Dissertations / Theses on the topic 'Remote sensing {Geographie}'

Although savanna ecosystems cover approximately 20 % of the terrestrial land surface and can have productivity equal to some closed forests, their role in the global carbon cycle is poorly understood. This study explored the applicability of a past spaceborne Lidar mission and the potential of future missions to estimate canopy height and carbon storage in these biomes.

The research used data from two Oak savannas in California, USA: the Tejon Ranch Conservancy in Kern County and the Tonzi Ranch in Santa Clara County. In the first paper we used non-parametric regression techniques to estimate canopy height from waveform parameters derived from the Ice Cloud and land Elevation Satellite’s Geoscience Laser Altimeter System (ICESat-GLAS) data. Merely adopting the methods derived for forests did not produce adequate results but the modeling was significantly improved by incorporating canopy cover information and interaction terms to address the high structural heterogeneity inherent to savannas. Paper 2 explored the relationship between canopy height and aboveground biomass. To accomplish this we developed generalized models using the classical least squares regression modeling approach to relate canopy height to above ground woody biomass and then employed Hierarchical Bayesian Analysis (HBA) to explore the implications of using generalized instead of species composition-specific models. Models that incorporated canopy cover proxies performed better than those that did not. Although the model parameters indicated interspecific variability, the distribution of the posterior densities of the differences between composition level and global level parameter values showed a high support for the use of global parameters, suggesting that these canopy height-biomass models are universally (large scale) applicable.

As the spatial coverage of spaceborne lidar will remain limited for the immediate future, our objective in paper 3 was to explore the best means of extrapolating plot level biomass into wall-to-wall maps that provide more ecological information. We evaluated the utility of three spatial modeling approaches to address this problem: deterministic methods, geostatistical methods and an image segmentation approach. Overall, the mean pixel biomass estimated by the 3 approaches did not differ significantly but the output maps showed marked differences in the estimation precision and ability of each model to mimic the primary variable’s trend across the landscape. The results emphasized the need for future satellite lidar missions to consider increasing the sampling intensity across track so that biomass observations are made and characterized at the scale at which they vary.

We used data from the Multiple Altimeter Beam Experimental Lidar (MABEL), an airborne photon counting lidar sensor developed by NASA Goddard to simulate ICESat-2 data. We segmented each transect into different block sizes and calculated canopy top and mean ground elevation based on the structure of the histogram of the block’s aggregated photons. Our algorithm was able to compute canopy height and generate visually meaningful vegetation profiles at MABEL’s signal and noise levels but a simulation of the expected performance of ICESat-2 by adjusting MABEL data's detected number of signal and noise photons to that predicted using ATLAS instrument model design cases indicated that signal photons will be substantially lower. The lower data resolution reduces canopy height estimation precision especially in areas of low density vegetation cover.

Given the clear difficulties in processing simulated ATLAS data, it appears unlikely that it will provide the kind of data required for mapping of the biophysical properties of savanna vegetation. Rather, resources are better concentrated on preparing for the Global Ecosystem Dynamics Investigation (GEDI) mission, a waveform lidar mission scheduled to launch by the end of this decade. In addition to the full waveform technique, GEDI will collect data from 25 m diameter contiguous footprints with a high across track density, a requirement that we identified as critically necessary in paper 3. (Abstract shortened by UMI.)

Climate warming is occurring at an unprecedented rate in the Arctic, seriously impacting sensitive environments, and triggering land cover change. These changes are compounded by localized human influences. This work classifies land cover change for the Lower Yenisei River, identifies those changes that were climate- and anthropogenic- induced, and discusses the implications for the underlying permafrost system. This is accomplished using a modified version of the “Landsat dense time stacking” methodology for three time periods spanning 29 years that are representative of Russian socio-economic transitions during the mid- to late-1980s (1985-1987), the early 2000s (2000-2002), and the contemporary 2010s (2012-2014). The classified area includes three cities indicative of different post-Soviet socio-economic situations, including continued population and infrastructure decline (Igarka), a relatively stable community (Dudinka), and a community receiving local reinvestment (Norilsk). The land cover classification, in tandem with regional climate reanalysis data, enabled climate- and anthropogenic- induced changes to be identified, characterized, and quantified. Climatic changes within the natural environments have produced a steady greening effect throughout the study area, as well as an increase in large lake abundance, indicative of permafrost degradation. Pollution, in close proximity to heavy industrial activity, caused a secondary plant succession process. The results of this work provide both map products that can be applied to future research in this region, as well as insights into the impacts of the warming climate and human presence on sensitive Arctic environments.

