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The construction industry relies on construction cost indexes to prepare cost estimate benchmarks and develop cost estimates. Subsequently, government agencies, non-profit organizations, and private companies routinely publish construction cost indexes for cities. Currently, all construction cost indexes are released in a tabular format for 649 cities across the conterminous United States, which is not effective in illustrating construction cost variations at the national level. This study explored the utility of various established interpolation methods and mapping techniques to visualize construction cost indexes at the national level. Geovisualization techniques such as thematic mapping provide a visual representation of construction cost data in addition to traditional tabular formats. This study explored the utility of Thiessen polygon and inverse distance weighted (IDW) methods to create thematic maps which can be used to interactively visualize construction costs at the national level. A qualitative comparison revealed that the IDW method can produce the most intuitive, interactive, and continuous surface maps to identify dynamic and previously unrecognized patterns. These continuous surface maps allow construction practitioners and academics, real estate developers, and the public to locate the geographic proximity of high or low construction costs while cost change maps allow investors and businesses to identify patterns in changing construction costs over a certain period. This work contributes to the body of knowledge by introducing interpolated maps for visualizing any construction cost-related indexes at a large scale such as the national level.

Advances in artificial intelligence for computer vision hold great promise for increasing the scales at which ecological systems can be studied. The distribution and behavior of individuals is central to ecology, and computer vision using deep neural networks can learn to detect individual objects in imagery. However, developing supervised models for ecological monitoring is challenging because it requires large amounts of human-labeled training data, requires advanced technical expertise and computational infrastructure, and is prone to overfitting. This limits application across space and time. One solution is developing generalized models that can be applied across species and ecosystems. Using over 250,000 annotations from 13 projects from around the world, we develop a general bird detection model that achieves over 65% recall and 50% precision on novel aerial data without any local training despite differences in species, habitat, and imaging methodology. Fine-tuning this model with only 1000 local annotations increases these values to an average of 84% recall and 69% precision by building on the general features learned from other data sources. Retraining from the general model improves local predictions even when moderately large annotation sets are available and makes model training faster and more stable. Our results demonstrate that general models for detecting broad classes of organisms using airborne imagery are achievable. These models can reduce the effort, expertise, and computational resources necessary for automating the detection of individual organisms across large scales, helping to transform the scale of data collection in ecology and the questions that can be addressed.

Drought intensity and duration are expected to increase over the coming century in the semiarid western United States due to anthropogenic climate change. Historic data indicate that megadroughts in this region have resulted in widespread ecosystem transitions. Landscape-scale monitoring with remote sensing can help land managers to track these changes. However, special considerations are required: traditional vegetation indices such as NDVI often underestimate vegetation cover in semiarid systems due to short and multimodal green pulses, extremely variable rainfall, and high soil fractions. Multi-endmember spectral mixture analysis (MESMA) may be more suitable, as it accounts for both green and non-photosynthetic soil fractions. To determine the suitability of MESMA for assessing drought vegetation dynamics in the western US, we test multiple endmember selection and model parameters for optimizing the classification of fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil (S) in semiarid grass- and shrubland in central New Mexico. Field spectra of dominant vegetation species were collected at the Sevilleta National Wildlife Refuge over six field sessions from May–September 2019. Landsat Thematic Mapper imagery from 2009 (two years pre-drought), and Landsat Operational Land Imager imagery from 2014 (final year of drought), and 2019 (five years post-drought) was unmixed. The best fit model had high levels of agreement with reference plots for all three classes, with R2 values of 0.85 (NPV), 0.67 (GV), and 0.74 (S) respectively. Reductions in NPV and increases in GV and S were observed on the landscape after the drought event, that persisted five years after a return to normal rainfall. Results indicate that MESMA can be successfully applied for monitoring changes in relative vegetation fractions in semiarid grass and shrubland systems in New Mexico.

Advances in artificial intelligence for computer vision hold great promise for increasing the scales at which ecological systems can be studied. The distribution and behavior of individuals is central to ecology, and computer vision using deep neural networks can learn to detect individual objects in imagery. However, developing supervised models for ecological monitoring is challenging because it needs large amounts of human-labeled training data, requires advanced technical expertise and computational infrastructure, and is prone to overfitting. This limits application across space and time. One solution is developing generalized models that can be applied across species and ecosystems. Using over 250,000 annotations from 13 projects from around the world, we develop a general bird detection model that achieves over 65% recall and 50% precision on novel aerial data without any local training despite differences in species, habitat, and imaging methodology. Fine-tuning this model with only 1000 local annotations increase these values to an average of 84% recall and 69% precision by building on the general features learned from other data sources. Retraining from the general model improves local predictions even when moderately large annotation sets are available and makes model training faster and more stable. Our results demonstrate that general models for detecting broad classes of organisms using airborne imagery are achievable. These models can reduce the effort, expertise, and computational resources necessary for automating the detection of individual organisms across large scales, helping to transform the scale of data collection in ecology and the questions that can be addressed.

This research examines the impact of urban development on three major development areas (Mesa del Sol, Volcano Mesa, and Santolina) in Albuquerque, New Mexico, based on the past pattern of urban gr...

Unmanned airborne systems (UAS), particularly the class of UAS referred to as small-unmanned airborne systems (S-UAS), have the potential to revolutionize the science, practice, and role of remote ...

Repeat Station Imaging (RSI) for image acquisition is compared with non-RSI to assess the methods’ effects on vertical and volumetric estimation using structure from motion (SFM). Aerial triangulation (i.e., SFM) is used to create three-dimensional reconstructions of the study area using unmanned aerial vehicle-acquired imagery. Targets of known volume were deployed throughout the scene and manipulated to create changes between the first and subsequent flights. An RSI flight and two non-RSI flights were compared to a baseline flight in order to estimate a series of introduced volumetric changes, which were then compared to known volume changes. Using images with a nominal ground sampling distance of 1.96 cm, results show a total root-mean-squared-error (RMSE) of 0.035 m3 and mean percent error (MPE) of 25.9% for the RSI flight, and average RMSE of 0.057 m3 and MPE 33.3% for the two non-RSI flights. For the measurement of volumetric changes to extant features, the RSI flight had an RMSE of 0.026 m3 and an MPE of 17.6%; the average RMSE and MPE of the two non-RSI flights were 0.071 m3 and 39.4%. These results show that RSI has the potential to improve the accuracy of volumetric and height change estimation.

When constructing Reinforced Concrete (RC) components, rebar spacing must be inspected in the field before pouring concrete. The traditional inspection of rebar spacing: (1) depends on the experience of the individual inspector; (2) is limited to a pass/fail outcome; and (3) is in general conducted with limited time. This paper presents a recognition algorithm that automatically identifies the rebar position using LiDAR. The result is an automatic Rebar Layout Quality Index (RLQI). Researchers collected LiDAR data from a real rebar mat, compared it with the design drawings, and obtained the RLQI for both top and bottom rebar mats. The authors also quantified the quality of the structural construction with a new flexural relative moment strength index. This methodology has the potential to reduce inspection time and increase the reliability of inspections through an objective assessment of rebar spacing, by creating an as-built record of actual rebar location in the structure.

The objective of this article is to synthesize research findings and requirements pertaining to development of time-sensitive remote-sensing systems (TSRSS) that support decision-making pertaining ...

All transportation management agencies face the yearly challenge of inspecting the condition of myriad miles of road surface. With routinely acquired and publicly available geospatial data and geospatial modelling techniques, there is potential to substantially reduce the number of survey sites required to characterize overall pavement surface distress condition. Using roadway pavement assets in the State of New Mexico as an example, this study investigated if overall pavement surface conditions could be estimated based on geospatial modelling. A total of 17 explanatory variables, which were extracted from three types of geospatial data, including traffic volumes, environmental conditions and topography, were used to estimate overall pavement surface distress conditions. Results show that overall pavement surface conditions can be effectively (R2 > 0.9) estimated based on the extent of geospatial data and inferential modelling techniques with fewer survey sites, substantially reducing the cost and...

Department of Geography and Environmental Studies, GIScience for Environmental Studies (GEM) Lab, Litvak Lab (UNM Department of Biology), and GPSA

Suitability models can help identify prime environments for a specific form of agricultural field, ak chin, that is difficult to locate with standard archaeological field methods. Using data from western New Mexico, we applied remote sensing and a geographic information system to develop suitability models based on a small training data sample. Three models, Mahalanobis Typicality, Maximum Entropy, and Multi-Criteria Evaluation Ordered Weighted Average, were used and the resulting suitability estimates were visually compared to known patterns of ak chin agriculture and quantitatively compared using receiver operating characteristic area under the curve. Although archaeological fieldwork is needed to verify these results, receiver operating characteristic validation indicates that Mahalanobis Typicality and Maximum Entropy performed well in identifying the potential location of previously undiscovered prehistoric agricultural fields.

