Application of geotechnologies to land use and land cover in the Upper Paraíba River basin

Authors

  • José Hugo Simplicio de Sousa Universidade Federal de Campina Grande
  • Geroge do Nascimento Ribeiro Universidade Federal de Campina Grande
  • Paulo Roberto Megna Francisco Universidade Federal de Campina Grande
  • Wesley dos Santos Carvalho Universidade Católica Dom Bosco

DOI:

https://doi.org/10.31416/rsdv.v12i2.683

Keywords:

Caatinga, Vegetation cover, Sentinel-2

Abstract

Through improper human activities over the years in the Upper Paraíba River basin, there have been significant transformations in its ecosystem. Maps depicting the distribution of activities and types of land cover have become essential tools for providing accurate data in the process of managing river basins. With the arrival of cloud computing platforms and advances in machine learning classifiers, new outlets are appearing for more accurate, large-scale classification of land uses and land covers. This study sought to achieve an accurate classification of land use and land cover (LULC) patterns in the analyzed basin area, by the year 2021, using the Classification and Regression Trees (CART), Random Forest (RF) and Minimum Distance - Euclidean (MMD) classifiers. To calculate the accuracy of the process, parameters such as the Kappa Index, Overall Accuracy, Producer Accuracy and User Accuracy were used. The Google Earth Engine (GEE) cloud computing platform for creating and evaluating LULC maps resulted in an effective and agile product. The RF classifier excelled in distinguishing the various classes with high precision, obtaining reduced spectral interference and an accuracy of over 85%.

Author Biographies

José Hugo Simplicio de Sousa, Universidade Federal de Campina Grande

Undergraduate student in Biosystems Engineering at the Federal University of Campina Grande, CDSA/Campus Sumé with an emphasis on Remote Sensing and Geoprocessing. He worked as a scholarship holder for the Institutional Scientific Initiation Program - PIBIC, in projects in the area of Remote Sensing and Geoprocessing, between 2021 and 2023 and worked on a project in the area of food drying between 2020 and 2021. Former Associate Junior Consultant at SustemBIO Jr, the Junior Company of the Biosystems Engineering course at UFCG/CDSA.

Geroge do Nascimento Ribeiro, Universidade Federal de Campina Grande

He holds a bachelor's degree in Agronomy (2003) and a master's degree in Soil and Water Management and Conservation (2006) from the Federal University of Paraíba, a doctorate in Agricultural Engineering from the Federal University of Campina Grande (2014) and a post-doctorate in Alternative Energy Sources from the Graduate Program in Chemical Engineering at UFCG (LABFREN/UFCG). He is currently a professor at the Federal University of Campina Grande/CDSA/Campus Sumé. He has experience in the areas of Geosciences, with an emphasis on Remote Sensing (natural resources, geotechnologies and thematic mapping) and Renewable Energies (production of hydrogen as a fuel source for fuel cells and solar energy - photovoltaic panels).

Paulo Roberto Megna Francisco, Universidade Federal de Campina Grande

He worked as a Researcher for Regional Scientific Development for Internalization at the Federal University of Paraíba-UFPB-CCA/Areia. Graduated in Agricultural Engineering from UFCG. PhD student in Natural Resources (Concentration in Natural Resources Engineering). He has a PhD in Agricultural Engineering (Concentration in Irrigation and Drainage) from the Federal University of Campina Grande - UFCG (2013), a Master's Degree in Agronomy - Soil and Water Management (Concentration - Sustainable Agriculture and Environmental Planning) from the Federal University of Paraíba - UFPB - Areia (2010) and also graduated as an Agricultural Technologist (Mechanization) from the São Paulo State University Júlio de Mesquita Filho - UNESP - Bauru (1990). He has teaching experience in the field of Agronomy, with an emphasis on Agricultural Mechanization, Agricultural Machinery and Implements and Agro-technical Machinery. He works as a researcher, collaborator and advisor on projects at the UFPB Areia Campus, UFCG - Campina Grande Campus, Sumé Campus and Patos Campus. He has experience in technical soil classification and mapping, agricultural and pedoclimatic suitability, land use capacity, geoprocessing, cartography, remote sensing, geostatistics, water balance generation and climate indices. He has worked as a consultant for INCRA/PB on PDAs. He was an ad hoc consultant for CONFEA as organizer of CONTECC. Editor-in-chief of Editora Portal Tecnológico-EPTEC. He is currently working as an organizer and on the scientific committee of CNMA-Poços de Caldas.

