Advertisement

A five-step protocol for estimating forest cover and rate of change in the New York City watershed

  • Mehmet Yavuz
  • Myrna H. P. Hall
Article

Abstract

New York City drinking water quality depends on retention of forest cover in its Catskill Mountains watersheds, yet multiple published analyses of temporally approximate satellite imagery derived no definitive nor agreed upon quantification of either forest cover in the watershed, or, more importantly, its rate of change over time. The objective of this work was to reduce uncertainty surrounding these estimates. We developed a five-pronged protocol that included (1) creation of a 1975–2002 time-series of land use/land cover (LULC) using Cross-Correlation Analysis (CCA); (2) a corrective post classification logic-based algorithm to correct for illogical transitions; (3) a probability-based stratified random sample accuracy assessment; (4) joint probability calculations of the “true” 2002 class proportions; and (5) verification of quantities of our LULC classification, and those of other researchers, versus the statistically derived true proportions. The estimated true percent of forest cover as of 2002 is 72%, far less than that reported by other studies, even with a net reforestation between 1975 and 2002. This protocol is an enhancement over previous LULC monitoring methods. Its more robust estimates of both historic trends and 2002 forest cover reveal information that is vitally important to monitoring and managing future water quality for the nation’s largest city.

Keywords

Cross-correlation analysis Change detection Catskill/Delaware watersheds Water quality 

Notes

Acknowledgments

This study is an enhanced version of Yavuz and Hall (2011), The land use and land cover classification of the Catskill/Delaware Watersheds for years 1975, 1987, 1991 and 2002, chapter 4.1.6 (pp. 76–100)) in M. Hall, R. Germain, M. Tyrrell, and N. Sampson (Eds.), Predicting Future Water Quality from Land Use Change Projections in the Catskill-Delaware Watersheds: Final Report to the New York State Department of Environmental Conservation (available online at: www.esf.edu/cue/documentsCatskill_Delaware_Study.pdf). We extend our gratitude to the following individuals for their participation, support, and encouragement: Mary Tyrrell and Neil Sampson at Yale University Global Institute of Sustainable Forestry, School of Forestry and Environmental Studies; David Smith, Jim Mayfield and Terry Spies at New York City Dept. of Environmental Protection; Bruce Musset and Ken Markussen at NYSDEC; Colin Homer, USGS, Sioux Falls, SD; Rene Germain, Eddie Bevilacqua, and Steve Stehman at State University of New York College of Environmental Science and Forestry; Brett Butler, USDA Forest Service Family Forest Research Center; Jeffrey Walton, USDA Forest Service, Syracuse, NY and to the following organizations for their collaboration: New York City Watershed Agricultural Council; Institute for Applied Geospatial Technology, Auburn, NY, for supplying additional imagery used in cloud-cover removal; All the Family Forest Owners who participated in the study. We are grateful to Daniel L. Civco and James D. Hurd at University of Connecticut Dept. of Natural Resources Management and Engineering, for their support in integrating the CCA algorithm into the ERDAS Imagine platform.

Author contributions

M.Y. and M.H.P.H. conceived, designed, and performed the experiments, analyzed the data, contributed materials/analysis tools and wrote the paper.

Funding information

It was funded by the New York State Department of Environmental Conservation (NYSDEC), under the US Environmental Protection Agency Safe Drinking Water Act for NYC Watershed Protection, with additional support from the Edna Bailey Sussman Fund and the McIntire-Stennis program.

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest.

References

  1. Anderson, J. R., Hardy, E. E., Roach, J. T., & Witmer, R. E. (1976). A land use and land cover classification system for use with remote sensor data, vol. 964. US Geological Survey, pp. 1–28.Google Scholar
  2. Anderson, N., Germain, R., & Hall, M. (2012). An assessment of forest cover and impervious surface area on family forests in the New York City watershed. Northern Journal of Applied Forestry, 29(2), 67–73.CrossRefGoogle Scholar
  3. Baker, C., Lawrence, R. L., Montagne, C., & Patten, D. (2007). Change detection of wetland ecosystems using Landsat imagery and change vector analysis. Wetlands, 27(3), 610–619.CrossRefGoogle Scholar
  4. Banskota, A., Kayastha, N., Falkowski, M. J., Wulder, M. A., Froese, R. E., & White, J. C. (2014). Forest monitoring using Landsat time series data: a review. Canadian Journal of Remote Sensing, 40(5), 362–384.  https://doi.org/10.1080/07038992.2014.987376.CrossRefGoogle Scholar
  5. Canty, M., Nielsen, A., & Schmidt, M. (2004). Automatic radiometric normalization of multitemporal satellite imagery. Remote Sensing of Environment, 91(3–4), 441–451.CrossRefGoogle Scholar
  6. Ceccato, P., Gobron, N., Flasse, S., Pinty, B., & Tarantola, S. (2002). Designing a spectral index to estimate vegetation water content from remote sensing data. Part 1: Theoretical approach. Remote Sensing of Environment, 82(2), 188–197.CrossRefGoogle Scholar
  7. Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5), 893–903.CrossRefGoogle Scholar
  8. CIE (2004). Colorimetry, Publication CIE 15: 2004. International Commission on Illumination, Vienna, Austria. ISBN 3-901-906-33-9.Google Scholar
  9. Civco, D. L., Hurd, J. D., Wilson, E. H., Song, M., & Zhang, Z. (2002). A comparison of land use and land cover change detection methods. In: ASPRS-ACSM Annual Conference, 22–26 April 2002.Google Scholar
  10. Congalton, R. G., & Green, K. (1999). Assessing the accuracy of remotely sensed data: principles and applications. Boca Raton, FL: Lewis Publishers.Google Scholar
  11. Coppin, P. R., & Bauer, M. E. (1996). Digital change detection in forest ecosystems with remote sensing imagery. Remote Sensing Reviews, 13(3–4), 207–234.CrossRefGoogle Scholar
  12. Czaplewski, R. (2003). Accuracy assessment of maps of forest condition: statistical design and methodological considerations. In M. Wulder & S. Franklin (Eds.), Remote sensing of forest environments (pp. 115–140). USA: Springer.CrossRefGoogle Scholar
  13. Dobson, J. E., Bright, E., Ferguson, R., Field, D., Wood, L., Haddad, K., et al. (1995). NOAA Coastal Change Analysis Program (C-CAP): guidance for regional implementation: US Department of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service.Google Scholar
  14. Dow, C. L., Arscott, D. B., & Newbold, J. D. (2006). Relating major ions and nutrients to watershed conditions across a mixed-use, water-supply watershed. Journal of the North American Benthological Society, 25(4), 887–911.CrossRefGoogle Scholar
  15. Dudley, N., & Stolton, S. (2003). Running pure: the importance of forest protected areas to drinking water: World Bank/WWF Alliance for Forest Conservation and Sustainable Use. https://openknowledge.worldbank.org/handle/10986/15006 License: CC BY 3.0 IGO. Acessed 25 Feb 2017.
  16. Duggin, M. J., Kinn, G. J., Muller, J. K., Myeong, S., Yavuz, M., Florack, C., & Walton, J. (1999). Effect of altitude, view angle and sun angle, view angle correction procedures, and the atmosphere on deduced vegetative indices. In: Airborne Reconnaissance XXIII: SPIE’s International Symposium on Optical Science, Engineering, and Instrumentation (7 December 1999), Denver, CO, USA, 1999 (Vol. 3751, pp. 102–112): Society of Photo-Optical Instrumentation Engineers (SPIE). doi: https://doi.org/10.1117/12.372644.
