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Landscape Ecology

, Volume 23, Issue 9, pp 1119–1132 | Cite as

Modeling grain-size dependent bias in estimating forest area: a regional application

  • Daolan Zheng
  • Linda S. Heath
  • Mark J. Ducey
Research Article

Abstract

A better understanding of scaling-up effects on estimating important landscape characteristics (e.g. forest percentage) is critical for improving ecological applications over large areas. This study illustrated effects of changing grain sizes on regional forest estimates in Minnesota, Wisconsin, and Michigan of the USA using 30-m land-cover maps (1992 and 2001) produced by the National Land Cover Datasets. The maps were aggregated to two broad cover types (forest vs. non-forest) and scaled up to 1-km and 10-km resolutions. Empirical models were established from county-level observations using regression analysis to estimate scaling effects on area estimation. Forest percentages observed at 30-m and 1-km land-cover maps were highly correlated. This intrinsic relationship was tested spatially, temporally, and was shown to be invariant. Our models provide a practical way to calibrate forest percentages observed from coarse-resolution land-cover data. The models predicted mean scaling effects of 7.0 and 12.0% (in absolute value with standard deviations of 2.2 and 5.3%) on regional forest cover estimation (ranging from 2.3 and 2.5% to 11.1 and 23.7% at the county level) with standard errors of model estimation 3.1 and 7.1% between 30 m and 1 km, and 30 m and 10 km, respectively, within a 95% confidence interval. Our models improved accuracy of forest cover estimates (in terms of percent) by 63% (at 1-km resolution) and 57% (at 10-km resolution) at the county level relative to those without model adjustment and by 87 and 84% at the regional level in 2001. The model improved 1992 and 2001 regional forest estimation in terms of area for 1-km maps by 15,141 and 7,412 km2 (after area weighting of all counties) respectively, compared to the corresponding estimates without calibration using 30 m-based regional forest areas as reference.

Keywords

Aggregation Confidence interval Land-cover map Pixel resolution Standard errors 3 Lake States of USA 

Notes

Acknowledgements

This study was in part funded by the USDA Forest Service through grant 05-DG-11242343-074.

