Spectral characterization and monitoring of mangrove forests with remote sensing in the Colombian Pacific Coast: Bajo Baudó, Chocó
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Published:
2021-08-27
Keywords:
Tide, Spectral signature, Vegetation indexes, Landsat, Reflectance
Main Article Content
Abstract
The Colombian Pacific has extensive areas in mangrove forests (MF), which is a strategic ecosystem of great environmental and socioeconomic for climate change mitigation. This work aimed to perform spectral characterization and monitoring of 66.59 km2 for four MF densities in Bajo Baudó (Colombia), using three Landsat images (1998, 2014 and 2017), combinations of spectral bands and three vegetation indices (VI) (Normalized Difference Vegetation Index - NDVI, Soil Adjusted Vegetation Index - SAVI and the Combined Mangrove Recognition Index - CMRI). The results showed that the best combination of spectral bands for visual identification of MF corresponded to infrared color (NIR, Red, Green) and false-color composite 1 (NIR, SWIR, Red). The spectral sign of MFs had different behaviors in four densities under the conditions of high tide and low tide. During the 19 years analyzed, there was a difference of up to 17.9% in the average reflectance value in MF. Similarly, the values of VI were proportional to the densities of MF, but their value was reduced by tidal effects at the time of capturing the images; the largest increases in VI were recorded over the coastal area of land-water transition, where there is a strong interaction with the tidal condition. This research contributes to the spatial characterization and monitoring of MF with remote sensors and the spectral study of this important ecosystem in Colombia.
Article Details
Section
Scientific Article
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References
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Holdridge, L. (1978). Ecología basada en zonas de vida. Centro Interamericano de Documentación e Información Agrícola-IICA.
Horning, N. (2014). Selecting the appropriate band combination for an RGB image using Landsat imagery Version 1.0. American Museum of Natural History, Center for Biodiversity y Conservation.
Huete, A. (1988). «A soil-adjusted vegetation index (SAVI)». En: Remote sensing of environment 25.3, 295-309. Online: https://bit.ly/3zuX8jY
Jia, M. y col. (2019). «New Vegetation Index to Detect Periodically Submerged Mangrove Forest Using Single-Tide Sentinel-2 Imagery». En: Remote Sensing 11, 1-17.Online: https://bit.ly/3iToERo
Kuenzer, C. y col. (2011). «Remote sensing of mangrove ecosystems: A review». En: Remote Sensing 3.5, 878-928. Online: https://bit.ly/2UHovII
Mohamed, E. (2017). «Consideration of landsat-8 Spectral band combination in typical mediterranean forest classification in Halkidiki, Greece». En: Open Geosciences 9.1, 468-479. Online: https://bit.ly/36Zo7Yt
Mondal, P., S. Trzaska y A. De Sherbinin (2018). «Landsat-derived estimates of mangrove extents in the Sierra Leone coastal landscape complex during 1990–2016». En: Sensors 18.12, 1-15. Online: https://bit.ly/3BxOpiF
Monirul, I., B. Helena y K. Lalit (2018). «Monitoring mangrove forest land cover changes in the coastline of Bangladesh from 1976 to 2015». En: Geocarto International 31.13, 1458-1476. Online: https://bit.ly/2VbEcI1
Muhsoni, F. y col. (2018). «Comparison of different vegetation indices for assessing mangrove density using sentinel-2 imagery». En: Int. J. Geomate 14, 42-51. Online: https://bit.ly/3eMwOcT
