Unmanned Aerial Vehicle Observations Research Articles

Evapotranspiration partitioning by integrating eddy covariance, micro-lysimeter and unmanned aerial vehicle observations: A case study in the North China Plain

Partitioning of evapotranspiration (ET) into soil evaporation (E) and plant transpiration (T) poses a significant challenge. This study proposed a novel approach that combines eddy covariance, micro-lysimeter, and high-resolution unmanned aerial vehicle (UAV) images within the flux footprint to optimize the canopy and soil resistances of the Shuttleworth-Wallace model (S-W) using a multi-objective optimization scheme. A two-year experiment was conducted at the Yucheng and Luancheng sites to investigate the effectiveness of the proposed method for the summer maize-wheat winter system in the North China Plain. Our results showed that the S-W model with multi-objective optimization by using high-resolution UAV images within the flux footprint significantly improved the estimation accuracy of ET components compared with the traditional one- and two-objective optimization schemes. The estimation of ET, T, and E of our method had an average root mean square error of 0.67 mm day−1, 0.66 mm day−1, and 0.28 mm day−1, respectively. The optimized S-W model using high-resolution UAV data within the flux footprint produced better ET partitioning than that optimized using Moderate Resolution Imaging Spectroradiometer (MODIS) data. We then applied the proposed method to estimate ET and ET components in the North China Plain. This study highlighted the importance of utilizing multi-source data (especially high-resolution UAV images) and multi-objective optimization for accurate ET partitioning.

Double Inversion Layers Affect Fog–Haze Events over Eastern China—Based on Unmanned Aerial Vehicles Observation

The goal of this work is to analyze how double inversion layers affect fog–haze events and boundary layers over Eastern China based on unmanned aerial vehicle observations. During 10–12 December 2020, two successive fog–haze events occurred in Eastern China. Based on the unmanned aerial vehicle (UAV), wind, temperature, relative humidity (RH), and aerosol mass concentration were monitored simultaneously in Lianyungang, China. Several observations were found after analyzing the physical fields of these data. The results show that the concentrations of air pollutants during fog events were negatively correlated with horizontal visibilities. The mass concentrations of particulate matter increased rapidly (After 08:00 BJT 11) in the fog dissipation stages (PM2.5 and PM10 increased from 97 and 150 μg/m3 to 213 and 300 μg/m3, respectively). Double temperature inversion significantly affected fog events, where the enhancement of the lower-level temperature inversion (where the inversion layer top height was between 150 and 300 m) corresponded to the explosive growth of fog and the dissipation of the upper-level inversion layer. (The bottom height of the inversion layer was between 350 m and 600 m) This indicates the end of fog events. Fog layer thicknesses are negatively correlated with the concentrations of air pollutants in the near-surface layer. The thickening of the inversion layer near the surface corresponds to an increase (after 05:00 BJT 11 and 03:00 BJT 12) in fog layer height. The fog event is maintained when water vapor conditions are favorable enough or when there is a deep near-surface temperature inversion but the maintenance of the near-surface temperature inversion at an intensity of above 2 °C (100 m)−1 mainly contributes to the durations of these two fog events. Calculations through the trajectory model reveal that the air pollutant source varied during these two events, which was the main reason for the difference in air pollutant concentrations between the two events.

Influence of coastal planetary boundary layer on PM2.5 with unmanned aerial vehicle observation

