Geospatial analysis with landsat series and sentinel-3B OLCI satellites to assess changes in land use and water quality over time in Brazil
Geospatial analyses have gained fundamental importance on a global scale following emphasis on sustainability. Here we geospatially analyze images from Landsat 2/5/7/8 satellites captured during 1975 to 2020 in order to determine changes in land use. Sentinel-3B OLCI (Ocean Land Color Instrument) im...
- Autores:
-
Dal Moro, Leila
Stolfo Maculan, Laércio
Pivoto, Dieisson
Tibério Cardoso, Grace
Pinto, Diana
Adelodun, Bashir
Bodah, Brian William
Santosh, M.
Guedes Bortoluzzi, Marluse
Branco, Elisiane
Neckel, Alcindo
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2022
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/10721
- Acceso en línea:
- https://hdl.handle.net/11323/10721
https://repositorio.cuc.edu.co/
- Palabra clave:
- Landscape metrics
Land use change
SDG
Food security
Remote sensing
- Rights
- openAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
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| dc.title.eng.fl_str_mv |
Geospatial analysis with landsat series and sentinel-3B OLCI satellites to assess changes in land use and water quality over time in Brazil |
| title |
Geospatial analysis with landsat series and sentinel-3B OLCI satellites to assess changes in land use and water quality over time in Brazil |
| spellingShingle |
Geospatial analysis with landsat series and sentinel-3B OLCI satellites to assess changes in land use and water quality over time in Brazil Landscape metrics Land use change SDG Food security Remote sensing |
| title_short |
Geospatial analysis with landsat series and sentinel-3B OLCI satellites to assess changes in land use and water quality over time in Brazil |
| title_full |
Geospatial analysis with landsat series and sentinel-3B OLCI satellites to assess changes in land use and water quality over time in Brazil |
| title_fullStr |
Geospatial analysis with landsat series and sentinel-3B OLCI satellites to assess changes in land use and water quality over time in Brazil |
| title_full_unstemmed |
Geospatial analysis with landsat series and sentinel-3B OLCI satellites to assess changes in land use and water quality over time in Brazil |
| title_sort |
Geospatial analysis with landsat series and sentinel-3B OLCI satellites to assess changes in land use and water quality over time in Brazil |
| dc.creator.fl_str_mv |
Dal Moro, Leila Stolfo Maculan, Laércio Pivoto, Dieisson Tibério Cardoso, Grace Pinto, Diana Adelodun, Bashir Bodah, Brian William Santosh, M. Guedes Bortoluzzi, Marluse Branco, Elisiane Neckel, Alcindo |
| dc.contributor.author.none.fl_str_mv |
Dal Moro, Leila Stolfo Maculan, Laércio Pivoto, Dieisson Tibério Cardoso, Grace Pinto, Diana Adelodun, Bashir Bodah, Brian William Santosh, M. Guedes Bortoluzzi, Marluse Branco, Elisiane Neckel, Alcindo |
| dc.subject.proposal.eng.fl_str_mv |
Landscape metrics Land use change SDG Food security Remote sensing |
| topic |
Landscape metrics Land use change SDG Food security Remote sensing |
| description |
Geospatial analyses have gained fundamental importance on a global scale following emphasis on sustainability. Here we geospatially analyze images from Landsat 2/5/7/8 satellites captured during 1975 to 2020 in order to determine changes in land use. Sentinel-3B OLCI (Ocean Land Color Instrument) images obtained in 2019 and 2021 were utilized to assess water resources, based on water turbidity levels (TSM_NN), suspended pollution potential (ADG_443_NN) and the presence of chlorophyll-a (CHL_NN) in order to temporally monitor the effectiveness of Brazilian legislation currently in force. This work on sustainability standards was applied to a hydrographic basin dedicated to agricultural production located in southern Brazil. Satellite images from Landsat 2/5/7/8 (1975 to 2020) and Sentinel-3B OLCI (2019 and 2021) revealed that changes in land use, vegetation cover and water in the Capinguí Dam reservoir detected high concentrations of ADG_443_NN (3830 m−1), CHL_NN (20,290 mg m−3) and TSM_NN (100 gm−3). These results can alert the population to the risks to public health and harm to hydrographic preservation, capable of covering large regions. |
| publishDate |
2022 |
| dc.date.issued.none.fl_str_mv |
2022-08-08 |
| dc.date.accessioned.none.fl_str_mv |
2024-02-16T22:36:01Z |
| dc.date.available.none.fl_str_mv |
2024-02-16T22:36:01Z |
| dc.type.spa.fl_str_mv |
Artículo de revista |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
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http://purl.org/redcol/resource_type/ART |
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info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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publishedVersion |
| dc.identifier.citation.spa.fl_str_mv |
Moro, L.D.; Maculan, L.S.; Pivoto, D.; Cardoso, G.T.; Pinto, D.; Adelodun, B.; Bodah, B.W.; Santosh, M.; Bortoluzzi, M.G.; Branco, E.; et al. Geospatial Analysis with Landsat Series and Sentinel-3B OLCI Satellites to Assess Changes in Land Use and Water Quality over Time in Brazil. Sustainability 2022, 14, 9733. https://doi.org/10.3390/su14159733 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/11323/10721 |
