- KOREAN
- P-ISSN2287-8327
- E-ISSN2288-1220
- SCOPUS, KCI
ISSN : 2287-8327
Comparison of field- and satellite-based vegetation cover estimation methods
Amartuvshin Narantsetseg (Mongolian Academy of Sciences)
Abstract
Background: Monitoring terrestrial vegetation cover condition is important to evaluate its current condition and to identify potential vulnerabilities. Due to simplicity and low cost, point intercept method has been widely used in evaluating grassland surface and quantifying cover conditions. Field-based digital photography method is gaining popularity for the purpose of cover estimate, as it can reduce field time and enable additional analysis in the future. However, the caveats and uncertainty among field-based vegetation cover estimation methods is not well known, especially across a wide range of cover conditions. We compared cover estimates from point intercept and digital photography methods with varying sampling intensities (25, 49, and 100 points within an image), across 61 transects in typical steppe, forest steppe, and desert steppe in central Mongolia. We classified three photosynthetic groups of cover important to grassland ecosystem functioning: photosynthetic vegetation, non-photosynthetic vegetation, and bare soil. We also acquired normalized difference vegetation index from satellite image comparison with the field-based cover. Results: Photosynthetic vegetation estimates by point intercept method were correlated with normalized difference vegetation index, with improvement when non-photosynthetic vegetation was combined. For digital photography method, photosynthetic and non-photosynthetic vegetation estimates showed no correlation with normalized difference vegetation index, but combining of both showed moderate and significant correlation, which slightly increased with greater sampling intensity. Conclusions: Results imply that varying greenness is playing an important role in classification accuracy confusion. We suggest adopting measures to reduce observer bias and better distinguishing greenness levels in combination with multispectral indices to improve estimates on dry matter.
- keywords
- Point intercept, Digital photography, Land cover estimate, NDVI, Photosynthetic vegetation, Greenness
Reference
Asner, G. P. (1998). Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensing of Environment 64, 234-253.
Beck, L. R., Hutchinson, C F., & Zauderer, J. (1 990). A comparison of greenness measures in two semi-arid grasslands. Climatic Change, 17, 287-303.
Bergstedt, J., Westerberg, L. & Milberg, P. (2009). In the eye of the beholder: bias and stochastic variation in cover estimates. Plant Ecology, 204, 271-283.
Bollard-Breen, B. Brooks, J. D. Jones, M. R. L. Robertson, J, Betschart, s. Kung, o.Craig Cary, S. Lee. C. K. & Pointing, S. B. (2015). Application of an unmanned aerial vehide in spatial mapping of terrestrial biology and human disturbance in the McMurdo Dry Valleys, East Antarctica. Polar Biology, 38, 573-578.
Boone, R. B., Bumsilver, S. B, Thornton, P. K, Worden, J. S, & Galvin, K A. (2005). Quantifying declines in livestock due to land subdivision. Rangeland Ecology & Management, 58, 523-532
Booth, D. T, Cox, S. E. Fifield, C., Phillips, M., & Williamson, N. (2005). Image analysis compared with other methods for measuring ground cover. Arid Land Research Management, 19, 91-100.
Booth, D. T, Cox, S. E. & Berryman, R. D. (2006). Point sampling digital imagery with "Samplepoint•. Environmental Monitoring and Assessment, 123, 97-108.
Boyd, C. S, & Svejcar, T. J. (2005). A visual obstruction technique for photo monitnring of willow clumps. Rangeland Erology & Management 58, 434-438.
Bradley, B. A., & Mustard, J. F. (2005}. Identifying land cover variability distinct from land cover change: cheatgrass in the Great Basin. Remote Sensing of Environmenc 94, 204-213.
Buckland, S. T., Borchers, D. L., Johnston, A., Henrys, P. A., & Marques, T. A. (2007). Line transect methods for plant surveys. Biometrics, 63, 989-998.
Burg, S, Rixen, C .. Stockli, v. & Wipf, S. (2015). Observation bias and its causes in botanical surveys on higtralpine summits. Journal of Vegetation Sdence, 26, 191-200.
Byambakhuu, I, Sugita, M, & Matsushima, D. (2010). Remote sensing of environment spectral unmixing model to assess land cover fractions in Mongolian steppe regions. Remote Sensing of Environment 114, 2361-2372.
Canfield, R. H. (1941 ). Application of the line interception method in sampling range vegetation. Journal of Forestry. 39, 388-394.
Chen, Z. M. Babiker, I. S, Chen, Z. X. Komaki, K, Mohamed, M. A. A, & Kato, K (2004). Estimation of interannual variation in productivity of global vegetation using NDVI data. International Journal of Remote Sensing, 25, 3139-3159.
Chen, X.-L., Zhao, H.-M., Li, P.-X. & Yin, Z.-Y. (2006). Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment 104, 133-146.
Cui, G. Lee, W.-K., Kwak, D.-A., Choi, S., Park, T. & Lee, J. (2011). Desertification monitoring by LANDSAT TM satellite imagery. Forest Science and Technology, 7. 11o-116.
Cunliffe, A M., Brazier, R. E., & Anderson, K. (2016). Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structurefrom-motion photogrammetry. Remote Sensing of Environment, 183, 129-143.
Daubenmire, R. (1959). A canopy-coverage method of vegetational analysis. Northwest Science. 33, 43-64.
Dethier, M. N., Graham, E. S, Cohen, S., & Tear, L M. (1993). Visual versus randompoint percent cover estimations: "objective" is not always better. Marine Ecology Progress Series, 96, 93-1 00.
Fensholt. R., Sandholt. I., & Rasmussen, M. S. (2004). Evaluation of MODIS LAI. fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements. Remote Sensing of Environment 91, 490-507.
