Skip to main content
Log in

An introduction to digital methods in remote sensing of forested ecosystems: Focus on the Pacific Northwest, USA

  • Environmental Auditing
  • Published:
Environmental Management Aims and scope Submit manuscript

Abstract

Aerial photography has been routinely used for several decades by natural resource scientists and managers to map and monitor the condition of forested landscapes. Recently, along with the emergence of concepts in managing forests as ecosystems, has come a significant shift in emphasis from smaller to larger spatial scales and the widespread use of geographic information systems. These developments have precipitated an increasing need for vegetation information derived from other remote sensing imagery, especially digital data acquired from high-elevation aircraft and satellite platforms. This paper introduces fundamental concepts in digital remote sensing and describes numerous applications of the technology. The intent is to provide a balanced, nontechnical view, discussing the shortcomings, successes, and future potential for digital remote sensing of forested ecosystems.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Literature Cited

  • Abuelgasim, A. A., and A. H. Strahler. 1994. Modeling bidirectional radiance measurements collected by the advanced solid-state array spectroradiometer (ASAS) over Oregon transect conifer forests.Remote Sensing of Environment 47:261–275.

    Google Scholar 

  • Ahern, F. J., R. J. Brown, J. Cihlar, R. Gauthier, J. Murphy, R. A. Neville, and P. M. Teillet. 1987. Radiometric correction of visible and infrared remote sensing data at the Canada Centre for Remote Sensing.International Journal of Remote Sensing 8:1349–1376.

    Google Scholar 

  • Barrett, E. C., and L. F. Curtis. 1992. Introduction to environmental remote sensing, 3rd ed. Chapman & Hall, London, 426 pp.

    Google Scholar 

  • Butera, M. K. 1986. A correlation and regression analysis of percent canopy closure versus TMS spectral response for selected forest sine in the San Juan National Forest, Colorado.IEEE Transactions on Geoscience & Remote Sensing 24:122–129.

    Google Scholar 

  • Chiesa, C. C., and W. A. Tyler. 1994. Beyond cubic convolution: ERIM restoration for remotely-sensed imagery.Earth Observation Magazine 3:40–44.

    Google Scholar 

  • Chuvieco, E., and R. G. Congalton. 1988. Using cluster analysis to improve the selection of training statistics in classifying remotely sensed data.Photogrammetric Engineering & Remote Sensing 54:1275–1281.

    Google Scholar 

  • Cibula, W. G., and M. O. Nyquist. 1987. Use of topographic and climatological models in a geographic data base to improve Landsat MSS classification for Olympic National Park.Photgrammetric Engineering & Remote Sensing 53:67–75.

    Google Scholar 

  • Cohen, W. B. and T. A. Spies. 1992. Estimating structural attributes of Douglas-fir/western hemlock forest stands from Landsat and SPOT imagery.Remote Sensing of Environment 41:1–17.

    Google Scholar 

  • Cohen, W. B., T. A. Spies, and G. A. Bradshaw. 1990. Semivariograms of digital imagery for analysis of conifer canopy structure.Remote Sensing of Environment 34:167–178.

    Google Scholar 

  • Cohen, W. B., T. A. Spies, and M. Fiorella. 1995. Estimating the age and structure of forests in a multiownership landscape of western Oregon, USA.International Journal of Remote Sensing 16:721–746.

    Google Scholar 

  • Collins, J. B., and Woodcock, C. E. 1994. Change detection using the Gramm-Schmidt transformation applied to mapping forest mortality.Remote Sensing of Environment 50:267–279.

    Google Scholar 

  • Colwell, J. E. 1974. Vegetation canopy reflectance.Remote Sensing of Environment 3:175–183.

    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.

    Google Scholar 

  • Congalton, R. G., K. Green, and J. Teply. 1993. Mapping old growth forests on national forest and park lands in the Pacific Northwest from remotely sensed data.Photogrammetric Engineering & Remote Sensing 59:529–535.

    Google Scholar 

  • Corr, D. G., Taylor, A. M., Cross, A., Hoggs, D. C., Lawrence, D. H., Mason. D. C., and Petrou, M. 1989. Progress in automatic analysis of multi-temporal remotely-sensed data.International Journal of Remote Sensing 10:1175–1195.

