Human alteration of the landscape for agricultural and urban land use has been linked to the degradation of streams and stream biota. Natural physical and climatic characteristics, or physiographic template, are important for determining natural land cover and constraining human land use, and are strongly related to stream habitat and stream biotic assemblages. Since the physiographic template differs among watersheds and is an important determinant of the processes being studied, it is important to account for these natural differences among watersheds so that the relationship between land cover and streams can be properly understood. The purpose of this thesis is to develop and assess the utility of a regional framework that classifies watersheds based on physical and climatic predictors of land cover. In Chapter 1, I identified physical and climatic predictors of land cover and classified watersheds into Land cover Distinguished Physiographic Regions (LDPRs) based on these predictors. I was able to identify and create classes based off eight climatic and landform characteristics that determined natural land cover and human land use patterns for both the Eastern and Western U.S. In Chapter 2, I utilized LDPRs to stratify a study region and investigated whether the relationships between land cover and stream fish assemblages varied between these regions. Five commonly used metrics covering trophic, reproductive and taxonomic groupings showed significant variation in their response to agricultural land use across LDPRs. The results suggest that the physiographic differences among LDPRs can result in different pathways by which land cover alterations impact stream fish communities. Unlike other commonly used regional frameworks, the rationale and methods used to develop LDPRs properly accounts for the causal relationship between physiography and land cover. Therefore, I recommend the use of LDPRs as a tool for stratifying watersheds based on physiography in future investigations so that the processes by which human land use results in stream degradation can be understood. / Master of Science

Whitetop Mountain is Virginia's second highest peak and hosts two globally rare, insular ecosystems: a southern Appalachian grass bald and a red spruce-dominated forest. These areas provide important ecosystem services and habitat for rare and endangered species. They are highly prized for their cultural value and recreational areas that support nearby rural economies. This thesis investigated spatial patterns in both ecosystems on Whitetop. We documented a 24.73% decrease of in the extent of the southern Appalachian grass bald across 68 years through analysis of historical aerial photography. In the red spruce-dominated forest, we used a consumer grade unmanned aerial vehicle (UAV) to survey the health of all trees within a 46 ha sample plot. We assessed (dead, dying, healthy) over 9,000 individual trees based on visual patterns in the imagery and produced spatial products that will inform land managers about where resources are most needed. About 7.4% of the red spruce trees in our study area were classified as dead or dying. A model relating spruce mortality to biophysical landscape factors identified no single predictive factor related to mortality. The addition of optical information from the UAV imagery into the model proved utility for remotely-sensed data in identification of dead spruce within the forest canopy at Whitetop and possibly in other similarly structured forests. This research contributed to the limited body of knowledge surrounding the decline of both southern Appalachian grass balds and red spruce forests and provided technical insights for future mortality monitoring. / Master of Science / This thesis investigates land cover changes in two rare ecosystems on Whitetop Mountain, Virginia. The mountain has important biological significance and is a cultural landmark. The high-elevation summit hosts plant and animal species characteristic of northern climates, including a red spruce-dominated forest and a southern Appalachian grass bald. This work documented a 24.73% decrease in the size of the rare southern Appalachian grass bald ecosystem at Whitetop Mountain over 68 years and discussed potential drivers and proposed management for conservation. We also successfully used a camera-equipped unmanned aerial vehicle (drone) to produce high quality imagery for spruce mortality detection within the red spruce forest. Of over 9,000 standing spruce trees, 7.4% were categorized as either dead or dying. We built a predictive model to investigate the relationship between mortality and biophysical environmental factors, but did not identify a single causal factor. A second model that included the color band information from the drone camera revealed that different types of aerial imagery could play a valuable role in detection of tree mortality in forests of similar structure. Overall this research contributes to the body of knowledge surrounding the decline of both southern Appalachian grass bald and red spruce ecosystems and provides insights for management.