Wesley dos Santos Carvalho, Universidade Católica Dom Bosco

Sanitary and Environmental Engineer, with a master's degree in Environmental Sciences and Agricultural Sustainability from the Dom Bosco Catholic University - UCDB. PhD student in Environmental Sciences and Agricultural Sustainability at the Dom Bosco Catholic University - UCDB. He currently works as a Fellow at the Technical Center of the Integrated Center for Environmental Protection and Research - CEIPPAM/UCDB, linked to the Public Prosecutor's Office of Mato Grosso do Sul. He worked as an intern at the Geoprocessing and Remote Sensing Center of the Public Prosecutor's Office of the State of Mato Grosso do Sul

References

AGUILAR, R.; ZURITA-MILLA, R.; IZQUIERDO-VERDIGUIER, E.; BY, R. A. A cloud-based multi-temporal ensemble classifier to map smallholder farming systems. Remote Sensing, v.10, n.5, e729, 2018.

ARPITHA, M.; AHMED, S. A.; HARISHNAIKA, N. Land use and land cover classification using machine learning algorithms in Google Earth Engine. Earth Science Informatics, v.1, n.1, p.1-17, 2023.

ABUBAKAR, G. A.; WANG, K.; KOKO, A. F.; HUSSEINI, M. I.; SHUKA, K. A. M.; DENG, J.; GAN, M. Mapping Maize Cropland and Land Cover in Semi-Arid Region in Northern Nigeria Using Machine Learning and Google Earth Engine. Remote Sensing, v.15, n.11, e2835, 2023.

AMANI, M.; MAHDAVI, S.; AFSHAR, M.; BRISCO, B.; HUANG, W.; MOHAMMAD JAVAD MIRZADEH, S.; WHITE, L.; BANKS, S.; MONTGOMERY, J.; HOPKINSON, C. Canadian wetland inventory using Google Earth Engine: The first map and preliminary results. Remote Sensing, v.11, n.7, e842, 2019.

ADAM, E.; MUTANGA, O.; ODINDI, J.; ABDEL-RAHMAN, E. M. Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers. International Journal of Remote Sensing, v.35, n.10, p.3440-3458, 2014.

ALTHOFF, T. D.; MENEZES, R. S. C.; SIQUEIRA PINTO, A.; PAREYN, F. G. C.; CARVALHO, A. L.; MARTINS, J. C. R.; CARVALHO, E. X.; SILVA, A. S. A.; DUTRA, E. D.; SAMPAIO, E. V. D. S. B. Adaptation of the century model to simulate C and N dynamics of Caatinga dry forest before and after deforestation. Agriculture, Ecosystems & Environment, v.254, n.1, p.26-34, 2018.

BOORI, M. S.; VOŽENÍLEK, V.; CHOUDHARY, K. Land use/cover disturbance due to tourism in Jeseníky Mountain, Czech Republic: A remote sensing and GIS based approach. The Egyptian Journal of Remote Sensing and Space Science, v.18, n.1, p.17-26, 2015.

BARBOSA NETO, M. V.; ARAÚJO, M. D. S. B.; ARAÚJO FILHO, J. C.; SAMPAIO, E. V. D. S. B.; ALMEIDA, B. G. Rill and sheet soil erosion estimation in an area undergoing desertification in the Brazilian semi-arid region. Modeling Earth Systems and Environment, v.7, n.1, p.1183-1191, 2021.

BRITO MORAIS, Y. C.; BEZERRA DE ARAÚJO, M. D. S.; MOURA, M. S. B.; GALVÍNCIO, J. D.; QUEIROGA MIRANDA, R. Análise do Sequestro de Carbono em áreas de Caatinga do Semiárido Pernambucano. Revista Brasileira de Meteorologia, v.32, n.1, p.585-599, 2017.

