Caracterización espectral de los bosques de mangles en Cuba a través de sensores remotos: un enfoque metodológico
Contenido principal del artículo
Resumen
El empleo de la información satelital para el estudio de los manglares cubanos ha sido limitado. Los métodos generalizados que obtienen datos de campo proveen perspectivas locales y de amplitud temporal restringida, insuficientes para generalizarse a bosques que se distribuyen por cientos de kilómetros de zonas costeras con alta variabilidad espacio temporal. En el presente trabajo se describe y aplica el método para evaluar las características espectrales y su variabilidad espacial de los bosques de mangles en Cuba, a partir de imágenes del Landsat 8 del año 2017, como línea base para futuros estudios. Con las imágenes procesadas se crearon mosaicos de diez índices espectrales de vegetación. La variabilidad espacial se muestreó estadísticamente a partir de 11 584 puntos que permitieron caracterizar las distribuciones de valores entre regiones, zonas costeras y los principales humedales de Cuba. Los índices se correlacionaron entre ellos, con la cobertura arbórea y con distancias a factores de influencia potencial como el mar, cuerpos de agua y poblaciones humanas. Los dos primeros componentes principales, explicaron el 80% de la varianza y permitieron detectar diferencias globales en las distribuciones de puntajes entre regiones. Los manglares de los cuatro sistemas de humedales más extensos del país mostraron patrones particulares de índices espectrales, posiblemente relacionados a sus características geomorfológicas y estructurales. Se discuten las aplicaciones de estas variables en el estudio y monitoreo de este importante ecosistema cubano y se describen sus potencialidades.
Detalles del artículo
Citas
Albuquerque-Ribeiro R, Scarlate A, Twilley RR, Castañeda-Moya E. 2019. Spatial variability of mangrove primary productivity in the neotropics. Ecosphere. 10: 1-13.
Areces AJ (Ed.). 2002. Ecoregionalización y clasificación de hábitats marinos en la plataforma cubana. Resultados del Taller celebrado del 20 al 23 de mayo del 2002. Instituto de Oceanología, World Wildlife Fund-Canada, Environmental Defense, Centro Nacional de Áreas Protegidas. La Habana.
Arfan A, Toriman ME, Maru R, Nyompa S. 2015. Reflectance characteristic of mangrove species using spectroradiometer HR-1024 in Suppa Coast, Pinrang, South Sulawesi, Indonesia. Asian Journal of Applied Sciences. 3: 642-648.
Asner GP, Martin RE. 2009. Airborne spectranomics: Mapping canopy chemical and taxonomic diversity in tropical forests. Frontiers in Ecology and Environment. 7: 269-276.
Asner GP, Martin RE. 2016. Spectranomics: Emerging science and conservation opportunities at the interface of biodiversity and remote sensing. Global Ecology and Conservation. 8: 212-219.
Asner GP, Martin RE, Knapp DE, Tupayachi R, Anderson C, Carranza L, Martinez P, Houcheime M, Sinca F, Weiss P. 2011.Spectroscopy of canopy chemicals in humid tropical forests. Remote Sensing of Environment. 115: 3587-3598.
Baret F, Guyot G. 1991. Potentials and limits of vegetation indices for LAI and PAR assessment. Remote Sensing of Environment. 35: 161-173.
Batllori-Sampedro E, Febles-Patrón JL. 2007. Límites máximos permisibles para el aprovechamiento del ecosistema de manglar. Gacela Ecológica INE-SEMARNAT. 82: 5-23.
Bèland M, Goita K, Bonn F., Pham TTH. 2006. Assessment of land?cover changes related to shrimp aquaculture using remote sensing data: a case study in the Giao Thuy District, Vietnam. International Journal of Remote Sensing 27(8), 1491-1510.
Binh T, Vromant N, Hung NT, Hens L, Boon EK. 2005. Land cover changes between 1968 and 2003 in CaiNuoc, Ca Mau Peninsula, Vietnam. Environmental Development Sustainable 7: 519-536.
