Biomass estimation of Sonneratia caseolaris (l.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multi-layer perceptron neural networks

References

• Ali, I., F. Greifeneder, J. Stamenkovic, M. Neumann, and C. Notarnicola. 2015. “Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data.” Remote Sensing 7 (12): 16398–16421. doi:10.3390/rs71215841.
   Web of Science ®Google Scholar
• Alongi, D. M. 2002. “Present State and Future of the World’s Mangrove Forests.” Environmental Conservation 29 (03): 331–349. doi:10.1017/S0376892902000231.
   Web of Science ®Google Scholar
• Anderson, J. E., L. C. Plourde, M. E. Martin, B. H. Braswell, M.-L. Smith, R. O. Dubayah, M. A. Hofton, and J. B. Blair. 2008. “Integrating Waveform Lidar with Hyperspectral Imagery for Inventory of a Northern Temperate Forest.” Remote Sensing of Environment 112 (4): 1856–1870. doi:10.1016/j.rse.2007.09.009.
   Web of Science ®Google Scholar
• Basuki, T. M., A. K. Skidmore, Y. A. Hussin, and I. Van Duren. 2013. “Estimating Tropical Forest Biomass More Accurately by Integrating ALOS PALSAR and Landsat-7 ETM+ Data.” International Journal of Remote Sensing 34 (13): 4871–4888. doi:10.1080/01431161.2013.777486.
   Web of Science ®Google Scholar
• Beh Boon, C., M. Z. M. Jafri, and S. L. Hwee. 2011. “Mangrove Mapping in Penang Island by Using Artificial Neural Network Technique.” Paper presented at the Open Systems (ICOS), 2011 IEEE Conference on, September 25–28.
   Google Scholar
• Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1023/a:1010933404324.
   Web of Science ®Google Scholar
• Bui, D., B. P. Pradhan, H. Nampak, Q.-T. Bui, Q.-A. Tran, and Q.-P. Nguyen. 2016. “Hybrid Artificial Intelligence Approach Based on Neural Fuzzy Inference Model and Metaheuristic Optimization for Flood Susceptibility Modeling in a High-Frequency Tropical Cyclone Area Using GIS.” Journal of Hydrology 540: 317–330. doi:10.1016/j.jhydrol.2016.06.027.
   Web of Science ®Google Scholar
• Burnham, K. P., and D. R. Anderson. 2004. “Multimodel Inference Understanding AIC and BIC in Model Selection.” Sociological Methods & Research 33 (2): 261–304. doi:10.1177/0049124104268644.
   Web of Science ®Google Scholar
• Burnham, K. P., D. R. Anderson, and K. P. Huyvaert. 2011. “AIC Model Selection and Multimodel Inference in Behavioral Ecology: Some Background, Observations, and Comparisons.” Behavioral Ecology and Sociobiology 65 (1): 23–35. doi:10.1007/s00265-010-1029-6.
   Web of Science ®Google Scholar
• Byrd, K. B., J. L. O’Connell, S. Di Tommaso, and M. Kelly. 2014. “Evaluation of Sensor Types and Environmental Controls on Mapping Biomass of Coastal Marsh Emergent Vegetation.” Remote Sensing of Environment 149: 166–180. doi:10.1016/j.rse.2014.04.003.
   Web of Science ®Google Scholar
• Carreiras, J., J. Melo, and M. Vasconcelos. 2013. “Estimating the Above-Ground Biomass in Miombo Savanna Woodlands (Mozambique, East Africa) Using L-Band Synthetic Aperture Radar Data.” Remote Sensing 5 (4): 1524–1548. doi:10.3390/rs5041524.
   Web of Science ®Google Scholar
• Chai, T., and R. R. Draxler. 2014. “Root Mean Square Error (RMSE) or Mean Absolute Error (Mae)?–Arguments against Avoiding RMSE in the Literature.” Geoscientific Model Development 7 (3): 1247–1250. doi:10.5194/gmd-7-1247-2014.
   Web of Science ®Google Scholar
• Chave, J., C. Andalo, S. Brown, M. A. Cairns, J. Q. Chambers, D. Eamus, H. Fölster, et al. 2005. “Tree Allometry and Improved Estimation of Carbon Stocks and Balance in Tropical Forests.” Oecologia 145 (1): 87–99. doi:10.1007/s00442-005-0100-x.
   PubMed Web of Science ®Google Scholar
• Chen, T., and H. Chen. 1995. “Approximation Capability to Functions of Several Variables, Nonlinear Functionals, and Operators by Radial Basis Function Neural Networks.” Ieee Transactions on Neural Networks 6 (4): 904–910. doi:10.1109/72.392252.
   PubMed Web of Science ®Google Scholar
• Civco, D. L. 1993. “Artificial Neural Networks for Land-Cover Classification and Mapping.” International Journal of Geographical Information Systems 7 (2): 173–186. doi:10.1080/02693799308901949.
