Exploring parameter selection for carbon monitoring based on Landsat-8 imagery of the aboveground forest biomass on Mount Tai

References

• Andersen, H.E., Strunk, J., Temesgen, H., Atwood, D., & Winterberger, K. (2012). Using multilevel remote sensing and ground data to estimate forest biomass resources in remote regions: A case study in the boreal forests of interior Alaska. Canadian Journal of Remote Sensing, 37(6), 596–611. doi:10.5589/m12-003
   Web of Science ®Google Scholar
• Baccini, A., Laporte, N., Goetz, S.J., Sun, M., & Dong, H. (2008). A first map of tropical Africa’s above-ground biomass derived from satellite imagery. Environmental Research Letters, 3, 045011. doi:10.1088/1748-9326/3/4/045011
   Web of Science ®Google Scholar
• Barbosa, J.M., Melendez-Pastor, I., Navarro-Pedreño, J., & Bitencourt, M.D. (2014). Remotely sensed biomass over steep slopes: An evaluation among successional stands of the Atlantic Forest, Brazil. ISPRS Journal of Photogrammetry and Remote Sensing, 88, 91–100. doi:10.1016/j.isprsjprs.2013.11.019
   Web of Science ®Google Scholar
• Birigazzi, L., Gregoire, T.G., Finegold, Y., Golec, R.D.C., Sandker, M., Donegan, E., & Gamarra, J.G. (2019). Data quality reporting: Good practice for transparent estimates from forest and land cover surveys. Environmental Science & Policy, 96, 85–94. doi:10.1016/j.envsci.2019.02.009
   Web of Science ®Google Scholar
• Blackard, J., Finco, M., Helmer, E., Holden, G., Hoppus, M., Jacobs, D., … Riemann, R. (2008). Mapping US forest biomass using nationwide forest inventory data and moderate resolution information. Remote Sensing of Environment, 112, 1658–1677. doi:10.1016/j.rse.2007.08.021
   Web of Science ®Google Scholar
• Cai, R., Zhang, Z., & Hao, Z. (2011). Bassum: A Bayesian semi-supervised method for classification feature selection. Pattern Recognition, 44, 811–820. doi:10.1016/j.patcog.2010.10.023
   Web of Science ®Google Scholar
• Darvishzadeh, R., Skidmore, A., Schlerf, M., & Atzberger, C. (2008). Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland. Remote Sensing of Environment, 112, 2592–2604. doi:10.1016/j.rse.2007.12.003
   Web of Science ®Google Scholar
• Ding, Y., Zhao, K., Zheng, X., & Jiang, T. (2014). Temporal dynamics of spatial heterogeneity over cropland quantified by time-series NDVI, near infrared and red reflectance of landsat 8 OLI imagery. International Journal of Applied Earth Observation Geoinformation, 30, 139–145. doi:10.1016/j.jag.2014.01.009
   Web of Science ®Google Scholar
• Doquire, G., & Verleysen, M. (2013). A graph Laplacian based approach to semi-supervised feature selection for regression problems. Neurocomputing, 121, 5–13. doi:10.1016/j.neucom.2012.10.028
   Web of Science ®Google Scholar
• Eckert, S. (2012). Improved forest biomass and carbon estimations using texture measures from WorldView-2 satellite data. Remote Sensing, 4, 810–829. doi:10.3390/rs4040810
   Web of Science ®Google Scholar
• Fang, H., & Liang, S. (2005). A hybrid inversion method for mapping leaf area index from MODIS data: Experiments and application to broadleaf and needleleaf canopies. Remote Sensing of Environment, 94, 405–424. doi:10.1016/j.rse.2004.11.001
   Web of Science ®Google Scholar
• Fernández-Manso, O., Fernández-Manso, A., & Quintano, C. (2014). Estimation of aboveground biomass in Mediterranean forests by statistical modelling of ASTER fraction images. International Journal of Applied Earth Observation Geoinformation, 31, 45–56. doi:10.1016/j.jag.2014.03.005
   Web of Science ®Google Scholar
