SUNY College of Environmental Science and Forestry
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Barriers in implementing Material Transparency in LEED® v4.0 projects
No full textDisclosure of the life cycle impact of materials and transparent documentation of material ingredients can help the process of material selection for designers. The LEED® v4.0 has placed emphasis on material transparency through the addition of Building Product Disclosure and Optimization credits under the Materials and Resources category. The intent of the study through interviews is to understand the perception of architects and manufacturers in the US on common barriers in implementing material transparency. The research identifies gaps in access to or availability of transparent material ingredient documentation that will help material producers better understand the needs of designers who are responsible for environmentally preferable material selection. The study resulted in four conclusions and the most important one is the need for education of all the stakeholders in a project to push the healthy material goals and in turn, bring a change in the construction industry
The Application of Lidar Data for Determining the Area of Potential Effect Associated with Offshore Wind Projects on the Outer Continental Shelf
Get PDFOne of the first steps in any visual impact assessment is to determine the preliminary area of potential effect (APE). To determine the APE, we must understand the geographic range of potential visibility, which typically requires a zone of visual influence (ZVI), or viewshed analysis. A viewshed analysis is a geographic information systems (GIS) analysis that uses topographic data to determine the availability of a clear line of sight from a viewer’s position and height, to a specific project’s position and height.
Generally, this basic calculation is still the foundation for all viewshed analyses completed today. However, the data sources and input capacity have changed dramatically, introducing a level of accuracy that is unprecedented in the industry. Traditional viewshed analysis was an accurate indicator of locations where a feature would not be visible. However, identifying areas of actual visibility was more speculative. With the technology available today, viewshed analysis can now tell us where a feature or project will be visible with astonishing accuracy.
Since the 1980’s, technology for viewshed analysis has rapidly evolved to allow both project sponsors and regulatory bodies to understand and predict the visibility of planned development projects with increasing accuracy and precision. This presentation will outline the history of viewshed analysis methods over the past few decades and expand upon the current frontiers of the technology. We will present a case study focusing on a proposed wind farm located on the outer continental shelf in the Atlantic Ocean to explain how recent advances in raw data availability, processing power, and analytical techniques have allowed exponential improvements in the quality and utility of viewshed analysis, in terms of identifying and evaluating potential visual effects on aesthetic resources within the project APE. We will compare viewsheds produced for the same study area using three different analytical techniques, representing three milestones in the evolution of viewshed analysis technology. These include: analysis based on bare earth topography (Bare Earth Viewshed); analysis based on topography plus mapped forest vegetation areas (Vegetation Viewshed); and analysis using raw lidar derived digital surface models (DSMs), which reflect topography, vegetation, and structures (DSM Viewshed).
Finally, we discuss how a lidar-based viewshed analysis informs the latter stages of a visual impact assessment and allows the consultant to focus efforts on resources that are included in the project APE.
The case study will include specific examples of how viewshed analysis is a powerful tool in predicting where impacts are likely to occur and the degree to which they should be further investigated. The viewshed analysis allows for a more focused application of field review, photography, visual simulations, and expert evaluation. The case study will demonstrate how lidar-based viewshed analysis allowed for a massive reduction in the investigation area, which allowed stakeholders, government agencies, and consultants to focus on the most likely affected resources, producing a substantially refined visual impact assessment
EFFECTS OF OVEREXPRESSION OF ACID PHOSPHATASE CIS GENE ON THE HEALTH, BLIGHT SUSCEPTIBILITY AND PHOTOCHEMISTRY OF TRANSGENIC AMERICAN CHESTNUT (CASTANEA DENTATA)
No full textFish Assemblage Succession Within a Recovering Urban Lake
Get PDFOnondaga Lake in Syracuse, New York was once the site of prolific chemical and municipal sewage dumping. However, over the last two decades it has become the target of restoration efforts including the rehabilitation of the fish assemblage. This study compared species richness and Shannon diversity between lake basins and over time, in conjunction with multivariate ordination to assess changes in fish assemblage structure. Species richness of offshore fish increased in this timeframe; however, both richness and diversity declined for the nearshore fish assemblage. There was significant annual variability in species composition for both offshore and nearshore samples based on permutational analyses of variance, but only the composition of offshore samples was significantly different between basins. These results suggest that offshore fish have been responding positively to increasing water quality, while the nearshore fish assemblage has likely been negatively impacted by nearshore habitat homogenization from introduced aquatic invasives
Forest Aboveground Biomass Estimation Using Multi-Source Remote Sensing Data in Temperate Forests
