Landsat satellites have been operating since 1972, providing the longest continuous observation record of the Earth's land surface. Over the past half century, the Landsat user community has grown exponentially, encompassing more diverse and evolving scientific research and operational uses. Understanding current and future user needs is crucial to informing the design of Landsat missions beyond Landsat 9. The U.S. Geological Survey (USGS) initiated a user needs collection process to document needs from U.S. Federal civil subject matter experts who rely on moderate-resolution land imaging data across a diverse range of scientific research and application domains. In total, 379 moderate-resolution land imaging user needs were collected through structured interviews. The findings indicate that, at present, users need continuity in Landsat capabilities with free and open data access. Improvements to future Landsat systems should include 10 m spatial resolution and at least weekly cloud-free observation frequency. Spectral enhancements should include the addition of red edge bands, and multiple, narrower visible, near infrared, shortwave infrared, and thermal infrared bands. Ideally, a variety of applications need continuous, full-spectrum coverage in 10 nm-wide bands spanning the visible to shortwave infrared (VSWIR) region (400–2500 nm) and 5 to 8 multispectral thermal infrared bands. Non-Federal (state, local, commercial, academic, and international) sources found similar results, but a more comprehensive comparison across these communities through a broader survey may provide additional insights. USGS-collected moderate-resolution land imaging user needs are an input to the Landsat 10 Architecture Study to develop and assess feasible Landsat 10 mission architectures.

The U. S. Geological Survey (USGS) initiated the Requirements, Capabilities and Analysis for Earth Observations (RCA-EO) activity in the Land Remote Sensing (LRS) program to provide a structured approach to collect, store, maintain, and analyze user requirements and Earth observing system capabilities information. RCA-EO enables the collection of information on current key Earth observation products, services, and projects, and to evaluate them at different organizational levels within an agency, in terms of how reliant they are on Earth observation data from all sources, including spaceborne, airborne, and ground-based platforms. Within the USGS, RCA-EO has engaged over 500 subject matter experts in this assessment, and evaluated the impacts of more than 1000 different Earth observing data sources on 345 key USGS products and services. This paper summarizes Landsat impacts at various levels of the organizational structure of the USGS and highlights the feedback of the subject matter experts regarding Landsat data and Landsat-derived products. This feedback is expected to inform future Landsat mission decision making. The RCA-EO approach can be applied in a much broader scope to derive comprehensive knowledge of Earth observing system usage and impacts, to inform product and service development and remote sensing technology innovation beyond the USGS.

Estimating the uncertainty or predicted accuracy of gridded products that are generated from historical bathymetric survey data is of high interest to the maritime navigation community. Surface interpolation methods used for gridding survey data in practice are well established. This paper investigates error estimation methods for gridded bathymetry in terms of their practical utility. Of particular interest are: 1) assessing the quality of a prior uncertainty of random error in survey data; 2) the significance of autocorrelated random errors; 3) the relationship between survey point density and propagated or product uncertainty; 4) the computational feasibility of Monte Carlo (MC) methods over large regions; and 5) the value of cross-validation to estimate error in the absence of controlled truth. K-fold cross-validation is used as the basis for performance evaluation of our approach to propagate a priori random errors via MC perturbation with spline-in-tension surface interpolation. Experiments are conducted with test areas in the Norwegian archipelago of Svalbard.

