An Evaluation of Spaceborne Imaging Spectrometry for Estimation of Forest Canopy Nitrogen Concentration in a Subtropical Conifer Plantation of Southern China

doi:10.5814/j.issn.1674-764x.2014.01.001

Abstract

Abstract: Canopy foliar Nitrogen Concentration (CNC) is one of the most important parameters influencing vegetation productivity in forest ecosystems. In this study, we explored the potential of imaging spectrometry (hyperspectral) remote sensing of CNC in conifer plantations in China's subtropical red soil hilly region. Our analysis included data from 57 field plots scattered across two transects covered by Hyperion images. Single regression and partial least squares regression (PLSR) were used to explore the relationships between CNC and hyperspectral data. The correlations between CNC and nearinfrared reflectance (NIR) were consistent in three data subsets (subsets A –C). For all subsets, CNC was significantly positively correlated with NIR in the two transects (R²=0.29, 0.33 and 0.36, P <0.05 or P <0.01, respectively). It suggested that the NIR-CNC relationship exist despite a weak one, and the relationship may be weakened by the single canopy structure. Besides, we also applied a shortwave infrared (SWIR) index—Normalized Difference Nitrogen Index (NDNI) to estimate CNC variation. NDNI presented a significant positive correlation with CNC in different subsets, but like NIR, it was also with low coefficient of determination (R²=0.38, 0.20 and 0.17, P <0.01, respectively). Also, the correlations between CNC and the entire spectrum reflectance (or its derivative and logarithmic transformation) by PLSR owned different significance in various subsets. We did not find the very robust relationship like previous literatures, so the data we used were checked again. The paired T-test was applied to estimate the influence of inter-annual variability of FNC on the relationships between CNC and Hyperion data. The inter-annual mismatch between period of fieldwork and Hyperion acquisition had no influence on the correlations of CNC-Hyperion data. Meanwhile, we pointed out that the lack of the canopy structure variation in conifer plantation area may lead to these weak relationships.

Key words: conifer plantation, foliar nitrogen concentration, forest canopy, Hyperion, imaging spectrometry, remote sensing

YU Quanzhou, WANG Shaoqiang, SHI Hao, HUANG Kun, ZHOU Lei. An Evaluation of Spaceborne Imaging Spectrometry for Estimation of Forest Canopy Nitrogen Concentration in a Subtropical Conifer Plantation of Southern China[J]. Journal of Resources and Ecology, 2014, 5(1): 1-10.

References

Abdel-Rahman E M, F B Ahmed, R Ismail, 2013. Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data. International Journal of Remote Sensing, 34: 712-728.

Asner G P, R E Martin RE. 2008. Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels. Remote Sensing of Environment, 112: 3958-3970.

Asner G P, R E Martin, K Carlson, et al. 2006. Vegetation-climate interactions among native and invasive species in Hawaiian rainforest. Ecosystems, 9: 1106-1117.

Barry P. 2001. EO-1/Hyperion science data user's guide, Level 1_B. TRW Space, Defense & Information Systems.

Beck R. 2003. EO-1 user guide (v. 2.3). University of Cincinnati. Bolster K, M E Martin, J D Aber. 1996. Determination of carbon fraction and nitrogen concentration in tree foliage by near infrared reflectance: A comparison of statistical methods. Canadian Journal of Forest Research, 26: 590-600.

Datt B, T R McVicar, T G VanNiel, et al. 2003. Preprocessing EO-1 hyperion hyperspectral data to support the application of agricultural indexes. IEEE Transactions on Geoscience and Remote Sensing, 41: 1246-1259.

Deel L N, B E McNeil, P G Curtis, et al. 2012. Relationship of a Landsat cumulative disturbance index to canopy nitrogen and forest structure. Remote Sensing of Environment, 118: 40-49.

Field C, H A Mooney. 1986. The photosynthesis–nitrogen relationship in wild plants. Cambridge, UK: Cambridge University Press, 25-55.

Herrmann I, A Karnieli, D J Bonfil, et al. 2010. SWIR-based spectral indices for assessing nitrogen content in potato fields. International Journal of Remote Sensing, 31: 5127-5143.

Homolová L, Z Malenovský, J G P W Clevers, et al. 2013. Review of optical-based remote sensing for plant trait mapping. Ecological Complexity, 15: 1-16.

Huang Z, B J Turner, S J Dury, et al. 2004. Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis. Remote Sensing of Environment, 93: 18-29.

Jiangxi Province Bureau of Statistics. 2012, 2013. Jiangxi statistical yearbook 2012, 2013. Beijing: China Statistics Press. (in Chinese)

Knyazikhin Y, M A Schull, P Stenberg, et al. 2012. Hyperspectral remote sensing of foliar nitrogen content. Proceedings of the National Academy of Sciences of the USA, 10: E185-E192.

Kokaly R F, G P Asner, S V Ollinger, et al. 2009. Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. Remote Sensing of Environment, 113: s78-s91.