| dc.identifier.doi.none.fl_str_mv |
10.3390/su14159733 |
| dc.identifier.eissn.spa.fl_str_mv |
2071-1050 |
| dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
| dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
| identifier_str_mv |
Moro, L.D.; Maculan, L.S.; Pivoto, D.; Cardoso, G.T.; Pinto, D.; Adelodun, B.; Bodah, B.W.; Santosh, M.; Bortoluzzi, M.G.; Branco, E.; et al. Geospatial Analysis with Landsat Series and Sentinel-3B OLCI Satellites to Assess Changes in Land Use and Water Quality over Time in Brazil. Sustainability 2022, 14, 9733. https://doi.org/10.3390/su14159733 10.3390/su14159733 2071-1050 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| url |
https://hdl.handle.net/11323/10721 https://repositorio.cuc.edu.co/ |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.relation.ispartofjournal.spa.fl_str_mv |
Sustainability |
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1. Yiran, G.A.B.; Ablo, A.D.; Asem, F.E. Urbanisation and domestic energy trends: Analysis of household energy consumption patterns in relation to land-use change in peri-urban Accra, Ghana. Land Use Policy 2020, 99, 105047. [CrossRef] 2. Chowdhury, S.; Khan, S.; Sarker, M.F.H.; Islam, M.K.; Tamal, M.A.; Khan, N.A. Does Agricultural Ecology Cause Environmental Degradation? Empirical Evidence from Bangladesh. Heliyon 2022, 8, e09750. [CrossRef] [PubMed] 3. Viana, C.M.; Freire, D.; Abrantes, P.; Rocha, J.; Pereira, P. Agricultural land systems importance for supporting food security and sustainable development goals: A systematic review. Sci. Total Environ. 2022, 806, 150718. [CrossRef] 4. Parven, A.; Pal, I.; Witayangkurn, A.; Pramanik, M.; Nagai, M.; Miyazaki, H.; Wuthisakkaroon, C. Impacts of disaster and land-use change on food security and adaptation: Evidence from the delta community in Bangladesh. Int. J. Disaster Risk Reduct. 2022, 78, 103119. [CrossRef] 5. Acuti, D.; Bellucci, M.; Manetti, G. Company disclosures concerning the resilience of cities from the Sustainable Development Goals (SDGs) perspective. Cities 2020, 99, 102608. [CrossRef] 6. Lu, X.; Zhang, Y.; Lin, C.; Wu, F. Analysis and comprehensive evaluation of sustainable land use in China: Based on sustainable development goals framework. J. Clean. Prod. 2021, 310, 127205. [CrossRef] 7. Dwivedi, P.P.; Sharma, D.K. Application of Shannon Entropy and COCOSO techniques to analyze performance of sustainable development goals: The case of the Indian Union Territories. Results Eng. 2022, 14, 100416. [CrossRef] 8. United Nations. Transforming our World: The 2030 Agenda for Sustainable Development. 2015. Available online: https:// sustainabledevelopment.un.org/post2015/transformingourworld/publication (accessed on 28 April 2022). 9. Dal Moro, L.; Maculan, L.S.; Neckel, A.; Mores, G.de.V.; Pivoto, D.; Bodah, E.T.; Bodah, B.W.; Oliveira, M.L. Geotechnologies applied to the analysis of buildings involved in the production of poultry and swine to the integrated food safety system and environment. J. Environ. Chem. Eng. 2021, 9, 106475. [CrossRef] 10. Hagos, Y.G.; Andualem, T.G. Geospatial and multi-criteria decision approach of groundwater potential zone identification in Cuma sub-basin, Southern Ethiopia. Heliyon 2021, 7, e07963. [CrossRef] 11. Andrieu, N.; Dumas, P.; Hemmerlé, E.; Caforio, F.; Falconnier, G.N.; Blanchard, M.; Vayssières, J. Ex ante mapping of favorable zones for uptake of climate-smart agricultural practices: A case study in West Africa. Environ. Dev. 2021, 37, 100566. [CrossRef] 12. Laufenberg, J.S.; Johnson, H.E.; Doherty, P.F.; Breck, S.W. Compounding effects of human development and a natural food shortage on a black bear population along a human development-wildland interface. Biol. Conserv. 2018, 224, 188–198. [CrossRef] 13. Bernard, B.M.; Song, Y.; Hena, S.; Ahmad, F.; Wang, X. Assessing Africa’s Agricultural TFP for Food Security and Effects on Human Development: Evidence from 35 Countries. Sustainability 2022, 14, 6411. [CrossRef] 14. Schürmann, A.; Kleemann, J.; Fürst, C.; Teucher, M. Assessing the relationship between land tenure issues and land cover changes around the Arabuko Sokoke Forest in Kenya. Land Use Policy 2020, 95, 104625. [CrossRef] 15. Chen, Y.; Lu, C. A Comparative Analysis on Food Security in Bangladesh, India and Myanmar. Sustainability 2018, 10, 405. [CrossRef] 16. Laborde, J.P.; Wortmann, C.S.; Blanco-Canqui, H.; Baigorria, G.A.; Lindquist, J.L. Identifying the drivers and predicting the outcome of conservation agriculture globally. Agric. Syst. 2020, 177, 102692. [CrossRef] 17. Bodah, B.W.; Neckel, A.; Maculan, L.S.; Milanes, C.B.; Korcelski, C.; Ramírez, O.; Mendez-Espinosa, J.F.; Bodah, E.T.; Oliveira, M.L. Sentinel-5P TROPOMI satellite application for NO2 and CO studies aiming at environmental valuation. J. Clean. Prod. 2022, 357, 131960. [CrossRef] 18. Marcinko, C.L.; Samanta, S.; Basu, O.; Harfoot, A.; Hornby, D.D.; Hutton, C.W.; Pal, S.; Watmough, G.R. Earth observation and geospatial data can predict the relative distribution of village level poverty in the Sundarban Biosphere Reserve, India. J. Environ. Manag. 