Blasco F, Gauquelin T, Rasolofoharinoro M, Denis J, Aizpuru M, Caldairou V. 1998. Recent advances in mangrove studies using remote sensing data. Marine and Freshwater Research. 49: 287-296.
Boegh E, Soegaard H, Broge N, Hasager C, Jensen N, Schelde K, Thomsen A. 2002. Airborne multi-spectral data for quantifying leaf area index, nitrogen concentration and photosynthetic ef?ciency in agriculture. Remote Sensing of Environment. 81: 179-193.
Boon B, Zubir M, Hwee L. 2011. Reflectance Characteristic of Certain Mangrove Species at Matang Mangrove Forest Reserve, Malaysia. En: Proceeding of the 2011 IEEE International Conference on Space Science and Communication (Icon Space), Penang, Malaysia (12-13 de julio 2011).
Cáceres J, Martín MP y Salas J. 2015. Análisis temporal de biomasa y stocks de carbono en un ecosistema de dehesa mediante imágenes Landsat, y su relación con factores climáticos. Ciencias Espaciales. 8: 190-211.
Camejo JA, Cobián D, Izquierdo K, Linares JL, Montero RV. 2013. Acercamiento al estado de salud del ecosistema de manglar de la franja norte en la Reserva de la Biosfera Península de Guanahacabibes, Cuba. ECOVIDA. 4: 36-52.
Cannicci S, Burrows D, Fratini S, Smith TJ, Offenberg J, Dahdouh-Guebas F. 2008. Faunal impact on vegetation structure and ecosystem function in mangrove forests: A review. Aquatic Botany. 89: 186-200.
Chaudhury MU. 1990. Digital Analysis of Remote Sensing Data for Monitoring the Ecological Status of the Mangrove Forests of Sunderbans in Bangladesh. En: Proceedings of the 23rd International Symposium on Remote Sensing of the Environment, Bangkok, Thailand. 1: 493-497.
Cruz DD, Curbelo-Benítez EA, Ferrer-Sánchez Y, Denis D. 2020. Variaciones espaciales y temporales en el Índice de Vegetación de Diferencia Normalizada en Cuba. Ecosistemas. 29(1):1885.
Dash J, Curran PJ. 2004. The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing. 25: 5403-5413.
Denis D, Curbelo EA, Madrigal-Roca LJ, Pérez-Lanyau RD. 2020. Variación espaciotemporal de la respuesta espectral en manglares de La Habana, Cuba, a través de sensores remotos. Revista de Biología Tropical. 68: 321-335.
Días D. 2007. Modelling canopy density variations from remotely sensed data: implications on monitoring floristic and macro-benthic properties of mangrove ecosystems. Tesis de Maestría en Geo-information Science and Earth Observation. International Institute for Geo-information Science and Earth Observation, The Netherlands.
Diaz BM, Blackburn GA. 2003. Remote sensing of mangrove biophysical properties: Evidence from a laboratory simulation of the possible effects of background variation on spectral vegetation indices. International Journal of Remote Sensing. 24: 53-73.
Didan K, Munoz AB, Solano R, Huete A. 2015. MODIS vegetation index user’s guide (MOD13 Series). Disponible en https://vip.arizona.edu/documents/MODIS/MODIS_VI_UsersGuide_June_2015_C6.pdf (consultado: 28 de marzo 2019).
Dobigeon N, Tourneret J, Chang C. 2008. Semi-supervised linear spectral unmixing using a hierarchical bayesian model for hyperspectral imagery. Signal Processing, IEEE Trans. 56: 2684-2695.
Drake N, White K. 1991. Linear mixture modeling of landsat thematic mapper data for mapping the distribution and abundance of gypsum in the Tunisian Southern. En: Spatial Data 2000: Proceedings of a joint conference of the Photogrammetric Society, the Remote Sensing Society, the American Society for Photogrammetry and Remote Sensing’, Christ Church.
