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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <title>Unmanned Aircraft Systems (UAS): Activities at the Department of the Interior</title>
      <link>https://trid.trb.org/View/1341502</link>
      <description><![CDATA[The U.S. Department of the Interior (DOI) is responsible for protecting and managing the natural resources and heritage on almost 20% of the land in the United States. The DOI’s mission requires access to remotely sensed data over vast lands, including areas that are remote and potentially dangerous to access. Unmanned Aircraft Systems (UAS) technology has the potential to enable the DOI to be a better steward of the land by: (1) Improving natural hazard forecasting and the analysis of the impacts. (2) Improving the understanding of climate change to better plan for likely impacts. (3) Developing precipitation and evaporation forecasting to better manage water resources. (4) Monitoring Arctic ice change and its impacts on ecosystems, coasts, and transportation. (5) Increasing safety and effectiveness of wildland fire management. (6) Enhancing search and rescue capabilities. (7) Broadening the abilities to monitor environmental or landscape conditions and changes. (8) Better understanding and protecting the Nation’s ecosystems. The initial operational testing and evaluations performed by the DOI have proven that UAS technology can be used to support many of the Department’s activities. UAS technology provides scientists a way to look longer, closer and more frequently at some of Earth’s most remote areas—places that were previously too dangerous or expensive to monitor in detail. The flexibility of operations and relative low cost to purchase and operate Small Unmanned Aerial System (sUAS) enhances the ability to track long-term landscape and environmental change. The initial testing indicates the operational costs are approximately 10% of traditional manned aircraft. In addition, users can quickly assess landscape-altering events such as wildland fires, floods and volcanoes. UAS technology will allow the DOI to do more with less and in the process enhance the Department’s ability to provide unbiased scientific information to help stakeholders make informed decisions. It will also provide a digital baseline record that can be archived and used when monitoring future events or conditions. One possible future scenario has scientists carrying sUAS into the field allowing quick deployment and operation to observe the environment or for emergency response. This scenario could also include a persistent monitoring capability provided by a UAS that can stay airborne over a small geographic area for days or weeks, or possibly longer. While the DOI focus is on sUAS, the Department recognizes that larger UAS systems will also play a role in meeting its mission. The Department anticipates meeting long-duration or specialized acquisition commitments, such as state or national aerial photography, by collaboration with other agencies or through commercial contracts. Even though the DOI continues to evaluate UAS and sensor technology to meet the Department’s mission, some of its bureaus are already moving towards an operational capability. The authors fully anticipate that by 2020 UAS will emerge as one of the primary platforms for DOI remote sensing applications.]]></description>
      <pubDate>Mon, 02 Feb 2015 10:27:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/1341502</guid>
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      <title>High Resolution Imagery Collection for Post- Disaster Studies Utilizing Unmanned Aircraft Systems (UAS)</title>
      <link>https://trid.trb.org/View/1341937</link>
      <description><![CDATA[This paper examines the use of unmanned aircraft systems (UAS) to capture imagery for use in post-disaster field studies at the neighborhood and individual building level. A discussion of post-disaster imagery collection including satellite, aerial, and ground-based platforms is first presented. Applications of UAS in recent disasters as described in the literature are then surveyed, and a case study investigating UAS capabilities for imagery collection following an EF-3 tornado in northern Alabama on 02 March 2012 is presented. Case study considerations include the multi-rotor unmanned aerial vehicle (UAV) equipment and ground station, onboard imagery devices, flight considerations and capabilities, and imagery and metadata collection capabilities of the UAS. Sample post-tornado imagery of building damage is shown, demonstrating the order of magnitude improvement in imagery resolution compared to traditional post-disaster aerial photography.]]></description>
      <pubDate>Mon, 02 Feb 2015 10:27:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/1341937</guid>
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    <item>
      <title>Modeling Above-Ground Biomass in Tallgrass Prairie Using Ultra-High Spatial Resolution sUAS Imagery</title>
      <link>https://trid.trb.org/View/1341930</link>
      <description><![CDATA[The authors examined the relationship between tallgrass above-ground biomass (AGB) and NDVI from ultra-high spatial resolution multispectral imagery collected by small unmanned aircraft systems (sUAS). This study was conducted at the Tallgrass Prairie National Preserve in Chase County, Kansas. Results show that NDVI values computed from sUAS imagery explained up to 94 percent of the variance (p <0.01) in AGB measurements. The model coefficient of determination (r₂) decreased with increasing aircraft flight altitude suggesting image spatial resolution is a key factor influencing the strength of the relationship. A scaling-up approach from small-scale sUAS imagery to broad-scale, digital aerial imagery collected at 1,200 meters by a piloted aircraft was used to provide AGB model estimates across the entire 4,500 ha of the Preserve. Spectral reflectance data measured by spectroradiometer were also used to identify three optimal regions of the spectrum that have the highest significant correlations with tallgrass AGB.]]></description>
