Skip to main content
SpringerLink
Log in

An Overview of Uncertainty in Optical Remotely Sensed Data for Ecological Applications

  • Chapter
Spatial Uncertainty in Ecology

Abstract

Remote sensing has become a widely used tool in ecology. Examples of ecological applications that use remote sensing include species conservation efforts such as GAP analysis Scott et al. 1993, land cover and land use change monitoring (Skole and Tucker 1993; DeFries and Townshend 1994), and estimation of ecosystem carbon assimilation rates and net primary production (Prince 1991). At biome to global scales, it has also been demonstrated that the utility of remote sensing for monitoring ecosystem dynamics at time scales is commensurate with global change processes (Braswell et al. 1997; Myneni et al. 1997). Developments in remote sensing technologies, including airborne radar, video imaging systems, and satellite instruments with high spatial and spectral resolution show substantial promise for ecological studies. Further, even though this chapter focuses on optical remote sensing, new technologies (e.g., radar, laser altimeter, and lidar systems, which provide detailed information regarding topography and vegetation structure in three dimensions) suggest that the use of remote sensing by ecologists is likely to increase in the future.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Adams, J., M. Smith, and P. Johnson. 1986. Spectral mixture modeling: a new analysis of rock and soil types at the Viking lander 1 site. Journal of Geophysical Research 91(B8):8098–8012.

    Article  Google Scholar 

  • Asrar, G., A.E. Kanemasu, G. Miller, and R. Weiser. 1986. Light interception and leaf area estimates from measurements of grass canopy reflectance. IEEE Transactions on Geoscience and Remote Sensing GE-24:76–81.

    Article  Google Scholar 

  • Bartlett, D., M. Hardisky, R. Johnson, M. Gross, and J. Hartman. 1988. Continental scale variability in vegetation reflectance and its relationship to canopy morphology. International Journal of Remote Sensing 9:1223–1241.

    Article  Google Scholar 

  • Belward, A. 1996. The IGBP-DIS Global 1 km land cover data set DISCOVER: proposal and implementation plans. IGBP-DIS Working Paper, IGBP-DIS Office, Meteo-France, 42 Av. G. Coriolis, F—31057, Toulouse, France.

    Google Scholar 

  • Braswell, B., D. Schimel, J. Privette, B. Moore, W. Emery, E. Sulzman, et al. 1996. Extracting ecological and biophysical information from AVHRR oprical data: an integrated algorithm based on inverse modeling. Journal of Geophysical Research 101(D18):23335–23348.

    Article  Google Scholar 

  • Braswell, B., D. Schimel, E. Linder, and B. Moore. 1997. The response of global terrestrial ecosystems to interannual temperature variability. Science 278:870–872.

    Article  CAS  Google Scholar 

  • Campbell, J. 1981, Spatial correlation effects upon accuracy of supervised classification of land cover. Photogrammetric Engineering and Remote Sensing 47:355–364.

    Google Scholar 

  • Campbell, J.B. 1996. Introduction to remote sensing, second ed. Guilford, New York.

    Google Scholar 

  • Chavez, P. 1996. Image-based atmospheric corrections revisited and improved. Photogrammetric Engineering and Remote Sensing 62(9): 1025–1036.

    Google Scholar 

  • Civco, D. 1989. Topographic normalization of Landsat Thematic Mapper digital imagery. Photogrammetric Engineering and Remote Sensing (55): 1303–1309.

    Google Scholar 

  • Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20:37–46.

    Article  Google Scholar 

  • Congalton, R.G., and R.A. Mead. 1983. Quantitative method to test for consistency and correctness of photointerpretation. Photogrammetric Engineering and Remote Sensing 49:69–74.

    Google Scholar 

  • Congalton, R.G., R.G. Oderwald, and R.A. Mead. 1983. Assessing Landsat classification accuracy using discrete, multivariate analysis statistical techniques. Photogrammetric Engineering and Remote Sensing 49:1671–1678.

    Google Scholar 

  • Congalton, R.G. 1988a. A comparision of sampling schemes used in generating error matrices for assessing the accuracy of maps generated from remotely sensed data. Photogrammetric Engineering and Remote Sensing 54:593–600.

    Google Scholar 

  • Congalton, R.G. 1988b. Using spatial autocorrelation analysis to explore the errors in maps generated from remotely sensed data. Photogrammetric Engineering and Remote Sensing 54:587–592.

    Google Scholar 

  • Congalton, R.G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment 37:35–46.

