1,846
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Performance evaluation of convolution neural networks in canopy height estimation using sentinel 2 data, application to Thailand

ORCID Icon, ORCID Icon, , ORCID Icon, ORCID Icon, & show all
Pages 1726-1748 | Received 29 Nov 2022, Accepted 03 Mar 2023, Published online: 23 Mar 2023

References

  • Aamir, S., A. Shah, M. A. Manzoor, and A. Bais. 2020. “Canopy Height Estimation at Landsat Resolution Using Convolutional Neural Networks.” Machine Learning and Knowledge Extraction, February 2 (1): 23–36. doi:10.3390/make2010003.
  • Alagialoglou, L., I. Manakos, M. Heurich, J. Červenka, and A. Delopoulos. 2021. “Canopy Height Estimation from Spaceborne Imagery Using Convolutional Encoder-Decoder.“ In MultiMedia Modeling: 27th International Conference, MMM 2021, Prague, Czech Republic, June 22–24, 2021, Proceedings, Part II 27, pp. 307–317. Springer International Publishing
  • Bannari, A., H. Asalhi, and P. M. Teillet. 2002. Transformed Difference Vegetation Index (TDVI) for Vegetation Cover Mapping. International Geoscience and Remote Sensing Symposium (IGARSS), 24-28 June 2002, Toronto, ON, Canada, Volume 5, pp. 3053–3055. doi:10.1109/IGARSS.2002.1026867.
  • Bisong, E., 2019. Google Colaboratory. Building Machine Learning and Deep Learning Models on Google Cloud Platform, pp. 59–64.
  • Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1, October): 5–32. doi:10.1023/A:1010933404324.
  • Chen, J. M. 1996. “Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications.” Canadian Journal of Remote Sensing 22 (3): 229–242. doi:10.1080/07038992.1996.10855178.
  • Cloude, S. R., and K. Papathanassiou, 1998. Polarimetric SAR Interferometry. IEEE Transactions on geoscience and remote sensing 36 (5): 1551–1565. doi:10.1109/36.718859.
  • Cloude, S. R., and K. P. Papathanassiou. 2003. “Three-Stage Inversion Process for Polarimetric SAR Interferometry.” IEE Proceedings: Radar, Sonar and Navigation 150 (3, June): 125–134. doi:10.1049/ip-rsn:20030449.
  • Dong, T., J. Liu, J. Shang, B. Qian, B. Ma, J. M. Kovacs, D. Walters, X. Jiao, X. Geng, and Y. Shi. 2019. “Assessment of Red-Edge Vegetation Indices for Crop Leaf Area Index Estimation.” Remote Sensing of Environment 222 (March): 133–143. doi:10.1016/j.rse.2018.12.032.
  • Gale, M. G., G. J. Cary, A. I. Van Dijk, and M. Yebra. 2021. “Forest Fire Fuel Through the Lens of Remote Sensing: Review of Approaches, Challenges and Future Directions in the Remote Sensing of Biotic Determinants of Fire Behaviour.” Remote Sensing of Environment 255: 112282. doi:10.1016/j.rse.2020.112282.
  • Gao, B. C. 1996. “Ndwi—a Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space.” Remote Sensing of Environment 58 (3, December): 257–266. doi:10.1016/S0034-4257(96)00067-3.
  • Ghilani, C. D. 2017. Adjustment Computations: Spatial Data Analysis. Hoboken, New Jersey, USA: John Wiley & Sons.
  • Gholz, H. L., K. Nakane, and H. Shimoda. 2012. The Use of Remote Sensing in the Modeling of Forest Productivity. s.l: Springer Science & Business Media.
  • Gitelson, A. A., Y. J. Kaufman, and M. N. Merzlyak. 1996. “Use of a Green Channel in Remote Sensing of Global Vegetation from EOS-MODIS.” Remote Sensing of Environment 58 (3, December): 289–298. doi:10.1016/S0034-4257(96)00072-7.
  • Gitelson, A. A., and M. N. Merzlyak. 1998. “Remote Sensing of Chlorophyll Concentration in Higher Plant Leaves.” Advances in Space Research 22 (5, January): 689–692. doi:10.1016/S0273-1177(97)01133-2.
  • Gitelson, A. A., A. Viña, T. J. Arkebauer, D. C. Rundquist, G. Keydan, and B. Leavitt. 2003. “Remote Estimation of Leaf Area Index and Green Leaf Biomass in Maize Canopies.” Geophysical Research Letters 30 (5, March). doi:10.1029/2002GL016450.