      <pubDate>Mon, 02 Feb 2015 10:27:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/1341930</guid>
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    <item>
      <title>Retrieval of Spectral Reflectance of High Resolution Multispectral Imagery Acquired with an Autonomous Unmanned Aerial Vehicle: AggieAir™</title>
      <link>https://trid.trb.org/View/1341929</link>
      <description><![CDATA[This research presents a new semi-automatic model for converting raw AggieAir™ footprints in visible and near-infrared (NIR) bands into reflectance images. AggieAir, a new unmanned aerial vehicle (UAV) platform, is flown autonomously using pre-programmed flight plans at low altitudes to limit atmospheric effects. The UAV acquires high-resolution, multispectral images and has a flight duration of about 30 minutes. The sensors on board are twin cameras with duplicate settings and automatic mode disabled. A white Barium Sulfate (BaSO4) panel is used for reflectance calibration and in situ irradiance measurements. The spatial resolution of the imagery is 25 cm; the radiometric resolution is 8-bit. The raw images are mosaicked and orthorectified and the model converts their digital numbers (DN) to reflectance values. Imagery, acquired around local solar noon over wetlands on the Great Salt Lake, Utah, is used to illustrate the results. The model generates high quality images and the results are good. The reflectance values of vegetation in the NIR, Green and Red bands extracted at the test locations are consistent. The image processing, reflectance calculations, accuracy issues, with the proposed method are discussed.]]></description>
      <pubDate>Mon, 02 Feb 2015 10:27:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/1341929</guid>
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    <item>
      <title>Multi-UAV Surveillance over Forested Regions</title>
      <link>https://trid.trb.org/View/1341928</link>
      <description><![CDATA[S-UAVs (Small-Unmanned Aerial Vehicles) have emerged as low-cost alternatives for aerial surveillance over forests. However, they provide limited coverage owing to their low altitudes and short endurance. Therefore, a quick and effective surveillance necessitates optimal flying paths, maximizing ground visibility. Even though the occlusion of ground points due to vegetation is significant in forests, it is generally neglected. This paper proposes a probabilistic sensing model that incorporates both occlusions due to terrain and vegetation, in the visibility computations and presents a two-step approach to determine near-optimal flight paths: (a) waypoints are strategically deployed to enhance visibility, using centroidal Voronoi tessellation, and (b) flyable paths are designed using a clustered spiral-alternating algorithm. Simulation studies conducted on synthetic terrains and a reconstructed terrain, from satellite data of tree-cover and a Digital Elevation Model (DEM), show the effectiveness of the proposed method in improving the terrain visibility as compared to commonly used grid-based waypoints.]]></description>
      <pubDate>Mon, 02 Feb 2015 10:27:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/1341928</guid>
    </item>
    <item>
      <title>Feasibility Study for Pose Estimation of Small UAS in Known 3D Environment Using Geometric Hashing</title>
      <link>https://trid.trb.org/View/1341927</link>
      <description><![CDATA[A novel self-localization method for small Unmanned Aerial Systems (UAS) is presented. The algorithm automatically establishes correspondence between the First-Person View (FPV) video streamed from a UAS flying in an urban environment and its 3D building model. The resulting camera pose provides a precise navigation solution in the densely structured environment. Initially, Vertical Line Features are extracted from the FPV video frames, as the forward and downward-looking camera is kept stabilized through a gimbal camera mount. Geometric hashing is then used to match the extracted image features with a database of Vertical Line Features extracted from synthetic images of the three dimensional (3D) building models. The exterior orientation parameters of the FPV video frames for localizing the UAS frames are determined by photogrammetric bundle adjustment. The results demonstrate that sub-meter accuracies in the UAS's X, Y, Z positional coordinates are achievable from flying 40 m above ground.]]></description>
      <pubDate>Mon, 02 Feb 2015 10:27:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/1341927</guid>
    </item>
    <item>
      <title>GPU Processing for UAS-Based LFM-CW Stripmap SAR</title>
      <link>https://trid.trb.org/View/1341505</link>
      <description><![CDATA[Unmanned air systems (UAS) provide an excellent platform for synthetic aperture radar (SAR), enabling surveillance and research over areas too difficult, dangerous, or costly to reach using manned aircraft. However, the nimble nature of the small UAS makes them more susceptible to external forces, thus requiring significant motion compensation in order for SAR images to focus properly. SAR backprojection has been found to improve the focusing of low-altitude stripmap SAR images compared to frequency domain algorithms. In this paper the authors describe the development and implementation of SAR backprojection appropriate for UAS based stripmap SAR that utilizes the unique architecture of a graphics processing unit (GPU) in order to produce high-quality imagery in real-time.]]></description>
      <pubDate>Mon, 02 Feb 2015 10:27:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/1341505</guid>
    </item>
    <item>
      <title>Extracting Parking Lot Structures from Aerial Photographs</title>
      <link>https://trid.trb.org/View/1316684</link>
