Hyperspectral database prediction of ecological characteristics for grass species of alpine grasslands
Huan Yu A F G , Bo Kong B , Guangxing Wang C , Hua Sun D and Lu Wang E
+ Author Affiliations
- Author Affiliations
A College of Earth Sciences, Chengdu University of Technology, 610059, Chengdu, China.
B Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, 610041, Chengdu, China.
C Department of Geography and Environmental Resources, Southern Illinois University, Carbondale, IL 62901, USA.
D Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, 410004, Changsha, China.
E College of Natural Resources and Environment, South China Agricultural University, 510642, Guangzhou, China.
F Key Laboratory of Geoscience Spatial Information Technology of Ministry of Land and Resources, 610059, Chengdu, China.
G Corresponding author. Email: yuhuan0622@126.com
The Rangeland Journal 40(1) 19-29 https://doi.org/10.1071/RJ17084
Submitted: 23 August 2017 Accepted: 21 November 2017 Published: 27 February 2018
Abstract
Alpine grasslands are being degraded because of human activities and associated global climate change. Mapping the spatial distributions and ecological characteristics of grass species is essential for scientific management of grasslands. However, traditional field-survey methods are costly or even impossible owing to poor accessibility. Hyperspectral remote sensing provides solutions for the purpose. This study was conducted in Shenzha County of the Qiangtang Plateau, north-western Qinghai–Tibet Plateau, to examine the potential of using hyperspectral data for identifying the grass species and predicting their ecological characteristics in the alpine grasslands dominated by Stipa purpurea with co-existing species Leontopodium nanum and Oxytropis microphylla. Hyperspectral data were collected in 106 sample quadrats and the ecological characteristics of each quadrat (number and height of plants, vegetation cover, etc.) were measured. The results of spectral data analysis and regression modelling showed the following. (i) The near- and middle-infrared region was more appropriate than the visible region for discriminating the grass species. (ii) The enhanced spectral variables had much higher correlations with the ecological characteristics than the original bands. (iii) Most of the 23 derived enhanced spectral variables were significantly correlated with the number and height of the dominant species plants within the quadrats. (iv) The vegetation cover could be accurately predicted by using the models based on the enhanced spectral variables of the field-collected hyperspectral data with the relative RMSE values <28%. (v) The ecological characteristics of the dominant species could be more accurately estimated than of co-existing species. Overall, this study suggests that the hyperspectral database method provided great potential to predict the ecological characteristics of grass species in alpine grasslands.
Additional keywords: dominant species, ecosystem, monitoring, prediction.
References
Adam, E., and Mutanga, O. (2009). Spectral discrimination of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands using field spectrometry. ISPRS Journal of Photogrammetry and Remote Sensing 64, 612–620.| Spectral discrimination of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands using field spectrometry.Crossref | GoogleScholarGoogle Scholar |
Adjorlolo, C., Cho, M. A., Mutanga, O., and Ismail, R. (2012). Optimizing spectral resolutions for the classification of C3 and C4 grass species, using wavelengths of known absorption features. Journal of Applied Remote Sensing 6, 063560.
| Optimizing spectral resolutions for the classification of C3 and C4 grass species, using wavelengths of known absorption features.Crossref | GoogleScholarGoogle Scholar |
Askins, R. A., Chavez-Ramırez, F., Dale, B. C., Haas, C. A., Herkert, J. R., Knopf, F. L., and Vickery, P. D. (2007). Conservation of grassland birds in North America: understanding ecological processes in different regions. Ornithological Monographs 64, 1–46.
