Abstract
Forest change happens constantly at different spatial scales. At the individual tree level, the biochemical and biophysical properties may vary according to environmental conditions; physical appearance such as colour and size may change due to phenology or human disturbance. At the stand level, the structure of forest canopies may change in terms of horizontal measures of canopy closure and gap size and shape and vertical measures of number of layers of understorey, height of each storey, etc. At the watershed level, forest ecosystem change can be evaluated by different landscape measurements such as fragmentation indices, matrix and corridor structure and system complexity. Changes at even larger scales are usually caused by human-induced environmental changes and climate changes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adams, J. B., Sabol, D. E., Kapos, V., Almeida Filho, R., Roberts, D. A., Smith, M. O., & Gillespie, A. R. (1995). Classification of multispectral images based on fractions of endmembers: application to land-cover change in the Brizilian Amazon. Remote Sensing of Environment, 52,137–154.
Allen, T. R., & Kupfer, J. A. (2001). Spectral response and spatial pattern of Fraser fir mortality and regeneration, Great Smoky Mountains, USA. Plant Ecology, 156, 59–74.
Armour, B., Tanaka, A., Ohkura, H., Saito, G. (1998). Radar interferometry for environmental change detection. Lunetta R. S. & Elvidge C. D. (Eds). Remote Sensing Change Detection, Environmental Monitoring Methods and Applications, 245–279. Ann Arbor Press. Chelsea, Michigan, USA.
Biging, G. S., Colby, D. R., & Congalton, R. G. (1998). Sampling systems for change detection accuracy assessment. Lunetta, R. S. & Elvidge C. D. (Eds.). Remote Sensing Change Detection, Environmental Monitoring Methods and Applications, 281–308. Ann Arbor Press. Chelsea, Michigan.
Bruzzone, L., & Fernandez Prieto, D. (2000). Automatic analysis of the difference image for unsupervised change detection. IEEE Transactions on Geoscience and Remote Sensing, 38,1171–1182.
Chalifoux, S., Cavayas, F., & Gray, J. T. (1998). Map-guided approach for automatic detection of Landsat TM images of forest stands damaged by spruce budworm. Photogrammetric Engineering and Remote Sensing, 64, 629–635.
Chen, J., Gong, P., He, C, Pu, R., & Shi, P. (in press). Land use/cover change detection using improved change vector analysis. Photogrammetric Engineering and Remote Sensing
Cohen, W. B., & Fiorella, M. (1998). Comparison of Methods for Detecting Conifer Forest Change with Thematic Mapper Imagery. Lunetta R.S. & Elvidge C. D. (Eds.). Remote Sensing Change Detection, Environmental Monitoring Methods and Applications, 89–102. Ann Arbor Press. Chelsea, Michigan.
Collins, J. B, & Woodcock, C. E. (1994). Change detection using the Gramm-Schmidt transformation applied to mapping forest mortality. Remote Sensing of Environment, 50, 267–279.
Collins, J. B., & Woodcock, C. E. (1996). An assessment of several linear change detection techniques for mapping forest mortality using multitemporal Landsat TM data. Remote Sensing of Environment, 56, 66–77.
Coppin, P. R., & Bauer, M. E. (1994). Processing of multitemporal Landsat TM imagery to optimize extraction of forest cover change detection. IEEE Transactions on Geoscience and Remote Sensing, 32, 918–927.
Coppin, P., Nackaerts, K., Queen, L., & Brewer, K. (2001). Operational monitoring of green biomass change for forest management. Photogrammetric Engineering and Remote Sensing, 67, 603–611.
Crist, E. P., (1985). A TM tasseled cap equivalent transformation for reflectance factor data. Remote Sensing of Environment, 17, 301–306.
Crist, E. P., & Cicone, R. C. (1984). A physically-based transformation of thematic mapper data-the TM tasseled cap, IEEE Transactions on Geoscience and Remote Sensing, 22, 256–263.
Cushman, S.A., & Wallin, D. O. (2000). Rates and patterns of landscape change in the Central Sikhote-alin Mountains, Russian Far East. Landscape Ecology, 15,643–659.
