Skip to main content


Log in

Satellite detection of land-use change and effects on regional forest aboveground biomass estimates

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript


We used remote-sensing-driven models to detect land-cover change effects on forest aboveground biomass (AGB) density (Mg·ha−1, dry weight) and total AGB (Tg) in Minnesota, Wisconsin, and Michigan USA, between the years 1992–2001, and conducted an evaluation of the approach. Inputs included remotely-sensed 1992 reflectance data and land-cover map (University of Maryland) from Advanced Very High Resolution Radiometer (AVHRR) and 2001 products from Moderate Resolution Imaging Spectroradiometer (MODIS) at 1-km resolution for the region; and 30-m resolution land-cover maps from the National Land Cover Data (NLCD) for a subarea to conduct nine simulations to address our questions. Sensitivity analysis showed that (1) AVHRR data tended to underestimate AGB density by 11%, on average, compared to that estimated using MODIS data; (2) regional mean AGB density increased slightly from 124 (1992) to 126 Mg ha−1 (2001) by 1.6%; (3) a substantial decrease in total forest AGB across the region was detected, from 2,507 (1992) to 1,961 Tg (2001), an annual rate of −2.4%; and (4) in the subarea, while NLCD-based estimates suggested a 26% decrease in total AGB from 1992 to 2001, AVHRR/MODIS-based estimates indicated a 36% increase. The major source of uncertainty in change detection of total forest AGB over large areas was due to area differences from using land-cover maps produced by different sources. Scaling up 30-m land-cover map to 1-km resolution caused a mean difference of 8% (in absolute value) in forest area estimates at the county-level ranging from 0 to 17% within a 95% confidence interval.

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


  • Alexandridis, T., & Chemin, Y. (2002). Landsat ETM+, Terra MODIS and NOAA AVHRR: Issues of scale and inter-dependency regarding land parameters. In: Proceedings of Asian Conference on Remote Sensing, 25–29 November, Kathmandu, Nepal.

  • Bechtold, W. A., & Patterson, P. L. (2005). The enhanced forest inventory and analysis program—national sampling design and estimation procedures. Gen. Tech. Rep. SRS-80, USDA Forest Service Southern Research Station, Asheville, NC, p 85.

  • Carleton, T. J. (2003). Old growth in the Great Lakes forest. Environment Reviews, 11, S115–S134.

    Article  Google Scholar 

  • Chaplin, S., Perera, A., Robinson, S., Adams, J. Gray, T., Whelan-Enns, G., et al. (2001). Western Great Lakes forests (NA0416). Retrieved from

  • Cole, K. L., Stearns, F., Guntenspergen, G., Davis, M. B., & Walker, K. (2003). Historical landcover changes in the Great Lakes Region. Retrieved from

  • Dale, V. H. (1997). The relationship between land-use change and climate change. Ecological Applications, 7, 753–769.

    Article  Google Scholar 

  • DeFries, R. S., & Townshend, J. R. G. (1994). Global land cover: comparison of ground-based data set to classification with AVHRR data. In G. M. Foody, & P. Curran (Eds.) Environmental remote sensing from regional to global scales. Chichester: Wiley.

    Google Scholar 

  • Dixon, R. K., Brown, S., Houghton, R. A., Solomon, A. M., Trexler, M. C., & Wisniewski, J. (1994). Carbon pools and flux of global forest ecosystems. Science, 63, 185–190.

    Article  Google Scholar 

  • Drake, J. B., Dubayah, R. O., Knox, R. G., Clark, D. B., & Blair, J. B. (2002). Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest. Remote Sensing of Environment, 81, 378–392.

    Article  Google Scholar 

  • ESRI. (2006). Retrieved December 19, 2006, from

  • Friedl, M. A., McIver, D. K., Hodges, J. C. F., Zhang, X., Muchoney, D., & Strahler, A. H., et al. (2002). Global land cover from MODIS: Algorithms and early results. Remote Sensing of Environment, 83, 135–148.

    Article  Google Scholar 

  • Hame, T., Salli, A., Andersson, K., & Lohi, A. (1997). A new methodology for the estimation of biomass of conifer dominated boreal forest using NOAA AVHRR data. International Journal of Remote Sensing, 18, 3211–3243.

