Prediction of Macronutrients at the Canopy Level Using Spaceborne Imaging Spectroscopy and LiDAR Data in a Mixedwood Boreal Forest
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site
2.2. Field Data
2.3. Remote Sensing Data
2.4. Statistical Analysis
2.5. Generation of Canopy Macronutrient Maps
3. Results
3.1. Variation of Macronutrient Concentration by Species and Functional Type
3.2. Relationships between Canopy Structure and Macronutrients
N | P | K | Ca | Mg | |
---|---|---|---|---|---|
Dominant Height (m) | 0.34 | 0.33 | 0.48** | 0.71* | 0.47** |
Mean Dominant Height (m) | 0.34 | 0.33 | 0.49** | 0.70* | 0.46** |
Lorey's Height (m) | 0.42 | 0.41 | 0.52** | 0.65* | 0.51** |
Average Height (m) | 0.32 | 0.30 | 0.41 | 0.48** | 0.39 |
Crown Closure | 0.74* | 0.74* | 0.69* | 0.18 | 0.76* |
Basal Area (m2·ha−1) | 0.45** | 0.46** | 0.63* | 0.56** | 0.55** |
Biomass (kg·ha−1) | 0.38 | 0.38 | 0.52** | 0.48** | 0.48** |
3.3. Predictive Models with IS Data
3.4. Predictive Models with LiDAR Data
Nutrient | LiDAR Metric | R2 | Adjusted R2 | Residuals Shapiro | Prediction RMSE | |
---|---|---|---|---|---|---|
N | 50th perc. ht., max. ht. | 0.71 | 0.69 | 0.97 | 0.37 | 19.5 |
P | 50th perc. ht., max. ht. | 0.65 | 0.63 | 0.96 | 0.30 | 12.9 |
K | 50th perc. ht. | 0.68 | 0.67 | 0.97 | 0.45 | 13.9 |
Ca | 50th perc. ht., cv. | 0.80 | 0.79 | 0.98 | 0.75 | 9.4 |
Mg | 50th perc. ht., max. ht. | 0.77 | 0.76 | 0.96 | 0.32 | 13.8 |
3.5. Predictive Models with IS and LiDAR Data
3.6. Spatial Distribution of Canopy Macronutrient Concentration
4. Discussion
5. Conclusions
Acknowledgements
Author Contributions
Conflicts of Interest
References
- Taiz, L.; Zeiger, E. Plant Physiology, 5th ed.; Sinauer Associates Inc.: Sunderland, MA, USA, 2010. [Google Scholar]
- Melillo, J.M.; Aber, J.D.; Muratore, J.M. Nitrogen and lignin control of hardwood leaf litter decomposition dynamics. Ecology 1982, 63, 621–626. [Google Scholar] [CrossRef]
- Smith, M.L.; Ollinger, S.V.; Martin, M.E.; Aber, J.D.; Hallett, R.A.; Goodale, C.L. Direct estimation of aboveground forest productivity through hyperspectral remote sensing of canopy nitrogen. Ecol. Appl. 2002, 12, 1286–1302. [Google Scholar] [CrossRef]
- Chapin, F.S. The mineral nutrition of wild plants. Annu. Rev. Ecol. Syst. 1980, 11, 233–260. [Google Scholar] [CrossRef]
- Marschner, H. Mineral Nutrition of Higher Plants, 2nd ed.; Academic Press: San Diego, CA, USA, 1995. [Google Scholar]
- Mengel, K.; Kirkby, E.A. Principles of Plant Nutrition, 5th ed.; Kluwer Academic Publishers: Dordrecht, The Netherlands, 2001. [Google Scholar]
- Hepler, P.K. Calcium: A central regulator of plant growth and development. Plant Cell 2005, 17, 2142–2155. [Google Scholar] [CrossRef] [PubMed]
- Olivier, J.G.J.; Janssens-Maenhout, G.; Peters, J.A.H.W.; Wilson, J. Long-Term Trend in Global CO2 Emissions 2011 Report; PBL/JRC: The Hague, The Netherlands, 2011. [Google Scholar]
- Ollinger, S.V.; Smith, M.L. Net primary production and canopy nitrogen in a temperate forest landscape: An analysis using imaging spectroscopy, modeling, and field data. Ecosystems 2005, 8, 1–19. [Google Scholar] [CrossRef]
- Coops, N.C.; Smith, M.L.; Martin, M.E.; Ollinger, S.V. Prediction of eucalypt foliage nitrogen content from satellite-derived hyperspectral data. IEEE Trans. Geosci. Remote Sens. 2003, 41, 1338–1346. [Google Scholar] [CrossRef]
