Ground Visibility Research Articles

How Does Visibility Count? An Open Data‐Driven Approach to Compare the Use of Ground Visibility in Archaeological Field‐Walking Surveys in the Mediterranean Region

ABSTRACTThere is a common agreement among archaeologists that assessing visibility in the field is essential to measure the accuracy of their observations. Archaeologists widely expect that low visibility negatively impacts the recovery rate of artefacts and sites during field‐walking surveys. However, they hold fundamentally divergent opinions on using recorded visibility values and on whether or how to weight the results. In this paper, I undertake a review and comparison of ground visibility assessments from three archaeological field‐walking surveys conducted in the eastern Mediterranean, all of which have published their data. Capitalizing on the availability of open data, I recode and analyse the algorithms employed in these surveys. The results highlight the impacts of weighting techniques, and I compare the maps produced with and without weighting. In all cases, the corrections do not substantially change the interpretations of the results at the scale of site identification. As such, this data‐driven experiment contributes to the ongoing debate on how to compare effectively and integrate data from various survey projects to study regional trends.

An Enhanced Collaborative Localization Method Based on Belief Propagation Aided by 3D Terrain Modelling

Navigation system performance degrades significantly in complex environments. It is important to analyze satellite visibility through 3D terrain modelling and separate the satellite signals propagated by NLOS to suppress the NLOS error. However, the traditional 3D terrain modelling visibility analysis method based on the pure terrain cover angle is only suitable for determining the visibility of GNSS satellites and may incorrectly separate LOS propagate measurement signals from members with low relative ranges and elevation angles under air–ground swarm conditions. To this end, this paper proposes a belief-propagating cooperative navigation method based on air–ground visibility analysis, which avoids mistakenly separating close-range LOS cooperative navigation signals by simultaneously considering the distances, elevation angles, and azimuths of the signal sources relative to the air–ground swarm members. The simulation shows that the cooperative navigation NLOS identification method based on air–ground visibility analysis proposed in this paper can more accurately realize the separation of NLOS signals under cooperative conditions than the traditional pure angular 3D terrain modelling visibility analysis method can, and the localization error of the members to be assisted is significantly reduced.

Ground visibility prediction using tree-based and random-forest machine learning algorithm: Comparative study based on atmospheric pollution and atmospheric boundary layer data

To mitigate haze impacts, three visibility simulation schemes were designed using decision tree and random forest algorithms, leveraging atmospheric boundary layer meteorological data, pollutant concentrations, and ground observations. The optimal approach was identified to investigate the boundary layer's effect on simulations. The results showed that the simulation effect of the random forest algorithm for two haze processes was better than that of the decision tree algorithm. In the first haze process, the random forest algorithm had a more significant reduction in root mean square error than the decision tree algorithm in the same visibility range (Scheme 3, visibility<200 m, mean absolute error reduced by 5.9%, root mean square error reduced by 19.1%). Simulation models significantly improved the accuracy of the models by adding atmospheric boundary layer observation data to the two fog-hazes process visibility. However, the addition of atmospheric boundary layer meteorological data in the first haze process had a better improvement effect (random forest: visibility<200 m, mean absolute errors of 25.0 (relative error<12.5%) and 25.5 m (relative error<12.8%) in Scheme 2 and 3, respectively). The addition of atmospheric boundary-layer pollutant concentrations data was more effective in the second haze process (random forest: visibility<200 m, scheme 2 and scheme 3 had mean absolute errors of 25.6 (relative error<12.8%) and 11.1 m (relative error<5.6%), respectively). The influence of atmospheric boundary layer meteorological data and pollutant data on the two fog processes is affected by the cause of the fog process.

