Mapeamentos Nacionais e Globais da Cobertura e Uso da Terra no Geoparque Mundial da UNESCO Caminhos dos Cânions do Sul: Conceitos, Metodologias e Tendências
DOI:
https://doi.org/10.26848/rbgf.v19.02.p831-852Keywords:
Land Cover and Land Use, Environment, GeoparkAbstract
Land cover and land use (LCLU) maps delineate different typologies and arrangements of human activity and the environment. The methods of LCLU mapping are constantly evolving, ranging from field surveys and photointerpretation to sattelite image classification by algorithms. The objective of this study is to analyze different LCLU maps, in particular those with national or global coverage. Through the exploration of concepts related to LCLU and the selection and analysis of the LCLU maps, the objective is to understand the similarities and differences between the methods used. The methodology consisted of bibliographic research and the spatialization of LCLU maps in the area of the Caminhos dos Cânions do Sul UNESCO Global Geopark. The LCLU maps analyzed were the Vegetation Mapping of Brazil, the Monitoring of Land Cover and Land Use, MapBiomas, ESA-CCI mapping, and ESRI mapping. The results obtained showed the concepts related to LCLU, as well as the trend of mapping large areas by satellite image classification by algorithms rather than photointerpretation and field surveys. The results highlighted the differences between classification systems and methodologies; however, it was possible to verify the similarity in the distribution and proportion of classes. Finally, due to the lower resource requirements and continuous technological advancement, it was noted that recent LCLU maps rely on new algorithms, new forms of image access, and new means of data processing.
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Abdi, A. M. (2020). Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GIScience & Remote Sensing, 57(1), 1–20. https://doi.org/10.1080/15481603.2019.165044 DOI: https://doi.org/10.1080/15481603.2019.1650447
Ahlqvist, O. (2016). Semantic Issues in Land-Cover Analysis Representation, Analysis, and Visualization. Em C. P. Giri (Org.), Remote Sensing of Land Use and Land Cover: Principles and Applications. CRC Press. https://doi.org/10.1201/b11964
Arpitha, M., Ahmed, S. A., & Harishnaika, N. (2023). Land use and land cover classification using machine learning algorithms in google earth engine. Earth Science Informatics, 16(4), 3057–3073. Scopus. https://doi.org/10.1007/s12145-023-01073-w DOI: https://doi.org/10.1007/s12145-023-01073-w
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. Scopus. https://doi.org/10.1023/A:1010933404324 DOI: https://doi.org/10.1023/A:1010933404324
Brown, C. F., Brumby, S. P., Guzder-Williams, B., Birch, T., Hyde, S. B., Mazzariello, J., Czerwinski, W., Pasquarella, V. J., Haertel, R., Ilyushchenko, S., Schwehr, K., Weisse, M., Stolle, F., Hanson, C., Guinan, O., Moore, R., & Tait, A. M. (2022). Dynamic World, Near real-time global 10 m land use land cover mapping. Nature, 9(1), 251. https://doi.org/10.1038/s41597-022-01307-4 DOI: https://doi.org/10.1038/s41597-022-01307-4
Burrell, A. L., Evans, J. P., & De Kauwe, M. G. (2020). Anthropogenic climate change has driven over 5 million km2 of drylands towards desertification. Nature Communications, 11(1). Scopus. https://doi.org/10.1038/s41467-020-17710-7 DOI: https://doi.org/10.1038/s41467-020-17710-7
Camara, G., Simoes, R., Souza, F., Menino, F., Pelletier, C., Andrade, P. R., Ferreira, K., & Queiroz, G. (2025). sits: Satellite Image Time Series Analysis on Earth Observation Data Cubes. National Institute for Space Research (INPE). https://e-sensing.github.io/sitsbook/machine-learning-for-data-cubes.html
