Mangrove mapping using Sentinel-1 data for improved decision support on sustainable conservation and restoration interventions in the Keta Lagoon Complex Ramsar Site, Ghana
George Ashiagbor
A D , Winston Adams Asante A , Jonathan Arthur Quaye-Ballard B , Eric Kwabena Forkuo B , Emmanuel Acheampong A and Ernest Foli C
+ Author Affiliations
- Author Affiliations
A Faculty of Renewable Natural Resources (FRNR), Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana.
B Department of Geomatic Engineering, KNUST, PMB, Kumasi, Ghana.
C CSIR–Forestry Research Institute of Ghana (CSIR-FORIG), University PO Box UP63, Kumasi, Ghana.
D Corresponding author. Emails: gashiagbor.canr@knust.edu.gh; gashiagbor@yahoo.com
Marine and Freshwater Research 72(11) 1588-1601 https://doi.org/10.1071/MF20105
Submitted: 8 April 2020 Accepted: 11 May 2021 Published: 1 July 2021
Abstract
Despite the conservation importance of the Keta Lagoon Complex Ramsar Site (KLCRS), obtaining information on the extent and distribution of mangroves is challenging due to the unavailability of optical satellite data. This research explored Sentinel-1 radar data to provide information on mangrove distribution in the KLCRS. Global positioning system points from 443 training and 196 validation sites were used. In addition, focus group discussions and key informant interviews were used to corroborate information on mangrove distribution. Sentinel-1 data were processed for backscatter coefficients and two backscatter derivatives. These were stacked into a four-layer image composite and classified using a support vector machine. An overall classification accuracy of 89.28% was obtained. In addition, user and producer accuracies of 100 and 97.3% respectively were obtained for the mangrove class. The results show that mangroves occupy a total area of 41.02 km2 in the KLCRS and are mostly found around the Salo, Bomigo, Anyanui and Dzita communities. This study demonstrates the possibility of using Sentinel-1 imagery to map mangroves within the KLCRS. Thus, this study serves as a guideline for other data-constrained mangrove landscapes to map and monitor mangroves for conservation and restoration actions.
Keywords: forest monitoring, Ramsar sites, random forest classifier, RF, support vector machine, SVM, wetlands.
References
Abdel-Hamid, A., Dubovyk, O., El-Magd, A., and Menz, G. (2018). Mapping mangroves extents on the red sea coastline in Egypt using polarimetric SAR and high resolution optical remote sensing data. Sustainability 10, 646.| Mapping mangroves extents on the red sea coastline in Egypt using polarimetric SAR and high resolution optical remote sensing data.Crossref | GoogleScholarGoogle Scholar |
Abdikan, S., Sanli, F. B., Ustuner, M., and Calò, F. (2016). Land cover mapping using Sentinel-1 SAR data. In ‘The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXIII ISPRS Congress’, 12–19 July 2016, Prague, Czech Republic. Volume XLI-B7, pp. 757–761. (ISPRS.)
| Crossref |
Ajonina, G., Diamé, A., and Kairo, J. (2008). Current status and conservation of mangroves in Africa: an overview. In World Rainforest Movement Bulletin 133, pp. 1–6. Available at https://wrmbulletin.wordpress.com/2008/08/25/current-status-and-conservation-of-mangroves-in-africa-an-overview/ [Verified 31 May 2021].
Akar, Ö., and Güngör, O. (2012). Classification of multispectral images using Random Forest algorithm. Journal of Geodesy and Geoinformation 1, 105–112.
| Classification of multispectral images using Random Forest algorithm.Crossref | GoogleScholarGoogle Scholar |
Asante, W. A., Acheampong, E., Boateng, K., and Adda, J. (2017). The implications of land tenure and ownership regimes on sustainable mangrove management and conservation in two Ramsar sites in Ghana. Forest Policy and Economics 85, 65–75.