Dai, X., & Khorram, S. (1998). The effects of image misregistration on the accuracy of remotely sensed change detection. IEEE Transactions on Geoscience and Remote Sensing, 36, 1566–1577.
Diem, J. E. (2002). Remote sensing of forest health in southern Arizona, USA: evidence for ozone-induced foliar injury. Environ Manage 29, 373–384.
Drake, J. B., Dubayah R. O., Clark, D. B., et al. (2002). Estimation of tropical forest structural characteristics using large-footprint lidar. Remote Sensing of Environment, 79, 305–319.
Foody, G. M., & Boyd, D. S. (1999). Detection of partial land cover change associated with the migration of inner-class transitional zones. International Journal of Remote Sensing, 20,2723–2740.
Franklin S. E., (1989). Classification of hemlock looper defoliation using SPOT HRV imagery. Canadian Journal of Remote Sensing, 15, 178–182.
Franklin S. E., & Raske, A. G. (1994). Satellite remote sensing of spruce budworm forest defoliation in western Newfoundland. Canadian Journal of Remote Sensing, 20, 37–48.
Fung, T., & LeDrew, E. F. (1987). Application of principal component analysis to change detection. Photogrammetric Engineering & Remote Sensing, 53, 1649–1658.
Fung T. (1990). An assessment of TM imagery for land cover change detection. IEEE Transactions on Geoscience and Remote Sensing, 28, 681–684.
Gong, P. (1993). Change detection using principal component analysis and fuzzy set theory. Canadian Journal of Remote Sensing, 19, 22–29.
Gong, P. (forthcoming). Photo-ecometrics for natural resources monitoring. Andrea, G., Fabbri, G. G., & McCammon, R. B. (Eds.). Deposit and Geoenvironmental Models for Resource Exploitation and Environmental Security. Kluwer Academic Press, Dordrecht, The Netherlands.
Gong, P. (2002). Remote Sensing and Image Analysis Textbook (web site: http://camfer.cnr.berkeley.edu/~gong/textbook(accessed August 14, 2002)
Gong, P., Biging, G., & Standiford, R. (2000). The potential of digital surface model for hardwood rangeland monitoring, Journal of Range Management, 53, 622–626.
Gong, P., Biging, G. S., Lee, S. M., Mei, X., Sheng, Y., Pu, R., Xu, B., & Schwarz, K.-P. (1999). Photo-ecometrics for forest inventory, Geographic Information Sciences, 5, 9–14.
Gong, P., LeDrew, E. F., & Miller, J. R. (1992) Registration noise reduction in difference images for change detection. International Journal of Remote Sensing, 13, 773–779.
Gong, P., Miller, J. R., & Spanner, M. (1994). Forest canopy closure from classification and spectral unmixing: a multi-sensor evaluation of application to an open canopy. IEEE Transactions on Geoscience and Remote Sensing, 32, 1067–1080.
Gopal, S., & Woodcock, C. (1996). Remote sensing of forest change using artificial neural networks IEEE Transactions on Geoscience and Remote Sensing, 34, 398–404.
Grover, K., Quegan, S., & Freitas, C. D. (1999). Quantitative estimation of tropical forest cover by SAR, IEEE Transactions on Geoscience and Remote Sensing, 37,479–490.
Hame, T., Heiler, I., & San Miguel-Ayanz, J. (1998). An unsupervised change detection and recognition system for forestry. International Journal of Remote Sensing, 19, 1079–1099.
Hayes, D. J., & Sader, S. A. (2001). Comparison of change detection techniques for monitoring tropical forest clearing and vegetation regrowth in a time series. Photogrammetric Engineering and Remote Sensing, 67, 1067–1075.
Hessburg, P. F., Smith, B. G., & Salter, R. B. (2000). Recent changes (1930s–1990s) in spatial patterns of interior northwest forests, USA. Forest Ecology and Management, 136,53–83.
Howarth, P. J., & Wickware, G. M. (1981). Procedure for change detection using Landsat digital data. International Journal of Remote Sensing, 2, 277–291.
Ingebritsen, S., & Lyon, R. (1985). Principal component analysis of multitemporal image pairs, Intematioanl Journal of Remote Sensing, 6, 687–696.