  • Goel, N. S., and W. Qin. 1994. “Influences of Canopy Architecture on Relationships Between Various Vegetation Indices and LAI and FPAR: A Computer Simulation.” Remote Sensing Reviews 10 (4): 309–347. doi:10.1080/02757259409532252.
  • Gorelick, N., M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore. 2017. “Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone.” Remote Sensing of Environment 202 (December): 18–27. doi:10.1016/j.rse.2017.06.031.
  • Hansen, M. C., P. V. Potapov, S. J. Goetz, S. Turubanova, A. Tyukavina, A. Krylov, A. Kommareddy, and A. Egorov. 2016. “Mapping Tree Height Distributions in Sub-Saharan Africa Using Landsat 7 and 8 Data.” Remote Sensing of Environment 185 (November): 221–232. doi:10.1016/j.rse.2016.02.023.
  • Huete, A. R. 1988. “A Soil-Adjusted Vegetation Index (SAVI).” Remote Sensing of Environment 25 (3, August): 295–309. doi:10.1016/0034-4257(88)90106-X.
  • Jiang, Z., A. R. Huete, K. Didan, and T. Miura. 2008. “Development of a Two-Band Enhanced Vegetation Index Without a Blue Band.” Remote Sensing of Environment 112 (10): 3833–3845. doi:10.1016/j.rse.2008.06.006.
  • Kaufman, Y. J., and D. Tanre. 1992. “Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS.” IEEE Transactions on Geoscience and Remote Sensing 30 (2): 261–270. doi:10.1109/36.134076.
  • Kingma, D. P., and J. Ba. 2014. “Adam: A Method for Stochastic Optimization.” arXiv preprint arXiv:1412.6980 December. https://arxiv.org/abs/1412.6980
  • Kugler, F., S. K. Lee, I. Hajnsek, and K. P. Papathanassiou. 2015. “Forest Height Estimation by Means of Pol-InSar Data Inversion: The Role of the Vertical Wavenumber.” IEEE Transactions on Geoscience and Remote Sensing 53 (10, October): 5294–5311. doi:10.1109/TGRS.2015.2420996.
  • Lang, N., K. Schindler, and J. D. Wegner. 2019. “Country-Wide High-Resolution Vegetation Height Mapping with Sentinel-2.” Remote Sensing of Environment 233 (November): 111347. doi:10.1016/j.rse.2019.111347.
  • Lavalle, M., M. Simard, and S. Hensley. 2011. “A Temporal Decorrelation Model for Polarimetric Radar Interferometers.” IEEE Transactions on Geoscience and Remote Sensing 50 (7): 2880–2888. doi:10.1109/TGRS.2011.2174367.
  • Li, W., Z. Niu, R. Shang, Y. Qin, L. Wang, and H. Chen. 2020. “High-Resolution Mapping of Forest Canopy Height Using Machine Learning by Coupling ICESat-2 LiDar with Sentinel-1, Sentinel-2 and Landsat-8 Data.” International Journal of Applied Earth Observation and Geoinformation 92 (October): 102163. doi:10.1016/j.jag.2020.102163.
  • Liu, X., Y. Su, T. Hu, Q. Yang, B. Liu, Y. Deng, H. Tang, Z. Tang, J. Fang, and Q. Guo. 2022. “Neural Network Guided Interpolation for Mapping Canopy Height of China’s Forests by Integrating GEDI and ICESat-2 Data.” Remote Sensing of Environment 269: 112844. doi:10.1016/j.rse.2021.112844.
  • Main-Knorn, M., B. Pflug, J. Louis, V. Debaecker, U. Müller-Wilm, and F. Gascon. 2017. “Sen2Cor for sentinel-2.” In Image and Signal Processing for Remote Sensing XXIII 2017 Oct 4, 10427, 37–48. Warsaw, Poland: Society of Photo-Optical Instrumentation Engineers (SPIE). doi:10.1117/12.2278218.
  • Myeong, S., D. J. Nowak, and M. J. Duggin. 2006. “A Temporal Analysis of Urban Forest Carbon Storage Using Remote Sensing.” Remote Sensing of Environment 101 (2): 277–282. doi:10.1016/j.rse.2005.12.001.
  • Nguyen, C. T., A. Chidthaisong, P. Kieu Diem, and L. -Z. Huo. 2021. “A Modified Bare Soil Index to Identify Bare Land Features During Agricultural Fallow-Period in Southeast Asia Using Landsat 8.” Land 10 (3, February): 231. doi:10.3390/land10030231.