   Web of Science ®Google Scholar
• Claeskens, G., and N. L. Hjort. 2008. Model Selection and Model Averaging. Vol. 330. Cambridge: Cambridge University Press.
   Google Scholar
• Clough, B. F., P. Dixon, and O. Dalhaus. 1997. “Allometric Relationships for Estimating Biomass in Multi-Stemmed Mangrove Trees.” Australian Journal of Botany 45 (6): 1023–1031. doi:10.1071/BT96075.
   Web of Science ®Google Scholar
• Clough, B. F., and K. Scott. 1989. “Allometric Relationships for Estimating Above-Ground Biomass in Six Mangrove Species.” Forest Ecology and Management 27 (2): 117–127. doi:10.1016/0378-1127(89)90034-0.
   Web of Science ®Google Scholar
• Conchedda, G., L. Durieux, and P. Mayaux. 2008. “An Object-Based Method for Mapping and Change Analysis in Mangrove Ecosystems.” ISPRS Journal of Photogrammetry and Remote Sensing 63 (5): 578–589. doi:10.1016/j.isprsjprs.2008.04.002.
   Web of Science ®Google Scholar
• Danielsen, F., M. K. Sørensen, M. F. Olwig, V. Selvam, F. Parish, N. D. Burgess, T. Hiraishi, et al. 2005. “The Asian Tsunami: A Protective Role for Coastal Vegetation.” Science 310: 643. doi:10.1126/science.1118387.
   PubMed Web of Science ®Google Scholar
• Darmawan, S., W. Takeuchi, Y. Vetrita, K. Wikantika, and D. K. Sari. 2015. “Impact of Topography and Tidal Height on ALOS PALSAR Polarimetric Measurements to Estimate Aboveground Biomass of Mangrove Forest in Indonesia.” Journal of Sensors 2015: 1–13. doi:10.1155/2015/641798.
   Web of Science ®Google Scholar
• Dat, P. T., and K. Yoshino. 2011. “Monitoring Mangrove Forest Using Multi-Temporal Satellite Data in the Northern Coast of Vietnam.” Paper presented at The 32nd Asian Conference on Remote Sensing, Taipei, Taiwan, October 3–7.
   Google Scholar
• De Leeuw, M. R., and L. M. T. De Carvalho. 2009. “Performance Evaluation of Several Adaptive Speckle Filters for SAR Imaging.” Anais XIV Simpósio Brasileiro De Sensoriamento Remoto, Natal, April 25-30, 7299–7305. http://marte.sid.inpe.br/col/dpi.inpe.br/sbsr@80/2008/11.18.12.44/doc/7299-7305.pdf
   Google Scholar
• Donato, D. C., J. Boone Kauffman, D. Murdiyarso, S. Kurnianto, M. Stidham, and M. Kanninen. 2011. “Mangroves among the Most Carbon-Rich Forests in the Tropics.” Nature Geosci 4 (5): 293–297. doi:10.1038/ngeo1123.
   Web of Science ®Google Scholar
• Dube, T., O. Mutanga, A. Elhadi, and R. Ismail. 2014. “Intra-And-Inter Species Biomass Prediction in a Plantation Forest: Testing the Utility of High Spatial Resolution Spaceborne Multispectral Rapideye Sensor and Advanced Machine Learning Algorithms.” Sensors 14 (8): 15348–15370. doi:10.3390/s140815348.
   PubMed Web of Science ®Google Scholar
• Duke, N. C., J.-O. Meynecke, S. Dittmann, A. M. Ellison, K. Anger, U. Berger, S. Cannicci, et al. 2007. “A World Without Mangroves?.” Science 317 (5834): 41–42. doi:10.1126/science.317.5834.41b.
   PubMed Web of Science ®Google Scholar
• Ellison, A. M. 2002. “Macroecology of Mangroves: Large-Scale Patterns and Processes in Tropical Coastal Forests.” Trees 16 (2–3): 181–194. doi:10.1007/s00468-001-0133-7.
   Web of Science ®Google Scholar
• FAO. 2007. “The World’s Mangroves 1980-2005. A Thematic Study Prepared in the Framework of the Global Forest Resources Assessment 2005.” FAO Forestry Paper, 89. Rome: Food and Agriculture Organization of the United Nations.
   Google Scholar
• FAO. 2009. “GTOS: Biomass Assessment of the Status of the Development of the Standards for the Terrestrial Essential Climate Variables.” 18p. Rome, Italy.
   Google Scholar
• Fatoyinbo, T. E., M. Simard, R. A. Washington-Allen, and H. H. Shugart. 2008. “Landscape-Scale Extent, Height, Biomass, and Carbon Estimation of Mozambique’s Mangrove Forests with Landsat ETM+ and Shuttle Radar Topography Mission Elevation Data.” Journal of Geophysical Research: Biogeosciences 113 (G2). doi:10.1029/2007JG000551.
