Spatial analysis of landfills in respect to flood events and sea-level rise

Recently in the news, media coverage of flood events has garnered much attention due to tropical storms like Hurricane Matthew and the costly damages that resulted. Under climate change, events like sea-level rise (SLR) and flooding threaten infrastructure, which make it necessary for proper planning before, during, and after installation to mitigate risk. Studies in Austria and the UK have revealed that many landfills are located in flood zones as well as coastal areas effected by coastal erosion. In the U.S. however, there have not been publications on landfill locations related to flood events and SLR. The interest of gaining knowledge on flood prone and SLR at-risk landfills is that studies reveal that inundation of landfills can spread contaminants to other areas (e.g. marshes) that can have both ecological and health risks. To begin addressing this issue, figuring out what landfills are at risk from floods or sea level rise, how many, and the extent to which they could become inundated or eroded is the focus of this study. Using GIS software, and publicly available data, maps of landfills were created in the gulf-state area of the United States with flood and rising sea level data to then be analyzed and categorized based on spatial risks. Although incomplete, the study has shown the potential for hundreds of landfills to be at risk from either flooding, SLR, or both

A Robust Stepwise Clustering Approach to Detect Individual Trees in Temperate Hardwood Plantations Using Airborne LiDAR Data

Precise tree inventory plays a critical role in sustainable forest planting, restoration, and management. LiDAR-based individual tree detection algorithms often focus on finding individual treetops to discern tree positions. However, deliquescent tree forms (broad, flattened crowns) in deciduous forests can make these algorithms ineffective. In this study, we propose a stepwise tree detection approach, by first identifying individual trees using horizontal point density and then analyzing their vertical structure profiles. We first project LiDAR data onto a 2D horizontal plane and apply mean shift clustering to generate candidate tree clusters. Next, we apply a series of structure analyses on the vertical phase, to overcome local variations in crown size and tree density. This study demonstrates that the horizontal point density of LiDAR data provides critical information to locate and isolate individual trees in temperate hardwood plantations with varied densities, while vertical structure profiles can identify spreading branches and reconstruct deliquescent crowns. One challenge of applying mean shift clustering is training a dynamic search kernel to identify trees of different sizes, which usually requires a large number of field measurements. The stepwise approach proposed in this study demonstrated robustness when using a constant kernel in clustering, making it an efficient tool for large-scale analysis. This stepwise approach was designed for quantifying temperate hardwood plantation inventories using relatively low-density airborne LiDAR, and it has potential applications for monitoring large-scale plantation forests. Further research is needed to adapt this method to natural stands with diverse tree ages and structures

Estimation of Forest Interior Condition in Southern Appalachian Mountains Using Airborne Lidar Data

Sustainability of forest ecosystem requires maintenance of forest cover and forest interior conditions. Sustaining forest cover supplies ecosystem goods and services and maintains biomass and carbon and nitrogen storage. Forest interior supports ecosystem functioning and provides particular physical and biological environments for plant and animal species that depend on a type of habitat isolated from non-forest land cover areas and prohibit the infestation of invasive species. Land cover maps derived from satellite imagery were broadly applied to assess spatial patterns of forest ecosystem and its dynamic. However, existing land cover maps at landscape scale are insufficient to provide information of canopy gaps within forest areas. They are also not able to distinguish original forest with forest sapling areas, which do not match physical and biological functions as original forest. We applied airborne Lidar data to identify the forest area without canopy gaps and young saplings in southern Appalachian Mountains. Then the forest fragmentation and forest interior condition were analyzed at three spatial scales from 2.25 ha to 56.25 ha. The results showed 5.5% reducing of forest interior and 42.5% increasing of fragmentation areas than the estimations derived by National Land Cover Data

Improving Woody Biomass Estimation Efficiency Using Double Sampling

Although double sampling has been shown to be an effective method to estimate timber volume in forest inventories, only a limited body of research has tested the effectiveness of double sampling on forest biomass estimation. From forest biomass inventories collected over 9,683 ha using systematic point sampling, we examined how a double sampling scheme would have affected precision and efficiency in these biomass inventories. Our results indicated that double sample methods would have yielded biomass estimations with similar precision as systematic point sampling when the small sample was ≥ 20% of the large sample. When the small to large sample time ratio was 3:1, relative efficiency (a combined measure of time and precision) was highest when the small sample was a 30% subsample of the large sample. At a 30% double sample intensity, there was a \u3c 3% deviation from the original percent margin of error and almost half the required time. Results suggest that double sampling can be an efficient tool for natural resource managers to estimate forest biomass

Shifts in dominant tree mycorrhizal associations in response to anthropogenic impacts