Duro DC, Coops NC, Wulder MA, Han T. 2007. Development of a large area biodiversity monitoring system driven by remote sensing. Programs in Physical Geography. 31: 235-260.
English S, Wilkinson C, Baker V. 1994. Survey Manual for Tropical Marine Resources. ASEAN-Australia Marine Science Project, Australian Institute of Marine Science.
Estrada R, Morales GM, Martínez P, Rodríguez SV, Capote RP, Reyes I, Galano S, Cabrera C, Martínez C, Mateo L, Guerra Y, Batte A, Coya L. 2015. Mapa (BD-SIG) de vegetación natural y seminatural de Cuba v1 sobre Landsat ETM 7 slc-off gap filled, circa 2011. En: IV Congreso sobre Manejo de Ecosistemas y Biodiversidad, Convención Internacional sobre Medio Ambiente y Desarrollo, La Habana (4 - 8 de julio de 2013).
Feller IC, Lovelock CE, Berger U, McKee KL, Joye SB, Ball MC. 2010.Biocomplexity in Mangrove Ecosystems. Annual Reviews of Marine Science. 2: 395-417.
Galford GL, Fernandez M, Roman J, Monasterolo I, Ahamed S, Fiske G, González-Díaz P, Kaufman L. 2018. Cuban land use and conservation, from rainforests to coral reefs. Bulletin of Marine Science. 94:1-23.
Gao X, Huete AR, Ni W, Miura T. 2000. Optical–biophysical relationships of vegetation spectra without background contamination. Remote Sensing of Environment. 74: 609-620.
GCOS. 2011. Systematic observation requirements for satellite-based products for climate. 2011 update. Supplemental details to the satellite-based component of the implementation plan for the global observing system for climate in support of the UNFCCC. GCOS-154: 138.
Gillespie TW, Foody GM, Rocchini D, Giorgi AP, Saatchi S. 2008. Measuring and modeling biodiversity from space. Programs in Physical Geography. 32: 203-221.
Giri C, Long J, Abbas S, Murali RM, Qamer FM, Pengra B, Thau D. 2015. Distribution and dynamics of mangrove forests of South Asia. Journal of Environmental Management. 148:101-111.
Giri C, Ochieng E, Tieszen LL, Zhu Z, Singh A, Loveland T, Masek J, Duke N. 2011. Status and distribution of mangrove forests of the world using earth observation satellite data: Status and distributions of global mangroves. Global Ecology and Biogeography. 20: 154-159.
Gitelson AA. 2005. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters. 32.
Gitelson A, Kaufman Y, Merzylak M. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment. 58: 289-298.
Gitelson AA, Kaufman YJ, Stark R, Rundquist DC. 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment. 80: 76-87.
Gitelson AA, Viña A, Arkebauer TJ, Rundquist DC, Keydan GP, Leavitt B. 2003. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters. 30(5):1248.
Green EP, Clark CD, Edwards AJ. 2000. Image classification and habitat mapping. En: Remote Sensing Handbook for Tropical Coastal Management. UNESCO, Paris.
Green EP, Clark CD, Mumby PJ, Edwards AJ, Ellis AC. 1998. Remote sensing techniques for mangrove mapping.International Journal of Remote Sensing. 19: 935-956.
Green E, Mumby P, Edwards A, Clark C. 2014. Remote sensing handbook for tropical coastal management. Disponible en http://www.unesco.org/csi/pub/source/rs.htm (consultado: julio de 2019).
Green EP, Mumby PJ, Edwards AJ, Clark CD, Ellis AC. 1997. Estimating leaf area index of mangroves from satellite data. Aquatic Botany. 58: 11-19.
Hamilton SE, Casey D. 2016. Creation of a high spatio?temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC?21). Global Ecology and Biogeography. 25: 729-738
Hendrawan GJL, Susilo SB. 2018. Study of density and change of mangrove cover using Satellite Imagery in Sebatik Island, North Borneo. JurnalIlmu Dan Teknologi Kelautan Tropis. 10: 99-109.