References

  1. Adams JB, Smith MO, Johnston PE (1986) Spectral mixture modeling: a new analysis of rock and soil types at the viking lander 1 site. J Geophys Res 91:8098–8112. doi: 10.1029/JB091iB08p08098 CrossRefGoogle Scholar
  2. Ahl DE, Gower ST, Mackay DS et al (2005) The effects of aggregated land cover data on estimating NPP in northern Wisconsin. Remote Sens Environ 97:1–14. doi: 10.1016/j.rse.2005.02.016 CrossRefGoogle Scholar
  3. Ajtay GL, Ketner P, Duvigneaud P (1979) Terrestrial primary production and phytomass. In: Bolin EDB, Kempe S, Ketner P (eds) The global carbon cycle. Wiley, New York, pp 129–182Google Scholar
  4. 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, NepalGoogle Scholar
  5. Bierkens MFP, Finke PA, Willigen PD (2000) Upscaling and downscaling methods for environmental research. Kluwer Academic Publisher, DordrechtGoogle Scholar
  6. Bloschl G, Sivapalan M (1995) Scale issues in hydrological modeling: a review. Hydrol Process 9:251–290. doi: 10.1002/hyp.3360090305 CrossRefGoogle Scholar
  7. Bresee MK, LeMoine JM, Mather S et al (2004) Disturbance and landscape dynamics in the Chequamegon National Forest, Wisconsin, USA, from 1972 to 2001. Landsc Ecol 19:291–309. doi: 10.1023/B:LAND.0000030419.27883.40 CrossRefGoogle Scholar
  8. Brown PJ (1982) Multivariate calibration. J R Stat Soc [Ser A] B44:287–321Google Scholar
  9. Buyantuyev A, Wu J (2007) Effects of thematic resolution on landscape pattern analysis. Landsc Ecol 22:7–13. doi: 10.1007/s10980-006-9010-5 CrossRefGoogle Scholar
  10. Carleton TJ (2003) Old growth in the Great Lakes forest. Environ Rev 11:S115–S134. doi: 10.1139/a03-009 CrossRefGoogle Scholar
  11. Clark WAV, Hosking PL (1986) Statistical methods for geographers. John Wiley & Sons, New York, p 518Google Scholar
  12. Cohen WB, Justice CO (1999) Validating MODIS terrestrial ecology products: linking in situ and satellite measurements. Remote Sens Environ 70:1–3. doi: 10.1016/S0034-4257(99)00053-X CrossRefGoogle Scholar
  13. Cohen WB, Maiersperger TK, Spies TA et al (2001) Modelling forest cover attributes as continuous variables in a regional context with Thematic Mapper data. Int J Remote Sens 2:2279–2310. doi: 10.1080/014311601300229827 CrossRefGoogle Scholar
  14. Czaplewski RL, Catts GP (1992) Calibration of remotely sensed proportion or area estimates for misclassification error. Remote Sens Environ 39:29–43. doi: 10.1016/0034-4257(92)90138-A CrossRefGoogle Scholar
  15. Dungan JL, Perry JN, Dale MRT et al (2002) A balanced view of scale in spatial statistical analysis. Ecography 25:626–640. doi: 10.1034/j.1600-0587.2002.250510.x CrossRefGoogle Scholar
  16. Emanuel WR, Shugart HH, Stevenson MP (1985) Climatic change and the broad-scale distribution of terrestrial ecosystem complexes. Clim Change 7:29–43. doi: 10.1007/BF00139439 CrossRefGoogle Scholar
  17. Fang H, Liang S, Chen M et al (2004) Statistical comparison of MISR, ETM+ and MODIS land surface reflectance and albedo products of the BARC land validation core site, USA. Int J Remote Sens 25:409–422. doi: 10.1080/0143116031000101666 CrossRefGoogle Scholar
  18. Fassnacht KS, Gower ST, MacKenzie MD et al (1997) Estimating the leaf area index of North Central Wisconsin forests using the Landsat Thematic Mapper. Remote Sens Environ 61:229–245. doi: 10.1016/S0034-4257(97)00005-9 CrossRefGoogle Scholar
  19. Friedl MA, McIver DK, Hodges JCF et al (2002) Global land cover from MODIS: algorithms and early results. Remote Sens Environ 83:135–148. doi: 10.1016/S0034-4257(02)00078-0 CrossRefGoogle Scholar
  20. Gardner RH, Lookingbill TR, Townsend PA, Ferrari J (2008) A new approach for rescaling land cover data. Landsc Ecol 23:513–526. doi: 10.1007/s10980-008-9213-z CrossRefGoogle Scholar
  21. Goetz SJ, Prince SD, Small J et al (2000) Interannual variability of global terrestrial primary production: results of a model driven with satellite observations. J Geog Res 105:20077–20091. doi: 10.1029/2000JD900274 CrossRefGoogle Scholar
  22. Goodchild MF, Quattrochi DA (1997) Scale, multiscaling, remote sensing, and GIS. In: Quattrochi DA, Goodchild MF (eds) Scale in remote sensing and GIS. Lewis Publisher, Boca Raton, pp 1–11Google Scholar
  23. Gower ST, Krankina ON, Olson RJ et al (2001) Net primary production and carbon allocation patterns of boreal forest ecosystems. Ecol Appl 11:1395–1411. doi: 10.1890/1051-0761(2001)011[1395:NPPACA]2.0.CO;2 CrossRefGoogle Scholar
  24. Hall O, Hay G, Bouchard A, Marceau D (2004) Detecting dominant landscape objects through multiple scales: an integration of object-specific methods and watershed segmentation. Landsc Ecol 19:59–76. doi: 10.1023/B:LAND.0000018371.43447.1f CrossRefGoogle Scholar
  25. Hansen MC, DeFries RS, Townshend JR et al (2000) Global land cover classification at 1 km spatial resolution using a classification tree approach. Int J Remote Sens 21:1331–1364. doi: 10.1080/014311600210209 CrossRefGoogle Scholar
