Leaf Area Index (LAI) is an important input variable for forest ecosystem modeling as it is a factor in predicting productivity and biomass, two key aspects of forest health. Current in situ methods of determining LAI are sometimes destructive and generally very time consuming. Other LAI derivation methods, mainly satellite-based in nature, do not provide sufficient spatial resolution or the precision required by forest managers. This thesis focused on estimating LAI from: i) height and density metrics derived from Light Detection and Ranging (LiDAR); ii) spectral vegetation indices (SVIs), in particular the Normalized Difference Vegetation Index (NDVI); and iii) a combination of these two remote sensing technologies. In situ measurements of LAI were calculated from digital hemispherical photographs (DHPs) and remotely sensed variables were derived from low density LiDAR and high resolution WorldView-2 data. Multiple Linear Regression (MLR) models were created using these variables, allowing forest-wide prediction surfaces to be created. Results from these analyses demonstrated: i) moderate explanatory power (i.e., R2 = 0.54) for LiDAR models incorporating metrics that have proven to be related to canopy structure; ii) no relationship when using SVIs; and iii) no significant improvement of LiDAR models when combining them with SVI variables. The results suggest that LiDAR models in boreal forest environments provide satisfactory estimations of LAI, even with low ranges of LAI for model calibration. On the other hand, it was anticipated that traditional SVI relationships to LAI would be present with WorldView-2 data, a result that is not easily explained. Models derived from low point density LiDAR in a mixedwood boreal environment seem to offer a reliable method of estimating LAI at a high spatial resolution for decision makers in the forestry community. / Thesis (Master, Geography) -- Queen's University, 2012-09-24 16:18:09.96

There are a great number of publications that apply different methods to estimate soil moisture from optical satellite imagery. However, none of the proposed methods have considered correcting solar illumination error that is caused by variation in topography before estimating soil moisture. In this research, an integrated approach is developed to improve the estimation of soil moisture. The integration is represented by removing the solar-illumination error from the data. Several modifications were made in the Ts-VI space based on the Universal Triangle Relationship. The data used in the research are obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. The research results show that the surface-illumination error, which is caused by variation in topography, misleads the estimation of soil moisture index. Based on statistical and visual analysis, the results are improved with removing error. The method is further enhanced with the application of enhanced vegetation index (EVI) to the Ts-VI relationship.

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Sirex noctilio is causing considerable mortality in commercial pine forests in KwaZulu- Natal, South Africa. The ability to remotely detect S. noctilio infestations remains crucial for monitoring the spread of the wasp and for the effective deployment of suppression activities. This thesis advocates the development of techniques based on remote sensing technology to accurately detect and map S. noctilio infestations. To date, no research has examined the potential of remote sensing technologies for the detection and mapping of Pinus patula trees infested by S. noctilio. In the first part of this thesis, the focus was on whether high spatial resolution imagery could characterize S. noctilio induced stress in P. patula forests. Results showed that, the normalized difference vegetation index derived from high spatial resolution imagery has the potential to accurately detect and map the later stages of S. noctilio infestations. Additionally, operational guidelines for the optimal spatial resolutions that are suitable for detecting and mapping varying levels of sustained S. noctilio mortality were defined. Results showed that a pixel size of 2.3 m is recommended to detect high (11-15%) infestation levels, and a pixel size of 1.75 m is recommended for detecting low to medium infestation levels (1-10%). In the second part of this thesis, the focus was on the ability of high spectral resolution (hyperspectral) data to discriminate between healthy trees and the early stages of S. noctilio infestation. Results showed that specific wavelengths located in the visible and near infrared region have the greatest potential for discriminating between healthy trees and the early stages of S. noctilio infestation. The researcher also evaluated the robustness and accuracy of various machine learning algorithms in identifying spectral parameters that allowed for the successful detection of S. noctilio infestations. Results showed that the random forest algorithm simplified the process by identifying the minimum number of spectral parameters that provided the best overall accuracies. In the final part of this thesis spatial modelling techniques were used to proactively identify pine forests that are highly susceptible to S. noctilio infestations. For the first time the random forest algorithm was used in conjunction with geographic information systems for mapping pine forests that are susceptible to S. noctilio infestations. Overall, there is a high probability of S. noctilio infestation for the majority (63%) of pine forest plantations located in Mpumalanga, South Africa. Compared to previous studies, the random forest model identified highly susceptible pine forests at a more regional scale and provided an understanding of localized variations of environmental conditions in relation to the distribution of the wasps. / Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2008.

