Study of wetlands in the Ecuadorian Andes through the comparison of Landsat-8 and Sentinel-2 images

C. Jara, J. Delegido, J. Ayala, P. Lozano, A. Armas, V. Flores


The objective of the present study was to compare the Landsat-8 and Sentinel-2 images to calculate the wetland´s extension, distribution and degree of conservation, in Reserva de Producción de Fauna Chinborazo (RPFCH) protected area located in the Andean region of Ecuador. This process was developed with in situ work in 16 wetlands, distributed in different conservation levels. The Landsat-8 and Sentinel-2 images were processed through a radiometric calibration (restoration of lost lines or píxels and correction of the stripe of the image) and an atmospheric correction (conversion of the digital levels to radiance values), to later calculate the Vegetation spectral indexes: NDVI, SAVI (L = 0.5) where L is a constant of the soil brightness component, EVI2 (improved vegetation index 2), NDWI (standard difference water index), WDRI (wide dynamic range vegetation index) and the Red Edge model that only this one has in Sentinel-2 in this study. Making a classification of the Bofedal ecosystem in satellite images by applying Random Forest, the most important variables with Landsat-8 were EVI2 (37.72%) and SAVI with L = 0.5 (30.97%), while with Sentinel-2 the most important variables correspond to the Red Edge (38.54%) and WDRI (27.06%). With the indices calculated, two categories of analysis were determined: a) wetland integrated by the levels: intervened [1], moderately conserved [2] and conserved [3] and b) other than wetland [4] integrated by areas that do not correspond to this ecosystem. Landsat-8 shows that the percentage of correct classifications of píxels belonging to the wetland category corresponds to: [1] 72.76%, [2] 58.38%, [3] 68.42%, while for the category other [4] were correct 95.15%. With Sentinel-2, the percentage of correct classifications corresponds to [1] 95.00%, [2] 82.60%, [3] 96.25%, while for the category other [4] the correct answers were 98.13%. In this way with Landsat-8 the wetland corresponds to 21.708,54 ha (41.21%), while with Sentinel-2 the wetland represents a total of 20,518 ha (38.95%), of the 52,560 ha that belong to the RPFCH, concluding that Sentinel-2, due to its better spatial resolution, and the incorporation of its new bands in Red Edge, obtains better results in image classification.


bofedal, Landsat8; Sentinel2; Random Forest; Red Edge

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Ahmed, K., Akter, S. 2017. Analysis of landcover change in southwest Bengal delta due to floods by NDVI, NDWI and K-Means cluster with Landsat multiSpectral surface reflectance satellite data. Remote Sensing Applications: Society and Environment, 8, 168-181.

Amin, M., Riza, N. 2018. Machine learning enhanced optical distance sensor. Optics Communications, 407, 262-270. optcom.2017.09.028

Andrade, J. 2016. Determinación del estado de conservación de los bofedales de La Reserva de Producción de Fauna Chimborazo. Escuela Superior Politécnica De Chimborazo.

Ayala, J., Márquez, C., García, V., Recalde, C., Rodríguez, M., Damián, D. 2017. Land Cover Classification in an Ecuadorian Mountain Geosystem Using a Random Forest Classifier, Spectral Vegetation Indices, and Ancillary Geographic Data. Geosciences, 7(2), 34.

Bansal, S., Katyal, D., Saluja, R., Chakraborty, M., Garg, J. 2018. Remotely sensed MODIS wetland components for assessing the variability of methane emissions in Indian tropical/subtropical wetlands. Int J Appl Earth Obs Geoinformation, 64(0303- 2434.

Cabello, J., Alcaraz-Segura, D., Reyes, A., Lourenço, P., Requena, J.M., Bonache, J., Serrada, J. 2016. Sistema para el Seguimiento del funcionamiento de ecosistemas en la Red de Parques Nacionales de España mediante Teledetección. Revista de Teledeteccion, 46, 119-131.

Delegido, J., Tenjo, C., Ruiz, A., Pereira, M., Pasqualotto, N., Gibaja, G., Verrelst, J., Peña, R., Urrego, E., Borràs, J., Sanchis, J., Pezzola, A., Mosquera, Z., Quinto, Z., Gómez, J., Moreno, J. 2016. Aplicaciones de Sentinel-2 a estudios de vegetación y calidad de aguas continentales. Conference: XVII Simposio Internacional En Percepción Remota Y Sistemas de Información Geográfica (SELPER).

Delegido, J., Pezzola, A., Casella, A., Winschel, C., Urrego, E.P., Jimenez, J.C., Soria, G., Sobrino, J.A., Moreno, J. 2018. Estimación del grado de severidad de incendios en el sur de la Provincia de Buenos Aires, Argentina, usando Sentinel-2 y su comparación con Landsat-8. Revista de Teledetección, 51, 47-60.

Di Vittorio, C., Georgakakos, A. 2018. Land cover classification and wetland inundation mapping using MODIS. Remote Sensing of Environment, 204, 1-17.

Dwire, K., Mellmann, S., Gurrieri, J., 2018. Potential effects of climate change on riparian areas, wetlands, and groundwater-dependent ecosystems in the Blue Mountains, Oregon, USA. Climate Services, 10, 44- 52.

ESA. 2015. SENTINEL-2 User Handbook. (1),1-64.

García, E., Lleellish, M.A. 2012. Cartografiado de bofedales usando imágenes de satellite Landsat en una cuenca altoandina del Perú. Revista de Teledeteccion, 38, 92-108. Disponible en: http:// pdf Últim acceso: junio de 2019.