Field measurement with an Unmanned Aerial Vehicle (UAV) platform was conducted at coastal area of Bohai in winter and summer. 135 flights were obtained and characteristics of boundary layer structure and its influence on PM2.5 were studied. The averaged profiles showed that meteorological parameters and PM2.5 changed significantly below 300 m. Weak suspension inversion existed between 30–300 m in the morning, ground inversion existed below 70 m at night in winter, temperature inversion was only observed below 200 m at night in summer. PM2.5 peaked at 300 m, 100 m and 200 m during morning, daytime and night period, respectively. During the middle and later stages of winter pollution episode, southwest and southeast wind, northwest and northeast wind formed obvious convergence at coastal area in turn, resulted in maximum PM2.5 concentration of 160 μg m−3 and 218 μg m−3 at night and morning, respectively. Isothermal layer was observed below 200 m, the maximum wind speed and high PM2.5 concentration all appeared at 200 m at night. Special stratification between 50–100 m was observed at 11:00, where wind speed decreased and high PM2.5 concentration maintained. During the summer pollution episode, the sea breeze formed at 13:00 and ended at 22:00, sea breeze extend westward 50 km to the central urban area at 16:00. Thermal internal boundary layer formed below 100 m at 15:00 and extended upward to 150 m at 17:00, weak temperature inversion layer was observed between 50 m ∼ 100 m, the maximum wind speed was 8 m s−1, the wind direction changed from southeast to south, and maximum PM2.5 concentrations of 160 μg m−3 were observed at 100 m.

Towards evaluation of the PID criteria based UAVs observation and tracking head within resizable selection by COA algorithm

The study of unmanned systems has garnered significant academic attention worldwide. The advancements in tracking technologies, particularly the unmanned aerial vehicle (UAV) developed by the US Air Force, have motivated scientists to extensively explore this field due to the abundance of available aerial resources. However, the UAV’s effectiveness in motion is influenced by its three fundamental motions: pitch, roll, and yaw. To address this, scientists have been developing optimization algorithms and models to enhance the UAV’s performance. Nonetheless, in practical scenarios, each motion requires specific measurements, and their relative importance varies. This presents several challenges, including the existence of multiple criteria for selecting optimization values, determining the criteria’s significance, and evaluating trade-offs between performance across different optimization cases. Consequently, the selection and evaluation of UAV motion control algorithms becomes complex. This study proposes a novel selection process that employs the cuckoo optimization algorithm (COA) and real measured resizable margins for goal detection instead of traditional fixed-size evaluation criteria. The framework consists of enhancing PID motion gains using the COA algorithm, focusing on altitude and attitude functional PID controllers. The results of the selection process demonstrate the significance of various criteria for different motions. Rigorous evaluation and analysis were conducted to validate the proposed research framework.

Impact of urban air pollutants on the night sky brightness and color in Hohhot

This study investigated the impact of urban air pollutants on the brightness and color of the night sky in Hohhot, China. The study used correlation analysis to determine the relationship between air pollutant concentrations and night sky brightness. Observations were conducted using a charge-coupled device(CCD) camera and an unmanned aerial vehicle (UAV) to assess changes in night sky brightness, color, and their spatial distribution before and after the aggravation of air pollution. The results revealed that particulate matter (PM) had the strongest correlation with night sky brightness among urban air pollutants, while the correlation between gaseous pollutants (except O3) and night sky brightness needs to be cautiously interpreted. The CCD camera observations showed that the night sky shifted toward red as the concentration of urban air pollutants increased. Meanwhile, the UAV observations demonstrated that the brightness of the night sky decreased with height above ground, and the area of night sky glow increased with higher urban air pollutant concentrations. Based on the research findings, controlling air pollutant emissions may have a positive impact on reducing the level and area of light pollution in the night sky, and thus promote sustainable urban environment development.

Topography reconstruction and evolution analysis of outlet glacier using data from unmanned aerial vehicles in Antarctica