   Web of Science ®Google Scholar
• Foody, G. M., D. S. Boyd, and M. E. J. Cutler. 2003. “Predictive Relations of Tropical Forest Biomass from Landsat TM Data and Their Transferability between Regions.” Remote Sensing of Environment 85 (4): 463–474. doi:10.1016/S0034-4257(03)00039-7.
   Web of Science ®Google Scholar
• Gleason, C. J., and J. Im. 2011. “A Review of Remote Sensing of Forest Biomass and Biofuel: Options for Small-Area Applications.” Giscience & Remote Sensing 48 (2): 141–170. doi:10.2747/1548-1603.48.2.141.
   Web of Science ®Google Scholar
• Güneralp, İ., A. M. Filippi, and J. Randall. 2014. “Estimation of Floodplain Aboveground Biomass Using Multispectral Remote Sensing and Nonparametric Modeling.” International Journal of Applied Earth Observation and Geoinformation 33: 119–126. doi:10.1016/j.jag.2014.05.004.
   Web of Science ®Google Scholar
• Hall, M., E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. 2009. “The WEKA Data Mining Software: An Update.” SIGKDD Explor. Newsl. 11 (1): 10–18. doi:10.1145/1656274.1656278.
   Google Scholar
• Hamdan, O., M. R. Khairunnisa, A. A. Ammar, I. Mohd Hasmadi, and H. Khali Aziz. 2013. “Mangrove Carbon Stock Assessment by Optical Satellite Imagery.” Journal of Tropical Forest Science 25 (4): 554–565.
   Web of Science ®Google Scholar
• Hamdan, O., H. Khali Aziz, and I. Mohd Hasmadi. 2014. “L-Band ALOS PALSAR for Biomass Estimation of Matang Mangroves, Malaysia.” Remote Sensing of Environment 155: 69–78. doi:10.1016/j.rse.2014.04.029.
   Web of Science ®Google Scholar
• Haykin, S. 1998. Neural Networks: A Comprehensive Foundation. 2nd ed. Upper Saddle River, NJ, USA: Prentice Hall.
   Google Scholar
• Heumann, B. W. 2011. “Satellite Remote Sensing of Mangrove Forests: Recent Advances and Future Opportunities.” Progress in Physical Geography 35 (1): 87–108. doi:10.1177/0309133310385371.
   Web of Science ®Google Scholar
• Hirata, Y., R. Tabuchi, P. Patanaponpaiboon, S. Poungparn, R. Yoneda, and Y. Fujioka. 2014. “Estimation of Aboveground Biomass in Mangrove Forests Using High-Resolution Satellite Data.” Journal of Forest Research 19 (1): 34–41. doi:10.1007/s10310-013-0402-5.
   Web of Science ®Google Scholar
• Hoang, N.-D., D. T. Bui, and K.-W. Liao. 2016. “Groutability Estimation of Grouting Processes with Cement Grouts Using Differential Flower Pollination Optimized Support Vector Machine.” Applied Soft Computing 45: 173–186. doi:10.1016/j.asoc.2016.04.031.
   Web of Science ®Google Scholar
• Hong, H., X. Chong, I. Revhaug, and D. T. Bui. 2015. “Spatial Prediction of Landslide Hazard at the Yihuang Area (China): A Comparative Study on the Predictive Ability of Backpropagation Multi-Layer Perceptron Neural Networks and Radial Basic Function Neural Networks.” In Cartography - Maps Connecting the World, edited by C. R. Sluter, C. B. Madureira Cruz, and P. M. L. de Menezes, 175–188. Cham, Switzerland: Springer International Publishing.
   Google Scholar
• Hong, P. N., and H. T. San. 1993. “Mangroves of Vietnam.” In Iucn, 173. Bangkok: The International Union for Conservation of Nature and Natural Resources (IUCN).
   Google Scholar
• Hong, P. N., H. T. San, and P. T. A. Dao. 1998. “The Role of Mangrove Ecosystem in the Environment and the Life of People in Coastal Mangrove Areas.” Paper presented at the Proceedings of the national workshop on Socio-economic status of women in coastal mangrove areas - Trends to improve their lives and environment, Hanoi, 31 Oct–4 Nov 1997.
   Google Scholar
• Howard, J., S. Hoyt, K. Isensee, E. Pidgeon, and M. Telszewski. 2014. “Coastal Blue Carbon: Methods for Assessing Carbon Stocks and Emissions Factors in Mangroves, Tidal Salt Marshes, and Seagrass Meadows.” In International Union for Conservation of Nature, edited by J. Howard, S. Hoyt, K. Isensee, E. Pidgeon, and M. Telszewski. Arlington, VA: Conservation International, Intergovernmental Oceanographic Commission of UNESCO.
   Google Scholar
• Ibanez, C. A. G., B. G. Carcellar III, E. C. Paringit, R. J. L. Argamosa, R. A. G. Faelga, M. A. V. Posilero, G. P. Zaragosa, and N. A. Dimayacyac. 2016. “Estimating Dbh of Trees Employing Multiple Linear Regression of the Best Lidar-Derived Parameter Combination Automated in Python in a Natural Broadleaf Forest in the Philippines.” ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8: 657–662. doi:10.5194/isprsarchives-XLI-B8-657-2016.