This dissertation presents new research into the study of simulation and measurement techniques for microwave remote sensing of sea ice. We have embarked on a major study of the microwave propagation and scattering properties of sea ice in an attempt to link the physics of the sea ice medium to experimentally obtained concomitant scatterometer measurements. During our fieldwork, we studied the polarimetric backscattering response of sea ice, focusing on newly-formed sea ice under a large assortment of surface coverage. Polarimetric backscattering results and physical data for 40 stations during the fall freeze-up of 2003, 2006, and 2007 are presented. Analysis of the co-polarization correlation coefficient showed its sensitivity to sea ice thickness and surface coverage and resulted in a statistically significant separation of ice thickness into two regimes: ice less than 6 cm thick and ice greater than 8 cm thick. A case study quantified the backscatter of snow-infiltrated frost fl owers on new sea ice, showing that the presence of the frost flowers enhanced the backscatter by more than 6 dB. In our simulation work, an efficient method for simulating scattering from objects in multi-layered media was incorporated into a scattered-field formulation of the FVTD method. A total-field 1D-FDTD solution to the plane-wave propagation through multi-layered meda was used as a source. The method was validated for a TE-polarized incident-field through comparisons with other numerical techniques involving examples of scattering from canonically-shaped objects. Methods for homogenization of inhomogeneous media were developed and validated using well-known dielectric mixture models. A Monte Carlo Method for simulating scattering from statistically rough surfaces was developed and was validated through favorable comparison with the SPM method for rough surface scattering. Finally, we presented a new Monte Carlo Method for simulating sea ice remote sensing that utilized the framework of the FVTD method for scattering simulations. The modeling process was driven by actual physical measurements of sea ice, wherein dielectric and physics-based modeling techniques were employed. The method was demonstrated through a series of case studies where the scattering from newly-formed sea ice was simulated using a TE-polarized incident- eld. Good agreement between experimental scatterometer measurements and simulated results was obtained for co-polarized returns, whereas cross-polarized results indicated that more depolarizing features must be taken into account.

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This thesis analyses the International and American laws and regulations structuring the commercialization of space based remote sensing images. / Chapter one outlines the historical evolution influencing the present regulatory structure. Chapter two describes the technology of space based remote sensing systems. Chapter three analyses the international treaties and institutions along with their application to commercial remote sensing by satellites. Chapter four analyses the American legislative evolution and regulatory structure pertaining to commercial remote sensing. / The argument of the thesis is that a contextual evolution of the global geopolitical climate is forcing a regulatory retooling for commercial space endeavors. The contextual evolution has created new paradigms based upon international economic market values. The initial international treaties and institutions must evolve to reflect these criteria.

Maintaining forage availability is challenging for managers of grazing systems, especially in spatially heterogeneous swards. Remote sensing may help to overcome this problem. The objectives of this study were to (i) determine a method by which NDVI may be calibrated to estimate biomass, (ii) determine if NDVI can be used to assess spatial variability of yield in extensive grasslands, and (iii) to determine if NDVI can be used to evaluate grazing systems. We found that the calibration of NDVI values for the estimation of biomass was better correlated with the destructive harvesting procedure (R2 = 0.68) but far more laborious and time-consuming than estimation of biomass from the rising plate meter (R2 = 0.54). Semivariograms revealed that sampling at a 0.76 m distance provided information about the spatial variability structure of NDVI values from grazed swards. Frequency distributions of sward biomass derived from NDVI reflected foraging strategies of cattle. Negative skewness and high kurtosis of histograms indicated selective grazing, while positive skewness and low kurtosis indicated the opposite. Histograms also allowed for estimation of available forage within each field. We concluded that grassland biomass may be derived from high resolution NDVI and RPM data and used to evaluate condition of grassland landscapes and aid decision-making of managed grazing systems.