- Smith, M.L.; Martin, M.E.; Ollinger, S.V.; Plourde, L. Analysis of hyperspectral data for estimation of temperate forest canopy nitrogen concentration: Comparison between an airborne (AVIRIS) and a spaceborne (Hyperion) sensor. IEEE Trans. Geosci. Remote Sens. 2003, 41, 1332–1337. [Google Scholar] [CrossRef]
- Townsend, P.A.; Foster, J.R.; Chastain, R.A., Jr.; Currie, W.S. Application of imaging spectroscopy to mapping canopy nitrogen in the forests of the central Appalachian Mountains using Hyperion and AVIRIS. IEEE Trans. Geosci. Remote Sens. 2003, 41, 1347–1354. [Google Scholar] [CrossRef]
- Wessman, C.A.; Aber, J.D.; Peterson, D.L.; Melillo, J.M. Remote sensing of canopy chemistry and nitrogen cycling in temperate forest ecosystems. Nature 1988, 335, 154–156. [Google Scholar] [CrossRef]
- Curran, P.J.; Kupiec, J.A.; Smith, G.M. Remote sensing the biochemical composition of a slash pine canopy. IEEE Trans. Geosci. Remote Sens. 1997, 35, 415–420. [Google Scholar] [CrossRef]
- Martin, M.E.; Aber, J.D. High spectral resolution remote sensing of forest canopy lignin, nitrogen, and ecosystem processes. Ecol. Appl. 1997, 7, 431–444. [Google Scholar] [CrossRef]
- Wicklein, H.F.; Ollinger, S.V.; Martin, M.E.; Hollinger, D.Y.; Lepine, L.C.; Day, M.E.; Bartlett, M.K.; Richardson, A.D.; Norby, R.J. Variation in foliar nitrogen and albedo in response to nitrogen fertilization and elevated CO2. Oecologia 2012, 169, 915–925. [Google Scholar] [CrossRef] [PubMed]
- Carlson, K.M.; Asner, G.P.; Hughes, R.F.; Ostertag, R.; Martin, R.E. Hyperspectral remote sensing of canopy biodiversity in Hawaiian lowland rainforests. Ecosystems 2007, 10, 536–549. [Google Scholar] [CrossRef]
- Asner, G.P.; Martin, R.E.; Tupayachi, R.; Anderson, C.B.; Sinca, F.; Carranza-Jimenez, L.; Martinez, P. Amazonian functional diversity from forest canopy chemical assembly. Proc. Natl. Acad. Sci. USA 2014, 111, 5604–5609. [Google Scholar] [CrossRef] [PubMed]
- Asner, G.P. Hyperspectral remote sensing of canopy chemistry, physiology, and biodiversity in tropical rainforests. In Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests; Kalacska, M., Sanches-Azofeifa, G.A., Eds.; CRC Press: Boca Raton, FL, USA, 2008; pp. 261–296. [Google Scholar]
- Kokaly, R.F.; Asner, G.P.; Ollinger, S.V.; Martin, M.E.; Wessman, C.A. Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. Remote Sens. Environ. 2009, 113, S78–S91. [Google Scholar] [CrossRef]
- Ollinger, S.V.; Richardson, A.D.; Martin, M.E.; Hollinger, D.Y.; Frolking, S.E.; Reich, P.B.; Katul, G.G.; Mungere, J.W.; Orend, R.; Smith, M.-L.; et al. Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: Functional relations and potential climate feedbacks. Proc. Natl. Acad. Sci. USA 2008, 105, 19336–19341. [Google Scholar] [CrossRef] [PubMed]
- Knyazikhin, Y.; Schull, M.A.; Stenberg, P.; Mottus, M.; Rautiainen, M.; Yang, Y.; Marshak, A.; Carmona, P.L.; Kaufmanna, R.K.; Lewis, P.; et al. Hyperspectral remote sensing of foliar nitrogen content. Proc. Natl. Acad. Sci. USA 2012, 110, E185–E192. [Google Scholar] [CrossRef] [PubMed]
- Al-Abbas, A.H.; Barr, R.; Hall, J.D.; Crane, F.L.; Baumgardner, M.F. Spectra of normal and nutrient deficient maize leaves. Agron. J. 1974, 66, 16–20. [Google Scholar] [CrossRef]