   Google Scholar
• Jachowski, N. R., M. S. Y. Quak, D. A. Friess, D. A. Duangnamon, E. L. Webb, and A. D. Ziegler. 2013. “Mangrove Biomass Estimation in Southwest Thailand Using Machine Learning.” Applied Geography 45: 311–321. doi:10.1016/j.apgeog.2013.09.024.
   Web of Science ®Google Scholar
• JAXA. 2014. ALOS-2/PALSAR-2 Level 1.1/1.5/2.1/3.1 CEOS SAR Product, 233. Sengen: Japan Aerospace Exploration Agency.
   Google Scholar
• Jin-Eong, O., G. W. Khoon, and B. F. Clough. 1995. “Structure and Productivity of a 20-Year-Old Stand of Rhizophora Apiculata Bl. Mangrove Forest.” Journal of Biogeography 22 (2/3): 417–424. doi:10.2307/2845938.
   Web of Science ®Google Scholar
• Kauffman, J. B., C. Heider, J. Norfolk, and F. Payton. 2014. “Carbon Stocks of Intact Mangroves and Carbon Emissions Arising from Their Conversion in the Dominican Republic.” Ecological Applications 24 (3): 518–527. doi:10.1890/13-0640.1.
   PubMed Web of Science ®Google Scholar
• Kelloway, E. K. 2014. Using Mplus for Structural Equation Modeling: A Researcher’s Guide. Thousand Oaks, CA: SAGE Publications.
   Google Scholar
• Komiyama, A., V. Jintana, T. Sangtiean, and S. Kato. 2002. “A Common Allometric Equation for Predicting Stem Weight of Mangroves Growing in Secondary Forests.” Ecological Research 17 (3): 415–418. doi:10.1046/j.1440-1703.2002.00500.x.
   Web of Science ®Google Scholar
• Komiyama, A., J. E. Ong, and S. Poungparn. 2008. “Allometry, Biomass, and Productivity of Mangrove Forests: A Review.” Aquatic Botany 89 (2): 128–137. doi:10.1016/j.aquabot.2007.12.006.
   Web of Science ®Google Scholar
• Komiyama, A., S. Poungparn, and S. Kato. 2005. “Common Allometric Equations for Estimating the Tree Weight of Mangroves.” Journal of Tropical Ecology 21 (04): 471–477. doi:10.1017/S0266467405002476.
   Web of Science ®Google Scholar
• Laurin, G., Q. C. Chen, J. A. Lindsell, D. A. Coomes, F. D. Del Frate, L. Guerriero, F. Pirotti, and R. Valentini. 2014. “Above Ground Biomass Estimation in an African Tropical Forest with Lidar and Hyperspectral Data.” ISPRS Journal of Photogrammetry and Remote Sensing 89: 49–58. doi:10.1016/j.isprsjprs.2014.01.001.
   Web of Science ®Google Scholar
• Le Toan, T., A. Beaudoin, J. Riom, and D. Guyon. 1992. “Relating Forest Biomass to SAR Data.” IEEE Transactions on Geoscience and Remote Sensing 30 (2): 403–411. doi:10.1109/36.134089.
   Web of Science ®Google Scholar
• Le Toan, T., S. Quegan, I. Woodward, M. Lomas, N. Delbart, and G. Picard. 2004. “Relating Radar Remote Sensing of Biomass to Modelling of Forest Carbon Budgets.” Climatic Change 67 (2–3): 379–402. doi:10.1007/s10584-004-3155-5.
   Web of Science ®Google Scholar
• Lee, S. J., J. R. Kim, and Y. S. Choi. 2013. “The Extraction of Forest CO2 Storage Capacity Using High-Resolution Airborne Lidar Data.” Giscience & Remote Sensing 50 (2): 154–171. doi:10.1080/15481603.2013.786957.
   Web of Science ®Google Scholar
• Lefsky, M. A., W. B. Cohen, G. G. Parker, and D. J. Harding. 2002. “Lidar Remote Sensing for Ecosystem Studies: Lidar, an Emerging Remote Sensing Technology that Directly Measures the Three-Dimensional Distribution of Plant Canopies, Can Accurately Estimate Vegetation Structural Attributes and Should Be of Particular Interest to Forest, Landscape, and Global Ecologists.” Bioscience 52 (1): 19–30. doi:10.1641/0006-3568(2002)052[0019:lrsfes]2.0.co;2.
   Web of Science ®Google Scholar
• Long, B. G., and T. D. Skewes. 1996. “A Technique for Mapping Mangroves with Landsat TM Satellite Data and Geographic Information System.” Estuarine, Coastal and Shelf Science 43 (3): 373–381. doi:10.1006/ecss.1996.0076.
   Web of Science ®Google Scholar
• Long, J. B., and C. Giri. 2011. “Mapping the Philippines’ Mangrove Forests Using Landsat Imagery.” Sensors 11 (12): 2972–2981. doi:10.3390/s110302972.
