Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest aboveground biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering – that is, excluding returns from LiDAR point clouds based on simple height thresholds – is a common practice meant to improve the `signal’ content of LiDAR returns by preventing ground returns from masking useful information about tree size and condition contained within canopy returns. However, ground returns may be helpful for making accurate aboveground biomass predictions in heterogeneous landscapes that include a patchy mosaic of vegetation heights and land cover types. In this paper, we applied several ground noise filtering thresholds while mapping forest AGB across New York State (USA), a heterogenous landscape composed of both contiguously forested and highly fragmented areas with mixed land cover types. We fit random forest models to predictor sets derived from each filtering intensity threshold and compared model accuracies, paying attention to how changes in accuracy correlated with landscape structure. We observed that removing ground noise via any height threshold systematically biases many of the LiDAR-derived variables used in AGB modeling, with mean correlation (Spearman’s $\rho$) between variables increasing from 0.183 to 0.266. We found that that ground noise filtering yields models of forest AGB with lower accuracy than models trained using predictors derived from unfiltered point clouds, with RMSE increasing by up to 2.2 Mg ha-1 statewide. Although we only modeled AGB for forest cover types, models fit to predictors derived from filtered point clouds performed worse as landscape heterogeneity (as measured by patch density and edge density) increased, suggesting ground returns are particularly useful when modeling edge forests. Our results suggest that ground filtering should be a carefully considered decision when mapping forest AGB, particularly when mapping heterogeneous and highly fragmented landscapes, as ground returns are more likely to represent useful `signal’ than extraneous `noise’ in these cases.

The January 1998 ice storm broke off tree crowns across a wide geographic area in northeastern North America, and forest tent caterpillar (Malacosoma disstria Hubner) defoliated some of the same stands in 2004–2007. We assessed the post-event growth responses of upper canopy sugar maples (Acersaccharum Marsh.) in previously thinned and recently rehabilitated even-aged northern hardwood stands in New York State, USA. Cores from ice-storm-damaged trees showed an initial radial growth reduction, a recovery after one year, and an increase to or above pre-storm levels after three years. A later forest tent caterpillar defoliation in the same stand caused a second reduction of growth, and another recovery after one year. We observed greater post-storm radial growth on trees released by a post-ice storm rehabilitation treatment than in the untreated control, with growth exceeding pre-storm rates. Cores from another site thinned 38 years earlier and impacted only by the forest tent caterpillar showed a more moderate growth reduction, and a prompt but smaller post-defoliation growth response than among trees affected by both the ice storm and defoliation. Findings reflect the potential for growth of upper canopy sugar maple trees to recover after a single or two closely occurring crown disturbances, and provide guidance to managers who must decide about removing or continuing to manage stands after similar kinds of ice storm damage or defoliation.

The historic New York City (NYC) Watershed Memorandum of Agreement signed in 1997, which established the Watershed Protection and Partnership Program (WPPP), aimed to safeguard water quality for downstream NYC communities and enhance community vitality within the upstream watershed. Up to now, the focus has been on maintaining water quality, with less attention on the socioeconomic and demographic well-being of the upstream communities. This study bridges this gap by examining the socioeconomic and demographic trends within the upstream watershed communities. We collected census data from the U.S. Census Bureau and American Community Survey (ACS) from 1990, 2000, 2010, and 2020. The data were collected primarily for the Catskill-Delaware Watershed region, as well as the five immediate watershed counties, New York State, the U.S., and two rural counties similar in population density to the Watershed to serve as comparison areas. This study analyzed demographic factors and economic, and community characteristics to identify trends and shifts over time. Our findings reveal the demographic shift towards an older retiree population, decreasing labor force, higher unemployment rates, higher disparities in income distribution, and longer commute times in the Watershed compared to our comparison areas. The Watershed also experiences higher home vacancies than the comparison areas, suggesting a shift towards seasonal or second-home use of properties. While this study offers insights into socioeconomic and demographic shifts in watershed communities, its reliance on census data may limit the precision in establishing causal links between watershed protection measures and these shifts. Incorporating qualitative analysis in future research will deepen our understanding of these relationships.

This study demonstrates the power of combining unmanned aerial vehicle (UAV) imagery and deep learning (DL) for monitoring forest regeneration, specifically focusing on young white pine (Pinus strobus). Using high-resolution three-band RGB and five-band multispectral orthomosaics derived from UAV flights, 20 DL object-detection models were evaluated within ArcGIS Pro 3.4 software (Esri Inc., Redlands, CA, USA). The models were tested across study sites in St. Lawrence County, NY, to assess performance on three distinct size classes of white pine, each stratified into low, medium, and high density areas. The Faster R-CNN (F-RCNN) model, particularly when trained with image rotation and no augmentation, significantly outperformed others, achieving an average precision of 0.88 across both imagery types. Subsequent confusion matrix analysis yielded 91% and 90% overall accuracy in medium and high-density white pine blocks, respectively. These findings validate the use of UAV-DL systems as an accurate and efficient tool for operational white pine regeneration assessment, reducing the need for labor-intensive fieldwork.

Riparian management zones (RMZs) represent the area surrounding streams that are managed for protecting resources found within a riparian area. They are often established using fixed-width buffers, which may or may not represent the extent of an actual riparian area. This two-pronged study conducted in the Catskill mountains in New York state compares and contrasts the difference in the area dedicated to RMZs along first- and second-order streams between a 100-foot, fixed-width buffer and a variable-width approach using field measurements and regional spatial data derived from high resolution (1 meter) digital terrain model data. The study also examined the opportunity cost of stumpage value for three harvesting scenarios: (1) unlimited harvesting within RMZ; (2) limited harvest restrictions within RMZ; and (3) no harvesting allowed within RMZ. Results indicate a 100-foot, fixed-width buffer could be substituted for a variable-width buffer along first-order streams but could be insufficient to protect riparian functions along second-streams that have wider ravines and floodplains. On average, RMZs along first- and second-order streams account for 8 percent of the timberland area and carry sawtimber volumes of nearly 7 MBF/ac. The residual timber values within RMZs of headwater streams can range from 7–12 percent of the total stumpage revenue.

Watershed stewardship programs succeed when intended users progress from awareness to participation. This study applies diffusion of innovations theory to five forestry, agricultural, and economic viability programs administered by the Watershed Agricultural Council in the New York City watershed. These five programs are voluntary and intended to support water quality and community vitality on working lands. A survey of nonindustrial private farm and forest landowners measured awareness, participation, and satisfaction across the watershed programs. Awareness was limited and participation was rare, yet nearly all participants reported high satisfaction, indicating an entry bottleneck at the awareness-to-participation stage. Logistic regression and generalized estimating equations models showed higher participation among landowners with larger acreage. The study recommends strengthening existing extension and outreach to better communicate programs’ relative advantages and increase observability through peer examples and targeted messaging to improve enrollment among eligible landowners.