      <description><![CDATA[A new method for the automatic extraction of parking lot structures from an aerial orthophoto is proposed which has two major stages. Line features of a parking lot are first extracted by using a line extraction method with principle orientation constraints which offers a reliable basis for the extraction of parking lots structures by emphasizing on the extraction of accurate parking line information. The structure of the parking lots are then determined from the extracted parking lines through a maximum intersection orientation method, parameter-estimating procedure, and a self-adaptive growth method. A comparative experiment is shown and the result indicates that the proposed method can effectively extract parking lot structures with high correctness, high-quality completeness, and good geometric accuracy.]]></description>
      <pubDate>Thu, 24 Jul 2014 15:22:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/1316684</guid>
    </item>
    <item>
      <title>Road Extraction from Lidar Data Using Support Vector Machine Classification</title>
      <link>https://trid.trb.org/View/1308764</link>
      <description><![CDATA[This paper presents a method for road extraction from lidar data based on support vector machine (SVM) classification. The lidar data are used exclusively to evaluate the potential in the road extraction process. First, the SVM algorithm is used to classify the lidar data into five classes: road, tree, building, grassland, and cement. Then, some misclassified pixels in the road class is removed using the road values in the normalized Digital Surface Model and Normalized Difference Distance features. In the postprocessing stage, a method based on Radon transform and Spline interpolation is employed to automatically locate and fill the gaps in the road network. The experimental results show that the proposed algorithm for gap filling works well on straight roads. The proposed road extraction algorithm is tested on three datasets. An accuracy assessment indicated 63.7%, 60.26% and 66.71% quality for three datasets. Finally, centerline of the detected roads is extracted using mathematical morphology. Road information plays an important role in many modern applications, including transportation, automatic navigation systems, traffic management, and crisis management, and enables existing geographic information system (GIS) databases to be updated more efficiently. In the past two decades, automatic road extraction has become an important topic in remote sensing, photogrammetry, and computer vision. In addition, recent advances in lidar systems and their enormous potential in automatic feature extraction motivate the development of automatic road extraction algorithms based on lidar data.]]></description>
      <pubDate>Wed, 28 May 2014 15:21:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/1308764</guid>
    </item>
    <item>
      <title>Emergent Imaging and Geospatial Technologies for Soil Investigations</title>
      <link>https://trid.trb.org/View/1306551</link>
      <description><![CDATA[The authors of this article encourage the community to advance understanding of these important imaging and geospatial technologies for soil investigations. The authors need to strengthen educational, outreach, and professional certification programs to ensure informed and ethical applications of emergent sensor capabilities and knowledge-based systems. Lastly, the authors must serve as effective advocates of advanced sensor development, standardized processing and modeling of soil data and environmental co-variates, and appropriate integration of soil information for effective assessment and monitoring of environmental impacts and societal decision making. The next generation of imaging and telecommunications systems integrated with complex analytical methods and computing technologies will revolutionize the way soil resources across a wide range of scientific disciplines and application domains are inventoried and managed. Papers in this special issue highlight some of those systems and methods for the direct benefit of environmental professionals and students who focus on imaging and geospatial information for improved understanding, management, and monitoring of soil resources. Five key emergent geospatial technologies not addressed in the special issue papers are profiled here: airborne topographic lidar, proximal sensing of soil properties, unmanned aerial systems (UAS), active and passive microwave sensing of soil moisture, and web-enabled soil database access, computing, and mapping.]]></description>
      <pubDate>Tue, 22 Apr 2014 16:07:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/1306551</guid>
    </item>
    <item>
      <title>Toward Linking Aboveground Vegetation Properties and Soil Microbial Communities Using Remote Sensing</title>
      <link>https://trid.trb.org/View/1306552</link>
      <description><![CDATA[Despite their vital role in terrestrial ecosystem function, the distributions and dynamics of soil microbial communities (SMCs) are poorly understood. Vegetation and soil properties are the primary factors that influence SMCs. This paper discusses the potential effectiveness of remote sensing science and technologies for mapping SMC biogeography by characterizing surface biophysical properties (e.g., plant traits and community composition) strongly correlated with SMCs. Using remotely sensed biophysical properties to predict SMC distributions is extremely challenging because of the intricate interactions between biotic and abiotic factors and between above- and below-ground ecosystems. However, the integration of biophysical and soil remote sensing with geospatial information about the environment holds great promise for mapping SMC biogeography. Additional research needs involve microbial taxonomic definition, soil environmental complexity, and scaling strategies. The collaborative effort of experts from diverse disciplines is essential to linking terrestrial surface biosphere observations with subsurface microbial community distributions using remote sensing.]]></description>