Jackson, R. D. (1983). Spectral indices in n-space, Remote Sensing of Environment, 13, 400–421.
Johnson, R. D., & Kasischke, E. S. (1998). Change vector analysis: a technique for the multispectral monitoring for land cover and condition. International Journal of Remote Sensing, 19,411–426.
Jensen, J. R. (1996). Introductory Digital Image Processing, A Remote Sensing Perspective. Prentice Hall, Upper Saddle River, New Jersey.
Kauth, R. J., & Thomas, G. S. (1976). The tasseled cap — a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, 4b, 41–51. June 21–July 1, Purdue University, West Lafayette, Indiana.
Lambin, E. F., & Strahler, A. H. (1994a). 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.
Lambin, E. F., & Strahler, A. H. (1994b). Indicators of land-cover change for change vector analysis in multitemporal space at coarse spatial scales. International Journal of Remote Sensing, 15,2099–2119.
Leckie, D. G. (1987). Factors affecting defoliation assessment using Airborne Multispectral Scanner data. Photogrammetric Engineering and Remote Sensing, 53, 1665–1674.
Li, Z., Nadon, S., & Cihlar, J. (2000). Satellite detection of Canadian boreal forest fires: Development and application of an algorithm, International Journal of Remote Sensing, 21,3057–3069.
Malila, W.A. (1980). Change vector analysis: an approach for detecting forest changes with Landsat. Proc. Symposium Machine Processing of Remotely Sensed Data, 326–336. Purdue University, West Lafayette, Indiana, USA, IEEE, Piscataway, New Jersey, USA.
McGarigal, K, & Marks, B. J. (1995). FRAGSTATS spatial pattern analysis program for quantifying landscape structure. Report No PNW-GTR 351, USFS, Pacific Northwest Research Station, Portland.
Michalek, J. L., Luczkovich, J. J., & Stofile, R. W. (1993). Multispectral change vector analysis for monitoring coastal marine environments. Photogrammetric Engineering and Remote Sensing, 59, 381–384.
Muchoney, D. M., & Haack, B. N. (1994). Change detection for monitoring forest defoliation. Photogrammetric Engineering and Remote Sensing, 60,1243–1251.
Myneni, R. B., Keeling, C. D., Tucker, C. J., Asrar, G., & Nemani, R. R. (1997). Increased plant growth in the northern high latitudes from 1981–1991. Nature, 386, 698–702.
Naesset, E, (2002). Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sensing of Environment, 80, 88–99
Nielsen A. A., Conradsen K., & Simpson, J. J. (1998). Multivariate alteration detection and MAF postprocessing in multispectral, bitemproal image data: new approaches to change detection studies. Remote Sensing of Environment, 64, 1–19.
Olsson, H. (1994). Changes in satellite-measured reflectances caused by thinning cuttings in boreal forest, Remote Sensing of Environment, 50, 221–230.
Olsson H. (1995). Reflectance calibration of thematic mapper data for forest change detection. International Journal of Remote Sensing, 16,81–96.
Pellikka, P. (2001). Application of vertical skyward wide-angle photography and airborne video data for phonological studies of beech forests in the German Alps. International Journal of Remote Sensing, 22,2675–2700.
Quegan S., Le Toan, T., & Yu, J. J. (2000). Multitemporal ERS SAR analysis applied to forest mapping. IEEE Transactions on Geoscience and Remote Sensing, 38,741–753.
Qi Y., & Gong, P. (1996). Metabolic and phenological response of vegetation to temperature gradient: evidence derived from AVHRR data. Geographic Information Sciences, 2, 64–72.
Radeloff, V. C, Mladenoff, D. J., & Boyce, M. S. (2000). Effects of interacting disturbances on landscape patterns: budworm defoliation and salvage logging. Ecological Applications, 10,233–247.
Remmel, T. K., & Perera, A. H. (2001). Fire mapping in a northern boreal forest: assessing AVHRR/NDVI methods of change detection. Forest Ecology and Management, 152, 119–129.