A Hybrid Genetic Algorithm for Ground Station Scheduling Problems

In recent years, the substantial growth in satellite data transmission tasks and volume, coupled with the limited availability of ground station hardware resources, has exacerbated conflicts among missions and rendered traditional scheduling algorithms inadequate. To address this challenge, this paper introduces an improved tabu genetic hybrid algorithm (ITGA) integrated with heuristic rules for the first time. Firstly, a constraint satisfaction model for satellite data transmission tasks is established, considering multiple factors such as task execution windows, satellite–ground visibility, and ground station capabilities. Leveraging heuristic rules, an initial population of high-fitness chromosomes is selected for iterative refinement. Secondly, the proposed hybrid algorithm iteratively evolves this population towards optimal solutions. Finally, the scheduling plan with the highest fitness value is selected as the best strategy. Comparative simulation experimental results demonstrate that, across four distinct scenarios, our algorithm achieves improvements in the average task success rate ranging from 1.5% to 19.8% compared to alternative methods. Moreover, it reduces the average algorithm execution time by 0.5 s to 28.46 s and enhances algorithm stability by 0.8% to 27.7%. This research contributes a novel approach to the efficient scheduling of satellite data transmission tasks.

Simulation of Ground Visibility Based on Atmospheric Boundary Layer Data Using K-Nearest Neighbors and Ensemble Model Algorithms

Simulation of Ground Visibility Based on Atmospheric Boundary Layer Data Using K-Nearest Neighbors and Ensemble Model Algorithms

Estimating ground surface visibility on thermal images from drone wildlife surveys in forests

A thermal drone – an unmanned aerial vehicle equipped with a thermal camera – is a new tool in wildlife research. It enables effective surveys of animal species difficult to monitor with conventional methods. Over open terrain the detection of warm-blooded large animals with a thermal drone is nearly certain. However, in forest surveys, individuals hidden under the tree canopy may remain undetected which can lead to biased estimates of population size. Thus, it is important to estimate the percentage of surveyed area not obscured by tree canopy where animals can be easily detected. This figure may be used as a simple correction factor for animal counts from thermal images (thermograms) or as one of the covariates in more advanced statistical models of population density. We tested four methods of automatic image segmentation: 1) Otsu global thresholding, 2) local thresholding with Gaussian kernel, 3) Chan-Vese active contour model without edges, and 4) logistic regression and assessed each of their performances in differentiating between canopy and forest floor on a sample of 100 thermograms acquired with a drone during wildlife surveys in a range of forest habitats. We used segmentation results to calculate the estimated percentage of visible ground surface on images and compared it to the percentages assessed by manual classification. The Otsu global thresholding gave results that were in best agreement with the manual classification, had the fastest computing time and was superior to the other three segmentation methods. Visual inspection of the Otsu segmentation results showed that this method accurately separates tree canopy and ground surface on thermograms obtained across all forest types as well as in partially open areas. However, landscape features such as roads or water bodies that have a temperature similar to that of trees may cause segmentation errors and lead to underestimation of the ground visibility. While the use of Otsu segmentation to automatically assess percentage of visible ground surface on thermal images from drone surveys has certain limitations, the method is fast and can be readily applied by wildlife researchers. This approach can aid in correcting bias in estimates of animal population density and contribute to a better understanding of the use of thermal drones for wildlife surveys in forests.

Retrieval of aerosol single scattering albedo using joint satellite and surface visibility measurements

Aerosol single scattering albedo (SSA) measuring the ratio of scattering to extinction is a critical parameter in determining aerosol radiative effect. However, the retrieval of SSA from satellite platforms is limited because most existing passive satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) only provide the measurements of reflected solar radiation at the top of the atmosphere (TOA), which are only capable of retrieving aerosol optical depth (AOD) on the basis of assumed SSA. In this study, we develop a machine learning based algorithm to retrieve SSA using joint surface visibility and satellite radiation measurements. With ancillary information such as meteorological condition, surface visibility can be regarded as a proxy of column AOD. By combining MODIS measured TOA apparent reflectance with ground visibility as well as other auxiliary parameters, we retrieve SSA at over 6000 visibility stations worldwide. The validation results during the period from 2010 to 2019 show high consistency with AERONET retrieved SSA, with daily, monthly, and seasonal retrievals all exhibiting high correlations of 0.68, 0.96, and 0.97, respectively. Daily, monthly, and seasonal RMSE values are 0.044, 0.012, and 0.011, respectively. The mean absolute bias (MAB) of daily retrievals is 0.031 and 62% of the samples fall within the uncertainty interval of ±0.03. Monthly and seasonal MAB values decrease to below 0.01. Over 97% of the monthly and seasonal retrievals are within the error envelope of ±0.03. Our work generates a global aerosol SSA dataset with extensive coverage over land, which can be used for validating and improving climate models and the estimation of aerosol radiative forcing.

