Master of Science / Department of Geography / Douglas G. Goodin / Volyn Oblast in Western Ukraine has experienced substantial land use/land cover change over the last 25 years as a result of a change in political systems. Remote sensing provides a framework to quantify this change without extensive field work or historical land cover records. In this study, land change is quantified utilizing a post-classification change detection technique comparing Landsat imagery from 1986-2011(Post-Soviet era began 1991). A variety of remote sensing classification methods are explored to take advantage of spectral and spatial variation within this complex study area, and a hybrid scheme is ultimately utilized. Land cover from the CORINE classification scheme is then converted to the EUNIS habitat classification scheme to analyze how land cover change has affected habitat fragmentation. I found large scale agricultural abandonment, increases in forested areas, shifts towards smaller scale farming practices, shifts towards mixed forest structures, and increases in fragmentation of both forest and agricultural habitat types. These changes could have several positive and negative on biodiversity, ecosystems, and human well-being.

Thermal remote sensing is a powerful tool for measuring the spatial variability of evapotranspiration due to the cooling effect of vaporization. The residual method is a popular technique which calculates evapotranspiration by subtracting sensible heat from available energy. Estimating sensible heat requires aerodynamic surface temperature which is difficult to retrieve accurately. Methods such as SEBAL/METRIC correct for this problem by calibrating the relationship between sensible heat and retrieved surface temperature. Disadvantage of these calibrations are 1) user must manually identify extremely dry and wet pixels in image 2) each calibration is only applicable over limited spatial extent. Producing larger maps is operationally limited due to time required to manually calibrate multiple spatial extents over multiple days. This dissertation develops techniques which automatically detect dry and wet pixels. LANDSAT imagery is used because it resolves dry pixels. Calibrations using 1) only dry pixels and 2) including wet pixels are developed. Snapshots of retrieved evaporative fraction and actual evapotranspiration are compared to eddy covariance measurements for five study areas in Florida: 1) Big Cypress 2) Disney Wilderness 3) Everglades 4) near Gainesville, FL. 5) Kennedy Space Center. The sensitivity of evaporative fraction to temperature, available energy, roughness length and wind speed is tested. A technique for temporally interpolating evapotranspiration by fusing LANDSAT and MODIS is developed and tested. The automated algorithm is successful at detecting wet and dry pixels (if they exist). Including wet pixels in calibration and assuming constant atmospheric conductance significantly improved results for all but Big Cypress and Gainesville. Evaporative fraction is not very sensitive to instantaneous available energy but it is sensitive to temperature when wet pixels are included because temperature is required for estimating wet pixel evapotranspiration. Data fusion techniques only slightly outperformed linear interpolation. Eddy covariance comparison and temporal interpolation produced acceptable bias error for most cases suggesting automated calibration and interpolation could be used to predict monthly or annual ET. Maps demonstrating spatial patterns of evapotranspiration at field scale were successfully produced, but only for limited spatial extents. A framework has been established for producing larger maps by creating a mosaic of smaller individual maps. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

Thesis (MSc. (Geography)) --University of Limpopo, 2018 / Water hyacinth (Eichhornia crassipes) is recognised as the most notorious invasive species the world-over. Although its threats and effects are fully documented, its distribution is not yet understood, especially in complex environments, such as river systems. This has been associated with the lack of accurate (high spatial resolution) and robust techniques, together with the reliable data sources necessary for its quantification and monitoring. The advent of new generation sensors i.e. Landsat 8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) data, with unique sensor design and improved sensing characteristics is therefore perceived to provide new opportunities for mapping the distribution of invasive water hyacinth in small waterbodies. This study aimed at mapping and understanding the spatio-temporal distribution of invasive water hyacinth in the Greater Letaba river system in Tzaneen, Limpopo Province of South Africa using Landsat 8 OLI and Sentinel-2 MSI data. Specifically, the study sought to identify multispectral remote sensing variables that can optimally detect and map invasive water hyacinth. Landsat 8 OLI and Sentinel-2 MSI were tested based on the spectral bands, vegetation indices, as well as the combined spectral bands plus vegetation indices, using discriminant analysis algorithm. From the findings, Sentinel-2 MSI outperformed Landsat 8 OLI in mapping water hyacinth, with an overall classification (OA) accuracy of 77.56% and 68.44%, respectively. This observation was further confirmed by a t-test statistical analysis which showed that there were significant differences (t=6.313, p<0.04) between the performance of the two sensors. Secondly, the study sought to map the spatial distribution of invasive water hyacinth in the river system over time (Seasonal). Multi-date 10 m Sentinel-2 MSI images were used to detect and monitor the seasonal distribution and variations of water hyacinth in the Greater Letaba River system. The study demonstrated that, about 63.82% of the river system was infested with water hyacinth during the wet season and 28.34% during the dry season. Sentinel-2 MSI managed to depict species spatio-temporal distribution with an OA of 80.79% during wet season and 79.04% in dry season, using integrated spectral bands and vegetation indices. New generation sensors provide new opportunities and potential for seasonal or long-term monitoring of aquatic invasive species like water hyacinth- a previously challenging task with broadband multispectral sensors. / Risk and Vulnerability Science Centre (RSVC)