    Google Scholar 

  • Cracknell, A., and L. Hayes. 1991. Introduction to remote sensing. Taylor and Francis, London, Eng. 293 pp.

    Google Scholar 

  • Crist, E. P., and R. C. Cicone. 1984. A physically-based transformation of thematic mapper data—the TM tasseled cap.IEEE Transactions on Geoscience & Remote Sensing 22:256–263.

    Google Scholar 

  • Czaplewski, R. L., and G. P. Catts. 1992. Calibration of remotely sensed proportion or area estimates for misclassification error.Remote Sensing of Environment 39:29–43.

    Google Scholar 

  • Eby, J. R. 1987. The use of sun incidence angle and infrared reflectance levels in mapping old-growth coniferous forests. Proceedings, ASPRS-ACSM fall convention: Prospecting new horizons, ASPRS Technical Paper. Reno, Nevada, 4–9 October, American Society of Photogrammetric & Remote Sensing, Falls Church Virginia, pp. 36–40.

  • Eby, J. R., and M. C. Snyder. 1990. The status of old growth in western Washington: A Landsat perspective. Report. Washington Department of Wildlife, Olympia, Washington, 34 pp.

    Google Scholar 

  • EOSAT. 1994. A process of compensation.EOSAT Notes 9:8.

    Google Scholar 

  • ERDAS. 1993. Earth resource data analysis system, vers. 8.01. ERDAS Inc., Atlanta, Georgia.

    Google Scholar 

  • Fiorella, M., and W. J. Ripple. 1993a. Determining successional stage of temperate coniferous forests with Landsat satellite data.Photogrammetric Engineering & Remote Sensing 59:239–246.

    Google Scholar 

  • Fiorella, M., and W. J. Ripple. 1993b. Analysis of conifer forest regeneration using Landsat thematic mapper data.Photogrammetric Engineering & Remote Sensing 59:1383–1388.

    Google Scholar 

  • Franklin, S. E., and B. A. Wilson. 1992. A three-stage classifier for remote sensing of mountainous environments.Photogrammetric Engineering & Remote Sensing 58:449–454.

    Google Scholar 

  • Fung, T., and E. LeDrew. 1987. Application of principal components analysis to change detection.Photogrammetric Engineering & Remote Sensing 53:1649–1658.

    Google Scholar 

  • Gong, P. 1993. Change detection using principal component analysis and fuzzy set theory.Canadian Journal of Remote Sensing 19:22–29.

    Google Scholar 

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

    Google Scholar 

  • Goward, S. N., D. G. Dye, S. Turner, and J. Yang. 1994. Visiblenear infrared spectral reflectance of landscape components in western Oregon.Remote Sensing of Environment 47:190–203.

    Google Scholar 

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

    Google Scholar 

  • Hall, F. G., D. B. Botkin, D. E. Streble, K. D. Woods, and S. J. Goetz. 1991b. Large-scale patterns of forest succession as determined by remote sensing.Ecology 72:628–640.

    Google Scholar 

  • Heard, M. I., P. M. Mather, and C. Higgins. 1992. GERES: A prototype expert system for the geometric rectification of remotely-sensed images.International Journal of Remote Sensing 13:3381–3385.

    Google Scholar 

  • Holbo, H. R., and J. C. Luvall. 1989. Modeling surface temperature distributions in forest landscapes.Remote Sensing of Environment 27:11–24.

    Google Scholar 

  • Hong, T. H., and A. Rosenfeld. 1984. Compact region extraction using weighted pixel linking in a pyramid.IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-6:222–229.

    Google Scholar 

  • Hord, R. M. 1986. Remote sensing: Methods and applications. John Wiley & Sons, New York, 362 pp.

    Google Scholar 

  • Isaacson, D. L., D. A. Leckenby, and C. J. Alexander. 1982. The use of large-scale aerial photography for interpreting Landsat digital data in an elk habitat-analysis project.Journal of Applied Photogrammetric Engineering 8:51–57.

    Google Scholar 

  • Jensen, J. R. 1986. Introductory digital image processing. Prentice-Hall, Englewood Cliffs, New Jersey, 379 pp.