Azpiroz, A. B., Isacch, J. P., Dias, R. A., Giacomo, A. S. D., Fontana, C. S., and Palarea, C. M. (2012). Ecology and conservation of grassland birds in southeastern South America: a review. Journal of Field Ornithology 83, 217–246.
| Ecology and conservation of grassland birds in southeastern South America: a review.Crossref | GoogleScholarGoogle Scholar |
Bayat, B., Christiaan, V. D. T., and Verhoef, W. (2016). Remote sensing of grass response to drought stress using spectroscopic techniques and canopy reflectance model inversion. Remote Sensing 8, 557.
| Remote sensing of grass response to drought stress using spectroscopic techniques and canopy reflectance model inversion.Crossref | GoogleScholarGoogle Scholar |
Chen, X., Li, B. L., and Collins, S. L. (2005). Multiscale monitoring of a multispecies case study: two grass species at Sevilleta. Plant Ecology 179, 149–154.
| Multiscale monitoring of a multispecies case study: two grass species at Sevilleta.Crossref | GoogleScholarGoogle Scholar |
de Bruin, H. A. R., Trigo, I. F., Bosveld, F. C., and Meirink, J. F. (2016). A thermodynamically based model for actual evapotranspiration of an extensive grass field close to FAO Reference, suitable for remote sensing application. Journal of Hydrometeorology 17, 1373–1382.
| A thermodynamically based model for actual evapotranspiration of an extensive grass field close to FAO Reference, suitable for remote sensing application.Crossref | GoogleScholarGoogle Scholar |
Donald, P. F., and Evans, A. D. (2006). Habitat connectivity and matrix restoration: the wider implications of agri-environmental schemes. Journal of Applied Ecology 43, 209–218.
| Habitat connectivity and matrix restoration: the wider implications of agri-environmental schemes.Crossref | GoogleScholarGoogle Scholar |
Duan, M. J., Gao, Q. Z., Wan, Y. F., Li, Y. E., Guo, Y. Q., Luobu, D. J., and Luosang, J. C. (2010). Comprehensive evaluation of eco-environmental sensitivity in Northern Tibet. Acta Ecologica Sinica 30, 3892–3900.
Editorial Board of Flora of China (2004). ‘Flora of China.’ Vol. 22. (Science Press: Beijing.)
Fernández-Prieto, D., and Sabia, R. (2016). ‘Remote Sensing Advances for Earth System Science.’ (Springer International Publishing: Berlin.)
Foley, B., Mcilwee, A., Lawler, I., Agragones, L., Woolnough, A. P., and Berding, N. (1998). Ecological applications of near infrared spectroscopy—a tool for rapid, cost effective prediction of the composition of plant and animal tissues and aspects of animal performance. Oecologia 116, 293–305.
| Ecological applications of near infrared spectroscopy—a tool for rapid, cost effective prediction of the composition of plant and animal tissues and aspects of animal performance.Crossref | GoogleScholarGoogle Scholar |
Foley, J. A., Defries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S., Coe, M. T., Daily, G. C., Gibbs, H. K., Helkowski, J. H., Holloway, T., Howard, E. A., Kucharik, C. J., Monfreda, C., Patz, J. A., Prentice, I. C., Ramankutty, N., and Snyder, P. K. (2005). Global consequences of land use. Science 309, 570–574.
| Global consequences of land use.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2MXmsFChtrs%3D&md5=fadf8858da93e4d8276c4c64e519b7b3CAS |
Hill, M. J. (2013). Vegetation index suites as indicators of vegetation state in grassland and savanna: an analysis with simulated SENTINEL 2 data for a North American transect. Remote Sensing of Environment 137, 94–111.
| Vegetation index suites as indicators of vegetation state in grassland and savanna: an analysis with simulated SENTINEL 2 data for a North American transect.Crossref | GoogleScholarGoogle Scholar |
Hunt, E. R. Hunt, E. R. (2004). Estimation of carbon sequestration by combining remote sensing and net ecosystem exchange data for northern mixed-grass prairie and sagebrush-steppe ecosystems. Environmental Management 33, S432–S441.
| Estimation of carbon sequestration by combining remote sensing and net ecosystem exchange data for northern mixed-grass prairie and sagebrush-steppe ecosystems.Crossref | GoogleScholarGoogle Scholar |
Jin, H. A., Liu, D. W., Song, K. S., Wang, Z. M., Li, F., and Liu, H. J. (2007). Comparing the performance of broad-band and narrow-band vegetation indices for estimation of soybean LAI. System Sciences and Comprehensive Studies in Agriculture 23, 503–508.