BECKER, W. R.; LÓ, T. B.; JOHANN, J. A.; MERCANTE, E. Statistical features for land use and land cover classification in Google Earth Engine. Remote Sensing Applications: Society and Environment, v.21, n.1, e100459, 2021.

BASHEER, S.; WANG, X.; FAROOQUE, A. A.; NAWAZ, R. A.; LIU, K.; ADEKANMBI, T.; LIU, S. Comparison of land use land cover classifiers using different satellite imagery and machine learning techniques. Remote Sensing, v.14, n.19, e4978, 2022.

BRITO, H. C.; BRITO, Y. M. A.; RUFINO, I. A. A. O índice de segurança hídrica do brasil e o semiárido brasileiro: desafios e riscos futuros. Revista Brasileira de Cartografia, v.74, n.1, p.1-15, 2022.

BREIMAN, L. Random forests. Machine Learning, v.45, n.1, p.5-32, 2001.

CARDOSO, E. C. M. Análise espaço-temporal do risco à desertificação no Auto Curso do rio Paraíba/PB a partir das modificações na caatinga. Trabalho de Conclusão de Curso (Graduação em Geografia) - Departamento de Geociências, Universidade Federal da Paraíba - UFPB, João Pessoa. 26p. 2016.

CUI, J.; ZHU, M.; LIANG, Y.; QIN, G.; LI, J.; LIU, Y. Land use/land cover change and their driving factors in the Yellow River Basin of Shandong Province based on google earth Engine from 2000 to 2020. International Journal of Geo-Information, v.11, n.3, e163, 2022.

CHURCHES, C. E.; WAMPLER, P. J.; SUN, W.; SMITH, A. J. Evaluation of forest cover estimates for Haiti using supervised classification of Landsat data. International Journal of Applied Earth Observation and Geoinformation, v.30, n.1, 203-216, 2014.

CARVALHO, W. S.; MAGALHÃES FILHO, F. J. C.; SANTOS, T. L. Uso e cobertura do solo utilizando a Plataforma Google Earth Engine (GEE): Estudo de caso em uma unidade de conservação. Brazilian Journal of Development, v.7, n.2, p.15280-15300, 2021.

CARVALHO, M. G. R. F. Estado da Paraíba: classificação geomorfológica. João Pessoa: Editora Universitária/UFPB, 1982. 72p.

CAMPBELL, J. B.; WYNNE, R. H. Introduction to remote sensing. 5 ed. New York: The Guilford Press, 2011. 667p.

CASTANHO, A. D.; COE, M.; ANDRADE, E. M.; WALKER, W.; BACCINI, A.; CAMPOS, D. A.; FARINA, M. A close look at above ground biomass of a large and heterogeneous Seasonally Dry Tropical Forest-Caatinga in North East of Brazil. Anais da Academia Brasileira de Ciências, v.92, n.1, 1-18, 2020.

DORNELLAS, P. C.; SEABRA, V. S.; XAVIER, R. A.; SILVA, R. M. Estimativa de perdas de solo na bacia do Alto Rio Paraíba, Região Semiárida do Estado da Paraíba. OKARA: Geografia em Debate, v.11, n.2, p.338-350, 2017.

DONG, J.; XIAO, X.; MENARGUEZ, M. A.; ZHANG, G.; QIN, Y.; THAU, D.; BIRADAR, C.; MOORE III, B. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sensing of Environment, v.185, n.1, p.142-154, 2016.

DOU, Y.; YANG, Y.; AN, S.; ZHU, Z. Effects of different vegetation restoration measures on soil aggregate stability and erodibility on the Loess Plateau, China. Catena, v.185, n.1, e104294, 2020.

