Environmental flows are important to maintain the ecological integrity of the estuary. In a watershed, it is influenced by land use land cover (LULC) change, climate variability, and water regulations. San Antonio, Texas, the 8th largest city in the US, is likely to affect environmental flows to the San Antonio Bay/Guadalupe Estuary, due to rapid urbanization. Time series analysis was conducted at several stream gauging stations to assess trends in hydrologic variables. A bootstrapping method was employed to estimate the critical value for global significance. Results suggested a greater number of trends are observed than are expected to occur by chance. Stream gauging stations present in lower half of the watershed experienced increasing trend, whereas upper half experienced decreasing trends. A similar spatial pattern was not observed for rainfall. Winter season observed maximum number of trends. Wavelet analysis on hydrologic variables, suggested presence of multi-scale temporal variability; dominant frequencies in 10 to 15 year scale was observed in some of the hydrologic variables, with a decadal cycle. Dominant frequencies were also observed in 17 to 23 year scale with repeatability in 20 to 30 years. It is therefore important to understand various ecological processes that are dominant in this scale and quantify possible linkages among them. Genetic algorithm (GA) was used for calibration of the Hydrologic Simulation Program in FORTRAN (HSPF) model. Although, GA is computationally demanding, it is better than manual calibration. Parameter values obtained for the calibrated model had physical representation and were well within the ranges suggested in the literature. Information from LANDSAT images for the years 1987, 1999, and 2003 were introduced to HSPF to quantify the impact of LULC change on environmental flows. Modeling studies indicated, with increase in impervious surface, peak flows increased over the years. Wavelet analysis pointed, that urbanization also impacted storage. Modeling studies quantified, on average about 50% of variability in freshwater inflows could be attributed to variation in precipitation, and approximately 10% of variation in freshwater inflows could be attributed to LULC change. This study will help ecologist, engineers, scientist, and politicians in policy making pertinent to water resources management.

In dryland regions, where water is a limited resource, land use/land cover has undergone and continues to undergo significant change mainly due to human activities. The nature of runoff from dryland regions and the influence of land use/land cover change are largely not quantified. The objective of this study is to examine runoff dynamics and the influence of land cover in drylands of western Texas across multiple spatial and temporal scales. The study consists of four major components: (1) an experimental study at Honey Creek upland catchment (19 ha) to assess vegetation treatment effects on runoff by hydrometric and isotopic methods; (2) a hydrochemical evaluation of hydrologic linkage between the upland and bottomland at the second-order Honey Creek watershed; (3) a detailed precipitation-streamflow analysis at North Concho River basin to assess long-term and large-scale precipitation-streamflowvegetation dynamics; and (4) a comparison of streamflow in North, Middle, and South Concho River basins and a regional streamflow trend analysis for the entire western Texas. The study indicates runoff production in the drylands of western Texas is dominated by a few large runoff-producing events. The small catchment experiment indicated that runoff increased about 40 mm per year when 60% of woody plants were removed. This effect may relate to the presence of a baseflow component, but was not verified in regional trend analysis for the Edwards Plateau region where most rivers are spring-fed. The decrease in streamflow in North Concho River basin after the 1950's is in large part related to the enhanced infiltration capacity from reduced grazing pressure and improved vegetation cover. Regional streamflow trend analysis suggests some headwater areas outside the Edwards Plateau region experienced patterns of streamflow change similar to those in North Concho River basin, although artificial impoundments complicated the analysis. The study has broader application in ecohydrological research beyond specific geographic areas and specific vegetation types when evaluating the impact of ecosystem structure change on hydrology and water resources.

