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

  • Mehmet Yavuz
  • Myrna H. P. Hall


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.


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



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: 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.


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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

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