FRANCISCO, P. R. M.; AYRES, G. D. J.; FARIAS, E. S. B.; MORAES NETO, J. M.; SILVA, V. F.; SANTOS, D.; OLIVEIRA, F. P. Detecção de mudanças da vegetação de caatinga da bacia hidrográfica do Alto Rio Paraíba através de técnicas de geoprocessamento. In: FRANCISCO, P. R. M.; FURTADO, D. A.; FERREIRA, A. C. (org.). Engenharia, Agronomia e Geociência 2014-2021. Campina Grande: EPTEC, pp. 37-53. 2021.

FRIEDL, M. A.; MCIVER, D. K.; HODGES, J. C.; ZHANG, X. Y.; MUCHONEY, D.; STRAHLER, A. H.; WOODCOCK, C. E.; GOPAL, S.; SCHNEIDER, A.; COOPER, A.; BACCINI, A.; GAO, F.; SCHAAF, C. Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of Environment, v.83, n.2, p.287-302, 2002.

FOODY, G. M.; MATHUR, A. A relative evaluation of multiclass image classification by support vector machines. Transactions on Geoscience and Remote Sensing, v.42, n.6, p.1335-1343, 2004.

FURTADO, A. V. B. D.? SOUZA, P. O. J. Mapeamento geomorfológico para a bacia do Alto Curso do Rio Paraíba de acordo com as normas do Manual Geomorfológico do IBGE. Revista de Geociências do Nordeste, v.2, n.1, p.83-91, 2016.

GORELICK, N.; HANCHER, M.; DIXON, M.; ILYUSHCHENKO, S.; THAU, D.; MOORE, R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, v.202, n.1, p.18-27, 2017.

Google Earth Engine. 2022. A planetary-scale platform for Earth science data & analysis. Disponível em: https://earthengine.google.com/. Acesso em: 10 de dezembro de 2022.

GOLDBLATT, R.; YOU, W.; HANSON, G.; KHANDELWAL, A. K. Detecting the boundaries of urban areas in india: A dataset for pixel-based image classification in google earth engine. Remote Sensing, v.8, n.8, e634, 2016.

GISLASON, P. O.; BENEDIKTSSON, J. A.; SVEINSSON, J. R. Random forests for land cover classification. Pattern Recognition Letters, v.27, n.4, p.294-300, 2006.

GHIMIRE, B.; ROGAN, J.; GALIANO, V. R.; PANDAY, P.; NEETI, N. An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA. GIScience & Remote Sensing, v.49, n.5, p.623-643, 2012.

HANSEN, M. C.; POTAPOV, P. V.; MOORE, R.; HANCHER, M.; TURUBANOVA, S. A.; TYUKAVINA, A.; THAU, D.; STEHMAN, S. V.; GOETZ, S. J.; LOVELAND, T. R.; KOMMAREDDY, A.; EGOROV, A.; CJINI, L.; JUSTICE, C. O.; TOWNSHEND, J. High-resolution global maps of 21st-century forest cover change. Science, v.342, n.6160, p.850-853, 2013.

JENSEN, J. R. Remote Sensing of the Environment: An Earth Resource. 2 ed. India: Pearson Education, 2009. 592p.

JOHANSEN, K.; PHINN, S.; TAYLOR, M. Mapping woody vegetation clearing in Queensland, Australia from Landsat imagery using the Google Earth Engine. Remote Sensing Applications: Society and Environment, v.1, n.1, p.36-49, 2015.

KARISHMA, C. G.; KANNAN, B.; NAGARAJAN, K.; PANNEERSELVAM, S.; PAZHANIVELAN, S. Land use land cover change detection in the lower Bhavani basin, Tamil Nadu, using geospatial techniques. Journal of Applied and Natural Science, v.14, n.1, p.58-64, 2022.

KOTHARI, N. S.; MEHER, S. K. Semisupervised classification of remote sensing images using efficient neighborhood learning method. Engineering Applications of Artificial Intelligence, v.90, n.1, e103520, 2020.

KELLEY, L. C.; PITCHER, L.; BACON, C. Using Google Earth engine to map complex shade-grown coffee landscapes in Northern Nicaragua. Remote Sensing, v.10, n.6, e952, 2018.

