Systematic Literature Review on the Use of Remote Sensing and Machine Learning-Based Algorithms in Tropical Dry Forest Mapping
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Abstract
Tropical dry forests are a forest ecosystem notably marked by climatic seasonality and predominance of tree species that present marked deciduousness during the dry season and great perenniality during the wet season. These environments are home to a rich diversity of fauna and flora, playing a crucial role in the quality of life of millions of people by providing essential ecosystem services. Due to their occurrence in fertile soils, there have been increasing records of anthropic pressures on their forest remnants for conversion to agriculture. For this reason, the demand for environmental monitoring actions has been growing. In this sense, the use of Machine Learning (ML) methods applied to remote sensing of tropical dry forests has stood out in recent literature. Thus, the present study proposed, through a systematic literature review, a quantitative evaluation of the main remote sensing and ML algorithms used in recent years, so that it was possible to obtain an overview of current methods, as well as promising topics for future research. As main results, it was found that the main approaches have prioritized the use of the Random Forest algorithm and data from optical sensors. By carrying out this review, it was observed that opportunities for future research lie in the analysis of the integrated use of SAR and optical sensors, as well as in comparative evaluation between different ML algorithms, so that new methods can be evaluated for progress in the monitoring and conservation of dry tropical forests.
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Abade, N., Carvalho Júnior, O. A., Guimarães, R., & De Oliveira, S. (2015). Comparative Analysis of MODIS Time-Series Classification Using Support Vector Machines and Methods Based upon Distance and Similarity Measures in the Brazilian Cerrado-Caatinga Boundary. Remote Sensing, 7(9), 12160–12191. https://doi.org/10.3390/rs70912160
Agrawal, S., & Khairnar, G. B. (2019). A comparative assessment of remote sensing imaging techniques: Optical, sar and lidar. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-5/W3, 1–6. https://doi.org/10.5194/isprs-archives-XLII-5-W3-1-2019
Al Shafian, S., & Hu, D. (2024). Integrating Machine Learning and Remote Sensing in Disaster Management: A Decadal Review of Post-Disaster Building Damage Assessment. Buildings, 14(8), 2344. https://doi.org/10.3390/buildings14082344
Alba, E., Alexandre, M. L. D. S., Marchesan, J., De Souza, L. S. B., Bezerra, A. C., & Silva, E. A. (2022). Comparação entre Algoritmos de Aprendizado de Máquina para a Identificação de Floresta Tropical Sazonalmente Seca. Anuário do Instituto de Geociências, 45, 1–10. https://doi.org/10.11137/1982-3908_2022_45_40758
Andres‐Mauricio, J., Valdez‐Lazalde, J. R., George‐Chacón, S. P., & Hernández‐Stefanoni, J. L. (2021). Mapping structural attributes of tropical dry forests by combining Synthetic Aperture Radar and high‐resolution satellite imagery data. Applied Vegetation Science, 24(2), e12580. https://doi.org/10.1111/avsc.12580
Azevedo, D., Urias, G., & Oliveira, L. L. D. (2023). A revisão de literatura como metódo de pesquisa na geografia: Uma scoping review. Boletim Paulista de Geografia, 109(1), 65–88. https://doi.org/10.54446/bpg.v109i1.2955
Barboza, E., Salazar, W., Gálvez-Paucar, D., Valqui-Valqui, L., Saravia, D., Gonzales, J., Aldana, W., Vásquez, H. V., & Arbizu, C. I. (2022). Cover and Land Use Changes in the Dry Forest of Tumbes (Peru) Using Sentinel-2 and Google Earth Engine Data. The 3rd International Electronic Conference on Forests—Exploring New Discoveries and New Directions in Forests, 2. https://doi.org/10.3390/IECF2022-13095
Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
Bendini, H. N., Fonseca, L. M. G., Matosak, B. M., Mariano, R. F., Haidar, R. F., & Valeriano, D. M. (2022). Evaluating the Separability Between Dry Tropical Forests and Savanna Woodlands in the Brazilian Savanna Using Landsat Dense Image Time Series and Artificial Intelligence. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences., XLIII-B3-2022, 841–847.