2022, 313, 114950. [CrossRef] 19. Acharki, S. PlanetScope contributions compared to Sentinel-2, and Landsat-8 for LULC mapping. Remote Sens. Appl. Soc. Environ. 2022, 27, 100774. [CrossRef] 20. Song, D.X.; Wang, Z.; He, T.; Wang, H.; Liang, S. Estimation and validation of 30 m fractional vegetation cover over China through integrated use of Landsat 8 and Gaofen 2 data. Sci. Remote Sens. 2022, 6, 100058. [CrossRef] 21. Shang, R.; Zhu, Z.; Zhang, J.; Qiu, S.; Yang, Z.; Li, T.; Yang, X. Near-real-time monitoring of land disturbance with harmonized Landsats 7–8 and Sentinel-2 data. Remote Sens. Environ. 2022, 278, 113073. [CrossRef] 22. Zhang, X.; Xiao, X.; Qiu, S.; Xu, X.; Wang, X.; Chang, Q.; Wu, J.; Li, B. Quantifying latitudinal variation in land surface phenology of Spartina alterniflora saltmarshes across coastal wetlands in China by Landsat 7/8 and Sentinel-2 images. Remote Sens. Environ. 2022, 269, 112810. [CrossRef] 23. Song, X.P.; Huang, W.; Hansen, M.C.; Potapov, P. An evaluation of Landsat, Sentinel-2, Sentinel-1 and MODIS data for crop type mapping. Sci. Remote Sens. 2021, 3, 100018. [CrossRef] 24. Neckel, A.; Oliveira, M.L.S.; Bolaño, L.J.C.; Maculan, L.S.; dal Moro, L.; Bodah, E.T.; Moreno-Ríos, A.L.; Bodah, B.W.; Silva, L.F.O. Biophysical matter in a marine estuary identified by the Sentinel-3B OLCI satellite and the presence of terrestrial iron (Fe) nanoparticles. Mar. Pollut. Bull. 2021, 173, 112925. [CrossRef] [PubMed] 25. Kusi, K.K.; Khattabi, A.; Mhammdi, N.; Lahssini, S. Prospective evaluation of the impact of land use change on ecosystem services in the Ourika watershed, Morocco. Land Use Policy 2020, 97, 104796. [CrossRef] 26. Oduro Appiah, J.; Agyemang-Duah, W.; Sobeng, A.K.; Kpienbaareh, D. Analysing patterns of forest cover change and related land uses in the Tano-Offin forest reserve in Ghana: Implications for forest policy and land management. Trees For. People 2021, 5, 100105. [CrossRef] 27. Sauer, S. Soy expansion into the agricultural frontiers of the Brazilian Amazon: The agribusiness economy and its social and environmental conflicts. Land Use Policy 2018, 79, 326–338. [CrossRef] 28. Bin, D. Agricultural dispossessions during the 1964–1985 Brazilian dictatorship. Political Geogr. 2021, 84, 102307. [CrossRef] 29. Roitman, I.; Vieira, L.C.G.; Jacobson, T.K.B.; Bustamante, M.M.da.C.; Silva Marcondes, N.J.; Cury, K.; Silva Estevam, L.; Ribeiro, R.J.da.C.; Ribeiro, V.; Stabile, M.C.; et al. Rural Environmental Registry: An innovative model for land-use and environmental policies. Land Use Policy 2018, 76, 95–102. [CrossRef] 30. Preto, M.D.F.; Garcia, A.S.; Nakai, R.S.; Casarin, L.P.; Vilela, V.M.D.F.N.; Ballester, M.V.R. The role of environmental legislation and land use patterns on riparian deforestation dynamics in an Amazonian agricultural frontier (MT, Brazil). Land Use Policy 2022, 118, 106132. [CrossRef] 31. Moraes, L.A.F.de.; Floreano, I.X. LULC zoning in the “Madeira river” settlement, legal Amazon, Brazil, before and after implementation of the rural environmental registry (CAR) (2008–2018). Environ. Dev. 2022, 43, 100725. [CrossRef] 32. Arvor, D.; Silgueiro, V.; Manzon Nunes, G.; Nabucet, J.; Pereira Dias, A. The 2008 map of consolidated rural areas in the Brazilian Legal Amazon state of Mato Grosso: Accuracy assessment and implications for the environmental regularization of rural properties. Land Use Policy 2021, 103, 105281. [CrossRef] 33. Leal Filho, W.; Azeiteiro, U.; Alves, F.; Pace, P.; Mifsud, M.; Brandli, L.L.; Caeiro, S.S.; Disterheft, A. Reinvigorating the sustainable development research agenda: The role of the sustainable development goals (SDG). Int. J. Sustain. Dev. World Ecol. 2017, 25, 131–142. [CrossRef] 34. Coelho Junior, M.G.; Biju, B.P.; Silva Neto, E.C.D.; Oliveira, A.L.D.; Tavares, A.A.D.O.; Basso, V.M.; Turetta, A.P.D.; Carvalho, A.G.D.; Sansevero, J.B.B. Improving the management effectiveness and decision-making by stakeholders’ perspectives: A case study in a protected area from the Brazilian Atlantic Forest. J. Environ. Manag. 2020, 272, 111083. [CrossRef] 35. IBGE. Brazilian Institute of Geography and Statistics, Demographic Data of 2022—Brazil. 2022. Available online: https://cidades. ibge.gov.br/brasil/rs/passo-fundo/panorama (accessed on 10 March 2022). 36. Zafar, M.; Tiecher, T.; Capoane, V.; Troian, A.; dos Santos, D.R. Characteristics, lability and distribution of phosphorus in suspended sediment from a subtropical catchment under diverse anthropic pressure in Southern Brazil. Ecol. Eng. 2017, 100, 28–45. [CrossRef] 37. USGS. Global Visualization Viewer. 2022. Available online: https://glovis.usgs.gov (accessed on 1 March 2022). 38. Wang, J.; Yang, D.; Chen, S.; Zhu, X.; Wu, S.; Bogonovich, M.; Guo, Z.; Zhu, Z.; Wu, J. Automatic cloud and cloud shadow detection in tropical areas for PlanetScope satellite images. Remote Sens. Environ. 