Detecting dust loads in the atmosphere over Thar desert by using MODIS and INSAT-3D data

• Dust detection algorithm based on MODIS and INSAT-3D were developed for Thar desert. • Brightness temperature difference and dust indices were used to detect dust load. • Integrated dust detection approach using MODIS and INSAT-3D was complementary. • Dust load detection over Indian Thar desert was successfully validated and tested. Suspended dust particles in atmosphere have adverse impacts on environment, ecosystem as well as on human health. To avoid negative impacts of dust storm events, early warning system to predict it well in advance may be a suitable option. However, for this purpose, assessment on magnitude of dust load and its dynamics in atmosphere is a primary requirement. The present study aims to develop remote sensing based assessment of dust load in atmosphere specifically over the Indian Thar Desert region. The severe dust storm event occurred on 5 th June 2017 over the Indian Thar Desert has been used in this study to develop integrated dust detection algorithm using split window technique, mid-infrared technique and different dust indices derived from MODIS and INSAT-3D data. Evaluation of the developed algorithm revealed that the area classified under dust load depends on threshold value of dust indices used in the algorithm, type of dust detection techniques followed and the specifications of remote sensing sensors used to retrieve the dust image. The integrated dust detection algorithm developed in this study has the capability to eliminate the problem in variations of predicted dust loadings in atmosphere. Validation of the developed algorithm to detect dust pixels showed good agreement with independent observations on aerosol optical depth (AOD), wind speed profile data and ground visibility data. The method adopted can be helpful to implement an operational system for detection and monitoring of dust storms over the Thar Desert region.

Developing a long-term high-resolution winter fog climatology over south Asia using satellite observations from 2002 to 2020

The vast Indo-Gangetic Plains (IGP) south of the Himalaya are subject to dense fog every year during winter months (December–January), severely disrupting rail, air and public transport of millions of people living in northern India, Pakistan, Nepal and Bangladesh. Air pollution combined with high moisture availability in the shallow boundary layer, are important factors affecting the persistence and widespread nature of fog over the IGP. Despite the environmental significance and impacts on the public at-large, an in depth understanding of the long-term spatial-temporal distribution of the south Asian fog, is presently not available in the literature. We utilize infrared remote sensing techniques to develop a high-resolution (≈1 km × 1 km) fog/low-stratus detection climatology over the past two decades (2002−2020), using Aqua/MODIS satellite observations. A dynamic brightness temperature difference threshold (involving 3.96 μm and 11.03 μm bands) for nighttime fog/low-stratus detection is constructed based on systematic radiative transfer simulations involving cloud effective radius, cloud top height, liquid water path and satellite viewing geometry. Our satellite-based fog/low-stratus detection is consistent with theoretical simulations of fog characterization and is also found to be well-correlated with near-surface visibility observations of dense fog (r = 0.89, p-value << 0.01). We also provide satellite-derived nighttime estimates of fog/low-cloud effective radius that are comparable to the operational daytime MODIS cloud data product and limited in situ observations. In terms of fog/low-stratus frequency, the IGP is relatively uniformly covered by widespread fog/low-stratus occurrences with the largest frequency found in the low-lying Terai region, bordering India and Nepal, which is also consistently observed in our daytime fog/low-stratus detection results over the last two decades. Additionally, the interannual variations in fog/low-stratus occurrences track closely with that of relative humidity in the IGP, which is associated with shallow boundary layer conditions during winter-time favoring the formation and persistence of fog. Overall, these long-term satellite-derived results present new high-resolution data and insights into the dense and often intense winter fog occurrences which routinely engulf the entire stretch of the Indo-Gangetic Plains and cause significant degradation to ground visibility in one of the world's most densely populated regions.

Scratching the Surface: Integrating Low-Visibility Zones and Large Rural Sites in Landscape Archaeology Using Point Sampling

ABSTRACT Whereas archaeological field survey is relatively fast and effective for the mapping of surface finds in Mediterranean landscapes, two challenges limit its potential for reconstructing ancient settlement patterns. First, field survey usually excludes portions of the landscape that are inaccessible or present low ground visibility due to vegetation or the terrain, and second, including large settlements sites poses logistical problems, as these tend to produce unmanageably high frequencies of finds. In this paper, we explore the potential value of point sampling for integrating these areas in standard transect field survey projects. For this experiment, we sampled a large rural Archaic–Roman period site and its off-site environs in Molise, central-southern Italy. We present a systematic statistical and spatial comparison between data derived from both methods on the same areas. As such, the experiment contributes to the current debate on how to compare and integrate data from survey projects that apply different techniques.

Ecological and methodological drivers of persistence and detection of bird fatalities at power lines: Insights from multi-project monitoring data

Environmental impact assessment (EIA) follow-up monitoring in energy projects, such as wind farms, solar facilities and power lines, frequently encompasses bird fatality surveys based on regular carcass searches. Since a large number of bird carcasses are often not found, field experiments to determine carcasses persistence (CP) and searcher efficiency (SE) biases in fatality surveys are typically performed for every single project. These data are, however, rarely used beyond the scope of each original project. We compiled data from CP and SE trials performed under 36 independent monitoring programs aiming to assess bird mortality at transmission lines in Portugal, whose results were until now unavailable or dispersed in grey literature. We used survival analysis and generalized linear mixed-effects models to investigate the ecological and methodological factors influencing CP times and SE rates. Bird carcass size was a key driving factor of both CP times and SE rates of human observers, which were consistently out-performed by scent detection dogs. Season and habitat interactions had also an important role in CP patterns, while variations in SE rates by human observers were largely influenced by ground visibility (i.e., a combination of ground cover and vegetation height). Our results reinforce previous studies indicating that CP and SE biases are site-specific and determined by a wide range of ecological and methodological factors not always accounted in standard trial designs. Overall, our study demonstrates that data routinely collected under bird monitoring programs from multiple projects can be combined to identify broad ecological patterns, limitations of current studies and, ultimately, improve EIA follow-up practice.

Ground Visibility in Satellite Optical Time Series Based on A Contrario Local Image Matching

Assessing ground visibility is a crucial step in automatic satellite image analysis. Some Earth observation satellites are provided with spectral bands specially designed for cloud detection. An alternative approach is to detect ground visibility by comparing locally the images in a temporal series: matching regions are necessarily cloud free. Indeed, the ground has persistent patterns that can be observed repetitively in the time series, while clouds change shape constantly. We describe here a ground visibility detection algorithm based on an a contrario local image matching method, coupled with an efficient greedy algorithm.

Dynamic Lidar Ratio Calculation and Aerosol Vertical Extinction Coefficient Retrieval Based on Observed Visibility

ABSTRACT Micropulse lidar (MPL) cannot directly retrieve the aerosol extinction coefficient under cloudy conditions and at night. Therefore, we used ground visibility, Fernald’s near-end solution method, and the linear correlation between the near-end lidar signal (photons) and ground aerosol extinction coefficient (correlation coefficient = 0.98), to calculate the lidar constant and lidar ratio (LR). We compared the aerosol optical depth (AOD) retrieved from MPL and the AOD retrieved from the multifilter rotating shadowband radiometer (MFRSR-7) at the same band (532 nm). The correlation coefficient was 0.77. The vertical distribution of aerosols in daytime and nighttime during summer was obtained from lidar in July at 00:00 and 12:00 Beijing time (UTC+8). In daytime, under clear sky conditions, the distribution displayed a unimodal and peak at approximately 2000 m. The distribution at night was more complicated than that in the day, with three results. The first was monotonically decreasing from ground to upper layer, with a peak at 600 m and two peaks at approximately 1200 m. In general, the aerosol extinction coefficient at nighttime is higher than that at daytime below 1200 m. The near-ground extinction coefficient at night is higher than in the day.