  17. Eastman, J. R., Sangermano, F., Ghimire, B., Zhu, H. L., Chen, H., Neeti, N., et al. (2009). Seasonal trend analysis of image time series. International Journal of Remote Sensing, 30(10), 2721–2726.  https://doi.org/10.1080/01431160902755338.CrossRefGoogle Scholar
  18. ERDAS (2008). ERDAS field guide. ERDAS software. ERDAS Inc. Georgia. USA.Google Scholar
  19. Feyisa, G. L., Meilby, H., Fensholt, R., & Proud, S. R. (2014). Automated water extraction index: a new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140, 23–35.CrossRefGoogle Scholar
  20. Foody, G. (2001). Monitoring the magnitude of land-cover change around the southern limits of the Sahara. Photogrammetric Engineering and Remote Sensing, 67(7), 841–848.Google Scholar
  21. Foody, G. M. (2003). Remote sensing of tropical forest environments: towards the monitoring of environmental resources for sustainable development. International Journal of Remote Sensing, 24(20), 4035–4046.  https://doi.org/10.1080/0143116031000103853.CrossRefGoogle Scholar
  22. Fry, J., Coan, M., Homer, C., Meyer, D., & Wickham, J. (2009). Completion of the National Land Cover Database (NLCD) 1992–2001 land cover change retrofit product. US Geological Survey.Google Scholar
  23. Hall, M., Germain, R., Tyrrell, M., & Sampson, N. (2011). Predicting future water quality from land use change projections in the Catskill-Delaware Watersheds: revised final report to the New York State Department of Environmental Conservation. The State University of New York College of Environmental Science and Forestry and the Global Institute of Sustainable Forestry Yale University School of Forestry and Environmental Studies: New York, NY, USA, 308pp. Available at: www.esf.edu/cue/documents/Catskill_Delaware_study.pdf. Accessed 01 Dec 2017.
  24. Henits, L., Jürgens, C., & Mucsi, L. (2016). Seasonal multitemporal land-cover classification and change detection analysis of Bochum, Germany, using multitemporal Landsat TM data. International Journal of Remote Sensing, 37(15), 3439–3454.  https://doi.org/10.1080/01431161.2015.1125558. Google Scholar
  25. Herold, M., Schiefer, S., Hostert, P., & Roberts, D. A. (2006). Applying imaging spectrometry in urban areas. Urban Remote Sensing, 137–161.Google Scholar
  26. Homer, C., Huang, C., Yang, L., Wylie, B., & Coan, M. (2004). Development of a 2001 National Land Cover Database for the United States. Photogrammetric Engineering & Remote Sensing, 70(7), 829–840.CrossRefGoogle Scholar
  27. Homer, C., Dewitz, J., Fry, J., Coan, M., Hossain, N., Larson, C., et al. (2007). Completion of the 2001 National Land Cover Database for the Counterminous United States. Photogrammetric Engineering and Remote Rensing, 73(4), 337–341.Google Scholar
  28. Honsinger, C. W. (2004). Method for detecting rotation and magnification in images. Patent No: US6711303B1. Intellectual Ventures Fund 83 LLC.Google Scholar
  29. Hu, Y., Liu, L., Liu, L., & Jiao, Q. (2011). Comparison of absolute and relative radiometric normalization use Landsat time series images. In MIPPR 2011: remote sensing image processing, geographic information systems, and other applications, vol. 8006, p. 800616. International Society for Optics and Photonics. doi:  https://doi.org/10.1117/12.902076.
  30. Hurd, J. D., Civco, D. L., LaBash, C., & August, P. (1992). Coastal wetland mapping and change detection in the northeast United States. In Proc. 1992 ASPRS/ACSM/RT'92 Convention, Washington, DC, vol. 1, pp. 130–139.Google Scholar
  31. Hurd, J. D., Wilson, E. H., Lammey, S. G., & Civco, D. L. (2001). Characterization of forest fragmentation and urban sprawl using time sequential Landsat imagery. In Proceedings of the ASPRS Annual Convention, St. Louis, MO, pp. 2001.Google Scholar
  32. Jensen, J. R. (1996). Introduction to digital image processing (p. 318). NewJersy: Printice-Hall.Google Scholar
  33. Koeln, G., & Bissonnette, J. (2000). Cross-correlation analysis: mapping landcover change with a historic landcover database and a recent, single-date multispectral image. In Proc. 2000 ASPRS Annual Convention, Washington, DC. Google Scholar
  34. Koeln, G. T., & Mitchell, R. A. (1998). Process and apparatus for cross-correlating digital imagery. Patent No. US5719949A, MDA Information Systems LLC.Google Scholar
  35. Krejmas, B. E., Paulachok, G. N., & Blanchard, S. F. (2006). Report of the River Master of the Delaware River for the Period December 1, 2001–November 30, 2002, pp. 80. US Geological Survey.Google Scholar
  36. Lahey, T. (1997). Lahey FORTRAN 90 language reference and user guide. LF90 v. 4.0. Lahey Computer Systems. Inc., NV.Google Scholar
  37. Lied, T. T., Geladi, P., & Esbensen, K. H. (2000). Multivariate image regression (MIR): implementation of image PLSR—first forays. Journal of Chemometrics, 14(5–6), 585–598.CrossRefGoogle Scholar
  38. Likens, G. E. (2013). Biogeochemistry of a forested ecosystem. Springer Science & Business Media.Google Scholar
  39. Lillesand, T., Kiefer, R. W., & Chipman, J. (2014). Remote sensing and image interpretation. John Wiley & Sons.Google Scholar
  40. Liu, H., & Zhou, Q. (2004). Accuracy analysis of remote sensing change detection by RuleBased rationality evaluation with post-classification comparison. International Journal of Remote Sensing, 5(25), 1037–1050.CrossRefGoogle Scholar
  41. Lunetta, R. S., & Elvidge, C. D. (1999). Remote sensing change detection: environmental monitoring methods and applications. Taylor & Francis Ltd.Google Scholar
  42. Malila, W. A. (1980). Change vector analysis: an approach for detecting forest changes with Landsat. In LARS Symposia, p. 385.Google Scholar
  43. Mas, J. F. (2005). Change estimates by map comparison: A method to reduce erroneous changes due to positional error. Transactions in GIS, 9(4), 619–629.CrossRefGoogle Scholar
  44. Mehaffey, M., Nash, M., Wade, T., Ebert, D., Jones, K., & Rager, A. (2005). Linking land cover and water quality in new York City’s water supply watersheds. Environmental Monitoring and Assessment, 107(1–3), 29–44.CrossRefGoogle Scholar
  45. NALC (2006). North American Landscape Characterization (NALC) Triplicates data set. https://lta.cr.usgs.gov/NALC. Accessed 20 Nov 2017.
  46. NOAA (2006). The Coastal Change Analysis Program (C-CAP). http://www.csc.noaa.gov/crs/lca/ccap.html. Accessed 10 Dec 2016.
  47. NY State GIS Clearing House (2001). Digital Ortho Quarter Quads Program. NYS Office of Information Technology Services (ITS). http://gis.ny.gov/gateway/mg/. Accessed 5 Dec 2015.
  48. NYC DEP (2010). Drinking water supply and quality report. New York City Department of Environmental Protection, New York, NY. http://www.nyc.gov/html/dep/pdf/wsstate10.pdf, Accessed 1 October 2017.
  49. NYC DEP (2011). New York City Watershed Forest Management Plan CAT-374. New York City Department of Environmental Protection, New York, NY. http://www.nyc.gov/html/dep/pdf/watershed_protection/dep_forest_management_plan_2011.pdf. Accessed 24 Nov 2017.
  50. NYC DEP (2016). Drinking water supply and quality report. New York City Department of Environmental Protection, New York, NY. http://www.nyc.gov/html/dep/pdf/wsstate16.pdf. Accessed 15 Nov 2017.
  51. NYC WAC (2010). New York City Watershed Forestry Program. http://www.nycwatershed.org/forest0027.html. Accessed 4 Dec 2015.
  52. NYS DEC (2013). Freshwater Wetlands Mapping. http://www.dec.ny.gov/lands/5124.html. Accessed 07 Nov 2017.
  53. NYSDOH (2017). New York City filtration avoidance determination: 2017 surface water treatment rule determination for New York City’s Catskill/Delaware Water Supply System, New York State Department of Health; https://www.health.ny.gov/environmental/water/drinking/nycfad/docs/final_draft_2017_fad.pdf. Accessed 12 Nov 2017.
  54. Pan, Y., Zhang, X., Tian, J., Jin, X., Luo, L., & Yang, K. (2017). Mapping asphalt pavement aging and condition using multiple endmember spectral mixture analysis in Beijing, China. Journal of Applied Remote Sensing, 11(1), 016003.  https://doi.org/10.1117/1.JRS.11.016003.CrossRefGoogle Scholar
  55. Pontius Jr., R. G., & Li, X. (2010). Land transition estimates from erroneous maps. Journal of Land Use Science, 5(1), 31–44.CrossRefGoogle Scholar
  56. Richards, J. A. & Jia, X. (2006). Remote sensing digital image analysis: an introduction, 4th edition. XXV + 439 pp. Berlin: Springer-Verlag.Google Scholar
  57. Schneiderman, E. M., Pierson, D. C., Lounsbury, D. G., & Zion, M. S. (2002). Modeling the hydrochemistry of the Cannonsville watershed with generalized watershed loading functions (GWLF). Journal of the American Water Resources Association, 38(5), 1323–1347.CrossRefGoogle Scholar
  58. Schneiderman, E. M., Steenhuis, T. S., Thongs, D. J., Easton, Z. M., Zion, M. S., Neal, A. L., Mendoza, G. F., & Todd Walter, M. (2007). Incorporating variable source area hydrology into a curve-number-based watershed model. Hydrological Processes, 21(25), 3420–3430.CrossRefGoogle Scholar
  59. Schowengerdt, R. A. (2007). Chapter 9—thematic classification. In Remote sensing (3rd ed.). Burlington: Academic Press.  https://doi.org/10.1016/B978-012369407-2/50012-7.Google Scholar
  60. Schroeder, T. A., Cohen, W. B., Song, C., Canty, M. J., & Yang, Z. (2006). Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon. Remote Sensing of Environment, 103(1), 16–26.CrossRefGoogle Scholar
  61. Singh, A. (1986). Change detection in the tropical forest environment of northeastern India using Landsat. In M.J. Eden and J.T. Parry (eds.) Remote sensing and tropical land management, John Wiley & Sons, London, 237–254.Google Scholar
  62. Singh, A. (1989). Review article digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6), 989–1003.CrossRefGoogle Scholar
  63. Stehman, S. V. (2012). Impact of sample size allocation when using stratified random sampling to estimate accuracy and area of land-cover change. Remote Sensing Letters, 3(2), 111–120.  https://doi.org/10.1080/01431161.2010.541950.CrossRefGoogle Scholar
  64. Stehman, S. V., & Czaplewski, R. L. (1998). Design and analysis for thematic map accuracy assessment: fundamental principles. Remote Sensing of Environment, 64, 331–344.CrossRefGoogle Scholar
  65. Stehman, S. V., & Foody, G. M. (2009). Accuracy assessment. In The SAGE handbook of remote sensing (pp. 297–309). Thousand Oaks, CA: SAGE Publications.CrossRefGoogle Scholar
  66. Stehman, S. V., & Wickham, J. D. (2006). Assessing accuracy of net change derived from land cover maps. Photogrammetric Engineering & Remote Sensing, 72(2), 175–185.CrossRefGoogle Scholar
  67. Stehman, S. V., Wickham, J., Smith, J., & Yang, L. (2003). Thematic accuracy of the 1992 National Land-Cover Data for the eastern United States: statistical methodology and regional results. Remote Sensing of Environment, 86(4), 500–516.CrossRefGoogle Scholar
  68. Teillet, P., Barker, J., Markham, B., Irish, R., Fedosejevs, G., & Storey, J. (2001). Radiometric cross-calibration of the Landsat-7 ETM+ and Landsat-5 TM sensors based on tandem data sets. Remote Sensing of Environment, 78(1), 39–54.CrossRefGoogle Scholar
  69. Tyrrell, M., Hall, M., & Sampson, R. (2004). Dynamic models of land use change in northeastern USA: Developing tools, techniques, and talents for effective conservation action. GISF Research Paper(003).Google Scholar
  70. USDA Forest Service (2010). Riparian forest buffer program. Watershed Agricultural Council: USDA Forest Service, New York, NY.Google Scholar
  71. USGS (2017). SLC-off products: background. https://landsat.usgs.gov/slc-products-background. Accessed 16 Sept 2017.
  72. Vogelmann, J., Sohl, T., Campbell, P., & Shaw, D. (1998). Regional land cover characterization using Landsat thematic mapper data and ancillary data sources. Environmental Monitoring and Assessment, 51(1–2), 415–428.CrossRefGoogle Scholar
  73. Weng, Q. (2007). Remote sensing of impervious surfaces. Boca Raton, FL: CRC Press.CrossRefGoogle Scholar
  74. Wickham, J., Stehman, S. V., Fry, J., Smith, J., & Homer, C. (2010). Thematic accuracy of the NLCD 2001 land cover for the conterminous United States. Remote Sensing of Environment, 114(6), 1286–1296.CrossRefGoogle Scholar
  75. Woodwell, G. M., Houghton, R. A., Stone, T. A., & Park, A. B. (1986). Changes in the area of forests in Rondônia, Amazon Basin, measured by satellite imagery. In J. R. Trabalka & D. E. Reichle (Eds.), The changing carbon cycle: a global analysis (pp. 242–257). Berlin: Springer.CrossRefGoogle Scholar
  76. Wyszecki, G., & Stiles, W. S. (1982). Color science (Vol. 8). New York: Wiley.Google Scholar
  77. Xie, H., Luo, X., Xu, X., Tong, X., Jin, Y., Pan, H., & Zhou, B. (2014). New hyperspectral difference water index for the extraction of urban water bodies by the use of airborne hyperspectral images. Journal of Applied Remote Sensing, 8(1), 085098_01-085098_15.Google Scholar
  78. Yang, X., & Lo, C. P. (2000). Relative radiometric normalization performance for change detection from multi-date satellite images. Photogrammetric Engineering and Remote Sensing, 66(8), 967–980.Google Scholar
  79. Yavuz, M. & Hall, M.P.H. (2011). The land use and land cover classification of the Catskill/Delaware Watersheds for years 1975, 1987, 1991 and 2002, section 4.1.6. In M. Hall, R. Germain, M. Tyrrell, N. Sampson (Eds.), Predicting future water quality from land use change projections in the Catskill-Delaware Watersheds: final report to the New York State Department of Environmental Conservation (pp. 79–98). The State University of New York College of Environmental Science and Forestry and the Global Institute of Sustainable Forestry Yale University, School of Forestry and Environmental Studies: New York, NY, USA, 308p. Available online at www.esf.edu/cue/documents/Catskill_Delaware_Study.pdf. Accessed 01 Dec 2017.
  80. Yuan, D., Elvidge, C. D., & Lunetta, R. S. (1999). Survey of multispectral methods for land cover change analysis. In R. S. Lunetta & C. D. Elvidge (Eds.), Remote sensing change detection: Environmental monitoring methods and applications (pp. 21–39). London, UK: Taylor & Francis Ltd..Google Scholar
  81. Zhang, Y., & Guindon, B. (2003). Quantitative assessment of a haze suppression methodology for satellite imagery: effect on land cover classification performance. IEEE Transactions on Geoscience and Remote Sensing, 41(5), 1082–1089.CrossRefGoogle Scholar
  82. Zhu, Z., & Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment, 144, 152–171.CrossRefGoogle Scholar
  83. Zohner, C. M., Benito, B. M., Svenning, J. C., & Renner, S. S. (2016). Day length unlikely to constrain climate-driven shifts in leaf-out times of northern woody plants. Nature Climate Change, 6(12), 1120–1123.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Environmental Science and ForestryState University of New YorkSyracuseUSA
  2. 2.Faculty of Forestry, Department of Forest EngineeringArtvin Coruh UniversityArtvinTurkey

Personalised recommendations