Capoane, V., & Fushimi, M. (2023). Alterações antropogeomorfológicas na bacia hidrográfica Córrego Lajeado, Campo Grande—MS. GEOUSP, 27, e. https://doi.org/10.11606/issn.2179-0892.geousp.2023.196393 DOI: https://doi.org/10.11606/issn.2179-0892.geousp.2023.196393
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. 13-17-August-2016, 785–794. Scopus. https://doi.org/10.1145/2939672.2939785 DOI: https://doi.org/10.1145/2939672.2939785
Congalton, R. G., & Green, K. (2019). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Third Edition (3o ed.). CRC Press. https://doi.org/10.1201/9780429052729 DOI: https://doi.org/10.1201/9780429052729
Dabiri, Z., & Blaschke, T. (2019). Scale matters: A survey of the concepts of scale used in spatial disciplines. European Journal of Remote Sensing, 52(1), 419–435. Scopus. https://doi.org/10.1080/22797254.2019.1626291 DOI: https://doi.org/10.1080/22797254.2019.1626291
Di Gregorio, A. (2016). Land Cover Classification System—Classification. https://openknowledge.fao.org/server/api/core/bitstreams/bb3fe826-5869-49c1-9b3f-87a160de8403/content
Diuk-Wasser, M. A., Vanacker, M. C., & Fernandez, M. P. (2021). Impact of Land Use Changes and Habitat Fragmentation on the Eco-epidemiology of Tick-Borne Diseases. Journal of Medical Entomology, 58(4), 1546–1564. Scopus. https://doi.org/10.1093/jme/tjaa209 DOI: https://doi.org/10.1093/jme/tjaa209
Duro, D. C., Franklin, S. E., & Dubé, M. G. (2012). A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118, 259–272. https://doi.org/10.1016/j.rse.2011.11.020 DOI: https://doi.org/10.1016/j.rse.2011.11.020
ESA. Agência Espacial Européia, (2017). Land Cover CCI: PRODUCT USER GUIDE. https://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf
Fan, X., Chen, L., Xu, X., Yan, C., Fan, J., & Li, X. (2023). Land Cover Classification of Remote Sensing Images Based on Hierarchical Convolutional Recurrent Neural Network. Forests, 14(9), Artigo 9. https://doi.org/10.3390/f14091881 DOI: https://doi.org/10.3390/f14091881
FAO. Organização das Nações Unidas para a Agricultura e Alimentação, (2016). Map Accuracy Assessment and Area Estimation: A practical Guide. Roma.
FAO. Organização das Nações Unidas para Alimento e Agricultura, (2024). SDG 15.4.2 Computation Tools Documentation. Roma. https://mgci-docs.readthedocs.io/en/latest/
García-Álvarez, D., Olmedo, M. T. C., & Paegelow, M. (2019). Sensitivity of a common Land Use Cover Change (LUCC) model to the Minimum Mapping Unit (MMU) and Minimum Mapping Width (MMW) of input maps. Computers, Environment and Urban Systems, 78. Scopus. https://doi.org/10.1016/j.compenvurbsys.2019.101389 DOI: https://doi.org/10.1016/j.compenvurbsys.2019.101389
Georganos, S., Grippa, T., Vanhuysse, S., Lennert, M., Shimoni, M., & Wolff, E. (2018). Very High Resolution Object-Based Land Use-Land Cover Urban Classification Using Extreme Gradient Boosting. IEEE Geoscience and Remote Sensing Letters, 15(4), 607–611. Scopus. https://doi.org/10.1109/LGRS.2018.2803259 DOI: https://doi.org/10.1109/LGRS.2018.2803259
Giri, C. P. (2016). Brief Overview of Remote Sensing of Land Cover. Em C. P. Giri (Org.), Remote Sensing of Land Use and Land Cover: Principles and Applications. CRC Press. https://doi.org/10.1201/b11964
Giuliani, G. (2024). Time-first approach for land cover mapping using big Earth observation data time-series in a data cube – a case study from the Lake Geneva region (Switzerland). Big Earth Data, 8(3), 435–466. https://doi.org/10.1080/20964471.2024.2323241 DOI: https://doi.org/10.1080/20964471.2024.2323241
Glimskär, A., & Skånes, H. (2018). Land Type Categories as a Complement to Land Use and Land Cover Attributes in Landscape Mapping and Monitoring. Em O. Ahlqvist, D. Varanka, S. Fritz, & K. Janowicz (Org.), Land Use and Land Cover Semantics: Principles, Best Practices, and Prospects. CRC Press. https://doi.org/10.1201/9781351228596