• Foody, G.M., Boyd, D.S., & Cutler, M.E.J. (2003). Predictive relations of tropical forest biomass from landsat TM data and their transferability between regions. Remote Sensing of Environment, 85, 463–474. doi:10.1016/S0034-4257(03)00039-7
   Web of Science ®Google Scholar
• Foody, G.M., Cutler, M.E., McMorrow, J., Pelz, D., Tangki, H., Boyd, D.S., & Douglas, I. (2001). Mapping the biomass of Bornean tropical rain forest from remotely sensed data. Global Ecology and Biogeography, 10, 379–387. doi:10.1046/j.1466-822X.2001.00248.x
   Web of Science ®Google Scholar
• Ghosh, K., Ramteke, M., & Srinivasan, R. (2014). Optimal variable selection for effective statistical process monitoring. Computers and Chemical Engineering, 60, 260–276. doi:10.1016/j.compchemeng.2013.09.014
   Web of Science ®Google Scholar
• Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46, 389–422. doi:10.1023/A:1012487302797
   Web of Science ®Google Scholar
• Houghton, R.A., Hall, F., & Goetz, S.J. (2009). Importance of biomass in the global carbon cycle. Journal of Geophysical Research Biogeosciences, 114, 2005–2012. doi:10.1029/2009JG000935
   Web of Science ®Google Scholar
• Huete, A.R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295–309. doi:10.1016/0034-4257(88)90106-X
   Web of Science ®Google Scholar
• Karnieli, A., Kaufman, Y.J., Remer, L., & Wald, A. (2001). AFRI – Aerosol free vegetation index. Remote Sensing of Environment, 77, 10–21. doi:10.1016/S0034-4257(01)00190-0
   Web of Science ®Google Scholar
• Kelsey, K., & Neff, J. (2014). Estimates of aboveground biomass from texture analysis of landsat imagery. Remote Sensing, 6, 6407–6422. doi:10.3390/rs6076407
   Web of Science ®Google Scholar
• Lee, K., Kook, M., Shin, J., Kim, S., & Kim, T. (2007). Spectral characteristics of forest vegetation in moderate drought condition observed by laboratory measurements and spaceborne hyperspectral data. Photogrammetric Engineering and Remote Sensing, 73, 1121–1127. doi:10.14358/PERS.73.10.1121
   Web of Science ®Google Scholar
• Li, W., Zhao, Y., Song, Y., & Yang, Z. (2008). SVM feature selection and sample regression for Chinese medicine research. In International Conference on Information and Automation. 2008. Piscataway, NJ: IEEE.
   Google Scholar
• Lu, D., Mausel, P., Brondízio, E., & Moran, E. (2004). Relationships between forest stand parameters and landsat TM spectral responses in the Brazilian Amazon Basin. Forest Ecology and Management, 198, 149–167. doi:10.1016/j.foreco.2004.03.048
   Web of Science ®Google Scholar
• Mardiatmoko, G., Kastanya, A., & Hatulesila, J.W. (2019). Allometric equation for estimating aboveground biomass of nutmeg (Myristica fragrans Houtt) to support REDD+. Agroforestry Systems, 93–1377. doi:10.1007/s10457-018-0245-3
   Web of Science ®Google Scholar
• Mundra, P., Kumar, M., Kumar, K.K., Jayaraman, V.K., & Kulkarni, B.D. (2007). Using pseudo amino acid composition to predict protein subnuclear localization: Approached with PSSM. Pattern recognition Letters, 28(13), 1610-1615. doi:10.1016/j.patrec.2007.04.001
   Google Scholar
• Mundra, P.A., & Rajapakse, J.C. (2010). SVM-RFE with MRMR filter for gene selection. IEEE Transactions on Nanobioscience, 9, 31–37. doi:10.1109/TNB.2009.2035284
   PubMed Web of Science ®Google Scholar
• Návar, J., Ríos-Saucedo, J., Pérez-Verdín, G., Rodríguez-Flores, J., & Domínguez-Calleros, P.A. (2013). Regional aboveground biomass equations for North American arid and semi-arid forests. Journal of Arid Environments, 97, 127–135. doi:10.1016/j.jaridenv.2013.05.016
   Web of Science ®Google Scholar
• Qiu, X., Fu, D., & Fu, Z. (2013). Feature selection of atmospheric corrosion data based on SVM-RFE method. Advances in Computer Science and Its Applications, 2, 443–448.