Reconstruction of glacial topography is important for assessing the ice dynamics of glaciers in the past and understanding how they may respond to climate change in the future. As an emerging strategy, unmanned aerial vehicles (UAVs) have been successfully used in glaciology applications to reconstruct surface topography and monitor the short-term dynamics of glaciers. However, none of these studies have focused on the ice dynamics of outlet glaciers in Antarctica. In this study, based on a combination of UAVs and a base station, we investigated Dalk Glacier, a typical marine-terminating glacier in East Antarctica, during two Chinese National Antarctic Research Expeditions from 2019 to 2020. By applying structure-from-motion and multi-view-stereo photogrammetry, high-resolution orthomosaics and digital elevation models of the glacial topography were reconstructed with centimeter-level accuracy via in situ validation, thus marking the first application of UAV observations in the monitoring of Antarctic outlet glaciers. Topographic evolution of the glacier was quantitatively analyzed from various aspects including ice velocity, surface elevation, crevasses, and ice front calving. A maximum ice velocity of ∼ 310 m a-1 at its terminus was observed, which was ∼ 90 m a-1 greater than that in satellite-based studies. We analyzed in detail the spatially heterogeneous changes in the ice velocity and surface elevation of the glacier under the influence of an ice rumple at its calving terminus. Combined with satellite images and existing datasets, we hypothesize that the large ice rumples at the glacier terminus could deform the glacier and potentially damage its structural integrity, thereby limiting the growth of its terminus.

A Novel Inversion Approach for the Kernel-Driven BRDF Model for Heterogeneous Pixels

The bidirectional reflectance distribution function (BRDF) of the land surface contains information relating to its physical structure and composition. Accurate BRDF modeling for heterogeneous pixels is important for global ecosystem monitoring and radiation balance studies. However, the original kernel-driven models, which many operational BRDF/Albedo algorithms have adopted, do not explicitly consider the heterogeneity within heterogeneous pixels, which may result in large fitting residuals. In this paper, we attempted to improve the fitting ability of the kernel-driven models over heterogeneous pixels by changing the inversion approach and proposed a dynamic weighted least squares (DWLS) inversion approach. The performance of DWLS and the traditional ordinary least squares (OLS) inversion approach were compared using simulated data. We also evaluated its ability to reconstruct multiangle satellite observations and provide accurate BRDF using unmanned aerial vehicle observations. The results show that the developed DWLS approach improves the accuracy of modeled BRDF of heterogeneous pixels. The DWLS approach applied to satellite observations shows better performance than the OLS method in study regions and exhibits smaller mean fitting residuals both in the red and near-infrared bands. The DWLS approach also shows higher BRDF modeling accuracy than the OLS approach with unmanned aerial vehicle observations. These results indicate that the DWLS inversion approach can be a better choice when kernel-driven models are used for heterogeneous pixels.

Prediction of heading date, culm length, and biomass from canopy-height-related parameters derived from time-series UAV observations of rice.

Unmanned aerial vehicles (UAVs) are powerful tools for monitoring crops for high-throughput phenotyping. Time-series aerial photography of fields can record the whole process of crop growth. Canopy height (CH), which is vertical plant growth, has been used as an indicator for the evaluation of lodging tolerance and the prediction of biomass and yield. However, there have been few attempts to use UAV-derived time-series CH data for field testing of crop lines. Here we provide a novel framework for trait prediction using CH data in rice. We generated UAV-based digital surface models of crops to extract CH data of 30 Japanese rice cultivars in 2019, 2020, and 2021. CH-related parameters were calculated in a non-linear time-series model as an S-shaped plant growth curve. The maximum saturation CH value was the most important predictor for culm length. The time point at the maximum CH contributed to the prediction of days to heading, and was able to predict stem and leaf weight and aboveground weight, possibly reflecting the association of biomass with duration of vegetative growth. These results indicate that the CH-related parameters acquired by UAV can be useful as predictors of traits typically measured by hand.