  26. Hansen MC, DeFries RS, Townshend JR et al (2003) Global percent tree cover at a spatial resolution of 500 meters: First results of the MODIS Vegetation Continuous Fields algorithm. Earth Interact 7:1–15. doi:10.1175/1087-3562(2003)007<0001:GPTCAA>2.0.CO;2CrossRefGoogle Scholar
  27. Heath LS, Birdsey RA, Row C (1996) Carbon pools and fluxes in US forest products. In: Apps MJ, Price DT, Price DT (eds) Forest ecosystems, forest management, and the global carbon cycle NATO ASI Series I: global environmental changes. Springer Verlag, NY, USA, pp 271–278Google Scholar
  28. Homer C, Huang C, Yang L et al (2004) Development of a 2001 national land-cover database for the United States. Photogramm Eng Remote Sens 70:829–840Google Scholar
  29. Huguenin RL, Karaska MA, Bralicom DV et al (1997) Subpixel classification of bald cypress and tupelo gum trees in thematic mapper imagery. Photogramm Eng Remote Sens 63:717–725Google Scholar
  30. Jelinski D, Wu J (1996) The modifiable areal unit problem and implications for landscape ecology. Landsc Ecol 11:129–140. doi: 10.1007/BF02447512 CrossRefGoogle Scholar
  31. Ju J, Gopal S, Kolaczyk ED (2005) On the choice of spatial and categorical scale in remote sensing land cover classification. Remote Sens Environ 96:62–77. doi: 10.1016/j.rse.2005.01.016 CrossRefGoogle Scholar
  32. Katz RW (2002) Techniques for estimating uncertainty in climate change scenarios and impact studies. Clim Res 20:167–185. doi: 10.3354/cr020167 CrossRefGoogle Scholar
  33. Levin SA (1992) The problem of pattern and scale in ecology. Ecology 73:1943–1983. doi: 10.2307/1941447 CrossRefGoogle Scholar
  34. Li H, Reynolds F (1993) A new contagion index to quantify spatial patterns of landscapes. Landsc Ecol 8:155–162. doi: 10.1007/BF00125347 CrossRefGoogle Scholar
  35. Li H, Wu J (2006) Uncertainty analysis in ecological studies: an overview. In: Wu J, Jones KB, Li H, Loucks O (eds) Scaling and uncertainty analysis in ecology: methods and applications. Springer, Dordrect, Netherlands, pp 43–64Google Scholar
  36. Lieth H (1975) Modelling the primary productivity of the world. In: Lieth H, Whittaker RH (eds) Primary productivity of the biosphere. Springer-Verlag, New York, pp 237–263Google Scholar
  37. Mayaux P, Lambin EF (1997) Tropical forest area measured from global land-cover classifications: inverse calibration models based on spatial textures. Remote Sens Environ 59:29–43. doi: 10.1016/S0034-4257(96)00077-6 CrossRefGoogle Scholar
  38. McWilliams WH, Heath LS, Reese GC et al (2000) Forest resources and conditions. In: Mickler RA, Birdsey RA, Hom J (eds) Responses of northern US forests to environmental change. Springer-Verlag, New York, pp 3–26Google Scholar
  39. Moody A, Woodcock CE (1994) Scale-dependent errors in the estimation of land-cover proportions: implications for global land-cover datasets. Photogramm Eng Remote Sens 60:585–596Google Scholar
  40. Moody A, Woodcock CE (1995) The influence of scale and the spatial characteristics of landscapes on land-cover mapping using remote sensing. Landsc Ecol 10:363–379. doi: 10.1007/BF00130213 CrossRefGoogle Scholar
  41. Moody A, Woodcock CE (1996) Calibration-based methods for correction of area estimates derived from coarse resolution land-cover data. Remote Sens Environ 58:225–241. doi: 10.1016/S0034-4257(96)00036-3 CrossRefGoogle Scholar
  42. MRLC (nd) (2007) Retrieved May 14, from http://wwwmrlcgov/
  43. Oki K, Uenishi TM, Omasa K (2004) Accuracy of land cover area estimated from coarse spatial resolution images using unmixing method. Int J Remote Sens 25:1673–1683. doi: 10.1080/0143116031000139854 CrossRefGoogle Scholar
  44. O’Neill RV (1988) Hierarchy theory and global change. In: Rosswall T, Woodmansee RG, Risser PG (eds) Scales and global change. Wiley, Melbourne, Australia, pp 29–46Google Scholar
  45. Running SW, Baldocchi DD, Turner DP et al (1999) A global terrestrial monitoring network integrating tower fluxes, flask sampling, ecosystem modeling and EOS satellite data. Remote Sens Environ 70:108–127. doi: 10.1016/S0034-4257(99)00061-9 CrossRefGoogle Scholar
  46. Running SW, Thornton PE, Nemani R et al (2000) Global terrestrial gross and net primary productivity from the earth observing system. In: Sala OE, Jackson R, Mooney HA, Hwarth R (eds) Methods in ecosystem science. Springer-Verlag, New York, pp 44–57Google Scholar
  47. Saura S (2004) Effects of remote sensor spatial resolution and data aggregation on selected fragmentation indices. Landsc Ecol 19:197–209. doi: 10.1023/B:LAND.0000021724.60785.65 CrossRefGoogle Scholar
  48. Settle JJ, Drake NA (1993) Linear mixing and the estimation of ground cover proportions. Int J Remote Sens 14:1159–1177. doi: 10.1080/01431169308904402 CrossRefGoogle Scholar
