan, N. P., Litvak, M. E., & Suyker, A. E. (2016). The sensitivity of carbon exchanges in Great Plains grasslands to precipitation variability. Journal of Geophysical Research: Biogeosciences, 121, 280–294. https://doi.org/10.1002/2015JG003205
doi: 10.1002/2015JG003205
Phua, M. H., Tsuyuki, S., Furuya, N., & Lee, J. S. (2008). Detecting deforestation with a spectral change detection approach using multitemporal Landsat data: A case study of Kinabalu Park, Sabah, Malaysia. Journal of Environmental Management, 88, 784–795. https://doi.org/10.1016/j.jenvman.2007.04.011
doi: 10.1016/j.jenvman.2007.04.011
Pinheiro, T. F., Escada, M. I. S., Valeriano, D. M., Hostert, P., Gollnow, F., & Muller, H. (2016). Forest degradation associated with logging Frontier expansion in the Amazon: The BR-163 region in Southwestern Pará, Brazil. Earth Interactions, 20(17), 1–16. https://doi.org/10.1175/EI-D-15-0016.1
doi: 10.1175/EI-D-15-0016.1
Porwal, S., & Katiyar, S. K. (2014). Performance evaluation of various resampling techniques on IRS imagery. Seventh International Conference on Contemporary Computing, IC3, pp. 489–494. https://doi.ieeecomputersociety.org/10.1109/IC3.2014.6897222
Rajão, R., Soares-Filho, B., Nunes, F., Börner, J., Machado, L., Assis, D., Oliveira, A., Pinto, L., Ribeiro, V., Rausch, L., Gibbs, H., & Figueira, D. (2020). The rotten apples of Braziĺs agribusiness. Science, 369(6501), 246–248. https://doi.org/10.1126/science.aba6646
doi: 10.1126/science.aba6646
Rondônia. (2002). Governo do Estado de Rondônia. Atlas Geoambiental de Rondônia. SEDAM: Secretaria de Estado do Desenvolvimento Ambiental (2 eds, pp. 1–142), Porto Velho, RO, Brasil.
Rossi, F. S., & Santos, G. A. A. (2020). Fire dynamics in Mato Grosso State, Brazil: The relative roles of gross primary productivity. Big Earth Data, 4, 23–44. https://doi.org/10.1080/20964471.2019.1706832
doi: 10.1080/20964471.2019.1706832
Running, S., Mu, Q., Zhao, M. (2015). MOD17A2H MODIS/terra gross primary productivity 8-day L4 global 500m SIN grid V006, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD17A2H.006
Running, S., Mu, Q., Zhao, M., & Moreno, A. (2019). MOD16A2GF MODIS/Terra net evapotranspiration gap-filled 8-day L4 global 500 m SIN grid V006. Distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD16A2GF.006 . Accessed 2021-07-15.
Saatchi, S., Asefi-Najafabady, S., Malhi, Y., Aragão, L. E. O. C., Anderson, L. O., Myneni, R. B., & Nemanim, R. (2013). Persistent effects of a severe drought on Amazonian forest canopy. Proceedings of the National Academy of Sciences of the United States of America (PNAS), 110, 565–570. https://doi.org/10.1073/pnas.1204651110
doi: 10.1073/pnas.1204651110
Sabajo, C. R., le Maire, G., June, T., Meijide, A., Roupsard, O., & Knohl, A. (2017). Expansion of oil palm and other cash crops causes an increase of the land surface temperature in the Jambi province in Indonesia. Biogeosciences, 14, 4619–4635. https://doi.org/10.5194/bg-14-4619-2017
doi: 10.5194/bg-14-4619-2017
Saha, S., Saha, M., Mukherje, K., Arabameri, A., Ngo, P. T. T., & Paul, G. C. (2020). Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River Basin, India. Science of the Total Environment., 730, 1–20. https://doi.org/10.1016/j.scitotenv.2020.139197
doi: 10.1016/j.scitotenv.2020.139197
Sales, F., Santiago, T., Biggs, T. W., Mullan, K., Sills, E. O., & Monteverde, C. (2020). Impacts of protected area deforestation on dry-season regional climate in the Brazilian Amazon. JRG Atmospheres, 125, 1–25. https://doi.org/10.1029/2020JD033048
doi: 10.1029/2020JD033048
Schaaf, C., & Wang, Z. (2015). MCD43A3 MODIS/Terra+Aqua BRDF/albedo daily L3 global - 500m V006, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MCD43A3.006
Schultz, M., Clevers, J. G. P. W., Carter, S., Verbesselt, J., Avitabile, V., Quang, H. V., & Herod, M. (2016). Performance of vegetation indices from Landsat time series in deforestation monitoring. International Journal of Applied Earth Observation and Geoinformation., 52, 318–327. https://doi.org/10.1016/j.jag.2016.06.020
doi: 10.1016/j.jag.2016.06.020