Multitemporal UAV surveys of geomorphological changes caused by postfire heavy rain in Kamaishi city, northeast Japan

Unmanned aerial vehicles (UAVs) are widely utilized for surveying various sediment disasters, and the high-resolution imaging they provide reveals detailed topographic characteristics. However, the availability of multitemporal UAV observations, which reveal geomorphological changes before and after the disaster, is still limited. In this research, we performed multitemporal UAV observations for geomorphological changes before and after extreme postfire heavy rain in the Ozaki Peninsula wildfire area in Japan. Significant geomorphological changes were observed associated with eight landslides and stream shifting, with a spatial resolution of 0.30 cm obtained by structure from motion (SfM) processing. To extract the surface changes based on multitemporal UAV observations, in addition to elevation displacement, vertex normal vectors were calculated for each mesh. We found that the elevation displacement showed comprehensive alterations in the entire area and that the changes in vertex normal vectors effectively captured the shape of these alterations. Deterioration, deposition, and scarping were shown for landslides, and the dynamics of erosion and deposition in the cross-section were for stream shifting. In addition, the detected geomorphological changes were compared with the predisaster topographic characteristics. Landslides occurred with a catchment area of 500 to 2,000 m2 and a slope inclination of 30 to 40°, and stream shifting occurred with a catchment area of 10,000 m2 or more and a slope inclination of less than 20°. In both regions affected by landslides and stream shifting, the logarithmic stream power index (logSPI), a natural logarithmic representation of the conventional stream power index (SPI), could describe the characteristics roughly. A logSPI value of 4 to 5 represented landslides, and 5 and greater represented stream shifting, with the probability of landslides and stream shifting occurring in areas with these values exceeding 99.5% and 99.9%, respectively. Therefore, the logSPI was utilized to roughly classify the terrain and geomorphological condition and the points among the unburnt, unlogged-burnt, and logged-burnt areas were compared. Even when the logSPI was similar, the damage was much more severe in the burnt areas. On slopes with a logSPI of approximately 4, there were signs of erosion in the logged-burnt area, and the increase in fallen trees was significant in the unlogged-burnt area, while there were no substantial changes in the unburnt area. In valleys with a logSPI of 8 or greater, sediment and woody debris were significantly deposited in the burnt areas following the postfire rain, while there were few changes in the unburnt area.

Bayesian Modeling of Crowd Dynamics by Aggregating Multiresolution Observations From UAVs and UGVs

Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) can be jointly deployed to form a collaborative surveillance system, where UAVs collect low-resolution images at a high altitude to obtain a global perception and UGVs observe high-resolution images within a focused detection range. Such multiresolution heterogeneous observations create opportunities yet pose challenges to model the dynamics of the targeted crowds. Existing approaches that integrate multiresolution observations rely on intensive computation of a large volume of historical data, and thus, result in a lack of computational efficiency, accuracy, and robustness. To address these limitations, this article proposes a new crowd dynamics modeling approach to aggregating the multiresolution information under a Bayesian inference framework. Beta-binomial and Normal–Wishart conjugate distributions were adopted to model crowd dynamics from low-resolution UAV observations and high-resolution UGV observations, respectively. Based on the proposed approach, a real-time model updating mechanism is developed to implement information aggregation for onboard crowd dynamics inference, where high efficiency, accuracy, and robustness are critical. Numerical simulations and onboard experiments were conducted to demonstrate the effectiveness and efficiency of the proposed modeling approach.

Evaluation of traditional rain-fed agricultural terraces for soil erosion control through UAV observation in the middle mountain of Nepal

The livelihood of most people in the mountainous region of Nepal depends on traditional farming practices. Farmers modify their lands by constructing narrow outward sloping terraces to control soil erosion in the mountain and hill of Nepal. However, preliminary observation reveals that farmers adopt different strategies to manage outward sloping terraces according to the amount of rainfall so as to avoid the impact from soil erosion due to excessive rainfall. Thus, this study aims to evaluate the effectiveness of the traditional practice of topographic management of outward sloping rain-fed terraces to control soil erosion. The study makes a comparative analysis of topographic values of terraces and the estimated rates of soil erosion in different rainfall regions. Topographic values were derived from high spatial resolution (20 cm) Digital Elevation Model (DEM) data acquired by Unmanned Aerial Vehicle (UAV). Revised Universal Soil Loss Equation (RUSLE) was used to estimate the rate of soil erosion based on this high-resolution DEM together with other required variables. The research revealed that the traditional practice of topographic modification is highly effective to control soil erosion, as no significant differences in the rates of soil erosion were found even though the amount of rainfall considerably varies at different sites.