    Article  Google Scholar 

  • Hansen, M. C., Defries, R. S., Townshend, J. R., & Sohlberg, R. (2000). Global land cover classification at 1 km spatial resolution using a classification tree approach. International Journal of Remote Sensing, 21, 1331–1364.

    Article  Google Scholar 

  • Heath, L. S., & Birdsey, R. A. (1993). Carbon trends of productive temperate forests of the conterminous United States. Water, Air, and Soil Pollution, 70, 279–293.

    Article  Google Scholar 

  • Houghton, R. A. (1995). Land-use change and the carbon cycle. Global Change Biology, 1, 275–287.

    Article  Google Scholar 

  • Houghton, R. A., Hobbie, J. E., Melillo, J. M., Moore, B., Peterson, B. J., & Shaver, G. R., et al. (1983). Changes in the carbon content of terrestrial biota and soils between 1860 and 1980: A net release of CO2 to the atmosphere. Ecological Monographs, 53, 235–262.

    Article  CAS  Google Scholar 

  • Jenkins, J. C., Chojnacky, D. C., Heath, L. S., & Birdsey, R. A. (2004). Comprehensive database of diameter-based biomass regressions for north American tree species. Gen. Tech. Rep. NE-319, USDA Forest Service, Northeastern Research Station, Newtown Square, PA 19073, p. 45.

  • Justice, C. O., Holben, B. N., & Gwynne, M. D. (1986). Monitoring East African vegetation using AVHRR data. International Journal of Remote Sensing, 7, 1453–1474.

    Article  Google Scholar 

  • King, M. D., Kaufman, Y. J., Menzel, W. P., & Tanre, D. (1992). Remote sensing of cloud aerosol and water vapor properties from the Moderate Resolution Imaging Spectrometer. I.E.E.E. Transactions on Geoscience and Remote Sensing, 30, 7–27.

    Google Scholar 

  • Lefsky, M. A., Cohen, W. B., Harding, D. J., Parker, G. G., Acker, S. A., & Gower, S. T. (2002). Lidar remote sensing of above-ground biomass in three biomes. Global Ecology and Biogeography, 11, 393–399.

    Article  Google Scholar 

  • Lefsky, M. A., Harding, D., Cohen, W. B., Parker, G., & Shugart, H. H. (1999). Surface lidar remote sensing of basal area and biomass in deciduous forests of eastern Maryland, USA. Remote Sensing of Environment, 67, 83–98.

    Article  Google Scholar 

  • LP DAAC. (2006). Retrieved August 5, 2006, from

  • Malingreau, J.-P., Stevens, G., & Fellows, C. (1985). 1982–83 forest fires of Kalimantan and North Borneo: satellite observations for detection and monitoring. Ambio, 14, 314–346.

    Google Scholar 

  • McGarical, K., & Marks, B. J. (1995). FRAGSTATS: spatial pattern analysis program for quantifying landscape structure (version 2.0). USDA Forest Service, PNW-GTR-351, Portland, OR.

  • MRLC. (2006). Retrieved November 14, 2006, from

  • National Association of Conservation Districts. (2007). Biomass & Forest Health Update. Retrieved March 15, 2007, from

  • Nemani, R. R., & Running, S. W. (1989). Estimation of regional surface resistance to evapotranspiration from NDVI and thermal-IR AVHRR data. Journal of Applied meteorology, 28, 276–284.

    Article  Google Scholar 

  • Prince, S. D., & Goward, S. N. (1995). Global primary production: a remote sensing approach. Journal of Biogeography, 22, 815–835.

    Article  Google Scholar 

  • Ramankutty, N., & Foley, J. A. (1999). Estimating historical changes in land cover: North American croplands from 1850 to 1992. Global Ecology and Biogeography, 8, 381–396.

    Article  Google Scholar 

  • Richards, J. F. (1990). Land transformation. In B. L. Turner, W. C. Clark, R. W. Kates, J. F. Richards, J. T. Mathews, & W. B. Meyer (Eds.) The earth as transformed by human action: global and regional changes in the biosphere over the past 300 years (pp. 163–178). Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Running, S. W., Justice, C. O., Salomonson, V., Hall, D., Barker, J., & Kaufmann, Y. J., et al. (1994). Terrestrial remote sensing science and algorithms for EOS/MODIS. International Journal of Remote Sensing, 15, 3587–3620.