    Google Scholar 

  • Johnson, L. F., C. A. Hlavka, and D. L. Peterson. 1994. Multivariate analysis of AVIRIS data for canopy biochemical estimation along the Oregon transect.Remote Sensing of Environment 47:216–230.

    Google Scholar 

  • Kauth, R. J., and G. S. Thomas. 1976. The tasselled cap—a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. Proceedings, second annual symposium on machine processing of remotely sensed data. Purdue University Laboratory of Applied Remote Sensing, West Lafayette, Indiana. 6 June–2 July, pp. 4B/41–51.

  • Kauth, R. J., A. P. Pentland, and G. S. Thomas. 1977. Blob: An unsupervised clustering approach to spatial preprocessing of MSS imagery. Proceedings, eleventh international symposium on remote sensing of environment, vol. II. Environmental Research Institute of Michigan, Ann Arbor, Michigan, 25–29 April.

    Google Scholar 

  • Kruse, F. A., A. B. Lefkoff, and J. B. Dietz. 1993. Expert systembased mineral mapping in northern Death Valley, California/Nevada, using airborne visible/infrared imaging spectrometer.Remote Sensing of Environment 44:309–336.

    Google Scholar 

  • Lambin, E. F., and A. H. Strahler. 1994. Change-vector analysis in multitemporal space: A tool to detect and categorize land-cover change processes using high temporal-resolution satellite data.Remote Sensing of Environment 48:231–244.

    Google Scholar 

  • Law, B. E., and R. H. Waring. 1994. Remote sensing of leaf area index and radiation intercepted by understory vegetation.Ecological Applications 4:272–279.

    Google Scholar 

  • Li, X., and A. H. Strahler. 1985. Geometric-optical modeling of a conifer forest canopy.IEEE Transactions on Geoscience & Remote Sensing GE-23:705–721.

    Google Scholar 

  • Lillesand, T. M., and R. W. Kiefer. 1994. Remote sensing and image interpretation, 3rd ed. John Wiley & Sons, New York, 750 pp.

    Google Scholar 

  • Luvall, J. C., and H. R. Holbo. 1989. Measurements of short term thermal responses of coniferous forest canopies using thermal scanner data.Remote Sensing of Environment 27:1–10.

    Google Scholar 

  • Luvall, J. C., and H. R. Holbo. 1991. Thermal remote sensing methods in landscape ecology. InEcological Studies Vol. 82, pp. 127–152.

    Google Scholar 

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

    Google Scholar 

  • Malila, W. A. 1980. Change vector analysis: An approach for detecting forest changes with Landsat. Proceedings, sixth annual symposium on machine processing of remotely sensed data. Pages 326–335in P. G. Burroff and D. B. Morrison (eds.), Soil information systems and remote sensing and soil survey. Purdue University Laboratory of Applied Remote Sensing, West Lafayette, Indiana, 3–6 June.

    Google Scholar 

  • Mather, P. M. 1987. Computer processing of remote sensed images: An introduction. John Wiley & Sons, Chichester, England, 352 pp.

    Google Scholar 

  • Matson, P., L. Johnson, C. Billow, J. Miller, and R. Pu. 1994. Seasonal patterns and remote spectral estimation of canopy chemistry across the Oregon transect.Ecological Applications 4:280–298.

    Google Scholar 

  • Michalek, J. L., T. W. Wagner, J. J. Luczkovich, and R. W. Stoffle. 1993. Multispectral change vector analysis for monitoring coastal marine environments.Photogrammetric Engineering & Remote Sensing 59:381–384.

    Google Scholar 

  • Moghaddam, M., S. Durden, and H. Zebker. 1994. Radar measurement of forested areas during OTTER.Remote Sensing of Environment 47:154–166.

    Google Scholar 

  • Morrison, P. H., D. Klopfer, D. A. Leversee, C. M. Socha, and D. L. Ferber. 1991. Ancient forests in the Pacific Northwest. Analysis and maps of twelve national forests. The Wilderness Society, Washington, DC, 22 pp.

    Google Scholar 

  • Muchoney, D. M., and B. N. Haack. 1994. Change detection for monitoring forest defoliation.Photogrammetric Engineering & Remote Sensing 60:1243–1251.

