The northern hardwood forest type is a critical habitat component for the endangered Carolina northern flying squirrel (CNFS; Glaucomys sabrinus coloratus) for denning sites and corridor habitats between montane conifer patches where the squirrel forages. This study examined terrain data, and patterns of occurrence for the northern hardwood forest type in the recovery areas of CNFS in western North Carolina and southwestern Virginia with the purpose of creating a more robust predictive model of this forest type for spatial delineation. I recorded overstory species composition as well as terrain variables at 338 points throughout the study area in order to quantitatively define the northern hardwood forest type. These data were used in conjunction with digital terrain data for creation of the predictive model. Terrain variables we examined to attempt to differentiate northern hardwoods from other forest types included elevation, aspect, slope gradient, curvature, and landform index. I used an information-theoretic approach to assess six models based on existing literature and a global model.  My results indicate that on a regional, multi-state scale, latitude, elevation, aspect, and landform index (LFI) of an area are significant predictors of the presence of the northern hardwood forest type in the southern Appalachians.  My model consisting of Elevation + LFI was the best approximating model based on lowest AICc score.  Our Elevation + LFI model correctly predicted northern hardwood presence at 78.2% of our sample points observed to be northern hardwoods. I then used this model to create a predictive map of the distribution of the northern hardwood forest type in CNFS recovery areas. / Master of Science

Forests have the ability to alleviate the impact of global warming through carbon sequestration. Six forest management scenarios for a 27,000 acre study area are modeled to determine the impact of forest management on carbon sequestration. Forest management determines annual harvested volume and end-use disposition category of wood products, and inventory volume. Shorter rotations tend to produce short-lived wood products, while longer rotations produce long-lived wood products. Thinning removes pulpwood, which increases the average diameter of the stand and increases the proportion of sawtimber products. Changing forest management complicates accounting for changes in future C storage. Understanding the distinction between pre- and post-regulation harvest volume and C storage is essential to understand the effects of forest regulation. Plotting harvested volume and C storage volume over time shows distinctive pre- and post-regulation characteristics. The pre-regulation curves exhibit irregularities and varying thinned volume due to the uneven area in the existing age classes. Post-regulation curves are level because a constant area is annually thinned and clearcut. Carbon storage is the amount of C that is sequestered into a C pool, which for the purposes of this study is either inventory volume or residual wood product volume. Converting volume flows to C storage involves tracking the accumulation of wood products and standing volume over time then converting volume to a measure of C. Once the forest is regulated, C stored in the inventory pool remains constant from year to year, while the C stored in wood products continually increases. Longer rotations store more carbon than shorter rotations because they have larger inventory pools. Wood products are a substantial carbon pool: at the end of 50 years; the ratio of incremental C in the wood products carbon pool to incremental C in the inventory pool ranges from 6 to 122 for the modeled scenarios. Three accounting periods are evaluated to examine the importance of C sequestration timing to determine if a market for C can influence forest management. Long rotations meet the objectives of maximizing C sequestration and NPV for the modeled regimes regardless of the accounting period considered, or if the forest is regulated or un-regulated. Model sensitivity to decomposition rate, discount rate and timber prices is assessed to determine the effects of uncertainty (measurement error and future trends) on the results of the model. Short rotations are most sensitive to decomposition assumptions and stumpage prices because they produce a large amount of fast-decaying wood products. Long rotations are most affected by discount rate. Carbon storage of all scenarios increases substantially when the pulpwood decomposition rate equals the sawtimber decomposition rate to reflect a potential future increase in composite lumber production. / Master of Science

Three watersheds located in Westmoreland County, Virginia were monitored to evaluate effectiveness of forestry best management practices (BMPs) for minimizing hydrologic and water quality impacts associated with timber harvesting. One watershed was clear-cut without implementation of BMPs, one watershed was clear-cut with the implementation of BMPs and the third watershed was left undisturbed as a control. The 27 months of pre-harvest monitoring data and 17 months of post-harvest monitoring data were compared using the paired watershed regression analysis and the minimum detectable change (MDC) statistic. Analysis of the hydrologic data showed that peak discharge rates were not impacted by harvesting, regardless of whether BMPs were implemented. Harvesting with or without the implementation of BMPs resulted in no statistically significant change in stormflow volume. BMP implementation was found to be effective in minimizing harvesting impacts on stormflow total suspended solids (TSS), ammonia, total Kjeldahl nitrogen (TKN), total nitrogen (TN), sediment-bound nitrogen and total phosphorous (TP) concentrations and total flow TSS and TP concentrations. BMP implementation was also effective in minimizing harvesting impacts on stormflow TSS, TKN, sediment-bound nitrogen, TP and sediment-bound phosphorous loadings and total ammonia loading. The BMPs were not effective in reducing nitrate concentrations and loadings. The BROOK90 forest hydrology model was used to predict the site hydrology. Model predictions compared favorably with streamflow measurements from watersheds QN3 and QN4 until the time of harvest, but did not predict streamflows as accurately for the control watershed, QN5. / Master of Science

Submitted by Jorge Silva (jorgelmsilva@ufrrj.br) on 2018-03-23T18:33:28Z No. of bitstreams: 1 2017 - Micael Cortopassi Booth.pdf: 3109489 bytes, checksum: 01bce83a5a84e68b19faf1907d31a751 (MD5) / Made available in DSpace on 2018-03-23T18:33:28Z (GMT). No. of bitstreams: 1 2017 - Micael Cortopassi Booth.pdf: 3109489 bytes, checksum: 01bce83a5a84e68b19faf1907d31a751 (MD5) Previous issue date: 2017-02-22 / CAPES / The use of geotechnologies assists in the monitoring and quantification of the development and modification of the landscape. The Amazon region has high diversity and plant richness and it is in this biome that is the study area, in the National Forest of Jamari, is a Conservation Unit of Multiple Use. In this FLONA there are eight mines under a PRAD because there has been since the 1960s the exploration and mining of cassiterite to obtain tin. The opening of cavas for the exploration is done in the open, performed almost mechanically, leaving the environment at the end very degraded and its restoration demands resources and time. In general, the recovery of the landscape of an area of cultivation is done with the use of planting heterogenous species of the same biome, aiming to cover the soil and induce natural regeneration. In the areas of mines the type of substrate influences in the development and fixation of the vegetal species in the area. The objectives were to map the soil cover, the creation of future scenarios of revegetation using a prediction program. In these mines the types of substrates that were classified in tillage (PL), dry tail (RS), wet tail (RU), washed tail and washing plant (WP) were mapped. Using four high spatial resolution images of the years 2009, 2011, 2013 and 2015, the typologies were classified as soil exposed, field dirty, capoeira, vegetation thin, intermediate, dense. The areas occupied by each typology for each of the years studied in the mines were compared in order to quantify the converted areas of exposed soil in vegetated areas and the dynamics of the present vegetation cover. Future scenarios for the years 2020 to 2065 were modeled using the Dinamica-EGO, a program used for simulation and prediction of natural environments in particular deforestation. The validation of the generated maps was done using fuzzy similarity. The results obtained for the future scenarios were similar to those observed and the dynamics of the five typologies used evolved in a manner consistent with that observed, with some mines having a better development in a shorter time according to the type of substrate and age of the plantations. / A utiliza??o de geotecnologias auxilia no acompanhamento e quantifica??o do desenvolvimento e modifica??o da paisagem. A regi?o Amaz?nica tem alta diversidade e riqueza vegetal e ? nesse bioma que est? a ?rea de estudo, na Florestal Nacional do Jamari, uma Unidade de Conserva??o de uso m?ltiplo. Nessa FLONA h? oito minas sob um PRAD pois houve desde a d?cada de 1960 a explora??o e lavra de cassiterita para obten??o de estanho. A abertura de cavas para a explora??o ? feita a c?u aberto, realizada de forma quase toda mec?nica, ficando o ambiente ao final muito degradado e sua restaura??o demanda recursos e tempo. De modo geral, a recupera??o da paisagem de uma ?rea de lavra ? feita com o uso de plantio de esp?cies heterogenias do mesmo bioma, visando recobrir o solo e induzir a regenera??o natural. Nas ?reas de minas o tipo de substrato influencia no desenvolvimento e fixa??o das esp?cies vegetais na ?rea. Os objetivos foram mapear a cobertura do solo, a cria??o de cen?rios futuros de revegeta??o usando um programa de predi??o. Nestas minas foram mapeados os tipos de substratos que s?o classificadas em piso de lavra (PL), rejeito seco (RS), rejeito ?mido (RU), rejeito capeado e washing plant (WP). Utilizando quatro imagens de alta resolu??o espacial dos anos 2009, 2011, 2013 e 2015, foram classificados as tipologias solo exposto, campo sujo, capoeira, vegeta??o rala, intermedi?ria, densa. As ?reas ocupadas por cada tipologia, para cada um dos anos estudados nas minas foram comparados visando quantificar as ?reas convertidas de solo exposto em ?reas vegetadas e a din?mica da cobertura vegetal presente. Cen?rios futuros para os anos de 2020 a 2065 foram modelados utilizando o Din?mica-EGO, programa utilizado para simula??o e predi??o de ambientes naturais em especial desmatamento. A valida??o dos mapas gerados foi feita utilizando similaridade fuzzy. Os resultados obtidos para os cen?rios futuros foram em similares ao observado e a din?mica das cinco tipologias utilizadas evolu?ram de forma condizente com o observado, tendo algumas minas melhor desenvolvimento em um menor tempo de acordo com o tipo de substrato e idade dos plantios.