The terrestrial ecosystem is an integral component of the Earth System. Constant atmosphere-biosphere exchanges of energy and material affect both the physics and chemistry of the atmosphere. While the general roles of terrestrial ecosystems in regulating ozone and particulate matter air pollution have long been acknowledged, our understanding at both individual process and system level are far from perfect. Also, new process-level discoveries about terrestrial atmosphere-biosphere exchanges are not timely incorporated in numerical models routinely used to study and forecast air quality. These hinder our ability to understand how air quality respond to environmental changes and variabilities. Chapter 1 of this dissertation provides a brief overview on these topics. In Chapter 2 of this dissertation (Wong et al., 2019), we conduct global long-term simulations of ozone dry deposition velocity with four different types of dry deposition parameterizations. We find that none of the tested parameterizations universally stands out in terms of matching observed ozone deposition velocity over different land cover types. Combining this with sensitivity simulations from a global 3-D atmospheric chemistry model (GEOS-Chem), we find that the choice of dry deposition parameterizations can affect the mean, trend and variability of simulated surface O3 level. In Chapter 3 of this dissertation (Wong et al., 2022), we analyze long-term ozone flux observation from three field sites to examine the effects of extreme heat and dryness on ozone deposition. We find that non-stomatal ozone uptake tends to increase during hot days, which either partially offsets or dominates over the reduction in stomatal ozone uptake anticipated by ecophysiological theory. While the response of ozone deposition to dryness is more varied, changes in non-stomatal deposition are usually important. Current dry deposition parameterizations often fail to capture such changes in non-stomatal ozone uptake, resulting in considerable potential error in simulated surface ozone level during hot and dry days. In Chapter 4 of this dissertation (Wong and Geddes, 2021), we conduct global GEOS-Chem numerical experiments with anthropogenic emission inventories and land surface remote sensing products to compare the effects land cover versus land management changes on O3 and fine particulate matter air quality over 1992 – 2014. We find that land cover has stronger effects on O3, while land management has stronger effects on fine particulate matter pollution. We also find that land management has significantly altered regional and global nitrogen deposition, and therefore the risk of critical load exceedance. Chapter 5 of this dissertation includes the concluding remarks and suggestions for future work. In addition, I outline and present the preliminary result from a project examining the future of soil reactive nitrogen emissions and their impacts on air quality.

Malaria is one of the leading causes of mortality and morbidity globally. Land use/land cover (LULC) change have been found to affect the transmission and distribution of malaria in other regions, but no study has attempted to examine such relationships in Nepal. Therefore, this study was conducted in Nepal to assess LULC change between 2000 and 2010, to study the spatial and temporal trend of malaria incidence rate (MIR) between 1999 and 2015, and to understand the relationship between LULC and malaria. The land cover types used for this study are forest, water bodies, agriculture, grassland, shrubland, barren areas, built-up areas and paddy areas. Change detection techniques were used to study LULC change. The temporal trend of MIR in 58 districts, and the relationship between MIR and LULC were evaluated using Poisson and negative binomial regression. Forest, water bodies, snow cover, and built-up area increased in Nepal by 28.5%, 2.96%, 55.12% and 21.19% respectively while the rest of the LULC variables decreased. MIR decreased significantly in 21 districts; however, four districts namely Pyuthan, Kaski, Rupandehi and Siraha had a significantly increasing trend of MIR. During 2001, 2002, and 2003, MIR was positively related to water bodies and paddy areas. Similarly, MIR of 2010 was negatively related to grassland. However, there was no relationship between LULC and MIR in 2000, 2011, 2012 and 2013. It may be because MIR is decreasing significantly in the country and thus the influence of LULC change is also decreasing. / MS / Malaria is one of the major public health concern worldwide. Among many other factors, Land use/land cover (LULC) change have impact in the transmission and distribution of malaria which have been studied in other regions, however, no study has attempted to examine such relationships in Nepal. Therefore, this study was conducted in Nepal to understand the relationship between LULC and malaria. The land cover types used for this study are forest, water bodies, agriculture, grassland, shrubland, barren areas, built-up areas and paddy areas. The relationship between malaria incidence rate (MIR) and LULC were evaluated using Poisson and negative binomial regression. Water bodies and paddy cultivation had positive relationship with MIR during 2001, 2002, and 2003. Similarly, MIR of 2010 was negatively related to grassland. However, there was no relationship between LULC and MIR in 2000, 2011, 2012 and 2013. It may be because MIR is decreasing significantly in the country and thus the influence of LULC change is also decreasing.

This dissertation comprises three interrelated studies exploring the effects of hurricanes on forestlands and the optimization of salvage logging practices. The first study examines land cover changes and salvage logging patterns following Hurricane Michael. It utilizes predictive models to identify key drivers of these changes, exploring the relative influence of storm intensity, forest vulnerability, and economic/operational factors. The second study builds upon these findings, focusing on the agent attribution for land cover change observations leveraging advanced remote sensing tools and relevant spatial data. By distinguishing between wind damage and salvage logging activities, it advances the understanding of post-hurricane land cover dynamics. The third study introduces a novel timber supply model that utilizes robust stochastic optimization to optimize salvage operations under uncertainty. It integrates various data sources to optimize site selection, transportation logistics, and resource allocation under uncertain timber stocks, aiming to enhance salvage operations' efficiency and economic returns. Collectively, these studies provide valuable insights for improved hurricane disturbance management.

Land alterations including deforestation, unsustainable land management practices and an increase in cultivated areas have occurred in the Upper Ruvu watershed in recent decades threatening water and natural resources. This study, which used a combination of remote sensing techniques, field experiments, watershed monitoring, and modeling was designed to investigate impacts of environmental changes on hydrology and soil erosion. The objectives were to: map the extent of land use and land cover change and its influence on soil erosion; correlate the contribution of climate variability and human activities to the changes in hydrology at headwater and watershed scales; estimate surface runoff, sediments and Curve Number at plot scale, and model streamflow responses to changes in land use and land cover using the SWAT watershed model. Results indicate that areas covered by forest decreased from 17% in 1991 to 4% of the total watershed area in 2015. However, areas covered by cropland increased from 14% to 30% of the total watershed area from 1991 to 2015, respectively. Further, results indicate that site characteristics affect runoff and sediment yield as higher soil loss was estimated from cropland with a mean of 28.4 tha-1 in 2015 from 19.8 tha-1 in 1991. Results from monitoring show high sediment loads were from the most disturbed watersheds, compared to Mbezi. Analysis of trends for the long term records at the watershed showed that rainfall had significant decreasing trends. At annual scale, climate variability contributed 46% and human activities contributed 54% of the changes in streamflow. Results from the rainfall simulation experiments show upland rice had higher runoff (48 mmh-1) and soil loss (94 gm-2) compared to grassland and forest. Results from the model outputs showed that average streamflow decreased by 13% between 1991 and 2015. Average peak flows increased by 5% and 12% for 2000 and 2015, respectively compared to the baseline. Land alterations had impacts on surface runoff which increased by 75% and baseflow decreased by 66% in 2015 from the baseline. These results highlight the main areas of changes and provide quantitative information to decision makers for sustainable land and water resources planning and management. / Ph. D. / Deforestation, unsustainable land management practices including cultivation in marginal areas, slash and burn, illegal forest harvest; and bush fires have been common threats to the landscapes of the Upper Ruvu watershed in recent decades. These practices have contributed to the deterioration of water and natural resource base and jeopardize sustainability. Our study was designed to investigate the impacts of environmental changes on the hydrology and soil erosion. We used a combination of methods including experiments in the field, remote sensing and mathematical modeling to investigate the extent of the problem and provide useful information for sustainable management of resources. The objectives were to understand the extent and dynamics of land use and land cover change and subsequent influences on soil erosion; to correlate contribution of climate variability and human activities to hydrology at different scales; to estimate surface runoff and sediments at plot scale; and to model and predict streamflow responses to changes in land use and land cover. Our results indicate that the watershed has been characterized by a loss of forest cover which decreased from 17% in 1991 to 4% of the total watershed area in 2015. Areas of the watershed occupied by cropland increased from 14% to 30% of the total watershed area from 1991 to 2015, respectively. Further, results indicate that the changes had effects on runoff and sediment yield as a high increase ofsoil loss was estimated from cropland which increased from 19.8 t ha^-1 in 1991 to 28.4 t ha^-1 in 2015 and areas occupied by forest were least contributors to soil erosion. The assertion is supported by results from a stream-monitoring which revealed that watersheds with least human interferences generated less sediments, and upland rice had higher soil loss compared to grassland and forest. Analysis of rainfall trends showed significant decreasing trends and fluctuations in climate contributed 46%, and human activities contributed 54% of the changes in streamflow signifying impacts on water availability. Results from the model outputs showed that average streamflow decreased by 13% between 1991 and 2015, with increase in peak flows and decrease in baseflow. Results highlight the changes and subsequent consequences on the hydrology of the watershed and water availability. The information is useful for watershed planning and water resources management.