| The implications of land tenure and ownership regimes on sustainable mangrove management and conservation in two Ramsar sites in Ghana.Crossref | GoogleScholarGoogle Scholar |
Ashiagbor, G., Forkuo, E. K., Asante, W. A., Acheampong, E., Quaye-Ballard, J. A., Boamah, P., Mohammed, Y., and Foli, E. (2020). Pixel-based and object-oriented approaches in segregating cocoa from forest in the Juabeso-Bia landscape of Ghana. Remote Sensing Applications: Society and Environment 19, 100349.
| Pixel-based and object-oriented approaches in segregating cocoa from forest in the Juabeso-Bia landscape of Ghana.Crossref | GoogleScholarGoogle Scholar |
Balzter, H., Cole, B., Thiel, C., and Schmullius, C. (2015). Mapping CORINE land cover from Sentinel-1A SAR and SRTM digital elevation model data using random forests. Remote Sensing 7, 14876–14898.
| Mapping CORINE land cover from Sentinel-1A SAR and SRTM digital elevation model data using random forests.Crossref | GoogleScholarGoogle Scholar |
Bayanudin, A. A., and Jatmiko, R. H. (2016). Orthorectification of Sentinel-1 SAR (synthetic aperture radar) data in some parts of south-eastern Sulawesi using Sentinel-1 toolbox. IOP Conference Series: Earth and Environmental Science 47, 012007.
| Orthorectification of Sentinel-1 SAR (synthetic aperture radar) data in some parts of south-eastern Sulawesi using Sentinel-1 toolbox.Crossref | GoogleScholarGoogle Scholar |
Bazzi, H., Baghdadi, N., Ienco, D., El Hajj, M., Zribi, M., Belhouchette, H., Escorihuela, M. J., and Demarez, V. (2019). Mapping irrigated areas using Sentinel-1 time series in Catalonia, Spain. Remote Sensing 11, 1836.
| Mapping irrigated areas using Sentinel-1 time series in Catalonia, Spain.Crossref | GoogleScholarGoogle Scholar |
Belgiu, M., Drăguţ, L., and Strobl, J. (2014). Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery. ISPRS Journal of Photogrammetry and Remote Sensing 87, 205–215.
| Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery.Crossref | GoogleScholarGoogle Scholar | 24623959PubMed |
Blaes, X., Defourny, P., Wegmuller, U., Della Vecchia, A., Guerriero, L., and Ferrazzoli, P. (2006). C-band polarimetric indexes for maize monitoring based on a validated radiative transfer model. IEEE Transactions on Geoscience and Remote Sensing 44, 791–800.
| C-band polarimetric indexes for maize monitoring based on a validated radiative transfer model.Crossref | GoogleScholarGoogle Scholar |
Blasco, F., Gauquelin, T., Rasolofoharinoro, M., Denis, J., Aizpuru, M., and Caldairou, V. (1998). Recent advances in mangrove studies using remote sensing data. Marine and Freshwater Research 49, 287–296.
| Recent advances in mangrove studies using remote sensing data.Crossref | GoogleScholarGoogle Scholar |
Boateng, I. (2018). An assessment of vulnerability and adaptation of coastal mangroves of West Africa in the face of climate change. In ‘Threats to Mangrove Forests’. (Eds C. Makowski and C. W. Finkl.) pp. 141–154. (Springer.)
| Crossref |
Brown, I., Mwansasu, S., and Westerberg, L. O. (2016). L-band polarimetric target decomposition of mangroves of the rufiji delta, Tanzania. Remote Sensing 8, 140.
| L-band polarimetric target decomposition of mangroves of the rufiji delta, Tanzania.Crossref | GoogleScholarGoogle Scholar |
Campomanes, F., Pada, A. V., and Silapan, J. (2016). Mangrove classification using support vector machines and random forest algorithm: a comparative study. In ‘GEOBIA 2016: Solutions and Synergies’, 14–16 September 2016, Twente, Netherlands. pp. 3–6. (University of Twente Faculty of Geo-Information and Earth Observation, ITC.)