KARAMAGE, F.; SHAO, H.; CHEN, X.; NDAYISABA, F.; NAHAYO, L.; KAYIRANGA, A.; OMIFOLAJI, J. K.; LIU, T.; ZHANG, C. Deforestation effects on soil erosion in the Lake Kivu Basin, DR Congo-Rwanda. Forests, v.7, n.11, e281, 2018.

KHATAMI, R.; MOUNTRAKIS, G.; STEHMAN, S. V. A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research. Remote Sensing of Environment, v.177, n.1, p.89-100, 2016.

LU, D.; WENG, Q. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, v.28, n.5, p.823-870, 2007.

LI, X.; MESSINA, J. P.; MOORE, N. J.; FAN, P.; SHORTRIDGE, A. M. MODIS land cover uncertainty in regional climate simulations. Climate Dynamics, v.49, n.11, p.4047-4059, 2017.

LAWRENCE, R. L.; WRIGHT, A. Rule-based classification systems using classification and regression tree (CART) analysis. Photogrammetric Engineering and Remote Sensing, v.67, n.10, p.1137-1142, 2001.

MATHER, P.; TSO, B. Classification methods for remotely sensed data. Boca Raton: CRC Press, 2009. 349p.

MAXWELL, A. E.; WARNER, T. A.; FANG, F. Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing, v.39, n.9, p.2784-2817, 2018.

MACHADO, P. D. O.; TORRES, F. T. P. Introdução à Hidrogeografia. 1.ed. São Paulo: Cengage Learning, 2012. 178p.

MITCHELL, T. M. Machine Learning. 1 ed. New York: McGraw-Hill, 1997. 414p.

MURTAZA, K. O.; ROMSHOO, S. A. Determining the suitability and accuracy of various statistical algorithms for satellite data classification. International Journal of Geomatics and Geosciences, v.4, n.4, p.585-599, 2014.

MENEZES, R. S. C.; SAMPAIO, E. V. S. B.; GIONGO, V.; PÉREZ-MARIN, A. M. Biogeochemical cycling in terrestrial ecosystems of the Caatinga Biome. Brazilian Journal of Biology, v.72, n.3, p.643-653, 2012.

MUBAKO, S.; BELHAJ, O.; HEYMAN, J.; HARGROVE, W.; REYES, C. Monitoring of land use/land-cover changes in the arid transboundary middle Rio grande basin using remote sensing. Remote Sensing, v.10, n.12, e2005, 2018.

NEVES, L. F. S.; NEVES, S. M. A. S.; CANALE, G. R. Análise da fragmentação de Cerrado na bacia hidrográfica do rio Aguapeí, Porto Esperidião (MT): um estudo de caso a partir das geotecnologias e métricas da paisagem. Ateliê Geográfico, v.8, n.2, p.130-149, 2014.

PEREZ-MARIN, A. M.; MENEZES, R. S. C.; SILVA, E. D.; SAMPAIO, E. V. D. S. B. Efeito da Gliricidia sepium sobre nutrientes do solo, microclima e produtividade do milho em sistema agroflorestal no Agreste Paraibano. Revista Brasileira de Ciência do Solo, v.30, n.3, p.555-564, 2006.

PINTO, G. D. R. M.; CENTENO, J. A. S. Monitoramento do espelho d'água da represa do Vossoroca entre 2010-2015 usando dados Landsat. Revista Brasileira de Geomática, v.4, n.3, p.163-168, 2016.

PATEL, N. N.; ANGIULI, E.; GAMBA, P.; GAUGHAN, A.; LISINI, G.; STEVENS, F. R.; TATEM, A. J.; TRIANNI, G. Multitemporal settlement and population mapping from Landsat using Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, v.35, n.1, p.199-208, 2015.

PADARIAN, J.; MINASNY, B.; MCBRATNEY, A. B. Using Google's cloud-based platform for digital soil mapping. Computers & Geosciences, v.83, n.1, p.80-88, 2015.

RAWAT, J. S.; KUMAR, M. Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, v.18, n.1, p.77-84, 2015.