Gribb, W. J., & Czerniak, R. J. (2018). Land Use/Land Cover Classification Systems and Their Relationship to Land Planning. Em O. Ahlqvist, D. Varanka, S. Fritz, & K. Janowicz (Org.), Land Use and Land Cover Semantics: Principles, Best Practices, and Prospects. CRC Press. https://doi.org/10.1201/978135122859
Guo, M., Li, J., Sheng, C., Xu, J., & Wu, L. (2017). A review of wetland remote sensing. Sensors (Switzerland), 17(4). Scopus. https://doi.org/10.3390/s17040777 DOI: https://doi.org/10.3390/s17040777
Harper, K., Lamarche, C., Hartley, A., Peylin, P., Ottle, C., Bastrikov, V., San Martin, R., Bohnenstengel, S., Kirches, G., Boettcher, M., Shevchuk, R., Brockmann, C., & Defourny, P. (2023). A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models. Earth System Science Data, 15, 1465–1499. https://doi.org/10.5194/essd-15-1465-2023 DOI: https://doi.org/10.5194/essd-15-1465-2023
Herold, M., Woodcock, C. E., Gregorio, A. di, Mayaux, P., Belward, A. S., Latham, J., & Schmullius, C. C. (2006). A joint initiative for harmonization and validation of land cover datasets. IEEE Transactions on Geoscience and Remote Sensing, 44(7), 1719–1727. https://doi.org/10.1109/TGRS.2006.871219 DOI: https://doi.org/10.1109/TGRS.2006.871219
IBGE. Instituto Brasileiro de Geografia e Estatística, (1992). Manual técnico da vegetação brasileira (1o ed.). Rio de Janeiro. https://biblioteca.ibge.gov.br/pt/biblioteca-catalogo?view=detalhes&id=223267
IBGE. Instituto Brasileiro de Geografia e Estatística, (2012). Manual técnico da vegetação brasileira (2o ed.). Rio de Janeiro. https://biblioteca.ibge.gov.br/index.php/biblioteca-catalogo?view=detalhes&id=263011
IBGE. Instituto Brasileiro de Geografia e Estatística, (2013). Manual técnico de uso da terra (3a̲ edição).Rio de Janeiro. https://biblioteca.ibge.gov.br/index.php/biblioteca-catalogo?view=detalhes&id=281615
IBGE. Instituto Brasileiro de Geografia e Estatística, (2017). MONITORAMENTO DA COBERTURA E USO DA TERRA DO BRASIL 2000 – 2010 – 2012 – 2014: EM GRADE TERRITORIAL ESTATÍSTICA. Rio de Janeiro. https://biblioteca.ibge.gov.br/index.php/biblioteca-catalogo?view=detalhes&id=2101469
IBGE. Instituto Brasileiro de Geografia e Estatística,(2018a). Desbravar, conhecer, mapear: Memórias do Projeto Radam/RadamBrasil. Rio de Janeiro. https://biblioteca.ibge.gov.br/index.php/biblioteca-catalogo?view=detalhes&id=2101614
IBGE. Instituto Brasileiro de Geografia e Estatística, (2018b). MAPEAMENTO DE USO E COBERTURA DA TERRA UTILIZANDO OS DADOS NUMERICOS DO CENSO AGROPECUARIO 2006. Rio de Janeiro
IBGE. Instituto Brasileiro de Geografia e Estatística, (2019). Macrocaracterização dos Recursos Naturais do Brasil: Províncias estruturais, compartimentos de relevo, tipos de solos e regiões fitoecológicas. Rio de Janeiro. https://biblioteca.ibge.gov.br/index.php/biblioteca-catalogo?view=detalhes&id=2101648
IBGE. Instituto Brasileiro de Geografia e Estatística, (2020). Contas de ecossistemas: O uso da terra nos biomas brasileiros: 2000- 2018. Rio de Janeiro. https://biblioteca.ibge.gov.br/index.php/biblioteca-catalogo?view=detalhes&id=2101754
IBGE. Instituto Brasileiro de Geografia e Estatística,(2022a). Contas econômicas ambientais da terra: Contabilidade Física, Brasil, 2000/2020. Rio de Janeiro. https://biblioteca.ibge.gov.br/index.php/biblioteca-catalogo?view=detalhes&id=2101965
IBGE. Instituto Brasileiro de Geografia e Estatística,(2022b). Monitoramento da Cobertura e Uso da Terra do Brasil: 2018/2020. Rio de Janeiro. https://biblioteca.ibge.gov.br/index.php/biblioteca-catalogo?view=detalhes&id=2101966
IBGE. Instituto Brasileiro de Geografia e Estatística, (2023). Banco de Dados e Informações Ambientais (BDiA): Mapeamento de Recursos Naturais (MRN) Escala 1:250 000. Rio de Janeiro. https://biblioteca.ibge.gov.br/index.php/biblioteca-catalogo?view=detalhes&id=2102042
Impact Observatory. (2022). Methodology & Accuracy Summary. https://www.impactobservatory.com/static/c033eb846160f6a0a35c63a64ef45e52/lulc-methodology-accuracy.pdf
Jansen, L. J. M. (2018). Parameterized Approaches to the Categorization of Land Use and Land Cover. Em O. Ahlqvist, D. Varanka, S. Fritz, & K. Janowicz (Org.), Land Use and Land Cover Semantics: Principles, Best Practices, and Prospects. CRC Press. https://doi.org/10.1201/9781351228596 DOI: https://doi.org/10.1201/9781351228596
Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J. C., Mathis, M., & Brumby, S. P. (2021). Global land use / land cover with Sentinel 2 and deep learning. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 4704–4707. https://doi.org/10.1109/IGARSS47720.2021.9553499 DOI: https://doi.org/10.1109/IGARSS47720.2021.9553499
Liu, L., Zhang, X., Gao, Y., Chen, X., Shuai, X., & Mi, J. (2021). Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects. Journal of Remote Sensing (United States), 2021. Scopus. https://doi.org/10.34133/2021/528969 DOI: https://doi.org/10.34133/2021/5289697
Loveland, T. R. (2016). History of LandCover Mapping. Em C. P. Giri (Org.), Remote Sensing of Land Use and Land Cover: Principles and Applications. CRC Press. https://doi.org/10.1201/b11964 DOI: https://doi.org/10.1201/b11964
Lv, W., & Wang, X. (2020). Overview of Hyperspectral Image Classification. Journal of Sensors, 2020. Scopus. https://doi.org/10.1155/2020/4817234 DOI: https://doi.org/10.1155/2020/4817234
MapBiomas Project. (2024). Collection 9 of the Annual Land Cover and Land Use Maps of Brazil (1985-2023) [Conjunto de dados]. MapBiomas Data. https://doi.org/10.58053/MAPBIOMAS/XXUKA
Mapbiomas Project. (2024). MapBiomas General “Handbook”: Algorithm Theoretical Basis Document (ATBD): Collection 9. https://brasil.mapbiomas.org/wp-content/uploads/sites/4/2024/08/ATBD-Collection-9-v2.docx.pdf
Mashala, M. J., Dube, T., Mudereri, B. T., Ayisi, K. K., & Ramudzuli, M. R. (2023). A Systematic Review on Advancements in Remote Sensing for Assessing and Monitoring Land Use and Land Cover Changes Impacts on Surface Water Resources in Semi-Arid Tropical Environments. Remote Sensing, 15(16). Scopus. https://doi.org/10.3390/rs15163926 DOI: https://doi.org/10.3390/rs15163926
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. Scopus. https://doi.org/10.1080/01431161.2018.1433343 DOI: https://doi.org/10.1080/01431161.2018.1433343
Mora, C., Spirandelli, D., Franklin, E. C., Lynham, J., Kantar, M. B., Miles, W., Smith, C. Z., Freel, K., Moy, J., Louis, L. V., Barba, E. W., Bettinger, K., Frazier, A. G., Colburn IX, J. F., Hanasaki, N., Hawkins, E., Hirabayashi, Y., Knorr, W., Little, C. M., … Hunter, C. L. (2018). Broad threat to humanity from cumulative climate hazards intensified by greenhouse gas emissions. Nature Climate Change, 8(12), 1062–1071. Scopus. https://doi.org/10.1038/s41558-018-0315-6 DOI: https://doi.org/10.1038/s41558-018-0315-6
Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247–259. https://doi.org/10.1016/j.isprsjprs.2010.11.001 DOI: https://doi.org/10.1016/j.isprsjprs.2010.11.001
Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel vs. Object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5), 1145–1161. Scopus. https://doi.org/10.1016/j.rse.2010.12.017 DOI: https://doi.org/10.1016/j.rse.2010.12.017
Nações Unidas. (2016). Sistema de Contas Econômicas Ambientais. Comissão Econômica para a América Latina e o Caribe (CEPAL).
Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42–57. Scopus. https://doi.org/10.1016/j.rse.2014.02.015 DOI: https://doi.org/10.1016/j.rse.2014.02.015
Ozturk, M. Y., & Colkesen, I. (2024). A novel hybrid methodology integrating pixel- and object-based techniques for mapping land use and land cover from high-resolution satellite data. International Journal of Remote Sensing, 45(16), 5640–5678. https://doi.org/10.1080/01431161.2024.2379515 DOI: https://doi.org/10.1080/01431161.2024.2379515
Pelletier, C., Webb, G. I., & Petitjean, F. (2019). Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series. Remote Sensing, 11(5), Artigo 5. https://doi.org/10.3390/rs11050523 DOI: https://doi.org/10.3390/rs11050523
Phiri, D., & Morgenroth, J. (2017). Developments in Landsat land cover classification methods: A review. Remote Sensing, 9(9). Scopus. https://doi.org/10.3390/rs9090967 DOI: https://doi.org/10.3390/rs9090967
Prăvălie, R., Patriche, C., Borrelli, P., Panagos, P., Roșca, B., Dumitraşcu, M., Nita, I.-A., Săvulescu, I., Birsan, M.-V., & Bandoc, G. (2021). Arable lands under the pressure of multiple land degradation processes. A global perspective. Environmental Research, 194. Scopus. https://doi.org/10.1016/j.envres.2020.110697 DOI: https://doi.org/10.1016/j.envres.2020.110697
Rahman, M., Ningsheng, C., Mahmud, G. I., Islam, M. M., Pourghasemi, H. R., Ahmad, H., Habumugisha, J. M., Washakh, R. M. A., Alam, M., Liu, E., Han, Z., Ni, H., Shufeng, T., & Dewan, A. (2021). Flooding and its relationship with land cover change, population growth, and road density. Geoscience Frontiers, 12(6). Scopus. https://doi.org/10.1016/j.gsf.2021.101224 DOI: https://doi.org/10.1016/j.gsf.2021.101224
Rong, C., & Fu, W. (2023). A Comprehensive Review of Land Use and Land Cover Change Based on Knowledge Graph and Bibliometric Analyses. Land, 12(8), 1573. https://doi.org/10.3390/land12081573 DOI: https://doi.org/10.3390/land12081573
Salah, M. (2017). A survey of modern classification techniques in remote sensing for improved image classification. 11(1).
Sheykhmousa, M., Mahdianpari, M., Ghanbari, H., Mohammadimanesh, F., Ghamisi, P., & Homayouni, S. (2020). Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 6308–6325. Scopus. https://doi.org/10.1109/JSTARS.2020.3026724 DOI: https://doi.org/10.1109/JSTARS.2020.3026724
Simões, R., Camara, G., Queiroz, G., Souza, F., Andrade, P., Santos, L., Carvalho, A., & Ferreira, K. (2021). Satellite Image Time Series Analysis for Big Earth Observation Data. Remote Sensing, 13(13), 2428. https://doi.org/10.3390/rs13132428 DOI: https://doi.org/10.3390/rs13132428
Sims, N. C., Newnham, G. J., England, J. R., Cox, S. J. D., Roxburgh, S. H., Viscarra Rossel, R. A., Fritz, S., & Wheeler, I. (2021). Good practice guidance. SDG indicator 15.3.1, Proportion of land that Is degraded over total land area. Version 2.0. (2.0). UNCCD. https://www.unccd.int/resources/manuals-and-guides/good-practice-guidance-sdg-indicator-1531-proportion-land-degraded
Song, X.-P. (2023). The future of global land change monitoring. International Journal of Digital Earth, 16(1), 2279–2300. Scopus. https://doi.org/10.1080/17538947.2023.2224586 DOI: https://doi.org/10.1080/17538947.2023.2224586