Plant-fungal symbioses play critical roles in vegetation dynamics and nutrient cycling, modulating the impacts of global changes on ecosystem functioning. Here, we used forest inventory data consisting of more than 3 million trees to develop a spatially resolved “mycorrhizal tree map” of the contiguous United States. We show that abundances of the two dominant mycorrhizal tree groups—arbuscular mycorrhizal (AM) and ectomycorrhizal trees—are associated primarily with climate. Further, we show that anthropogenic influences, primarily nitrogen (N) deposition and fire suppression, in concert with climate change, have increased AM tree dominance during the past three decades in the eastern United States. Given that most AM-dominated forests in this region are underlain by soils with high N availability, our results suggest that the increasing abundance of AM trees has the potential to induce nutrient acceleration, with critical consequences for forest productivity, ecosystem carbon and nutrient retention, and feedbacks to climate change

Effect of ultrasound pretreatment on wood prior to vacuum drying

The influence of ultrasonic pretreatment prior to vacuum drying of Chinese fir specimens was examined in this work. In the pretreatments, wood samples were immerged in a distilled water bath and were treated using two wave frequencies for four different elapsed times to investigate effects of ultrasonic frequency and treatment duration. Then the wood samples were vacuum-dried at 80°C and absolute pressure of 0,05 MPa. After the pretreatment, microscopic analysis was carried out on the wood samples to check micro-cracks, the loss of extractives from the cell walls and other micro-structural changes on the wood. Results show that the ultrasonic treatment prior to vacuum drying significantly shortened the wood drying time. The drying time decreased with increase in the wave frequency and the treatment time. Furthermore, ultrasound pretreatment tended to reduce the content of extractives in the wood cell walls and cause cell-wall micro-cracking

How Structural Complexity of Vegetation Facilitates Invasion: Integrating LiDAR and FIA Invasive Species Plot Data in the Appalachian Mountains of the USA

This study examines how the vertical structure of forests and the variation in forest canopy tree composition relates to where forest plant invasions occur at a regional scale. We used LiDAR data on vertical structure of forests collected across 16 counties of western North Carolina, and Forest Inventory and Analysis (FIA) abundance data of invasive plant species and canopy tree species from 575 plots. We found that nearly one third of these plots were invaded by at least one invasive plant species (range = 1 to 8 species). We derived canopy gaps/clear-cut areas of the study site using LiDAR data matrix (RH100) and 2006 NLCD image to compare invasive species richness at the vegetation gap and closed canopy areas. The most frequently occurring invasive species of the 22 recorded invasive species in the vegetation gap and closed canopy areas were Lonicera japonica (Japanese honeysuckle; 67% & 49%), Rosa spp. (non-native rose; 58% & 51%) and Ligustrum sinense (Chinese privet; 36% & 25%) respectively. Majority of invasive species in both vegetation gap and closed canopy areas are dispersed by birds/ small mammals. Preliminary results suggest that plots in areas having greater forest structural complexity have less invasive plant species present. A variety of mechanisms can explain how forest structural complexity may impact invasivability. We conclude by summarizing these possible mechanisms and the role that LiDAR can play in studying and managing forested landscapes threatened by invasive species

An integrated assessment of the potential impacts of climate change on Indiana forests

Forests provide myriad ecosystem services, many of which are vital to local and regional economies. Consequently, there is a need to better understand how predicted changes in climate will impact forests dynamics and the implications of such changes for society as a whole. Here we focus on the impacts of climate change on Indiana forests, which are representative of many secondary growth broadleaved forests in the greater Midwest region in terms of their land use history and current composition. We find that predicted changes in climate for the state – warmer and wetter winters/springs and hotter and potentially drier summers – will dramatically shape forest communities, resulting in new assemblages of trees and wildlife that differ from forest communities of the past or present. Overall, suitable habitat is expected to decline for 17-29 percent of tree species and increase for 43-52 percent of tree species in the state, depending on the region and climate scenario. Such changes have important consequences for wildlife that depend on certain tree species or have ranges with strong sensitivities to climate. Additionally, these changes will have potential economic impacts on Indiana industries that depend on forest resources and products (both timber and non-timber). Finally, we offer some practical suggestions on how management may minimize the extent of climate-induced ecological impacts, and highlight a case study from a tree planting initiative currently underway in the Patoka River National Wildlife Refuge and Management Area

Development of microsatellite markers in Cocos nucifera and their application in evaluating the level of genetic diversity of Cocos nucifera

Cocos nucifera (coconut) is an economically important tropical crop, but opportunities for molecular breeding are limited by lack of DNA sequence information for this species. Previous assessments of coconut germplasm have been conducted based solely on phenotypic data for agronomic and quality traits, due to lack of available molecular markers. In this study, we developed 30 novel microsatellite markers from Illumina transcriptome sequence data, and used these markers to evaluate coconut genetic diversity in 30 individuals representing accessions from China (12 samples) and Southeast Asia (18 samples). The microsatellite markers displayed low to high genetic polymorphism across the population: observed heterozygosity varied from 0.06 to 0.79, with an average of 0.39 ± 0.15. Our results indicated that the Southeast Asian population had a significantly higher number of alleles (p = 0.02), but not significantly different (