Groundwater use is of fundamental importance to meet rapidly expanding urban, industrial, and agricultural water requirements, particularly in semiarid zones. To quantify the current rate of groundwater recharge is thus a prerequisite for efficient and sustainable groundwater resource management in these dry areas, where such resources are often the key to economic development. Increased groundwater recharge has been documented where native vegetation or forest/shrub land was converted to grassland or pasture, or where the land was cleared for agricultural purposes. The basic argument for increased recharge is that evapotranspiration, primarily interception and transpiration, is higher in shrublands than grasslands. Chloride mass balance (CMB) has been used to estimate ancient recharge, but recharge from recent land-use change has also been documented, specifically where vegetation has been altered and deep-rooted species replaced with shallow-rooted grasses. Chloride concentrations are inversely related to recharge rates: low Clconcentrations indicate high recharge rates as Cl- is leached from the system; high Cl concentrations indicate low recharge rates since Cl- accumulates as a result of evapotranspiration. The objectives were (1) to assess the hypothesis that removal of woody-shrub vegetation and replacement with grasses increases deep drainage, (2) to quantify the amount of deep drainage after land-use change, and (3) to provide science-based data for a better understanding of changing land-use impacts on deep drainage. Eight soils from five locations in the Central Rolling Red Plains near Abilene and Sweetwater were sampled. Each location consisted of a pair of similar soils with contrasting vegetative cover: shrubland and grassland. At each site three to five soil cores were taken as deep as possible and samples were taken by horizon, but horizons were split when their thickness exceeded 0.25 m. Soil Cl- profiles under shrubland at three sites showed that virtually no water escapes beyond the root zone. High Cl- concentrations and inventories reflect soil moisture fluxes that approached 0 mm yr-1 with depth. Evapotranspiration may be largely responsible for Cl- enrichment in those profiles. Surprisingly, soil moisture flux past 200 cm under juniper woodlands was the highest with 2.6 mm yr-1. Evapotranspirative Cl- enrichment in the upper 300 cm was not observed and may suggest a different water uptake mechanism for this plant community. Soil Cl- profiles showed increased recharge rates under grassland vegetation ecosystem. Estimated deep drainage past 200 cm of 0.1 to 1.3 mm yr-1 was observed. Low Cl- concentrations and inventories suggest a leaching environment that may be in response to changes in land use/land cover.

In this thesis, land surface emissivity variations are examined with respect to the land surface type, wavelength, and time (season and month) for the seven selected regions in Turkey using MODIS emissivity database and precipitation amount. Investigating land surface emissivity variations are important in many applications and it is known that studies about these variations are done for many regions except Turkey. This study is prior knowledge for Turkey to be used in infrared (IR) background models, surface radiation budget calculations, and land cover type classifications specific for Turkey. The results indicate that precipitation has a great influence on monthly/seasonal emissivity values depending on the land cover type and causes spectral emissivity variations. As a result, we determined appropriate IR wavelengths for the investigation of the seasonal emissivity variations and seasonal factors causing emissivity variations according to the land cover types.

The Houston-Galveston-Brazoria Consolidated Metropolitan Statistical Area (Houston CMSA) has experienced rapid population growth during the past decades and is the only major US metropolitan area with no zoning regulations. We use SLEUTH, a spatially explicit cellular automata model, to simulate future (2002-2030) urban growth in the Houston metropolitan area, one of the fastest growing metropolises in the United States during the past decades. The model is calibrated with historical data for the period 1974-2002 that are extracted from a time series of satellite images. The dataset consists of four historical urban extents (1974, 1984, 1992, 2002), two land use layers (1992, 2002), five transportation layers (1974, 1984, 1990, 2002, 2025), slope layer, hillshade layer, and excluded layer. Future growth patterns are predicted based on growth coefficients derived during the calibration phase. After calibrating the model successfully, the spatial pattern of urban growth of the Houston CMSA for the period from 2002 to 2030 is predicted. Within SLEUTH, growth in the Houston CMSA is predominately "organic" with most growth occurring along the urban/rural fringe. Projected increases in urban area from 2002 to 2030 parallel projected increases in population growth within the Houston CMSA. We design three specific scenarios to simulate the spatial consequences of urban growth under different environmental conditions. The first scenario is to simulate the unmanaged growth with no restrictions. The second scenario is to project the moderate growth trend by taking into consideration environmental protection, specifically for agricultural areas, forests and wetlands. The last scenario is to simulate the managed growth with maximum environmental protection. Adjusting the level of protection for different land cover types was found to markedly affect the land use changes in the Houston CMSA. Without any protection on resource lands, Houston CMSA is estimated to lose 2,000 km2 of forest land by 2030, about 600 km2 of agricultural land, and approximately 400 km2 of wetland. Approximately half of all resource land could be saved by the third scenario, managed growth with maximum protection.

An ecological approach to land-use planning is essential to maintain the long-term sustainability of ecosystem benefits, services, and resources. Concern about environmental quality and the long-term livability of urban areas is now a driving force in urban planning and design. The interrelated issues of growth management, smart growth, sustainable development, and new urbanism are topics in the most vibrant discussions at all levels of planning and landscape architecture. Within this context, this study starts from the interest in the ecological planning and management in urban areas, especially related to the issue of local development regulation and guidelines. Landscape regulations have come into existence recently in communities across the nation and these regulations vary from one region to another and from one community to another. The aims of this study were to investigate the relationship between ecological landscape structure and local development regulations over time. Comparison analysis was conducted between two areas that had similar pre-development ecological conditions but were developed under vastly different regulatory environments. The Woodlands (regulated to protect ecological condition) and the North Houston area (which followed traditional subdivision regulations) were examined at three different developmental time periods: predevelopment, early development (after 10 years), and matured development (after 30 years). Aerial photos of each site from the three time periods were classified into forested and non-forested classes and the landscape structure was quantified with a number of landscape metrics related to fragmentation??an indicator of habitat degradation. Two factors, the ecological approach to landscape planning and the adoption of more restrictive landscape regulations and guidelines, are discussed on the premise that they exert influence in developing and maintaining the long-term sustainability of ecosystems. In conclusion, this study provides the quantified landscape configuration and composition of the effects of development regulations on landscape structure. The ecologically planned community shows a less fragmented forest pattern and more restrictive development guidelines result in more ecologically structured environments. Understanding how elements of local development regulations affect ecological landscape patterns is important for landscape architects, planners, and administrators because it can lead to better strategies for planning and designing sustainable communities.