Get PDFForests are a crucial part of global ecosystems. Accurately estimating aboveground biomass (AGB) is important in many applications including monitoring carbon stocks, investigating forest degradation, and designing sustainable forest management strategies. Remote sensing techniques have proved to be a cost-effective way to estimate forest AGB with timely and repeated observations. This dissertation investigated the use of multiple remotely sensed datasets for forest AGB estimation in temperate forests. We compared the performance of Landsat and lidar data—individually and fused—for estimating AGB using multiple regression models (MLR), Random Forest (RF) and Geographically Weight Regression (GWR). Our approach showed MLR performed similarly to GWR and both were better than RF. Integration of lidar and Landsat inputs outperformed either data source alone. However, although lidar provides valuable three-dimensional forest structure information, acquiring comprehensive lidar coverage is often cost prohibitive. Thus we developed a lidar sampling framework to support AGB estimation from Landsat images. We compared two sampling strategies—systematic and classification-based—and found that the systematic sampling selection method was highly dependent on site conditions and had higher model variability. The classification-based lidar sampling strategy was easy to apply and provides a framework that is readily transferable to new study sites. The performance of Sentinel-2 and Landsat 8 data for quantifying AGB in a temperate forest using RF regression was also tested. We modeled AGB using three datasets: Sentinel-2, Landsat 8, and a pseudo dataset that retained the spatial resolution of Sentinel-2 but only the spectral bands that matched those on Landsat 8. We found that while RF model parameters impact model outcomes, it is more important to focus attention on variable selection. Our results showed that the incorporation of red-edge information increased AGB estimation accuracy by approximately 6%. The additional spatial resolution improved accuracy by approximately 3%. The variable importance ranks in the RF regression model showed that in addition to the red- edge bands, the shortwave infrared bands were important either individually (in the Sentinel-2 model) or in band indices. With the growing availability of remote sensing datasets, developing tools to appropriately and efficiently apply remote sensing data is increasingly important
Sources of Atmospheric Fine Particles and Adsorbed Polycyclic Aromatic Hydrocarbons in Syracuse, New York
Get PDFLand surface temperature (LST) images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor have been widely utilized across scientific disciplines for a variety of purposes. The goal of this dissertation was to utilize MODIS LST for three spatial modeling applications within the conterminous United States (CONUS). These topics broadly encompassed agriculture and human health.
The first manuscript compared the performance of all methods previously used to interpolate missing values in 8-day MODIS LST images. At low cloud cover (\u3c30%), the Spline spatial method outperformed all of the temporal and spatiotemporal methods by a wide margin, with median absolute errors (MAEs) ranging from 0.2°C-0.6°C. However, the Weiss spatiotemporal method generally performed best at greater cloud cover, with MAEs ranging from 0.3°C-1.2°C. Considering the distribution of cloud contamination and difficulty of implementing Weiss, using Spline under all conditions for simplicity would be sufficient.
The second manuscript compared the corn yield predictive capability across the US Corn Belt of a novel killing degree day metric (LST KDD), computed with daily MODIS LST, and a traditional air temperature-based metric (Tair KDD). LST KDD was capable of predicting annual corn yield with considerably less error than Tair KDD (R2 /RMSE of 0.65/15.3 Bu/Acre vs. 0.56/17.2 Bu/Acre). The superior performance can be attributed to LST’s ability to better reflect evaporative cooling and water stress. Moreover, these findings suggest that long-term yield projections based on Tair and precipitation alone will contain error, especially for years of extreme drought.
Finally, the third manuscript assessed the extent to which daily maximum heat index (HI) across the CONUS can be estimated by MODIS multispectral imagery in conjunction with land cover, topographic, and locational factors. The derived model was capable of estimating HI in 2012 with an acceptable level of error (R 2 = 0.83, RMSE = 4.4°F). LST and water vapor (WV) were, by far, the most important variables for estimation. Expanding this analytical framework to a more extensive study area (both temporally and spatially) would further validate these findings. Moreover, identifying an appropriate interpolation and downscaling approach for daily MODIS imagery would substantially increase the utility of the corn yield and HI models
Integrating Land Surface Temperature (LST) Images from the Moderate Resolution Imaging Spectroradiometer (MODIS) Sensor for Agricultural and Human Health Studies
No full textLand surface temperature (LST) images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor have been widely utilized across scientific disciplines for a variety of purposes. The goal of this dissertation was to utilize MODIS LST for three spatial modeling applications within the conterminous United States (CONUS). These topics broadly encompassed agriculture and human health. The first manuscript compared the performance of all methods previously used to interpolate missing values in 8-day MODIS LST images. At low cloud cover (\u3c30%), the Spline spatial method outperformed all of the temporal and spatiotemporal methods by a wide margin, with median absolute errors (MAEs) ranging from 0.2°C-0.6°C. However, the Weiss spatiotemporal method generally performed best at greater cloud cover, with MAEs ranging from 0.3°C-1.2°C. Considering the distribution of cloud contamination and difficulty of implementing Weiss, using Spline under all conditions for simplicity would be sufficient. The second manuscript