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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Combined Use of Vegetation and Water Indices from Remotely-Sensed AVIRIS and MODIS Data to Monitor Riparian and Semiarid Vegetation

Kim, Ho J January 2006 (has links)
The objectives of dissertation were to examine vegetation and water indices from AVIRIS and MODIS data for monitoring semiarid and upland vegetation communities related with moisture condition and their spatial and temporal dependencies in estimating evapotranspiration (ET). The performance of various water indices, including the normalized difference water index (NDWI) and land surface water index (LSWI), with the chlorophyll-based vegetation indices (VIs), the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) was evaluated in 1) investigating sensitivity of vegetation and land surface moisture condition 2) finding optimal indices in detecting seasonal variations in vegetation water status at the landscape level, and 3) their spatial and temporal scale dependency on estimating ET. The analyses were accomplished through field radiometric measurement, airborne-based and satellite data processing accompanied with water flux data.The results of these studies showed vegetation and landscape moisture condition could be identified in VI - WI scatter-plot. LSWI (2100) showed the biggest sensitivity to variation of vegetation and background soil moisture condition as well. Multi-temporal MODIS data analysis was able to show water use characteristic of riparian vegetation and upland vegetation. Results showed water use characteristics of riparian vegetation are relatively insensitive to summer monsoon pulse, while upland vegetation is highly tied to summer monsoon rain. The relationship between water flux measurement from eddy covariance tower and satellite data has shown that MODIS derived EVI and LSWI (2100) have similar merit to estimate ET rate, but better correlation was observed from the relationship between MODIS EVI and ET.Pixel aggregation results using fine resolution AVIRIS data showed moderate resolution spatial scale 250m or 500m, best predicted ET rates over all study areas. Surface fluxes temporally aggregated to weekly or biweekly intervals showed the strongest ET versus EVI relationships. ET measured at flux towers can be scaled over heterogeneous vegetation associations by simple statistical methods that use meteorological data and flux tower data as ground input, and using the MODIS Enhanced Vegetation Index (EVI) as the only source of remote sensing data.
2

Multisensor Translation and Continuity of Vegetation Indices Using Hyperspectral Data

Kim, Youngwook January 2007 (has links)
The earth surface is monitored periodically by numerous satellite sensors which have different spectral response functions, image acquisition heights, atmosphere correction schemes, overpass times, and sun/view angle geometries. Temporal and spatial variations of land surface properties, such as vegetation index, Leaf Area Index (LAI), land surface temperature, and soil moisture, have been provided by long-term time series of various remote sensing datasets. Inter-sensor translation equations are required to build long-term time series by the combination of multiple sensors from historical to advanced and new satellite datasets. In the first chapter, inter-sensor translation equations of band reflectances and two vegetation indices (e.g. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) were derived using linear regression equations relative to Moderate Resolution Imaging Spectroradiometer (MODIS) values. The consistency and validation of inter-sensor transforms were investigated through statistical student's t-test and the root mean square error (RMSE).In the second chapter, cross-sensor extension of EVI and a 2-band EVI (without the blue band; EVI2) were investigated based on the continuity of both EVI's. Sensor specific red-blue coherencies were examined for the possibility of the EVI and EVI2 extension from MODIS sensor. The EVI continuity to MODIS was particularly problematic for the Visible Infrared Imager / Radiometer Suite (VIIRS) and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) that have dissimilar blue bands from that of MODIS. The cross-sensor extension and compatibility of EVI2 were improved and provided the possibility to be lengthened to the Advanced Very High Resolution Radiometer (AVHRR) using its translation equation.Finally, we evaluated the use of sensor-specific EVI and NDVI data sets, using a time sequence of Hyperion images over Amazon rainforest in Tapajos National Forest, Brazil for the 2001 and 2002 dry seasons. We computed NDVI, EVI, and EVI2 with the convolution data of different global monitoring and high temporal resolution sensor systems (AVHRR, MODIS, VIIRS, SPOT-VGT, and SeaWiFS) from Hyperion, and evaluated their spectral deviations and continuity in the characterization of tropical forest phenology. Our analyses show that EVI2 maintains the desirable properties of increased sensitivity in high biomass forests across all sensor systems evaluated.
3