Repeat station imaging (RSI) is an approach to precise aerial image registration and change detection. It involves planning and conducting aerial imaging flights such that the same sensor returns to the same imaging stations over time, replicating view geometry, and the resultant images are geometrically processed on a frame-by-frame basis. The objective of this paper is to elucidate and model the geometric elements associated with RSI capture and processing that influence the accuracy of co-registration. When each type of offset or difference in orientation is modeled separately, relative displacement of features in RSI pairs are linear functions of the varying orientation parameters and focal length, with the exception of an inverse square relationship with altitude above ground level; the combination of higher altitude and longer focal length reduces image displacements approximately in half. The average difference (bias) between modeled and measured parallax in ground dimensions is 0.1 m. Isolating the effects of orientation and offset factors is a useful first step towards developing a robust analytical model of the geometry of RSI pairs.

Remote sensing for hazard response requires a priori identification of sensor, transmission, processing, and distribution methods to permit the extraction of relevant information in timescales sufficient to allow managers to make a given time-sensitive decision. This study applies and demonstrates the utility of the Remote Sensing Communication Model RSCM to improve a tactical wildfire remote-sensing system to better meet the time-sensitive information requirements of emergency response managers in San Diego County, USA. A thermal infrared airborne remote-sensing system designed and operated by the United States Forest Service Pacific Southwest Research Station for active wildfire monitoring is documented and updated based on the RSCM. Analysis of the thermal infrared remote-sensing system in the context of the RSCM identified three configuration changes that can improve the effectiveness of the information produced when employed by wildfire incident commanders for suppression prioritization: 1 limit spectral sampling collection to a single waveband; 2 complete image processing steps on-board the aircraft; and 3 provide information on wildfire locations to incident commanders in the form of a static map.

This paper examines the potential of remote sensing–derived metrics of vegetation phenology and a Multi-Layer Perceptron neural network to model the most likely locations of large, agglomerated archaeological sites. Focusing on two different environments in central New Mexico, the Galisteo Basin and the Sandia-Manzano Mountain range, this pilot study distinguishes between archaeological sites and their surroundings based on differential growth in vegetation. Using data derived from Landsat Thematic Mapper, a time series of Normalized Difference Vegetation Indices were created to characterize vegetation phenology in the study areas. Distinguishing between archaeological sites and their surroundings, the neural network was trained on a series of known sites to develop an output activation layer indicating the possible locations of other, previously unknown sites. This output activation layer, treated as a site suitability model, was validated using the receiver operating characteristic area under the curve using known sites excluded from the training procedure. Results show promise in large, open areas such as basin environments. While differences in vegetation type have relatively little effect, differences in elevation, or more directly the changes in phenology that go along with them, negatively impact the ability to infer the presence of archaeological sites using this approach.

Forests provide many benefits (e.g., esthetics, water purification, habitat, carbon sequestration) to humans when they are conserved. However, forest conservation efforts may conflict with other uses, particularly timber harvest. Incentives for forest conservation can be established through payment for ecosystem services (PES) policies. PES policies rely on forest valuation studies which traditionally have valued the whole forest, or characteristics of a forest. Methods that allow variability in estimated forest values over space can provide valuation information at a finer scale and aid in the optimization of PES policies. These spatially explicit values provide information critical to many policy decisions, particularly conservation prioritization and planning. Using moderate resolution satellite remote sensing (i.e., LandSat) and a forest use survey conducted in Kam Cha i, Thailand, methods to estimate forest product valuation for spatially heterogeneous plots are demonstrated. These estimates are produced using geographic information system (GIS) multicriteria decision analysis (MCDA). The MCDA method combines forest use data with associated travel costs and vegetation cover estimates to provide spatially explicit estimates of forest values. This information is then used to compare local use values against potential carbon credit values estimated from the literature. The results show that a majority of the forest plots have higher PES values than for local forest products. Some do not, however, which suggests a possible cause for resistance against potential PES policies implemented in these areas.

In recent years, unmanned airborne systems (UAS) have emerged as an important platform for collecting hyper-spatial resolution airborne remote sensing data. Using this hyper-spatial resolution imagery as input, modern aerial triangulation (AT) techniques, also known as structure-from-motion or SfM, can rapidly produce orthophotos and digital surface models (DSMs) at fine scales. Such data hold great promise for a number of applications, including routine and post-disaster assessment of transportation infrastructure, which provided the impetus for this research. Using hyper-spatial resolution (0.002 m) natural colour digital aerial photography acquired from a low-altitude UAS as input images, this research systematically investigated the horizontal and vertical accuracy of the AT generated orthophotos and DSMs, respectively. Hyper-spatial resolution aerial data were collected for a total of 28 study sites and, for each study site, coordinate information of 16 ground control points (GCPs) was collec...