In order to better understand the issues associated with Full Motion Video (FMV) geopositioning and to develop corresponding strategies and algorithms, an integrated test bed is required. It is used to evaluate the performance of various candidate algorithms associated with registration of the video frames and subsequent geopositioning using the registered frames. Major issues include reliable error propagation or predicted solution accuracy, optimal vs. suboptimal vs. divergent solutions, robust processing in the presence of poor or non-existent a priori estimates of sensor metadata, difficulty in the measurement of tie points between adjacent frames, poor imaging geometry including small field-of-view and little vertical relief, and no control (points). The test bed modules must be integrated with appropriate data flows between them. The test bed must also ingest/generate real and simulated data and support evaluation of corresponding performance based on module-internal metrics as well as comparisons to real or simulated “ground truth”. Selection of the appropriate modules and algorithms must be both operator specifiable and specifiable as automatic. An FMV test bed has been developed and continues to be improved with the above characteristics. The paper describes its overall design as well as key underlying algorithms, including a recent update to “A matrix” generation, which allows for the computation of arbitrary inter-frame error cross-covariance matrices associated with Kalman filter (KF) registration in the presence of dynamic state vector definition, necessary for rigorous error propagation when the contents/definition of the KF state vector changes due to added/dropped tie points. Performance of a tested scenario is also presented.

Geostatistical modeling of spatial uncertainty has its roots in the mining, water and oil reservoir exploration communities, and has great potential for broader applications as proposed in this paper. This paper describes the underlying statistical models and their use in both the estimation of quantities of interest and the Monte-Carlo simulation of their uncertainty or errors, including their variance or expected magnitude and their spatial correlations or inter-relationships. These quantities can include 2D or 3D terrain locations, feature vertex locations, or any specified attributes whose statistical properties vary spatially. The simulation of spatial uncertainty or errors is a practical and powerful tool for understanding the effects of error propagation in complex systems. This paper describes various simulation techniques and trades-off their generality with complexity and speed. One technique recently proposed by the authors, Fast Sequential Simulation, has the ability to simulate tens of millions of errors with specifiable variance and spatial correlations in a few seconds on a lap-top computer. This ability allows for the timely evaluation of resultant output errors or the performance of a "down-stream" module or application. It also allows for near-real time evaluation when such a simulation capability is built into the application itself.

A simple, reliable, and fast sequential simulation (FSS) technique based upon the exponential variogram model is demonstrated. FSS is shown to be significantly faster and simpler than standard implementations of sequential Guassian simulation, but with a corresponding tradeoff of modeling flexibility. This tradeoff may be adequate for many analysis scenarios in GIScience, which may in turn motivate broader community appeal. Monte Carlo experiments with FSS are demonstrated with evaluating conflation matching performance for geographic information system (GIS) features.

This paper presents practical methods for the sequential generation or simulation of a Gaussian two-dimensional random field. The specific realizations typically correspond to geospatial errors or perturbations over a horizontal plane or grid. The errors are either scalar, such as vertical errors, or multivariate, such as , , and errors. These realizations enable simulation-based performance assessment and tuning of various geospatial applications. Both homogeneous and non-homogeneous random fields are addressed. The sequential generation is very fast and compared to methods based on Cholesky decomposition of an a priori covariance matrix and Sequential Gaussian Simulation. The multi-grid point covariance matrix is also developed for all the above random fields, essential for the optimal performance of many geospatial applications ingesting data with these types of errors.

Optimal full motion video (FMV) registration is a crucial need for the Geospatial community. It is required for subsequent and optimal geopositioning with simultaneous and reliable accuracy prediction. An overall approach being developed for such registration is presented that models relevant error sources in terms of the expected magnitude and correlation of sensor errors. The corresponding estimator is selected based on the level of accuracy of the a priori information of the sensor's trajectory and attitude (pointing) information, in order to best deal with non-linearity effects. Estimator choices include near real-time Kalman Filters and batch Weighted Least Squares. Registration solves for corrections to the sensor a priori information for each frame. It also computes and makes available a posteriori accuracy information, i.e., the expected magnitude and correlation of sensor registration errors. Both the registered sensor data and its a posteriori accuracy information are then made available to "down-stream" Multi-Image Geopositioning (MIG) processes. An object of interest is then measured on the registered frames and a multi-image optimal solution, including reliable predicted solution accuracy, is then performed for the object's 3D coordinates. This paper also describes a robust approach to registration when a priori information of sensor attitude is unavailable. It makes use of structure-from-motion principles, but does not use standard Computer Vision techniques, such as estimation of the Essential Matrix which can be very sensitive to noise. The approach used instead is a novel, robust, direct search-based technique.