Omar, H., M. Misman y V. Linggok (2018). «Characterizing and monitoring of mangroves in Malaysia using Landsat-based spatial-spectral variability». En: IOP Conference Series: Earth and Environmental Science. Vol. 169, 24-25. Online: https://bit.ly/3hWSAwK
Pagkalinawan, E. (2014). «Mangrove forest mapping using Landsat 8 images». En: State of the mangrove summit: Northwestern Luzon Proceedings, 60-64. Online: https://bit.ly/2TxBOLj
Perea-Ardila, M., F. Oviedo-Barrero y J. LealVillamil (2019). «Cartografía de bosques de manglar mediante imágenes de sensores remotos: estudio de caso: Buenaventura, Colombia.» En: Revista de Teledetección 53.1, 73-86. Online: https://bit.ly/3ygOWU8
Pérez, F. y J. De la Riva (1998). «El empleo de imágenes Landsat TM para la detección y cartografía de áreas incendiadas en el Prepirineo occidental oscense». En: Geographicalia 36, 131-145. Online: https://bit.ly/36XtEij
Pham, T. y col. (2019). «Remote sensing approaches for monitoring mangrove species, structure, and biomass: Opportunities and challenges». En: Remote Sensing 11.3, 1-24. Online: https://bit.ly/3rFlMf2
Pimple, U. y col. (2018). «Google earth engine based three decadal landsat imagery analysis for mapping of mangrove forests and its surroundings in the trat province of Thailand». En: Journal of Computer and Communications 6, 246-264. Online: https://bit.ly/3BBa7SU
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Rouse, J., J. Haas R. Shell y D. Deering (1974). Monitoring vegetation systems in the Great Plains with ERTS. Goddard Space Flight Center.
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Umroh, A. y S. Sari (2016). «Detection of mangrove distribution in Pongok Island». En: Procedia Environmental Sciences 33, 253-257. Online: https://n9.cl/w48d
Vaghela, B. y col. (2018). «Multi criteria decision making (MCDM) approach for mangrove health assessment using geo-informatics technology». En: International Journal of Environment and Geoinformatics 5.2, 114-131. Online: https://bit.ly/3rx4Zuo
Wang, L. y col. (2019). «A review of remote sensing for mangrove forests: 1956-2018». En: Remote Sensing of Environment 231, 1-150. Online: https://bit.ly/2WcAxdr
Wilkie, M. y S. Fortuna (2003). Status and trends in mangrove area extent worldwide. Food y Agriculture Organization of the United Nations.
Winarso, G. y A. Purwanto (2017). «Evaluation of mangrove damage level based on Landsat 8 image». En: International Journal of Remote Sensing and Earth Sciences 11.2, 105-116. Online: https://bit.ly/3rutwjS.
Xia, Q. y col. (2018). «Mapping mangrove forests based on multi-tidal high-resolution satellite imagery». En: Remote Sensing 10.1343, 2-20. Online: https://bit.ly/3l6iPD5
Xia, Q. y col. (2020). «Evaluation of submerged mangrove recognition index using multi-tidal remote sensing data». En: Ecological Indicators 113, 1-140. Online: https://bit.ly/3kRKpDH
Zhang, X. y Q. Tian (2013). «A mangrove recognition index for remote sensing of mangrove forest from space.» En: Current Science 105.8, 1149-1155.
Online:https://bit.ly/3i0V11a. Zhang, X. y col. (2017). «Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure». En: International journal of applied earth observation and geoinformation 62, 201-214. Online: https://bit.ly/3i0jVOr
Zhu, Z. y C. Woodcock (2014). «Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change». En: Remote Sensing of Environment 152, 217-234. Online: https://bit.ly/3ByuHDD
Ariza, A. (2013). Descripción y Corrección de Productos Landsat 8 LDCM (Landsat Data Continuity Mission) Versión 1.0. Inf. téc. Bogotá: Centro de Investigación y Desarrollo – CIAF, Instituto Geográfico Agustín Codazzi.