- Osborne, S.L.; Schepers, J.S.; Francis, D.D.; Schlemmer, M.R. Detection of phosphorous and nitrogen deficiencies in corn using spectral radiance measurements. Agron. J. 2002, 94, 1215–1221. [Google Scholar] [CrossRef]
- Gong, P.; Pu, R.; Heald, R.C. Analysis of in situ hyperspectral data for nutrient estimation of giant sequoia. Int. J. Remote Sens. 2002, 23, 1827–1850. [Google Scholar] [CrossRef]
- Ponzoni, F.J.; de M. Gonçalves, J.L. Spectral features associated with nitrogen, phosphorus, and potassium deficiencies in Eucalyptus saligna seedling leaves. Int. J. Remote Sens. 1999, 20, 2249–2264. [Google Scholar] [CrossRef]
- Mutanga, O.; Skidmore, A.K.; Prins, H.H.T. Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features. Remote Sens. Environ. 2004, 89, 393–408. [Google Scholar] [CrossRef]
- Ferwerda, J.G.; Skidmore, A.K. Can nutrient status of four woody plant species be predicted using field spectrometry? ISPRS J. Photogramm. Remote Sens. 2007, 62, 406–414. [Google Scholar] [CrossRef]
- Porder, S.; Asner, G.P.; Vitousek, P.M. Ground-based and remotely sensed nutrient availability across a tropical landscape. Proc. Natl. Acad. Sci. USA 2005, 102, 10909–10912. [Google Scholar] [CrossRef] [PubMed]
- Mutanga, O.; Kumar, L. Estimating and mapping grass phosphorus concentration in an African savanna using hyperspectral image data. Int. J. Remote Sens. 2007, 28, 4897–4911. [Google Scholar] [CrossRef]
- Mirik, M.; Norland, J.E.; Crabtree, R.L.; Biondini, M.E. Hyperspectral one-meter-resolution remote Sensing in Yellowstone National Park, Wyoming: I. Forage nutritional values. Rangel. Ecol. Manag. 2005, 58, 452–458. [Google Scholar] [CrossRef]
- NASA HyspIRI Mission Website. Available online: http://hyspiri.jpl.nasa.gov (accessed on 9 April 2015).
- EnMAP Website. Available online: http://www.enmap.org/sites/default/files/pdf/pub/EnMAP_komplett_web_eng.pdf (accessed on 14 April 2015).
- Curran, P.J. Remote sensing of foliar chemistry. Remote Sens. Environ. 1989, 30, 271–278. [Google Scholar] [CrossRef]
- Kokaly, R.F.; Clark, R.N. Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sens. Environ. 1999, 67, 267–287. [Google Scholar] [CrossRef]
- Yoder, B.J.; Pettigrew-Cosby, R.E. Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales. Remote Sens. Environ. 1995, 53, 199–211. [Google Scholar] [CrossRef]
- Asner, G.P. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens. Environ. 1998, 64, 234–253. [Google Scholar] [CrossRef]
- Ollinger, S.V. Sources of variability in canopy reflectance and the convergent properties of plants. New Phytol. 2011, 189, 375–394. [Google Scholar] [CrossRef] [PubMed]
- Dixit, L.; Ram, S. Quantitative analysis by derivative electronic spectroscopy. Appl. Spectrosc. Rev. 1985, 21, 311–418. [Google Scholar] [CrossRef]
- Elvidge, C.D.; Chen, Z. Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sens. Environ. 1995, 54, 38–48. [Google Scholar] [CrossRef]
- Curran, P.J.; Dungan, J.L.; Peterson, D.L. Estimating the foliar biochemical concentration of leaves with reflectance spectrometry-testing the Kokaly and Clark methodologies. Remote Sens. Environ. 2001, 76, 349–359. [Google Scholar] [CrossRef]
- Huang, Z.; Turner, B.J.; Dury, S.J.; Wallis, I.R.; Foley, W.J. Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis. Remote Sens. Environ. 2004, 93, 8–29. [Google Scholar] [CrossRef]