Fernandez.Gimenez, M. E.. Batkhishig, B., Batbuyan, B., & Ulambayar, T. (201 5). Lessons from the dzud: community-based rangeland management increases the adaptive capacity of Mongolian herders to winter disasters. World Development 68, 48-65.
Floyd, D. A. & Anderson, J. E. (1987). A comparison of three methods for estimating plant cover. Journal of Ecology, 75, 221-228.
Gemmell, F. (1999]. Estimating conifer forest cover with Thematic Mapper data using reflectance model inversion and two spectral indices in a site with variable background characteristics. Remote Sensing of Environment 69, 105-121.
Guerschman, J. P. Hill, M. J., Renzullo, L J, Barrett. D. J. Marks, A. s. & Botha, E. J. (2009). Estimating fractional cover of photosynthetic vegetation, nonphotosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors. Remote Sensing of Environment, 113, 928-945.
Hervouet A., Dunford, R., Piegay, H., Belletti, B.. & Tremelo, M.-L. (2011). Analysis of post41ood recruitment patterns in braided-channel rivers at multiple sca les based on an image series collected by unmanned aerial vehicles, ultra-light aerial vehicles, and satellites. GIScience & Remote Sensing, 48, 50-73.
Hi robe, M., & Kondo, J. (2012). Effects of climate and grazing on surface soil in grassland. In N. Yamamura, N. Fujita, & A Maekilwa (Eds.), The Mongolian Ecosystem Network: Environmental Issues Under Climate and Social Changes (pp. 105-114). Japan: Springer.
In, H.-J., & Park, SAJ. (2002). A simulation of long-range transport of Yellow Sand observed in April 1998 in Korea. Atmospheric Environmenc 36, 4173-4187.
Iverson, L R, Cook, E. A. & Graham, R. L. (1989). A technique for extrapolating and validating forest cover across large regions calibrating AVHRR data with TM data. International Jaumal of Remote Sensing, 10, 1805-1812.
Jang, K, Kang, S., Kim, J, Lee, C. B, Kim, T., Kim, J, Hirata, R., & Saigusa, N. (2010). Mapping evapotranspiration using MODIS and MM5 four-dimensional data assimilation. Remote Sensing of Environment 114, 657-673.
Karamysheva, Z. V. & Khramtsov, V. N. (1995). The steppes of Mongolia. Broun-Blanquetia, 11, 5-79.
McCarthy, J. J. (2001). Oimate change 2001: impacts, adaptation, and vulnerability: contribution of working group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge:Cambridge University Press.
Meyer, W. B. Turner II, B. L (1994). Changes in land use and land cover. a global perspective. Cambridge: Cambridge University Press.
Milberg, P., Bergstedt, J. Fridman, J. Odell, G., & Westerberg, L. (2008). Observer bias and random variation in vegetation monitoring data. Journal of Vegetation Science, 19, 633-644.
Moody, A., & Woodcock, C E. (1995). The influence of scale and the spatial characteristics of landscapes on land-cover mapping using remote sensing. Landscape Ecology, 10, 363-379.
Mosier, A., Schimel, D, Valentine, D. Bronson, K., & Parton, W. (1991). Methane and nitrous oxide fluxes in native, fertilized and cultivated grasslands. Nature, 350, 330-332.
Phadnis, M. J., & Carmichael, G. R. (2000]. Numerical investigation of the influence of mineral dust on the tropospheric chemistry of East Asia. Journal of Atmospheric Chemistry, 36, 285-323.
Pickup, G., Chewings, V. H., & Nelson, D. J. (1993). Estimating changes in vegetation cover over time in arid rangelands using Landsat MSS data. Remote Sensing of Environmenc 43, 243-263.
Ramsey, F. L (1979). Parametric models for line transect surveys. Biometrika, 66, 505-512.
Rango, A., Laliberte, A. Herrick. J. E. Winters, C., Havstad, K, Steele, C.. & Browning, D. (2009). Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitnring, and management. Journal of Applied Remote Sensing, 3, 33542.
Reinke, K, Reinke, K, Jones, S., & Jones, S. (2006). Integrating vegetation field surveys with remotely sensed data. Ecological Management and Restoration. 7, 518-523.
Richardson, M. D., Karcher, D. E. & Purcell, L. C (2001). Quantifying turfgrass cover using digital image analysis. Crop Science. 41, 1884-1888.
Stoner, D. C.. Sexton, J. O. Nagai, J. Bernales, H. H. & Edwards, T. C. (201 6). Ungulate reproductive parameters track satellite observations of plant phenology across latitude and climatological regimes. PloS One, 11, e0148780.
Sutherland, W. J. (2006). Ecological census techniques: a handbook. Cambridge:Cambridge University Press.
Turner, M. G, Dale, V. H., & Gardner, R. H. (1989). Predicitng across scales: theory development and testing. Landscape Ecology, 3, 245-252.
Turner, B. L., Lambin, E. F., & Reenberg, A. (2007). The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences, 104, 20666-20671.
Wang, J., Rich, P. M., Price, K. P., & Kettle, W. D. (2004). Relations between NDVI and tree productivity in the central Great Plains. International Journal of Remote Sensing, 25, 3127-3138.
White, R. P., Murray, S., Rohweder, M., Prince, S. D., & Thompson, K M. (2000). Grassland ecosystems. Washington DC: World Resources Institute.
Vim, J., Kleinn, c. Cho, H. & Shin, M. (2010). Integration of digital satellite data and forest inventory data for forest cover mapping in Korea. Forest Science and Technology, 6, 87-96.
- Downloaded
- Viewed
- 0KCI Citations
- 0WOS Citations