Forest ecosystems are being altered by climate change, invasive species, and additional stressors. Our ability to detect these changes and quantify their impacts relies on detailed data across spatial and temporal scales. This dissertation expands the ecological utility of long-term satellite imagery by developing high quality forest mapping products and examining spatiotemporal changes in tree species abundance and phenology across the northeastern United States (US; the ‘Northeast’). Species/genus-level forest composition maps were developed by integrating field data and Landsat images to model abundance at a sub-pixel scale. These abundance maps were then used to 1) produce a more detailed, accurate forest classification compared to similar products and 2) construct a 30-year time-series of abundance for eight common species/genera. Analyzing the time-series data revealed significant abundance trends in notable species, including increases in American beech (Fagus grandifolia) at the expense of sugar maple (Acer saccharum). Climate was the dominant predictor of abundance trends, indicating climate change may be altering competitive relationships. Spatiotemporal trends in deciduous forest phenology – start and end of the growing season (SOS/EOS) – were examined based on MODIS imagery from 2001-2015. SOS exhibited a slight advancing trend across the Northeast, but with a distinct spatial pattern: eastern ecoregions showed advance and western ecoregions delay. EOS trended substantially later almost everywhere. SOS trends were linked to winter-spring temperature and precipitation trends; areas with higher elevation and fall precipitation anomalies had negative associations with EOS trends. Together, this work demonstrates the value of remote sensing in furthering our understanding of long-term forest responses to changing environmental conditions. By highlighting potential changes in forest composition and function, the research presented here can be used to develop forest conservation and management strategies in the Northeast.

<p>The pearl oyster <em>Pinctada martensii </em>or <em>Pinctada fucata </em>is the oyster for produce the South China Sea Pearl, and the production of pearl oyster <em>Pinctada martensii</em> plays a key role for the economic and social welfare of the coastal areas. To guarantee both rich and sustainability of providing pearl oyster productions, addressing the suitable areas for aquaculture is a very important consideration in any aquaculture activities. Relatively rarely, in the case of site selection research, the researchers use GIS analysis to identify suitable sites in fishery industry in China. Therefore, I decided to help the local government to search suitable sites form the view of GIS context. This study was conducted to find the optimal sites for suspended culture of pearl oyster <em>Pinctada martensii </em>using GIS-based multi-criteria analysis. The original idea came from the research of Radiarta and his colleagues in 2008 in Japan. Most of the parameters in the GIS model were extracted from remote sensing data (Moderate Resolution Imaging Spectroradiometer and Landsat 7). Eleven thematic layers were arranged into three sub-models, namely: biophysical model, social-economic model and constraint model. The biophysical model includes sea surface temperature, chlorophyll-α concentration, suspended sediment concentration and bathymetry. The criteria in the social-economic model are distance to cities and towns and distance to piers. The constraint model was used to exclude the places from the research area where the natural conditions cannot be fulfilled for the development of pearl oyster aquaculture; it contains river mouth, tourism area, harbor, salt fields / shrimp ponds, and non-related water area. Finally those GIS sub-models were used to address the optimal sites for pearl oyster <em>Pinctada martensii</em> culture by using weighted linear combination evaluation. In the final result, suitability levels were arranged from 1 (least suitable) to 8 (most suitable), and about 2.4% of the total potential area had the higher levels (level 6 and 7). These areas were considered to be the places that have the most suitable conditions for pearl oyster <em>Pinctada martensii </em>for costal water of Yingpan.</p>

Terrestrial laser scanning (TLS) may provide a way to increase timber value recovery by replacing manual timber cruising with a simple-to-use, cost-effective alternative. TLS has been studied in several trials worldwide. Past studies have not compared TLS based estimates with mill estimates of stem value and volume. Three differently stocked stands of hybrid poplar were selected for diameter, stem sinuosity and height measurement using manual cruising and TLS. Selected trees were harvested and transported to a mill where they were scanned and then processed into lumber and chips. Data gathered using both manual and TLS methods were used to obtain stem volume and value estimates to compare with mill estimates. Results indicated that TLS diameter measurements were more accurately matched to mill and manual measurements up to about 7.5 meters on the stem than above 7.5 meters on the stem in all three stands. Stem curvature comparisons indicated that the variation between TLS and mill centerline measurements was similar to the variation between repeat mill scan measurements of the same stems. Using TLS as a pre-harvest inventory tool showed that additional revenue could be obtained from the reallocation of saw-log and chip log volume to veneer logs of various sizes in all three stands. It was also shown that the sampling error required to estimate stand value was greater than was required to estimate stand volume within the same error limits. / Graduation date: 2012

The pearl oyster Pinctada martensii or Pinctada fucata is the oyster for produce the South China Sea Pearl, and the production of pearl oyster Pinctada martensii plays a key role for the economic and social welfare of the coastal areas. To guarantee both rich and sustainability of providing pearl oyster productions, addressing the suitable areas for aquaculture is a very important consideration in any aquaculture activities. Relatively rarely, in the case of site selection research, the researchers use GIS analysis to identify suitable sites in fishery industry in China. Therefore, I decided to help the local government to search suitable sites form the view of GIS context. This study was conducted to find the optimal sites for suspended culture of pearl oyster Pinctada martensii using GIS-based multi-criteria analysis. The original idea came from the research of Radiarta and his colleagues in 2008 in Japan. Most of the parameters in the GIS model were extracted from remote sensing data (Moderate Resolution Imaging Spectroradiometer and Landsat 7). Eleven thematic layers were arranged into three sub-models, namely: biophysical model, social-economic model and constraint model. The biophysical model includes sea surface temperature, chlorophyll-α concentration, suspended sediment concentration and bathymetry. The criteria in the social-economic model are distance to cities and towns and distance to piers. The constraint model was used to exclude the places from the research area where the natural conditions cannot be fulfilled for the development of pearl oyster aquaculture; it contains river mouth, tourism area, harbor, salt fields / shrimp ponds, and non-related water area. Finally those GIS sub-models were used to address the optimal sites for pearl oyster Pinctada martensii culture by using weighted linear combination evaluation. In the final result, suitability levels were arranged from 1 (least suitable) to 8 (most suitable), and about 2.4% of the total potential area had the higher levels (level 6 and 7). These areas were considered to be the places that have the most suitable conditions for pearl oyster Pinctada martensii for costal water of Yingpan.

No description available.

<p>Land use and land cover changes significantly affect C storage in terrestrial ecosystems. Programs intended to compensate land owners for the maintenance or enhancement to C stocks are promising, but require detailed and spatially explicit C distribution estimates to monitor the effectiveness of management interventions. Savanna ecosystems are significant components of the global C cycle, however, they have not received much attention for the development of C monitoring approaches. In this dissertation I have investigated three of the aspects related to woody plant cover dynamics in semiarid savannas of central Argentina: spatio-temporal dynamics, precise field surveying and scaling from field to region with the use of freely available remotely sense data. </p><p>To examine the long term changes in woody plant cover, I first carefully extracted information from historical maps of the Caldenal savannas of central Argentina (190,000 km2) in the 1880s to generate a woody cover map that was compared to a 2000s dataset. Over the last ~120 years, woody cover increased across ~12,200 km2 (14.2 % of the area). During the same period, ~5,000 km2 of the original woody area was converted to croplands and ~7,000 km2 to pastures, about the same total land area as was affected by woody plant encroachment. A smaller area, fine scale analysis between the 1960s and the 2000s revealed that tree cover increased overall by 27%, shifting from open savannas to a mosaic of dense woodlands along with additional agricultural clearings. Statistical models indicate that woody cover dynamics in this region were affected by a combination of environmental and human factors.</p><p>To assess the consequences of woody cover dynamics on C, we also measured ecosystem C stocks along a gradient of woody plant density. I characterized changes in C stocks in live biomass (woody and herbaceous, above- and belowground), litter, and soil organic carbon (to 1.5 m depth) pools along a woody plant cover gradient (0 to 94 %). I found a significant increase in ecosystem C stocks with increasing woody cover, with mean values of 4.5, 8.4, 12.4, and 16.5 kg C m-2 for grasslands, shrublands, open and closed forests, respectively. Woody plant cover and soil silt content were the two primary factors accounting for the variability of ecosystem C. I developed simple regression models that reliably predict soil, tree and ecosystem C stocks from basic field measurements of woody plant cover and soil silt content. These models are valuable tools for broad scale estimation if linked to regional soil maps and remotely sensed data, allowing for precise and spatially explicit estimation of C stocks and change at regional scales.</p><p>Finally, I used the field survey data and high resolution panchromatic images (2.5 m resolution) to identify tree canopies and train a regional tree percent cover model using the Random Forests (RF) algorithm. I found that a model with summer and winter tasseled cap spectral indices, climate and topography performed best. Sample spatial distribution highly affected the performance of the RF models. The regression model built to predict tree C stocks from percent tree cover explained 83 % of the variability, and the spatially explicit tree C model prediction presented an root mean squared error (RMSE) of 8.2 tC/ha which represented ~30% of the mean C stock for areas with tree cover. Our analysis indicates that regionally over the last ~120 years, increases in woody plant cover have stored significant amounts of C (95.9 TgC), but not enough to compensate for in C generated by the conversions of woodlands and natural grasslands to croplands and pastures (166.7 TgC), generating a regional net loss of 70.9 TgC. C losses could be even larger in the future if, as predicted, energy crops would trigger a new land cover change phase in this region.</p> / Dissertation