   Google Scholar
• Quinzán, I., Sotoca, J.M., & Pla, F. (2009). Clustering-based feature selection in semi-supervised problems. In 2009 Ninth International conference on intelligent systems design and applications. New York, NY: ISDA.
   Google Scholar
• Roy, D.P., Wulder, M.A., Loveland, T.R., Woodcock, C.E., Allen, R.G., Anderson, M.C., … Scambos, T.A. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172. doi:10.1016/j.rse.2014.02.001
   Web of Science ®Google Scholar
• Samb, M.L., Camara, F., Ndiaye, S., Slimani, Y., & Esseghir, M.A. (2012). A novel RFE-SVM-based feature selection approach for classification. International Journal Advanced Science and Technology, 43, 27-36.
   Google Scholar
• Sarker, L.R., & Nichol, J.E. (2011). Improved forest biomass estimates using ALOS AVNIR-2 texture indices. Remote Sensing of Environment, 115, 968–977. doi:10.1016/j.rse.2010.11.010
   Web of Science ®Google Scholar
• Schlerf, M., & Atzberger, C. (2006). Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data. Remote Sensing of Environment, 100, 281–294. doi:10.1016/j.rse.2005.10.006
   Web of Science ®Google Scholar
• Shen, W., Guo, X., Wu, C., & Wu, D. (2011). Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm. Knowledge-Based System, 24, 378–385. doi:10.1016/j.knosys.2010.11.001
   Web of Science ®Google Scholar
• Tan, B., Masek, J.G., Wolfe, R., Gao, F., Huang, C., Vermote, E.F., … Ederer, G. (2013). Improved forest change detection with terrain illumination corrected landsat images. Remote Sensing of Environment, 136, 469–483. doi:10.1016/j.rse.2013.05.013
   Web of Science ®Google Scholar
• Tan, B., Wolfe, R., Masek, J., Gao, F., & Vermote, E.F. (2010). An illumination correction algorithm on landsat-TM data. In Geoscience and remote sensing symposium (IGARSS). Piscataway, NJ: IEEE.
   Google Scholar
• Tejada, G., Görgens, E.B., Espírito-Santo, F.D.B., Cantinho, R.Z., & Ometto, J.P. (2019). Evaluating spatial coverage of data on the aboveground biomass in undisturbed forests in the Brazilian Amazon. Carbon Balance and Management, 14(1), 11. doi:10.1186/s13021-019-0126-8
   Web of Science ®Google Scholar
• Uller, H.F., Oliveira, L.Z., Klitzke, A.R., Eleotério, J.R., Fantini, A.C., & Vibrans, A.C. (2019). Aboveground biomass quantification and tree-level prediction models for the Brazilian subtropical Atlantic Forest. Southern Forests: a Journal of Forest Science, 81(3), 261–271. doi:10.2989/00306525.2019.1581498
   Web of Science ®Google Scholar
• Utanga, O., Adam, E., & Cho, M.A. (2012). High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. International Journal of Applied Earth Observation and Geoinformation, 18, 399–406. doi:10.1016/j.jag.2012.03.012
   Web of Science ®Google Scholar
• Vaglio Laurin, G., Chen, Q., Lindsell, J.A., Coomes, D.A., Frate, F.D., Guerriero, L., … Valentini, R. (2014). Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data. ISPRS Journal Photogrammetry and Remote Sensing, 89, 49–58. doi:10.1016/j.isprsjprs.2014.01.001
   Web of Science ®Google Scholar
• Xiao, Z., Liang, S., Wang, J., Song, J., & Wu, X. (2009). A temporally integrated inversion method for estimating leaf area index from MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 47, 2536–2545. doi:10.1109/TGRS.2009.2015656
   Web of Science ®Google Scholar
• Zhou, X., & Tuck, D.P. (2007). MSVM-RFE: Extensions of SVM-RFE for multiclass gene selection on DNA microarray data. BioInformatics, 23, 1106–1114. doi:10.1093/bioinformatics/btm036
   PubMed Web of Science ®Google Scholar