| Crossref |
Chatziantoniou, A., Petropoulos, G. P., and Psomiadis, E. (2017). Co-orbital Sentinel 1 and 2 for LULC mapping with emphasis on wetlands in a mediterranean setting based on machine learning. Remote Sensing 9, 1259.
| Co-orbital Sentinel 1 and 2 for LULC mapping with emphasis on wetlands in a mediterranean setting based on machine learning.Crossref | GoogleScholarGoogle Scholar |
Clerici, N., Valbuena Calderón, C. A., and Posada, J. M. (2017). Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia. Journal of Maps 13, 718–726.
| Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia.Crossref | GoogleScholarGoogle Scholar |
Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment 37, 35–46.
| A review of assessing the accuracy of classifications of remotely sensed data.Crossref | GoogleScholarGoogle Scholar |
Cutler, M. E. J., Boyd, D. S., Foody, G. M., and Vetrivel, A. (2012). Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: an assessment of predictions between regions. ISPRS Journal of Photogrammetry and Remote Sensing 70, 66–77.
| Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: an assessment of predictions between regions.Crossref | GoogleScholarGoogle Scholar |
Duro, D. C., Franklin, S. E., and Dubé, M. G. (2012). A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classi fi cation of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment 118, 259–272.
| A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classi fi cation of agricultural landscapes using SPOT-5 HRG imagery.Crossref | GoogleScholarGoogle Scholar |
Filipponi, F. (2019). Sentinel-1 GRD preprocessing workflow. Proceedings 18, 11.
| Sentinel-1 GRD preprocessing workflow.Crossref | GoogleScholarGoogle Scholar |
Giri, C. (2016). Observation and monitoring of mangrove forests using remote sensing: opportunities and challenges. Remote Sensing 8, 783.
| Observation and monitoring of mangrove forests using remote sensing: opportunities and challenges.Crossref | GoogleScholarGoogle Scholar |
Giri, C., Ochieng, E., Tieszen, L. L., Zhu, Z., Singh, A., Loveland, T., Masek, J., and Duke, N. (2011). Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography 20, 154–159.
| Status and distribution of mangrove forests of the world using earth observation satellite data.Crossref | GoogleScholarGoogle Scholar |
Goetz, S., and Dubayah, R. (2011). Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change. Carbon Management 2, 231–244.
| Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change.Crossref | GoogleScholarGoogle Scholar |
Green, E. P., Clark, C. D., Mumby, P. J., Edwards, A. J., and Ellis, A. C. (1998). Remote sensing techniques for mangrove mapping. International Journal of Remote Sensing 19, 935–956.
| Remote sensing techniques for mangrove mapping.Crossref | GoogleScholarGoogle Scholar |
Heumann, B. W. (2011). An object-based classification of mangroves using a hybrid decision tree – support vector machine approach. Remote Sensing 3, 2440–2460.
| An object-based classification of mangroves using a hybrid decision tree – support vector machine approach.Crossref | GoogleScholarGoogle Scholar |
Heydari, S. S., and Mountrakis, G. (2018). Effect of classifier selection, reference sample size, reference class distribution and scene heterogeneity in per-pixel classification accuracy using 26 Landsat sites. Remote Sensing of Environment 204, 648–658.
| Effect of classifier selection, reference sample size, reference class distribution and scene heterogeneity in per-pixel classification accuracy using 26 Landsat sites.Crossref | GoogleScholarGoogle Scholar |
Hu, L., Xu, N., Liang, J., Li, Z., Chen, L., and Zhao, F. (2020). Advancing the mapping of mangrove forests at national-scale using Sentinel-1 and Sentinel-2 time-series data with google earth engine: a case study in China. Remote Sensing 12, 3120.
| Advancing the mapping of mangrove forests at national-scale using Sentinel-1 and Sentinel-2 time-series data with google earth engine: a case study in China.Crossref | GoogleScholarGoogle Scholar |
Huang, W., DeVries, B., Huang, C., Lang, M. W., Jones, J. W., Creed, I. F., and Carroll, M. L. (2018). Automated extraction of surface water extent from Sentinel-1 data. Remote Sensing 10, 797.