State transportation agencies regularly collect data on pavement surface distresses. These data are used to assess overall pavement conditions and to make maintenance and repair decisions. Routinely-acquired and publically-available high spatial resolution (hsr) multispectral digital aerial photography provides a potential method for collecting distress information that can supplement or replace currently-used technologies. Principal component analysis and linear least squares regression models were used to evaluate the potential of using HSR multispectral digital aerial photographs to estimate pavement surface overall distress conditions. Various models were developed using HSR multispectral digital aerial photographs of different spatial resolution (6-inch, 12-inch, and 24-inch) and reference pavement surface distress data collected manually at multiple sample sites using standard protocols. The results show that the spectral response of HSR multispectral digital aerial photographs correlate strongly with reference distress rates at all tested spatial resolutions, but the 6-inch aerial photos exhibit the strongest correlation (R 2 > 0.95), even when using only half of the sample sites (R 2 > 0.92). These results indicate that straightforward analysis of HSR multispectral digital aerial photographs, routinely acquired by most municipalities and states, can permit assessment of pavement surface distress conditions as well as current manual evaluation protocols.

A fundamental process in construction cost estimation is the appropriate adjustment of costs to reflect project location. Unfortunately, location adjustment factors are not available for all locations. To overcome this lack of data, cost estimators in the United States often use adjustment factors from adjacent locations, referred to as the nearest neighbor (NN) method. However, these adjacent locations may not have similar economic conditions, which limit the accuracy of the NN method. This research proposes a new method of using nighttime light satellite imagery (NLSI) to estimate location adjustment factors where they do not exist. The NLSI method for estimating location adjustment factors was evaluated against an established cost index database, and results show that NLSI can be used to effectively estimate location adjustment factors. When compared with NN and other alternative proximity-based location adjustment methods, the proposed NLSI method leads to a 25–40% reduction of the median abso...

Remote sensing employs a range of conceptual and mechanistic models. Several conceptual models have been proposed to explain remote sensing systems (RSSs) and either directly prescribe or elucidate the configuration of such systems based on scene conditions and information requirements. This study interrogates the utility of these models for the design and practical implementation of RSSs to address time-sensitive information requirements and proposes a novel conceptual model, the remote sensing communication model, that places remote sensing within a decision support context.Three pertinent remote sensing conceptual models are critically assessed based on their satisfaction of three basic requirements: (1) prescribe or explicate RSS configuration based on user information requirements, (2) elucidate dependencies between information type, accuracy, and timeliness, and (3) describe the effect of RSS configuration on the effectiveness of users’ decisions. The conceptual remote sensing models that are evalua...

This letter demonstrates the potential of a repeat station imaging and registration approach applied to rapid sequence aerial images captured along the same flightline for detecting moving objects and measuring their velocities. Sequential images share similar view geometries and thus can be precisely registered with simple warping transformations based on just a few matching points. Moving objects were detected as brightness anomalies between sequential images. Precise registration and low-pass filtering minimized false detections associated with misregistration. Velocities were estimated by measuring displacements of moving objects depicted in registered sequential image frames and dividing by the time interval between exposures.

Infrastructure is the backbone of the US economy and a necessary input to every economic output [1]. The cost of infrastructure maintenance and management demands significant expense for government and private companies. Infrastructure owners want to increase efficiency and improve their bottom-line from existing infrastructure rather than building new ones [2]. One of the significant challenges for the engineering community has been adopting new technologies such as low-cost wireless smart sensors, augmented reality, Unmanned Aerial System (UAS)-based Structural Health Monitoring (SHM). To receive first-hand insight from infrastructure owners, industry professionals and researchers, a workshop entitled ‘Infrastructure, Maintenance and Management Using New Technology’ was conducted in Fort Worth, Texas. In this paper the findings from the workshop are discussed. Stakeholders highlighted safety of the bridge inspectors as the priority in the maintenance and management work. Based on the findings of this workshop it now clear that adopting new technologies leads to higher safety for field inspectors. Key aspects include importance of new technologies for obtaining actionable data for maintenance and management, owner’s perspectives on development of future technologies, current research progress and challenges faced by infrastructure industry in implementing new technologies are presented.

The objectives are to (1) quantify, map, and analyze vegetation cover distributions and changes across Accra, Ghana, for 2002 and 2010; and (2) examine the statistical relationship between vegetation cover and a housing quality index (HQI) for 2000 at the neighborhood level. Pixel-level vegetation cover maps derived using threshold classification of 2002 and 2010 QuickBird normalized difference vegetation index images have very high overall accuracies and yield an estimate of 5.9 percent vegetation cover reduction over the study area between 2002 and 2010. A high degree of variance in vegetation cover for individual dates is explained by HQI at the neighborhood level, although minimal covariability between absolute or relative vegetation cover change and HQI for 2000 was observed.