2021, 264, 112604. [CrossRef] 39. Qingyun, F.; Zhaokui, W. Cross-modality attentive feature fusion for object detection in multispectral remote sensing imagery. Pattern Recognit. 2022, 130, 108786. [CrossRef] 40. Fauvel, M.; Dechesne, C.; Zullo, A.; Ferraty, F. Fast Forward Feature Selection of Hyperspectral Images for Classification With Gaussian Mixture Models. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2015, 8, 2824–2831. [CrossRef] 41. Zhang, Y.; Balzter, H.; Zou, C.; Xu, H.; Tang, F. Characterizing bi-temporal patterns of land surface temperature using landscape metrics based on sub-pixel classifications from Landsat TM/ETM+. Int. J. Appl. Earth Obs. Geoinf. 2015, 42, 87–96. [CrossRef] 42. Abalo, M.; Badabate, D.; Fousseni, F.; Kpérkouma, W.; Koffi, A. Landscape-based analysis of wetlands patterns in the Ogou River basin in Togo (West Africa). Environ. Chall. 2021, 2, 100013. [CrossRef] 43. Jung, M. LecoS—A python plugin for automated landscape ecology analysis. Ecol. Inform. 2016, 31, 18–21. [CrossRef] 44. Zatelli, P.; Gobbi, S.; Tattoni, C.; Cantiani, M.G.; La Porta, N.; Rocchini, D.; Zorzi, N.; Ciolli, M. Relevance of the Cell Neighborhood Size in Landscape Metrics Evaluation and Free or Open Source Software Implementations. ISPRS Int. J. Geo-Inf. 2019, 8, 586. [CrossRef] 45. ESA. European Space Agency. Sentinel-5P Pre-Operations Data Hub—European. 2022. Available online: https://s5phub.copernicus. eu/dhus/ (accessed on 3 March 2022). 46. Maroni, D.; Cardoso, G.T.; Neckel, A.; Maculan, L.S.; Oliveira, M.L.; Bodah, E.T.; Bodah, B.W.; Santosh, M. Land surface temperature and vegetation index as a proxy to microclimate. J. Environ. Chem. Eng. 2021, 9, 105796. [CrossRef] 47. Niu, G.; Ji, Y.; Zhang, Z.; Wang, W.; Chen, J.; Yu, P. Clustering analysis of typical scenarios of island power supply system by using cohesive hierarchical clustering based K-Means clustering method. Energy Rep. 2021, 7, 250–256. [CrossRef] 48. Borlea, I.D.; Precup, R.E.; Borlea, A.B. Improvement of K-means Cluster Quality by Post Processing Resulted Clusters. Procedia Comput. Sci. 2022, 199, 63–70. [CrossRef] 49. Ahmad, A.; Khan, S.S. initKmix-A novel initial partition generation algorithm for clustering mixed data using k-means-based clustering. Expert Syst. Appl. 2021, 167, 114149. [CrossRef] 50. Cusworth, G.; Garnett, T.; Lorimer, J. Agroecological break out: Legumes, crop diversification and the regenerative futures of UK agriculture. J. Rural. Stud. 2021, 88, 126–137. [CrossRef] 51. Musyoki, M.E.; Busienei, J.R.; Gathiaka, J.K.; Karuku, G.N. Linking farmers’ risk attitudes, livelihood diversification and adoption of climate smart agriculture technologies in the Nyando basin, South-Western Kenya. Heliyon 2022, 8, e09305. [CrossRef] 52. Burbano-Figueroa, O.; Sierra-Monroy, A.; David-Hinestroza, A.; Whitney, C.; Borgemeister, C.; Luedeling, E. Farm-planning under risk: An application of decision analysis and portfolio theory for the assessment of crop diversification strategies in horticultural systems. Agric. Syst. 2022, 199, 103409. [CrossRef] 53. David Raj, A.; Kumar, S.; Sooryamol, K. Modelling climate change impact on soil loss and erosion vulnerability in a watershed of Shiwalik Himalayas. Catena 2022, 214, 106279. [CrossRef] 54. Guo, Y.; Wang, J. Poverty alleviation through labor transfer in rural China: Evidence from Hualong County. Habitat Int. 2021, 116, 102402. [CrossRef] 55. Reisman, E. Protecting provenance, abandoning agriculture? Heritage products, industrial ideals and the uprooting of a Spanish turrón. J. Rural. Stud. 2022, 89, 45–53. [CrossRef] 56. Langewitz, T.; Wiedner, K.; Polifka, S.; Eckmeier, E. Pedological properties related to formation and functions of ancient ridge and furrow cultivation in Central and Northern Germany. Catena 2021, 198, 105049. [CrossRef] 57. Arora, A.; Pandey, M.; Mishra, V.N.; Kumar, R.; Rai, P.K.; Costache, R.; Punia, M.; Di, L. Comparative evaluation of geospatial scenario-based land change simulation models using landscape metrics. Ecol. Indic. 2021, 128, 107810. [CrossRef] 58. Estoque, R.C.; Ooba, M.; Togawa, T.; Hijioka, Y.; Murayama, Y. Monitoring global land-use efficiency in the context of the UN 2030 Agenda for Sustainable Development. Habitat Int. 2021, 115, 102403. [CrossRef] 59. Vasiliev, D.; Greenwood, S. Making green pledges support biodiversity: Nature-based solution design can be informed by landscape ecology principles. Land Use Policy 2022, 117, 106129. [CrossRef] 60. Hargis, C.D.; Bissonette, J.A.; David, J.L. The behavior of landscape metrics commonly used in the study of habitat fragmentation. Landsc. Ecol. 1998, 13, 167–186. [CrossRef] 61. Mungai, L.M.; Messina, J.P.; Zulu, L.C.; Qi, J.; Snapp, S. Modeling Spatiotemporal Patterns of Land Use/Land Cover Change in Central Malawi Using a Neural Network Model. Remote Sens. 2022, 14, 3477. [CrossRef] 62. Jaeger, J.A. Landscape division, splitting index, and effective mesh size: New measures of landscape fragmentation. Landsc. Ecol. 2000, 15, 115–130. [CrossRef] 63. Marine, N.; Arnaiz-Schmitz, C.; Herrero-Jáuregui, C.; Cabrera, M.R.d.L.O.; Escudero, D.; Schmitz, M.F. Protected