| Automated extraction of surface water extent from Sentinel-1 data.Crossref | GoogleScholarGoogle Scholar |
Intergovernmental Panel on Climate Change (2013). Summary for Policymakers. In ‘Climate Change 2013: the Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change’. pp. 1–30. (Cambridge University Press: Cambridge, UK.)
| Crossref |
Jacob, A. W., Vicente-Guijalba, F., Lopez-Martinez, C., Lopez-Sanchez, J. M., Litzinger, M., Kristen, H., Mestre-Quereda, A., Ziółkowski, D., Lavalle, M., Notarnicola, C., and Suresh, G. (2020). Sentinel-1 InSAR coherence for land cover mapping: a comparison of multiple feature-based classifiers. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, 535–552.
| Sentinel-1 InSAR coherence for land cover mapping: a comparison of multiple feature-based classifiers.Crossref | GoogleScholarGoogle Scholar |
Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., Jepsen, M. R., Kuemmerle, T., Meyfroidt, P., Mitchard, E. T., and Reiche, J. (2016). A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sensing 8, 70.
| A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring.Crossref | GoogleScholarGoogle Scholar |
Kamusoko, C. (2019). ‘Remote Sensing Image Classification in R.’ (Springer: Singapore)
| Crossref |
Lapini, A., Pettinato, S., Santi, E., Paloscia, S., Fontanelli, G., and Garzelli, A. (2020). Comparison of machine learning methods applied to SAR images for forest classification in mediterranean areas. Remote Sensing 12, 369.
| Comparison of machine learning methods applied to SAR images for forest classification in mediterranean areas.Crossref | GoogleScholarGoogle Scholar |
Li, Q., Wong, F. K. K., and Fung, T. (2019). Classification of mangrove species using combined WordView-3 and LiDAR data in Mai Po Nature Reserve, Hong Kong. Remote Sensing 11, 2114.
| Classification of mangrove species using combined WordView-3 and LiDAR data in Mai Po Nature Reserve, Hong Kong.Crossref | GoogleScholarGoogle Scholar |
Liu, C. (2016). Analysis of Sentinel-1 SAR data for mapping standing water in the Twente region. M.Sc. Thesis, University of Twente, Twente, Netherlands. Available at www.itc.nl/library/papers_2016/msc/wrem/cliu.pdf.
Liu, T., Abd-Elrahman, A., Morton, J., and Wilhelm, V. L. (2018). Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system. GIScience & Remote Sensing 55, 243–264.
| Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system.Crossref | GoogleScholarGoogle Scholar |
Lucas, R., Rebelo, L. M., Fatoyinbo, L., Rosenqvist, A., Itoh, T., Shimada, M., Simard, M., Souza-Filho, P. W., Thomas, N., Trettin, C., and Accad, A. (2014). Contribution of L-band SAR to systematic global mangrove monitoring. Marine and Freshwater Research 65, 589–603.
| Contribution of L-band SAR to systematic global mangrove monitoring.Crossref | GoogleScholarGoogle Scholar |
Mandal, D., Kumar, V., Ratha, D., Dey, S., Bhattacharya, A., Lopez-Sanchez, J. M., McNairn, H., and Rao, Y. S. (2020). Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data. Remote Sensing of Environment 247, 111954.
| Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data.Crossref | GoogleScholarGoogle Scholar |
Manson, F. J., Loneragan, N. R., McLeod, I. M., and Kenyon, R. A. (2001). Assessing techniques for estimating the extent of mangroves: topographic maps, aerial photographs and Landsat TM images. Marine and Freshwater Research 52, 787–792.
| Assessing techniques for estimating the extent of mangroves: topographic maps, aerial photographs and Landsat TM images.Crossref | GoogleScholarGoogle Scholar |
Maxwell, A. E., Warner, T. A., and Fang, F. (2018). Implementation of machine-learning classification in remote sensing: an applied review. International Journal of Remote Sensing 39, 2784–2817.