Garcia, E., Otto, M. 2015. Caracterización ecohidrológica de humedales alto andinos usando imágenes de satélite multitemporales en la cabecera de cuenca del río Santa, Ancash, Perú. Ecología Aplicada, 14(2):115-125. rea.v14i1-2.88

Gitelson, A. 2004. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology, 161(2), 165-173.

Gitelson, A., Viña, A., Ciganda, V., Rundquist, D., Arkebauer, T. 2005. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32(8), 1-4. https://doi. org/10.1029/2005GL022688

Granizo, T., Molina, M., Secaira, E., Herrera, B., Benítez, S., Maldonado, O., Libby, M., Arroyo, P., Ísola, S., Castro, M. 2006. Manual de Planificación Para La Conservación de Áreas, PCA. edited by M. Cuvi. Quito- Ecuador: TNC y USAID.

Halabisky, M., Moskal, L., Gillespie, A., Hannam, M. 2016. Reconstructing semi-arid wetland surface water dynamics through spectral mixture analysis of a time series of Landsat satellite images (1984- 2011). Remote Sensing of Environment, 177, 171- 183.

ENVI - Environment for Visualizing Images v5.1. 2019. Harris Geospatial Solutions. Recuperado en mayo de 2019, disponible en: https://www.harrisgeospatial. com/

Heynes, S., Gonzáles, M., Ruacho, L., Gonzáles, M., López, I. 2017. Vegetación de humedales del municipio de Durango, Durango, México. Revista Mexicana de Biodiversidad, 88, 358-364. https://

Houborg, R., Fisher, J., Skidmore, A. 2015. Advances in remote sensing of vegetation function and traits. International Journal of Applied Earth Observation and Geoinformation, 43, 1-6. https://

Jiang, Z., Huete, A., Didan, H., Miura., T. 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112(10), 383-345. https://doi. org/10.1016/j.rse.2008.06.006

Kaplan, G., Avdan., U. 2019. Evaluating the utilization of the red edge and radar bands from sentinel sensors for wetland classification. Catena, 178, 109-119.

Lary, D.J., Faruque, F., Malakar, N., Moore, A. 2014. Estimating the global abundance of ground level particulate matter (PM2. 5) Since 1997. Geospatial Health, 9(1), 1-40. gh.2014.292

Li, H., Zhong, X.C., Zhi, W.J.,Wen, B.W., Jian, Q.R., Bin, L., Hasi, T. 2017. Comparative analysis of GF1, HJ-1, and Landsat-8 data for estimating the leaf area index of winter wheat. Journal of Integrative Agriculture, 16(2), 266-285. S2095-3119(15)61293-X

Liang, L., Di, L., Huang, T., Wang, J., Lin, L., Wang, L., Yang, M. 2018. Estimation of Leaf Nitrogen Content in Wheat Using New Hyperspectral Indices and a Random Forest Regression Algorithm. Remote Sensing, 10(12), 1940. rs10121940

Liu, J., Pattey, E., Jégo, G. 2012. Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons. Remote Sensing of Environment 123, 347-358.

Mahdianpari, M., Salehi, B., Mohammadimanesh, F., Brisco, B., Mahdavi, S., Amani, M., Granger, J. 2018. Fisher Linear Discriminant Analysis of coherency matrix for wetland classification using PolSAR imagery. Remote Sensing of Environment, 206, 300- 317.

McFeeters, S.K. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425-1432.

Ministerio del Ambiente. 2014. Actualización Del Plan de Manejo de La Reserva de Producción de Funa Chimborazo. EcoCiencia. Riobamba.

Nie, S., Wang, C., Xi, X., Lou, S., Li, S. 2018. Estimating the height of wetland vegetation using airborne discrete-return LiDAR data. Optik, 154, 267-274.

O’Neil, G., Goodall, J., Watson, J. 2018. Evaluating the potential for site-specific modification of LiDAR DEM derivatives to improve environmental planning-scale wetland identification using Random Forest classification. Journal of Hydrology, 559, 192- 208.

Orimoloye, I.R., Kalumba, A., Mazinyo, S.P., Nel, W. 2018. Geospatial analysis of wetland dynamics: wetland depletion and biodiversity conservation of Isimangaliso Wetland, South Africa. Journal of King Saud University - Science. (in press).

Qi, J., Chehbouni, A., Huete, A.R., Kerr, Y.H., Sorooshian, S. 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment 48(2), 119-126.

Ren, H., Zhou, G., Zhang, F. 2018. Using negative soil adjustment factor in soil-adjusted vegetation index (SAVI) for aboveground living biomass estimation in arid grasslands. Remote Sensing of Environment, 209, 439-445.

Rouse, J. W., Hass, R.H., Schell, J.A., Deering, D.W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. Third Earth Resources Technology Satellite (ERTS) Symposium, 1, 309-317.

SNAP - ESA Sentinel Application Platform v2.0.2. 2019. STEP | Science Toolbox Exploitation Platform. Recuperado en mayo de 2019, de

Secretaría de la Convención de Ramsar. 2013. Manual de La Convención de Ramsar , 6a Edición. Ramsar 6, 118.

Wang, Y., Yésou, H. 2018. Remote Sensing of Floodpath Lakes and Wetlands: A Challenging Frontier in the Monitoring of Changing Environments. Remote Sensing, 10(12), 1955.

Zarco, P., Hornero, A., Hernández, R., Beck, P. 2018. Understanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2a imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 137, 134- 148.

Zhang, H., Roy, D., Yan, L., Li, Z., Huang, H., Vermote, E., Skakun, S., Roger, J. 2018. Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences. Remote Sensing of Environment 215, 482-494.

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