A case study of evaluating the GRAPES_Meso V5.0 forecasting performance utilizing observations from South China Sea Experiment 2020 of the “Petrel Project”

The evaluations of numerical weather prediction (NWP) models are pivotal for their development. However, there is a big challenge to carry out over oceans because of the rarity of in-situ observations. With a unique suite of marine observations from South China Sea Experiment 2020 of the “Petrel Project” that undertook a mission to observe the peripheral cloud system of Typhoon Sinlaku (2020), this study evaluates the performance of GRAPES_Meso v5.0 forecasts operated by the China Meteorological Administration. Compared with the observations by dropsondes from the unmanned aerial vehicles (UAV), nine forecast cycles all well forecast the atmospheric vertical profiles with a basically consistent bias structure over ocean, which reveals that the model has a roughly accurate and stable forecast performance. The temperature bias demonstrates a pattern of warm-cool-warm distribution from model bottom level to near 300 hPa, corresponding to a wet-dry-wet pattern in humidity fields with considerable relative errors. The positive (negative) temperature (humidity) biases at heights below 925 hPa are related to the underestimation of total hydrometeor content. The pressure field has been systematically overestimated with a maximum value of ∼20 hPa at 6 km height. The vertical velocities have been underestimated, yet, they are closer to the observation in its spatial distribution when the leading time is shorter. The forecasted sea level pressure and temperature at 2-m have consistent temporal variation with two buoy observations, with biases varying from −0.82 hPa to 1.38 hPa and − 1.74 °C to 2.1 °C, respectively. These results reveal that GRAPES_Meso V5.0 has stable forecast performance with relatively small biases except for humidity field. Although the conclusions may not be universal since they are only based on the evaluation for one single typhoon event, this study highlights the potential of UAVs observations in model evaluation, especially over oceans. More observational experiments conducted by UAVs at different weather events are expected to obtain more robust results for the performance of NWP models.

How can UAV bridge the gap between ground and satellite observations for quantifying the biomass of desert shrub community?

Accurate estimation of the shrub above-ground biomass (AGB) is an essential basis for determining carbon storage and monitoring desertification risk in arid ecosystems. However, significant uncertainties and biases exist in large-scale monitoring of desert AGB due to the scale mismatch and spatio-temporal topological errors between the coarse resolution of satellite images and the limited area that can be surveyed via ground measurements. The rapid development of unmanned aerial vehicle (UAV) technology has the prospective advantage of multi-scale vegetation observation. However, its potential to bridge the gap between satellites and the ground in desert AGB estimation has not yet been explored. This study developed a procedure to fill the gap between satellite and ground measurements based on low-cost and easy-to-use UAV visible-light technology in typical desert shrub communities in Inner Mongolia, China. First, canopy area (CA), canopy maximum height (CH), and canopy volume (CV) metrics derived from UAV-RGB (Red, Green, Blue) images, coupled with structure-from-motion photogrammetry, were used to invert the UAV-based AGB. Then, the UAV-based AGB data were aggregated to different scales to align with satellite data. Here, we focused on examining the performance of generalized additive models between the upscaled UAV-based AGB and vegetation indices (VIs) generated from PlanetScope (resolution: 3 m), Sentinel-2A MSI (resolution: 10 m, 20 m), and Landsat 8 OLI (resolution: 30 m). Finally, we investigated the effects of scale and spectra in the upscaling and modeling process. Results showed that the UAV-based AGB linear prediction models developed by the CV metric performed best for Reaumuria soongarica (R2 = 0.749, RMSE = 65.5 g) and Salsola passerina (R2 = 0.919, RMSE = 56.7 g), which allowed for accurately mapping the desert AGB at the individual plant level with ultra-high resolution (2 cm). The performance of satellite biomass models was excellent based on the upscaled UAV-based AGB and VIs (best-performing models for different satellites: adjusted R2 = 0.625–0.934, RMSE = 35.1–119.1 g/m2). As the resolution of satellite data increases, the increase in variance is a big scale-related challenge in AGB model performance evaluation. It is worth noting that the UAV-based AGB data are unmodifiable due to their area-independent nature, which can avoid the scale-related effect. Moreover, the satellite VIs most closely related to the desert AGB were associated with Red, NIR, and Red-Edge bands. Specifically, we recommend the Red-Edge band when using Sentinel-2A data. This study proves that UAV technology can not only provide exhaustive information on vegetation biomass but also can train and validate satellite data beyond individual UAV observations, which can significantly improve large-scale biomass monitoring if widely applied in the future.

Unveiling the complex canopy spatial structure of a Mediterranean old-growth beech (Fagus sylvatica L.) forest from UAV observations

In front of climate change scenarios and global loss of biodiversity, it is essential to monitor the structure of old-growth forests to study ecosystem status and dynamics to inform future conservation and restoration programmes. We propose an Unmanned Aerial Vehicle (UAV)-based framework to monitor fine-grained forest top canopy structure in a primary old-growth beech (Fagus sylvatica L.) forest in Pollino National Park, Italy, which belongs to the UNESCO World Heritage (UNESCO WH) serial site “Ancient and Primeval beech forests of the Carpathians and other regions of Europe”. Canopy profile, gap properties and their spatial distribution patterns were analysed using the canopy height model (CHM) derived from UAV surveys. Very high-resolution orthomosaic images coupled with direct field measurement data were used to assess gap detection accuracy and CHM validation. Forest canopy properties along with the vertical layering of the canopy were further explored using second-order statistics. The reconstructed canopy profile revealed a bimodal top height frequency distribution. The upper canopy layer (h > 14 m) was the most represented canopy height, with the remaining 50% split between the medium and lowest layer; 551 gaps were identified within 11.5 ha. Gap size varied between 2 m2 and 353 m2, and 19 m2was the mean gap size; the gap size-frequency relationship reflected a power-law probability distribution. About 97 % of the gaps were <100 m2 in size, showing a significant tendency to cluster. Most gaps were located in the upper and medium canopy layers; however, the highest relative gap area was found in the lowest layer. These results confirmed the high natural integrity of the ecosystem processes that distinguish the old-growth beech stands in respect to managed woodlands. Our findings demonstrate that the low-cost UAV-DAP (Digital Aerial Photogrammetry) workflow has the potential to generate realistic old-growth forest canopy attributes at a very fine scale. The proposed protocol can be adopted for monitoring the structural dynamics of high-value natural forest ecosystems as in the case of UNESCO WH sites or other old-growth stands. This approach is also helpful for mapping and deriving spatially explicit canopy structure information over confined forest areas and determining where conservation actions should be directed to preserve or restore natural ecosystem function.

The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing

Unmanned aerial vehicle (UAV)-based multispectral remote sensing effectively monitors agro-ecosystem functioning and predicts crop yield. However, the timing of the remote sensing field campaigns can profoundly impact the accuracy of yield predictions. Little is known on the effects of phenological phases on skills of high-frequency sensing observations used to predict maize yield. It is also unclear how much improvement can be gained using multi-temporal compared to mono-temporal data. We used a systematic scheme to address those gaps employing UAV multispectral observations at nine development stages of maize (from second-leaf to maturity). Next, the spectral and texture indices calculated from the mono-temporal and multi-temporal UAV images were fed into the Random Forest model for yield prediction. Our results indicated that multi-temporal UAV data could remarkably enhance the yield prediction accuracy compared with mono-temporal UAV data (R2 increased by 8.1% and RMSE decreased by 27.4%). For single temporal UAV observation, the fourteenth-leaf stage was the earliest suitable time and the milking stage was the optimal observing time to estimate grain yield. For multi-temporal UAV data, the combination of tasseling, silking, milking, and dough stages exhibited the highest yield prediction accuracy (R2 = 0.93, RMSE = 0.77 t·ha−1). Furthermore, we found that the Normalized Difference Red Edge Index (NDRE), Green Normalized Difference Vegetation Index (GNDVI), and dissimilarity of the near-infrared image at milking stage were the most promising feature variables for maize yield prediction.

Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations.

With the widespread use of high-throughput phenotyping systems, growth process data are expected to become more easily available. By applying genomic prediction to growth data, it will be possible to predict the growth of untested genotypes. Predicting the growth process will be useful for crop breeding, as variability in the growth process has a significant impact on the management of plant cultivation. However, the integration of growth modeling and genomic prediction has yet to be studied in depth. In this study, we implemented new prediction models to propose a novel growth prediction scheme. Phenotype data of 198 soybean germplasm genotypes were acquired for 3 years in experimental fields in Tottori, Japan. The longitudinal changes in the green fractions were measured using UAV remote sensing. Then, a dynamic model was fitted to the green fraction to extract the dynamic characteristics of the green fraction as five parameters. Using the estimated growth parameters, we developed models for genomic prediction of the growth process and tested whether the inclusion of the dynamic model contributed to better prediction of growth. Our proposed models consist of two steps: first, predicting the parameters of the dynamics model with genomic prediction, and then substituting the predicted values for the parameters of the dynamics model. By evaluating the heritability of the growth parameters, the dynamic model was able to effectively extract genetic diversity in the growth characteristics of the green fraction. In addition, the proposed prediction model showed higher prediction accuracy than conventional genomic prediction models, especially when the future growth of the test population is a prediction target given the observed values in the first half of growth as training data. This indicates that our model was able to successfully combine information from the early growth period with phenotypic data from the training population for prediction. This prediction method could be applied to selection at an early growth stage in crop breeding, and could reduce the cost and time of field trials.

Satellite-based interferometric monitoring of deformation characteristics and their relationship with internal hydrothermal structures of an earthflow in Zhimei, Yushu, Qinghai-Tibet Plateau

Under the influence of climate change, permafrost landforms are sensitive to seasonal heave and contraction, thus exacerbating surface instability and fostering landslides as a consequence. In the pastureland of Zhimei on the Qinghai-Tibet Plateau (QTP), a typical earthflow has drawn significant attention through social media. However, detailed knowledge of the deformation characteristics, internal hydrothermal regime, and structure is still scarce. In this study, we aim to enhance traditional satellite synthetic aperture radar interferometry to divide ground deformation into the seasonal oscillation and slope deformation components and identify the magnitude and spatial distribution of unstable slopes in frozen regions. Then, the use of unmanned aerial vehicles (UAVs) was combined with geophysical monitoring techniques to recognise the deformation dynamics from the pre- to post-failure stages. Sentinel-1 images, covering almost five years, highlighted that obvious creep behaviour dominated at the pre-failure stage, while a seasonal deformation pattern characterised by a piecewise distribution associated with the hydrothermal regime was observed at the post-failure stage. Fast retrogressive erosion on the head scarps at the post-failure stage was clearly identified by multidifferential digital surface models from the UAV observations. To better understand the internal structure, both electrical resistivity tomography and ground-penetrating radar were combined to determine the seasonal frozen thickness, underlying thawing materials, and vertical cracks, which controlled the kinematic evolution from the initial creep to the narrow and long oversaturated flow that represented the terminal portion of the landslide. Finally, by comparing in situ monitoring data with field investigations, the main driving factors controlling the movement mechanism are discussed. Our results highlight the specific kinematic behaviour of an earthflow and can provide a reference for slope destabilisation on the QTP under the influence of climate change.