| Implementation of machine-learning classification in remote sensing: an applied review.Crossref | GoogleScholarGoogle Scholar |
Mensah, J. C. (2013). Remote sensing application for mangrove mapping in the Ellembelle District in Ghana, USAID Integrated Coastal and Fisheries Governance Program for the Western Region of Ghana. (Coastal Resources Center, Graduate School of Oceanography, University of Rhode Island, Narragansett, RI, USA.) Available at https://www.crc.uri.edu/download/GH2009DAZ004_508.pdf [Verified 1 June 2021].
Mercier, A., Betbeder, J., Rumiano, F., Baudry, J., Gond, V., Blanc, L., Bourgoin, C., Cornu, G., Marchamalo, M., Poccard-Chapuis, R., and Hubert-Moy, L. (2019). Evaluation of Sentinel-1 and 2 time series for land cover classification of forest–agriculture mosaics in temperate and tropical landscapes. Remote Sensing 11, 979.
| Evaluation of Sentinel-1 and 2 time series for land cover classification of forest–agriculture mosaics in temperate and tropical landscapes.Crossref | GoogleScholarGoogle Scholar |
Mleczko, M., and Mróz, M. (2018). Wetland mapping using SAR data from the Sentinel-1A and TanDEM-X missions: a comparative study in the Biebrza Floodplain (Poland). Remote Sensing 10, 78.
| Wetland mapping using SAR data from the Sentinel-1A and TanDEM-X missions: a comparative study in the Biebrza Floodplain (Poland).Crossref | GoogleScholarGoogle Scholar |
Nascimento, W. R., Souza-Filho, P. W. M., Proisy, C., Lucas, R. M., and Rosenqvist, A. (2013). Mapping changes in the largest continuous Amazonian mangrove belt using object-based classification of multisensor satellite imagery. Estuarine, Coastal and Shelf Science 117, 83–93.
| Mapping changes in the largest continuous Amazonian mangrove belt using object-based classification of multisensor satellite imagery.Crossref | GoogleScholarGoogle Scholar |
Ntiamoa-Baidu, Y., and Gordon, C. (1991). Coastal wetlands management plans: Ghana. Report prepared for the World Bank and Environmental Protection Council, Ghana. Available at http://extwprlegs1.fao.org/docs/pdf/gha101175.pdf [Verified 1 June 2021].
Numbisi, F. N., Van Coillie, F. M. B., and De Wulf, R. (2019). Delineation of cocoa agroforests using multiseason Sentinel-1 SAR images: a low grey level range reduces uncertainties in GLCM texture-based mapping. ISPRS International Journal of Geo-Information 8, 179.
| Delineation of cocoa agroforests using multiseason Sentinel-1 SAR images: a low grey level range reduces uncertainties in GLCM texture-based mapping.Crossref | GoogleScholarGoogle Scholar |
Okoree, E. (2010) Keta Lagoon Complex, Clearing House mechanism of Ghana: Convention of Biodiversity. Available at http://gh.chm-cbd.net/biodiversity/faunal-diversity-ghana/-situ-conservation-2/ramsar-sites/keta-lagoon-complex [Verified 11 January 2021].
Onojeghuo, A. O., Blackburn, G. A., Wang, Q., Atkinson, P. M., Kindred, D., and Miao, Y. (2018). Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data. International Journal of Remote Sensing 39, 1042–1067.
| Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data.Crossref | GoogleScholarGoogle Scholar |
Pham, L. H., Pham, L. T., Dang, T. D., Tran, D. D., and Dinh, T. Q. (2021). Application of Sentinel-1 data in mapping land-use and land cover in a complex seasonal landscape: a case study in coastal area of Vietnamese Mekong Delta. Geocarto International , .