Classification of sets of mixed pixels in remote sensing

Faraklioti, M. January 2000 (has links)
Recently, remotely sensed multispectral data have been proved to be very useful for many applications in the field of Earth surveys. For certain applications, however, limits in the spatial resolution of satellite sensors and variation in ground surface restrict the usefulness of the available data, since the observed spectral signature of the pixels is the result of a number of surface materials found in the area of the pixel. Two mixed pixel classification techniques which have shown high correlation with vegetation coverage of single pixels are described in this thesis: the vegetation indices and the linear mixing model. The two approaches are adjusted in order to deal with sets of pixels and not individual pixels. The sets of pixels are treated as statistical distributions and moments can be estimated. The vegetation indices and the linear mixing model can then be expressed in terms of these statistics. The illumination direction is an important factor that should be taken into account in mixed pixel classification, since it modifies the statistics of the distributions of pixels, and has received no attention until now. The effect of illumination on the relation between the vegetation indices and the proportion of sets of mixed pixels is examined. It is demonstrated that some vegetation indices, which are defined from the ratio of statistics in two spectral bands, can be considered relatively invariant to illumination changes. Finally, a new illumination invariant mixing model is proposed which is expressed in terms of some photometric invariant statistics. It is shown to perform very well and it can be used to un-mix accurately sets of pixels under many illumination angles. The newly introduced mixing model can be considered a suitable choice in the mixed pixel classification field. Key words: Mixed pixels, sets of pixels, vegetation index, illumination invariants.
4

Utilization of Canopy Reflectance to Predict Yield Response of Corn and Cotton to Varying Nitrogen Rates

Rattanakaew, Totsanat 11 December 2015 (has links)
Fertilizer N is one of the most costly inputs in corn (Zea mays L.) and cotton (Gossypium hirsutum L.) production and is a strong yield determining factor. Variable rate N fertilization has the potential to improve resource use efficiency, profitability, and help to minimize adverse environmental impacts. Vegetation indices (VIs) may be useful for in-season crop health monitoring to assist in fertilizer N management and yield prediction. This research determined the utility of aerial imagery in detecting corn and cotton response to varying N supply using five selected VIs. The VIs derived from aerial images, chlorophyll readings and tissue N for corn from V5 to V9 growth stages and cotton beginning the 1st week of flowering through to latelowering were used to relate to fertilizer N rates and plant N status and yield. The results showed that VIs derived from aerial imagery could be used to differentiate N supply and in-season grain yield of corn beginning at V5 to V6; however, models from later growth stages had greater r2 values than earlier growth stages. Single variable models that used VI, chlorophyll content, or plant N concentration as an independent variable were overall stronger than 2 variable Multiple Linear Regression models (MLRs). Three independent variables used in MLRs contained multicollinearity. For cotton, the use of VIs derived from aerial imagery to differentiate N supply may depend on environmental factors such as soil and weather. However, VIs may be useful for in-season lint yield prediction beginning the 1st week of flowering although later stages improved accuracy. The MLRs that were developed with 2 independent variables may be more suitable for in-season lint yield prediction than single independent variable models.
5

Deforestation and the Transformation of the Landscape of North China: prehistory - present

Moore, Alan H. 01 November 2010 (has links)
Environmental evidence shows that 10,000 years ago North China was primarily a lush deciduous forest. Like many other regions of the planet, this landscape has been dramatically transformed by human activity, yet unusually this mostly occurred long ago under pre-industrial conditions. Fortunately China has a long recorded history of human activity. Complementary environmental evidence helps to extend this record into prehistory, for even prehistoric Chinese substantially altered their environment. The first half of this study examines historical and physical evidence in order to better explain how North China's forests disappeared. Only recently have there been regional scale activities focused on reversing this tragic trend. Despite many claims of successes in afforestation, there are serious shortcomings in the collection of government statistics and known limitations to area-based forest assessments, so it is difficult to say with much confidence what is happening with North China's forests today. Phenological measurements from space-based instruments have been effectively used to characterize vegetation trends. In the second half of this study, MODIS sensor observations for 2000-2009 are collected for five study sites and are used to characterize vegetation change over the past decade, independent of government statistics and area-based estimates. Forests provide tangible benefits to environmental and human well-being. Forest health and growth are critical to addressing global climate change. Much attention has been focused on China's efforts to combat deforestation. A better understanding of North China's forest trends — both past and present — may offer valuable lessons for our environmental future. / Master of Science
6