  49. Smith JE, Heath LS (2007) US agriculture and forestry greenhouse gas inventory: 1990–2005. Tech Bull Wash DC (in press)Google Scholar
  50. Smith JE, Heath LS, Skog KE (2006) Methods for calculating forest ecosystem and harvested carbon with standard estimates for forest types of the United States. Gen Tech Rep NE-343. USDA Forest Service, Northeastern Research Station, Newtown Square, PA, p 216Google Scholar
  51. Strahler AH, Woodcock CE, Smith JA (1986) On the nature of models in remote sensing. Remote Sens Environ 20:121–139. doi: 10.1016/0034-4257(86)90018-0 CrossRefGoogle Scholar
  52. Stuckens J, Coppin PR, Bauer ME (2000) Integrating contextual information with per-pixel classification for improved land cover classification. Remote Sens Environ 71:282–296. doi: 10.1016/S0034-4257(99)00083-8 CrossRefGoogle Scholar
  53. Townshend J, Justice CO, Li W et al (1991) Global landcover classification by remote sensing: present capabilities and future possibilities. Remote Sens Environ 35:243–255. doi: 10.1016/0034-4257(91)90016-Y CrossRefGoogle Scholar
  54. Tucker CJ, Townshend JRG, Goff TE (1985) African land-cover classification using satellite data. Sci 227:369–375. doi: 10.1126/science.227.4685.369 CrossRefGoogle Scholar
  55. Turner MG (1989) Landscape Ecol: the effect of pattern on process. Annu Rev Ecol Syst 20:171–197. doi: 10.1146/annurev.es.20.110189.001131 CrossRefGoogle Scholar
  56. Turner MG, Dale VH, Gardner RH (1989) Predicting across scales: theory development and testing. Landscape Ecol 3:245–252. doi: 10.1007/BF00131542 CrossRefGoogle Scholar
  57. Turner DP, Cohen WB, Kennedy RE (2000) Alternative spatial resolutions and estimates of carbon flux over a managed forest landscape in Western Oregon. Landsc Ecol 15:441–452. doi: 10.1023/A:1008116300063 CrossRefGoogle Scholar
  58. USDA (2007) Natural Resources Conservation Service. http://wwwnrcsusdagov/TECHNICAL/land/nri03/statereports/2003summaryreportpdf
  59. Vogelmann JE, Howard SM, Yang L et al (2001) Completion of the 1990s National Land Cover Dataset for the conterminous United States from Landsat Thematic Mapper data and ancillary data sources. Photogramm Eng Remote Sens 67:650–652Google Scholar
  60. Walsh TA, Burk TE (1993) Calibration of satellite classification of land area. Remote Sens Environ 46:281–290. doi: 10.1016/0034-4257(93)90048-3 CrossRefGoogle Scholar
  61. Woodcock CE, Strahler AH (1987) The factor of scale in remote sensing. Remote Sens Environ 21:311–332. doi: 10.1016/0034-4257(87)90015-0 CrossRefGoogle Scholar
  62. Wu J (1999) Hierarchy and scaling: extrapolating information along a scaling ladder. Can J Rem Sens 25:367–380Google Scholar
  63. Wu J (2004) Effects of changing scale on landscape pattern analysis: scaling relations. Landscape Ecol 19:125–138. doi: 10.1023/B:LAND.0000021711.40074.ae CrossRefGoogle Scholar
  64. Wu J, David JL (2002) Spatially explicit hierarchical approach to modeling complex ecological systems: theory and applications. Ecol Modell 153:7–26. doi: 10.1016/S0304-3800(01)00499-9 CrossRefGoogle Scholar
  65. Wu J, Hobbs R (2002) Key issues and research priorities in landscape ecology: an idiosyncratic synthesis. Landscape Ecol 17:355–365. doi: 10.1023/A:1020561630963 CrossRefGoogle Scholar
  66. Wu J, Li H (2006) Concepts of scale and scaling. In: Wu J, Jones B, Li H, Loucks OL (eds) Scaling and uncertainty analysis in ecology: methods and applications. Springer, Dordrecht, The Netherlands, pp 3–15CrossRefGoogle Scholar
  67. Wu J, Marceau DJ (2002) Modeling complex ecological systems: an introduction. Ecol Modell 153:1–6. doi: 10.1016/S0304-3800(01)00498-7 CrossRefGoogle Scholar
  68. Zheng D, Prince SD, Wright R (2003) Terrestrial net primary production estimates for 050 grid cells from field observations—a contribution to global biogeochemical modeling. Glob Change Biol 9:46–64. doi: 10.1046/j.1365-2486.2003.00534.x CrossRefGoogle Scholar
  69. Zheng D, Prince SD, Hame T (2004a) Estimating net primary production of boreal forests in Finland and Sweden from field data and remote sensing. J Veg Sci 15:161–170. doi: 10.1658/1100-9233(2004)015[0161:ENPPOB]2.0.CO;2 CrossRefGoogle Scholar
  70. Zheng D, Rademacher J, Chen J et al (2004b) Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sens Environ 93:402–411. doi: 10.1016/j.rse.2004.08.008 CrossRefGoogle Scholar
  71. Zheng D, Heath LS, Ducey MJ (2007) Forest biomass estimated from MODIS and FIA data in the Lake States: MN, WI, and MI, USA. Forestry 80:265–278. doi: 10.1093/forestry/cpm015 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.Department of Natural Resources and the EnvironmentUniversity of New HampshireDurhamUSA
  2. 2.USDA Forest Service, Northern Research StationDurhamUSA

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