Asner, G. (1998). «Biophysical and biochemical sources of variability in canopy reflectance». En: Remote sensing of Environment 64.3, 234-253. Online: https://bit.ly/3hVvgzg
Ávila, D. y col. (2020). «Variación espacio-temporal de la respuesta espectral en manglares de La Habana, Cuba, evaluada con sensores remotos». En: Revista de Biología Tropical 68.1, 321-335. Online: https://bit.ly/3kF9SQM
Baloloy, A. y col. (2020). «Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping». En: ISPRS Journal of Photogrammetry and Remote Sensing 166, 95-117. Online: https://bit.ly/2Tw2dcq
Bannari, A y col. (1995). «A review of vegetation indices». En: Remote sensing reviews 13.1-2, 95-120. Online: https://bit.ly/3Bw8ENw
Blanco, J. F., C. Escobar-Sierra y J. D. CarvajalQuintero (2014). «Gorgona, Baudó y Darién (Chocó Biogeográfico, Colombia): ecorregiones modelo para los estudios ecológicos de comunidades de quebradas costeras». En: Revista de Biología Tropical 62.1, 43-64. Online: https://bit.ly/2UQYl6z
Chander, G. y B. Markham (2003). «Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges». En: IEEE Transactions on geoscience and remote sensing 41.11, 2674-2677. Online: https://n9.cl/wygmu
Chen, N. (2020). «Mapping mangrove in Dongzhaigang, China using Sentinel-2 imagery». En: Journal of Applied Remote Sensing 14.1, 1-11. Online: https://bit.ly/3hY0I06
Chow, J. (2017). «Mangrove management for climate change adaptation and sustainable development in coastal zones». En: Journal of Sustainable Forestry 37.2, 139-156. Online: https://bit.ly/36UsrbI
Chuvieco, E. (1995). Fundamentos de teledetección espacial. 2nd ed. Madrid: Ediciones RIALP S.A.
Chuvieco, E. (2010). Teledetección Ambiental. 3rd ed. Barcelona: Ariel Editorial.
Congalton, R. (1991). «A review of assessing the accuracy of classifications of remotely sensed data». En: Remote Sensing of Environment 37.1, 35-46. Online: https://bit.ly/3kUfMgX
Conti, L., C. Sampaio y M. Cunha (2016). «Spatial database modeling for mangrove forests mapping; example of two estuarine systems in Brazil». En: Modeling Earth Systems and Environment 2.73, 1-12. Online: https://bit.ly/3kPkInf
DIMAR-CCCP (2013). Zonificación fisiográfica del litoral Pacífico colombiano. Fase I. Inf. téc. Dirección General Marítima: San Andrés de Tumaco: Dirección General Marítima-Centro de Investigaciones Oceanográficas e Hidrográficas del Pacífico.
Diniz, C. y col. (2019). «Brazilian mangrove status: Three decades of satellite data analysis». En: Remote Sensing 11.7, 1-19. Online: https://bit.ly/3y78oCI
Dirección General Marítima., ed. (2020). Red de Medición de Parámetros Oceanográficos y de Meteorología Marina (REDMPOMM). Infraestructura de Datos Espaciales Marítima, Fluvial y Costera de Colombia.
ESRI (2014). Redlands, USA.
FAO (2007). The world’s mangroves 1980-2005. Food y Agriculture Organization of the United Nations. Composiciones Landsat en ARCGIS. Guía Básica (2017). MIXDYR. Online: https://bit.ly/3iXYcWX
Galeano, A. y col. (2017). «Mangrove resilience to climate extreme events in a Colombian Caribbean Island». En: Wetlands ecology and management 25.6, 743-760. Online: https://bit.ly/36UD5PJ
Gao, B. (1996). «NDWI A Normalized Difference Water Index for remote sensing of vegetation liquid water from space». En: Remote Sensing of Environment 358, 257-266. Online: https://bit.ly/3x1kps2
Ghosh, S. y col. (2020). «A preliminary study on upstream migration of mangroves in response to changing environment along River Hooghly, India». En: Marine pollution bulletin 151, 1-14. Online: https://bit.ly/3iFWKYT
Giri, C. (2016). «Observation and Monitoring of Mangrove Forests Using Remote Sensing: Opportunities and Challenges.» En: Marine pollution bulletin 8.9, 1-8. Online: https://bit.ly/3iCgAV0
Gupta, K. y col. (2018). «An index for discrimination of mangroves from non-mangroves using LANDSAT 8 OLI imagery». En: MethodsX 5, 1129-1139. Online: https://bit.ly/2UzJIEz
Holdridge, L. (1978). Ecología basada en zonas de vida. Centro Interamericano de Documentación e Información Agrícola-IICA.