    Article  Google Scholar 

  • Sader, S. A., Waide, R. B., Lawrence, W. T., & Joyce, A. T. (1989). Tropical forest biomass and successional age class relationships to a vegetation index derived from Landsat TM data. Remote Sensing of Environment, 28, 143–156.

    Article  Google Scholar 

  • Sannier, C. A. D., Taylor, J. C., & Plessis, W. D. (2002). Real-time monitoring of vegetation biomass with NOAA-AVHRR in Etosha National Park, Namibia, for a fire risk assessment. International Journal of Remote Sensing, 23, 71–89.

    Article  Google Scholar 

  • Schimel, D. S. (1995). Terrestrial ecosystems and the carbon cycle. Global Change Biology, 1, 77–91.

    Article  Google Scholar 

  • Schulte, L. A., Crow, T. R., Vissage, J., & Cleland, D. (2003). Seventy years of forest change in the northern Great Lake Region, USA. In L. J. Buse, & A. H. Perera (Eds.) Comps. meeting emerging ecological, economic, and social challenges in the Great Lakes region: Popular summaries (pp. 99–101). Sault Ste. Marie, Ontario, Canada: Ontario Forest Research Institute.

    Google Scholar 

  • Steininger, M. K. (2000). Satellite estimation of tropical secondary forest above-ground biomass: data from Brazil and Bolivia. International Journal of Remote Sensing, 21, 1139–1157.

    Article  Google Scholar 

  • Townshend, J. R. G., Justice, C. O., & Kalb, V. T. (1987). Characterization and classification of South American land cover types using satellite data. International Journal of Remote Sensing, 8, 1189–1207.

    Article  Google Scholar 

  • Tucker, C. J., Gaitlin, J. A., & Schneider, S. R. (1984). Monitoring vegetation in the Nile Delta with NOAA-6 and NOAA-7 AVHRR imagery. Photogrammetric Engineering and Remote Sensing, 50, 53–61.

    Google Scholar 

  • Tucker, C. J., Townshend, J. R. G., & Goff, T. E. (1985). African land-cover classification using satellite data. Science, 227, 369–375.

    Article  Google Scholar 

  • Turner, D. P., Koerper, G. J., Gucinski, H., Peterson, C. J., & Dixon, R. K. (1993). Monitoring global change: comparison of forest cover estimates using remote sensing and inventory approaches. Environmental Monitoring and Assessment, 26, 295–305.

    Article  Google Scholar 

  • USDA Forest Service. (2006). FIA Data Mart: Download Files. Retrieved September 28, 2006, from

  • USGS. Earth Resources Observation and Science (2006). Retrieved October 19, 2006, from

  • USGS-NASA. LP DAAC (2006). Retrieved August 25, 2006, from

  • Zhan, X., Sohlberg, R. A., Townshend, J. R. G., DiMiceli, C., Carroll, M. L., & Eastman, J. C., et al. (2002). Detection of land cover changes using MODIS 250 m data. Remote Sensing of Environment, 83, 336–350.

    Article  Google Scholar 

  • Zheng, D., Chen, J., Noormets, A., Euskirchen, E. S., & Le Moine, J. M. (2005). Effects of climate and land use on landscape soil respiration in northern Wisconsin, USA: 1972 to 2001. Climate Research, 28, 163–173.

    Article  Google Scholar 

  • Zheng, D., Heath, L. S., & Ducey, M. J. (2007). Forest biomass estimated from MODIS and FIA data in the Lake States: MN, WI, and MI, USA. Forestry, DOI 10.1093/forestry/cpm015.

  • Zheng, D., Rademacher, J., Chen, J., Crow, T., Bresee, M., & LeMoine, J. (2004). Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sensing of Environment, 93, 402–411.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Daolan Zheng.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zheng, D., Heath, L.S. & Ducey, M.J. Satellite detection of land-use change and effects on regional forest aboveground biomass estimates. Environ Monit Assess 144, 67–79 (2008).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: