Machine Learning Approach to Retrieving Physical Variables from Remotely Sensed Data

Scientists from all over the world make use of remotely sensed data from hundreds of satellites to better understand the Earth. However, physical measurements from an instrument is sometimes missing either because the instrument hasn\u27t been launched yet or the design of the instrument omitted a particular spectral band. Measurements received from the instrument may also be corrupt due to malfunction in the detectors on the instrument. Fortunately, there are machine learning techniques to estimate the missing or corrupt data. Using these techniques we can make use of the available data to its full potential. We present work on four different problems where the use of machine learning techniques helps to extract more information from available data. We demonstrate how missing or corrupt spectral measurements from a sensor can be accurately interpolated from existing spectral observations. Sometimes this requires data fusion from multiple sensors at different spatial and spectral resolution. The reconstructed measurements can then be used to develop products useful to scientists, such as cloud-top pressure, or produce true color imagery for visualization. Additionally, segmentation and image processing techniques can help solve classification problems important for ocean studies, such as the detection of clear-sky over ocean for a sea surface temperature product. In each case, we provide detailed analysis of the problem and empirical evidence that these problems can be solved effectively using machine learning techniques

ERTS-1 Virgin Islands experiment 589: Determine boundaries of ERTS and aircraft data within which useful water quality information can be obtained

The author has identified the following significant results. The boundaries of application of ERTS-1 and aircraft data are established for St. Thomas harbor within which useful water quality information can be obtained. In situ physical, chemical, and biological water quality and benthic data were collected. Moored current meters were employed. Optical measurements of solar irradiance, color test panel radiance and water absorption were taken. Procedures for correlating in situ optical, biological, and chemical data with underflight aircraft I2S data and ERTS-1 MSS scanner data are presented. Comparison of bulk and precision CCT computer printout data for this application is made, and a simple method for geometrically locating bulk data individual pixels based on land-water interface is described. ERTS spacecraft data and I2S aircraft imagery are correlated with optical in situ measurements of the harbor water, with the aircraft green photographic and ERTS-1 MSS-4 bands being the most useful. The biological pigments correlate inversely with the optical data for inshore areas and directly further seaward. Automated computer data processing facilitated analysis

Productivity and carbon sequestration potential of seagrass ecosystems in the eastern Aegean Sea

Atmospheric CO₂ levels have been increasing at ever faster rates, fueled by anthropogenic activity. Natural ecosystems,which typically form net autotrophic habitats such as seagrass meadows, could be crucial to counteracting CO₂ emissions. Increased fragmentation of Posidonia oceanica seagrass meadows within the eastern Mediterranean basin, linked to increased sea surface temperature, places these meadows at high risk of loss. Annual metabolism estimates showed patchy shallow water P. oceanica within the eastern region of the Aegean Sea to be overall autotrophic. P. oceanica net apparent productivity was heterotrophic in Autumn and significantly less than Summer when autotrophic, influenced by relative changes in irradiance and seagrass aboveground biomass. Seagrass biometrics also acted as predictors of carbon sequestration spatially, demonstrating higher productivity in the meadow center compared to the meadow edge. Future forecasts of autochthonous carbon storage must consider seasonal changes in productivity, potentially alongside seasonal changes in irradiance and aboveground biomass. Ultimately shallow patchy P. oceanica meadow’s contribution to carbon sequestration should not be overlooked. The non-indigenous seagrass Halophila Stipulacea was first recorded in the Mediterranean, within the Aegean Sea. Its tropical origin may enable it to thrive given global climate change predictions for the Mediterranean. However, the H. stipulacea community was highly heterotrophic during Autumn. Utilising periods of increased irradiance in Summer may enable the plant to persist at this locality, but it seems to live near its limits for survival.The presence of an uncommon endosymbiotic phytomyxid is documented and its potential influence on H.stipulacea metabolism discussed. Overall, this work demonstrates shallow water P. oceanica meadows in the Aegean Sea are annually autotrophic and if able to persist will continue to remove atmospheric carbon. Knowing H.stipulacea is near its limits in terms of metabolic balance and survival, indicates Mediterranean autochthonous carbon sequestration may decrease should H. stipulacea increase in abundance simultaneous to knownP. oceanica regression

Evaluating the effectiveness of large-scale marine reserves on wide-ranging sharks: a case study of the Cook Islands Shark Sanctuary

Jessica Cramp investigated shark sanctuary effectiveness on oceanic sharks, using a case study in the Cook Islands. She found that sharks benefited from the Sanctuary, but benefits varied based on sharks' movement ecology and release condition. Additionally, an adapted systematic conservation planning approach would close loopholes in shark conservation policy

The influence of grazing on surface climatological variables of tallgrass prairie

Mass and energy exchange between most grassland canopies and the atmosphere are mediated by grazing activities. Ambient temperatures can be increased or decreased by grazers. Data have been assembled from simulated grazing experiments on Konza Prairie Research Natural Area and observations on adjacent pastures grazed by cattle show significant changes in primary production, nutrient content, and bidirectional reflectance characteristics as a function of grazing intensity. The purpose of this research was to provide algorithms that would allow incorporation of grazing effects into models of energy budgets using remote sensing procedures. The approach involved: (1) linking empirical measurements of plant biomass and grazing intensities to remotely sensed canopy reflectance, and (2) using a higher resolution, mechanistic grazing model to derive plant ecophysiological parameters that influence reflectance and other surface climatological variables

Science-based restoration monitoring of coastal habitats, Volume Two: Tools for monitoring coastal habitats

Healthy coastal habitats are not only important ecologically; they also support healthy coastal communities and improve the quality of people’s lives. Despite their many benefits and values, coastal habitats have been systematically modified, degraded, and destroyed throughout the United States and its protectorates beginning with European colonization in the 1600’s (Dahl 1990). As a result, many coastal habitats around the United States are in desperate need of restoration. The monitoring of restoration projects, the focus of this document, is necessary to ensure that restoration efforts are successful, to further the science, and to increase the efficiency of future restoration efforts

Geoengineering the climate: science, governance and uncertainty

Geoengineering, or the deliberate large-scale manipulation of the planetary environment to counteract anthropogenic climate change, has been suggested as a new potential tool for addressing climate change. Efforts to address climate change have primarily focused on mitigation, the reduction of greenhouse gas emissions, and more recently on addressing the impacts of climate change—adaptation. However, international political consensus on the need to reduce emissions has been very slow in coming, and there is as yet no agreement on the emissions reductions needed beyond 2012. As a result global emissions have continued to increase by about 3% per year (Raupach et al. 2007), a faster rate than that projected by the Intergovernmental Panel on Climate Change (IPCC) (IPCC 2001)7 even under its most fossil fuel intensive scenario (A1FI8) in which an increase in global mean temperature of about 4°C (2.4 to 6.4°C) by 2100 is projected (Rahmstorf et al. 2007). The scientifi c community is now becoming increasingly concerned that emissions will not be reduced at the rate and magnitude required to keep the increase in global average temperature below 2°C (above pre-industrial levels) by 2100. Concerns with the lack of progress of the political processes have led to increasing interest in geoengineering approaches. This Royal Society report presents an independent scientifi c review of the range of methods proposed with the aim of providing an objective view on whether geoengineering could, and should, play a role in addressing climate change, and under what conditions

Remote sensing applications to resource problems in South Dakota

There are no author-identified significant results in this report

Predicting evapotranspiration from sparse and dense vegetation communities in a semiarid environment using Ndvi from satellite and ground measurements

One of the most critical issues associated with using satellite data-based products to study and estimate surface energy fluxes and other ecosystem processes, has been the lack of frequent acquisition at a spatial scale equivalent to or finer than the footprint of field measurements. In this study, we incorporated continuous field measurements based on using Normalized difference vegetation index (NDVI) time series analysis of individual shrub species and transect measurements within 625 m2 size plots equivalent to the Landsat-5 Thematic Mapper spatial resolution. The NDVI system was a dual channel SKR-1800 radiometer that simultaneously measured incident solar radiation and upward reflectance in two broadband red and near-infrared channels comparable to Landsat-5 TM band 3 and band 4, respectively. The two study sites identified as Spring Valley 1 site (SV1) and Snake Valley 1 site (SNK1) were chosen for having different species composition, soil texture and percent canopy cover; NDVI time-series of greasewood (Sarcobatus vermiculatus) from the SV1 site allowed for clear distinction between the main phenological stages of the entire growing season during the period from January to November, 2007. Comparison of greasewood NDVI values between the two sites revealed a significant temporal difference associated with early canopy development and early dry down of greasewood at the SNK1 site. NDVI time series values were also significantly different between sagebrush (Artemisia tridentata ) and rabbitbrush (Chrysothamnus viscidiflorus) at SV1 as well as between the two bare soil types at the two sites, indicating the ability of the ground-based NDVI to distinguish between different plant species as well as between different desert soils based on their moisture level and color. The difference in phenological characteristics of greasewood between the two sites and between sagebrush, rabbitbrush and greasewood within the same site were not captured by the spatially integrated Landsat NDVI acquired during repeated overpasses. Greasewood NDVI from the SNK1 site produced significant correlations with many of the measured plant parameters, most closely with chlorophyll index (r = 0.97), leaf area index (r = 0.98) and leaf xylem water potential (r = 0.93). Whereas greasewood NDVI from the SV1 site produced lower correlations ( r = 0.89, r = 0.73), or non significant correlations (r = 0.32) with the same parameters, respectively. Total percent cover was estimated at 17.5% for SV1 and at 63% for SNK1; Transect measurements provided detailed information with regard to the spectral properties of shrub species and soil types, differentiating the two sites, which was not possible to discern with the spatial resolution of Landsat. Correlation between transect NDVI data and Landsat NDVI produced an r of 0.79. While correlation between transect NDVI data and ground-based NDVI sensors produced an r of 0.73. The linear regression equation between daily ET measured by the eddy covariance method and Landsat NDVI yielded a strong relationship (r = 0.88) for data combined across the experimental period (May to September) and across the two sites. The ET prediction equation was improved (r2 = 0.86) by introducing net solar radiation (Rn) which was the meteorological variable that had the highest prediction of ET (r2 = 0.82). A high correlation was found between weighted ground-based sensor NDVI estimates and Landsat derived NDVI at the pixel scale (r = 0.97) for the two study sites combined over time. While results from this study in scaling ground-based NDVI measurements and estimating ET were very promising, further verification and improvement is needed to determine the performance level of this approach over larger heterogeneous areas and over extended time periods

Application of LANDSAT to the management of Delaware's marine and wetland resources

The author has identified the following significant results. LANDSAT data were found to be the best source of synoptic information on the distribution of horizontal water mass discontinuities (fronts) at different portions of the tidal cycle. Distributions observed were used to improve an oil slick movement prediction model for the Delaware Bay. LANDSAT data were used to monitor the movement and dispersion of industrial acid waste material dumped over the continental shelf. A technique for assessing aqueous sediment concentration with limited ground truth was proposed