      <pubDate>Tue, 22 Apr 2014 16:07:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/1306552</guid>
    </item>
    <item>
      <title>Semi-Automated Disaggregation of a Conventional Soil Map Using Knowledge Driven Data Mining and Random Forests in the Sonoran Desert, USA</title>
      <link>https://trid.trb.org/View/1306554</link>
      <description><![CDATA[Conventional soil maps (CSM) have provided baseline soil information for land use planning for over 100 years. Although CSMhave been widely used, they are not suitable to meet growing demands for high resolution soil information at field scales. The authors present a repeatable method to disaggregate CSM data into ~30-meter resolution rasterized soil class maps that include continuous representation of probabilistic map uncertainty. Methods include training set creation for original CSM component soil classes from soil-landscape descriptions within the original survey database. Training sets are used to build a random forest predictive model utilizing environmental covariate maps derived from ASTER satellite imagery and the United States Geological Survey (USGS) National Elevation Dataset. Results showed agreement at 70 percent of independent field validation sites and equivalent accuracy between original CSM map units and the disaggregated map. Uncertainty of predictions was mapped by relating prediction frequencies of the random forest model and success rates at validation sites.]]></description>
      <pubDate>Tue, 22 Apr 2014 16:07:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/1306554</guid>
    </item>
    <item>
      <title>Geostatistical Methods for Predicting Soil Moisture Continuously in a Subalpine Basin</title>
      <link>https://trid.trb.org/View/1306553</link>
      <description><![CDATA[This study presents spatial statistical methods for examining the distribution of soil moisture in a sub-alpine environment. The high local variability of soil moisture is not well characterized by spatial interpolation from dispersed data points. Interpolation using only field samples from Loch Vale, Rocky Mountain National Park, Colorado produced coarse estimates that followed mean soil moisture trends, but failed to capture local mid-slope variation. A properly specified regression model was identified by using dispersed field samples and ancillary data derived from Ikonos-2 and lidar data. This model predicted soil moisture patterns at a much finer spatial resolution. An intensive field campaign provided independent soil moisture measurements that were used to assess the model’s accuracy. The modeled soil moisture estimates captured local variability associated with topographic terrain differences along mid-slope areas.]]></description>
      <pubDate>Tue, 22 Apr 2014 16:07:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/1306553</guid>
    </item>
    <item>
      <title>Hierarchical Method of Urban Building Extraction Inspired by Human Perception</title>
      <link>https://trid.trb.org/View/1285662</link>
      <description><![CDATA[In a high-resolution satellite image, buildings can be considered as clustered objects belonging to the same category. Human perception of such objects consists of an initial identification of simple instances followed by recognition of more complicated ones by deduction. Inspired by this observation, a hierarchical building extraction framework is proposed to simulate the process, which includes three major components. First, a total variation-based segmentation algorithm is presented to decompose the given image into object-level elements. Then, a shape analysis is applied to extract some common and easily identified rectangular buildings. Finally, the detection of buildings with complex structures is formulated as a deduction problem based on preceding extracted information in terms of maximum a posteriori (MAP) estimation and a Bayesian based approach is proposed to deal with it. The experimental results demonstrate that the proposed framework is capable of efficiently identified urban buildings from high-resolution satellite images.]]></description>
      <pubDate>Fri, 24 Jan 2014 14:29:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/1285662</guid>
    </item>
    <item>
      <title>A Combined Object-and Pixel-Based Image Analysis Framework for Urban Land Cover Classification of VHR Imagery</title>
      <link>https://trid.trb.org/View/1276018</link>
      <description><![CDATA[This paper aims at exploiting the advantages of pixel-based and object-based image analysis approaches for urban land cover classification of very high resolution (vHR) satellite imagery through a combined object- and pixel-based image analysis framework. The framework starts with segmenting the image resulting in several spectral and spatial features of segments. To overcome the curse of dimensionality, a wavelet ­ based feature extraction method is proposed to reduce the number of features. The wavelet-based method is automatic, fast, and can preserve local variations in objects' spectral/ spatial signatures. Finally, the extracted features together with the original bands of the image are classified using the conventional pixel-based Maximum Likelihood classifica­tion. The proposed method was tested on the WorldView-2, QuickBird, and Ikonos images of the same urban area for comparison purposes. Results show up to 17 percent, 10 percent, and 11 percent improvement in kappa coefficients compared to the case in which only the original bands of the image are used for WV-2, QB, and IK, respectively. Furthermore, the objects' spectral features contribute more to increasing classification accuracy than spatial features.]]></description>
      <pubDate>Thu, 21 Nov 2013 09:14:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/1276018</guid>
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