    Google Scholar 

  • Mustard, J. F. 1993. Relationships of soil, grass, and bedrock over the Kaweah Serpentine Melange through mixture analysis of AVIRIS data.Remote Sensing of Environment 44:293–308.

    Google Scholar 

  • Nazif, A. M., and M. D. Levine. 1984. Low level image segmentation: an expert system.IEEE Transactions on Interactions of Pattern Analysis and Machine Intelligence PAMI-6:555–577.

    Google Scholar 

  • Nelson, R. F. 1981. A comparison of two methods for classifying forestland.International Journal of Remote Sensing 2:49–60.

    Google Scholar 

  • Paine, D. P. 1981. Aerial photography and image interpretation for resource management. John Wiley & Sons, New York, 571 pp.

    Google Scholar 

  • Peddle, D. R., and S. E. Franklin. 1991. Image texture processing and data integration for surface pattern discrimination.Photogrammetric Engineering & Remote Sensing 57:413–420.

    Google Scholar 

  • Perry, C. R., and L. F. Lautenschlager. 1984. Functional equivalence of spectral vegetation indices.Remote Sensing of Environment 14:583–597.

    Google Scholar 

  • Peterson, D. L., and R. H. Waring. 1994. Overview of the Oregon transect ecosystem research project.Ecological Applications 4:211–225.

    Google Scholar 

  • Peterson, D. L., W. E. Westman, N. J. Stephenson, V. G. Ambrosia, J. A. Brass, and M. A. Spanner. 1986. Analysis of forest structure using thematic mapper simulator data.IEEE Transactions on Geoscience & Remote Sensing GE-24:113–121.

    Google Scholar 

  • Peterson, D. L., M. A. Spanner, S. W. Running, and K. B. Teuber. 1987. Relationship of thematic mapper simulator data to leaf area index of temperate coniferous forests.Remote Sensing of Environment 22:323–341.

    Google Scholar 

  • Richards, J. A. 1984. Thematic mapping from multitemporal image data using principal components transformation.Remote Sensing of Environment 16:35–46.

    Google Scholar 

  • Richards, J. A. 1993. Remote sensing digital image analysis: An introduction, 2nd ed. Springer-Verlag, Berlin, 340 pp.

    Google Scholar 

  • Ripple, W. J. 1994. Determining coniferous forest cover and forest fragmentation with NOAA-9 advanced very high resolution radiometer data.Photogrammetric Engineering & Remote Sensing 60:533–540.

    Google Scholar 

  • Ripple, W. J., S. Wang, D. L. Isaacson, and D. P. Paine. 1991. A preliminary comparison of Landsat thematic mapper and SPOT-1 HRV multispectral data for estimating coniferous forest volume.International Journal of Remote Sensing 12:1971–1977.

    Google Scholar 

  • Roberts, D. A., M. O. Smith, and J. B. Adams. 1993. Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS.Remote Sensing of Environment 44:255–269.

    Google Scholar 

  • Running, S. W., D. L. Peterson, M. A. Spanner, and K. B. Teuber. 1986. Remote sensing of coniferous forest leaf area.Ecology 67:273–276.

    Google Scholar 

  • Sader, S. A. 1986. Analysis of effective radiant temperatures in a Pacific Northwest forest using thermal infrared multispectral scanner data.Remote Sensing of Environment 19:105–115.

    Google Scholar 

  • Sader, S. A., and J. C. Winne. 1992. RGB-NDVI colour composites for visualizing forest change dynamics.International Journal of Remote Sensing 13:3055–3067.

    Google Scholar 

  • Singh, A. 1986. Change detection in the tropical forest environment of northeastern India using Lansat. Pages 237–254in M. J. Eden and J. T. Parry (eds.), Remote sensing and tropical land management. John Wiley & Sons, New York.

    Google Scholar 

  • Skole, D., and Tucker, C. 1993. Tropical deforestation and habitat fragmentation in the Amazon: Satellite data from 1978 to 1988.Science 260:1905–1910.

