Spectral features and separability of alpine wetland grass species

References

• Hirota, M.; et al. Methane emissions from different vegetation zones in a Qinghai-Tibetan Plateau wetland. Soil Biology & Biochemistry 2004, 36(5), 737–748.
   Web of Science ®Google Scholar
• Villa, J. A.; et al. Contribution of different wetland plant species to the DOC exported from a mesocosm experiment in the Florida Everglades. Ecological Engineering 2014, 71, 118–125.
   Web of Science ®Google Scholar
• Sekulova, L.; et al. Alpine wetlands in the West Carpathians: vegetation survey and vegetation-environment relationships. Preslia 2011, 83(1), 1–24.
   Web of Science ®Google Scholar
• Hirota, M.; et al. Net primary productivity and spatial distribution of vegetation in an alpine wetland, Qinghai–Tibetan Plateau. Limnology 2007, 8(2), 161–170.
   Web of Science ®Google Scholar
• Becker, B. L.; Lusch, D. P.; Qi, J. A classification-based assessment of the optimal spectral and spatial resolutions for Great Lakes coastal wetland imagery. Remote Sensing of Environment 2007, 108(1), 111–120.
   Web of Science ®Google Scholar
• Bell, I. E.; Baranoski, G. V. G. Reducing the dimensionality of plant spectral databases. IEEE Transactions on Geoscience and Remote Sensing 2004, 42(3), 570–576.
   Web of Science ®Google Scholar
• Blackburn, G. A.; Ferwerda, J. G. Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis. Remote Sensing of Environment 2008, 112(4), 1614–1632.
   Web of Science ®Google Scholar
• Thornhill, N. F.; et al. Spectral principal component analysis of dynamic process data. Control Engineering Practice 2002, 10(8), 833–846.
   Web of Science ®Google Scholar
• Silva, C. S.; et al. Classification of blue pen ink using infrared spectroscopy and linear discriminant analysis. Microchemical Journal 2013, 109, 122–127.
   Web of Science ®Google Scholar
• Marconcini, M.; Camps-Valls, G.; Bruzzone, L. A Composite Semisupervised SVM for Classification of Hyperspectral Images. IEEE Geoscience and Remote Sensing Letters 2009, 6(2), 234–238.
   Web of Science ®Google Scholar
• Xu, L.; et al. Protected geographical indication identification of a Chinese green tea (Anji-White) by near-infrared spectroscopy and chemometric class modeling techniques. Journal of Spectroscopy 2013, 2013(1).
   Google Scholar
• Du, P.; Tan, K.; Xing, X. Wavelet SVM in reproducing kernel Hilbert space for hyperspectral remote sensing image classification. Optics Communications 2010, 283(24), 4978–4984.
   Web of Science ®Google Scholar
• Leardi, R. Application of genetic algorithm-PLS for feature selection in spectral data sets. Journal of Chemometrics 2000, 14(5–6), 643–655.
   Web of Science ®Google Scholar
• Rahilly, P.; et al. Mapping swamp timothy (Crypsis schoenoides) seed productivity using spectral values and vegetation indices in managed wetlands. International Journal of Remote Sensing 2012, 33(16), 4902–4918.
   Web of Science ®Google Scholar
• Kokaly, R. F.; et al. Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data. Remote Sensing of Environment 2003, 84(3), 437–456.
   Web of Science ®Google Scholar
• Adam, E.; Mutanga, O.; Rugege, D. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecology and Management 2010, 18(3), 281–296.
   Web of Science ®Google Scholar
• Ide, R.; Nakaji, T.; Oguma, H. Assessment of canopy photosynthetic capacity and estimation of GPP by using spectral vegetation indices and the light-response function in a larch forest. Agricultural and Forest Meteorology 2010, 150(3), 389–398.
   Web of Science ®Google Scholar
• Gao, Z. G.; Zhang, L. Q. Multi-seasonal spectral characteristics analysis of coastal salt marsh vegetation in Shanghai, China. Estuarine Coastal and Shelf Science 2006, 69(1–2), 217–224.
   Web of Science ®Google Scholar
• Fernandes, M. R.; et al. Spectral separability of riparian forests from small and medium-sized rivers across a latitudinal gradient using multispectral imagery. International Journal of Remote Sensing 2013, 34(7), 2375–2401.
   Web of Science ®Google Scholar
• Mirzaie, M.; et al. Comparative analysis of different uni- and multi-variate methods for estimation of vegetation water content using hyper-spectral measurements (vol 26, p. 1, 2014). International Journal of Applied Earth Observation and Geoinformation 2014, 28, 260–260.
   Web of Science ®Google Scholar
• Stagakis, S.; et al. Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: An application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations. Remote Sensing of Environment 2010, 114(5), 977–994.
   Web of Science ®Google Scholar
• Pan, Z. K.; Huang, J. F.; Wang, F. M. Multi range spectral feature fitting for hyperspectral imagery in extracting oilseed rape planting area. International Journal of Applied Earth Observation and Geoinformation 2013, 25, 21–29.