SHELESTOV, A.; LAVRENIUK, M.; KUSSUL, N.; NOVIKOV, A.; SKAKUN, S. Exploring Google Earth Engine platform for big data processing: Classification of multi-temporal satellite imagery for crop mapping. Frontiers in Earth Science, v.5, n.1, e17, 2017.

SOUSA, J. H. S.; MOREIRA, A. R.; NASCIMENTO, A. A.; RIBEIRO, G. N.; NETO, J. N. O.; PRADO JÚNIOR, L. S. Assessment of land use and cover in the Sucuru Watershed using Google Earth Engine. Revista Verde de Agroecologia e Desenvolvimento Sustentável, v.17, n.4, p.235-241, 2022.

SOUSA, J. H. S.; RIBEIRO, G. N.; FRANCISCO, P. R. M.; NASCIMENTO, A. A.; ALVES, J. I. P.; CAVALCANTI, M. Classificação e mapeamento do uso e cobertura das terras da bacia hidrográfica do rio Taperoá-PB utilizando o Google Earth Engine. Revista Geama, v.9, n.2, p.44–52, 2023.

SAMPAIO, E. V. S. B.; SAMPAIO, Y. D. S. B.; VITAL, T.; ARAÚJO, M. S. B.; SAMPAIO, G. R. Desertificação no Brasil: conceitos, núcleos e tecnologias de recuperação e convivência. Recife: Editora Universitária/UFPE, 2003. 202p.

SANTOS ALVES, W.; MARTINS, A. P.; PÔSSA, É. M.; MOURA, D. M. B.; MORAIS, W. A.; FERREIRA, R. S.; SANTOS, L. N. S. Geotechnologies applied in the analysis of land use and land cover (LULC) transition in a hydrographic basin in the Brazilian Cerrado. Remote Sensing Applications: Society and Environment, v.22, n.1, e100495, 2021.

STEHMAN, S. V.; FOODY, G. M. Key issues in rigorous accuracy assessment of land cover products. Remote Sensing of Environment, v.231, n.1, e111199, 2019.

SOUSA, J. H. S. Aplicação do algoritmo Random Forest para classificação do uso e cobertura da terra na Bacia Hidrográfica do Rio Sucuru utilizando o Google Earth Engine. Trabalho de Conclusão de Curso (Graduação em Engenharia de Biossistemas) - Centro de Desenvolvimento Sustentável do Semiárido, Universidade Federal de Campina Grande, Sumé. 43p. 2023.

SANTOS, A. H. L. Análise dos estilos fluviais e da capacidade de ajuste do Alto Curso do Rio Paraíba – PB. Trabalho de Conclusão de Curso (Graduação em Geografia) - Departamento de Geociências, Universidade Federal da Paraíba, João Pessoa. 87p. 2017.

SILVA, P. C. G.; MOURA, M. S. B.; KIILL, L.H. P.; BRITO, L. T. L.; PEREIRA, L. A.; SÁ, I. B.; CORREIA, R. C.; TEIXEIRA, A. H. C.; CUNHA, T. J. F.; GUIMARÃES-FILHO, C. G. Caracterização do Semiárido brasileiro: fatores naturais e humanos. In: SA, I. B.; SILVA, P. C. G. (org). Semiárido brasileiro: pesquisa, desenvolvimento e inovação. Petrolina: Embrapa Semiárido, pp. 18-48. 2010.

SANTANA, I. L. Classificação do uso e cobertura da terra utilizando algoritmo de aprendizado de máquina no Google Earth Engine para o semiárido de Pernambuco. Dissertação (Mestrado em Tecnologias Energéticas e Nucleares) - Departamento De Energia Nuclear, Universidade Federal de Pernambuco - UFPE, Recife. 61p. 2021.

SCEPAN, J. Thematic validation of high-resolution global land-cover data sets. Photogrammetric Engineering and Remote Sensing, v.65, n.9, p.1051-1060, 1999.

SHALABY, A.; TATEISHI, R. 2007. Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Applied Geography, 27, (1), 28-41.