    Article  Google Scholar 

  • Craig, R. 1979. Autocorrelation in Landsat data. Pages 1517–1524 in Proceedings of the thirteenth international symposia on remote sensing of environment. Environmental Research Institute of Michigan, Ann Arbor.

    Google Scholar 

  • Curran, P., and A. Hay. 1986. The importance of measurement error for certain procedures in remote sensing at optical wavelengths. Photogrammetric Engineering and Remote Sensing 52:229–241.

    Google Scholar 

  • Curran, P. 1988. The semi-variogram in remote sensing: an introduction. Remote Sensing of Environment 24:493–507.

    Article  Google Scholar 

  • Deering, D. 1989. Field measurements of bidirectional reflectance. Pages 14–65 in G. Asrar, ed. Theory and applications of optical remote sensing. John Wiley & Sons, New York.

    Google Scholar 

  • DeFries, R., and J. Townshend. 1994. NDVI-derived land cover classifications at a global scale. International Journal of Remote Sensing 5:3567–3586.

    Article  Google Scholar 

  • DeFries, R., M. Hansen, and J. Townshend. 2000. Global continuous fields of vegetation characteristics: a linear mixture model applied to multi-year 8 km AVHRR data. International Journal of Remote Sensing 21:1389–1414.

    Article  Google Scholar 

  • Davis, F., and D. Roberts. 2000. Stand structure in terrestrial ecosystems. Pages 7–30 in O. Sala, R.B. Jackson, H.A. Mooney, and R.W. Howarth, eds. Methods in Ecosystem Science (chapter in review). Springer-Verlag, New York.

    Chapter  Google Scholar 

  • Dubayah, R. 1992. Estimating net solar radiation using landsat thematic mapper and digital elevation data. Water Resources Research 28:2469–2484.

    Article  Google Scholar 

  • Duggin, M., and C. Robinove. 1990. Assumptions implicit in remote sensing data acquisition and analysis. International Journal of Remote Sensing 11(10): 1669–1694.

    Article  Google Scholar 

  • Edwards, T.C., G.G. Moisen, and D.R. Cutler. 1988. Assessing map accuracy in a remotely sensed ecoregion-scale cover map. Remote Sensing of Environment 63:73–83.

    Article  Google Scholar 

  • Friedl, M., J. Michaelsen, F. Davis, and D. Schimel. 1994. Estimating grassland biomass and leaf area index using ground and satellite data. International Journal of Remote Sensing 15(7): 1401–1420.

    Article  Google Scholar 

  • Friedl, M., F. Davis, J. Michaelsen, and M. Moritz. 1995. Scaling and uncertainty in the relationship between the NDVI and land surface biophysical variables: an analysis using a scene simulation model and data from FIFE. Remote Sensing of Environment 54:233–246.

    Article  Google Scholar 

  • Friedl, M. 1997. Examining the effects of sensor resolution and sub-pixel heterogeneity on spectral vegetation indices: implications for biophysical modeling. Pages 113–140 in D. Quattrochi, and M. Goodchild, eds. Scale in Remote Sensing and GIS. Lewis, Boca Raton, FL.

    Google Scholar 

  • Friedl, M., and C. Brodley. 1997. Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment 61:399–409.

    Article  Google Scholar 

  • Friedl, M., C. Woodcock, S. Gopal, D. Muchoney, A.H. Strahler, and C. Barker-Schaaf. 2000. A note on procedures used for accuracy assessment in land cover maps derived from AVHRR data. International Journal of Remote Sensing 21:1073–1077.

    Article  Google Scholar 

  • Goel, N. 1987. Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data. Remote Sensing Reviews 3:1–212.

    Article  Google Scholar 

  • Gopal, S., and C. Woodcock. 1996. Remote sensing of forest change using artificial neural networks. IEEE Transactions on Geoscience and Remote Sensing 34(2):398–404.

    Article  Google Scholar 

  • Goward, S., B. Markham, D. Dye, W. Dulaney, and J. Yang, 1991. Normalized difference vegetation index measurements from the advanced very high resolution radiometer. Remote Sensing of Environment 35:257–277.

    Article  Google Scholar 

  • Goward, S., and K. Huemmrich. 1992. Vegetation canopy PAR absorptance and the normalized difference vegetation index: an assessment using the SAIL model. Remote Sensing of Environment 39:119–140.

    Article  Google Scholar 

  • Hall, F., D. Strebel, J. Nickeson, and S. Goetz. 1991. Radiometric rectification: toward a common radiometric response among multidate, multisensor images. Remote Sensing of Environment 35:11–27.

