Vermont Forest Indicators Dashboard

Crown Dieback

Crown dieback is a visual assessment of the percentage of the tree’s crown that has recent fine twig mortality. Crown dieback has been assessed annually on Vermont Forest Health Monitoring¹ (FHM, a collaboration between FEMC and VT FPR) and North American Maple Project (NAMP) plots². For the FHM program, plots have been assessed annually since 1994, but between 2008 and 2013, plots were assessed on a 3-year rotation. To generate values for the missing years of 2009 and 2012, we used the average dieback from 2008-2010 and 2011-2013, respectively. FHM dieback scores were averaged per plot per year of survey data for dominant and codominant trees of all species. NAMP plots have assessed on an annual basis, beginning in 1988. While maple species are the focus of the NAMP project, co-occurring species have also been assessed. To approach the unequal species composition on NAMP plots, we selected only those plots where dominant and codominant trees were less than 80% sugar maple. We averaged dieback within species in a plot prior to averaging across plots. We then computed the mean crown dieback for all species across all plots per year in NAMP. From these two data sources, we computed an overall mean crown dieback for all species for each year by taking the mean of the NAMP and FHM yearly dieback averages. For crown dieback, the target was set as the long-term mean. We computed the current year score as the distance between the target and the upper scoring bounds (maximum value in the dataset plus 10% of the range), scaled to be between 1 and 5. Values below the target were scored as 5.

Forest Damage

Insect and Disease Surveys (IDS) are annual aerial surveys of forests conducted by the State of Vermont and the USDA Forest Service to map forest disturbance, including damage caused by insects, diseases, fire, and weather events¹. While the program has been conducted for many decades in Vermont, the first year of digitally available data was 1995. This metric is computed as a sum of forest area identified with visible damage in a given year (excluding any areas identified as current-year mortality) that has occurred since the previous survey. We set the target for this dataset to be the minimum value in the dataset minus 10% of the range. The current year is scored as the difference between the target and the upper scoring bounds (maximum value in the dataset plus 10% of the range), scaled to be between 1 and 5.

Tree Growth

Data on tree growth were extracted from the USFS Forest Inventory and Analysis Program EVALIDator¹. We used an FIA-established query (“Average annual net growth of sound bole volume of trees (at least 5 inches d.b.h./d.r.c.), in cubic feet, on forest land”) for net growth of live trees ≥5 inch DBH on sampled P1 plots. The first usable panel is for the year 2008. We relied on FIA’s statistical models for computing this value over time. We set the target for this dataset as the long-term mean. The current year is scored for where it falls between the target and the upper scoring bounds (maximum value in the dataset plus 10% of the range) or the lower scoring bounds (minimum value in the dataset minus 10% of the range), scaled to be between 1 and 5.

Canopy Density

Using MODIS phenology remote sensing products, we selected the Time Integrated (TIN) Normalized Difference Vegetation Index (NDVI) to quantify maximum canopy greenness over a growing season¹. Available TIN images began in 2001. We first excluded all pixels with a value of either 0 where no chlorophyll was detected throughout the growing season, or 255 for water or areas of no data. Using National Land Cover Dataset (2011), we created a forest cover layer by masking out all non-forest pixels (i.e. those not identified as forest/woody wetlands [values 41, 42, 43, 90]). We used this forest mask to compute the mean TIN and standard deviation of the mean TIN across all forest-cover pixels per year. We set the target for this dataset as the maximum value. The current year was scored based on where it falls between zero and the target, scaled to be between 1 and 5.

Forest Mortality

Insect and Disease Surveys (IDS) are annual aerial surveys of forests conducted by the state of Vermont and the USDA Forest Service to map areas of tree mortality¹. While the program has been conducted for many decades in Vermont, the first year of digitally available data is 1995. This metric is computed as a sum of total area mapped with visible mortality in a given year that has occurred since the previous survey. We set the target for this dataset to be lowest possible acreage (either the minimum value in the data minus 10% of range or 0, whichever was greater). The current year is scored as the difference between the target and the upper scoring bounds (maximum value in the dataset plus 10% of the range), scaled to be between 1 and 5.

Damage and Decay

Data on tree damage and decay were extracted from the USFS Forest Inventory and Analysis (FIA) Program EVALIDator¹. We used an FIA-established query to retrieve the number of live trees (“0004 Number of live trees (at least 1 inch d.b.h./d.r.c.), in trees, on forest land”) filtered by trees ≥5 inch DBH (“and DIA>=5”). We computed tree damage and decay as any living tree ≥5 inch DBH with a recorded damage agent (“and DAMAGE_AGENT_CD1>0)². We set the target for this dataset as the long-term mean. The current year is scored for where it falls between the target and the upper scoring bounds (maximum value in the dataset plus 10% of the range) or the lower scoring bounds (minimum value in the dataset minus 10% of the range), scaled to be between 1 and 5.

Tree Mortality

Data on annual tree mortality were extracted from the USFS Forest Inventory and Analysis Program EVALIDator¹. We used FIA-established queries to retrieve volume estimates for tree mortality (“0213 Average annual mortality of sound bole volume of trees (at least 5 inches d.b.h./d.r.c.), in cubic feet, on forest land”) and live trees (“0024 Sound bole volume of live trees (at least 5 inches d.b.h./d.r.c.), in cubic feet, on forest land”) filtered by trees ≥5 inch DBH (“and tree.DIA>=5”). We computed tree mortality as the ratio of the annual mortality tree volume relative to the volume of all live trees ≥5 inch DBH on sampled plots. Annual data began in 2008 with a more complete inventory in 2010. We relied on FIA’s statistical models for computing this value over time. We set the target for this dataset as the long-term mean. The current year is scored for where it falls between the target and the upper scoring bounds (maximum value in the dataset plus 10% of the range) or the lower scoring bounds (minimum value in the dataset minus 10% of the range), scaled to be between 1 and 5.

Timber Harvest

Using data collected in the Vermont Forest Resource Harvest Report¹, we presented the total quantity of timber harvested in Vermont per year, which is reported as cords of wood. We set the target for these data as the maximum value in the dataset plus 10% of the range. The annual score was computed as the difference between the lower scoring bounds (either the minimum value in the data minus 10% of the range or 0, whichever was greater) and the target, was scaled between 1 and 5.

Stream Indicator Species

Species richness was collected from the Vermont Integrated Watershed Information System¹. We selected one location, Ranch Brook (site #502032) in Underhill VT, where species richness metrics were computed for macroinvertebrates. Data from other locations in the state did not have annually-resolved data with a sufficient long-term record to warrant inclusion. We used the EPT richness score as computed by the datasetauthors². EPT richness is a count of the unique number of taxa identified in the sample that belong to a trio of particularly sensitive macroinvertebrate orders - Ephemeroptera (mayflies), Plecoptera (stoneflies), and Tricoptera (caddisflies). In cases where a specimen could not be identified to the species level, it was only counted as unique taxon if no other specimens were identified to that level or below. In some cases, there were multiple samples in a single year. In these cases, we took the mean of the EPT richness scores as the value for the year. The target for these data was set to be the maximum value plus 10% of the range. The annual score was computed as the difference between the lower scoring bound of 0 (indicating no EPT taxa were identified) and the target, scaled between 1 and 5.

Hunting Harvests

We used data on the annual number of meals procured from harvesting big game animals (white-tailed deer, black bear, moose, and wild turkey), as reported by the Vermont Department of Fish and Wildlife¹. We set the data target as the data maximum plus 10% of the range. The annual score was computed as the difference between the lower scoring bound of 0 (indicating that no hunting harvests were reported) and the target, scaled to be between 1 and 5.

Carbon Storage

Data on carbon storage were extracted from the USFS Forest Inventory and Analysis Program EVALIDator¹. We used an FIA-established query (“0103 Forest carbon total: all 5 pools, in metric tonnes, on forest land”) accessed by the FIA EVALIDator¹. This query computed the total carbon storage (in MgT) on FIA plots across all carbon pools. The first available year of data was 1997. The data target was set at the maximum value in the dataset plus 10% of the range. The annual score was computed as the difference between the lower scoring bounds (either the minimum value in the data minus 10% of range or 0, whichever was greater) and the target, scaled between 1 and 5.

Maple Syrup Production

We used the total annual maple syrup revenue (in dollars) reported by the USDA National Agriculture Statistics Service¹. We set the dataset target as the maximum value in the dataset plus 10% of range. The annual score was then computed as the distance between the lower scoring bounds (either the minimum value in the data minus 10% of range or 0, whichever was greater) and the target, scaled between 1 and 5.

Recreation Rates

Data on camping and day use visitation counts for state park in Vermont were accessed from the Vermont Department of Forest Parks and Recreation¹. Note that these data do not contain all Vermont public lands visitation counts; for example, Camel’s Hump State Park is managed by the Green Mountain Club and those data are not collected by the state of Vermont. To process these data, we first classified each park parcel² in the FPR dataset by the percent of forest cover using the National Land Cover Dataset³. The goal of this step was to subset state parks where forests were a predominant reason visitors came to the park, as opposed to water bodies or other types of non-forested natural resources. We classified parks based on the percent of Deciduous, Evergreen, and Mixed Forest Cover according to the NLCD (codes 41, 42, 43). We did not include Woody Wetland cover (code 90) because many parks that are adjacent to water bodies were classified as having a high amount of forest cover when this land cover type was included. Based on our familiarity with many of the state parks, we selected 60% forest cover as the threshold for a park considered forested. We subsetted all park visitation data to include only those parks with >60% forest cover as determined by NLCD. Total visitation counts (both camping and day use) of the 41 forested state parks were averaged by the number of parks with data per year beginning in 1970. We set the dataset target as the maximum value in the dataset plus 10% of the range. The annual score was computed as the difference between the lower scoring bounds (minimum value in the data minus 10% of the range) and the target, scaled between 1 and 5.

Forest Bird Diversity

Data on forest bird counts by species were collected by Vermont Center for Ecostudies¹ at forested locations throughout Vermont beginning in 1989, with a more complete survey of the selected sites in 1990. From these data, we computed a Living Planet Index (LPI) ². We used a beta package for R, rlpi³, to compute the LPI for all bird species. We used equal weighting among species and no sub-groupings. Only those Forest Bird Monitoring sites with a complete record were included: Bear Swamp, Concord Woods, Dorset Bat Cave, Galick Preserve, Maypond, Moosebog, Pease Mountain, Roy Mt WMA, Sandbar WMA, Sugar Hollow, The Cape, and Underhill State Park. Living Planet Index ranges from zero to two. We computed the index and set the target to 1, and the current year is scored as the difference between the target and the current year value, scaled to be between 1 and 5.

Precipitation Acidity

Data on the pH of precipitation were accessed from the National Atmospheric Deposition Program (NADP) ¹. Data were collected from the Underhill, Vermont, station. We set a target for precipitation acidity of 5.6, based on a previously established value ². The annual score was computed as the difference between the lower scoring bounds (minimum value in the data minus 10% of the range) and the target value (5.6). This difference was then scaled between 1 and 5. Values above the target receive a 5.

Growing Season Length

Using MODIS remotely sensed phenology products, we selected the Growing Season Duration (DUR) dataset as an assessment of the total length of the functional growing season¹. We clipped imagery to the state of Vermont boundary², and masked to limit only those pixels identified as forested (Deciduous (41), Evergreen (42), and Mixed (43), and Woody Wetlands (90) cover pixels), according to 2011 National Land Cover Dataset³. In each MODIS image, we excluded values of-1000 (unknown) and 1000 (water), then calculated the mean pixel value per image. We also computed the mean for each pixel over all image years and then computed the mean across pixels to establish the long-term mean of the dataset. We set the data target as the long-term mean. The current year is scored for where it falls between the target and the upper scoring bounds (maximum value in the dataset plus 10% of the range) or the lower scoring bounds (minimum value in the dataset minus 10% of the range), scaled to be between 1 and 5.

Ozone Exposure

Daily ozone data (ppm-hour) was accessed from the air quality monitoring sites at Underhill (Proctor Maple Research Forest) and Bennington (Morse Airport), Vermont¹. To compute an ozone exposure index, we selected the W126 standard² because it is thought to be better representation of ozone injury to plants. Under this, Vermont’s ozone monitoring season runs April 1 to September 30 and growing hours are 8:00 am to 8:00 pm. We determined the minimum detection limit per site (here 0.005 for both sites over the entire record). We backfilled missing values with the minimum value observed per year, restricted to the established monitoring season and growing hours. All months must have >75% completeness in daily records to be utilized. Data were transform following the equation: OZ*(1/(1+4403*EXP((OZ*(-126))))). A daily index value was calculated by summing the transformed values per day; these were then summed per month to compute a monthly index value. These monthly indices were multiplied by the ratio of collected and backfilled samples to total possible samples within the month. For each month, we computed a three-month maximum -- the current month plus the two preceding months (i.e., the3-month maximum for June is the maximum of June, May and April). In Vermont, this results in 3-monthmaximum values for June, July, August and September. To compute the annual W126, we took the mean of the largest 3-month maximum over the current year and the previous two years. We set the target for ozone exposure at 7 ppm-hour³. The annual score was computed as a difference between 7 and the upper scoring bounds (maximum value in the dataset plus 10% of the range), scaled between 1 and 5. Values below 7 were given a score of 5.

Mercury Deposition

Data on mercury deposition (μg/m²) were accessed from the National Atmospheric Deposition Program (NADP) Mercury Deposition Network (MDN) for sites at Underhill, Vermont¹. Annual values are computed from weekly samples collected at the site. We set the target for mercury deposition to zero². The annual score was computed as the difference between the target concentration of 0 μg/m² and upper scoring bounds (maximum value in the data plus 10% of the range), scaled between 1 and 5.

Minimum Temperature

Data of mean minimum annual temperature for Vermont were gathered from NOAA National Centers for Environmental Information¹. We set the target for the dataset as the mean minimum temperature from1961-1990 which is used as the baseline normal for climate comparisons by the Intergovernmental Panel on Climate Change (IPCC). The current year is scored for where it falls between the target and the upper scoring bounds (maximum value in the dataset) or the lower scoring bounds (minimum value in the dataset), scaled to be between 1 and 5.

Maximum Temperature

Data of mean maximum annual temperature for Vermont were gathered from NOAA National Centers for Environmental Information¹. We set the target for the dataset as the mean maximum temperature from1961-1990 which is used as the baseline normal for climate comparisons by the Intergovernmental Panel on Climate Change (IPCC). The current year is scored for where it falls between the target and the upper scoring bounds (maximum value in the dataset) or the lower scoring bounds (minimum value in the dataset), scaled to be between 1 and 5.

Precipitation

Data of annual total precipitation (inches) for Vermont were collected from NOAA National Centers for Environmental Information¹. We set the target for the dataset as the total precipitation from 1961-1990 which is used as the baseline normal for climate comparisons by the Intergovernmental Panel on Climate Change (IPCC). The current year is scored for where it falls between the target and the upper scoring bounds (maximum value in the dataset) or the lower scoring bounds (minimum value in the dataset), scaled to be between 1 and 5.

Snow Cover

The total annual number of days with snow cover >1 inch in Vermont were collected from NOAA National Centers for Environmental Information¹. We set the target for the dataset as the mean duration of snow cover from 1961-1990 which is used as the baseline normal for climate comparisons by the Intergovernmental Panel on Climate Change (IPCC). The current year is scored for where it falls between the target and the upper scoring bounds (maximum value in the dataset) or the lower scoring bounds (minimum value in the dataset), scaled to be between 1 and 5.

Climate Extremes

NOAA National Centers for Environmental Information (NCEI) provides a robust Climate Extremes Index for the northeastern US¹. NCEI computes the regional CEI based on a set of climate extreme indicators: (1) monthly maximum and minimum temperature, (2) daily precipitation, and (3) monthly Palmer Drought Severity Index (PDSI). The CEI is a combination of the proportion of the year and the area in the region that has experienced an extreme event for these three indices. NCEI has defined extremes as those CEI values that fall in the upper (or lower) tenth percentile of the local period of record. Please refer to NCEI documentation for more details on calculations. Accordingly, a value of 0% for the CEI indicates that no portion of the year was subject to any of the extremes considered in the index. In contrast, a value of 100% indicates that the entire northeast region had extreme conditions throughout the year for each of the indicators. The long-term variation or change in the CEI represents the tendency for extremes of climate to either decrease, increase, or remain the same¹. We set the target for the dataset as the long-term mean. The current year is scored for where it falls between the target and the upper scoring bounds (maximum value in the dataset) or the lower scoring bounds (minimum value in the dataset), scaled to be between 1 and 5.

Drought

Drought was assessed through the Standardized Precipitation Evapotranspiration Index (SPEI) for Vermont¹. The SPEI more fully captures the effect of drought on plants than the Palmer Drought Severity Index, as the former includes the loss of water through evapotranspiration. We selected a six month SPEI value which spans from April-September to capture drought in the functional growing season. We set the target for the dataset as the long-term mean from 1961-1990 based on the baseline set by the Intergovernmental Panel on Climate Change (IPCC). The current year is scored for where it falls between the target and the upper scoring bounds (maximum value in the dataset plus 10% of the range) or the lower scoring bounds (minimum value in the dataset minus 10% of the range), scaled to be between 1 and 5.

Damage by Invasive Pests

To quantify the damage caused by invasive insects and diseases, we use Insect and Disease Surveys (IDS), which are annual aerial surveys of forests conducted by the State of Vermont and the USDA Forest Service to map forest disturbance¹. Here, we summed the total area mapped by pests that we could determine as invasive to Vermont. The target was set as the lowest possible acreage (either the minimum value in the data minus 10% of range or 0, whichever was greater), and the current year is scored for where it falls between the target and the upper scoring bounds (maximum value in the dataset plus 10% of the range), scaled to be between 1 and 5.

Forest Cover

We used forest cover estimates generated by the USFS Forest Inventory and Analysis Program EVALIDator¹ (“0079 Area of sampled land and water, in acres”). We relied on the query and methods set by FIA to compute these estimates (Accessible forest as defined by FIA)/(Total land– (Noncensus Water + Census Water)) x 100%). We then converted them to a percentage of the state of Vermont using the total area of the state. The target was set as the long-term mean value. The current year is scored for where it falls between the target and the upper scoring bounds (maximum value in the dataset +10% of the range) or the lower scoring bounds (minimum value in the dataset -10% of the range), scaled to be between 1 and 5.

Hardwood Regeneration

We used sugar maple (Acer saccharum) seedling counts per acre in maple-beech-birch forests estimated from USFS Forest Inventory and Analysis (FIA) Program Phase 2 subplots. Seedling data were collected using an FIA-established query in EVALIDator;>¹ (“0045 Number of live seedlings (less than 1 inch d.b.h./d.r.c.), in seedlings, on forest land”) filtered by sugar maple (“and seedling.spcd = 318”) and grouped by forest type. Per FIA protocol, plots were reassessed every 5 years until 2014 (i.e. data reported for 2007 used 2003, 2004, 2005, 2006 and 2007), and then every 7 years after that;>². Estimates are calculated by scaling the seedling data to represent the maple-beech-birch forests of Vermont. The target for this dataset was set to the maximum value plus 10% of the range. The current year is scored for where it falls between zero and the target, scaled to be between 1 and 5.

Softwood Regeneration

We used red spruce (Picea rubens) seedling counts per acre in spruce-fir forests estimated from USFS Forest Inventory and Analysis (FIA) Program Phase 2 subplots. Seedling data were collected using an FIA-established query in EVALIDator¹ (“0045 Number of live seedlings (less than 1 inch d.b.h./d.r.c.), in seedlings, on forest land”) filtered by red spruce (“and seedling.spcd = 97”) and grouped by forest type. Per FIA protocol, plots were reassessed every 5 years until 2014 (i.e. data reported for 2007 used 2003, 2004, 2005, 2006 and 2007), and then every 7 years after that². Estimates are calculated by scaling the seedling data to represent the spruce-fir forests of Vermont. The target for this dataset was set to the maximum value plus 10% of the range. The current year is scored for where it falls between zero and the target, scaled to be between 1 and 5.