Landscapes in Spain: Reasons for Protection and Sustainability of Conservation Management. Sustainability 2020, 12, 6913. [CrossRef] 64. Xu, H.; Xiao, X.; Qin, Y.; Qiao, Z.; Long, S.; Tang, X.; Liu, L. Annual Maps of Built-Up Land in Guangdong from 1991 to 2020 Based on Landsat Images, Phenology, Deep Learning Algorithms, and Google Earth Engine. Remote Sens. 2022, 14, 3562. [CrossRef] 65. Xie, Z.; Liu, J.; Huang, J.; Chen, Z.; Lu, X. Linking Land Cover Change with Landscape Pattern Dynamics Induced by Damming in a Small Watershed. Remote Sens. 2022, 14, 3580. [CrossRef] 66. Hou, M.; Bao, X.; Ge, J.; Liang, T. Land cover pattern and habitat suitability on the global largest breeding sites for Black-necked Cranes. J. Clean. Prod. 2021, 322, 128968. [CrossRef] 67. Tang, Y.; Wang, Q.; Tong, X.; Atkinson, P.M. Integrating spatio-temporal-spectral information for downscaling Sentinel-3 OLCI images. ISPRS J. Photogramm. Remote Sens. 2021, 180, 130–150. [CrossRef] |
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Atribución 4.0 Internacional (CC BY 4.0)© 2022 by the authors. Licensee MDPI, Basel, Switzerland.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Dal Moro, Leila Stolfo Maculan, LaércioPivoto, DieissonTibério Cardoso, GracePinto, DianaAdelodun, BashirBodah, Brian WilliamSantosh, M.Guedes Bortoluzzi, MarluseBranco, ElisianeNeckel, Alcindo2024-02-16T22:36:01Z2024-02-16T22:36:01Z2022-08-08Moro, L.D.; Maculan, L.S.; Pivoto, D.; Cardoso, G.T.; Pinto, D.; Adelodun, B.; Bodah, B.W.; Santosh, M.; Bortoluzzi, M.G.; Branco, E.; et al. Geospatial Analysis with Landsat Series and Sentinel-3B OLCI Satellites to Assess Changes in Land Use and Water Quality over Time in Brazil. Sustainability 2022, 14, 9733. https://doi.org/10.3390/su14159733https://hdl.handle.net/11323/1072110.3390/su141597332071-1050Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Geospatial analyses have gained fundamental importance on a global scale following emphasis on sustainability. Here we geospatially analyze images from Landsat 2/5/7/8 satellites captured during 1975 to 2020 in order to determine changes in land use. Sentinel-3B OLCI (Ocean Land Color Instrument) images obtained in 2019 and 2021 were utilized to assess water resources, based on water turbidity levels (TSM_NN), suspended pollution potential (ADG_443_NN) and the presence of chlorophyll-a (CHL_NN) in order to temporally monitor the effectiveness of Brazilian legislation currently in force. This work on sustainability standards was applied to a hydrographic basin dedicated to agricultural production located in southern Brazil. Satellite images from Landsat 2/5/7/8 (1975 to 2020) and Sentinel-3B OLCI (2019 and 2021) revealed that changes in land use, vegetation cover and water in the Capinguí Dam reservoir detected high concentrations of ADG_443_NN (3830 m−1), CHL_NN (20,290 mg m−3) and TSM_NN (100 gm−3). These results can alert the population to the risks to public health and harm to hydrographic preservation, capable of covering large regions.17 páginasapplication/pdfengMDPI AGSwitzerlandhttps://www.mdpi.com/2071-1050/14/15/9733Geospatial analysis with landsat series and sentinel-3B OLCI satellites to assess changes in land use and water quality over time in BrazilArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85BrazilSustainability1. Yiran, G.A.B.; Ablo, A.D.; Asem, F.E. Urbanisation and domestic energy trends: Analysis of household energy consumption patterns in relation to land-use change in peri-urban Accra, Ghana. Land Use Policy 2020, 99, 105047. [CrossRef]2. Chowdhury, S.; Khan, S.; Sarker, M.F.H.; Islam, M.K.; Tamal, M.A.; Khan, N.A. Does Agricultural Ecology Cause Environmental Degradation? Empirical Evidence from Bangladesh. Heliyon 2022, 8, e09750. [CrossRef] [PubMed]3. Viana, C.M.; Freire, D.; Abrantes, P.; Rocha, J.; Pereira, P. Agricultural land systems importance for supporting food security and sustainable development goals: A systematic review. Sci. Total Environ. 2022, 806, 150718. [CrossRef]4. Parven, A.; Pal, I.; Witayangkurn, A.; Pramanik, M.; Nagai, M.; Miyazaki, H.; Wuthisakkaroon, C. Impacts of disaster and land-use change on food security and adaptation: Evidence from the delta community in Bangladesh. Int. J. Disaster Risk Reduct. 2022, 78, 103119. [CrossRef]5. Acuti, D.; Bellucci, M.; Manetti, G. Company disclosures concerning the resilience of cities from the Sustainable Development Goals (SDGs) perspective. Cities 2020, 99, 102608. [CrossRef]6. Lu, X.; Zhang, Y.; Lin, C.; Wu, F. Analysis and comprehensive evaluation of sustainable land use in China: Based on sustainable development goals framework. J. Clean. Prod. 2021, 310, 127205. [CrossRef]7. Dwivedi, P.P.; Sharma, D.K. Application of Shannon Entropy and COCOSO techniques to analyze performance of sustainable development goals: The case of the Indian Union Territories. Results Eng. 2022, 14, 100416. [CrossRef]8. United Nations. Transforming our World: The 2030 Agenda for Sustainable Development. 