UMETSU, R. K.; PEREIRA, N.; CAMPOS, É. M. F. D. P. D.; UMETSU, C. A.; MENDONÇA, R. A. M. D.; BERNASCONI, P.; CAMARGO, M. F. Análise morfométrica e socioambiental de uma bacia hidrográfica amazônica, Carlinda, MT. Revista Árvore, v.36, n.1, p.83-92, 2012.

VELASTEGUI-MONTOYA, A.; MONTALVÁN-BURBANO, N.; CARRIÓN-MERO, P.; RIVERA-TORRES, H.; SADECK, L.; ADAMI, M. Google Earth Engine: A Global Analysis and Future Trends. Remote Sensing, v.15, n.14, e3675, 2023.

VIANA, C. M.; GIRÃO, I.; ROCHA, J. Long-term satellite image time-series for land use/land cover change detection using refined open source data in a rural region. Remote Sensing, v.11, n.9, e1104, 2019.

VANACKER, V.; AMEIJEIRAS-MARIÑO, Y.; SCHOONEJANS, J.; CORNÉLIS, J. T.; MINELLA, J. P.; LAMOULINE, F.; VERMEIRE, M. L.; CAMPFORTS, B.; ROBINET, J.; VAN DE BROEK, M.; DELMELLE, P.; OPFERGELT, S. Land use impacts on soil erosion and rejuvenation in Southern Brazil. Catena, v.178, n.1, p.256-266, 2019.

VERGOLINO, J. R.; DANTAS, M. Os determinantes do processo de urbanização da região Nordeste do Brasil: 1970-1996. Revista de Economia, v.31, n.2, p.7-33, 2005.

WANG, C.; GAO, Q.; WANG, X.; YU, M. Spatially differentiated trends in urbanization, agricultural land abandonment and reclamation, and woodland recovery in Northern China. Scientific Reports, v.6, n.1, e37658, 2016.

XAVIER, R. A.; DORNELLAS, P. C.; MACIEL, J. S.; BÚ, J. C. Caracterização do regime fluvial da Bacia hidrográfica do Rio Paraíba – PB. Revista Tamoios, v.8, n.2, p.15-28, 2012.

YU, L.; LIANG, L.; WANG, J.; ZHAO, Y.; CHENG, Q.; HU, L.; LIU, S.; YU, L.; WANG, X.; ZHU, P.; LI, X.; XU, Y.; LI, C.; FU, W.; LI, X.; LI, W.; LIU, C.; CONG, N.; ZHANG, H.; SUN, F.; BI, X.; XIN, Q.; LI, D.; YAN, D.; ZHU, Z.; GOODCJILD, M. F.; GONG, P. Meta-discoveries from a synthesis of satellite-based land-cover mapping research. International Journal of Remote Sensing, v.35, n.13, p.4573-4588, 2014.

ZHAO, Y.; YANG, S.; LIU, L.; JIA, X.; HAN, L.; YUAN, X.; ZHAO, M.; ZHANG, P. Variations and driving mechanisms of desertification in the southeast section of the China-Mongolia-Russia Economic Zone. Science of The Total Environment, v.887, n.1, e164004, 2023.

ZURQANI, H. A.; POST, C. J.; MIKHAILOVA, E. A.; SCHLAUTMAN, M. A.; SHARP, J. L. Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, v.69, n.1, p.175-185, 2018.

ZHANG, D. D.; ZHANG, L. Land cover change in the central region of the lower Yangtze River based on Landsat imagery and the Google Earth Engine: A case study in Nanjing, China. Sensors, v.20, n.7, e2091, 2020.

Published

2024-06-07

How to Cite

SOUSA, J. H. S. de; RIBEIRO, G. do N.; FRANCISCO, P. R. M.; CARVALHO, W. dos S. Application of geotechnologies to land use and land cover in the Upper Paraíba River basin. Revista Semiárido De Visu, [S. l.], v. 12, n. 2, p. 644–657, 2024. DOI: 10.31416/rsdv.v12i2.683. Disponível em: https://semiaridodevisu.ifsertao-pe.edu.br/index.php/rsdv/article/view/683. Acesso em: 21 dec. 2024.