- Mutanga, O.; Skidmore, A.K.; Kumar, L.; Ferwerda, J. Estimating tropical pasture quality at canopy level using band depth analysis with continuum removal in the visible domain. Int. J. Remote Sens. 2005, 26, 1093–1108. [Google Scholar] [CrossRef]
- Kramer, R. Chemometric Techniques for Quantitative Analysis; Marcel Dekker: New York, NY, USA, 1998. [Google Scholar]
- Feilhauer, H.; Asner, G.P.; Marten, R.E.; Schmidtlein, S. Brightness-normalized Partial Least Squares Regression for hyperspectral data. J. Quant. Spectrosc. Radiat. Transf. 2010, 111, 1947–1957. [Google Scholar] [CrossRef]
- Dahlin, K.M.; Asner, G.P.; Field, C.B. Environmental and community controls on plant canopy chemistry in a Mediterranean-type ecosystem. Proc. Natl. Acad. Sci. USA 2013, 110, 6895–6900. [Google Scholar] [CrossRef] [PubMed]
- Mitchell, J.; Glenn, N.; Sankey, T.; Derryberry, D.W.; Germino, M. Remote sensing of sagebrush canopy nitrogen. Remote Sens. Environ. 2012, 124, 217–223. [Google Scholar] [CrossRef]
- McNeil, B.E.; Read, J.M.; Sullivan, T.J.; McDonnell, T.C.; Fernandez, I.J.; Driscoll, C.T. The spatial pattern of nitrogen cycling in the Adirondack Park, New York. Ecol. Appl. 2008, 18, 438–452. [Google Scholar] [CrossRef] [PubMed]
- Asner, G.P.; Martin, R.E.; Ford, A.J.; Metcalfe, D.J.; Liddell, M.J. Leaf chemical and spectral diversity of Australian tropical forests. Ecol. Appl. 2009, 19, 236–253. [Google Scholar] [CrossRef] [PubMed]
- Asner, G.P.; Martin, R.E.; Suhaili, A. Sources of canopy chemical and spectral diversity in lowland Bornean forest. Ecosystems 2012, 15, 504–517. [Google Scholar] [CrossRef]
- Wright, I.J.; Reich, P.B.; Westoby, M.; Ackerly, D.D.; Baruch, Z.; Bongers, F.; Cavender-Bares, J.; Chapin, T.; Cornelissen, J.H.C.; Diemer, M.; et al. The worldwide leaf economics spectrum. Nature 2004, 428, 821–827. [Google Scholar] [CrossRef] [PubMed]
- Reich, P.B.; Walters, M.B.; Ellsworth, D.S. From tropics to tundra: Global convergence in plant functioning. Proc. Natl. Acad. Sci. USA 1997, 94, 13730–13734. [Google Scholar] [CrossRef] [PubMed]
- Asner, G.P.; Martin, R.E.; Carranza-Jimenez, L.; Sinca, F.; Tupayachi, R.; Anderson, C.B.; Martinez, P. Functional and biological diversity of foliar spectra in tree canopies throughout the Andes to Amazon region. New Phytol. 2014, 204, 127–139. [Google Scholar] [CrossRef] [PubMed]
- Gökkaya, K.; Thomas, V.; Noland, T.; McCaughey, H.; Morrison, I.; Treitz, P. Mapping continuous forest type variation by means of correlating remotely sensed metrics to canopy N:P ratio in a boreal mixedwood forest. Appl. Veg. Sci. 2015, 18, 143–157. [Google Scholar] [CrossRef]
- Thomas, V.; Treitz, P.; McCaughey, J.H.; Noland, T.; Rich, L. Canopy chlorophyll concentration estimation using hyperspectral and LiDAR data for a boreal mixedwood forest in northern Ontario, Canada. Int. J. Remote Sens. 2008, 29, 1029–1052. [Google Scholar] [CrossRef]
- Korhonen, L.; Korpela, I.; Heiskanen, J.; Maltamo, M. Airborne discrete-return LiDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index. Remote Sens. Environ. 2011, 115, 1065–1080. [Google Scholar]
- Vepakomma, U.; St-Onge, B.; Kneeshaw, D. Response of a boreal forest to canopy opening: Assessing vertical and lateral tree growth with multi-temporal LiDAR data. Ecol. Appl. 2011, 21, 99–121. [Google Scholar] [CrossRef] [PubMed]
- Magnussen, S.; Wulder, M. Post-fire canopy height recovery in Canada’s boreal forests using Airborne Laser Scanner (ALS). Remote Sens. 2012, 4, 1600–1616. [Google Scholar] [CrossRef]