compared the corn yield predictive capability across the US Corn Belt of a novel killing degree day metric (LST KDD), computed with daily MODIS LST, and a traditional air temperature-based metric (Tair KDD). LST KDD was capable of predicting annual corn yield with considerably less error than Tair KDD (R2 /RMSE of 0.65/15.3 Bu/Acre vs. 0.56/17.2 Bu/Acre). The superior performance can be attributed to LST’s ability to better reflect evaporative cooling and water stress. Moreover, these findings suggest that long-term yield projections based on Tair and precipitation alone will contain error, especially for years of extreme drought. Finally, the third manuscript assessed the extent to which daily maximum heat index (HI) across the CONUS can be estimated by MODIS multispectral imagery in conjunction with land cover, topographic, and locational factors. The derived model was capable of estimating HI in 2012 with an acceptable level of error (R 2 = 0.83, RMSE = 4.4°F). LST and water vapor (WV) were, by far, the most important variables for estimation. Expanding this analytical framework to a more extensive study area (both temporally and spatially) would further validate these findings. Moreover, identifying an appropriate interpolation and downscaling approach for daily MODIS imagery would substantially increase the utility of the corn yield and HI models
IMPROVING ESTIMATES OF WHITE-TAILED DEER ABUNDANCE UNDER SUB-OPTIMAL SURVEY CONDITIONS IN SYRACUSE, NEW YORK
Get PDFI conducted weekly counts of deer in a 39 km2 , peri-urban landscape. Counts were conducted during the hours after sunrise along public roads. Using conventional distance sampling methods, I estimated and compared deer density between 2016 and 2017 from data collected between the months of May and October. I also modeled deer abundance as a function of percent tree cover and the inverse of the route length in each 1-km2 unit. Hierarchical models were constructed in the unmarked R-package and in the WinBUGS programming environment. None of the models analyzed with unmarked fit the data. Simulation of data with characteristics of actual counts revealed widespread heterogeneity due to important, unmodeled random effects in the survey protocol. Using WinBUGS, I incorporated specific random effects and produced models with little or no overdispersion. These models were used to predict density variation across the study area to facilitate management planning
Spatiotemporal adaptations of Pseudomonas fluorescens to the herbicide Dicamba in a Microbial Evolutionary Growth Arena
Get PDFIn natural settings, bacteria respond to selective agents across spatiotemporal concentration gradients. In this work, a Microbial Evolutionary Growth Arena (MEGA plate) was constructed, optimized and tested for reproducibility as a tool to visualize bacterial adaptation to spatiotemporal gradients of selection. Using this system, Escherichia coli and Pseudomonas fluorescens adapted to a gradient of the antibiotic ciprofloxacin and the herbicide dicamba, respectively. Both gradients elicited adaptive responses from their respective study organisms, with MEGA plate E. coli isolates able to grow at high ciprofloxacin concentrations and P. fluorescens isolates demonstrating increased growth capabilities in subsequent exposures to dicamba, a loss of pigment production and an increase in antibiotic susceptibility. These experiments highlight how dicamba applications affect beneficial soil bacteria and confirm the reproducibility and utility of the MEGA plate system for visualizing spatiotemporal adaptation
Drivers of jaguar (Panthera onca) distribution, density, and movement in the Brazilian Pantanal
Get PDFGlobally, conversion of land for livestock production is a major driver of changes in prey availability for and conflict with large carnivores – notably so for Neotropical species including jaguars (Panthera onca). Using camera traps and GPS-collared individuals, I investigated the degree to which jaguars altered their activity patterns, population density, and selection of resources in response to native and non-native prey, and the degree to which these processes interacted (e.g., density-dependent resource selection), across a network of protected areas and working cattle ranches (where hunting of jaguars was prohibited) in the Brazilian Pantanal. Overall, I found that local jaguar populations were more patchily distributed in the ranches and more uniform in the parks, with the most consistent driver of distribution being canopy cover. Similar trends were observed for the activity and distribution of wild prey in the parks, although within the ranches cattle (Bos taurus) and feral water buffalo (Bubalus bubalis) were important drivers. Both temporal and spatial jaguar activity positively and most consistently tracked with wild prey, and negatively with cattle. Canopy cover and a composite of wild prey activity drove local jaguar density in both the parks and ranches. In the ranches, jaguar densities tended to be higher within remaining forest patches – such that, on average, jaguar density was statistically similar between the ranches and the parks. Jaguar density, wild prey and cattle availability, and forest canopy cover influenced local resource selection by jaguars. From broad (home range) to fine (foraging steps) scales, forest cover was the single- most consistent metric predicting prey and jaguar distributions – underscoring the importance of forest cover for wildlife conservation in the Neotropics, spotlighting concerns over recent and pending changes land use policy throughout jaguar range, and indicating a potentially simple metric for monitoring jaguar habitat potential where they are protected from hunting. Importantly, despite increasingly rare and fragmented forest cover in ranching landscapes, the “wildlife-friendly” practices in my study area helped to support a density of jaguars equivalent to protected areas with intact forest canopies, indicating their potential value as a conservation tool