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Forest above-ground biomass (AGB) estimation provides valuable information about the carbon cycle. Thus, the overall goal of this paper is to present an approach to enhance the accuracy of the AGB estimation. The main objectives are to: 1) investigate the performance of remote sensing data sources, including airborne light detection and ranging (LiDAR), optical, SAR, and their combination to improve the AGB predictions, 2) examine the capability of tree-based machine learning models, and 3) compare the performance of pixel-based and object-based image analysis (OBIA). To investigate the performance of machine learning models, multiple tree-based algorithms were fitted to predictors derived from airborne LiDAR data, Landsat, Sentinel-2, Sentinel-1, and PALSAR2/PALSAR SAR data collected within New York’s Adirondack Park. Combining remote sensing data from multiple sources improved the model accuracy (RMSE: 52.14 Mg ha-1 and R² : 0.49). There was no significant difference among gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGBoost) models. In addition, pixel-based and object-based models were compared using the airborne LiDAR-derived AGB raster as a training/testing sample. The OBIA provided the best results with the RMSE of 33.77 Mg ha-1 and R² of 0.81 for the combination of optical and SAR data in the GBM model. [ABSTRACT FROM AUTHOR]

Novel plant communities reshape landscapes and pose challenges for land cover classification and mapping that can constrain research and stewardship efforts. In the US Northeast, emergence of low-statured woody vegetation, or shrublands, instead of secondary forests in post-agricultural landscapes is well documented by field studies, but poorly understood from a landscape perspective, which limits the ability to systematically study and manage these lands. To address gaps in classification/mapping of low-statured cover types where they have been historically rare, we developed models to predict shrubland distributions at 30 m resolution across New York State (NYS), using a stacked ensemble combining a random forest, gradient boosting machine, and artificial neural network to integrate remote sensing of structural (airborne LIDAR) and optical (satellite imagery) properties of vegetation cover. We first classified a 1 m canopy height model (CHM), derived from a patchwork of available LIDAR coverages, to define shrubland presence/absence. Next, these non-contiguous maps were used to train a model ensemble based on temporally segmented imagery to predict shrubland probability for the entire study landscape (NYS). Approximately 2.5% of the CHM coverage area was classified as shrubland. Models using Landsat predictors trained on the classified CHM were effective at identifying shrubland (test set AUC = 0.893, real-world AUC = 0.904), in discriminating between shrub/young forest and other cover classes, and produced qualitatively sensible maps, even when extending beyond the original training data. After ground-truthing, we expect these shrubland maps and models will have many research and stewardship applications including wildlife conservation, invasive species mitigation and natural climate solutions. Our results suggest that incorporation of airborne LiDAR, even from a discontinuous patchwork of coverages, can improve land cover classification of historically rare but increasingly prevalent shrubland habitats across broader areas. [ABSTRACT FROM AUTHOR]

The need for reliable landscape-scale monitoring of forest disturbance has grown with increased policy and regulatory attention to promoting the climate benefits of forests. Change detection algorithms based on satellite imagery can address this need but are largely untested for the forest types and disturbance regimes of the US Northeast, including management practices common in northern hardwoods and mixed hardwood-conifer forests. This study ground-truthed the "off-the-shelf" outputs of three satellite-based change detection algorithms using detailed harvest records and maps covering 43,000 ha of working forests in northeastern New York. [ABSTRACT FROM AUTHOR]

Forest aboveground biomass (AGB) provides valuable information about the carbon cycle, carbon sink monitoring, and understanding of climate change factors. Remote sensing data coupled with machine learning models have been increasingly used for forest AGB estimation over local and regional extents. Landsat series provide a 50-year data archive, which is a valuable source for historical mapping over large areas. As such, this paper proposed a machine learning-based workflow for historical AGB estimation and its change analysis from 2001 to 2019 for the New York State's forests using Landsat historical imagery, airborne LiDAR, and forest plot data. As the object-based image analysis (OBIA) is able to incorporate spectral, contextual, and textural features into the regression model, the proposed method utilizes an OBIA approach and a random forest (RF) regression model implemented on the Google Earth Engine (GEE) cloud computing platform. Results demonstrated that there is a considerable decrease of 983.79 × 106 Mg/ha in the AGB of deciduous forests from 2001 to 2006, followed by an increase of 618.28 × 106 Mg/ha from 2006 to 2011, continued with an increase of 229.12 × 106 Mg/ha of deciduous forests from 2011–2016. Finally, the results demonstrated a slight change in AGB from 2016 to 2019. The transferability of the proposed framework provides a practical solution for monitoring forests in other states or even on a national scale. [ABSTRACT FROM AUTHOR]