In this paper we demonstrate a technique for extracting 3-dimensional data from 2-dimensional GPS-tagged video. We call our method Minimum Separation Vector Mapping (MSVM), and we verify it's performance versus traditional Structure From Motion (SFM) techniques in the field of GPS-tagged aerial imagery, including GPS-tagged full motion video (FMV). We explain how MSVM is better posed to natively exploit the a priori content of GPS tags when compared to SFM. We show that given GPS-tagged images and moderately well known intrinsic camera parameters, our MSVM technique consistently outperforms traditional SFM implementations under a variety of conditions.

As open source volunteered geographic information continues to gain popularity, the user community and data contributions are expected to grow, e.g., CloudMade, Apple, and Ushahidi now provide OpenStreetMap© (OSM) as a base layer for some of their mapping applications. This, coupled with the lack of cartographic standards and the expectation to one day be able to use this vector data for more geopositionally sensitive applications, like GPS navigation, leaves potential users and researchers to question the accuracy of the database. This research takes a photogrammetric approach to determining the positional accuracy of OSM road features using stereo imagery and a vector adjustment model. The method applies rigorous analytical measurement principles to compute accurate real world geolocations of OSM road vectors. The proposed approach was tested on several urban gridded city streets from the OSM database with the results showing that the post adjusted shape points improved positionally by 86%. Furthermore, the vector adjustment was able to recover 95% of the actual positional displacement present in the database. To demonstrate a practical application, a head-to-head positional accuracy assessment between OSM, the USGS National Map (TNM), and United States Census Bureau’s Topologically Integrated Geographic Encoding Referencing (TIGER) 2007 roads was conducted.

With the rapid growth of sensor platforms for imagery collection, from micro-unmanned aerial systems (UAS) to smart phones, an ability to geo-register image data is a fundamental need for many downstream applications. Approaches to georegistration for sensor imagery have deep roots in photogrammetry, and more recently with the integration of computer vision techniques. Georegistration solutions are increasingly sought for inexpensive and non-metric quality sensors and/or those that may lack the metadata needed to support rigorous coordinate transfer with error estimation. This indicates a range of solution quality, with situational awareness at one end, and rigorous accuracy at the other. There are a variety of correspondence and transformation models from which to select, with tradeoffs among simplicity, accuracy, and error estimation. The continually expanding vernacular of terms and methods can lead to confusion of application among the broader community of users. A sorting of representative terminology, processes, and techniques, is proposed as a framework. The goal is to motivate discussion for application guidelines.

The classic problem of computer-assisted conflation involves the matching of individual features (e.g., point, polyline, or polygon vectors) as stored in a geographic information system (GIS), between two different sets (layers) of features. The classical goal of conflation is the transfer of feature metadata (attributes) from one layer to another. The age of free public and open source geospatial feature data has significantly increased the opportunity to conflate such data to create enhanced products. There are currently several spatial conflation tools in the marketplace with varying degrees of automation. An ability to evaluate conflation tool performance quantitatively is of operational value, although manual truthing of matched features is laborious and costly. In this paper, we present a novel methodology that uses spatial uncertainty modeling to simulate realistic feature layers to streamline evaluation of feature matching performance for conflation methods. Performance results are compiled for DCGIS street centerline features.

The topic of data uncertainty handling is relevant to essentially any scientific activity that involves making measurements of real world phenomena. A rigorous accounting of uncertainty can be crucial to the decision-making process. The purpose of this paper is to provide a brief overview on select issues in handling uncertainty in geospatial data. We begin with photogrammetric concepts of uncertainty handling, followed by investigating uncertainty issues related to processing vector (object) representations of geospatial information. Suggestions are offered for enhanced modeling, visualization, and exploitation of local uncertainty information in applications such as fusion and conflation. Stochastic simulation can provide an effective approach to improve understanding of the consequences uncertainty propagation in common geospatial processes such as path finding. Future work should consider the development of standardized modeling techniques for stochastic simulation for more complex object data, to include spatial and attribute information.