   PubMed Web of Science ®Google Scholar
• Lu, D. 2006. “The Potential and Challenge of Remote Sensing‐Based Biomass Estimation.” International Journal of Remote Sensing 27 (7): 1297–1328. doi:10.1080/01431160500486732.
   Web of Science ®Google Scholar
• Lu, D., Q. Chen, G. Wang, L. Liu, G. Li, and E. Moran. 2016. “A Survey of Remote Sensing-Based Aboveground Biomass Estimation Methods in Forest Ecosystems.” International Journal of Digital Earth 9 (1): 63–105. doi:10.1080/17538947.2014.990526.
   Web of Science ®Google Scholar
• Lucas, R. M., P. Bunting, D. Clewley, C. Proisy, P. W. M. Souza Filho, K. Viergever, I. Woodhouse, C. Ticehurst, J. Carreiras, and A. Rosenqvist. 2009. Characterisation and Monitoring of Mangroves Using ALOS PALSAR Data. Sengen: JAXA.
   Google Scholar
• Lucas, R. M., A. L. Mitchell, A. Rosenqvist, C. Proisy, A. Melius, and C. Ticehurst. 2007. “The Potential of L-Band SAR for Quantifying Mangrove Characteristics and Change: Case Studies from the Tropics.” Aquatic Conservation: Marine and Freshwater Ecosystems 17 (3): 245–264. doi:10.1002/aqc.833.
   Web of Science ®Google Scholar
• Luo, S., C. Wang, X. Xiaohuan, F. Pan, D. Peng, J. Zou, S. Nie, and H. Qin. 2017. “Fusion of Airborne Lidar Data and Hyperspectral Imagery for Aboveground and Belowground Forest Biomass Estimation.” Ecological Indicators 73: 378–387. doi:10.1016/j.ecolind.2016.10.001.
   Web of Science ®Google Scholar
• Mahdizadeh Khasraghi, M., M. A. Sefidkouhi, and M. Valipour. 2015. “Simulation of Open- and Closed-End Border Irrigation Systems Using SIRMOD.” Archives of Agronomy and Soil Science 61 (7): 929–941. doi:10.1080/03650340.2014.981163.
   Web of Science ®Google Scholar
• Marchand, M. 2008. Mangrove Restoration in Vietnam: Key Considerations and a Practical Guide. Delft: Deltares.
   Google Scholar
• Mas, J. F. 2004. “Mapping Land Use/Cover in a Tropical Coastal Area Using Satellite Sensor Data, GIS and Artificial Neural Networks.” Estuarine, Coastal and Shelf Science 59 (2): 219–230. doi:10.1016/j.ecss.2003.08.011.
   Web of Science ®Google Scholar
• Mazda, Y., M. Magi, Y. Ikeda, T. Kurokawa, and T. Asano. 2006. “Wave Reduction in a Mangrove Forest Dominated by Sonneratia Sp.” Wetlands Ecology and Management 14 (4): 365–378. doi:10.1007/s11273-005-5388-0.
   Google Scholar
• Mazda, Y., M. Magi, M. Kogo, and P. N. Hong. 1997. “Mangroves as a Coastal Protection from Waves in the Tong King Delta, Vietnam.” Mangroves and Salt Marshes 1 (2): 127–135. doi:10.1023/a:1009928003700.
   Google Scholar
• Mitchard, E. T. A., S. S. Saatchi, I. H. Woodhouse, G. Nangendo, N. S. Ribeiro, M. Williams, C. M. Ryan, S. L. Lewis, T. R. Feldpausch, and P. Meir. 2009. “Using Satellite Radar Backscatter to Predict Above-Ground Woody Biomass: A Consistent Relationship across Four Different African Landscapes.” Geophysical Research Letters 36 (23). doi:10.1029/2009GL040692.
   Web of Science ®Google Scholar
• Moore, A. W. 2001. Cross-Validation for Detecting and Preventing. Pittsburgh, PA: School of Computer Science Carneigie Mellon University.
   Google Scholar
• Mougin, E., C. Proisy, G. Marty, F. Fromard, H. Puig, J. L. Betoulle, and J. P. Rudant. 1999. “Multifrequency and Multipolarization Radar Backscattering from Mangrove Forests.” IEEE Transactions on Geoscience and Remote Sensing 37 (1): 94–102. doi:10.1109/36.739128.
   Web of Science ®Google Scholar
• Mountrakis, G., J. Im, and C. Ogole. 2011. “Support Vector Machines in Remote Sensing: A Review.” ISPRS Journal of Photogrammetry and Remote Sensing 66 (3): 247–259. doi:10.1016/j.isprsjprs.2010.11.001.
   Web of Science ®Google Scholar
• Nam, V. N., S. D. Sasmito, D. Murdiyarso, J. Purbopuspito, and R. A. MacKenzie. 2016. “Carbon Stocks in Artificially and Naturally Regenerated Mangrove Ecosystems in the Mekong Delta.” Wetlands Ecology and Management 24 (2): 231–244. doi:10.1007/s11273-015-9479-2.