2015. Available online: https:// sustainabledevelopment.un.org/post2015/transformingourworld/publication (accessed on 28 April 2022).9. Dal Moro, L.; Maculan, L.S.; Neckel, A.; Mores, G.de.V.; Pivoto, D.; Bodah, E.T.; Bodah, B.W.; Oliveira, M.L. Geotechnologies applied to the analysis of buildings involved in the production of poultry and swine to the integrated food safety system and environment. J. Environ. Chem. Eng. 2021, 9, 106475. [CrossRef]10. Hagos, Y.G.; Andualem, T.G. Geospatial and multi-criteria decision approach of groundwater potential zone identification in Cuma sub-basin, Southern Ethiopia. Heliyon 2021, 7, e07963. [CrossRef]11. Andrieu, N.; Dumas, P.; Hemmerlé, E.; Caforio, F.; Falconnier, G.N.; Blanchard, M.; Vayssières, J. Ex ante mapping of favorable zones for uptake of climate-smart agricultural practices: A case study in West Africa. Environ. Dev. 2021, 37, 100566. [CrossRef]12. Laufenberg, J.S.; Johnson, H.E.; Doherty, P.F.; Breck, S.W. Compounding effects of human development and a natural food shortage on a black bear population along a human development-wildland interface. Biol. Conserv. 2018, 224, 188–198. [CrossRef]13. Bernard, B.M.; Song, Y.; Hena, S.; Ahmad, F.; Wang, X. Assessing Africa’s Agricultural TFP for Food Security and Effects on Human Development: Evidence from 35 Countries. Sustainability 2022, 14, 6411. [CrossRef]14. Schürmann, A.; Kleemann, J.; Fürst, C.; Teucher, M. Assessing the relationship between land tenure issues and land cover changes around the Arabuko Sokoke Forest in Kenya. Land Use Policy 2020, 95, 104625. [CrossRef]15. Chen, Y.; Lu, C. A Comparative Analysis on Food Security in Bangladesh, India and Myanmar. Sustainability 2018, 10, 405. [CrossRef]16. Laborde, J.P.; Wortmann, C.S.; Blanco-Canqui, H.; Baigorria, G.A.; Lindquist, J.L. Identifying the drivers and predicting the outcome of conservation agriculture globally. Agric. Syst. 2020, 177, 102692. [CrossRef]17. Bodah, B.W.; Neckel, A.; Maculan, L.S.; Milanes, C.B.; Korcelski, C.; Ramírez, O.; Mendez-Espinosa, J.F.; Bodah, E.T.; Oliveira, M.L. Sentinel-5P TROPOMI satellite application for NO2 and CO studies aiming at environmental valuation. J. Clean. Prod. 2022, 357, 131960. [CrossRef]18. Marcinko, C.L.; Samanta, S.; Basu, O.; Harfoot, A.; Hornby, D.D.; Hutton, C.W.; Pal, S.; Watmough, G.R. Earth observation and geospatial data can predict the relative distribution of village level poverty in the Sundarban Biosphere Reserve, India. J. Environ. Manag. 2022, 313, 114950. [CrossRef]19. Acharki, S. PlanetScope contributions compared to Sentinel-2, and Landsat-8 for LULC mapping. Remote Sens. Appl. Soc. Environ. 2022, 27, 100774. [CrossRef]20. Song, D.X.; Wang, Z.; He, T.; Wang, H.; Liang, S. Estimation and validation of 30 m fractional vegetation cover over China through integrated use of Landsat 8 and Gaofen 2 data. Sci. Remote Sens. 2022, 6, 100058. [CrossRef]21. Shang, R.; Zhu, Z.; Zhang, J.; Qiu, S.; Yang, Z.; Li, T.; Yang, X. Near-real-time monitoring of land disturbance with harmonized Landsats 7–8 and Sentinel-2 data. Remote Sens. Environ. 2022, 278, 113073. [CrossRef]22. Zhang, X.; Xiao, X.; Qiu, S.; Xu, X.; Wang, X.; Chang, Q.; Wu, J.; Li, B. Quantifying latitudinal variation in land surface phenology of Spartina alterniflora saltmarshes across coastal wetlands in China by Landsat 7/8 and Sentinel-2 images. Remote Sens. Environ. 2022, 269, 112810. [CrossRef]23. Song, X.P.; Huang, W.; Hansen, M.C.; Potapov, P. An evaluation of Landsat, Sentinel-2, Sentinel-1 and MODIS data for crop type mapping. Sci. Remote Sens. 2021, 3, 100018. [CrossRef]24. Neckel, A.; Oliveira, M.L.S.; Bolaño, L.J.C.; Maculan, L.S.; dal Moro, L.; Bodah, E.T.; Moreno-Ríos, A.L.; Bodah, B.W.; Silva, L.F.O. Biophysical matter in a marine estuary identified by the Sentinel-3B OLCI satellite and the presence of terrestrial iron (Fe) nanoparticles. Mar. Pollut. Bull. 2021, 173, 112925. [CrossRef] [PubMed]25. Kusi, K.K.; Khattabi, A.; Mhammdi, N.; Lahssini, S. Prospective evaluation of the impact of land use change on ecosystem services in the Ourika watershed, Morocco. Land Use Policy 2020, 97, 104796. [CrossRef]26. Oduro Appiah, J.; Agyemang-Duah, W.; Sobeng, A.K.; Kpienbaareh, D. Analysing patterns of forest cover change and related land uses in the Tano-Offin forest reserve in Ghana: Implications for forest policy and land management. Trees For. People 2021, 5, 100105. [CrossRef]27. Sauer, S. Soy expansion into the agricultural frontiers of the Brazilian Amazon: The agribusiness economy and its social and environmental conflicts. Land Use Policy 2018, 79, 326–338. [CrossRef]28. Bin, D. Agricultural dispossessions during the 1964–1985 Brazilian dictatorship. Political Geogr. 