The relationships between the spectral properties of Arizona soils and rangelands and their characteristics were studied. The per cent reflectance of soils was determined using a multispectral hand-held radiometer, and the spectral response of Arizona rangeland sites was measured by scanners aboard an orbiting satellite. These spectral properties were related, by means of stepwise multiple regressions, to various soil and site characteristics. This research is presented in three chapters. The first chapter describes the relationships between soil properties and their spectral reflectance as determined in a laboratory environment. The second chapter attempts to correlate spectral properties of soils measured with a radiometer and that measured by scanners aboard an orbiting satellite for a small area near Winkelman, Arizona. The third chapter describes the relationships between the properties of 243 rangeland sites in central and southeastern Arizona and Landsat spectral data values. Determinations of Munsell soil colors and the radiometrically measured reflectance of 163 soils led to the development of charts for converting Munsell color to reflectance. Little difference was found between Munsell color measured in the sun and that measured indoors, and on the average, soil scientists were in agreement 80 per cent of the time. Munsell value, organic carbon, carbonates, and Munsell chroma explained 80 per cent of the variability within the reflectance measurements of these soils. The spectral response of the less than 2 mm soil fraction collected from rangeland surfaces was significantly different from the spectral response of coarser fragments collected from the same surface. In the Winkelman area the radiometrically measured reflectance of the less than 2 mm fraction alone accounted for 46 per cent of the variability and the reflectance of the 13 to 76 mm fraction accounted for 17 per cent of the variability within the satellite measured response. This area had a low vegetative cover and soil-geologic features, particularly soil color, correlated best with the Landsat digital data. Seventy-six per cent of the satellite data were explained by the interaction of the per cent coarse fragments times its reflectance, the average slope of the sites and the per cent soil less than 2 mm fraction times its reflectance. The relationship between the properties of 110 rangeland sites in central Arizona and the sum of the four Landsat spectral bands was determined. The sum of brush and forest crown densities, elevation, soil color,Geology of the site, and the per cent of surface covered with cobbles explained 82 per cent of this variation. An evaluation of field measurements only to explain the variability among mapping units showed the sum of brush and forest crown densities, elevation, clay content, and fragments greater than 2 mm explained 67 per cent of this variation. When satellite data were added to the field measurement site characteristics, the ratio of satellite scanner bands 4+5 to 6+7 becomes the most significant factor in explaining the variation among mapping unit symbols and a greater per cent of the variability could be explained. A similar study conducted on 133 sites in southeastern Arizona gave different results as only 41 per cent of the variability could be explained. It was shown that for central and southern Arizona rangelands, it is possible to define specific relationships between site characteristics and satellite measured spectral response. Less than ten site characteristics and their interactions explain considerable portions of the variability between mapping units for a given survey. These relationships are unique for specific locations, but they could easily be developed for a survey area and effectively used in the mapping process.