Knox, N. M., Skidmore, A. K., Werff, H. M. A. V. D., Groen, T. A., Boer, W. F. D., Prins, H. H. T., Kohi, E., and Peel, M. (2013). Differentiation of plant age in grasses using remote sensing. International Journal of Applied Earth Observation and Geoinformation 24, 54–62.
| Differentiation of plant age in grasses using remote sensing.Crossref | GoogleScholarGoogle Scholar |
Landmann, T., Piiroinen, R., Makori, D. M., Abdel-Rahman, E. M., Makau, S., Pellikka, P., and Raina, S. K. (2015). Application of hyperspectral remote sensing for flower mapping in African savannas. Remote Sensing of Environment 166, 50–60.
| Application of hyperspectral remote sensing for flower mapping in African savannas.Crossref | GoogleScholarGoogle Scholar |
Lindgren, D. T. (1985). ‘Land Use Planning and Remote Sensing.’ (Springer: Dordrecht, The Netherlands.)
Lu, L., Jiang, L., Qin, Z., Xu, B., and Tao, W. (2007). Remote sensing monitoring of grass growing in grassland ecosystems of China. Proceedings of the Society for Photo-Instrumentation Engineers 6752, 675225.
| Remote sensing monitoring of grass growing in grassland ecosystems of China.Crossref | GoogleScholarGoogle Scholar |
Mansour, K., Mutanga, O., Everson, T., and Adam, E. (2012). Discriminating indicator grass species for rangeland degradation assessment using hyperspectral data resampled to AISA Eagle resolution. ISPRS Journal of Photogrammetry and Remote Sensing 70, 56–65.
| Discriminating indicator grass species for rangeland degradation assessment using hyperspectral data resampled to AISA Eagle resolution.Crossref | GoogleScholarGoogle Scholar |
Muchoney, D., and Haack, B. (1994). Change detection for monitoring forest defoliation. Photogrammetric Engineering and Remote Sensing 60, 1243–1251.
Mueller-Warrant, G. W., Whittaker, G. W., Griffith, S. M., Banowetz, G. M., Dugger, B. D., Garcia, T. S., Giannico, G., Boyer, K. L., and McComb, B. C. (2011). Remote sensing classification of grass seed cropping practices in western Oregon. International Journal of Remote Sensing 32, 2451–2480.
| Remote sensing classification of grass seed cropping practices in western Oregon.Crossref | GoogleScholarGoogle Scholar |
Mutanga, O., Skidmore, A. K., and Prins, H. H. T. (2004). Discriminating sodium concentration in a mixed grass species environment of the Kruger National Park using field spectrometry. International Journal of Remote Sensing 25, 4191–4201.
| Discriminating sodium concentration in a mixed grass species environment of the Kruger National Park using field spectrometry.Crossref | GoogleScholarGoogle Scholar |
Pôҫas, I., Cunha, M., Pereira, L. S., and Allen, R. G. (2013). Using remote sensing energy balance and evapotranspiration to characterize montane landscape vegetation with focus on grass and pasture lands. International Journal of Applied Earth Observation and Geoinformation 21, 159–172.
| Using remote sensing energy balance and evapotranspiration to characterize montane landscape vegetation with focus on grass and pasture lands.Crossref | GoogleScholarGoogle Scholar |
Root, K. V., Akcakaya, R., and Ginzburg, L. (2003). A multispecies approach to ecological valuation and conservation. Conservation Biology 17, 196–206.
| A multispecies approach to ecological valuation and conservation.Crossref | GoogleScholarGoogle Scholar |
Shoko, C., Mutanga, O., and Dube, T. (2016). Progress in the remote sensing of C3 and C4 grass species aboveground biomass over time and space. ISPRS Journal of Photogrammetry and Remote Sensing 120, 13–24.