Horning, N. (2014). Selecting the appropriate band combination for an RGB image using Landsat imagery Version 1.0. American Museum of Natural History, Center for Biodiversity y Conservation.
Huete, A. (1988). «A soil-adjusted vegetation index (SAVI)». En: Remote sensing of environment 25.3, 295-309. Online: https://bit.ly/3zuX8jY
Jia, M. y col. (2019). «New Vegetation Index to Detect Periodically Submerged Mangrove Forest Using Single-Tide Sentinel-2 Imagery». En: Remote Sensing 11, 1-17.Online: https://bit.ly/3iToERo
Kuenzer, C. y col. (2011). «Remote sensing of mangrove ecosystems: A review». En: Remote Sensing 3.5, 878-928. Online: https://bit.ly/2UHovII
Mohamed, E. (2017). «Consideration of landsat-8 Spectral band combination in typical mediterranean forest classification in Halkidiki, Greece». En: Open Geosciences 9.1, 468-479. Online: https://bit.ly/36Zo7Yt
Mondal, P., S. Trzaska y A. De Sherbinin (2018). «Landsat-derived estimates of mangrove extents in the Sierra Leone coastal landscape complex during 1990–2016». En: Sensors 18.12, 1-15. Online: https://bit.ly/3BxOpiF
Monirul, I., B. Helena y K. Lalit (2018). «Monitoring mangrove forest land cover changes in the coastline of Bangladesh from 1976 to 2015». En: Geocarto International 31.13, 1458-1476. Online: https://bit.ly/2VbEcI1
Muhsoni, F. y col. (2018). «Comparison of different vegetation indices for assessing mangrove density using sentinel-2 imagery». En: Int. J. Geomate 14, 42-51. Online: https://bit.ly/3eMwOcT
Omar, H., M. Misman y V. Linggok (2018). «Characterizing and monitoring of mangroves in Malaysia using Landsat-based spatial-spectral variability». En: IOP Conference Series: Earth and Environmental Science. Vol. 169, 24-25. Online: https://bit.ly/3hWSAwK
Pagkalinawan, E. (2014). «Mangrove forest mapping using Landsat 8 images». En: State of the mangrove summit: Northwestern Luzon Proceedings, 60-64. Online: https://bit.ly/2TxBOLj
Perea-Ardila, M., F. Oviedo-Barrero y J. LealVillamil (2019). «Cartografía de bosques de manglar mediante imágenes de sensores remotos: estudio de caso: Buenaventura, Colombia.» En: Revista de Teledetección 53.1, 73-86. Online: https://bit.ly/3ygOWU8
Pérez, F. y J. De la Riva (1998). «El empleo de imágenes Landsat TM para la detección y cartografía de áreas incendiadas en el Prepirineo occidental oscense». En: Geographicalia 36, 131-145. Online: https://bit.ly/36XtEij
Pham, T. y col. (2019). «Remote sensing approaches for monitoring mangrove species, structure, and biomass: Opportunities and challenges». En: Remote Sensing 11.3, 1-24. Online: https://bit.ly/3rFlMf2
Pimple, U. y col. (2018). «Google earth engine based three decadal landsat imagery analysis for mapping of mangrove forests and its surroundings in the trat province of Thailand». En: Journal of Computer and Communications 6, 246-264. Online: https://bit.ly/3BBa7SU
Purwanto, A. y W. Asriningrum (2019). «Identification of mangrove forests using multispectral satellite imageries». En: International Journal of Remote Sensing and Earth Sciences (IJReSES) 16.1, 63-86. Online: https://bit.ly/36YbtJn
Rebelo-Mochel, F. y F.J. Ponzoni (2007). «Spectral characterization of mangrove leaves in the Brazilian Amazonian Coast: Turiaçu Bay, Maranhão State». En: Anais da Academia Brasileira de Ciências 79.4, 683-692. Online: https://bit.ly/3rEKZGj
Rhyma, P. y col. (2020). «Integration of normalised different vegetation index and Soil-Adjusted Vegetation Index for mangrove vegetation delineation». En: Remote Sensing Applications: Society and Environment 17, 1-70. Online: https://bit.ly/3y2s4ru
Rodríguez-Rodríguez, J.A. y col. (2016). «The Wetland Book». En: Dordrecht: Springer Netherlands. Cap. Mangroves of Colombia.