Evaluating the relationship between Modis and AVHRR vegetation indices

Malherbe, Johan 14 November 2006 (has links)
Student Number : 0216831W - MSc research report - School of Environmental Sciences - Faculty of Science / This report deals with the relationship between the NDVI obtained from the NOAA AVHRR sensor and that obtained from the MODIS sensor. The relationship is quantitatively assessed for distinct polygons over various land-cover types in the northeastern Kwa-Zulu Natal Province of South Africa. Spatial and temporal variations in the relationships are addressed and discussed with reference to spectral response, sunsensor- target geometries and atmospheric factors. Specifically, various methods are investigated to estimate a MODIS-equivalent NDVI from the AVHRR NDVI and in so doing create the potential to develop a self-consistent NDVI between the historically available AVHRR NDVI dataset and the relatively new MODIS NDVI dataset. NOAA-16 AVHRR NDVI data and AQUA MODIS NDVI data for the two-year period from January 2002 to December 2003 are used to develop the method. A linear relationship exists between the AVHRR and MODIS NDVI. However, spatial variations in the relationship in terms of land-cover and mean NDVI are pointed out. The potential of atmospheric corrections applied to AVHRR data through a radiative transfer atmospheric correction model to improve the relationship between the two NDVI datasets is also investigated. The importance of geo-location accuracy of the AVHRR NDVI dataset is assessed in the light of the accuracy obtainable with the proposed method to estimate a MODIS-equivalent NDVI from the AVHRR NDVI. A method to estimate the MODIS NDVI from the AVHRR NDVI that takes the mean AVHRR NDVI value into account, as opposed to a constant linear relationship over all the points, is proposed. Atmospheric correction is shown not to improve the accuracy of the method in a statistically significant way. The root-mean-square error of the proposed method is in the order of 0.05 NDVI units and varies between 0.5 and 2 standard deviations of the MODIS NDVI over an entire season.
7

Avaliação e comparação de imagens LISS-III/ResourceSat-1 e TM/Landsat 5 para estimar volume de madeira de um plantio de Pinus elliottii