Depletion of Ca from forest soils due to acidic deposition has had potentially pervasive effects on forest communities, but these impacts remain largely unknown. Because snails, salamanders, and plants play essential roles in the Ca cycle of northern hardwood forests, we hypothesized that their community diversity, abundance, and structure would vary with differences in biotic Ca availability. To test this hypothesis, we sampled 12 upland hardwood forests representing a soil Ca gradient in the Adirondack Mountains, New York (USA), where chronic deposition has resulted in acidified soils but where areas of well-buffered soils remain Ca rich due to parent materials. Along the gradient of increasing soil [[Ca.sup.2+]], we observed increasing trends in snail community richness and abundance, live biomass of redback salamanders (Plethodon cinereus (Green, 1818)), and canopy tree basal area. Salamander communities were dominated by mountain dusky salamanders (Desmognathus ochrophaeus Cope, 1859) at Ca-poor sites and changed continuously along the Ca gradient to become dominated by redback salamanders at the Ca-rich sites. Several known calciphilic species of snails and plants were found only at the highest-Ca sites. Our results indicated that Ca availability, which is shaped by geology and acidic deposition inputs, influences northern hardwood forest ecosystems at multiple trophic levels, although the underlying mechanisms require further study. Resume: L'epuisement du Ca dans les sols forestiers a cause des depots acides a eu des repercussions potentiellement generalisees sur les communautes forestieres mais ces impacts demeurent largement inconnus. Etant donne que les escargots, les salamandres et les plantes jouent un role essentiel dans le cycle du Ca des forets feuillues nordiques, nous avons emis l'hypothese que la diversite, l'abondance et la structure de leurs communautes varieraient en fonction de la disponibilite du Ca. Pour tester cette hypothese, nous avons echantillonne 12 forets feuillues en milieu sec dans les monts Adirondacks, dans l'Etat de New York aux Etats-Unis d'Amerique, ou des depots chroniques ont acidifie les sols mais ou il y a encore des en-droits ou les sols ont un pouvoir tampon eleve et demeurent riches en Ca a cause du materiau originel. A mesure que la concentration de [Ca.sup.2+] augmentait le long du gradient, nous avons observe des tendances a la hausse de l'abondance et de la richesse des communautes d'escargots, de la biomasse vivante de salamandres rayees (Plethodon cinereus (Green, 1818)) et de la surface terriere des arbres qui forment la canopee. Les communautes de salamandres etaient dominees par la salaman-dre sombre des montagnes (Desmognathus ochrophaeus Cope, 1859) dans les stations pauvres en Ca et changeaient progressivement le long du gradient de Ca pour devenir dominees par la salamandre rayee dans les stations riches en Ca. Plusieurs especes calcicoles connues d'escargots et de plantes ont ete observees seulement dans les stations les plus riches en Ca. Nos resultats indiquent que la disponibilite du Ca, qui est determinee par les conditions geologiques et les apports de depots acides, influence les ecosystemes de feuillus nordiques a de multiples niveaux trophiques bien que les mecanismes sous-jacents necessitent plus de recherche. [Traduit par la Redaction], Introduction Research on forest ecosystem processes has suggested that Ca availability plays a critical role in shaping ecosystem structure, function, and responses to change (McLaughlin and Wimmer 1999). In the [...]

Abstract: Decline of yellow-cedar (Chamaecyparis nootkatensis ((D. Don) Spach) has occurred on 200 000 ha of temperate rainforests across southeastern Alaska. Because declining forests appeared soon after the Little Ice [...]

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Projected changes in temperature and drought regime are likely to reduce carbon (C) storage in forests, thereby amplifying rates of climate change. While such reductions are often presumed to be greatest in semi-arid forests that experience widespread tree mortality, the consequences of drought may also be important in temperate mesic forests of Eastern North America (ENA) if tree growth is significantly curtailed by drought. Investigations of the environmental conditions that determine drought sensitivity are critically needed to accurately predict ecosystem feedbacks to climate change. We matched site factors with the growth responses to drought of 10,753 trees across mesic forests of ENA, representing 24 species and 346 stands, to determine the broad-scale drivers of drought sensitivity for the dominant trees in ENA. Here we show that two factors--the timing of drought, and the atmospheric demand for water (i.e., local potential evapotranspiration; PET)--are stronger drivers of drought sensitivity than soil and stand characteristics. Droughtinduced reductions in tree growth were greatest when the droughts occurred during early-season peaks in radial growth, especially for trees growing in the warmest, driest regions (i.e., highest PET). Further, mean species trait values (rooting depth and w50) were poor predictors of drought sensitivity, as intraspecific variation in sensitivity was equal to or greater than interspecific variation in 17 of 24 species. From a general circulation model ensemble, we find that future increases in earlyseason PET may exacerbate these effects, and potentially offset gains in C uptake and storage in ENA owing to other global change factors. [ABSTRACT FROM AUTHOR]

We estimated the potential economic value of recreational fisheries in lakes altered by acid pollution in the Adirondack Mountains (USA). We found that the expected value of recreational fisheries has been diminished because of acid deposition but may improve as lakes recover from acidification under low emissions scenarios combined with fish stocking. Fishery value increased with lake pH, from a low of $4.41 angler day-1 in lakes with pH < 4.5, to a high of $38.40 angler day-1 in lakes with pH > 6.5 that were stocked with trout species. Stocking increased the expected fishery value by an average of $11.50 angler day-1 across the entire pH range of the lakes studied. Simulating the future long-term trajectory of a subset of lakes, we found that pH and expected fishery value increased over time in all future emissions scenarios. Differences in estimated value among pollution reduction scenarios were small (<$1 angler day-1) compared to fish stocking scenarios (>$4 angler day-1). Our work provides a basis for assessing the costs and benefits of emissions reductions and management efforts that can hasten recovery of the economic and cultural benefits of ecosystems degraded by chronic pollution. [ABSTRACT FROM AUTHOR]

We evaluated the effects of hydrologic setting on the growth sensitivity of two conifer species to recent atmospheric climatic variability and change in a region experiencing a warming (annual Tmin: 0.07 °C/decade) and wetting trend (total annual PPT 9.8 mm/decade; 1911–2012). Tree-ring chronologies were constructed for the boreal disjunct balsam fir ( Abies balsamea ; n = 72; 1916–2012) and range-centered eastern white pine ( Pinus strobus ; n = 84; 1707–2012) at three forested, groundwater-fed wetlands (fens) and their neighboring uplands in New York State, USA. Soil temperature monitoring in 2010 confirmed that upland soils were significantly warmer than fen soils during the spring and summer months (11.5–13.9 °C; p < 0.05). Climate-growth relationships for Abies balsamea varied substantially based on hydrologic setting and season. Compared with a remnant upland population that showed an increasingly negative sensitivity to warm summer temperatures over time A. balsamea positioned in fens were less sensitive to recent warming. While atmospheric climate-growth relationships were more consistent across hydrologic settings for Pinus strobus , we still observed qualitatively different responses to atmospheric climate variables between fen and upland populations. Overall, the climate-growth relationships identified in this study suggest that relative to trees growing in mesic upland soils, growth sensitivity to warm ambient climate in summer is ameliorated by groundwater inputs. The climate modulating effects of groundwater on tree-growth observed in our study suggests that for temperate and boreal regions where fens are abundant further consideration of groundwater influences on climate-growth relationships is warranted. [ABSTRACT FROM AUTHOR]

Sugar maple ( Acer saccharum ) is among the most ecologically and economically important tree species in North America, and its growth and regeneration is often the focus of silvicultural practices in northern hardwood forests. A key stressor for sugar maple (SM) is acid rain, which depletes base cations from poorly-buffered forest soils and has been associated with much lower SM vigor, growth, and recruitment. However, the potential interactions between forest management and soil acidification – and their implications for the sustainability of SM and its economic and cultural benefits – have not been investigated. In this study, we simulated the development of 50 extant SM stands in the western Adirondack region of NY (USA) for 100 years under different soil chemical conditions and silvicultural prescriptions. We found that interactions between management prescription and soil base saturation will strongly shape the ability to maintain SM in managed forests. Below 12% base saturation, SM did not regenerate sufficiently after harvest and was replaced mainly by red maple ( Acer rubrum ) and American beech ( Fagus grandifolia ). Loss of SM on acid-impaired sites was predicted regardless of whether the shelterwood or diameter-limit prescriptions were used. On soils with sufficient base saturation, models predicted that SM will regenerate after harvest and be sustained for future rotations. We then estimated how these different post-harvest outcomes, mediated by acid impairment of forest soils, would affect the potential monetary value of ecosystem services provided by SM forests. Model simulations indicated that a management strategy focused on syrup production – although not feasible across the vast areas where acid impairment has occurred – may generate the greatest economic return. Although pollution from acid rain is declining, its long-term legacy in forest soils will shape future options for sustainable forestry and ecosystem stewardship in the northern hardwood forests of North America. [ABSTRACT FROM AUTHOR]

Hardwood forests of eastern North America have experienced decades of acidic deposition, leading to soil acidification where base cation supply was insufficient to neutralize acid inputs. Negative impacts of soil acidity on amphibians include disrupted embryonic development, lower growth rates, and habitat loss. However, some amphibians exhibit intraspecific variation in acid tolerance, suggesting the potential for local adaptation in areas where soils are naturally acidic. The eastern red-backed salamander ( Plethodon cinereus) is a highly abundant top predator of the northern hardwood forest floor. Early research found that P. cinereus was sensitive to acidic soils, avoiding substrates with pH < 3.8 and experiencing decreased growth rates in acidic habitats. However, recent studies have documented P. cinereus populations in lower pH conditions than previously observed, suggesting some populations may persist in acidic conditions. Here, we evaluated relationships between organic horizon soil pH and P. cinereus abundance, adult health (body size and condition), and microhabitat selection, based on surveys of 34 hardwood forests in northeastern United States that encompass a regional soil pH gradient. We found no associations between soil pH and P. cinereus abundance or health, and observed that this salamander used substrates with pH similar to that available, suggesting that pH does not mediate their fine-scale distributions. The strongest negative predictor of P. cinereus abundance was the presence of dusky salamanders ( Desmognathus spp.), which were most abundant in the western Adirondacks. Our results indicate that P. cinereus occupies a wider range of soil pH than has been previously thought, which has implications for their functional role in forest food webs and nutrient cycles in acid-impaired ecosystems. Tolerance of P. cinereus for more acidic habitats, including anthropogenically acidified forests, may be due to local adaptation in reproductively isolated populations and/or generalist life history traits that allow them to exploit a wider resource niche. [ABSTRACT FROM AUTHOR]

The article reports on evaluation of gastric lavage method for diet analysis of Plethodon cinereus. It states that animals were dissected using stereomicroscope to assess stomach contents while identified prey items were sent to taxonomic resolution possible. It mentions that stomach flushing is a method to collect diet samples removing most of the stomach contents and gastric lavage is an effective technique for studying plethodontid diets without removing individuals from population.

Large-footprint waveform light detection and ranging (lidar) data have been widely used in above-ground forest biomass estimation. Waveform metrics derived from basic statistics (e.g. percentile of energy) of the lidar waveform, such as canopy height and height of median energy, have been applied to biomass estimation in numerous studies. In this study, a set of metrics based on Gaussian decomposition (GD) results were developed and evaluated for forest above-ground biomass estimation using NASA's laser vegetation imaging sensor (LVIS) data. The GD metrics were designed to explicitly incorporate lidar intensity and vertical structures of canopy layers for biomass estimation. The proposed GD metrics used information related to the above-ground height of each Gaussian centroid and the Gaussian area index (GAI), where GAI is the area covered by a Gaussian function. Two types of novel GD metrics were developed: (1) percentileheight GAI metrics expressing the GAI summation of a subset of Gaussian centroids located within a certain percentile height range; and (2) height-weighted GAI metrics, a summation of GAIs of a waveform weighted by the corresponding heights of their Gaussian centroids. A biomass regression model was built by eight newly developed GD metrics using GAI information and five pre-existing GD-derived metrics that have not previously been used for biomass estimation. The performance of the regression model was then compared to another regression model using 12 previously published metrics (non-GD metrics). The Random Forests (RF) regression algorithm was employed for predicting biomass. The RF out-of-bag results indicated that above-ground biomass estimations using GD metrics achieved significantly better results than those derived from non-GD metrics for deciduous plots (19% lower root mean square error (RMSE), 25% higher coefficient of determination (R²), and marginally better results in coniferous plots (4% lower RSME, 6% higher R²). The combination of GD and non-GD metrics achieved comparable biomass estimation results to the model using exclusively GD metrics. GD metrics also showed strong correlation with forest attributes such as mean diameter at breast height (DBH) and stem density. This study contributes to the usage of GD results for accurate estimation of forest above-ground biomass in large-footprint lidar waveform data in temperate deciduous forests, because temperate deciduous forests have been proved challenging in regard to lidar-derived biomass estimations. [ABSTRACT FROM AUTHOR]

Local and regional-scale knowledge of climate change is needed to model ecosystem responses, assess vulnerabilities and devise effective adaptation strategies. High-resolution gridded historical climate (GHC) products address this need, but come with multiple sources of uncertainty that are typically not well understood by data users. To better understand this uncertainty in a region with a complex climatology, we conducted a ground-truthing analysis of two 4 km GHC temperature products (PRISM and NRCC) for the US Northeast using 51 Cooperative Network (COOP) weather stations utilized by both GHC products. We estimated GHC prediction error for monthly temperature means and trends (1980–2009) across the US Northeast and evaluated any landscape effects (e.g., elevation, distance from coast) on those prediction errors. Results indicated that station-based prediction errors for the two GHC products were similar in magnitude, but on average, the NRCC product predicted cooler than observed temperature means and trends, while PRISM was cooler for means and warmer for trends. We found no evidence for systematic sources of uncertainty across the US Northeast, although errors were largest at high elevations. Errors in the coarse-scale (4 km) digital elevation models used by each product were correlated with temperature prediction errors, more so for NRCC than PRISM. In summary, uncertainty in spatial climate data has many sources and we recommend that data users develop an understanding of uncertainty at the appropriate scales for their purposes. To this end, we demonstrate a simple method for utilizing weather stations to assess local GHC uncertainty and inform decisions among alternative GHC products. [ABSTRACT FROM AUTHOR]