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Campos, V. E., & Figueroa-Masanet, A. (2024). A systematic review of remote sensing data to assess dry forests attributes. Bosque (Valdivia), 45(1), 17–41. https://doi.org/10.4067/s0717-92002024000100017
Cardoso, P. V., Seabra, V. da S., Xavier, R. A., Rodrigues, E. de M., & Gomes, A. S. (2021). Mapeamento de áreas de caatinga através do random forrest: Estudo de caso na bacia do rio taperoá. Geoaraguaia, 11(Especial Geotecnologias), 55–68. https://periodicoscientificos.ufmt.br/ojs/index.php/geo/article/view/12743/8441
Carvalho Júnior, O. A. (2018). Aplicações e perspectivas do sensoriamento remoto para o mapeamento de áreas inundáveis. Revista de Geografia, 35(4), 412–431. https://doi.org/10.51359/2238-6211.2018.238239
Chaves, M., Picoli, M., & Sanches, I. (2020). Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review. Remote Sensing, 12(18), 3062. https://doi.org/10.3390/rs12183062
Farrick, K. K., & Branfireun, B. A. (2013). Left high and dry: A call to action for increased hydrological research in tropical dry forests. Hydrological Processes, 27(22), 3254–3262. https://doi.org/10.1002/hyp.9935
Ganem, K. A., Xue, Y., Rodrigues, A. D. A., Franca-Rocha, W., Oliveira, M. T. D., Carvalho, N. S. D., Cayo, E. Y. T., Rosa, M. R., Dutra, A. C., & Shimabukuro, Y. E. (2022). Mapping South America’s Drylands through Remote Sensing—A Review of the Methodological Trends and Current Challenges. Remote Sensing, 14(3), 736. https://doi.org/10.3390/rs14030736
Gao, Y., Solórzano, J. V., Estoque, R. C., & Tsuyuzaki, S. (2023). Tropical Dry Forest Dynamics Explained by Topographic and Anthropogenic Factors: A Case Study in Mexico. Remote Sensing, 15(5), 1471. https://doi.org/10.3390/rs15051471
Garcia-Millan, V. E., Sanchez-Azofeifa, G. A., & Malvarez, G. C. (2015). Mapping Tropical Dry Forest Succession With CHRIS/PROBA Hyperspectral Images Using Nonparametric Decision Trees. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6), 3081–3094.
Garcia-Millan, V., & Sanchez-Azofeifa, A. (2018). Quantifying Changes on Forest Succession in a Dry Tropical Forest Using Angular-Hyperspectral Remote Sensing. Remote Sensing, 10(12), 1865. https://doi.org/10.3390/rs10121865
Gyamfi-Ampadu, E., & Gebreslasie, M. (2021). Two Decades Progress on the Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review. Forests, 12(6), 739. https://doi.org/10.3390/f12060739
Hermuche, P., & Sano, E. (2011). Identificação da floresta estacional decidual no vão do paranã, estado de goiás, a partir da análise da reflectância acumulada de imagens do sensor etm+/landsat-7. Revista Brasileira de Cartografia, 63(3). https://doi.org/10.14393/rbcv63n3-43750
Hernández-Stefanoni, J. L., Castillo-Santiago, M. Á., Mas, J. F., Wheeler, C. E., Andres-Mauricio, J., Tun-Dzul, F., George-Chacón, S. P., Reyes-Palomeque, G., Castellanos-Basto, B., Vaca, R., & Dupuy, J. M. (2020). Improving aboveground biomass maps of tropical dry forests by integrating LiDAR, ALOS PALSAR, climate and field data. Carbon Balance and Management, 15(1), 15. https://doi.org/10.1186/s13021-020-00151-6
Herrmann, P. B., Nascimento, V. F., & Freitas, M. W. D. D. (2022). Sensoriamento Remoto Aplicado à Análise de Fogo em Formações Campestres: Uma Re-visão Sistemática. Revista Brasileira de Cartografia, 74(2), 437–458. https://doi.org/10.14393/rbcv74n2-63739
Hesketh, M., & Sanchez-Azofeifa, A. (2014). A Review of Remote Sensing of Tropical Dry Forests. Em A. Sanchez-Azofeifa, J. S. Powers, G. W. Fernandes, & M. Quesada (Orgs.), Tropical Dry Forests in the Americas: Ecology, Conservation and Management (p. 83–100). CRC Press.