Berra, Elias Fernando January 2013 (has links)
O objetivo deste trabalho foi estimar o volume de madeira de um povoamento jovem de Pinus elliottii, localizado no litoral sudeste do Rio Grande do Sul, com imagens dos sensores LISS-III/ResourceSat-1 e TM/Landsat 5, comparando o desempenho destes para tal. Obtiveram-se imagens de setembro de 2010, mês coincidente com o inventário florestal feito na área de estudo. Os valores de reflectância espectral de superfície foram recuperados das imagens originais. Após o georreferenciamento, dos pixels coincidentes com a localização das unidades amostrais do inventário florestal foram extraídos os valores das reflectâncias nas quatro bandas espectrais equivalentes aos dois sensores, cujas respostas foram comparadas. Além das bandas espectrais foram utilizados os índices de vegetação (IV’s) SR, NDVI, SAVI, MVI e GNDVI. Também, foi proposto o ajuste destes IV’s originais pela idade do povoamento, os quais foram identificados por SR_i, NDVI_i, MVI_i e GNDVI_i. A aplicação do logaritmo nas bandas espectrais melhorou os valores dos coeficientes de correlação linear (r), à exceção do IVP, retornando valores entre 0,69 (IVP) a 0,83 (Verde) para o LISS-III e entre 0,68 (Vermelho) a 0,79 (IVM) para o TM; Com os IV’s o logaritmo melhorou os valores de r somente para os IV’s originais, retornando valores de r entre 0,77 (NDVI) a 0,84 (GNDVI) com o LISS-III e entre 0,73 (NDVI) a 0,82 (MVI) para o TM. Com os IV’s ajustados pela idade do povoamento a logaritimização não se mostrou necessária para melhorar a associação linear, retornando valores de r entre 0,79 (NDVI_i) a 0,82 (MVI_i) com o LISS-III e entre 0,74 (SR_i) a 0,80 (MVI_i) com o TM. Além disso, o ajuste pela idade aumentou o intervalo dinâmico dos IV’s ajustados, e, aparentemente, aumentou a sensibilidade nos povoamentos de maior volume. Diferenças significativas na associação linear entre os dados espectrais do TM e LISS-III com o volume só foram encontradas na banda equivalente do verde. Com dados TM, a equação melhor ajustada explicou 68% da variabilidade do volume; com dados LISS-III a equação explicou 72% da variabilidade. Estas equações geraram dois mapas de volume de madeira, onde as médias das estimativas obtidas com LISS-III estiveram dentro do intervalo de confiança da média do inventário florestal em 70% dos talhões considerados; para o TM a coincidência foi de 65% dos talhões. Conclui-se que os sensores LISS-III e TM apresentam alta similaridade e que a metodologia empregada pode ser utilizada para auxiliar no inventário florestal dos povoamentos jovens de P. elliottii na área de estudo principalmente pelo fato das estimativas obtidas pelas imagens cobrirem todo o talhão, ao passo que a amostragem do inventário florestal contempla menos de 2% da área. / The aim of this work was to estimate the wood volume of a young stand of Pinus elliottii, located on the southeastern coast of the state of Rio Grande do Sul, by imagery from LISS-III/ResourceSat-1 and TM/Landsat 5 sensors, comparing their performance for such. Images were obtained on September 2010, the month coincident with the forest inventory made in the study area. The surface spectral reflectance values were retrieved from the original images. After the georeferencing, the sampling units location from the forest inventory were used to select the pixels to extract the reflectance values on the four spectral bands equivalents for the two sensors, which answers were compared. In addition to the bands were used the Vegetation Indices (VI’s) SR, NDVI, SAVI, MVI and GNDVI. Also proposed was the adjusting of these original VI’s by the stand age, which ones were identified by SR_i, NDVI_i, MVI_i and GNDVI_i. The application of logarithm in the spectral bands improved the r values, with exception to NIR, achieving values between 0.69 (NIR) and 0.83 (Green) for LISS-III and between 0.68 (Red) and 0.79 (SWIR) for TM; With the VI’s, the logarithm improved the r values only for the original VI’s, returning r values from 0.77 (NDVI) to 0.84 (GNDVI) with LISS-III and r values from 0.73 (NDVI) to 0.82 (MVI) for TM. With the VI’s adjusted by stand age the logarithm was not necessary to improve the linear association, returning r values from 0.79 (NDVI_i) to 0.82 (MVI_i) with LISS-III and r values from 0.74 (SR_i) to 0.80 (MVI_i) with TM. Moreover, adjusting by age increased the dynamic range of the VI’s adjusted, and apparently increased the sensitivity in stands with larger volume. Significant differences in the linear association between TM and LISS-III spectral data with volume were just found on the green equivalent band. With TM data, the best fitted model explained 68% of the volume variability; with LISS-III data the model explained 72% of the variability. These models generated two wood volume maps, where the average of the estimates achieved with LISS-III were within the confidence level of the average from the forest inventory on 70% of the compartments considered; for TM the coincidence was on 65% of the compartments. It is conclude that the sensors LISS-III and TM presented high similarity and the methodology applied can be used to aid in forest inventory of young stands of P. elliottii in the study area mainly because the estimates obtained by the images cover the entire compartment, while the forest inventory sampling contemplates less than 2% of the area.
8

Estimation of grass photosynthesis rates in mixed-grass prairie using field and remote sensing approaches

Black, Selena Compton 24 July 2006
With the increase in atmospheric CO2 concentrations, and the resulting potential for climate change, there has been increasing research devoted to understanding the factors that determine the magnitude of CO2 fluxes and the feedback of ecosystem fluxes on climate. This thesis is an effort to investigate the feasibility of using alternate methods to measure and estimate the CO2 exchange rates in the northern mixed grass prairie. Specifically, the objectives are to evaluate the capability of using ground-level hyperspectral, and satellite-level multispectral data in the estimation of mid-season leaf CO2 exchange rates as measured with a chamber, in and around Grasslands National Park (GNP), Saskatchewan. Data for the first manuscript was collected during June of 2004 (the approximate period for peak greenness for the study area). Spectral reflectance and CO2 exchange measurements were collected from 13 sites in and around GNP. Linear regression showed that the Photochemical Reflectance Index (PRI) calculated from hyperspectral ground-level data explained 46% of the variance seen in the CO2 exchange rates. This indicates that the PRI, which has traditionally been used only in laboratory conditions to predict CO2 exchange, can also be applied at the canopy level in grassland field conditions. <p>The focus of the second manuscript is to establish if the relationship found between ground-level hyperspectral data and leaf CO2 exchange is applicable to satellite-level derived vegetation indices. During June of 2005, biophysical and CO2 exchange measurements were collected from 24 sites in and around GNP. A SPOT satellite image was obtained from June 22, midway through the field data collection. Cubic regression showed that Normalized Difference Vegetation Index (NDVI) explained 46% of the variance observed in the CO2 exchange rates. To our knowledge, this is the first time that a direct correlation between satellite images and leaf CO2 fluxes has been shown within the grassland biome.
9