   Web of Science ®Google Scholar
• Nandy, S., and S. Kushwaha. 2011. “Study on the Utility of IRS 1D LISS-III Data and the Classification Techniques for Mapping of Sunderban Mangroves.” Journal of Coastal Conservation 15 (1): 123–137. doi:10.1007/s11852-010-0126-z.
   Web of Science ®Google Scholar
• Nguyen, H. T., R. Yoneda, I. Ninomiya, K. Harada, T. V. Dao, T. M. Sy, and H. N. Phan. 2004. “The Effects of Stand-Age and Inundation on Carbon Accumulation in Mangrove Plantation Soil in Namdinh, Northern Vietnam.” Tropics 14 (1): 21–37. doi:10.3759/tropics.14.21.
   Google Scholar
• Nguyen-Thanh, S., C.-F. Chen, N.-B. Chang, C.-R. Cheng-Ru, L.-Y. Chang, and B.-X. Thanh. 2015. “Mangrove Mapping and Change Detection in Ca Mau Peninsula, Vietnam, Using Landsat Data and Object-Based Image Analysis.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (2): 503–510. doi:10.1109/JSTARS.2014.2360691.
   Web of Science ®Google Scholar
• Nilsson, M. 1996. “Estimation of Tree Heights and Stand Volume Using an Airborne Lidar System.” Remote Sensing of Environment 56 (1): 1–7. doi:10.1016/0034-4257(95)00224-3.
   Web of Science ®Google Scholar
• Pasqualini, V., J. Iltis, N. Dessay, M. Lointier, O. Guelorget, and L. Polidori. 1999. “Mangrove Mapping in North-Western Madagascar Using SPOT-XS and SIR-C Radar Data.” Hydrobiologia 413 (0): 127–133. doi:10.1023/a:1003807330375.
   Web of Science ®Google Scholar
• Patel, M. J., A. Khalaf, and H. J. Aizenstein. 2016. “Studying Depression Using Imaging and Machine Learning Methods.” Neuroimage: Clinical 10: 115–123. doi:10.1016/j.nicl.2015.11.003.
   PubMed Web of Science ®Google Scholar
• Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, and V. Dubourg. 2011. “Scikit-Learn: Machine Learning in Python.” Journal of Machine Learning Research 12 (Oct): 2825–2830.
   Google Scholar
• Proisy, C., E. Mougin, F. Fromard, and M. A. Karam. 2000. “Interpretation of Polarimetric Radar Signatures of Mangrove Forests.” Remote Sensing of Environment 71 (1): 56–66. doi:10.1016/S0034-4257(99)00064-4.
   Web of Science ®Google Scholar
• Proisy, C., P. Couteron, and F. Fromard. 2007. “Predicting and Mapping Mangrove Biomass from Canopy Grain Analysis Using Fourier-Based Textural Ordination of IKONOS Images.” Remote Sensing of Environment 109 (3): 379–392. doi:10.1016/j.rse.2007.01.009.
   Web of Science ®Google Scholar
• Ranson, K. J., and G. Sun. 1994. “Mapping Biomass of a Northern Forest Using Multifrequency SAR Data.” IEEE Transactions on Geoscience and Remote Sensing 32 (2): 388–396. doi:10.1109/36.295053.
   Web of Science ®Google Scholar
• Ross, M. S., P. L. Ruiz, G. J. Telesnicki, and J. F. Meeder. 2001. “Estimating Above-Ground Biomass and Production in Mangrove Communities of Biscayne National Park, Florida (U.S.A.).” Wetlands Ecology and Management 9 (1): 27–37. doi:10.1023/A:1008411103288.
   Google Scholar
• Sasikala, S., S. Appavu Alias Balamurugan, and S. Geetha. 2014. “Multi Filtration Feature Selection (MFFS) to Improve Discriminatory Ability in Clinical Data Set.” Applied Computing and Informatics. doi:10.1016/j.aci.2014.03.002.
   Google Scholar
• Scholkopf, B., -K.-K. Sung, C. J. C. Burges, F. Girosi, P. Niyogi, T. Poggio, and V. Vapnik. 1997. “Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers.” Ieee Transactions on Signal Processing 45 (11): 2758–2765. doi:10.1109/78.650102.
   Web of Science ®Google Scholar
• Schwarz, C. J. 2011. Sampling, Regression, Experimental Design and Analysis for Environmental Scientists, Biologists, and Resource Managers, 57. Burnaby: Department of Statistics and Actuarial Science, Simon Fraser University.
   Google Scholar
• Şenkal, O., and T. Kuleli. 2009. “Estimation of Solar Radiation over Turkey Using Artificial Neural Network and Satellite Data.” Applied Energy 86 (7–8): 1222–1228. doi:10.1016/j.apenergy.2008.06.003.
