Enhancing Landsat time series through multi-sensor fusion and integration of meteorological data

Over 50 years ago, the United States Interior Secretary, Stewart Udall, directed space agencies to gather "facts about the natural resources of the earth." Today global climate change and human modification make earth observations from all variety of sensors essential to understand and adapt to environmental change. The Landsat program has been an invaluable source for understanding the history of the land surface, with consistent observations from the Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors since 1982. This dissertation develops and explores methods for enhancing the TM/ETM+ record by fusing other data sources, specifically, Landsat 8 for future continuity, radar data for tropical forest monitoring, and meteorological data for semi-arid vegetation dynamics. Landsat 8 data may be incorporated into existing time series of Landsat 4-7 data for applications like change detection, but vegetation trend analysis requires calibration, especially when using the near-infrared band. The improvements in radiometric quality and cloud masking provided by Landsat 8 data reduce noise compared to previous sensors. Tropical forests are notoriously difficult to monitor with Landsat alone because of clouds. This dissertation developed and compared two approaches for fusing Synthetic Aperture Radar (SAR) data from the Advanced Land Observation Satellite (ALOS-1) with Landsat in Peru, and found that radar data increased accuracy of deforestation. Simulations indicate that the benefit of using radar data increased with higher cloud cover. Time series analysis of vegetation indices from Landsat in semi-arid environments is complicated by the response of vegetation to high variability in timing and amount of precipitation. We found that quantifying dynamics in precipitation and drought index data improved land cover change detection performance compared to more traditional harmonic modeling for grasslands and shrublands in California. This dissertation enhances the value of Landsat data by combining it with other data sources, including other optical sensors, SAR data, and meteorological data. The methods developed here show the potential for data fusion and are especially important in light of recent and upcoming missions, like Sentinel-1, Sentinel-2, and NASA-ISRO Synthetic Aperture Radar (NISAR)

Applications of active microwave imagery

The following topics were discussed in reference to active microwave applications: (1) Use of imaging radar to improve the data collection/analysis process; (2) Data collection tasks for radar that other systems will not perform; (3) Data reduction concepts; and (4) System and vehicle parameters: aircraft and spacecraft

FIREX mission requirements document for renewable resources

The initial experimental program and mission requirements for a satellite synthetic aperture radar (SAR) system FIREX (Free-Flying Imaging Radar Experiment) for renewable resources is described. The spacecraft SAR is a C-band and L-band VV polarized system operating at two angles of incidence which is designated as a research instrument for crop identification, crop canopy condition assessments, soil moisture condition estimation, forestry type and condition assessments, snow water equivalent and snow wetness assessments, wetland and coastal land type identification and mapping, flood extent mapping, and assessment of drainage characteristics of watersheds for water resources applications. Specific mission design issues such as the preferred incidence angles for vegetation canopy measurements and the utility of a dual frequency (L and C-band) or dual polarization system as compared to the baseline system are addressed

Potential of ALOS2 and NDVI to estimate forest above-ground biomass, and comparison with lidar-derived estimates

Remote sensing supports carbon estimation, allowing the upscaling of field measurements to large extents. Lidar is considered the premier instrument to estimate above ground biomass, but data are expensive and collected on-demand, with limited spatial and temporal coverage. The previous JERS and ALOS SAR satellites data were extensively employed to model forest biomass, with literature suggesting signal saturation at low-moderate biomass values, and an influence of plot size on estimates accuracy. The ALOS2 continuity mission since May 2014 produces data with improved features with respect to the former ALOS, such as increased spatial resolution and reduced revisit time. We used ALOS2 backscatter data, testing also the integration with additional features (SAR textures and NDVI from Landsat 8 data) together with ground truth, to model and map above ground biomass in two mixed forest sites: Tahoe (California) and Asiago (Alps). While texture was useful to improve the model performance, the best model was obtained using joined SAR and NDVI (R2 equal to 0.66). In this model, only a slight saturation was observed, at higher levels than what usually reported in literature for SAR; the trend requires further investigation but the model confirmed the complementarity of optical and SAR datatypes. For comparison purposes, we also generated a biomass map for Asiago using lidar data, and considered a previous lidar-based study for Tahoe; in these areas, the observed R2 were 0.92 for Tahoe and 0.75 for Asiago, respectively. The quantitative comparison of the carbon stocks obtained with the two methods allows discussion of sensor suitability. The range of local variation captured by lidar is higher than those by SAR and NDVI, with the latter showing overestimation. However, this overestimation is very limited for one of the study areas, suggesting that when the purpose is the overall quantification of the stored carbon, especially in areas with high carbon density, satellite data with lower cost and broad coverage can be as effective as lidar

The future of Earth observation in hydrology

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smart-phones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the "internet of things" as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems

Quarterly literature review of the remote sensing of natural resources

The Technology Application Center reviewed abstracted literature sources, and selected document data and data gathering techniques which were performed or obtained remotely from space, aircraft or groundbased stations. All of the documentation was related to remote sensing sensors or the remote sensing of the natural resources. Sensors were primarily those operating within the 10 to the minus 8 power to 1 meter wavelength band. Included are NASA Tech Briefs, ARAC Industrial Applications Reports, U.S. Navy Technical Reports, U.S. Patent reports, and other technical articles and reports

Automated Satellite-Based Landslide Identification Product for Nepal

Landslide event inventories are a vital resource for landslide susceptibility and forecasting applications. However, landslide inventories can vary in accuracy, availability, and timeliness as a result of varying detection methods, reporting, and data availability. This study presents an approach to use publicly available satellite data and open source software to automate a landslide detection process called the Sudden Landslide Identification Product (SLIP). SLIP utilizes optical data from the Landsat 8 OLI sensor, elevation data from the Shuttle Radar Topography Mission (SRTM), and precipitation data from the Global Precipitation Measurement (GPM) mission to create a reproducible and spatially customizable landslide identification product. The SLIP software applies change detection algorithms to identify areas of new bare-earth exposures that may be landslide events. The study also presents a precipitation monitoring tool that runs alongside SLIP called the Detecting Real-time Increased Precipitation (DRIP) model that helps identify the timing of potential landslide events detected by SLIP. Using SLIP and DRIP together, landslide detection is improved by reducing problems related to accuracy, availability, and timeliness that are prevalent in the state-of-the-art of landslide detection. A case study and validation exercise was performed in Nepal for images acquired between 2014 and 2015. Preliminary validation results suggest 56% model accuracy, with errors of commission often resulting from newly cleared agricultural areas. These results suggest that SLIP is an important first attempt in an automated framework that can be used for medium resolution regional landslide detection, although it requires refinement before being fully realized as an operational tool

Evaluation of SIR-A space radar for geologic interpretation: United States, Panama, Colombia, and New Guinea

Comparisons between LANDSAT MSS imagery, and aircraft and space radar imagery from different geologic environments in the United States, Panama, Colombia, and New Guinea demonstrate the interdependence of radar system geometry and terrain configuration for optimum retrieval of geologic information. Illustrations suggest that in the case of space radars (SIR-A in particular), the ability to acquire multiple look-angle/look-direction radar images of a given area is more valuable for landform mapping than further improvements in spatial resolution. Radar look-angle is concluded to be one of the most important system parameters of a space radar designed to be used for geologic reconnaissance mapping. The optimum set of system parameters must be determined for imaging different classes of landform features and tailoring the look-angle to local topography