Estimation of grass photosynthesis rates in mixed-grass prairie using field and remote sensing approaches

Black, Selena Compton 24 July 2006 (has links)
With the increase in atmospheric CO2 concentrations, and the resulting potential for climate change, there has been increasing research devoted to understanding the factors that determine the magnitude of CO2 fluxes and the feedback of ecosystem fluxes on climate. This thesis is an effort to investigate the feasibility of using alternate methods to measure and estimate the CO2 exchange rates in the northern mixed grass prairie. Specifically, the objectives are to evaluate the capability of using ground-level hyperspectral, and satellite-level multispectral data in the estimation of mid-season leaf CO2 exchange rates as measured with a chamber, in and around Grasslands National Park (GNP), Saskatchewan. Data for the first manuscript was collected during June of 2004 (the approximate period for peak greenness for the study area). Spectral reflectance and CO2 exchange measurements were collected from 13 sites in and around GNP. Linear regression showed that the Photochemical Reflectance Index (PRI) calculated from hyperspectral ground-level data explained 46% of the variance seen in the CO2 exchange rates. This indicates that the PRI, which has traditionally been used only in laboratory conditions to predict CO2 exchange, can also be applied at the canopy level in grassland field conditions. <p>The focus of the second manuscript is to establish if the relationship found between ground-level hyperspectral data and leaf CO2 exchange is applicable to satellite-level derived vegetation indices. During June of 2005, biophysical and CO2 exchange measurements were collected from 24 sites in and around GNP. A SPOT satellite image was obtained from June 22, midway through the field data collection. Cubic regression showed that Normalized Difference Vegetation Index (NDVI) explained 46% of the variance observed in the CO2 exchange rates. To our knowledge, this is the first time that a direct correlation between satellite images and leaf CO2 fluxes has been shown within the grassland biome.
10

Detection and analysis of changes in desertification in the Caspian Sea Region

Abbasova, Tahira January 2010 (has links)
The Caspian Region includes the Caspian Sea and five littoral states: Azerbaijan, Iran, Turkmenistan, Kazakhstan and Russian. 40% of the Caspian coastal zone is arid, 69% of this territory undergone desertification according to international reports. Among the reasons are soil erosion caused by water, wind and irrigation, the salinization of soil, intense bioresources usage, and soil pollution due to oil extraction and production. Desertification is a serious problem, at global, national and local scales. It is important to know what should be sustained or developed in order to protect land from desertification. The generalization of data over desertification processes in Caspian countries, studying the dynamics of this process in space and time could help facilitate measures to counter regional desertification. To understand Caspian Region coastal desertification phenomenon, vegetation cover satellite images for the years 1982 – 2006 were investigated to give map vegetation changes over time. The Normalized Difference Vegetation Index (NDVI) data for this study was derived from the Global Inventory Modeling and Mapping Studies (GIMMS) dataset, with the spatial resolution of 8 km. A coastal strip 160 km from the coast, divided by countries, was investigated. Theanalyses were focused on extent and severity of vegetation cover degradation, and possible causes such as landscape, land use history and culture, climatic changes and policies. The aim was to address questions related to desertification phenomenon, by focusing on Caspian Region time-series of vegetation cover data and investigation patterns of desertification in the region. In this study evidence of land degradation in the Caspian Region countries was found to occur on local scales or sub-national scales rather than across the regional as a whole. Changes in vegetation cover revealed by AVHRR NDVI appeared to be reversible in character and were dependent on the climate conditions, and anthropogenic impact in approximately equal proportions.

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