This dissertation employed a random forest algorithm for Land Use Land Cover (LULC) classification and proposed and tested a modified forest transition framework in the Greater Shawnee National Forest (GSNF), Illinois. Subsequently, a machine learning-based multilayer artificial neural network was used to assess the LULC of the GSNF between 2019 and 2050 utilizing IPCC-based projected climate data. The accuracy of LULC classification was evaluated using Kappa statistics and Producer and User accuracies. The Stepwise Regression, Support Vector Machine, Random Forest, and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models were compared to quantify terrestrial carbon stock. Similarly, InVEST, FRAGSTAT, and Maxent models were used for habitat quality analysis and to estimate the probability of bobcat distribution. The terrestrial carbon stock, habitat quality, and bobcat distribution were quantified across three spatial resolutions, 30, 60, and 90 meters, to assess if there were substantial differences in the represented trends of these measures of Ecosystem Services (ES). The LULC analysis showed varying levels of temporal and spatial variabilities with increased deciduous forest (1.35%), mixed forest (26.40%), agricultural land (2.15%), and urbanized areas (6.70%) between 1990 and 2019. Notably, the LULC intensity analysis exhibited stability from 2001 to 2019, consistent with the forest transition framework proposed in the study. However, when integrating temperature and precipitation projections derived from the IPCC, notable changes in forest cover were observed from the western to eastern sectors within the central region of the GSNF. In all IPCC based scenarios, overall forest cover (deciduous, evergreen, and mixed) declined. The classification accuracy of the LULC assessment ranged from 92.9% to 95.9%, accompanied by kappa statistics ranging from 0.89 to 0.94. The prediction accuracy of LULC change was validated for the year 2019, ranging from 77.99% to 84.67%, with kappa statistics between 0.79 and 0.81, depending on the scenario, and predictions were extended to the year 2050. The terrestrial carbon stock in GSNF varied from 15 to 212 MgC per hectare across different models. The RF model performed best at 90 meters resolution with FIA-based data, with RMSE values of 17.45, 18.73, and 20.05, and R-squared values of 0.53, 0.48, and 0.43 for 2001, 2010, and 2019, respectively. The findings indicated that while the InVEST model provides a broad and generalized approach to quantifying carbon storage, the random forest (RF) model is essential for obtaining more accurate and precise estimations. LULC has gradually become more fragmented over time, leading to a decline in average habitat quality from 1990 (0.724±0.215) to 2019 (0.689±0.192). Regardless of increased forest density, the proportion of high-quality habitats (habitat quality score above 0.83) decreased by 5% during the study period. Interestingly, there was a notable increase in the probability score of bobcat distribution from 1990 (0.327±0.123) to 2019 (0.347±0.084). The study revealed a strong correlation between habitat quality and the probability of bobcat distributions, indicating a mutual influence between the two factors. This dissertation suggests that the LULC change of the GSNF follows the forest transition framework and has a significant implication on ecosystem services, such as carbon storage and habitat quality. These results are instrumental for sustainable land management to optimize terrestrial carbon stock and habitat quality, thereby mitigating the impacts of climate change.

An examination of temporal and spatial variation of water quality across the whole watershed is undertook in this research. It is observed that the seasonal variation is apparent in all of the water quality parameters measured. And the spatial variation of water quality parameters gives us the general ideas that water quality is correlation with the watershed landscape. / An integrated approach involving Remote Sensing (RS) technology, Geographic Information System (GIS), Statistical and Spatial Analysis, as well as hydrologic modeling is put forward to perf0ll11 a comprehensive study on the relationship between land use-land cover and water quality in Wenyu River Watershed. Landsat TM data is used to extract land use-land cover information of the study area; while Arc Hydro model is employed to perform the stream network tracing and watershed delineation. / Based on an exponential model, separate multiple regression models are developed to estimate the contributions of different land types on six stream water quality variables, including TN, NO3- N, TP, PO4- P, COD and DO, in Wenyu River watershed. The resulted models are identified to well explain the water quality variables using land use types. And the goodness-of-fit of these modles are reasonably satisfactory. / Finally, this research also discusses the future-oriented studies: l) Higher resolution remote sensing imagery and more in-situ water quality data will be employed to improve the models with higher degree of "goodness of fit" in linking land uses and water quality. 2) Except LULC-related variables, other controlling factors will be considered to establish the more rigorous linkage models. 3) Identifying the relationship between the buffer landscape and stream water quality will be another subject of the future study. 4) Estimating the links between land use-land cover and water quality over an extended period is crucially important job in the future works. (Abstract shortened by UMI.) / The above results and analysis provide insight into the linkages between land-use practices and stream water quality, and the developed models can help in examining the relative sensitivity of water quality variables to alterations in land use made within a watershed. The predicted values are close to the actual monitored values, which indicates that with little calibration and validation, the regression model can be used in another watershed under a different geographical scale, in a different region with variable landscapes. / The results of water quality comparison between different land-use structures tell us that land use types are significantly correlated to water quality variables in Wenyu River Watershed. And the Spearman's rank correlation analyses verify this conclusion, and reveal that urban and village have strong positive relationships with the water quality variables of total nitrogen (TN), total phosphorous (TP), phosphate (PO4-P) and chemical oxygen demand (COD). On the contrary, forest land represents the negative correlation with all the above variables and only positively correlates with dissolved oxygen (DO), which demenstrates that forest land is acting as a "sink" or active transformation zone. / The study offers supporting evidence for previous studies and can serve as a reference to similar studies estimating the response of water quality to the land use-land cover change. The results also indicate that with the integration of GIS and ecological modeling, a decision-making support system can be developed to manage land development and control non-point sources pollution at the watershed scales. This study also suggests that if we pursue a sustainable development, the land management with respect to its development must consider the further erosion on water quality in this area. / Wang, Yufei. / Adviser: Yuanzhi Zhang. / Source: Dissertation Abstracts International, Volume: 73-06, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 123-136). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

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Recently, there is a tremendous amount of high resolution imagery that wasn’t available years ago, mainly because of the advancement of the technology in capturing such images. Most of the very high resolution (VHR) imagery comes in three bands only the red, green and blue (RGB), whereas, the importance of using such imagery in remote sensing studies has been only considered lately, despite that, there are no enough studies examining the usefulness of these imagery in urban applications. This research proposes a method to investigate high resolution imagery to analyse an urban area using UAV imagery for land use and land cover classification. Remote sensing imagery comes in various characteristics and format from different sources, most commonly from satellite and airborne platforms. Recently, unmanned aerial vehicles (UAVs) have become a very good potential source to collect geographic data with new unique properties, most important asset is the VHR of spatiotemporal data structure. UAV systems are as a promising technology that will advance not only remote sensing but GIScience as well. UAVs imagery has been gaining popularity in the last decade for various remote sensing and GIS applications in general, and particularly in image analysis and classification. One of the concerns of UAV imagery is finding an optimal approach to classify UAV imagery which is usually hard to define, because many variables are involved in the process such as the properties of the image source and purpose of the classification. The main objective of this research is evaluating land use / land cover (LULC) classification for urban areas, whereas the data of the study area consists of VHR imagery of RGB bands collected by a basic, off-shelf and simple UAV. LULC classification was conducted by pixel and object-based approaches, where supervised algorithms were used for both approaches to classify the image. In pixel-based image analysis, three different algorithms were used to create a final classified map, where one algorithm was used in the object-based image analysis. The study also tested the effectiveness of object-based approach instead of pixel-based in order to minimize the difficulty in classifying mixed pixels in VHR imagery, while identifying all possible classes in the scene and maintain the high accuracy. Both approaches were applied to a UAV image with three spectral bands (red, green and blue), in addition to a DEM layer that was added later to the image as ancillary data. Previous studies of comparing pixel-based and object-based classification approaches claims that object-based had produced better results of classes for VHR imagery. Meanwhile several trade-offs are being made when selecting a classification approach that varies from different perspectives and factors such as time cost, trial and error, and subjectivity.       Classification based on pixels was approached in this study through supervised learning algorithms, where the classification process included all necessary steps such as selecting representative training samples and creating a spectral signature file. The process in object-based classification included segmenting the UAV’s imagery and creating class rules by using feature extraction. In addition, the incorporation of hue, saturation and intensity (IHS) colour domain and Principle Component Analysis (PCA) layers were tested to evaluate the ability of such method to produce better results of classes for simple UAVs imagery. These UAVs are usually equipped with only RGB colour sensors, where combining more derived colour bands such as IHS has been proven useful in prior studies for object-based image analysis (OBIA) of UAV’s imagery, however, incorporating the IHS domain and PCA layers in this research did not provide much better classes. For the pixel-based classification approach, it was found that Maximum Likelihood algorithm performs better for VHR of UAV imagery than the other two algorithms, the Minimum Distance and Mahalanobis Distance. The difference in the overall accuracy for all algorithms in the pixel-based approach was obvious, where the values for Maximum Likelihood, Minimum Distance and Mahalanobis Distance were respectively as 86%, 80% and 76%. The Average Precision (AP) measure was calculated to compare between the pixel and object-based approaches, the result was higher in the object-based approach when applied for the buildings class, the AP measure for object-based classification was 0.9621 and 0.9152 for pixel-based classification. The results revealed that pixel-based classification is still effective and can be applicable for UAV imagery, however, the object-based classification that was done by the Nearest Neighbour algorithm has produced more appealing classes with higher accuracy. Also, it was concluded that OBIA has more power for extracting geographic information and easier integration within the GIS, whereas the result of this research is estimated to be applicable for classifying UAV’s imagery used for LULC applications.