Data registration is the foundational step for fusion applications such as change detection, data conflation, ATR, and automated feature extraction. The efficacy of data fusion products can be limited by inadequate selection of the transformation model, or characterization of uncertainty in the registration process. In this paper, three components of image-to-image registration are investigated: 1) image correspondence via feature matching, 2) selection of a transformation function, and 3) estimation of uncertainty. Experimental results are presented for photogrammetric versus non-photogrammetric transfer of point features for four different sensor types and imaging geometries. The results demonstrate that a photogrammetric transfer model is generally more accurate at point transfer. Moreover, photogrammetric methods provide a reliable estimation of accuracy through the process of error propagation. Reliable local uncertainty derived from the registration process is particularly desirable information to have for subsequent fusion processes. To that end, uncertainty maps are generated to demonstrate global trends across the test images. Recommendations for extending this methodology to non-image data types are provided.

As the availability of geospatial data increases, there is a growing need to match these datasets together. However, since these datasets often vary in their origins and spatial accuracy, they frequently do not correspond well to each other, which create multiple problems. To accurately align with imagery, analysts currently either: 1) manually move the vectors, 2) perform a labor-intensive spatial registration of vectors to imagery, 3) move imagery to vectors, or 4) redigitize the vectors from scratch and transfer the attributes. All of these are time consuming and labor-intensive operations. Automated matching and fusing vector datasets has been a subject of research for years, and strides are being made. However, much less has been done with matching or fusing vector and raster data. While there are initial forays into this research area, the approaches are not robust. The objective of this work is to design and build robust software called MapSnap to conflate vector and image data in an automated/semi-automated manner. This paper reports the status of the MapSnap project that includes: (i) the overall algorithmic approach and system architecture, (ii) a tiling approach to deal with large datasets to tune MapSnap parameters, (iii) time comparison of MapSnap with re-digitizing the vectors from scratch and transfer the attributes, and (iv) accuracy comparison of MapSnap with manual adjustment of vectors. The paper concludes with the discussion of future work including addressing the general problem of continuous and rapid updating vector data, and fusing vector data with other data.

Image registration has been a broadly applied topic across the photogrammetric/remote sensing and computer vision communities. It is a foundational step for many applications such geopositioning, data fusion, change detection, conflation, and object recognition and extraction. The efficacy of many automated geospatial processes can be limited or nullified by an inadequate registration process. The task of automated image registration presents two main challenges: 1) establishing image-to-image correspondence through feature matching, and 2) determining an appropriate transformation model for a given registration scenario. When imaging 3D environments, a goal of the transformation function is to accurately relate the 2D pixel spaces of candidate images with potential geometric distortions and surface discontinuities projected from a 3D object space. When sensor model metadata and 3D surface information is available (e.g. a digital surface model), a 3D-to-2D photogrammetric transformation will generally provide the most reliable registration solution. Moreover, photogrammetric solutions propagate error to provide a statistically rigorous estimation of registration accuracy. On the other hand, direct 2D-to-2D transformations such as affine, homographic, and polynomials are often used when sensor metadata and/or object space information is limited or unavailable. Owing to their convenience of use and implementation, direct 2D-to-2D registration methods abound in commercial software application. However, such registration solutions are generally more suspect in terms of accuracy and uncertainty estimation. Nonetheless, they do have practical utility, provided appropriate care is exercised in their application. The goal of this paper is to quantitatively demonstrate different scenarios and solutions that users should consider when applying 3D-to-2D photogrammetric versus direct 2D-to-2D image registration methods.