Huechacona-Ruiz, A. H., Dupuy, J. M., Schwartz, N. B., Powers, J. S., Reyes-García, C., Tun-Dzul, F., & Hernández-Stefanoni, J. L. (2020). Mapping Tree Species Deciduousness of Tropical Dry Forests Combining Reflectance, Spectral Unmixing, and Texture Data from High-Resolution Imagery. Forests, 11(11), 1234. https://doi.org/10.3390/f11111234
Klompenburg, T., Kassahun, A., & Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics in Agriculture, 177, 105709. https://doi.org/10.1016/j.compag.2020.105709
Lechner, A. M., Foody, G. M., & Boyd, D. S. (2020). Applications in Remote Sensing to Forest Ecology and Management. One Earth, 2(5), 405–412. https://doi.org/10.1016/j.oneear.2020.05.001
Li, W., Cao, S., Campos-Vargas, C., & Sanchez-Azofeifa, A. (2017). Identifying tropical dry forests extent and succession via the use of machine learning techniques. International Journal of Applied Earth Observation and Geoinformation, 63, 196–205. https://doi.org/10.1016/j.jag.2017.08.003
Lycarião, D., Roque, R., & Costa, D. (2023). Revisão Sistemática de Literatura e Análise de Conteúdo na Área da Comunicação e Informação: O problema da confiabilidade e como resolvê-lo. Transinformação, 35, e220027. https://doi.org/10.1590/2318-0889202335e220027
Maxwell, A. E., Warner, T. A., & Fang, F. (2018). Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing, 39(9), 2784–2817. https://doi.org/10.1080/01431161.2018.1433343
Miles, L., Newton, A. C., DeFries, R. S., Ravilious, C., May, I., Blyth, S., Kapos, V., & Gordon, J. E. (2006). A global overview of the conservation status of tropical dry forests. Journal of Biogeography, 33(3), 491–505. https://doi.org/10.1111/j.1365-2699.2005.01424.x
Pandey, P. C., Koutsias, N., Petropoulos, G. P., Srivastava, P. K., & Ben Dor, E. (2021). Land use/land cover in view of earth observation: Data sources, input dimensions, and classifiers—a review of the state of the art. Geocarto International, 36(9), 957–988. https://doi.org/10.1080/10106049.2019.1629647
Pennington, R. T., Prado, D. E., & Pendry, C. A. (2000). Neotropical seasonally dry forests and Quaternary vegetation changes. Journal of Biogeography, 27(2), 261–273. https://doi.org/10.1046/j.1365-2699.2000.00397.x
Portal de Periódicos CAPES. (2024). Acesso CAFe. https://www.periodicos.capes.gov.br
Reyes-Palomeque, G., Dupuy, J. M., Portillo-Quintero, C. A., Andrade, J. L., Tun-Dzul, F. J., & Hernández-Stefanoni, J. L. (2021). Mapping forest age and characterizing vegetation structure and species composition in tropical dry forests. Ecological Indicators, 120, 106955. https://doi.org/10.1016/j.ecolind.2020.106955
Rocha, A. M., Leite, M. E., & Espírito-Santo, M. M. D. (2020). Monitoring of Brazilian Deciduous Seasonal Forest by Remote Sensing. Mercator, 19(2020), 1–20. https://doi.org/10.4215/rm2020.e19022
Sampaio, R., & Mancini, M. (2007). Estudos de revisão sistemática: Um guia para síntese criteriosa da evidência científica. Revista Brasileira de Fisioterapia, 11(1), 83–89. https://doi.org/10.1590/S1413-35552007000100013