Roadway pavement surface distress information is critical for effective pavement asset management, and subsequently, transportation management agencies at all levels (i.e., federal, state, and local) dedicate a large amount of time and money to routinely evaluate pavement surface distress conditions as the core of their asset management programs. However, currently adopted ground-based evaluation methods for pavement surface conditions have many disadvantages, like being time-consuming and expensive. Aircraft-based evaluation methods, although getting more attention, have not been used for any operational evaluation programs yet because the acquired images lack the spatial resolution to resolve finer scale pavement surface distresses. Hyper-spatial resolution natural color aerial photography (HSR-AP) provides a potential method for collecting pavement surface distress information that can supplement or substitute for currently adopted evaluation methods. Using roadway pavement sections located in the State of New Mexico as an example, this research explored the utility of aerial triangulation (AT) technique and HSR-AP acquired from a low-altitude and low-cost small-unmanned aircraft system (S-UAS), in this case a tethered helium weather balloon, to permit characterization of detailed pavement surface distress conditions. The Wilcoxon Signed Rank test, Mann-Whitney U test, and visual comparison were used to compare detailed pavement surface distress rates measured from HSR-AP derived products (orthophotos and digital surface models generated from AT) with reference distress rates manually collected on the ground using standard protocols. The results reveal that S-UAS based hyper-spatial resolution imaging and AT techniques can provide detailed and reliable primary observations suitable for characterizing detailed pavement surface distress conditions comparable to the ground-based manual measurement, which lays the foundation for the future application of HSR-AP for automated detection and assessment of detailed pavement surface distress conditions.

The rapid and accurate assessment of above ground biomass (AGB) of woody vegetation is a critical component of climate mitigation strategies, land management practices and process-based models of ecosystem function. This is especially true of semi-arid ecosystems, where the high variability in precipitation and disturbance regimes can have dramatic impacts on the global carbon budget by rapidly transitioning AGB between live and dead pools. Measuring regional AGB requires scaling ground-based measurements using remote sensing, an inherently challenging task in the sparsely-vegetated, spatially-heterogeneous landscapes characteristic of semi-arid regions. Here, we test the ability of canopy segmentation and statistic generation based on aerial LiDAR (light detection and ranging)-derived 3D point clouds to derive AGB in clumps of vegetation in a juniper savanna in central New Mexico. We show that single crown segmentation, often an error-prone and challenging task, is not required to produce accurate estimates of AGB. We leveraged the relationship between the volume of the segmented vegetation clumps and the equivalent stem diameter of the corresponding trees (R2 = 0.83, p < 0.001) to drive the allometry for J. monosperma on a per segment basis. Further, we showed that making use of the full 3D point cloud from LiDAR for the generation of canopy object statistics improved that relationship by including canopy segment point density as a covariate (R2 = 0.91). This work suggests the potential for LiDAR-derived estimates of AGB in spatially-heterogeneous and highly-clumped ecosystems.

West Africa has a rapidly growing population, an increasing fraction of which lives in urban informal settlements characterized by inadequate infrastructure and relatively high health risks. Little is known, however, about the spatial or health characteristics of cities in this region or about the spatial inequalities in health within them. In this article we show how we have been creating a data-rich field laboratory in Accra, Ghana, to connect the dots between health, poverty, and place in a large city in West Africa. Our overarching goal is to test the hypothesis that satellite imagery, in combination with census and limited survey data, such as that found in demographic and health surveys (DHSs), can provide clues to the spatial distribution of health inequalities in cities where fewer data exist than those we have collected for Accra. To this end, we have created the first digital boundary file of the city, obtained high spatial resolution satellite imagery for two dates, collected data from a longitudinal panel of 3,200 women spatially distributed throughout Accra, and obtained microlevel data from the census. We have also acquired water, sewerage, and elevation layers and then coupled all of these data with extensive field research on the neighborhood structure of Accra. We show that the proportional abundance of vegetation in a neighborhood serves as a key indicator of local levels of health and well-being and that local perceptions of health risk are not always consistent with objective measures.

The effect of using spectral transform images as input data on segmentation quality and its potential effect on products generated by object-based image analysis are explored in the context of land cover classification in Accra, Ghana. Five image data transformations are compared to untransformed spectral bands in terms of their effect on segmentation quality and final product accuracy. The relationship between segmentation quality and product accuracy is also briefly explored. Results suggest that input data transformations can aid in the delineation of landscape objects by image segmentation, but the effect is idiosyncratic to the transformation and object of interest.