A novel approach to semi-automated road centerline extraction from remotely sensed imagery is introduced. Providing inspiration is Kohonen’s self-organizing map (SOM) algorithm. With IFOV < 2m, road features are open to regionbased analysis. A variation of the basic SOM algorithm is implemented in a region-based approach to road vectorization from high spatial (1.0m) and spectral resolution imagery. Using spectrally classified road pixels as input, centerline nodes are located via cluster analysis of the local density fluctuations in the input space. Linking the self-organized locations of the nodes with a minimum spanning tree algorithm provides global topological structure, which is subsequently refined. The idea is use contextual analysis from which to derive optimum topology. The result is a vectorized road centerline network suitable for direct GIS database population. Preliminary results demonstrate the algorithm’s potential for robust vectorization when presented with noisy input.

In northern climates, locating overwintering fish can be very challenging due to thick ice cover. Areas near the coast of the Beaufort Sea provide valuable overwintering habitat for both resident and anadromous fish species; identifying them and understanding their use of overwintering areas is of special interest. Synthetic aperture radar (SAR) imagery from two spaceborne satellites was examined as an alternative to radiotelemetry for identifying anadromous fish overwintering. The presence of water and ice were sampled at 162 sites, and fish were sampled at 16 of these sites. From SAR imagery alone, we successfully identified large pools inhabited by overwintering fish in the ice-covered Sagavanirktok River, Alaska. In addition, the imagery was able to identify all of the larger pools (mean minimum length = 138 m, SD = 131, range = 15-470 m) of water located by field sampling. The effectiveness of SAR in identifying these pools varied from 31% to 100%, depending on imagery polarization, the incidence angle range, and the orbit. Horizontal transmit-vertical receive (HV) polarization appeared to be best. The accuracy of SAR was also assessed at a finer pixel-by-pixel scale (30 x 30 m). The best correspondence at this finer scale was obtained with an image having HV polarization. The levels of agreement ranged from 54% to 69%. The presence of broad whitefish Coregonus nasus (the only anadromous species present) was associated with salinity and pool size (estimated with SAR imagery); fish were more likely to be found in larger pools with low salinity. This research illustrates that SAR imaging has great potential for identifying under-ice overwintering areas of riverine fish. These techniques should allow managers to identify critical overwintering areas more easily and at lower cost than traditional techniques permit.

The methods used to evaluate automation tools are a critical part of the development process. In general, the most meaningful measure of an automation method from an operational standpoint is its effect on productivity. Both timed comparison between manual and automation based-extraction, as well as measures of spatial accuracy are needed. In this paper, we introduce the notion of correspondence to evaluate spatial accuracy of an automated update method. Over time, existing vector data becomes outdated because 1) land cover changes occur, or 2) more accurate overhead images are acquired, and/or vector data resolution requirements by the user may increase. Therefore, an automated vector data updating process has the potential to significantly increase productivity, particularly as existing worldwide vector database holdings increase in size, and become outdated more quickly. In this paper we apply the proposed evaluation methodology specifically to the process of automated updating of existing road centerline vectors. The operational scenario assumes that the accuracy of the existing vector data is in effect outdated with respect to newly acquired imagery. Whether the particular approach used is referred to as 1) vector-to-image registration, or 2) vector data updating-based automated feature extraction (AFE), it is open to interpretation of the application and bias of the developer or user. The objective of this paper is to present a quantitative and meaningful evaluation methodology of spatial accuracy for automated vector data updating methods.

The application of quantitative performance evaluation methods can provide useful insights in determining the utility of computer-assisted methods for delineating geographic features from remotely sensed images. Evaluation concepts are demonstrated with road centerlines in particular, but are applicable to similar feature types such as paths, trails, or rivers. The two comparative measures used to differentiate conventional versus computer-assisted delineation are 1) user clock time, and 2) spatial consistency. Our evaluation results with road centerlines demonstrate how such quantitative analyses can be used to determine the utility of computer-assisted methods from both developmental and operational perspectives.