| Application of Sentinel-1 data in mapping land-use and land cover in a complex seasonal landscape: a case study in coastal area of Vietnamese Mekong Delta.Crossref | GoogleScholarGoogle Scholar |
Plank, S., Jüssi, M., Martinis, S., and Twele, A. (2017). Mapping of flooded vegetation by means of polarimetric Sentinel-1 and ALOS-2 / PALSAR-2 imagery. International Journal of Remote Sensing 38, 3831–3850.
| Mapping of flooded vegetation by means of polarimetric Sentinel-1 and ALOS-2 / PALSAR-2 imagery.Crossref | GoogleScholarGoogle Scholar |
Portengen, E. G. (2017) Classifying mangroves in Vietnam using radar and optical satellite remote sensing. Master’s Thesis, Delft University of Technology, Delft, Netherlands. Available at https://repository.tudelft.nl/islandora/object/uuid%3A55f31164-c84e-474a-9620-955d9aecc150
Raczko, E., and Zagajewski, B. (2017). Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images. European Journal of Remote Sensing 50, 144–154.
| Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images.Crossref | GoogleScholarGoogle Scholar |
Refice, A., Zingaro, M., D’Addabbo, A., and Chini, M. (2020). Integrating C-and L-Band SAR imagery for detailed flood monitoring of remote vegetated areas. Water 12, 2745.
| Integrating C-and L-Band SAR imagery for detailed flood monitoring of remote vegetated areas.Crossref | GoogleScholarGoogle Scholar |
Reiche, J., Lucas, R., Mitchell, A. L., Verbesselt, J., Hoekman, D. H., Haarpaintner, J., Kellndorfer, J. M., Rosenqvist, A., Lehmann, E. A., Woodcock, C. E., Martin Seifert, F., and Herold, M. (2016). Combining satellite data for better tropical forest monitoring. Nature Climate Change 6, 120–122.
| Combining satellite data for better tropical forest monitoring.Crossref | GoogleScholarGoogle Scholar |
Rozenstein, O., and Karnieli, A. (2011). Comparison of methods for land-use classification incorporating remote sensing and GIS inputs. Applied Geography 31, 533–544.
| Comparison of methods for land-use classification incorporating remote sensing and GIS inputs.Crossref | GoogleScholarGoogle Scholar |
Rüetschi, M., Schaepman, M. E., and Small, D. (2018). Using multitemporal Sentinel-1 C-band backscatter to monitor phenology and classify deciduous and coniferous forests in Northern Switzerland. Remote Sensing 10, 55.
| Using multitemporal Sentinel-1 C-band backscatter to monitor phenology and classify deciduous and coniferous forests in Northern Switzerland.Crossref | GoogleScholarGoogle Scholar |
Sica, F., Pulella, A., Nannini, M., Pinheiro, M., and Rizzoli, P. (2019). Repeat-pass SAR interferometry for land cover classification: a methodology using Sentinel-1 short-time-series. Remote Sensing of Environment 232, 111277.
| Repeat-pass SAR interferometry for land cover classification: a methodology using Sentinel-1 short-time-series.Crossref | GoogleScholarGoogle Scholar |
Souza-Filho, P. W. M., and Paradella, W. R. (2003). Use of synthetic aperture radar for recognition of coastal geomorphological features, land-use assessment and shoreline changes in Braganca coast, Para, Northern Brazil. Anais da Academia Brasileira de Ciências 75, 341–356.
| Use of synthetic aperture radar for recognition of coastal geomorphological features, land-use assessment and shoreline changes in Braganca coast, Para, Northern Brazil.Crossref | GoogleScholarGoogle Scholar |
Tavares, P. A., Beltrão, N. E. S., Guimarães, U. S., and Teodoro, A. C. (2019). Integration of Sentinel-1 and Sentinel-2 for classification and LULC mapping in the urban area of Belém, eastern Brazilian Amazon. Sensors 19, 1140.
| Integration of Sentinel-1 and Sentinel-2 for classification and LULC mapping in the urban area of Belém, eastern Brazilian Amazon.Crossref | GoogleScholarGoogle Scholar |
Thanh Noi, P., and Kappas, M. (2017). Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors 18, 18.
| Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery.Crossref | GoogleScholarGoogle Scholar |
Toosi, N. B., Soffianian, A. R., Fakheran, S., Pourmanafi, S., Ginzler, C., and Waser, L. T. (2019). Comparing different classification algorithms for monitoring mangrove cover changes in southern Iran. Global Ecology and Conservation 19, e00662.
| Comparing different classification algorithms for monitoring mangrove cover changes in southern Iran.Crossref | GoogleScholarGoogle Scholar |
Uddin, K., Matin, M. A., and Meyer, F. J. (2019). Operational flood mapping using multi-temporal Sentinel-1 SAR images: a case study from Bangladesh. Remote Sensing 11, 1581.
| Operational flood mapping using multi-temporal Sentinel-1 SAR images: a case study from Bangladesh.Crossref | GoogleScholarGoogle Scholar |
Valderrama-Landeros, L., Flores-de-Santiago, F., Kovacs, J. M., and Flores-Verdugo, F. (2018). An assessment of commonly employed satellite-based remote sensors for mapping mangrove species in Mexico using an NDVI-based classification scheme. Environmental Monitoring and Assessment 190, 23.
| An assessment of commonly employed satellite-based remote sensors for mapping mangrove species in Mexico using an NDVI-based classification scheme.Crossref | GoogleScholarGoogle Scholar |
van Beijma, S., Comber, A., and Lamb, A. (2014). Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data. Remote Sensing of Environment 149, 118–129.
| Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data.Crossref | GoogleScholarGoogle Scholar |
Veci, L., and Braun, A. (2020). Sentinel-1 toolbox SAR basics tutorial. (Array Systems Computing.) Available at http://step.esa.int/docs/tutorials/S1TBX%20SAR%20Basics%20Tutorial.pdf
Wang, D., Wan, B., Qiu, P., Su, Y., Guo, Q., and Wu, X. (2018). Artificial mangrove species mapping using Pléiades-1: an evaluation of pixel-based and object-based classifications with selected machine learning algorithms. Remote Sensing 10, 294.
| Artificial mangrove species mapping using Pléiades-1: an evaluation of pixel-based and object-based classifications with selected machine learning algorithms.Crossref | GoogleScholarGoogle Scholar |
Woodhouse, I. H. (2017). ‘Introduction to Microwave Remote Sensing, Introduction to Microwave Remote Sensing.’ (CRC Press: Boca Raton, FL, USA.)
| Crossref |
Xia, Q., Qin, C. Z., Li, H., Huang, C., and Su, F. Z. (2018). Mapping mangrove forests based on multi-tidal high-resolution satellite imagery. Remote Sensing 10, 1343.
| Mapping mangrove forests based on multi-tidal high-resolution satellite imagery.Crossref | GoogleScholarGoogle Scholar |
Xia, J., Yokoya, N., and Pham, T. D. (2020). probabilistic mangrove species mapping with multiple-source remote-sensing datasets using label distribution learning in Xuan Thuy National Park, Vietnam. Remote Sensing 12, 3834.
| probabilistic mangrove species mapping with multiple-source remote-sensing datasets using label distribution learning in Xuan Thuy National Park, Vietnam.Crossref | GoogleScholarGoogle Scholar |
Xiong, J., Thenkabail, P. S., Tilton, J. C., Gumma, M. K., Teluguntla, P., Oliphant, A., Congalton, R. G., Yadav, K., and Gorelick, N. (2017). Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on google earth engine. Remote Sensing 9, 1065.
| Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on google earth engine.Crossref | GoogleScholarGoogle Scholar |
Yongzhu, X., Zhengdong, Z., and Feng, C. (2010). Comparison of artificial neural network and support vector machine methods for urban land use/cover classifications from remote sensing images: a case study of Guangzhou, South China. In ‘2010 International Conference on Computer Application and System Modeling (ICCASM 2010)’. pp. V13-52–V13-56. (IEEE.)
| Crossref |