UAV-Based Terrain Modeling under Vegetation in the Chinese Loess Plateau: A Deep Learning and Terrain Correction Ensemble Framework

Accurate topographic mapping is a critical task for various environmental applications because elevation affects hydrodynamics and vegetation distributions. UAV photogrammetry is popular in terrain modelling because of its lower cost compared to laser scanning. However, this method is restricted in vegetation area with a complex terrain, due to reduced ground visibility and lack of robust and automatic filtering algorithms. To solve this problem, this work proposed an ensemble method of deep learning and terrain correction. First, image matching point cloud was generated by UAV photogrammetry. Second, vegetation points were identified based on U-net deep learning network. After that, ground elevation was corrected by estimating vegetation height to generate the digital terrain model (DTM). Two scenarios, namely, discrete and continuous vegetation areas were considered. The vegetation points in the discrete area were directly removed and then interpolated, and terrain correction was applied for the points in the continuous areas. Case studies were conducted in three different landforms in the loess plateau of China, and accuracy assessment indicated that the overall accuracy of vegetation detection was 95.0%, and the MSE (Mean Square Error) of final DTM (Digital Terrain Model) was 0.024 m.

TEMPORAL REPETITION DETECTION FOR GROUND VISIBILITY ASSESSMENT

Abstract. Assessing ground visibility is a crucial step in automatic satellite image analysis. Nevertheless, several recent Earth observation satellite constellations lack specially designed spectral bands and use a frame camera, precluding spectrum-based and parallax-based cloud detection methods. An alternative approach is to detect the parts of each image where the ground is visible. This can be done by comparing locally pairs of registered images in a temporal series: matching regions are necessarily cloud free. Indeed, the ground has persistent patterns that can be observed repetitively in the time series while the appearance of clouds changes at each date. To detect reliably the “visible” ground, we propose here an a contrario local image matching method coupled with an efficient greedy algorithm.

Multi-sensor comparison of aerosol optical properties profiles observed during a pollution episode in a mega city of the North China Plain

A fine aerosol particle pollution episode happened in mega city Beijing during February 25th – March 5th. Aerosol extinction coefficient profiles were observed at 7 L idar s ta ti ons. Different aerosol optical property instruments were used to verify Lidar retrievals. Ground visibility observations were used to verify near ground Lidar observations. High correlation coefficient was found (0.8) between visiometer and Lidar observations. Lidar’s Column integrated aerosol extinction coefficient was also verified by Himawari-8 aerosol optical depth (AOD) data. The correlation coefficient between two AOD retrievals was 0.4. And the mean bias was -0.17. CALIPSO aerosol property profiles was also used to verify Lidar retrievals. Lidar retrievals was inconsistent with CALIPSO in the boundary layer. Overall, the Lidar retrievals are consistent with other optical property sensors. And the retrials could be used for observation research and model validation.

Cultural Resources Survey of the Proposed Moore Park Athletic Complex, Plano, Collin County, Texas

In March 2020, an intensive cultural resources survey of the proposed Moore Park athletic complex in Plano, Collin County, Texas was completed in order to inventory all cultural resources. The project parcel currently lies within an open field at the southwest corner of the intersection of Chaparral Road and Cottonwood Creek. It is bounded by Chaparral Road on the north, Cottonwood Creek to the east, Bright Star Way on the south, and Cloverhaven Way on the west in a densely residential developed area. A North Texas Municipal Water District building is located in the far northwestern corner near Cottonwood Creek. The archeological area of potential effects (APE) covers an area of approximately 103 acres (42 hectares). Because the project is owned and funded by the City of Plano, a political subdivision of the State of Texas, the project is subject to the Antiquities Code of Texas (9 Texas Natural Resources Code [TNRC] 191), which requires consideration of effects on properties designated as—or eligible to be designated as—State Antiquity Landmarks (SALs), which includes archeological resources. The survey was carried out for the City of Plano, Parks and Recreation under Texas Antiquities Permit 9334 by Brett Lang (Project Archeologist) of Cox|McLain Environmental Consulting, Inc. (CMEC). Melissa M. Green was the Principal Investigator. The parcel is situated on an upper terrace above Cottonwood Creek. From the western boundary, an unnamed tributary of Cottonwood Creek flows from the northwest corner to the southwest corner of the project parcel (as the terrace slopes southeastward) into Cottonwood Creek approximately 860 meters or 2821.5 feet away. Cottonwood Creek parallels the eastern boundary of the APE. Ground surfaces within the project area parcel were mostly covered in short, ankle-high prairie grasses used for active cattle grazing allowing for some limited visibility ranging from 20 to 50 percent. A densely wooded section was observed along the eastern boundary and along part of the tributary, allowing for 30 to 80 percent ground visibility. In all, 19 shovel test units were excavated judgmentally across the project area, of which none contained cultural materials. All materials (notes, photographs, administrative documents, and other project data) generated from this work will be housed at the Center for Archeological Studies at Texas State University at San Marcos, where they will be made permanently available to future researchers per 13 Texas Administrative Code 26.16-17. If any unanticipated cultural materials or deposits are found at any stage of clearing, preparation, or construction, the work should cease and Texas Historical Commission personnel should be notified immediately. The Texas Historical Commission concurred with the findings and recommendations of this report on 8 May 2020.