Searle, S. R., Casella, G., & Mcculloch, C. E. (1992). Analysis of Variance estimation for unbalanced data, Wiley Series in Probability and Statistics; John Wiley: New York, NY, USA, Chapter 5, 168–231. https://doi.org/10.1002/9780470316856.ch5
Shimabukuro, Y. E., Santos, J. R., Formaggio, A. R., Duarte, V., Rudorff, B. F. T. (2012). The Brazilian Amazon monitoring program: PRODES and DETER projects. Global Forest Monitoring from Earth Observation, 1st edition , 354, https://doi.org/10.1201/b13040
Silva Dias, M. A., Avissar, R., & Silva Dias, P. (2009). Modeling the regional and remote climatic impact of deforestation. In: Amazonia and global change, 186, 251–260. Wiley, Washington. https://doi.org/10.1029/2008GM000778
Silva Junior, C. H. L., Aragão, L. E. O. C., Fonseca, M. G., Almeida, C. T., Vedovato, L. B., & Anderson, L. O. (2018). Deforestation-induced fragmentation increases forest fire occurrence in central Brazilian Amazonia. Forests, 9(6), 1–16. https://doi.org/10.3390/f9060305
doi: 10.3390/f9060305
Silva Júnior, C. H. L., Pessôa, A. C. M., Carvalho, N. S., Reis, J. B., Anderson, L. O., & Aragão, E. O. C. (2021). The Brazilian Amazon deforestation rate in 2020 is the greatest of the decade. Nature Ecology & Evolution, 5, 144–145. https://doi.org/10.1038/s41559-020-01368-x
doi: 10.1038/s41559-020-01368-x
Smith, V., Portillo-Quintero, C., Sanchez-Azofeifa, A., & Hernandez-Stefanoni, J. L. (2019). Assessing the accuracy of detected breaks in Landsat time series as predictors of small scale deforestation in tropical dry forests of Mexico and Costa Rica. Remote Sensing of Environment, 221, 707–721. https://doi.org/10.1016/j.rse.2018.12.020
doi: 10.1016/j.rse.2018.12.020
Spracklen, D. V., Baker, J. C. A., Garcia-Carreras, L., & Marsham, J. H. (2018). The effects of tropical vegetation on rainfall. Annual Review of Environment and Resources, 43, 193–218. https://doi.org/10.1146/annurev-environ-102017-030136
doi: 10.1146/annurev-environ-102017-030136
Srivastava, S., Singh, T. P., Singh, H., Kushwaha, S. P. S., & Roy, P. S., (2002). Assessment of large-scale deforestation in Sonitpur district of Assam. Current Science, 82(12), 1479–1484. https://www.jstor.org/stable/24106185
Studley, H., & Weber, K. T. (2011). Comparison of image resampling techniques for satellite imagery, in final report: Assessing post-fire recovery of sagebrush-steppe rangelands in southeastern Idaho. In K. T. Weber, & K. Davis (Eds.), Pocatello, ID: Idaho State Univ, pp. 185–196. https://www.semanticscholar.org/paper/Comparison-of-Image-Resampling-Techniques-for-Studley/fa316fc18b22fe466f818bcea0edae76d08c91f9
Tarazona, Y., & Miyasiro-López, M. (2020). Monitoring tropical forest degradation using remote sensing. Challenges and opportunities in the Madre de Dios region, Peru. Remote Sensing Applications: Society and Environment, 19, 1–13. https://doi.org/10.1016/j.rsase.2020.100337
doi: 10.1016/j.rsase.2020.100337
Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research, 106(7), 7183–7192. https://doi.org/10.1029/2000JD900719
doi: 10.1029/2000JD900719
Trambauer, P., Dutra, E., Maskey, S., Werner, M., Pappenberger, F., van Beek, L. P. H., & Uhlenbrook, S. (2014). Comparison of different evaporation estimates over the African continent. Hydrology and Earth System Sciences, 18, 193–212. https://doi.org/10.5194/hess-18-193-2014
doi: 10.5194/hess-18-193-2014
Twisk, J. W. R. (2006). Applied multilevel analysis. Cambridge University Press, Amsterdam. https://doi.org/10.1017/CBO9780511610806
Valeriano, D. M., Mello, E. M. K., Moreira, J. C., Shimabukuro, Y. E., Duarte, V. (2004). Monitoring tropical forest from space: The PRODES digital project. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 35, 272–274. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.183.8466
Valeriano, D.M., Escada, M.I.S., Camara, G., Amaral, S.; Maurano, L.E., Rennó, C.D., Almeida, C.A., & Monteiro, A.M.V. (2012). O monitoramento do desmatamento. In: MARTINE, George; OJIMA, Ricardo; BARBIERI, Alisson; CARMO, Roberto do (Ed.). População e Sustentabilidade na era das mudanças ambientais globais. ABEP: [s.n.]. 223-238. ISBN 978-85-85543-25-9.