   Web of Science ®Google Scholar
• Shataee Joibary, S. 2013. “Forest Attributes Estimation Using Aerial Laser Scanner and TM Data.” 2013 22 (3): 13. doi:10.5424/fs/2013223-03874.
   Google Scholar
• Shimada, M., O. Isoguchi, T. Tadono, and K. Isono. 2009. “PALSAR Radiometric and Geometric Calibration.” IEEE Transactions on Geoscience and Remote Sensing 47 (12): 3915–3932. doi:10.1109/TGRS.2009.2023909.
   Web of Science ®Google Scholar
• Shugart, H. H., S. Saatchi, and F. G. Hall. 2010. “Importance of Structure and Its Measurement in Quantifying Function of Forest Ecosystems.” Journal of Geophysical Research: Biogeosciences 115 (G2). doi:10.1029/2009JG000993.
   Web of Science ®Google Scholar
• Sibanda, M., O. Mutanga, and M. Rouget. 2016. “Comparing the Spectral Settings of the New Generation Broad and Narrow Band Sensors in Estimating Biomass of Native Grasses Grown under Different Management Practices.” Giscience & Remote Sensing 53 (5): 614–633. doi:10.1080/15481603.2016.1221576.
   Web of Science ®Google Scholar
• Simard, M., K. Zhang, V. H. Rivera-Monroy, M. S. Ross, P. L. Ruiz, E. Castañeda-Moya, R. R. Twilley, and E. Rodriguez. 2006. “Mapping Height and Biomass of Mangrove Forests in Everglades National Park with SRTM Elevation Data.” Photogrammetric Engineering & Remote Sensing 72 (3): 299–311. doi:10.14358/PERS.72.3.299.
   Web of Science ®Google Scholar
• Smola, A., and V. Vapnik. 1997. “Support Vector Regression Machines.” Advances in Neural Information Processing Systems 9: 155–161.
   Google Scholar
• Tamai, S., T. Nakasuga, R. Tabuchi, and K. Ogino. 1986. “Standing Biomass of Mangrove Forests in Southern Thailand.” Journal of the Japanese Forestry Society 68 (9): 384–388.
   Google Scholar
• Tesfamichael, S. G., and C. Beech. 2016. “Combining Akaike’s Information Criterion and Discrete Return Lidar Data to Estimate Structural Attributes of Savanna Woody Vegetation.” Journal of Arid Environments 129: 25–34. doi:10.1016/j.jaridenv.2016.02.006.
   Web of Science ®Google Scholar
• Tien Bui, D., B. Pradhan, O. Lofman, I. Revhaug, and O. B. Dick. 2012. “Landslide Susceptibility Assessment in the Hoa Binh Province of Vietnam: A Comparison of the Levenberg-Marquardt and Bayesian Regularized Neural Networks.” Geomorphology 171-172 (0): 12–29. doi:10.1016/j.geomorph.2012.04.023.
   Web of Science ®Google Scholar
• Tien Bui, D., T. A. Tuan, H. Klempe, B. Pradhan, and I. Revhaug. 2016. “Spatial Prediction Models for Shallow Landslide Hazards: A Comparative Assessment of the Efficacy of Support Vector Machines, Artificial Neural Networks, Kernel Logistic Regression, and Logistic Model Tree.” Landslides 13 (2): 361–378. doi:10.1007/s10346-015-0557-6.
   Web of Science ®Google Scholar
• Tien Dat, P., and K. Yoshino. 2016. “Characterization of Mangrove Species Using ALOS-2 PALSAR in Hai Phong City, Vietnam.” IOP Conference Series: Earth and Environmental Science 37 (1): 012036. doi:10.1088/1755-1315/37/1/012036.
   Google Scholar
• Tien Dat, P., and K. Yoshino. 2012. “Mangrove Analysis Using ALOS Imagery in Hai Phong City, Vietnam.” 85250U-U. doi:10.1117/12.977261.
   Google Scholar
• Tien Dat, P., and K. Yoshino. 2016. “Impacts of Mangrove Management Systems on Mangrove Changes in the Northern Coast of Vietnam.” Tropics 24 (4). doi:10.3759/tropics.24.
   Web of Science ®Google Scholar
• Tuan, L. X., Y. Munekage, Q. T. Q. Dao, N. H. Tho, and P. T. A. Dao. 2003. “Environmental Management in Mangrove Areas.” Environmental Informatics Archives 1: 38–52.
   Google Scholar
• Unger, D. R., I. Kuai Hung, R. Brooks, and H. Williams. 2014. “Estimating Number of Trees, Tree Height and Crown Width Using Lidar Data.” Giscience & Remote Sensing 51 (3): 227–238. doi:10.1080/15481603.2014.909107.
   Web of Science ®Google Scholar
• Valipour, M. 2012. “Sprinkle and Trickle Irrigation System Design Using Tapered Pipes for Pressure Loss Adjusting.” Journal of Agricultural Science 4 (12): 125. doi:10.5539/jas.v4n12p125.