Souza, C. M., Z. Shimbo, J., Rosa, M. R., Parente, L. L., A. Alencar, A., Rudorff, B. F. T., Hasenack, H., Matsumoto, M., G. Ferreira, L., Souza-Filho, P. W. M., de Oliveira, S. W., Rocha, W. F., Fonseca, A. V., Marques, C. B., Diniz, C. G., Costa, D., Monteiro, D., Rosa, E. R., Vélez-Martin, E., … Azevedo, T. (2020). Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine. Remote Sensing, 12(17), Artigo 17. https://doi.org/10.3390/rs12172735 DOI: https://doi.org/10.3390/rs12172735
Sun, X., Wang, M., Wang, J., Li, G., & Hou, X. (2025). Deep learning classification of winter wheat from Sentinel optical-radar image time series in smallholder farming areas. Advances in Space Research, 75(3), 2683–2695. Scopus. https://doi.org/10.1016/j.asr.2024.11.038 DOI: https://doi.org/10.1016/j.asr.2024.11.038
UNESCO. Organização das Nações Unidas para Educação, Ciência e Cultura, (2015). STATUTES OF THE INTERNATIONAL GEOSCIENCE AND GEOPARKS PROGRAMME. Paris. https://unesdoc.unesco.org/ark:/48223/pf0000260675?posInSet=3&queryId=18a01105-ae82-4fb2-9824-08747d2c9257
Vali, A., Comai, S., & Matteucci, M. (2020). Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review. Remote Sensing, 12(15). Scopus. https://doi.org/10.3390/RS12152495 DOI: https://doi.org/10.3390/rs12152495
Van Thinh, T., Duong, P. C., Nasahara, K. N., & Tadono, T. (2019). How does land use/land cover map’s accuracy depend on number of classification classes? Scientific Online Letters on the Atmosphere, 15, 28–31. Scopus. https://doi.org/10.2151/SOLA.2019-006 DOI: https://doi.org/10.2151/sola.2019-006
Veiga, A. J. P., Matta, J. M. B. da, Veiga, D. A. M., & Bomfim, C. S. S. (2020). Análise do uso e cobertura da terra em Itapetinga no estado da Bahia, Brasil, com uso de Sensoriamento Remoto e SIG / Analysis of the use and land cover in Itapetinga in the state of Bahia, Brazil, using Remote Sensing and GIS. Brazilian Journal of Development, 6(9), 73928–73947. https://doi.org/10.34117/bjdv6n9-741 DOI: https://doi.org/10.34117/bjdv6n9-741
Verdum, R. (2016). As Múltiplas Abordagens para o Estudo da Paisagem The Multiples Approaches For The Landscape Study. Espaço Aberto, 6, 131–150. DOI: https://doi.org/10.36403/espacoaberto.2016.5240
Wang, Y., Sun, Y., Cao, X., Wang, Y., Zhang, W., & Cheng, X. (2023). A review of regional and Global scale Land Use/Land Cover (LULC) mapping products generated from satellite remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 206, 311–334. https://doi.org/10.1016/j.isprsjprs.2023.11.014 DOI: https://doi.org/10.1016/j.isprsjprs.2023.11.014
Wu, L., Sun, C., & Fan, F. (2021). Estimating the characteristic spatiotemporal variation in habitat quality using the invest model—A case study from guangdong–hong kong–macao greater bay area. Remote Sensing, 13(5), 1–24. Scopus. https://doi.org/10.3390/rs13051008 DOI: https://doi.org/10.3390/rs13051008
Xie, G., Bai, X., Peng, Y., Li, Y., Zhang, C., Liu, Y., Liang, J., Fang, L., Chen, J., Men, J., Wang, X., Wang, G., Wang, Q., & Ren, S. (2024). Aquaculture Ponds Identification Based on Multi-Feature Combination Strategy and Machine Learning from Landsat-5/8 in a Typical Inland Lake of China. Remote Sensing, 16(12). Scopus. https://doi.org/10.3390/rs16122168 DOI: https://doi.org/10.3390/rs16122168
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