- Bolton, D.K.; Coops, N.C.; Wulder, M.A. Measuring forest structure along productivity gradients in the Canadian boreal with small-footprint Lidar. Environ. Monit. Assess. 2013, 185, 6617–6634. [Google Scholar] [CrossRef] [PubMed]
- Li, J.; Hu, B.; Noland, T.L. Classification of tree species based on structural features derived from high density LiDAR data. Agric. For. Meteorol. 2013, 171–172, 104–114. [Google Scholar] [CrossRef]
- Van Leeuwen, M.; Nieuwenhuis, M. Retrieval of forest structural parameters using LiDAR remote sensing. Eur. J. For. Res. 2010, 129, 749–770. [Google Scholar] [CrossRef]
- Gökkaya, K.; Thomas, V.; Noland, T.; McCaughey, H.; Treitz, P. Testing the robustness of predictive models for chlorophyll generated from spaceborne imaging spectroscopy data for a mixedwood boreal forest canopy. Int. J. Remote Sens. 2014, 35, 218–233. [Google Scholar] [CrossRef]
- National Forest Inventory Ground Sampling Guidelines, version 4.0. Available online: http://www.proprights.org/PDFs/workshop_2011/References/Forest%20Humus/GroundSamplingGuidelines_v.4.0._back.pdf (accessed on 31 August 2013).
- Alemdag, I.S. Mass Equations and Merchantability Factors for Ontario Softwoods; Information Report PI-X-23; Canadian Forest Service: Chalk River, ON, Canada, 1983.
- Alemdag, I.S. Total Tree and Merchantable Stem Biomass Equations for Ontario Hardwoods; Information Report PI-X-46; Canadian Forestry Service: Chalk River, ON, Canada, 1984.
- Optech Inc. ALTM 2050 Airborne Laser Terrain Mapper on-line technical specifications document. Available online: http://als.nyme.hu/fileadmin/dokumentumok/als/Leirasok/Optech_ALTM_2050.pdf (accessed on 9 April 2015).
- Thomas, V.; Treitz, P.; McCaughey, J.H.; Noland, T.; Morrison, I. Mapping stand-level forest biophysical variables for a mixedwood boreal forest using lidar: an examination of scanning density. Can. J. For. Res. 2006, 36, 34–47. [Google Scholar] [CrossRef]
- Wold, S. PLS for multivariate linear modeling. In Chemometric Methods in Molecular Design (Methods and Principles in Medicinal Chemistry); de Vaterbeemd, H., Ed.; Verlag-Chemie: Weinheim, Germany, 1984; pp. 195–218. [Google Scholar]
- Croft, H.; Chen, J.M.; Noland, T.L. Stand age effects on Boreal forest physiology using a long time-series of satellite data. For. Ecol. Manag. 2014, 328, 202–208. [Google Scholar] [CrossRef]
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Gökkaya, K.; Thomas, V.; Noland, T.L.; McCaughey, H.; Morrison, I.; Treitz, P. Prediction of Macronutrients at the Canopy Level Using Spaceborne Imaging Spectroscopy and LiDAR Data in a Mixedwood Boreal Forest. Remote Sens. 2015, 7, 9045-9069. https://doi.org/10.3390/rs70709045
Gökkaya K, Thomas V, Noland TL, McCaughey H, Morrison I, Treitz P. Prediction of Macronutrients at the Canopy Level Using Spaceborne Imaging Spectroscopy and LiDAR Data in a Mixedwood Boreal Forest. Remote Sensing. 2015; 7(7):9045-9069. https://doi.org/10.3390/rs70709045
Chicago/Turabian StyleGökkaya, Kemal, Valerie Thomas, Thomas L. Noland, Harry McCaughey, Ian Morrison, and Paul Treitz. 2015. "Prediction of Macronutrients at the Canopy Level Using Spaceborne Imaging Spectroscopy and LiDAR Data in a Mixedwood Boreal Forest" Remote Sensing 7, no. 7: 9045-9069. https://doi.org/10.3390/rs70709045