2021, 84, 102307. [CrossRef]29. Roitman, I.; Vieira, L.C.G.; Jacobson, T.K.B.; Bustamante, M.M.da.C.; Silva Marcondes, N.J.; Cury, K.; Silva Estevam, L.; Ribeiro, R.J.da.C.; Ribeiro, V.; Stabile, M.C.; et al. Rural Environmental Registry: An innovative model for land-use and environmental policies. Land Use Policy 2018, 76, 95–102. [CrossRef]30. Preto, M.D.F.; Garcia, A.S.; Nakai, R.S.; Casarin, L.P.; Vilela, V.M.D.F.N.; Ballester, M.V.R. The role of environmental legislation and land use patterns on riparian deforestation dynamics in an Amazonian agricultural frontier (MT, Brazil). Land Use Policy 2022, 118, 106132. [CrossRef]31. Moraes, L.A.F.de.; Floreano, I.X. LULC zoning in the “Madeira river” settlement, legal Amazon, Brazil, before and after implementation of the rural environmental registry (CAR) (2008–2018). Environ. Dev. 2022, 43, 100725. [CrossRef]32. Arvor, D.; Silgueiro, V.; Manzon Nunes, G.; Nabucet, J.; Pereira Dias, A. The 2008 map of consolidated rural areas in the Brazilian Legal Amazon state of Mato Grosso: Accuracy assessment and implications for the environmental regularization of rural properties. Land Use Policy 2021, 103, 105281. [CrossRef]33. Leal Filho, W.; Azeiteiro, U.; Alves, F.; Pace, P.; Mifsud, M.; Brandli, L.L.; Caeiro, S.S.; Disterheft, A. Reinvigorating the sustainable development research agenda: The role of the sustainable development goals (SDG). Int. J. Sustain. Dev. World Ecol. 2017, 25, 131–142. [CrossRef]34. Coelho Junior, M.G.; Biju, B.P.; Silva Neto, E.C.D.; Oliveira, A.L.D.; Tavares, A.A.D.O.; Basso, V.M.; Turetta, A.P.D.; Carvalho, A.G.D.; Sansevero, J.B.B. Improving the management effectiveness and decision-making by stakeholders’ perspectives: A case study in a protected area from the Brazilian Atlantic Forest. J. Environ. Manag. 2020, 272, 111083. [CrossRef]35. IBGE. Brazilian Institute of Geography and Statistics, Demographic Data of 2022—Brazil. 2022. Available online: https://cidades. ibge.gov.br/brasil/rs/passo-fundo/panorama (accessed on 10 March 2022).36. Zafar, M.; Tiecher, T.; Capoane, V.; Troian, A.; dos Santos, D.R. Characteristics, lability and distribution of phosphorus in suspended sediment from a subtropical catchment under diverse anthropic pressure in Southern Brazil. Ecol. Eng. 2017, 100, 28–45. [CrossRef]37. USGS. Global Visualization Viewer. 2022. Available online: https://glovis.usgs.gov (accessed on 1 March 2022).38. Wang, J.; Yang, D.; Chen, S.; Zhu, X.; Wu, S.; Bogonovich, M.; Guo, Z.; Zhu, Z.; Wu, J. Automatic cloud and cloud shadow detection in tropical areas for PlanetScope satellite images. Remote Sens. Environ. 2021, 264, 112604. [CrossRef]39. Qingyun, F.; Zhaokui, W. Cross-modality attentive feature fusion for object detection in multispectral remote sensing imagery. Pattern Recognit. 2022, 130, 108786. [CrossRef]40. Fauvel, M.; Dechesne, C.; Zullo, A.; Ferraty, F. Fast Forward Feature Selection of Hyperspectral Images for Classification With Gaussian Mixture Models. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2015, 8, 2824–2831. [CrossRef]41. Zhang, Y.; Balzter, H.; Zou, C.; Xu, H.; Tang, F. Characterizing bi-temporal patterns of land surface temperature using landscape metrics based on sub-pixel classifications from Landsat TM/ETM+. Int. J. Appl. Earth Obs. Geoinf. 2015, 42, 87–96. [CrossRef]42. Abalo, M.; Badabate, D.; Fousseni, F.; Kpérkouma, W.; Koffi, A. Landscape-based analysis of wetlands patterns in the Ogou River basin in Togo (West Africa). Environ. Chall. 2021, 2, 100013. [CrossRef]43. Jung, M. LecoS—A python plugin for automated landscape ecology analysis. Ecol. Inform. 2016, 31, 18–21. [CrossRef]44. Zatelli, P.; Gobbi, S.; Tattoni, C.; Cantiani, M.G.; La Porta, N.; Rocchini, D.; Zorzi, N.; Ciolli, M. Relevance of the Cell Neighborhood Size in Landscape Metrics Evaluation and Free or Open Source Software Implementations. ISPRS Int. J. Geo-Inf. 2019, 8, 586. [CrossRef]45. ESA. European Space Agency. Sentinel-5P Pre-Operations Data Hub—European. 2022. Available online: https://s5phub.copernicus. eu/dhus/ (accessed on 3 March 2022).46. Maroni, D.; Cardoso, G.T.; Neckel, A.; Maculan, L.S.; Oliveira, M.L.; Bodah, E.T.; Bodah, B.W.; Santosh, M. Land surface temperature and vegetation index as a proxy to microclimate. J. Environ. Chem. Eng. 2021, 9, 105796. [CrossRef]47. Niu, G.; Ji, Y.; Zhang, Z.; Wang, W.; Chen, J.; Yu, P. Clustering analysis of typical scenarios of island power supply system by using cohesive hierarchical clustering based K-Means clustering method. Energy Rep. 2021, 7, 250–256. [CrossRef]48. Borlea, I.D.; Precup, R.E.; Borlea, A.B. Improvement of K-means Cluster Quality by Post Processing Resulted Clusters. Procedia Comput. Sci. 2022, 199, 63–70. [CrossRef]49. Ahmad, A.; Khan, S.S. initKmix-A novel initial partition generation algorithm for clustering mixed data using k-means-based clustering. Expert Syst. Appl. 2021, 167, 114149. [CrossRef]50. Cusworth, G.; Garnett, T.; Lorimer, J. Agroecological break out: Legumes, crop diversification and the regenerative futures of UK agriculture. J. Rural. Stud. 2021, 88, 126–137. [CrossRef]51. Musyoki, M.E.; Busienei, J.R.; Gathiaka, J.K.; Karuku, G.N. Linking farmers’ risk attitudes, livelihood diversification and adoption of climate smart agriculture technologies in the Nyando basin, South-Western Kenya. Heliyon 2022, 8, e09305. [CrossRef]52. Burbano-Figueroa, O.; Sierra-Monroy, A.; David-Hinestroza, A.; Whitney, C.; Borgemeister, C.; Luedeling, E. Farm-planning