In California, natural vegetation is experiencing an increasing amount of stress due to prolonged droughts, wildfires, insect infestation, and disease. Remote sensing technologies provide a means for monitoring plant species presence and function temporally across landscapes. In this his dissertation, I used hyperspectral visible shortwave infrared (VSWIR), hyperspectral thermal (TIR), and hyperspectral VSWIR + broadband TIR imagery to derive key observations of plant species across a gradient of environmental conditions and time frames. In Chapter 2, I classified plant species using hyperspectral VSWIR imagery from 2013–2015 spring, summer, and fall. Plant species maps had the highest classification accuracy using spectra from a single date (mean kappa 0.80–0.86). The inclusion of spectra from other dates decreased accuracy (mean kappa 0.78–0.83). Leave-one-out analysis emphasized the need to have spectra from the image date in the classification training, otherwise classification accuracy dropped significantly (mean kappa 0.31–0.73). In Chapter 3, I used hyperspectral TIR imagery to determine the extent that high precision spectral emissivity and canopy temperature can be exploited for vegetation research at the canopy level. I found that plant species show distinct spectral separation at the leaf level, but separability among species is lost at the canopy level. However, species’ canopy temperatures exhibited different distributions among dates and species. Variability in canopy temperatures was largely explained by LiDAR derived canopy structural attributes (e.g. canopy density) and the surrounding environment (e.g. presence of pavement). In Chapter 4, I used combined hyperspectral VSWIR and broadband TIR imagery to monitor plant stress during California’s 2013–2015 severe drought. The temperature condition index (TCI) was calculated to measure plant stress by using plant species’ surface minus air temperature distributions across dates. Plant stress was not evenly distributed across the landscape or time with lower elevation open shrub/meadows, showing the largest amount of stress in June 2014, and August 2015 imagery. Plant stress spatial variability across the study area was related to a slope’s aspect with highly stressed plants located on south or south-southwest facing slopes. Overall, this dissertation quantifies the ability to temporally study plant species using hyperspectral VSWIR, hyperspectral TIR, and combined VSWIR+TIR imagery. This analysis supports a range of current and planned missions including Surface Biology and Geology (SBG), Environmental Mapping and Analysis Program (EnMAP), National Ecological Observatory Network (NEON), Hyperspectral Thermal Emission Spectrometer (HyTES), and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS).

This dissertation describes the acquisition and analysis of tropical wave cloudiness. Tropical wave positions for the years 2003 through 2013 were extracted via text mining, from the National Hurricane Center’s Tropical Weather Discussion, a bulletin released every six hours and published on-line. Tropical wave tracks were developed from these positions using the Multiple Hypothesis Tracking algorithm. Satellite data from the Atmospheric Infrared Sounder (AIRS) was downloaded from the NASA Mirador website based on time and position of tracked tropical waves. The AIRS data was mosaicked to provide complete coverage between satellite swaths. The AIRS Level 2 Cloud Fraction Standard product was used exclusively in the analysis. Cloud fraction data was divided into upper and lower levels as provided in the AIRS product. A cloud fraction ratio was also developed to provide some indication of the insulating quality of clouds. The analysis discovered secular trends of varying degrees and direction depending on location of tropical waves. The analysis also found significant periodic variability within cloud fraction values, much of which correlated to known global oscillations such as El Nino and the Madden-Julian Oscillation. However a number of periodic signals found within tropical wave cloudiness could not be correlated with any of the known global and non-earth oscillations tested against. Future research ideas in the conclusions include an examination of those uncorrelated periodic signals. Also included in the conclusions are theories about differences in correlations to periodic signals within a tropical wave core versus correlations that are seen in surrounding cloud patterns.