Schröder, J. M., Rodríguez, L. P., & Günter, S. (2021). Research trends: Tropical dry forests: The neglected research agenda? Forest Policy and Economics, 122, 102333. https://doi.org/10.1016/j.forpol.2020.102333
Sesnie, S., Espinosa, C., Jara-Guerrero, A., & Tapia-Armijos, M. (2023). Ensemble Machine Learning for Mapping Tree Species Alpha-Diversity Using Multi-Source Satellite Data in an Ecuadorian Seasonally Dry Forest. Remote Sensing, 15(3), 583. https://doi.org/10.3390/rs15030583
Shafri, H. Z. M. (2017). Machine Learning in Hyperspectral and Multispectral Remote Sensing Data Analysis. Artificial Intelligence Science and Technology, 3–9. https://doi.org/10.1142/9789813206823_0001
Shimizu, K., Ota, T., & Mizoue, N. (2019). Detecting Forest Changes Using Dense Landsat 8 and Sentinel-1 Time Series Data in Tropical Seasonal Forests. Remote Sensing, 11(16), 1899. https://doi.org/10.3390/rs11161899
Singh, C., Karan, S. K., Sardar, P., & Samadder, S. R. (2022). Remote sensing-based biomass estimation of dry deciduous tropical forest using machine learning and ensemble analysis. Journal of Environmental Management, 308, 114639. https://doi.org/10.1016/j.jenvman.2022.114639
Smith, W. K., Dannenberg, M. P., Yan, D., Herrmann, S., Barnes, M. L., Barron-Gafford, G. A., Biederman, J. A., Ferrenberg, S., Fox, A. M., Hudson, A., Knowles, J. F., MacBean, N., Moore, D. J. P., Nagler, P. L., Reed, S. C., Rutherford, W. A., Scott, R. L., Wang, X., & Yang, J. (2019). Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities. Remote Sensing of Environment, 233, 111401. https://doi.org/10.1016/j.rse.2019.111401
Talukdar, S., Singha, P., Mahato, S., Shahfahad, Pal, S., Liou, Y.-A., & Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review. Remote Sensing, 12(7), 1135. https://doi.org/10.3390/rs12071135
Vargas-Sanabria, D., & Campos-Vargas, C. (2018). Sistema multi-algoritmo para la clasificación de coberturas de la tierra en el bosque seco tropical del Área de Conservación Guanacaste, Costa Rica. Revista Tecnología en Marcha, 31(1), 58–69. https://doi.org/10.18845/tm.v31i1.3497
Verhegghen, A., Kuzelova, K., Syrris, V., Eva, H., & Achard, F. (2022). Mapping Canopy Cover in African Dry Forests from the Combined Use of Sentinel-1 and Sentinel-2 Data: Application to Tanzania for the Year 2018. Remote Sensing, 14(6), 1522. https://doi.org/10.3390/rs14061522
Vizzari, M. (2022). PlanetScope, Sentinel-2, and Sentinel-1 Data Integration for Object-Based Land Cover Classification in Google Earth Engine. Remote Sensing, 14(11), 2628. https://doi.org/10.3390/rs14112628
Wohlfart, C., Wegmann, M., & Leimgruber, P. (2014). Mapping Threatened Dry Deciduous Dipterocarp Forest in South-East Asia for Conservation Management. Tropical Conservation Science, 7(4), 597–613. https://doi.org/10.1177/194008291400700402
Zeferino, L. B., Souza, L. F. T. D., Amaral, C. H. D., Fernandes Filho, E. I., & Oliveira, T. S. D. (2020). Does environmental data increase the accuracy of land use and land cover classification? International Journal of Applied Earth Observation and Geoinformation, 91, 102128. https://doi.org/10.1016/j.jag.2020.102128