Hu YH, Lee HB, Scarpace FL. 1999. Optimal linear spectral unmixing. IEEE Transactions on Geoscience and Remote Sensing 37(1): 639-644
Huete AR. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment. 25: 295-309.
Huete AR, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG. 2002. Overview of radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment. 83: 195-213.
Huete AR, Liu HQ, Batchily K, van Leeuwn WJD. 1997. A comparison of vegetation indices over a global set of TM images for EOSMODIS. Remote Sensing of Environment. 59: 440-451.
Jensen JR. 2007. Remote Sensing of the Environment: An Earth Resource Perspective. 2da ed. Pearson Prentice Hall: Upper Saddle River, NJ, USA.
Jensen JR, Lin H, Yang X, Ramsey III EW, Davis BA, Thoemke CW. 1991.The measurement of mangrove characteristics in southwest Florida using SPOT multispectral data. Geocarto International. 6: 13-21.
Jetz W, McGeoch MA, Guralnick R, Ferrier S, Beck J, Costello MJ, Fernandez M, Geller GN, Keil? P, Merow C, Meyer C, Muller-Karger FE, Pereira HM, Regan EC, Schmeller DS, Turak E. 2019. Essential biodiversity variables for mapping and monitoring species populations. Nature Ecology & Evolution. DOI.org/10.1038/s41559-019-0826-1.
Jian Z, Huete AR, Didan K, Miura T. 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment. 112: 3833-3845.
Julien Y, Sobrino JA. 2009. Global land surface phenology trends from GIMMS database. International Journal of Remote Sensing. 30: 3495-3513.
Kathiresan K, Bingham BL. 2001. Biology of mangroves and mangrove ecosystems. Advances in Marine Biology. 40: 81-251.
Kaufman YJ, Tanre D. 1992. Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS. IEEE Trans Geoscience Remote Sensing. 30: 261-270.
Kauth RJ, Thomas GS. 1976. The Tasseled Cap- a graphic description of the spectral-temporal devel- opment of agricultural crops as seen by Landsat. Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, Purdue University, West Lafayette, Indiana, pp. 4B41-4B51.
Kerr JT, Ostrovsky M. 2003. From space to species: Ecological applications for remote sensing. Trends in Ecology and Evolution. 18: 299-305.
Kovacs JM, Wang J, Blanco-Correa M. 2001. Mapping disturbance in a mangrove forest using multi-date Landsat imagery. Environmental Management. 27:763-776.
Kuenzer C, Bluemel A, Gebhardt S, Vo Quoc T, Dech S. 2011. Remote sensing of mangrove ecosystems: A Review. Remote Sensing. 3: 878-928.
Lee TM, Yeh HC. 2009. Applying remote sensing techniques to monitor shifting wetland vegetation: A case study of Danshui River estuary mangrove communities, Taiwan. Ecological engineering. 35: 487-496.
Lugo AE, Snedaker SC. 1974.The ecology of mangroves. Annual Review of Ecology and Systematics. 5: 39-64.
Marvin DC, Asner GP, Knapp DE, Anderson CB, Martin RE, Sinca F, Tupayachi R. 2014. Amazonian landscapes and the bias in field studies of forest structure and biomass. Proceedings of the National Academy of Science. 111: E5224–E5232.
Menéndez L, Guzmán JM, 2006. Ecosistema de manglar en el Archipiélago Cubano Estudios y experiencias enfocados a su gestión. Editorial Academia, La Habana.
Monsef HA, Smith SE. 2017. A new approach for estimating mangrove canopy cover using Landsat 8 imagery. Computers and Electronics in Agriculture. 135: 183-194.
Moran MS, Inoue Y, Barnes EM. 1997. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment. 61: 319-346.
Myneni RB, Hall FG, Sellers PJ, Marshak AL. 1995. The interpretation of spectral vegetation indexes. IEEE Trans Geoscience Remote Sensing. 33: 481-486.