Rouse, J., J. Haas R. Shell y D. Deering (1974). Monitoring vegetation systems in the Great Plains with ERTS. Goddard Space Flight Center.
USGS (1998). «USGS EROS Archive - Landsat Archives - Landsat 4-5 Thematic Mapper (TM) Level-1 Data Products». En: Landsat 4-5 TM Collection 1. Landsat Scene ID LT50100561998003CPE00. U.S Geological Survey. Online: https://bit.ly/3b7KDjI
USGS (2014). «USGS EROS Archive - Landsat Archives - Landsat 8 OLI Level-1 Data Products». En: Landsat 8 Operational Land Imager (OLI) Collection 1. Landsat Scene ID LC80100562014239LGN01. U.S Geological Survey. Online: https://bit.ly/3b7KDjI
USGS (2017). «USGS EROS Archive - Landsat Archives - Landsat 7 ETM+ Level-1 Data Products». En: Landsat 7 Enhanced Thematic Mapper Plus (ETM+) Collection 1. Landsat Scene ID LE70100562017111EDC00. U.S Geological Survey. Online: https://bit.ly/3b7KDjI
USGS (2018a). Landsat 7 Data Users Handbook. Version 2.0. Inf. téc. U.S Geological Survey.
USGS (2018b). Landsat 8 Data Users Handbook - Versión 3.0. Inf. téc. U.S Geological Survey.
USGS (2020). EarthExplorer. Prog. U.S Geological Survey.
Umroh, A. y S. Sari (2016). «Detection of mangrove distribution in Pongok Island». En: Procedia Environmental Sciences 33, 253-257. Online: https://n9.cl/w48d
Vaghela, B. y col. (2018). «Multi criteria decision making (MCDM) approach for mangrove health assessment using geo-informatics technology». En: International Journal of Environment and Geoinformatics 5.2, 114-131. Online: https://bit.ly/3rx4Zuo
Wang, L. y col. (2019). «A review of remote sensing for mangrove forests: 1956-2018». En: Remote Sensing of Environment 231, 1-150. Online: https://bit.ly/2WcAxdr
Wilkie, M. y S. Fortuna (2003). Status and trends in mangrove area extent worldwide. Food y Agriculture Organization of the United Nations.
Winarso, G. y A. Purwanto (2017). «Evaluation of mangrove damage level based on Landsat 8 image». En: International Journal of Remote Sensing and Earth Sciences 11.2, 105-116. Online: https://bit.ly/3rutwjS.
Xia, Q. y col. (2018). «Mapping mangrove forests based on multi-tidal high-resolution satellite imagery». En: Remote Sensing 10.1343, 2-20. Online: https://bit.ly/3l6iPD5
Xia, Q. y col. (2020). «Evaluation of submerged mangrove recognition index using multi-tidal remote sensing data». En: Ecological Indicators 113, 1-140. Online: https://bit.ly/3kRKpDH
Zhang, X. y Q. Tian (2013). «A mangrove recognition index for remote sensing of mangrove forest from space.» En: Current Science 105.8, 1149-1155.
Online:https://bit.ly/3i0V11a. Zhang, X. y col. (2017). «Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure». En: International journal of applied earth observation and geoinformation 62, 201-214. Online: https://bit.ly/3i0jVOr
Zhu, Z. y C. Woodcock (2014). «Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change». En: Remote Sensing of Environment 152, 217-234. Online: https://bit.ly/3ByuHDD