  • Papathanassiou, K. P., and S. Cloude. 2001. “Single-Baseline Polarimetric SAR Interferometry.” IEEE Transactions on Geoscience and Remote Sensing 39 (11): 2352–2363. doi:10.1109/36.964971.
  • Pettorelli, N. 2013. The Normalized Difference Vegetation Index. s.l: Oxford University Press.
  • Qi, J., A. Chehbouni, A. R. Huete, Y. H. Kerr, and S. Sorooshian. 1994. “A Modified Soil Adjusted Vegetation Index.” Remote Sensing of Environment 48 (2, May): 119–126. doi:10.1016/0034-4257(94)90134-1.
  • Rondeaux, G., M. Steven, and F. Baret. 1996. “Optimization of Soil-Adjusted Vegetation Indices.” Remote Sensing of Environment 55 (2, February): 95–107. doi:10.1016/0034-4257(95)00186-7.
  • Roujean, J. L., and F. M. Breon. 1995. “Estimating PAR Absorbed by Vegetation from Bidirectional Reflectance Measurements.” Remote Sensing of Environment 51 (3, March): 375–384. doi:10.1016/0034-4257(94)00114-3.
  • Roy, P., K. Sharma, and A. Jain. 1996. “Stratification of Density in Dry Deciduous Forest Using Satellite Remote Sensing Digital Data—an Approach Based on Spectral Indices.” Journal of Biosciences 21 (5): 723–734. doi:10.1007/BF02703148.
  • Sripada, R. P., R. W. Heiniger, J. G. White, and A. D. Meijer. 2006. “Aerial Color Infrared Photography for Determining Early In-Season Nitrogen Requirements in Corn.” Agronomy Journal 98 (4, July): 968–977. doi:10.2134/agronj2005.0200.
  • Sun, H., Q. Wang, G. Wang, H. Lin, P. Luo, J. Li, S. Zeng, X. Xu, and L. Ren. 2018. “Optimizing kNn for Mapping Vegetation Cover of Arid and Semi-Arid Areas Using Landsat Images.” Remote Sensing 10 (8, August): 1248. doi:10.3390/rs10081248.
  • Takaku, J., T. Tadono, and K. Tsutsui. 2014. “GENERATION OF HIGH RESOLUTION GLOBAL DSM FROM ALOS PRISM.” ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences 4 (2): 243–248. doi:10.5194/isprsarchives-XL-4-243-2014.
  • Wallis, C. I. B., J. Homeier, J. Peña, R. Brandl, N. Farwig, and J. Bendix. 2019. “Modeling Tropical Montane Forest Biomass, Productivity and Canopy Traits with Multispectral Remote Sensing Data.” Remote Sensing of Environment 225 (May): 77–92. doi:10.1016/j.rse.2019.02.021.
  • Wenxue, F., G. Huadong, L. Xinwu, T. Bangsen, and S. Zhongchang. 2015. “Extended Three-Stage Polarimetric SAR Interferometry Algorithm by Dual-Polarization Data.” IEEE Transactions on Geoscience and Remote Sensing 54 (5, May): 2792–2802. doi:10.1109/TGRS.2015.2505707.
  • Xu, H. 2006. “Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery.” International Journal of Remote Sensing 27 (14, July): 3025–3033. doi:10.1080/01431160600589179.
  • Yang, Z., P. Willis, and R. Mueller. 2008. “Impact of Band-Ratio Enhanced AWIFS Image to Crop Classification Accuracy.” Proceeding Pecora 17 (1): 1–11.
  • Zha, Y., J. Gao, and S. Ni. 2003. “Use of Normalized Difference Built-Up Index in Automatically Mapping Urban Areas from TM Imagery.” International Journal of Remote Sensing 24 (3): 583–594. doi:10.1080/01431160304987.
  • Zhao, K., S. Popescu, and R. Nelson. 2009. “Lidar Remote Sensing of Forest Biomass: A Scale-Invariant Estimation Approach Using Airborne Lasers.” Remote Sensing of Environment 113 (1): 182–196. doi:10.1016/j.rse.2008.09.009.
  • Zhao, Y., Y. Zeng, Z. Zheng, W. Dong, D. Zhao, B. Wu, and Q. Zhao. 2018. “Forest Species Diversity Mapping Using Airborne LiDar and Hyperspectral Data in a Subtropical Forest in China.” Remote Sensing of Environment 213: 104–114. doi:10.1016/j.rse.2018.05.014.