under risk: An application of decision analysis and portfolio theory for the assessment of crop diversification strategies in horticultural systems. Agric. Syst. 2022, 199, 103409. [CrossRef]53. David Raj, A.; Kumar, S.; Sooryamol, K. Modelling climate change impact on soil loss and erosion vulnerability in a watershed of Shiwalik Himalayas. Catena 2022, 214, 106279. [CrossRef]54. Guo, Y.; Wang, J. Poverty alleviation through labor transfer in rural China: Evidence from Hualong County. Habitat Int. 2021, 116, 102402. [CrossRef]55. Reisman, E. Protecting provenance, abandoning agriculture? Heritage products, industrial ideals and the uprooting of a Spanish turrón. J. Rural. Stud. 2022, 89, 45–53. [CrossRef]56. Langewitz, T.; Wiedner, K.; Polifka, S.; Eckmeier, E. Pedological properties related to formation and functions of ancient ridge and furrow cultivation in Central and Northern Germany. Catena 2021, 198, 105049. [CrossRef]57. Arora, A.; Pandey, M.; Mishra, V.N.; Kumar, R.; Rai, P.K.; Costache, R.; Punia, M.; Di, L. Comparative evaluation of geospatial scenario-based land change simulation models using landscape metrics. Ecol. Indic. 2021, 128, 107810. [CrossRef]58. Estoque, R.C.; Ooba, M.; Togawa, T.; Hijioka, Y.; Murayama, Y. Monitoring global land-use efficiency in the context of the UN 2030 Agenda for Sustainable Development. Habitat Int. 2021, 115, 102403. [CrossRef]59. Vasiliev, D.; Greenwood, S. Making green pledges support biodiversity: Nature-based solution design can be informed by landscape ecology principles. Land Use Policy 2022, 117, 106129. [CrossRef]60. Hargis, C.D.; Bissonette, J.A.; David, J.L. The behavior of landscape metrics commonly used in the study of habitat fragmentation. Landsc. Ecol. 1998, 13, 167–186. [CrossRef]61. Mungai, L.M.; Messina, J.P.; Zulu, L.C.; Qi, J.; Snapp, S. Modeling Spatiotemporal Patterns of Land Use/Land Cover Change in Central Malawi Using a Neural Network Model. Remote Sens. 2022, 14, 3477. [CrossRef]62. Jaeger, J.A. Landscape division, splitting index, and effective mesh size: New measures of landscape fragmentation. Landsc. Ecol. 2000, 15, 115–130. [CrossRef]63. Marine, N.; Arnaiz-Schmitz, C.; Herrero-Jáuregui, C.; Cabrera, M.R.d.L.O.; Escudero, D.; Schmitz, M.F. Protected Landscapes in Spain: Reasons for Protection and Sustainability of Conservation Management. Sustainability 2020, 12, 6913. [CrossRef]64. Xu, H.; Xiao, X.; Qin, Y.; Qiao, Z.; Long, S.; Tang, X.; Liu, L. Annual Maps of Built-Up Land in Guangdong from 1991 to 2020 Based on Landsat Images, Phenology, Deep Learning Algorithms, and Google Earth Engine. Remote Sens. 2022, 14, 3562. [CrossRef]65. Xie, Z.; Liu, J.; Huang, J.; Chen, Z.; Lu, X. Linking Land Cover Change with Landscape Pattern Dynamics Induced by Damming in a Small Watershed. Remote Sens. 2022, 14, 3580. [CrossRef]66. Hou, M.; Bao, X.; Ge, J.; Liang, T. Land cover pattern and habitat suitability on the global largest breeding sites for Black-necked Cranes. J. Clean. Prod. 2021, 322, 128968. [CrossRef]67. Tang, Y.; Wang, Q.; Tong, X.; Atkinson, P.M. Integrating spatio-temporal-spectral information for downscaling Sentinel-3 OLCI images. ISPRS J. Photogramm. Remote Sens. 2021, 180, 130–150. [CrossRef]1711514Landscape metricsLand use changeSDGFood securityRemote sensingPublicationORIGINALGeospatial Analysis with Landsat Series and Sentinel-3B OLCI Satellites to Assess Changes in Land Use and Water Quality over Time in Brazil.pdfGeospatial Analysis with Landsat Series and Sentinel-3B OLCI Satellites to Assess Changes in Land Use and Water Quality over Time in Brazil.pdfArtículoapplication/pdf5167993https://repositorio.cuc.edu.co/bitstreams/51e24de3-b3f2-4e03-9661-f77c36ce26dc/downloadae0e743ee1e91e60cbc70ff3f344c9aeMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/9ac25505-a806-45b6-bf1a-f319c571c129/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTGeospatial Analysis with Landsat Series and Sentinel-3B OLCI Satellites to Assess Changes in Land Use and Water Quality over Time in Brazil.pdf.txtGeospatial Analysis with Landsat Series and Sentinel-3B OLCI Satellites to Assess Changes in Land Use and Water Quality over Time in Brazil.pdf.txtExtracted texttext/plain71154https://repositorio.cuc.edu.co/bitstreams/2e4f5814-9f9d-44f6-a80c-3a8600d5842d/download1a0c419fa22a06b73e14867579056980MD53THUMBNAILGeospatial Analysis with Landsat Series and Sentinel-3B OLCI Satellites to Assess Changes in Land Use and Water Quality over Time in Brazil.pdf.jpgGeospatial Analysis with Landsat Series and Sentinel-3B OLCI Satellites to Assess Changes in Land Use and Water Quality over Time in Brazil.pdf.jpgGenerated Thumbnailimage/jpeg16304https://repositorio.cuc.edu.co/bitstreams/8034556c-87ea-48f9-afdf-7f4dc95b1449/download952f66e4fa7429887be365d4d8c6307cMD5411323/10721oai:repositorio.cuc.edu.co:11323/107212024-09-17 11:09:35.27https://creativecommons.org/licenses/by/4.0/© 2022 by the authors. 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ada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
 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