    Article  Google Scholar 

  • Hay, A. 1979. Sampling designs to test land-use map accuracy. Photogrammetric Engineering and Remote Sensing 45:529–533.

    Google Scholar 

  • Huete, A. 1986. Separation of soil-plant spectral mixtures by factor analysis. Remote Sensing of Environment 19:237–251.

    Article  Google Scholar 

  • Hunter, G., and M.F. Goodchild. 1997. Modeling the uncertainty in slope and aspect estimates from spatial databases. Geographical Analysis 29:35–49.

    Article  Google Scholar 

  • Jupp, D., A. Strahler, and C. Woodcock. 1988. Autocorrelation and regularization in digital images I. Basic theory. IEEE Transactions in Geoscience and Remote Sensing 26:463–473.

    Article  Google Scholar 

  • Jupp, D., A. Strahler, and C. Woodcock. 1989. Autocorrelation and regularization in digital images. IL Simple Image Models. IEEE Transactions in Geoscience and Remote Sensing 27:247–256.

    Article  Google Scholar 

  • Jupp, D., and A. Strahler. 1991. A hotspot model for leaf canopies. Remote Sensing of Environment 38:193–210.

    Article  Google Scholar 

  • Kaufman, Y. 1989. The atmospheric effect in remote sensing and its corrections. Pages 336–428 in G. Asrar, ed. Theory and applications of optical remote sensing. John Wiley & Sons, New York.

    Google Scholar 

  • Kauth, R., and G. Thomas. 1976. The tasselled cap—a graphic description of the spectral-temporal development of crops as seen by Landsat. Proc. symp. on machine processing of remotely sensed data 41–51.

    Google Scholar 

  • Kerekes, J., and D. Landgrebe. 1989. Simulation of optical remote sensing systems. IEEE Transactions on Geoscience and Remote Sensing GE-27(6):762–771.

    Article  Google Scholar 

  • Kerekes, J., and D. Landgrebe. 1991. An analytical model of earth-observational remote sensing systems. IEEE Transactions on Systems, Man, and Cybernetics 21(1):125–133.

    Article  Google Scholar 

  • Kramer, H. 1996. Observation of the earth and its environment, third ed. Springer, New York.

    Book  Google Scholar 

  • Labovitz, M. 1984. The influence of autocorrelation in signature extraction—an example from the Cotter Basin, Montana. International Journal of Remote Sensing 50:315–332.

    Article  Google Scholar 

  • Li, X., and A. Strahler. 1986. Geometric-optical bidirectional reflectance modeling of a conifer forest canopy. IEEE Transactions on Geoscience and Remote Sensing GE-24:281–293.

    Article  Google Scholar 

  • Ma, Z., and R.L. Redmond. 1995. Tau coefficient for accuracy assessment of classification of remote sensing data. Photogrammetric Engineering and Remote Sensing 61:435–439.

    Google Scholar 

  • Markham, B. 1985. The Landsat sensor spatial responses. IEEE Transactions on Geoscience and Remote Sensing GE-23(6):864–875.

    Article  Google Scholar 

  • Markham, B., and J. Barker. 1985. Spectral characterization of the LANDSAT thematic mapper sensors. International Journal of Remote Sensing 6(5):697–716.

    Article  Google Scholar 

  • Martin, M. 1998. Determining forest species composition using high spectral resolution remote sensing data. Remote Sensing of Environment 65:255–266.

    Article  Google Scholar 

  • Martin, M., and J. Aber. 1998. High spectral resolution remote sensing of forest canopy lignin, nitrogen and ecosystem processes. Ecological Applications 7:431–443.

    Article  Google Scholar 

  • McGwire, K. 1992. Analyst variability in labeling of unsupervised classifications. Photogrammetric Engineering and Remote Sensing 58(12): 1673–1677.

    Google Scholar 

  • McGwire, K., M. Friedl, and J. Estes. 1993. Spatial structure, sampling design and scale in remotely sensed imagery of a California savanna woodland. International Journal of Remote Sensing 14(11):2137–2164.

    Article  Google Scholar 

  • Muchoney, D., J. Borak, H. Chi, M. Friedl, J. Hodges, N. Morrow, et al 2000. Application of the MODIS global supervised classification model to vegetation and land cover mapping of Central America. International Journal of Remote Sensing. 21:1115–1138.