| Progress in the remote sensing of C3 and C4 grass species aboveground biomass over time and space.Crossref | GoogleScholarGoogle Scholar |
Skjøth, C. A., Ørby, P. V., Becker, T., Geels, C., Schlunssen, V., Sigsgaard, T., Bønløkke, J. H., Sommer, J., Søgaard, P., and Hertel, O. (2013). Identifying urban sources as cause of elevated grass pollen concentrations using GIS and remote sensing. Biogeosciences 10, 541–554.
| Identifying urban sources as cause of elevated grass pollen concentrations using GIS and remote sensing.Crossref | GoogleScholarGoogle Scholar |
Song, W. Z., Jia, H. F., Liu, S. J., Liang, S. D., Wang, Z., Hao, L. Z., and Chai, S. T. (2014). A remote sensing based forage biomass yield inversion model of alpine-cold meadow during grass-withering period in Sanjiangyuan area. IOP Conference Series: Earth and Environmental Science 17, 012042.
| A remote sensing based forage biomass yield inversion model of alpine-cold meadow during grass-withering period in Sanjiangyuan area.Crossref | GoogleScholarGoogle Scholar |
Ustin, S. L., Roberts, D. A., Gamon, J. A., Asner, G. P., and Green, R. O. (2004). Using imaging spectroscopy to study ecosystem processes and properties. Bioscience 54, 523–534.
| Using imaging spectroscopy to study ecosystem processes and properties.Crossref | GoogleScholarGoogle Scholar |
Ustin, S. L., Jacquemoud, S., Palacios-Orueta, A., Li, L., and Whiting, M. L. (2009). Remote sensing based assessment of biophysical indicators for land degradation and desertification. In ‘Recent Advances in Remote Sensing and Geoinformation Processing for Land Degradation Assessment’. (Eds J. A. Roeder and J. Hill.) pp. 1–20. (CRC Press: Boca Raton, FL, USA.)
Vaiphasa, C., Skidmore, A., de Boer, W., and Vaiphasa, T. (2007). A hyperspectral band selector for plant species discrimination. ISPRS Journal of Photogrammetry and Remote Sensing 62, 225–235.
| A hyperspectral band selector for plant species discrimination.Crossref | GoogleScholarGoogle Scholar |
Wang, H. J., Zhou, L., Yan, B. Y., Cui, Y. K., Wu, D. H., Fan, W. J., and Xu, X. R. (2009). The identification of indicator grass species of grassland degradation based on the field spectral characteristics. Proceeding of Geoscience & Remote Sensing Symposium 3, 455–458.
Weng, Q. (2002). Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. Journal of Environmental Management 64, 273–284.
| Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling.Crossref | GoogleScholarGoogle Scholar |
Yu, L., Zhou, L., Liu, W., and Zhou, H. K. (2010). Using remote sensing and GIS technologies to estimate grass yield and livestock carrying capacity of alpine grasslands in Golog Prefecture, China. Pedosphere 20, 342–351.
| Using remote sensing and GIS technologies to estimate grass yield and livestock carrying capacity of alpine grasslands in Golog Prefecture, China.Crossref | GoogleScholarGoogle Scholar |
Yu, L., Song, X. L., Zhao, J. N., Wang, H., Bai, L., and Yang, D. L. (2015). Responses of plant diversity and primary productivity to nutrient addition in a Stipa baicalensis grassland, China. Journal of Integrative Agriculture 14, 2099–2108.
| Responses of plant diversity and primary productivity to nutrient addition in a Stipa baicalensis grassland, China.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2MXhvFKktbfM&md5=269ea45388c27a0bde8a2e862d577f2eCAS |
Zhang, L. P., Liu, Q. H., Wang, D. G., and Chen, S. C. (2010). The universal analysis of vegetation indices for hyperspectral remote sensing data. Bulletin of Surveying and Mapping 46, 1–4.