    Google Scholar 

  • Smith, M. O., S. L. Ustin, J. B. Adams, and A. R. Gillespie. 1990a. Vegetation in deserts: I. A regional measure of abundance from multispectral images.Remote Sensing of Environment 31:1–26.

    Google Scholar 

  • Smith, M. O., S. L. Ustin, J. B. Adams, and A. R. Gillespie. 1990b. Vegetation in deserts: II. Environmental influences on regional abundance.Remote Sensing of Environment 31:27–52.

    Google Scholar 

  • Spanner, M. A., W. Acevedo, K. W. Teuber, S. W. Running, D. L. Peterson, D. H. Card, and D. A. Mouat. 1984. Remote sensing of the leaf area index of temperate coniferous forests. Proceedings, tenth international symposium on machine processing of remotely sensed data. Pages 362–369in M. M. Klepfer and D. B. Morrison (eds.), Thematic mapper data and geographic information systems. Purdue University Laboratory of Applied Remote Sensing, West Lafayette, Indiana, 12–14 June, pp. 362–369.

    Google Scholar 

  • Spanner, M. A., L. L. Pierce, S. W. Running, and D. L. Peterson. 1990. The seasonality of AVHRR data of temperate coniferous forests: relationship with leaf area index.Remote Sensing of Environment 33:97–112.

    Google Scholar 

  • Spies, T. A., W. J. Ripple, and G. A. Bradshaw. 1994. Dynamics and pattern of a managed coniferous forest landscape.Ecological Applications 4:555–568.

    Google Scholar 

  • Strahler, A. H. 1981. Stratification of natural vegetation for forest and rangeland inventory using Landsat digital imagery and collateral data.International Journal of Remote Sensing 2:15–41.

    Google Scholar 

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

    Google Scholar 

  • Strahler, A. H., Y. Wu, and J. Franklin. 1988. Remote estimation of tree size and density from satellite imagery by inversion of a geometric-optical canopy model. Proceedings, twenty-second international symposium of remote sensing of environment. Abidjan, Cote d'Ivoire, 20–26 October.

  • Teillet, P. M., B. Guindon, and D. G. Goodenough. 1982. On the slope-aspect correction of multispectral scanner data.Canadian Journal of Remote Sensing 8:84–106.

    Google Scholar 

  • Townshend, J. R., C. O. Justice, C. Gurney, and J. McManus. 1992. The impact of misregistration on change detection.IEEE Transactions on Geoscience & Remote Sensing 30:1054–1060.

    Google Scholar 

  • Tucker, C. J. 1979. Red and photographic infrared linear combinations for monitoring vegetation.Remote Sensing of Environment 8:127–150.

    Google Scholar 

  • Vane, G., and A. F. Goetz. 1993. Terrestrial imaging spectrometry: Current status, future trends.Remote Sensing of Environment 44:117–126.

    Google Scholar 

  • Vogelmann, J. E., and B. N. Rock. 1986. Assessing forest decline in coniferous forests of Vermont using NS-001 thematic mapper simulator data.International Journal of Remote Sensing 7:1301–1321.

    Google Scholar 

  • Walsh, S. J. 1980. Coniferous tree species mapping using Landsat data.Remote Sensing of Environment 9:11–26.

    Google Scholar 

  • Walsh, S. J. 1987. Variability of Landsat MSS spectral response of forests in relation to stand and site characteristics.International Journal of Remote Sensing 8:1289–1299.

    Google Scholar 

  • Woodcock, C., J. Collins, S. Gopal, V. Jakabhazy, X. Li, S. Macomber, S. Ryherd, Y. Wu, V. J. Harward, J. Levitan, and R. Warbington. 1994. Mapping forest vegetation using Landsat TM imagery and a canopy reflectance model.Remote Sensing of Environment 50:240–254.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  • Wu, Y., and A. H. Strahler. 1994. Remote estimation of crown size, stand density, and biomass on the Oregon transect.Ecological Applications 4:299–312.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Cohen, W.B., Kushla, J.D., Ripple, W.J. et al. An introduction to digital methods in remote sensing of forested ecosystems: Focus on the Pacific Northwest, USA. Environmental Management 20, 421–435 (1996). https://doi.org/10.1007/BF01203849

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01203849

Key words

Navigation