Land cover is a reliable source for studying changes in the land use patterns at a large scale. With advent of satellite images and remote sensing technologies, land cover classification has become easier and more reliable. In contrast to the conventional land cover classification methods that make use of land and aerial photography, this research uses small scale Digital Elevation Maps and it’s corresponding land cover image obtained from Google Earth Engine. Two machine learning techniques, Boosted Regression Trees and Image Analogy, have been used for classification of land cover regions in continental United States. The topographical features selected for this study include slope, aspect, elevation and topographical index (TI). We assess the efficiency of machine learning techniques in land cover classification using satellite data to establish the topographic-land cover relation. The thesis establishes the topographic-land cover relation, which is crucial for conservation planning, and habitat or species management. The main contribution of the research is its demonstration of the dominance of various topographical attributes and the ability of the techniques used to predict land cover over large regions and to reproduce land cover maps in high resolution. In comparison to traditional remote sensing methods such as, aerial photography, to develop land cover maps, both the methods presented are inexpensive, faster. The need for this research is in synergy with past studies, which show that large-scale data, processing, along with integration and interpretation make automated and accurate methods of change in land cover mapping highly desirable.

Land use and land cover (LULC) change information is essential in urban planning and management. With the rapid urbanization in China, many illegal land developments have emerged in some rapidly developing regions and have caused irreversible environmental problems, posing a threat to sustainable urban development. Short-interval monitoring of LULC change therefore is necessary in these regions to control and prevent illegal land developments at an early stage. Conventional optical remote sensing is limited by weather conditions and has difficulties collecting timely data in tropical regions characterized by frequent cloud cover. Radar remote sensing, not affected by clouds, is therefore a potential tool for collecting timely LULC information in these regions. Polarimetric SAR (PolSAR) is more suitable than single-polarization SAR for monitoring LULC change because it can discriminate different types of scattering mechanisms. The overall objective of this study is to conduct short-interval monitoring of LULC change using RADARSAT-2 PolSAR images. Classification methods that achieve high accuracy for PolSAR images are essential in monitoring LULC change. In this study, a new method, based on the integration of polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms, is proposed for LULC classification using RADARSAT-2 PolSAR data. A comparison between the proposed classification method and Wishart supervised classification which is commonly used for the classification of PolSAR data showed that the proposed method can significantly improve LULC classification accuracy. Polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms have been determined to contribute to the improvement achieved by the proposed classification method. Selection of appropriate incidence angle is important in LULC classification using PolSAR images because incidence angle influences the intensity and patterns of radar return. Based on the proposed classification method, the present study further investigates the influence of incidence angle on LULC classification using RADARSAT-2 PolSAR images. LULC classifications using incidence angles of 31.50 and 37.56° were conducted separately. The influence of incidence angle on the classification was investigated by comparing the results of the two independent classifications. The comparison showed that large incidence angle performs much better than small incidence angle in the classification of different vegetation types, whereas small incidence angle outperforms large incidence angle in reducing the confusion between urban/built-up areas and vegetation, that between vegetable and barren land, and that among barren land, water, and lawn. Considering that the detection of urban/built-up areas and barren land is important in monitoring illegal land developments, small incidence angle is more suitable than large incidence angle in monitoring illegal land developments. Change detection methods that achieve high accuracy for PolSAR data are also essential in monitoring LULC change. The current study proposes a new method for LULC change detection using RADARSAT-2 PolSAR images. The proposed change detection method combines change vector analysis (CVA) and post-classification comparison (PCC) to detect LULC changes using object-oriented image analysis. The classification of PolSAR images is based on the proposed classification method. Compared with the PCC based on Wishart supervised classification, the proposed change detection method can achieve much higher accuracy for LULC change detection. Further investigation indicated that CVA, PCC, and object-oriented image analysis all contribute to the higher accuracy achieved by the proposed change detection method. Short-interval monitoring of LULC change was carried out using a time series of RADARSAT-2 PolSAR images. The monitoring was based on monthly LULC change detection using the proposed change detection method and appropriate incidence angle. The influence of environmental factors on short-interval monitoring of LULC change was investigated by analyzing the monthly change detection results. Paddy harvesting and planting, seasonal crop growth, and change in soil moisture and surface roughness were found to exert significant influence on the short-interval monitoring of LULC change. High accuracy can be achieved for short-interval monitoring of construction sites and bulldozed land using RADARSAT-2 PolSAR images. However, paddy harvesting and growth still cause false alarms on the monitoring of these two LULC classes. The study indicated that conducting short-interval monitoring of LULC change using RADARSAT-2 PolSAR images is effective. High accuracy can be achieved for short-interval monitoring of construction sites and bulldozed land using the proposed change detection and classification methods, which can provide important information for the control and prevention of illegal land developments at an early stage. / published_or_final_version / Urban Planning and Design / Doctoral / Doctor of Philosophy