Stand Complexity

Using the USFS Forest Inventory and Analysis Program population estimate data on Phase 2 plots accessed via the FIA Datamart¹, we computed tree size class diversity using a Shannon-Weiner Diversity Index calculation (see equation below). The first available data year was 1997. We divided all sampled trees into 5 inch classes and tallied the total proportion of each size class measured in the plots. To compute the annual score, we used FIA’s plot evaluation group panels to create population estimates for each year. The target for this dataset was set as the maximum value plus 10% of the range. The target was set as the upper scoring bounds (dataset maximum plus 10% of the range), and the current year is scored for where it falls between the lower scoring bounds (dataset minimum minus 10% of the range) and the target, scaled to be between 1 and 5.

Shannon-Weiner Index (H’)

Where p_i is the proportion of trees in the ith size class

Mean Forest Patch Size

Using the National Land Cover Database (NLCD)¹, we mapped forest cover (41 Deciduous Forest, 42 Evergreen Forest, 43 Mixed Forest, and 90 Woody Wetlands) at 30 meter resolution. The NLCD begins in 2001 from the reprocessed release in 2019. We used FragStats² to compute the mean size of forest patches. We used the 8 cell neighborhood rule with a ‘no sampling’ strategy. We selected all Area-Edge Class metrics for computation. We set the target for this dataset to be the long-term mean. The current year is scored for where it falls between target and either the lower scoring bounds (dataset minimum minus 10% of the range) or the upper scoring bounds (dataset maximum plus 10% of the range) and the target when values are above the target, scaled to be between 1 and 5.

Forest Connectivity

Using the National Land Cover Database (NLCD)¹ we mapped forest cover (41 Deciduous Forest, 42 Evergreen Forest, 43 Mixed Forest, and 90 Woody Wetlands) at 30 meter resolution. The 2019 reprocessed release of the NLCD begins in 2001. We used FragStats² to compute forest connectivity. We used the 8 cell neighborhood rule with a ‘no sampling’ strategy. We selected ‘Contagion’ as the Landscape Aggregation metric for computation. We set the target for this dataset to be the long-term mean. The current year is scored for where it falls between target and either the lower scoring bounds (dataset minimum minus 10% of the range) or the upper scoring bounds (dataset maximum plus 10% of the range) and the target when values are above the target, scaled to be between 1 and 5.

Tree Species Diversity

Using the USFS Forest Inventory and Analysis Program population estimate data on Phase 2 plots accessed via the FIA Datamart¹, we tallied the number of trees (≥5 in diameter) per species to compute a Shannon-Weiner Diversity Index calculation (see equation below). The first available data year was 1997. We divided all sampled trees into 5 inch classes and tallied the total proportion of each size class measured in the plots. To compute the annual score, we used FIA’s plot evaluation group panels to create population estimates for each year. The target for this dataset was set as the maximum value plus 10% of the range. The target was set as the upper scoring bounds (dataset maximum plus 10% of the range), and the current year is scored for where it falls between the acceptable species diversity thresholds of 1.5 and 3.5², scaled to be between 1 and 5.

Shannon-Weiner Index (H’)

Where p_i is the proportion of trees in the ith size class

Stand Age Diversity

Using the USFS Forest Inventory and Analysis Program data accessed via the FIA EVALIDator¹ , we extracted the acreage of forest for every 20 year age class. We re-categorized stand ages into 40 year buckets (0-40, 41-80, and 81-140 years) and computed age class diversity using a Shannon-Weiner Diversity Index calculation (see equation below). Per FIA protocol, plots were reassessed every 5 years until 2014 (i.e. data reported for 2007 used 2003, 2004, 2005, 2006 and 2007), and then every 7 years after that². The target for this dataset was set to the long-term mean, and the score was then computed as the deviation from this target, scaled to be between 1 and 5.

Shannon-Weiner Index (H’)

Where p_i is the proportion of trees in the ith size class