Li G, Zhou G Y, Wu Z M, et al. 2012. Aboveground biomass of a naturalyregenerated Schima superba community at Xiaokeng of the Nanling Mountain. Scientia Silvae Sinicae, 48: 143-147. (in Chinese)

Li X R, Liu Q J, Chen Y R, et al. 2006. Aboveground biomass of three conifers in Qianyanzhou plantation. Chinese Journal of Applied Ecology, 17: 1382-1388. (in Chinese)

Louis J, H Genet, S Meyer, et al. 2012. Tree age-related effects on sun acclimated leaves in a chronosequence of beech (Fagus sylvatica) stands. Functional Plant Biology, 39: 323-331.

Martin M E, J D Aber. 1997. High spectral resolution remote sensing of forest canopy lignin, nitrogen, and ecosystem processes. Ecological Applications, 4: 431-443.

Martin M E, L C Plourde, S V Ollinger, et al. 2008. A generalizable method for remote sensing of canopy nitrogen across a wide range of forest ecosystems. Remote Sensing of Environment, 112: 3511-3519.

McNeil B E. 2006. Spatial variability of foliar nitrogen in the Adirondack Park, New York. Syracuse, US: Syracuse University.

Ollinger S V. 2011. Sources of variability in canopy reflectance and the convergent properties of plants. New Phytologist, 189: 375-394.

Ollinger S V, P B Reich, S Frolking, et al. 2013. Nitrogen cycling, forest canopy reflectance, and emergent properties of ecosystems. Proceedings of the National Academy of Sciences of the USA, 110: E2437.

Ollinger S V, A D Richardson, M E Martin, et al. 2008. Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: Functional relations and potential climate feedbacks. Proceedings of the National Academy of Sciences of the USA, 105: 19336-19341.

Ollinger S V, M L Smith. 2005. Net primary production and canopy nitrogen in a temperate forest landscape: An analysis using imaging spectroscopy, modeling and field data. Ecosystems, 8: 760-778.

Ollinger S V, M L Smith, M E Martin, et al. 2002. Regional variation in foliar chemistry and N cycling among forests of diverse history and composition. Ecology, 83: 339-355.

Ouyang S L, Dai C D, Hou Y N, et al. 2010. Establishment of main constructive species biomass model for protection forest system around Dongting Lake. Hunan Forestry Science and Technology, 37: 22-24. (in Chinese)

Pan Y D, J Hom, J Jenkins, et al. 2004. Importance of foliar nitrogen concentration to predict forest productivity in the Mid-Atlantic Region. Forest Science, 50: 279-289.

Reich P B, B D Kloeppel, D S Ellsworth, et al. 1995. Different photosynthesis-nitrogen relations in deciduous hardwood and evergreen coniferous tree species. Oecologia, 104: 24-30.

Ryu S R, Chen J Q, A Noormets, et al. 2008. Comparisons between PnETDay and eddy covariance based gross ecosystem production in two Northern Wisconsin forests. Agricultural and Forest Meteorology, 148: 247-256.

Serranoa L, J Penuelasa J, S L Ustin. 2002. Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals. Remote Sensing of Environment, 81: 355-364.

Smith M L, M E Martin. 2001. A plot-based method for rapid estimation of forest canopy chemistry. Canadian Journal of Forest Research, 31: 549-555.

Smith M L, M E Martin, L Plourde, et al. 2003. Analysis of hyperspectral data for estimation of temperate forest canopy nitrogen concentration: Comparison between an airborne (AVIRIS) and a spaceborne (HYPERION) sensor. IEEE Transactions on Geoscience and Remote Sensing, 41: 1332-1337.

Sullivan F B, S V Ollinger, M E Martin, et al. 2013. Foliar nitrogen in relation to plant traits and reflectance properties of New Hampshire forests. Canadian Journal of Forest Research, 43: 18-27.

Townsend P A, J R Foster, R A Chastain, et al. 2003. Imaging spectroscopy and canopy nitrogen: Application to the forests of the central Appalachian Mountains using Hyperion and AVIRIS. IEEE Transactions on Geoscience and Remote Sensing, 41: 1347-1354.

Verrelst J, M E Schaepman, M E Malenovský, et al. 2010. Effects of woody elements on simulated canopy reflectance: Implications for forest chlorophyll content retrieval. Remote Sensing of Environment, 114, 647-656.

Vetter M, G Churkina, Jung M, et al. 2008. Analyzing the causes and spatial pattern of the European 2003 carbon flux anomaly using seven models. Biogeosciences, 5: 561-583.

Wen X F, Yu G R, Sun X M, et al. 2006. Soil moisture effects on the temperature dependence of ecosystem respiration in a subtropical Pinus plantation of southeastern China. Agricultural and Forest Meteorology, 137: 166-175.

Wold S, M Sjöström, L Eriksson. 2001. PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58: 109-130.

Wright I J, P B Reich, W Mark, et al. 2004. The worldwide leaf economics spectrum. Nature, 428: 821-827.

Yang X G, Yu Y, Huang H J, et al. 2012. Estimation of forest canopy nitrogen content based on remote sensing. Journal of Infrared and Millimeter Waves, 12: 536-544. (in Chinese)

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