Deforestation in the tropics is considered to be a primary cause for worldwide loss of biological diversity. Future land use decisions have the potential to escalate or ameliorate this global problem. The goal of this research is to present a case study of an analysis of forest change within the Chocó-Andean Conservation Corridor in the Upper Guayllabamba Watershed in Northwestern Ecuador. Fieldwork, remote sensing, and a Geographic Information System (GIS) were used to analyze land use/land cover changes within the corridor. Change detection from 1986 to 2001 using Landsat imagery confirmed that forests were rapidly being converted to other land covers, but patterns of deforestation rates varied dramatically for different types of forests. The average annual rate of overall loss of forest was 2.7% for lower montane forest, 1.7% for mid-slope cloud forest 2.1% for upper montane forest, and 2.0% for riparian forests. The patterns of deforestation also varied based on scales of analysis. For example, the overall loss of forest within the southern portion of the Chocó-Andean Conservation Corridor occurred at an average rate of 1.3% per year, while the overall annual rate of forest loss within particular sub-watersheds varied from 0.2%-3.1% and the annual average rate of overall forest loss surrounding particular communities ranged from 0.3%-3.3%. Fifty interviews were conducted in 2003 in seven communities within the conservation corridor to determine local perspectives of current land use practices, past land use trends, and future land use goals; regional changes in the forest; and opinions of local conservation projects. An intriguing finding of the study is that remote sensing in isolation of fieldwork would have provided incomplete or misleading results. For example, the community that had the most deforestation between 1986 and 2001 was the community where the conservation projects were actually the most successful, based upon local resident opinion. This report asserts that a holistic approach to conservation is needed to reconcile environmental and socio-cultural needs in order to maintain and improve forest habitat and hydrologic connectivity at multiple spatial scales (including community-level, watershed, and regional) by extending conservation efforts beyond protected areas and utilizing a basin-scale perspective to make land use decisions that maintain biodiversity and promote watershed protection. / text

Distributed models of the land surface are essential for global climate models because of the importance of land-atmosphere exchanges of water, energy, momentum. They are also used for high resolution hydrologic simulation because of the need to capture non-linear responses to spatially variable inputs. Continued improvements to these models, and the data which they use, is especially important given ongoing changes in climate and land cover. In hydrologic models, important aspects are sometimes neglected due to the need to simplify the models for operational simulation. For example, operational flash flood models do not consider the role of snow and are often lumped (i.e. do not discretize a watershed into multiple units, and so do not fully consider the effect of intense, localized rainstorms). To address this deficiency, an overland flow model is coupled with a subsurface flow model to create a distributed flash flood forecasting system that can simulate flash floods that involve rain on snow. The model is intended for operational use, and there are extensive algorithms to incorporate high-resolution hydrometeorologic data, to assist in the calibration of the models, and to run the model in real time. A second study, which is designed to improve snow simulation in forested environments, demonstrates the importance of explicitly representing a near canopy environment in snow models, instead of only representing open and canopy covered areas (i.e. with % canopy fraction), as is often done. Our modeling, which uses canopy structure information from Aerial Laser Survey Mapping at 1 meter resolution, suggests that areas near trees have more net snow water input than surrounding areas because of the lack of snow interception, shading by the trees, and the effects of wind. In addition, the greatest discrepancy between our model simulations that explicitly represent forest structure and those that do not occur in areas with more canopy edges. In addition, two value-added Land Cover products (land cover type and maximum green vegetation fraction; MGVF) are developed and evaluated. The new products are good successors to current generation land cover products that are used in global models (many of which rely on 20 year old AVHRR land cover data from a single year) because they are based on 10 years of recent MODIS data. There is substantial spurious interannual variability in the MODIS land cover type data, and the MGVF product can vary substantially from year to year depending on climate conditions, suggesting the importance of using climatologies for land cover data. The new land cover type climatology also agrees better with validation sites, and the MGVF climatology is more consistent with other measures of vegetation (e.g. Leaf Area Index) than the older land cover data.