A Novel Method of Monitoring Surface Subsidence Law Based on Probability Integral Model Combined with Active and Passive Remote Sensing Data

For the accurate and high-precision measurement of the deformation field in mining areas using different data sources, the probability integral model was used to process deformation data obtained from an Unmanned Aerial Vehicle (UAV), Differential InSAR (DInSAR), and Small Baseline Subset InSAR (SBAS-InSAR) to obtain the complete deformation field. The SBAS-InSAR, DInSAR, and UAV can be used to obtain small-scale, mesoscale, and large-scale deformations, respectively. The three types of data were all superimposed by the Kriging interpolation, and the deformation field was integrated using the probability integral model to obtain the complete high-precision deformation field with complete time series in the study area. The study area was in the WangJiata mine in Western China, where mining was carried out from 12 July 2018 to 25 October 2018, on the 2S201 working face. The first observation was made in June 2018, and steady-state observations were made in April 2019, totaling four UAV observations. During this period, the Canadian Earth Observation Satellite of Radarsat-2 (R2) was used to take 10 SAR images, the surface subsidence mapping was undertaken using DInSAR and SBAS-InSAR techniques, and the complete deformation field of the working face during the 106-day mining period was obtained by using the UAV technique. The results showed that the subsidence basin gradually expanded along the mining direction as the working face advanced. When the mining advance was greater than 1.2–1.4 times the coal seam burial depth, the supercritical conditions were reached, and the maximum subsidence stabilized at the value of 2.780 m. The subsidence rate was basically maintained at 0.25 m/d. Finally, the accuracy of the method was tested by the Global Navigation Satellite System (GNSS) data, and the medium error of the strike was 0.103 m. A new method is reached by the fusion of active and passive remote sensing data to construct efficient, complete and high precision time-series subsidence basins with high precision.

Citizen-Science with off-the-shelf UAV for Coastal Monitoring

Accurate and repetitive observation and quantification of the shoreline position and the coastal feature are essential aspects of coastal management and planning. Commonly, the dataset associated with coastal observation and quantification is obtained with in-situ coastal surveys. The current methods are mostly quite expensive, time-consuming, and require trained individuals to do the task. With the availability of the off-the-shelf low cost, lightweight, and reliable Unmanned Aerial Vehicle (UAV) with the advances of the algorithms such as structure-from-motion (SfM), UAV-based measurement becomes a promising tool. Open SfM initiative, open topographical database, and UAV communities are the enablers that make it possible to collect accurate and frequent coastal monitoring and democratize data. This paper provides a review and discussions that highlight the possibility of conducting scientific coastal monitoring or collaborating with the public. Literature was examined for the advances in coastal monitoring, challenges, and recommendations. We identified and proposed the use of UAV along with the strategies and systems to encourage citizen-led UAV observation for coastal monitoring while attaining the quality.

Analysis of potential collisions between pedestrians and personal transportation devices in a university campus: an application of unmanned aerial vehicles

Objective To identify factors that contribute to near-miss collisions between pedestrians and personal transportation devices (PTDs) in a university campus using a novel data collection method, unmanned aerial vehicle (UAV). Participants A total of 3,349 pedestrians and 173 PTD riders were detected through UAV observations. Methods The researchers employed UAV technology to capture and geocode the interactions and behavior of pedestrians and PTD riders. Then, a multilevel logistic regression model examined factors that contribute to near-miss collisions between pedestrians and PTDs. Results The model outputs indicate that higher speed, non-bicycle PTDs (eg, skateboard and scooter), and some preventive actions, like reducing speed, deviating, and weaving, increase the probability of a PTD rider getting involved in a near-miss collision. Conclusions Findings can guide campus planners to redesign the streets as a safe environment for all transportation modes and implement appropriate regulations and education programs, especially for non-bicycle PTD riders.