Myneni RB, Hoffman S, Knyazikhin Y, Privette JL, Glassy J, Tian Y, Wang Y, Song X, Zhang Y, Smith GR, Lotsch A, Friedl M, Morisette JT, Votava P, Nemani RR, Running SW. 2002. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment. 83: 214-231.
Nagelkerken I, Blaber SJ, Bouillon S, Green P, Haywood M, Kirton LG, Meynecke JO, Pawlik J, Penrose HM, Sasekumar A, Somerfield PJ. 2008. The habit function of mangroves for terrestrial and marine fauna: A review. Aquatic Botany. 89: 155-185.
Nagendra H, Lucas R, Honrado JP, Jongman RH, Tarantino C, Adamo M, Mairota P. 2013. Remote sensing for conservation monitoring: assessing protected areas, habitat extent, habitat condition, species diversity, and threats. Ecological Indicators 33: 45-59.
Nagendra H, Nagendran S, Paul S, Pareeth S. 2012. Graying, greening and fragmentation in the rapidly expanding Indian city of Bangalore, Landscape and Urban Planning. 105: 400-406.
Navarro LM, Fernández N, Guerra G, Guralnick R, Kissling WD, Londoño MC, Muller-Karger F, Turak E, Balvanera P, Costello MJ, Delavaud A, El Serafy GY, Ferrier S, Geijzendorffer I, Geller GN, Jetz W, Kim ES, Kim HJ, Martin CS, McGeoch MA, Mwampamba TH, Nel JL, Nicholson E, Pettorelli N, Schaepman ME, Skidmore A, Pinto IS, Vergara S, Vihervaara P, Xu H, Yahara T, Gill M, Pereira HM. 2017. Monitoring biodiversity change through effective global coordination. Current Opinion in Environmental Sustainability 29: 158-169.
Neumann W, Martinuzzi S, Estes AB, Pidgeon AM, Dettki H, Ericsson G, Radeloff VC. 2015. Opportunities for the application of advanced remotely-sensed data in ecological studies of terrestrial animal movement. Movement Ecology. 3:8, 12 pp.
Pasaribu RA, Cakasana N, Maduppa H, Subhan B, Arafat D, Sangadji MS, Savana MS. 2020. Mangrove density level and area change analysis in small islands case study: UntungJawa Island, Seribu Islands, DKI Jakarta. IOP Conferences Series: Earth and Environmental Science. 429 (1): 012060.
Pastor-Guzman J, Atkinson PM, Dash J, Rioja-Nieto R. 2015. Spatiotemporal variation in mangrove chlorophyll concentration using Landsat 8. Remote Sensing. 7: 14530-14558.
Pastor-Guzman J, Dash J, Atkinson PM. 2018. Remote sensing of mangrove forest phenology and its environmental drivers. Remote Sensing of Environment. 205: 71-84.
Pereira HM, Ferrier S, Walters M, Geller GN, Jongman RHG, Scholes RJ, Bruford MW, Brummitt N, Butchart SHM, Cardoso AC, Coops NC, Dulloo E, Faith DP, Freyhof J, Gregory RD, Heip C, Höft R, Hurtt G, Jetz W, Karp DS, McGeoch MA, Obura D, Onoda Y, Pettorelli N, Reyers B, Sayre R, Scharlemann JPW, Stuart SN, Turak E, Walpole M, Wegmann M. 2013. Essential biodiversity variables. Science. 339: 277-278.
Pettorelli N, Laurance WF, O'Brien TG, Wegmann M, Nagendra H, Turner W. 2014. Satellite remote sensing for applied ecologists: opportunities and challenges, Journal of Applied Ecology. 51: 839-848.
Prasad KA, Gnanappazham L. 2014. Species discrimination of mangroves using Derivative Spectral Analysis.ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2: 45-52.
Rahman AF, Dragoni D, Didan K, Barreto A, Hutabarat JA. 2013. Detecting large scale conversion of mangroves to aquaculture with change point and mixed-pixel analyses of high-?delity MODIS data. Remote Sensing of Environment. 30: 96-107.