Varamesh, S., Hosseini, S. M., & Rahimzadegan, M. (2017). Detection of land use changes in Northeastern Iran by Landsat satellite data. Applied Ecology and Environmental Research, 15, 1443–1454. https://doi.org/10.15666/aeer/1503_14431454
doi: 10.15666/aeer/1503_14431454
von Randow, C., Manzi, A. O., Kruijt, B., Oliveira, P. J., Zanchi, F. B., Silva, R. L., Hodnett, M. G., Gash, J. H. C., Elbers, J. A., Waterloo, M. J., Cardoso, F. L., & Kabat, P. (2004). Comparative measurements and seasonal variations in energy and carbon exchange over forest and pasture in South West Amazonia. Theoretical and Applied Climatology, 78, 5–26. https://doi.org/10.1007/s00704-004-0041-z
doi: 10.1007/s00704-004-0041-z
Walker, W. S., Gorelik, S. R., Baccini, A., Aragon-Osejo, J. L., Josse, C., Meyer, C., et al. (2020). The role of forest conversion, degradation, and disturbance in the carbon dynamics of Amazon indigenous territories and protected areas. Proceedings of the National Academy of Sciences - PNAS, 117(6), 3015–3025. https://doi.org/10.1073/pnas.1913321117
doi: 10.1073/pnas.1913321117
Wan, Z., Hook, S., & Hulley, G. (2015). MOD11A2 MODIS/Terra land surface temperature/emissivity 8-day L3 global 1km SIN grid V006, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD11A2.006
Wang, D., Liang, S., He, T., Yu, W., Schaaf, C., & Wang, Z. (2015). Estimating daily mean land surface albedo from MODIS data. Journal of Geophysical Research: Atmospheres, 120, 4825–4841. https://doi.org/10.1002/2015JD023178
doi: 10.1002/2015JD023178
Wilks, D. S. (2009). Extending logistic regression to provide full-probability-distribution MOS forecasts. Meteorological Applications, 16(3), 361–368. https://doi.org/10.1002/met.134
doi: 10.1002/met.134
Wilson, K., Newton, A., Echeverria, C., Weston, C., & Burgman, M. (2005). A vulnerability analysis of the temperate forests of south central Chile. Biological Conservation, 122, 9–21. https://doi.org/10.1016/j.biocon.2004.06.015
doi: 10.1016/j.biocon.2004.06.015
Yamamoto, J. K., & Landim, P. M. B. (2013). Geoestatística: Conceitos e Aplicações. Oficina de Textos (1ª). São Paulo.
Yang, X., Ren, L., Singh, V. P., Liu, X., Yuan, F., Jiang, S., & Yong, B. (2012). Impacts of land use land cover changes on evapotranspiration and runoff at Shalamulun River watershed, China. Hdrology Research, 4, 1–2. https://doi.org/10.2166/nh.2011.120
doi: 10.2166/nh.2011.120
Zhang, L., & Gove, J. H. (2005). Spatial assessment of model errors from four regression techniques. Forest Science, 51(4), 334–346. https://doi.org/10.1093/forestscience/51.4.334
doi: 10.1093/forestscience/51.4.334
Zhang, L., Ma, Z., & Guo, L. (2008). Spatially assessing model errors of four regression techniques for three types of forest stands. Forestry: An International Journal of Forest Research, 81(2), 209–225. https://doi.org/10.1093/forestry/cpn014
doi: 10.1093/forestry/cpn014