   Web of Science ®Google Scholar
• Delegido, J.; et al. A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems. European Journal of Agronomy 2013, 46, 42–52.
   Web of Science ®Google Scholar
• Diqiang Li, J. L. Appendix: List of Animals and Flora, in Investigation Report of Three Rivers Reserves. 2001, Forestry Administration of Qinghai Province: Qinghai, China. p. 176.
   Google Scholar
• Ju, C. H.; et al. Estimating leaf chlorophyll content using red edge parameters. Pedosphere 2010, 20(5), 633–644.
   Web of Science ®Google Scholar
• Qian Tan, Y. Z.; Qingxi, Tong.; Lanfen, Zheng. The extraction model of vegetation spectral feature. Remote Sensing Information 2001, 1, 5.
   Google Scholar
• Yang, C. H.; Everitt, J. H. Using spectral distance, spectral angle and plant abundance derived from hyperspectral imagery to characterize crop yield variation. Precision Agriculture 2012, 13(1), 62–75.
   Web of Science ®Google Scholar
• Gong, Z. N.; et al. Canopy water content estimation for typical emerged plant community using WorldView-2 imagery: A case study in Wild Duck Lake wetland, Beijing. Journal of Infrared and Millimeter Waves 2014, 33(5), 533–545.
   Web of Science ®Google Scholar
• Yao, X.; et al. Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat. International Journal of Applied Earth Observation and Geoinformation 2010, 12(2), 89–100.
   Web of Science ®Google Scholar
• Stroppiana, D.; et al. Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field Crops Research 2009, 111(1–2), 119–129.
   Web of Science ®Google Scholar
• Shafri, H. Z. M.; Yusof, M. R. M. Determination of optimal wavelet denoising parameters for red edge feature extraction from hyperspectral data. Journal of Applied Remote Sensing 2009, 3(1), 131–136.
   Google Scholar
• Yu, X.; et al. Discrimination of three dimensional fluorescence spectra based on wavelet analysis and independent component analysis. Spectrochimica Acta Part a-Molecular and Biomolecular Spectroscopy 2014, 124, 52–58.
   PubMed Web of Science ®Google Scholar
• Chen, H. M.; Varshney, P. K.; Arora, M. K. Performance of mutual information similarity measure for registration of multitemporal remote sensing images. IEEE Transactions on Geoscience and Remote Sensing 2003, 41(11), 2445–2454.
   Web of Science ®Google Scholar
• Feng, J.; et al. Hyperspectral band selection based on trivariate mutual information and clonal selection. IEEE Transactions on Geoscience and Remote Sensing 2014, 52(7), 4092–4105.
   Web of Science ®Google Scholar
• Shao, Y.; et al. An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data. Remote Sensing of Environment 2016, 174, 258–265.
   Web of Science ®Google Scholar
• Qiu, B. W.; et al. A new approach for crop identification with wavelet variance and JM distance. Environmental Monitoring and Assessment 2014, 186(11), 7929–7940.
   PubMed Web of Science ®Google Scholar
• He, L.; et al. Estimating canopy leaf nitrogen concentration in winter wheat based on multi-angular hyperspectral remote sensing. European Journal of Agronomy 2016, 73, 170–185.
   Web of Science ®Google Scholar
• Vaiphasa, C.; et al. Tropical mangrove species discrimination using hyperspectral data: A laboratory study. Estuarine Coastal and Shelf Science 2005, 65(1–2), 371–379.
   Web of Science ®Google Scholar
• Wong, K. K.; He, Y. H. Estimating grassland chlorophyll content using remote sensing data at leaf, canopy, and landscape scales. Canadian Journal of Remote Sensing 2013, 39(2), 155–166.
   Web of Science ®Google Scholar
• Yin, C. M.; et al. Chlorophyll content estimation in arid grasslands from Landsat-8 OLI data. International Journal of Remote Sensing 2016, 37(3), 615–632.
   Web of Science ®Google Scholar
• Darvishzadeh, R.; et al. LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements. ISPRS Journal of Photogrammetry and Remote Sensing 2008, 63(4), 409–426.
   Web of Science ®Google Scholar
• Schlerf, M.; Atzberger, C.; Hill, J. Remote sensing of forest biophysical variables using HyMap imaging spectrometer data. Remote Sensing of Environment 2005, 95(2), 177–194.
   Web of Science ®Google Scholar
• Clevers, J.; Gitelson, A. A. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and-3. International Journal of Applied Earth Observation and Geoinformation 2013, 23, 344–351.
   Web of Science ®Google Scholar
• Wang, J. J.; et al. Evaluating different methods for grass nutrient estimation from canopy hyperspectral reflectance. Remote Sensing 2015, 7(5), 5901–5917.
   Web of Science ®Google Scholar