    Article  Google Scholar 

  • Myneni, R., G. Asrar, and F. Hall, 1992. A three-dimensional radiative transfer method for optical remote sensing of vegetated land surfaces. Remote Sensing of Environment 41:105–121.

    Article  Google Scholar 

  • Myneni, R., and G. Asrar. 1993. Simulation of space measurements of vegetation canopy bidirectional reflectance factors. Remote Sensing Reviews 7:19–41.

    Article  Google Scholar 

  • Myneni, R. 1994. Atmospheric effects and spectral vegetation indices. Remote Sensing of Environment 47:390–402.

    Article  Google Scholar 

  • Myneni, R., C. Keeling, C. Tucker, G. Asrar, and R. Nemani. 1997. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386:698–702.

    Article  CAS  Google Scholar 

  • Price, J. 1987. Calibration of satellite radiometers and the calibration of vegetation indices. Remote Sensing of Environment 21:15–27.

    Article  Google Scholar 

  • Price, J. 1988. An update on visible and near infrared calibration of satellite instruments. Remote Sensing of Environment 24:419–422.

    Article  Google Scholar 

  • Prince, S. 1991. Satellite remote sensing of primary production: comparison of results for Sahelian grasslands 1981-1988. International Journal of Remote Sensing 12: 1301–1311.

    Article  Google Scholar 

  • Privette, J., W. Emery, and D. Schimel. 1996. Inversion of a vegetation reflectance model with NOAA AVHRR Data. Remote Sensing of Environment 58:187–200.

    Article  Google Scholar 

  • Proy, C., D. Tanre, and P. Deschamps. 1989. Evaluation of topographic effects in remotely sensed data. Remote Sensing of Environment 30:21–32.

    Article  Google Scholar 

  • Rahman, H., M. Verstraete, and B. Pinty. 1993. Coupled surface-atmosphere reflectance (CSAR) model, 1. Model description and inversion on synthetic data. Journal of Geophysical Research 98:20779–20789.

    Article  Google Scholar 

  • Richards, J. 1993. Remote sensing digital image analysis: an introduction, second ed. Springer-Verlag, New York.

    Book  Google Scholar 

  • Richter, R. 1997. Correction of atmospheric and topographic effects for high spatial resolution satellite imagery. International Journal of Remote Sensing 18:1099–1111.

    Article  Google Scholar 

  • Rosenfield, G.H., and K. Fitzpatrick-Lins. 1986. A coefficient of agreement as a measure of thematic classification accuracy. Photogrammetric Engineering and Remote Sensing 52:223–227.

    Google Scholar 

  • Sandmeier, S., and K. Itten. 1997. A physically-based model to correct atmospheric and illumination effects in optical satellite data of rugged terrain. IEEE Transactions on Geoscience and Remote Sensing 35:708–717.

    Article  Google Scholar 

  • Schowengerdt, R. 1997. Remote sensing: models and methods for image processing. Academic Press, New York.

    Google Scholar 

  • Schwartz, M. 1994. Monitoring global change with phenology: the case of the spring green wave. International Journal of Biometeorology 38(1): 18–22.

    Article  Google Scholar 

  • Scott, J., F. Davis, B. Csuti, R. Noss, B. Butterfield, C. Groves, et al. 1993. GAP analysis: a geographic approach to protection of biological diversity. Wildlife Monographs 123: 1–41.

    Google Scholar 

  • Sellers, P., F. Hall, G. Asrar, D. Strebel, and R. Murphy. 1992. An overview of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE). Journal of Geophysical Research 97(D 17): 18345–18371.

    Article  Google Scholar 

  • Skole, D., and C. Tucker. 1993. Tropical deforestation and habitat fragmentation in the Amazon: satellite data from 1978 to 1988. Science 260:1905–1901.

    Article  PubMed  CAS  Google Scholar 

  • Stehman, S.V. 1992. Comparision of systematic and random sampling for estimating the accuracy of maps generated from remotely sensed data. Photogrammetric Engineering and Remote Sensing 58:1343–1350.

    Google Scholar 

  • Stehman, S.V. 1995. Thematic map accuracy assessment from the perspective of finite population sampling. International Journal of Remote Sensing 16:589–593.

    Article  Google Scholar 

  • Stehman, S.V. 1996. Estimating the kappa coefficient and its variance under stratified random sampling. Photogrammetric Engineering and Remote Sensing 62:401–407.

    Google Scholar 

  • Stehman, S.V. 1997a. Estimating standard errors of accuracy assessment statistics under cluster sampling. Remote Sensing of Environment 60:258–269.