Richards, J. A., & Jia, X. (1999). Remote Sensing Digital Image Analysis. Springer, Berlin.
Rigina O., Baklanov, A., & Hagner, O. (1999). Monitoring of forest damages in the Kola Peninsula, Northern Russia due to smelting industry. Science of the Total Environment, 229,147–163.
Rignot, E. J. M., & Vanzyl, J. J. (1993). Change detection techniques for ERS-1 SAR data. IEEE Transactions on Geoscience and Remote Sensing, 31, 1039–1046.
Royle, D. D., & Lathrop, R. G., (1997). Monitoring hemlock forest health in New Jersey using Landsat TM data and change detection techniques. Forest Science, 42,327–335.
Sheng, Y., Gong, P., & Biging, G. S. (2001). Model-based conifer crown surface reconstruction from high-resolution aerial images. Photogrammetric Engineering and Remote Sensing, 67, 957–965.
Silapaswan, C. S., Verbyla, D. L., & McGuire, A. D. (2001). Land cover change on the Seward Peninsula: The use of remote sensing to evaluate the potential influences of climate warming on historical vegetation dynamics. Canadian Journal of Remote Sensing, 27, 542–554.
Singh, A. (1989). Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10, 989–1003.
Smits, P. C, & Annoni, A. (2000). Toward specification-driven change detection. IEEE Transactions on Geoscience and Remote Sensing, 38, 1484–1488.
Smits, P. C, & Myers, W. L. (2000). Echelon approach to characterize and understand spatial structures of change in multitemporal remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 38, 2299–2309.
Sohl, T. L. (1999). Change Analysis in the United Arab Emirates: An Investigation of techniques, Photogrammetric Engineering and Remote Sensing, 65, 475–484.
Song, C, Woodcock, C. E., Seto, K., Lenney, M., & Macomber, S. (2001). Classification and change detection using landsat TM data: when and how to correct atmospheric effects? Remote Sensing of Environment, 75, 230–244.
Sun, G. Q., & Ranson, K. J., (2000). Modeling lidar returns from forest canopies. IEEE Transactions On Geoscience and Remote Sensing, 38, 2617–2626
Tokola, T., Lofman, S., & Erkkila, A. (1999). Relative calibration of multitemporal Landsat data for forest cover change detection. Remote Sensing of Environment, 68, 1–11.
Townshend, J. R. G., Justice, C. O., Gurney, C, & McManus, J. (1992). The impact of misregistration on change detection. IEEE Transactions on Geoscience and Remote Sensing, 30,1054–1060.
Varjo, J., & Folving, S. (1997). Monitoring of forest changes using unsupervised methods: a case study from boreal forest on mineral soils. Scandinavian Journal of Forest Research, 12,362–369.
Verbyla, D. L., & Boles, S. H. (2000). Bias in land cover change estimates due to misregistration. International Journal of Remote Sensing, 3553–3560.
Yuan, D., Elvidge C. D., & Lunetta, R. S. (1998). Survey of multispectral methods for land cover change analysis. Lunetta R. S. & Elvidge C. D. (Eds.). Remote Sensing Change Detection, Environmental Monitoring Methods and Applications, 21–39. Ann Arbor Press. Chelsea, Michigan.
Zhan, X, Defries R., & Townshend, J. R. G., et al. (2000). The 250 m global land cover change product from the Moderate Resolution Imaging Spectroradiometer of NASA’s Earth Observing System. International Journal of Remote Sensing, 21, 1433–1460.
Zheng, D. L., Wallin, D. O., & Hao, Z. Q. (1997). Rates and patterns of landscape change between 1972 and 1988 in the Changbai Mountain area of China and North Korea. Landscape Ecology, 12,241–254.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer Science+Business Media New York
About this chapter
Cite this chapter
Gong, P., Xu, B. (2003). Remote Sensing of Forests Over Time. In: Wulder, M.A., Franklin, S.E. (eds) Remote Sensing of Forest Environments. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0306-4_11
Download citation
DOI: https://doi.org/10.1007/978-1-4615-0306-4_11
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-5014-9
Online ISBN: 978-1-4615-0306-4
eBook Packages: Springer Book Archive