Assessment of Regional Forest Health and Stream and Soil Chemistry Using a Mulit-Scale Approach and New Methods of Remote Sensing Interpretation in the Catskill Mountains of New York
The overall goal of this project has been the development of forest health and sensitivity indicators and “1st-generation” maps of potential sensitivity to disturbance for lands within watersheds of the NYC water supply in the Catskill Mountains of New York. The methodologies and data layers created in this effort can now be used to aid management decisions and help determine the extent and magnitude of terrestrial and aquatic responses to acidic deposition. The data products derived from this effort have been produced and documented in such a manner that stakeholders can now use these products for site evaluation as well as to perform more extensive analysis on the suite of readily available GIS and image-based data products.
The value of a spatially explicit dataset such as this one lies in the ability to test a wide variety of hypotheses or ask specific management related questions of the data and have the answer mapped across 700,000 acres in the Catskills. In this report we take a case study approach to illustrate this flexibility. We discuss three case studies that ask questions ranging from the highly practical “Where is sugar maple most susceptible to decline?”; to “Can we predict and map a key streamwater acidification index without sampling a stream?”; and finally we create a theoretical index of “ecosystem sensitivity” using streamwater, soil, and foliar chemistry, forest health, and nitrogen deposition. Ultimately we hope to see this tool deployed on the web allowing land managers and scientists to design their own queries based upon criteria and thresholds that are important to them.
The project will facilitate future assessments of forest condition and the creation of more detailed forest sensitivity maps to be made at reduced expense.
Project Location by County
Catskill Region:
Delaware County, NY, Greene County, NY, Schoharie County, NY, Sullivan County,
NY, Ulster County, NY
- Source: USGS Sciencebase (id: 55c9f580e4b08400b1fdb74f)
The overall goal of this project has been the development of forest health and sensitivity indicators and “1st-generation” maps of potential sensitivity to disturbance for lands within watersheds of the NYC water supply in the Catskill Mountains of New York. The methodologies and data layers created in this effort can now be used to aid management decisions and help determine the extent and magnitude of terrestrial and aquatic responses to acidic deposition. The data products derived from this effort have been produced and documented in such a manner that stakeholders can now use these products for site evaluation as well as to perform more extensive analysis on the suite of readily available GIS and image-based data products.
The value of a spatially explicit dataset such as this one lies in the ability to test a wide variety of hypotheses or ask specific management related questions of the data and have the answer mapped across 700,000 acres in the Catskills. In this report we take a case study approach to illustrate this flexibility. We discuss three case studies that ask questions ranging from the highly practical “Where is sugar maple most susceptible to decline?”; to “Can we predict and map a key streamwater acidification index without sampling a stream?”; and finally we create a theoretical index of “ecosystem sensitivity” using streamwater, soil, and foliar chemistry, forest health, and nitrogen deposition. Ultimately we hope to see this tool deployed on the web allowing land managers and scientists to design their own queries based upon criteria and thresholds that are important to them.
The project will facilitate future assessments of forest condition and the creation of more detailed forest sensitivity maps to be made at reduced expense.
Project Location by County
Catskill Region:
Delaware County, NY, Greene County, NY, Schoharie County, NY, Sullivan County,
NY, Ulster County, NY
- Source: USGS Sciencebase (id: 55c9f580e4b08400b1fdb74f)