A comparison of automated object extraction methods for mound and shell-ring identification in coastal South Carolina

One persistent archaeological challenge is the generation of systematic documentation for the extant archaeological record at the scale of landscapes. Often our information for landscapes is the result of haphazard and patchy surveys that stem from opportunistic and historic efforts. Consequently, overall knowledge of some regions is the product of ad hoc survey area delineation, degree of accessibility, effective ground visibility, and the fraction of areas that have survived destruction from development. These factors subsequently contribute unknown biases to our understanding of chronology, settlements patterns, interaction, and exchange. Aerial remote sensing offers one potential solution for improving our knowledge of landscapes. With sensors that include LiDAR, remote sensing can identify archaeological features that are otherwise obscured by vegetation. Object-based image analyses (OBIA) of remote sensing data hold particular promise to facilitate regional analyses thorough the automation of archaeological feature recognition. Here, we explore four OBIA algorithms for artificial mound feature detection using LiDAR from Beaufort County, South Carolina: multiresolution segmentation, inverse depression analysis, template matching, and a newly designed algorithm that combines elements of segmentation and template matching. While no single algorithm proved to be consistently superior to the others, a combination of methods is shown to be the most effective for detecting archaeological features.

Weather Forecasting Using Merged Long Short-term Memory Model

Over decades, weather forecasting has attracted researchers from worldwide communities due to itssignificant effect to global human life ranging from agriculture, air trafic control to public security. Although formal study on weather forecasting has been started since 19th century, research attention to weather forecasting tasks increased significantly after weather big data are widely available. This paper proposed merged-Long Short-term Memory for forecasting ground visibility at the airpot using timeseries of predictor variable combined with another variable as moderating variable. The proposed models were tested using weather timeseries data at Hang Nadim Airport, Batam. The experiment results showedthe best average accuracy for forecasting visibility using merged Long Short-term Memory model and temperature and dew point as a moderating variable was (88.6%); whilst, using basic Long Short-term Memory without moderating variablewasonly (83.8%) respectively (increased by 4.8%).

Assessment of MODIS, OMI, MISR and CALIOP Aerosol Products for Estimating Surface Visual Range: A Mathematical Model for Hong Kong

Estimation of atmospheric visibility (VR) using ground and satellite sensors is ineffective under Hong Kong’s complex atmosphere and climate. Therefore, the relationship between columnar Aerosol Optical Depth (AOD) from four space-borne sensors (OMI, MODIS, MISR and CALIOP) and Bext from two visibility-recording stations was evaluated, to recommend an effective satellite-based method and spatial resolution, for estimation of VR over Hong Kong. Since most column-integrated aerosol particle extinction occurs within a mixing layer height (MLH) of 1–3 km, column-based AOD from satellites is expected to give a good indication of surface-level conditions, especially when MLH is a known input. The AOD from both MODIS and MISR showed high correlations with Bext; therefore, both were subjected to rigorous statistical analysis along with climatic data to simulate visibility. The best estimate of ground visibility was obtained from MODIS AOD combined with surface-level climatic data, and this explained 84% of the variance in VR, with a low distance error of 0.27 km. Results suggest that the water vapor mixing ratio (Q) alone can explain the combined effect of Atmospheric Pressure (P), Temperature (T) and Relative Humidity (RH) on VR, and that the advection term (VT) alone is sufficient to explain the effects of T, WS and WD on dispersion of aerosols, and hence on VR.