   Google Scholar
• Valipour, M. 2016. “Optimization of Neural Networks for Precipitation Analysis in a Humid Region to Detect Drought and Wet Year Alarms.” Meteorological Applications 23 (1): 91–100. doi:10.1002/met.1533.
   Web of Science ®Google Scholar
• Valipour, M., M. E. Banihabib, and S. M. R. Behbahani. 2013. “Comparison of the ARMA, ARIMA, and the Autoregressive Artificial Neural Network Models in Forecasting the Monthly Inflow of Dez Dam Reservoir.” Journal of Hydrology 476: 433–441. doi:10.1016/j.jhydrol.2012.11.017.
   Web of Science ®Google Scholar
• Valipour, M., M. A. G. Sefidkouhi, and S. Eslamian. 2015. “Surface Irrigation Simulation Models: A Review.” International Journal of Hydrology Science and Technology 5 (1): 51–70. doi:10.1504/IJHST.2015.069279.
   Google Scholar
• Viana, H., J. Aranha, D. Lopes, and W. B. Cohen. 2012. “Estimation of Crown Biomass of Pinus Pinaster Stands and Shrubland Above-Ground Biomass Using Forest Inventory Data, Remotely Sensed Imagery and Spatial Prediction Models.” Ecological Modelling 226: 22–35. doi:10.1016/j.ecolmodel.2011.11.027.
   Web of Science ®Google Scholar
• Vu, T. D., W. Takeuchi, and N. A. Van. 2014. “Carbon Stock Calculating and Forest Change Assessment toward REDD+ Activities for the Mangrove Forest in Vietnam.” Transactions of the Japan Society for Aeronautical and Space Sciences, Aerospace Technology Japan 12 (ists29): Pn_23–Pn_31. doi:10.2322/tastj.12.Pn_23.
   Google Scholar
• Wang, L., J. L. Silván-Cárdenas, and W. P. Sousa. 2008. “Neural Network Classification of Mangrove Species from Multi-Seasonal Ikonos Imagery.” Photogrammetric Engineering & Remote Sensing 74 (7): 921–927. doi:10.14358/PERS.74.7.921.
   Web of Science ®Google Scholar
• Were, K., D. T. Bui, Ø. B. Dick, and B. R. Singh. 2015. “A Comparative Assessment of Support Vector Regression, Artificial Neural Networks, and Random Forests for Predicting and Mapping Soil Organic Carbon Stocks across an Afromontane Landscape.” Ecological Indicators 52: 394–403. doi:10.1016/j.ecolind.2014.12.028.
   Web of Science ®Google Scholar
• Wisz, M. S., R. J. Hijmans, J. Li, A. T. Peterson, C. H. Graham, and A. Guisan; Nceas Predicting Species Distributions Working Group. 2008. “Effects of Sample Size on the Performance of Species Distribution Models.” Diversity and Distributions 14 (5): 763–773. doi:10.1111/j.1472-4642.2008.00482.x.
   Web of Science ®Google Scholar
• Wu, C., H. Shen, A. Shen, J. Deng, M. Gan, J. Zhu, H. Xu, and K. Wang. 2016. “Comparison of Machine-Learning Methods for Above-Ground Biomass Estimation Based on Landsat Imagery.” Journal of Applied Remote Sensing 10 (3): 035010. doi:10.1117/1.JRS.10.035010.
   Web of Science ®Google Scholar
• Yu, X., H.-B. Shao, and D.-Z. Zhao Liu. 2010. “Applying Neural Network Classification to Obtain Mangrove Landscape Characteristics for Monitoring the Travel Environment Quality on the Beihai Coast of Guangxi, P. R. China.” CLEAN – Soil, Air, Water 38: 289–295. doi:10.1002/clen.200900195.
   Web of Science ®Google Scholar
• Zhu, Y., K. Liu, L. Liu, S. Wang, and H. Liu. 2015. “Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with Worldview-2 Images.” Remote Sensing 7 (9): 12192–12214. doi:10.3390/rs70912192.
   Web of Science ®Google Scholar
• Zimble, D. A., D. L. Evans, G. C. Carlson, R. C. Parker, S. C. Grado, and P. D. Gerard. 2003. “Characterizing Vertical Forest Structure Using Small-Footprint Airborne Lidar.” Remote Sensing of Environment 87 (2–3): 171–182. doi:10.1016/S0034-4257(03)00139-1.
   Web of Science ®Google Scholar
• Zolkos, S. G., S. J. Goetz, and R. Dubayah. 2013. “A Meta-Analysis of Terrestrial Aboveground Biomass Estimation Using Lidar Remote Sensing.” Remote Sensing of Environment 128: 289–298. doi:10.1016/j.rse.2012.10.017.
   Web of Science ®Google Scholar
• Zucchini, W. 2000. “An Introduction to Model Selection.” Journal of Mathematical Psychology 44 (1): 41–61. doi:10.1006/jmps.1999.1276.
   PubMed Web of Science ®Google Scholar