Thesis (M.S.)--Tufts University, 2002. / Submitted to the Dept. of Mechanical Engineering. Includes bibliographical references (leaves 122-123).

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A general analytical framework was established to investigate the scale-related variabilities in remote sensing. The variabilities were studied first by investigating canopy structure, canopy interaction with light, relation between spectral reflectance and plant phenological parameters. The variabilities simulated by the plant model were compared with the actual spectral data acquired by ground spectroradiometer and satellite sensors. The theoretical relation between orthogonal-basedtransform and Kahunen-Lo6ve transform was investigated in the vector space. The role of spectral indices in identifying the status of phenological parameters was briefly studied. The radiometric corrections of the remotely sensed data were carefully controlled to avoid the unwanted noise introduced by typical resampling/correction procedures from commercial operation. The non-linearity and sensor response corrections were applied to the spectral data as necessary. Variability analysis was conducted to illustrate the complexities of spectral variability embedded in the remotely sensed data. The information extraction in spatial frequency domain was investigated with emphasis in Fourier domain feature extraction. The Radon transform was introduced as the potential tool to enhance the spatial information of the Fourier transformed image. The adequacy of entropy and fractal dimension as image information measures was proved. A functional link between entropy and fractal dimension was established. The image information content was extracted using various first and second order statistics, entropy, and fractal dimension. Results were presented for different remote sensors based on the full image information content and specific agricultural ground features. The quality of spatial resampling algorithms was tested by investigating the capability to maintain image information in the resampled image. Finally, two applications utilizing this analytical framework were presented to show its potential in land-use classification and multiscale data fusion. / Graduation date: 1993

Thesis (M.S.)--Naval Postgraduate School, 2002. / Thesis advisor(s): Richard C. Olsen, David M. Task. Includes bibliographical references (p. 141-142). Also available online.

Thesis (M.S. in Applied Physics)--Naval Postgraduate School, June 2003. / Thesis advisor(s): Richard C. Olsen, Alan A. Ross. Includes bibliographical references (p. 127-129). Also available online.

Using the remote sensing data, this study aims to enhance our understanding of land surface features, including ecosystem distribution in association with topographic controls and climatic controls, vegetation disturbance due to natural hazards, and surface temperature changes with consideration of the influence of urbanization. In this study, the Global Inventory Monitoring and Modeling System (GIMMS) Normalized Difference Vegetation Index (NDVI) data sets from 1982 to 2006 were used to explore vegetation variation. A data mining method, Exhaustive Chi-squared Automatic Interaction Detector algorithm, was successfully applied to investigate the topographic influences on vegetation distribution in China. The study revealed that elevation is a predominant factor for controlling vegetation distribution among different topographic attributes (slope, aspect, Compound Topographic Index (CTI) and distance to the nearest river). Further, the study results indicated that solar radiation is the limited factor for plant growth in majority of the Northern Hemisphere in summer, and temperature is the main limitation for other seasons. Partial correlation coefficient (PCC) method was adopted to investigate the complex relationships of NDVI with weather variables (i.e., temperature, precipitation and solar radiation) and key climate indices (such as, El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Arctic Oscillation (AO), and Antarctic Oscillation (AAO)). The study indicated that AO is the most significant index in affecting the temperatures in spring and winter in the Northern Hemisphere. This study enhanced the understanding of vegetation responds to asymmetric daytime (Tmax) and nighttime (Tmin) warming in different seasons. The result revealed that asymmetric warming of Tmax and Tmin may influence vegetation photosynthesis and respiration in the plant growth in different periods across biomes. In spring and autumn, vegetation in boreal and wet temperate regions of the Northern Hemisphere is positively correlated with Tmax and negatively correlated with Tmin, whereas, in dry regions the NDVI is always negatively correlated with Tmax and positively correlated with Tmin. In summer, the NDVI is negatively correlated with Tmax in many dry regions. In addition, this study developed a new index, Continued Vegetation Decrease Index (CVDI), to detect vegetation disturbance due to extreme natural hazards (such as, earthquake, wildfire, ice storms and so on). Using the Wenchuan earthquake occurred in Sichuan China on 12 May 2008 as an example, this study confirmed that the CVDI method can effectively identify the regions with severe vegetation damage, and it is expected that the newly-developed index can be used for detecting vegetation disturbance in other regions of the world. Finally, using the remote sensing data (land use data and surface temperature data) and weather station data, this study developed a new method to evaluate the urbanization influence on the temperature recorded at weather stations. The results revealed that the weather stations with most fast increase temperature are not in developed countries, but in developing countries. The results also imply that the global warming trend may be overestimated due to the under-estimation of urbanization influence on temperature increase. / published_or_final_version / Civil Engineering / Doctoral / Doctor of Philosophy

<p>The Richtersveld falls within the succulent karoo and dester biomes. This studu made use of remote sensing technologies in order to investigate possible vegetation cover changes that have taken place over time, and which have manifested through a combination of threats to the region. Te aims of the study were adressed using three key questions that sought to gainan understanding of the relationship between vegetation response and moisture, in order to interpret teporal and spatial vegetyation cover changes. A spartially and temporarily representative remotely sensed dataset was used together with techniques that are repeatable and able to quantify change with a limited human bias.</p>

<p>The Richtersveld falls within the succulent karoo and dester biomes. This studu made use of remote sensing technologies in order to investigate possible vegetation cover changes that have taken place over time, and which have manifested through a combination of threats to the region. Te aims of the study were adressed using three key questions that sought to gainan understanding of the relationship between vegetation response and moisture, in order to interpret teporal and spatial vegetyation cover changes. A spartially and temporarily representative remotely sensed dataset was used together with techniques that are repeatable and able to quantify change with a limited human bias.</p>

Thesis (Master of Spatial Information Science) -- University of Adelaide, Key Centre for Social Applications of Geographical Information Systems, 1999. / Includes bibliography.

Thesis (Ph. D.)--Ohio State University, 2003. / Title from first page of PDF file. Document formatted into pages; contains xiv, 157 p.; also includes graphics. Includes bibliographical references (p. 143-148).