Ramón AM, Martínez L, López O, Suarez C, Zamora Y. 2013. Estimación del patrimonio forestal y su categorización a partir de imágenes Landsat TM y modelación SIG, del municipio Guisa. Cuba. Terra. XXVIII (44): 39-52.
Roman J. 2018. The ecology and conservation of Cuba’s coastal and marine ecosystems. Bulletin of Marine Science. 94:149-169.
Rondeaux G, Steven M, Baret F. 1996. Optimization of soil adjusted vegetation indices. Remote Sensing of Environment. 55: 95-107.
Rossi E, Rogan J, Schneider L. 2013. Mapping forest damage in northern Nicaragua after Hurricane Felix (2007) using MODIS enhanced vegetation index data. GISci Remote Sensing. 50: 171-194.
Running SW, Justice CO, Salomonson V, Hall D, Barker J, Kaufmann YJ, Strahler AH, Huete AR, Muller JP, Vanderbilt V, Wan ZM, Teillet P, Carneggie D. 1994. Terrestrial remote sensing science and algorithms planned for EOS/MODIS. International Journal of Remote Sensing. 15: 3587-3620.
Schimel DS, Asner GP, Moorcroft PR. 2013.Observing changing ecological diversity in the Anthropocene.Frontiers in Ecology and Environment. 11: 129-137.
Sellers PJ.1985 Cano.py reflectance, photosynthesis and transpiration. International Journal of Remote Sensing. 6: 1335-1372.
Selvam V, Ravichandran KK, Gnanappazham L, Navamuniyammal M. 2003. Assessment of community - based restoration of Pichavaram mangrove wetland using remote sensing data. Current Science. 85: 794-798.
Serafy JE, Shideler GS, Araújo RJ, Nagelkerken I. 2015. Mangroves enhance reef fish abundance at the Caribbean regional scale. PLoS One. 10:e0142022.
Small C. 2004.The landsat ETM+ spectral mixing space. Remote Sensing of Environment. 93: 1-17.
Spalding M, Kainuma M, Collins L. 2010. World Atlas of Mangroves. Earthscan: London.
Susan B, Behzad R, Mehdi S, Alireza S, Masoud N. 2011. Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas. Egyptian Journal of Remote Sensing and Space Science. 14: 49-56.
Thu PM, Populus J. 2007. Status and changes of mangrove forest in Mekong Delta: Case study in TraVinh, Vietnam. Estuarine, Coastal and Shelf Science. 71: 98-109.
Tucker CJ, Townshend JRG. 2000. Strategies for monitoring tropical deforestation using satellite data. International Journal of Remote Sensing. 21:1461-1471.
Ustin SL, Roberts DA, Gamon JA, Asner GP, Green RO. 2004. Using imaging spectroscopy to study ecosystem processes and properties. BioScience. 54: 523-534.
Verstraete MM, Pinty B, Myneni RB. 1996. Potential and limitations of information extraction on the terrestrial biosphere from satellite remote sensing. Remote Sensing of Environment. 58: 201-214.
Wang K, Franklin SE, Guo X, Cattet M. 2010. Remote sensing of ecology, biodiversity and conservation: A review from the perspective of remote sensing specialists. Sensors. 10: 9647-9667.
Wang W, Lin P. 1999. Transfer of salt and nutrients in Bruguiera gymnorrhiza leaves during development and senescence. Mangrove Salt Marshes 3:1-7.
Winarso G, Purwanto AD. 2017. Evaluation of mangrove damage level based on Landsat 8 image. International Journal of Remote Sensing and Earth Sciences. 11: 105-116.
Xue J, B Su. 2017. Significant remote sensing vegetation indices: a review of developments and applications. Journal of Sensors. DOI:.org/10.1155/2017/1353691.
Zulfa AW, Norizah KP. 2018. Remotely sensed imagery data application in mangrove forest: a review. Journal of Science & Technology. 26: 899-922.