    Article  Google Scholar 

  • Stehman, S.V. 1997b. Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment 62:77–89.

    Article  Google Scholar 

  • Stoms, D.M., M.J. Bueno, F.W. Davis, K.M. Cassidy, K.L. Driese, and J.S. Kagan. 1998. Map-guided classification of regional land-cover with multi-temporal AVHRR data. Photogrammetric Engineering and Remote Sensing 64(8):831–838.

    Google Scholar 

  • Story, M., and R.G. Congalton. 1986. Accuracy assessment: a user’s perspective. Photogrammetric Engineering and Remote Sensing 52:397–399.

    Google Scholar 

  • Strahler, A. 1980. The use of prior probabilities on maximum likelihood classification of remotely sensed data. Remote Sensing of Environment 10:135–163.

    Article  Google Scholar 

  • Strahler, A., C. Woodcock, and J. Smith. 1986. On the nature of models in remote sensing. Remote Sensing of Environment 20:121–139.

    Article  Google Scholar 

  • Strahler, A. 1997. Vegetation canopy reflectance developments and remote sensing perspectives. Remote Sensing Reviews 15:179–194.

    Article  Google Scholar 

  • Tanre, D., C. Deroo, M. Herman, J. Morcrette, J. Perbos, and P. Deschamps. 1990. Description of a computer code to simulate the satellite signal in the solar spectrum. International Journal of Remote Sensing 11:659–668.

    Article  Google Scholar 

  • Townshend, J., and C. Justice. 1988. Selecting the spatial resolution of satellite sensors for global monitoring of land transformations. International Journal of Remote Sensing 9:187–236.

    Article  Google Scholar 

  • Walthall, C, J. Norman, J. Welles, G. Campbell, and B. Blad. 1985. Simple equation to approximate the bidirectional reflectance from vegetation canopies and bare soil surfaces. Applied Optics 24:383–387.

    Article  PubMed  CAS  Google Scholar 

  • Wessman, C, J. Aber, D. Peterson, and J. Melillo. 1988. Remote sensing of canopy chemistry and nitrogen cycling in temperate forest ecosystems. Nature 335:154–156.

    Article  Google Scholar 

  • Woodcock, C, A. Strahler, and D. Jupp. 1988a. The use of variograms in remote sensing: I Scene models and simulated images. Remote Sensing of Environment 25:323–348.

    Article  Google Scholar 

  • Woodcock, C, A. Strahler, and D. Jupp. 1988b. The use of variograms in remote sensing: II. Real digital images. Remote Sensing of Environment 25:349–379.

    Article  Google Scholar 

  • Woodcock, C, and A.H. Strahler. 1987. The factor of scale in remote sensing. Remote Sensing of Environment 21:311–332.

    Article  Google Scholar 

  • Woodcock, C., and V. Harward. 1992. Nested-hierarchical scene models and image segmentation. International Journal of Remote Sensing 13:3167–3187.

    Article  Google Scholar 

  • Woodcock, C., and S. Gopal. 1994. Theory and methods for accuracy assessment of thematic maps using fuzzy sets. Photogrammetric Engineering and Remote Sensing 60:181–188.

    Google Scholar 

Download references

Authors
  1. Mark A. Friedl
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kenneth C. McGwire
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Douglas K. McIver
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Forestry Sciences Laboratory, USDA Forest Service, Pacific Southwest Research Station, Fresno, CA, 93710-4639, USA

    Carolyn T. Hunsaker

  2. National Center for Geographic Information and Analysis Department of Geography, University of California, Santa Barbara, Santa Barbara, CA, 93106-4060, USA

    Michael F. Goodchild

  3. Department of Geography, Boston University, Boston, MA, 02215-1401, USA

    Mark A. Friedl

  4. Department of Biology, University of California, San Diego, La Jolla, CA, 92093-0116, USA

    Ted J. Case

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer Science+Business Media New York

About this chapter

Cite this chapter

Friedl, M.A., McGwire, K.C., McIver, D.K. (2001). An Overview of Uncertainty in Optical Remotely Sensed Data for Ecological Applications. In: Hunsaker, C.T., Goodchild, M.F., Friedl, M.A., Case, T.J. (eds) Spatial Uncertainty in Ecology. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-0209-4_12

Download citation

  • DOI: https://doi.org/10.1007/978-1-4613-0209-4_12

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-98889-4

  • Online ISBN: 978-1-4613-0209-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation