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In the granite regions of southern China, benggang poses a substantial threat to the ecological environment due to significant soil erosion. This phenomenon also imposes constraints on economic development, necessitating substantial investments in restoration efforts in recent decades. Despite these efforts, there remains a notable gap in comprehensive risk assessment that integrates both the erosion risk and disaster risk associated with benggang. This study focuses on a representative benggang area in Wuhua County, Guangdong province, employing transformer methods and high-resolution imagery to map the spatial pattern of the benggang. The integrated risk of benggang was assessed by combining soil-erosion risk and disaster risk, and cultivated land, residential land, and water bodies were identified as key disaster-affected entities. The machine-learning Segformer model demonstrated high precision, achieving an Intersection over Union (IoU) of 93.17% and an accuracy (Acc) of 96.73%. While the number of large benggang is relatively small, it constitutes the largest area proportion (65.10%); the number of small benggang is more significant (62.40%) despite a smaller area proportion. Prioritization for benggang management is categorized into high, medium, and low priority, accounting for 17.98%, 48.34%, and 33.69%, respectively. These priorities cover areas of 30.27%, 42.40%, and 27.33%, respectively. The findings of this study, which offer benggang management priorities, align with the nature-based solutions approach. Emphasizing the importance of considering costs and benefits comprehensively when formulating treatment plans, this approach contributes to sustainable solutions for addressing the challenges posed by benggang. [ABSTRACT FROM AUTHOR]

Frequent spontaneous combustion activities restrict ecological restoration of coal waste dumps after reclamation. Effective monitoring of vegetation restoration is important for ensuring land reclamation success and preserving the ecological environment in mining areas. Development of unmanned aerial vehicle (UAV) technology has enabled fine-scale vegetation monitoring. In this study, we focused on Medicago sativa L. (alfalfa), a representative herbaceous vegetation type, in a coal waste dump after reclamation in Shanxi province, China. The alfalfa aboveground biomass (AGB) was used as an indicator for assessing vegetation restoration. The objective of this study was to evaluate the capacity of UAV-based fusion of RGB, multispectral, and thermal infrared information for estimating alfalfa AGB using various regression models, including random forest regression (RFR), gradient boosting decision tree (GBDT), K-nearest neighbor (KNN), support vector regression (SVR), and stacking models. The main results are as follows: (i) UAV multi-source data fusion improved alfalfa AGB estimation accuracy, although the enhancement diminished with the increasing number of sensor types. (ii) The stacking model consistently outperformed RFR, GBDT, KNN, and SVR regression models across all feature fusion combinations. It achieved high accuracy with R2 of 0.86–0.88, RMSE of 80.06–86.87 g/m2, and MAE of 60.24–62.69 g/m2. Notably, the stacking model based on only RGB imagery features mitigated the accuracy loss from limited types of features, potentially reducing equipment costs. This study demonstrated the potential of UAV in improving vegetation restoration management of coal waste dumps after reclamation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In recent years, the development of airborne magnetic survey technology based on unmanned aerial vehicles (UAVs) has been rapidly advancing. The commonly used systems are the fixed-wing UAV-based, multi-rotors UAV-based, and unmanned helicopters-based magnetic survey systems. And, a type of hybrid UAV that uses a vertical takeoff and landing (VTOL) and fixed-wing cruise mode is increasingly being used to carry airborne magnetic survey systems. To meet the requirements of most UAVs for small-sized and lightweight payloads, a miniature magnetic survey system was developed and integrated into a hybrid fixed-wing UAV and formed an aeromagnetic survey system. And, a peripheral mineral exploration test was conducted in a known porphyry copper–gold deposit in southeastern China using the system. By processing the collected magnetic data with 3D inversion of susceptibilities, potential ore-bearing intrusive rocks were quickly identified and delineated, providing clues for peripheral mineral exploration in the mining area. [ABSTRACT FROM AUTHOR]

Cosmic ray neutron probes (CRNPs) provide continuous monitoring of average near-surface soil water content (SWC) on hectometer scales. However, the performance of CRNPs on surfaces of highly heterogeneous vegetation and SWC remains uncertain. This study evaluated three vegetation calibration methods with the correction of vegetation distribution developed for a CRNP on the Loess Plateau of China. Three plots with different vegetation distributions were selected and equipped with CRNPs and SWC sensors, and their biomass as well as distribution were measured by an unmanned aerial vehicle (UAV) equipped with a RedEdge multispectral camera. We found that the parameter N0, which is neutron flux in dry soil, was best represented by the biomass at average growth conditions of the monitoring period, yielding the lowest RMSE (0.068). The Veg–N0 vegetation calibration method reduced the RMSE the most between the CRNP SWC and the Kriging-weighted SWC, and the correction of the spatial distribution of the vegetation further reduced the RMSE. The cooperation between the CRNP and the UAV could obtain the regional averaged SWC accurately. This study makes up for the lack of vegetation calibration for the CRNP on the Loess Plateau, which should help develop sustainable vegetation management and ecohydrological management strategies on the Loess Plateau, so as to protect water security in the region. [ABSTRACT FROM AUTHOR]

Mining of mineral resources exposes various minerals to oxidizing environments, especially sulfide minerals, which are decomposed by water after oxidation and make the water in the mine area acidic. Acid mine drainage (AMD) from mining can pollute surrounding rivers and lakes, causing serious ecological problems. Compared with traditional field surveys, unmanned aerial vehicle (UAV) technology has advantages in terms of real-time imagery, security, and image accuracy. UAV technology can compensate for the shortcomings of traditional technology in mine environmental surveys and effectively improve the implementat ion efficiency of the work. UAV technology has gradually become one of the important ways of mine environmental monitoring. In this study, a UAV aerial photography system equipped with a Red, Green, Blue (RGB) camera collected very-high-resolution images of the stone coal mining area in Ziyang County, northwest China, and classified the very-high-resolution images by support vector machine (SVM), random forest (RF), and U-Net methods, and detected the distribution of five types of land cover, including AMD, roof, water, vegetation, and bare land. Finally, the accuracy of the recognition results was evaluated based on the land-cover map using the confusion matrix. The recognition accuracy of AMD using the U-Net method is significantly better than that of SVM and RF traditional machine-learning methods. The results showed that a UAV aerial photography system equipped with an RGB camera and the depth neural network algorithm could be combined for the competent detection of mine environmental problems. [ABSTRACT FROM AUTHOR]

Accurate monitoring of soil moisture content (SMC) in agricultural fields can greatly contribute to the utilization of water resources and sustainable development. Low SMC can cause the soil to harden during crop growth, which in turn affects the absorption of the crops' water and nutrients. In this study, the field-scale wheat canopy spectra were gathered to quantitatively estimate and map the SMC using an unmanned aerial vehicle (UAV) hyperspectral sensor. The study area was selected as Fukang City, Xinjiang Uygur Autonomous Region, China (87°51'15′′E, 44°21'14′′N) at the transition zone between a desert and an oasis. The soil samples were also collected concurrently with the hyperspectral data. 70 sampling units of 0.5 m×0.5 m were evenly selected in the target field, where the GPS locations were recorded. The wheat canopy spectra were smoothed using the savitzky-golay (SG). The spectral data were then transformed using the continuous wavelet transform (CWT) with seven different wavelet functions (bior4.4, coif4, db4, fk14, haar, rbio3.9, and sym 4). The wavelet coefficients were extracted by the genetic algorithm (GA), and finally combined with partial least squares (PLSR), Support Vector Machine (SVM), artificial neural network (ANN), radom forest (RF), and extreme gradient boosting (XGBoost) to predict the SMC. A neural Network was used to predict the SMC, and then map the SMC at a spatial scale. The results demonstrate that the accuracy of SMC estimation greatly increased using the GA-based feature band selection. The determination coefficient (R²) of the feature band was 0.35-0.82 with the wavelet coefficients, while the R² of the complete wavelet band ranged from 0.20 to 0.44. Furthermore, the R² of SMC reached 0.82, 0.72, 0.79, 0.76, and 0.46, respectively, using PLSR, SVM, ANN, RF, and XGBoost models. The best estimation accuracy was achieved in the feature band with the db4 wavelet coefficients, compared with the rest wavelet functions. The predicted and measured values were remarkably similar. The GA significantly reduced the number of variables to maximize the feature factors in the remote sensing bands. The majority of the feature bands were retained to remove the redundant bands that frequently presented in the hyperspectral data. The more stable ANN model was achieved in the small number of sample points on the big sample, even though the accuracy was lower than that of PLSR. Consequently, the perfect estimation accuracy of SMC with excellent mapping was obtained in the continuous wavelet transform and GA feature extraction of the hyperspectral data acquired by the UAV. The improved inversion can be expected to achieve accurate SMC prediction using crop growth period spectra at the field scale. The finding can provide a strong reference for high-precision SMC monitoring using remote sensing. [ABSTRACT FROM AUTHOR]

Global Position System (GPS) tags are widely used in animal tracking to address far‐ranging ecological, behavioural and conservation‐related questions. However, GPS tags with transmitting functions via Global System for Mobile communications (GSM) and long‐lasting batteries are too cumbersome for animals with small body sizes. In addition, GSM is expensive with strict signal limitations in remote areas. Downloading locations from small GPS tags also requires animal recapture, which can result in large workloads. Combined, these problems limit the use of GPS tags in tracking small animals.To overcome these challenges, we present an integrated technology to pinpoint animal locations, especially those of medium‐ and small‐sized species. Our system consists of 3.7 g solar‐powered GPS tags (Nano), whose sampling frequency can be changed flexibly, and a combination of a receiver and an unmanned aerial vehicle (UAV) to retrieve data. The UAV carries the receiver that can retrieve data from Nanos via Bluetooth. Data are uploaded to a platform via GSM by the receiver and then downloaded by users in real time. Users can also directly download data from tags or the receiver via Bluetooth.We conducted stationary and field tests to assess the system's performance in Thousand Island Lake, China. Stationary tests were conducted at eight sites near the buildings, in open fields and on an island. Nanos can transmit data to the receiver within distances of 600–1000 m and at speeds of 6–72 records/s. The mean accuracy was 10.44–23.32 m and the fix success rates (FSRs) were larger than 97%. In field tests, we captured and attached Nanos on six Eurasian jays Garrulus glandarius. The FSRs were larger than 98%. Using the UAV reduced the time spent in field.Our system is labour‐saving and accurate in locating animals larger than 80 g as tested in a land‐bridge island system. Bluetooth transmission assisted by the UAV can overcome signal limitations and landscape blocks, and has the potential to be applied in inaccessible landscapes. The large memory of Nanos also allows for the retrieval of data from migratory species with strong site fidelity on their returns. [ABSTRACT FROM AUTHOR]

Using deep learning-based object detection algorithms for landslide hazards detection is very popular and effective. However, most existing algorithms are designed for landslides in a specific geographical range. This paper constructs a set of landslide detection models YOLOX-Pro, based on the improved YOLOX (You Only Look Once) target detection model to address the poor detection of complex mixed landslides. Wherein the VariFocal is used to replace the binary cross entropy in the original classification loss function to solve the uneven distribution of landslide samples and improve the detection recall; the coordinate attention (CA) mechanism is added to enhance the detection accuracy. Firstly, 1200 historical landslide optical remote sensing images in thirty-eight areas of China were extracted from Google Earth to create a mixed sample set for landslide detection. Next, the three attention mechanisms were compared to form the YOLOX-Pro model. Then, we tested the performance of YOLOX-Pro by comparing it with four models: YOLOX, YOLOv5, Faster R-CNN, and Single Shot MultiBox Detector (SSD). The results show that the YOLOX-Pro(m) has significantly improved the detection accuracy of complex and small landslides than the other models, with an average precision (AP0.75) of 51.5%, APsmall of 36.50%, and ARsmall of 49.50%. In addition, optical remote sensing images of a 12.32 km 2 group-occurring landslides area located in Mibei village, Longchuan County, Guangdong, China, and 750 Unmanned Aerial Vehicle (UAV) images collected from the Internet were also used for landslide detection. The research results proved that the proposed method has strong generalization and good detection performance for many types of landslides, which provide a technical reference for the broad application of landslide detection using UAV. [ABSTRACT FROM AUTHOR]

Chinese mangroves have been recovered in area over the past two decades from previous declining trend, and about half of existing mangroves are still in their young growth stage. This provides a unique opportunity to assess mangrove conservation by examining the growth dynamics of young mangroves over different conservation periods. However, we are currently short of effective assessment tools for spatially explicit quantification of mangrove conservation effects. To fill up this gap, we proposed a novel remote sensing approach using readily available unmanned aerial vehicle (UAV) and Landsat enhanced vegetation index (EVI) data to assess the spatial evolution of aboveground biomass (AGB) of young mangroves. With the space‐for‐time hypothesis, the approach implemented with an empirical EVI‐height‐AGB equation was tested in four subtropical estuarine mangroves in the southeastern coast of China. The results indicated: (a) the UAV‐based Structure from Motion (SfM) technology served as an effective and low‐cost means for capturing the spatial heterogeneity of mangrove canopy heights; (b) a maximum stand age of 15 years could be used to define the young growth stage of mangroves, for which the EVI‐height relationships could be described by exponential equations without suffering significant spectral saturation effects; (c) mangrove forests had overall faster annual AGB accumulation during the young growth stage over the post‐2000 versus pre‐2000 conservation period. This study is one of the first attempts to develop a remote sensing approach for quantifying spatially explicit AGB accumulation rates of young mangroves. It highlights the practicability and advantage of the UAV‐SfM technology and confirms that stronger conservation efforts promote mangrove AGB accumulation over the past two decades. The developed EVI‐height‐AGB framework fueled with readily available UAV and Landsat data provides a unique tool for assessing mangrove conservation effects from landscape to regional scales. [ABSTRACT FROM AUTHOR]

The hybrid aerial underwater vehicle (HAUV) merges the best of unmanned aerial vehicle (UAV) and unmanned underwater vehicle into one platform makes it possible to operate in both the air and water. Various possible applications of HAUV have aroused much research on it. However, the underwater endurance and operation depth of current HAUVs are still limited. This hinders the application and popularization of HAUV in rugged environments. This paper presented the design, fabrication, and testing of a novel concept HAUV, Nezha III. It is featured by its piston‐driven underwater glide strategy and combination of an underwater glider and fixed‐wing vertical takeoff and landing aircraft. In‐depth design and evaluation of the amphibious wings with tradeoffs between aerodynamic and hydrodynamic performance in both fluids was conducted. The vehicle prototype was built, and field experiments, including domain transitions and underwater operations, were conducted in Qiandao Lake, China. Due to the experiment site constraints, the computational fluid dynamics technique evaluated horizontal flight performance with fixed wings. The experimental results show that the prototype can realize rotor flight and stable domain transitions. Moreover, this piston‐driven underwater glide strategy extended the HAUV's underwater endurance over 24 h and operational depth to 25.5 m. To the best of our knowledge, Nezha III possesses the most extended underwater endurance among existing HAUVs. [ABSTRACT FROM AUTHOR]

Non-grain production has emerged as a potential threat to grain production capacity and security in China. Agricultural products with higher economic returns are beginning to replace traditional grain crops, which have relatively low economic returns on a large scale. In this study, we proposed and verified an identification method utilizing an unmanned aerial vehicle and a U-net algorithm to distinguish peach trees in cultivated land; the overall accuracy for verification and prediction were 0.90 and 0.92, respectively. Additionally, a non-grain production index was developed to assess the degree of non-grain production in target plots. The index was 76.90% and 91.38% in the projected plots, representing a high degree of non-grain production. This combination of an identification method and non-grain production index could provide efficient tools for agricultural management to inspect peach trees in cultivated land, thus replacing field measurements to achieve significant labor savings. Furthermore, this method can provide a reference for creating high-standard farmland, sustainable development of cultivated land, and policymaking. [ABSTRACT FROM AUTHOR]

Northern China's agropastoral ecotone has been a key area of desertification control for decades, and digital maps of its soil organic carbon (SOC) stocks are needed to reveal the gaps between the actual SOC levels and baseline to support land degradation neutrality (LDN) under the Sustainable Development Goals. However, reliable soil information is scarce, and accurate prediction is hindered by the fragmented landscape, which is a dominant characteristic of desertified land. To improve the patchiness identification and accuracy of SOC prediction, we conducted field surveys and collected low-altitude aerial images along the desertification degrees (severe and extremely severe, moderate, slight) in the Horqin Sandy Land. Linear regressions were performed on the relationships between the normalized difference vegetation index and the fractional vegetation cover (FVC) extracted from aerial images, and regression kriging was applied to predict SOC stocks based on the soil-forming factors (vegetation, climate, and topography). Our prediction and cross-validation showed that the fragmented structure and prediction accuracy of SOC stocks were both greatly improved for desertified land. The FVC (R2c = 0.94) and evapotranspiration (R2c = 0.86) had significant positive effects on SOC stocks, respectively, with indirect and direct causal relationships. Our results could provide soil information with better patchiness and accuracy to help policymakers determine the future LDN status in this fragmented desertification landscape. As drone technology becomes more available, it will fully support digital mapping of soil properties. [ABSTRACT FROM AUTHOR]

Artificial glacier melt reduction is gaining increasing attention because of rapid glacier retreats and the projected acceleration of future mass losses. However, quantifying the effect of artificial melt reduction on glaciers in China has not been currently reported. Therefore, the case of Urumqi Glacier No.1 (eastern Tien Shan, China) is used to conduct a scientific evaluation of glacier cover efficiency for melt reduction between 24 June and 28 August 2021. By combining two high-resolution digital elevation models derived from terrestrial laser scanning and unmanned aerial vehicles, albedo, and meteorological data, glacier ablation mitigation under three different cover materials was assessed. The results revealed that up to 32% of mass loss was preserved in the protected areas compared with that of the unprotected areas. In contrast to the unprotected glacier surface, the nanofiber material reduced the glacier melt by up to 56%, which was significantly higher than that achieved by geotextiles (29%). This outcome could be attributed to the albedo of the materials and local climate factors. The nanofiber material showed higher albedo than the two geotextiles, dirty snow, clean ice, and dirty ice. Although clean snow had a higher albedo than the other materials, its impact on slowing glacier melt was minor due to the lower snowfall and relatively high air temperature after snowfall in the study area. This indicates that the efficiencies of nanofiber material and geotextiles can be beneficial in high-mountain areas. In general, the results of our study demonstrate that the high potential of glacier cover can help mitigate issues related to regions of higher glacier melt or lacking water resources, as well as tourist attractions. [ABSTRACT FROM AUTHOR]

Plant species diversity (PSD) is essential in evaluating the function and developing the management and conservation strategies of grassland. However, over a large region, an efficient and high precision method to monitor multiscale PSD (α-, β-, and γ-diversity) is lacking. In this study, we proposed and improved an unmanned aerial vehicle (UAV)-based PSD monitoring method (UAVB) and tested the feasibility, and meanwhile, explored the potential relationship between multiscale PSD and precipitation on the alpine grassland of the source region of the Yellow River (SRYR), China. Our findings showed that: (1) UAVB was more representative (larger monitoring areas and more species identified with higher α- and γ-diversity) than the traditional ground-based monitoring method, though a few specific species (small in size) were difficult to identify; (2) UAVB is suitable for monitoring the multiscale PSD over a large region (the SRYR in this study), and the improvement by weighing the dominance of species improved the precision of α-diversity (higher R 2 and lower P values of the linear regressions); and (3) the species diversity indices (α- and β-diversity) increased first and then they tended to be stable with the increase of precipitation in SRYR. These findings conclude that UAVB is suitable for monitoring multiscale PSD of an alpine grassland community over a large region, which will be useful for revealing the relationship of diversity–function, and helpful for conservation and sustainable management of the alpine grassland. [ABSTRACT FROM AUTHOR]

Alfalfa has been one of the most widely grown forage crops around the world. The "king of forage", alfalfa can be known as the high production of grass rich in nutrients. Timely and accurate monitoring of alfalfa growth and yield can be a high demand for large-scale agricultural production in recent years. In this study, a series of yield prediction models were established for the alfalfa using Unmanned Aerial Vehicle (UAV) remote sensing and machine learning. A field experiment was also performed in Zhuozhou City, Hebei Province, China. Five irrigation modes and a rainfed treatment were set for different alfalfa growth statuses and yield prediction. The UAV multispectral platform was used to monitor the alfalfa during the growing stage of branching, budding, and early blooming. A correlation analysis was then made to determine the alfalfa yields and 11 spectral parameters (vegetation indices). The top five correlation indexes were picked out for each growth stage. Then, a Structure from Motion (SfM) imaging technology was used to reconstruct the plant height of alfalfa. The high accuracy of prediction was achieved with the determination coefficient (R2) of 0.86, the Root Mean Square Error (RMSE) of 6.8 cm, and the Normalized Root Mean Square Error (NRMSE) of 14.4%, compared with the measurement. Eventually, three yield prediction models were established by Support Vector Regression (SVR). Specifically, the measured yield was always used as the output, but the input was used the vegetation indices, the plant height, as well as the combination of vegetation indices and plant height. Soil noise was also removed from the vegetation indices for the best performance. The results showed that there was the most significant correlation of vegetation indices with the measured alfalfa yield during the branching, budding, and early blooming stage. In the branching stage, the R2 values of the three models were between 0.4 and 0.6. The NRMSEs of the models were greater than 30% with the vegetation indices and plant height as the input variables. In the budding stage, the R2 values were above 0.8, and the NRMSEs were less than 20% for all three models. The highest accuracy was achieved in the model with the combination of vegetation indices and plant height as the input variables, with the R2 of 0.87, the RMSE of 564 kg/hm2, and the NRMSE of 16.1%. The R2 value was 0.72 in the yield prediction model with the vegetation indices only as the input variable in the early blooming stage, indicating a lower accuracy than that in the budding stage. However, the R2 values were 0.89 and 0.90 in the models with the plant height only, and the combination of vegetation indices and plant height as the input variables, respectively, while the NRMSEs were lower than 15%, indicating a higher accuracy than that in the budding stage. A higher accuracy of the yield prediction model was found, as the alfalfa grew. The combination of plant height and vegetation indices as the input variables can also be expected to improve the accuracy of the yield prediction model. The best yield prediction was achieved in the model with the combination of five vegetation indices and plant height as the inputs at an early blooming stage, with the R2 of 0.90, the RMSE of 500 kg/hm2, and the NRMSE of 14.3%. The optimal model can be strongly recommended for the rapid and accurate prediction of alfalfa yield. The finding can provide technical support to the large-scale production and precision management of alfalfa. [ABSTRACT FROM AUTHOR]

Field surveys and questionnaires are a cornerstone of rural socioeconomic research, providing invaluable firsthand data regarding on-the-ground situations. However, cost-effective and efficient methods for validating the accuracy of self-reported data in such questionnaires are lacking. Biased data are likely to lead to incorrect conclusions. In this study, we propose a new index, the survey bias index (SBI), for evaluating the degree of survey bias in field surveys. This index was obtained by comparing the data recorded in questionnaires with those from portable unmanned aerial vehicles (UAVs). In a case study, we employed SBI to reveal the degree of survey bias of questionnaires in field surveys on rural homesteads. The SBI of self-reported areas of rural homesteads reached 0.439, implying that 43.9% of data were significantly different from those collected using UAVs. A greater SBI was obtained in the pre-urban zone (0.515) than in the pure rural zone (0.258). These results indicate that homestead areas in the pre-urban zone have more incentive to expand than those in the pure rural zone. UAV remote sensing can strongly support research in the field of social economy, which reveals key information hidden in field surveys and questionnaires. [ABSTRACT FROM AUTHOR]

Methane (CH 4 ) is the second greenhouse gas and has a profound impact on global climate change due to its high global warming potential and concentration. By 2022, the CH 4 concentration was approximately 1.9 ppm, which was 264% of the pre-industrial level. The spatiotemporal distribution of CH 4 was investigated by a portable CH 4 detector on an unmanned aerial vehicle and electric bicycles in Shaoxing, a city situated in the Yangtze River Delta, China. The vertical distribution revealed CH 4 concentration generally decreased slowly with height. However, the inversion condition and low atmospheric boundary layer height (ABLH) leaded to the enhancement of CH 4 with height. The highest CH 4 concentration (2.2 ± 0.1 ppm, n = 1428) was observed in winter and the lowest (2.0 ± 0.2 ppm, n = 1530) in spring. Regarding the daily variation, CH 4 concentration peaked at 5:00 local time (LT) and reached its lowest level at 14:00 LT, which was attributed to the daily variation of ABLH, lowest in the early morning and highest in the noon. In urban areas, CH 4 concentrations showed higher levels near restaurants, natural gas stations and sewerage well, with a maximum value of 13.1 ppm, which was caused by CH 4 emission and natural gas leakage from these places. The annual CH 4 emission in Shaoxing were estimated to be approximately 69 ton/(km 2 ·year) by the mass balance approach. Compared with other cities in the world, the CH 4 emission is in higher level which imply some control measures should be conducted to reduce CH 4 emission in Shaoxing., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Chinese olive trees (Canarium album L.) are broad-leaved species that are widely planted in China. Accurately obtaining tree crown information provides important data for evaluating Chinese olive tree growth status, water and fertilizer management, and yield estimation. To this end, this study first used unmanned aerial vehicle (UAV) images in the visible band as the source of remote sensing (RS) data. Second, based on spectral features of the image object, the vegetation index, shape, texture, and terrain features were introduced. Finally, the extraction effect of different feature dimensions was analyzed based on the random forest (RF) algorithm, and the performance of different classifiers was compared based on the features after dimensionality reduction. The results showed that the difference in feature dimensionality and importance was the main factor that led to a change in extraction accuracy. RF has the best extraction effect among the current mainstream machine learning (ML) algorithms. In comparison with the pixel-based (PB) classification method, the object-based image analysis (OBIA) method can extract features of each element of RS images, which has certain advantages. Therefore, the combination of OBIA and RF algorithms is a good solution for Chinese olive tree crown (COTC) extraction based on UAV visible band images. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is a key indicator for the growth of crops. The crop yield is also closely related to the LAI, particularly for the decision-making in modern agriculture. Rapid and accurate detection of the crop LAI is of great significance to field production management. Single sensors have been mostly used to monitor the winter wheat LAI in the past, such as high-definition digital cameras, multispectral and hyperspectral cameras. Fortunately, Unmanned Aerial Vehicles (UAV) remote sensing technology has been developed for crop LAI monitoring by virtue of the high timeliness and low cost at present. The multi-source image data can also be combined for parameter monitoring. In addition, previous LAI estimation is limited to either only the correlation of image features with LAI, or only the importance of image features. Therefore, this study aims to comprehensively consider the correlation of image feature with the LAI and image feature importance, and then construct the multi-source remote sensing LAI estimation models using the selection of optimal image features. Baihu Farm in Lujiang County and Shucheng County Agricultural Science Institute in Anhui Province of China were selected as the study areas, where the canopy visible and hyperspectral images of winter wheat at flowering and filling stages were collected by a UAV platform equipped with a high-definition digital camera and an imaging hyperspectrometer. Meanwhile, the ground LAI data was collected using LAI-2200C. The multiple linear regression (MLR), support vector regression (SVR), and random forest regression (RFR) algorithms were selected to estimate the wheat LAI using the visible and hyperspectral image data. Firstly, the correlation between the visible and hyperspectral image features with the winter wheat LAI was analyzed as well as the importance of image features were calculated, where the optimal image features were selected. Secondly, the MLR, SVR, and RFR estimation models (single-sensor data sources) were constructed, where the input was taken as the visible vegetation index, the texture features, visible vegetation index combined with texture features, hyperspectral band, hyperspectral vegetation index, and hyperspectral band combined with vegetation index. The RFR and SVR LAI estimation models (two sensor data sources) were constructed with two image-preferred features to compare the performance of single-source and multi-source image features for the monitoring of wheat LAI. Thirdly, the spatial heterogeneity of plot soils was considered for the wheat LAI monitoring. The wheat LAI estimation model was also constructed to combine the single image features under different image sampling areas. The results showed that the best accuracy of the RFR LAI estimation model was achieved at the flowering and filling stages using two image preferred features, with the validation set R² of 0.733 and 0.929 and RMSE of 0.193 and 0.118, respectively. By contrast, the model using the combination of single image features performed the best at the flowering and filling stages, when the sampling areas of visible images were 30% and 50%, and the sampling areas of hyperspectral images were 65%, respectively. In summary, the study can provide a valuable reference for the UAV remote sensing monitoring of crop physiological parameters. [ABSTRACT FROM AUTHOR]

Background: China has a unique cotton planting pattern. Cotton is densely planted in alternating wide and narrow rows to increase yield in Xinjiang, China, causing the difficulty in the accurate estimation of cotton yield using remote sensing in such field with branches occluded and overlapped. Results: In this study, unmanned aerial vehicle (UAV) imaging and deep convolutional neural networks (DCNN) were used to estimate densely planted cotton yield. Images of cotton fields were acquired by the UAV at an altitude of 5 m. Cotton bolls were manually harvested and weighed afterwards. Then, a modified DCNN model (CD-SegNet) was constructed for pixel-level segmentation of cotton boll images by reorganizing the encoder-decoder and adding dilated convolutions. Besides, linear regression analysis was employed to build up the relationship between cotton boll pixels ratio and cotton yield. Finally, the estimated yield for four cotton fields were verified by weighing harvested cotton. The results showed that CD-SegNet outperformed the other tested models, including SegNet, support vector machine (SVM), and random forest (RF). The average error in yield estimates of the cotton fields was as low as 6.2%. Conclusions: Overall, the estimation of densely planted cotton yields based on low-altitude UAV imaging is feasible. This study provides a methodological reference for cotton yield estimation in China. [ABSTRACT FROM AUTHOR]

More than 80% of pesticide formulations are used in plant protection by spraying in China. The spraying drift, including wind drift and evaporation drift, caused by the spraying liquid from the nozzle to the target crop is one of the important factors that pesticides cause human and animal health risks and ecological damage. With the pesticide application technology system shifting from grounded based application to ground-based and aviation application in China, spray drift has become the focus and hot spot of application technology research. In order to better understand the spray drift, including spray droplets (size distribution, viscosity, surface tension, vapor pressure, volatility, density, etc.), spray pattern(nozzle type, spray speed and height, spray method) and external conditions(wind speed, wind direction, temperature, humidity, airflow and operator technical level, etc.) are systematically analyzed in this article. Meanwhile, the advantages and shortcomings of spray drift determination methods like field tests, indoor tests, computer simulation and new testing techniques are reviewed in detail, and proposed that the study of spray drift for plant protection unmanned aerial vehicle should be carried out combining indoor determination, field tests and computer simulation together. Furthermore, the risk control techniques of spray drift are analyzed and summarized, which are directly controlled by changing the movement mode and physicochemical properties of spray droplets and indirectly controlled by field layout. [ABSTRACT FROM AUTHOR]

Asymmetric responses of aboveground net primary productivity (ANPP) to precipitation were identified as a signal to predict ecosystem state shifts at temperate grassland zones in Inner Mongolia, China. However, mechanism studies were still lacking. This study hypothesized that the enhanced growth and newly emerged herbaceous after increased precipitation resulted in the highest asymmetry at the transition zone between desert and typical steppe. We monitored the responses of the normalized difference vegetation index (NDVI) of different species to precipitation events using un-manned aerial vehicle technology to test this hypothesis. NDVI and species richness were measured twice at fixed points in July and August with a time interval of 15 days. Results showed that: (1) From July to August, NDVI in the transition zone increased significantly after precipitation (P < 0.05), but NDVI in both the desert and typical steppe showed a non-significant change (P > 0.05). (2) In the transition zone, NDVI increases from the shrub and herbaceous contributed to 37 and 63% increases of the site NDVI, respectively. (3) There was a significant difference in species richness between July and August in the transition zone (P < 0.05), mainly caused by the herbaceous (Chenopodiaceae, Composite, Convolvulaceae, Gramineae, Leguminosae, and Liliaceae), which either emerged from soil or tillers growth from surviving plants. This study demonstrated that herbaceous dominant the changes of NDVI in the transition zone, which provides a scientific basis for the mechanism studies of ANPP asymmetric response to precipitation and warrants long-term measurements. [ABSTRACT FROM AUTHOR]

Breeding for wheat stripe rust-resistance could effectively reduce the harm caused by stripe rust. Accurate and efficient phenotyping is an essential step in crop breeding. Furthermore, UAV multispectral imaging had been used for crops growth monitoring, pathological phenotyping, biochemical index estimation and yield prediction widely for it could overcome the limitation of traditional phenotyping methods which has low efficiency and accuracy. In order to solve the problems of poor means and low efficiency of phenotyping of stripe rust in wheat, a dynamic high-throughput phenotyping framework for field stripe rust in population wheat using the UAV multispectral imaging was proposed. The experiment was carried out in Caoxinzhuang experimental farm, Yangling, Shaanxi Province, China. The breeding wheat population with 516 genotypes was used as experimental materials and planted into 600 plots according to the augmented design. Using UAV low altitude remote sensing and multispectral imaging, canopy multispectral images of experimental population wheat infected with stripe rust naturally were collected. At the same time, the data of disease stage and disease severity of stripe rust were recorded according to the disease time and the traditional manual identification standard of stripe rust. After image mosaic, radiometric correction, geometric correction, segmentation of region of interest and background, image clipping and other operations are carried out on the UAV remote sensing raw data, 22 vegetation indices were calculated to analyze. Using 22 spectral vegetation indices as classification features, we firstly established classification models of disease stage and disease severity in stripe rust, and screen out the vegetation indices sensitive to stripe rust disease stage and disease severity through random forest algorithm. At the same time, the random frog algorithm is also used to filter the original features to reduce the contingency caused by using the random forest algorithm to realize the feature screening merely. Using vegetation index with the selected probability ranking of about the top 1/3 according to the random frog algorithm as the input feature, the Support Vector Machine (SVM) algorithm was used to establish the classification model of disease stage and disease severity level in stripe rust, so as to verify the effectiveness of the features screened by the random frog algorithm. Combining the results of two feature selections, three vegetation indices sensitive to disease stage and disease severity were selected, and the differences of disease dynamics of six reference varieties in the population were analyzed based on the time series of response of these indices. The results showed that 1) for disease stages classification of stripe rust, the F1-score of the random forest classifier based on 22 original features and the SVM classifier based on the features sensitive to disease stage selected by random frog were 0.970 and 0.985 respectively, which proved the sensitive features selected by both random forest and random frog are reasonable and the two machine learning classifiers can be used in the classification of the disease stage of stripe rust in wheat population; 2) for disease severity classification of stripe rust, the F1-score of the random forest classifier based on 22 original features and the SVM classifier based on the sensitive features selected by random frog were 0.741 and 0.780 respectively. Although they are lower than the classifier for the disease stage of stripe rust, they have also achieved relatively satisfactory results, which also proved the features sensitive to disease severity selected by both random forest and random frog are reasonable and the two machine learning classifiers can be used in the classification of the disease severity of stripe rust in wheat population; 3) the response of Difference Vegetation Index RedEdge (DVIRE), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Vegetation Index rededge (NDVIrededge) were more sensitive to both disease stage and disease severity in classification models. Especially, DVIRE was selected as the most sensitive feature to disease stress, and can describe both disease stage and severity of stripe rust in field meanwhile. So, dynamic phenotyping by using multitemporal vegetation indices extracted from UAV multispectral images may accurately and finely quantify stripe rust stress state in wheat population and could be considered as a reliable high-throughput method. This study proposed a new method for the field phenotype investigation for wheat stripe rust-resistance breeding, and also provided a reference for the phenotyping of stress resistance breeding of other crops. [ABSTRACT FROM AUTHOR]

Cliff ecosystems are considered the 'Last Unknown' because cliffs may host ancient and unique species that are located in extremely hostile environments and are difficult to reach. However, comprehensive and systematic information and databases of cliff flora are unavailable; and obtaining information on these undisturbed ecosystems and cliff flora is fundamental and is of high priority for botanists. We present the first systematically sampled expert‐verified checklist of cliff flora from the ground to the top of cliffs in these harsh environments based on high‐resolution visible imagery captured with close‐range photogrammetry by a smart remote‐controlled unmanned aerial system (UAS). Using approximately 9200 records retrieved from high‐resolution images of 197 cliffs in the Wulingyuan global cultural heritage area, a mid‐subtropical region in China, we identified 267 vascular plant species, 140 genera and 58 families, excluding herbaceous plants. The maximum diversity was attributable to a few dominant families and genera, and most of the genera and families were represented by only a few species. Our findings highlight the unique floristic patterns as well as morphology on these cliffs that warrant further study and the potential for the use of a smart UAS to investigate cliff flora at large and global scales. [ABSTRACT FROM AUTHOR]

China is facing a great challenge with grain demand. Meanwhile, the decline of farmland quality will affect the quality and safety of food. The existing methods of farmland quality monitoring are characterized by a long time period and a lack of detailed spatial information of farmland quality. They could not provide the data that meet the requirements of farmland quality monitoring. This study explored a new methods and mobile laboratory for farmland quality monitoring. A farmland quality monitoring indicator system consisting of the variables or predictors that can be fast measured spatially and temporally was first developed. In this study, a framework which integrated satellite remote sensing monitoring, unmanned aerial vehicle（UAV）monitoring, wireless sensor network（WSN）monitoring and in situ measurement method into a farmland quality monitoring mobile laboratory was built. This study also explored the integration of remote sensing, UAV, WSN and ground-based monitoring methods through middleware technologies that connect various components of the system. Moreover, this study developed of a farmland quality monitoring mobile laboratory with an optimal design of routines for fast collection of data. A on-site test was carried out in the area of farmland upgrade and reconstruction program, which took less than 2 hours to quick test 13 indicators for 5 sampling points showed higher efficiency and on-site analysis capability compared with traditional farmland quality monitoring method. This study overcame the gaps that currently exist in the traditional farmland quality monitoring methods, realized comprehensive and multi-scale monitoring of farmland quality, promoted the effectiveness of monitoring cultivated land, and enhanced the capacity of farmland management and protection. [ABSTRACT FROM AUTHOR]

Accelerating the development of agricultural aviation technology is the need of China's modern agricultural construction. With the rise of emerging industries such as artificial intelligence, biotechnology, autonomous navigation, and the Internet of Things, agricultural aviation is further developing toward the direction of intelligence to meet the requirements of efficient and sophisticated agricultural aviation operations. Bionics is a multi-discipline and comprehensive border subject. It is produced by the mutual penetration and integration of life science and engineering science. Bionic technology has received more and more attention in recent years, and breakthroughs have been made in the fields of biomedicine and health, military, brain science and brain-like navigation, and advanced manufacturing. This study summarized the research progress of biomimetic technology in the field of agricultural aviation from three aspects of biological perception, biological behavior, and biological intelligence. On this basis, problems of related research and application of agricultural aircraft in real-time obstacle avoidance, path planning, and intelligent navigation were analyzed. Combined with the practice of the rapid development of agricultural aircraft, research and application of bionic technology suitable for agricultural aircraft were then proposed. Finally, prospects of agricultural aero-bionic technology were also discussed from multiple bionic target fusion, three-dimensional spatial information exploration, sensors, and animal brain system mechanism. This review provides a reference for the development of bionic technology in China's agricultural aviation. [ABSTRACT FROM AUTHOR]

Purpose: There are a large number of open-pit coal mines in the world, and their dependent, heterogeneous dump sites are one of the world's largest chronic waste concerns. The long-term existence of dumps is vulnerable to non-uniform deformation and erosion simultaneously, seriously threatening also the surrounding geological and ecological security. Hence, it is critical to monitor the potential geologic hazards erosion activity of such a large, unstable accumulation. Materials and methods: In this study, topographic observations were performed using the Terrain Observation by Progressive Scans (TOPS) mode of the up-to-date high-quality synthetic aperture radar (SAR) images from the Sentinel-1B (S-1B) satellite, while unmanned aerial vehicle (UAV) photogrammetric data were used to assist in phase unwrapping, showing the deformation and soil erosion of the Baorixile open-pit coal mine dump in China from September 2016 to September 2019. At the same time, the possible areas of geological disasters were predicted, and the formation mechanism of different types of erosion was analyzed. Results and discussion: The validation indicates that the deformation monitoring results by S-1B data matched well with the measured value. The soil consolidation has a positive effect on improving dump geological stability, while soil erosion has the opposite effect. The high correlation between accumulation consolidation and stacking height was used to distinguish soil consolidation and soil erosion from total deformation. The dump has undergone subsidence and/or erosion under gravity and non-gravity factors. The overall cumulative vertical subsidence and slope deformation ranged from 12 to − 298 mm (negative values indicate that the target moved away from the sensor) and from 4 to 365 mm, respectively. The deformation was aggravated with the increase of the stacking height of the dump site, especially at the edge of the top area. Soil erosion and slope instability were the results of the combined action of multiple factors, such as spring snowmelt erosion, summer (rainy season) surface runoff erosion and internal seepage erosion, autumn wind erosion, and winter frost heaving erosion. Potential slope destabilization hazards and hidden danger areas of slope instability need continuous attention. Conclusion: In summary, the deformation analysis suggests that the collaborative application of S-1B SAR image and UAV photogrammetric data demonstrates the practicability of the fine deformation monitoring service in the dump site and can be a useful tool for long-term continuous deformation and soil erosion monitoring. [ABSTRACT FROM AUTHOR]

• A UAV-AI framework to locate Aedes albopictus potential breeding sites. • A potential breeding dataset for Aedes albopictus based on drone images. • A YOLO v7 model to identify Aedes albopictus potential breeding sites. Aedes albopictus (Diptera: Culicidae) plays a crucial role as a vector for mosquito-borne diseases like dengue and zika. Given the limited availability of effective vaccines, the prevention of Aedes -borne diseases mainly relies on extensive efforts in vector surveillance and control. In multiple mosquito control methods, the identification and elimination of potential breeding sites (PBS) for Aedes are recognized as effective methods for population control. Previous studies utilizing unmanned aerial vehicles (UAVs) and deep learning to identify PBS have primarily focused on large, regularly-shaped containers. However, there has been a small amount of empirical research into their practical application in the field. We have thus constructed a PBS dataset specifically tailored for Ae. albopictus , including items such as buckets, bowls, bins, aquatic plants, jars, lids, pots, boxes, and sinks that were common in the Yangtze River Basin in China. Then, a YOLO v7 model for identifying these PBS was developed. Finally, we recognized and labeled the area with the highest PBS density, as well as the subarea with the most urgent need for source reduction in the empirical region, by calculating the kernel density value. Based on the above research, we proposed a UAV-AI-based methodological framework to locate the spatial distribution of PBS, and conducted empirical research on Jinhulu New Village, a typical model community. The results revealed that the YOLO v7 model achieved an excellent result on the F1 score and mAP(both above 0.99), with 97% of PBS correctly located. The predicted distribution of different PBS categories in each subarea was completely consistent with true distribution; the five houses with the most PBS were correctly located. The results of the kernel density map indicate the subarea 4 with the highest density of PBS, where PBS needs to be removed or destroyed with immediate effect. These results demonstrate the reliability of the prediction results and the feasibility of the UAV-AI-based methodological framework. It can minimize repetitive labor, enhance efficiency, and provide guidance for the removal and destruction of PBS. The research can shed light on the investigation of mosquito PBS investigation both methodologically and practically. [ABSTRACT FROM AUTHOR]

Paleoseismic offsets are important parameters for evaluating fault activity. With the development and popularization of unmanned aerial vehicles (UAVs) and structure-from-motion (SfM) photogrammetry, more and more low-cost mini-UAVs have been used for geoscience studies like active faults and paleoearthquakes. In this study, we take the Gebi ridge in the middle of the Altyn Tagh fault (ATF) in western China as an example of using the control-free images acquired by a mini-UAV and SfM to measure paleoseismic offsets. The measurement accuracies of the control-free images acquired by a mini-UAV were evaluated, and then the horizontal offsets of the land surface caused by paleoearthquakes were measured. After comparing the total number and anomalies of paleoearthquake events identified by UAV-based photos with those revealed by geological trenches nearby, the following conclusions can be drawn. (1) Although the absolute positioning accuracy of the control-free image from the mini-UAV and SfM is poor, the accuracy of relative horizontal measurement is acceptable. (2) Without the help of ground control points (GCPs), the accuracy of relative vertical measurement for aerial images is not sufficient to measure vertical offset. (3) Oblique photography can improve not only the accuracy of paleoseismic landform mapping but also flight safety. (4) Up to 11 paleoearthquakes have been identified through paleoseismic offset measurements using control-free images in the study area. And both the total number and the anomalies of paleoearthquakes are consistent with the geological evidence in nearby geological trenches. By taking into account the above factors, it can be concluded that it is feasible to measure the horizontal offset of the land surface caused by paleoearthquakes using control-free images from mini-UAVs and SfM. [ABSTRACT FROM AUTHOR]

Rodent-infested bald spots are crucial indicators of rodent infestation in grasslands. Leveraging Unmanned Aerial Vehicle (UAV) remote sensing technology for discerning detrimental bald spots among plateau pikas has significant implications for assessing associated ecological hazards. Based on UAV-visible light imagery, we classified and recognized the characteristics of plateau pika habitats with five supervised classification algorithms, i.e., minimum distance classification (MinD), maximum likelihood classification (ML), support vector machine classification (SVM), Mahalanobis distance classification (MD), and neural network classification (NN) . The accuracy of the five methods was evaluated using a confusion matrix. Results showed that NN and SVM exhibited superior performance than other methods in identifying and classifying features indicative of plateau pika habitats. The mapping accuracy of NN for grassland and bald spots was 98.1% and 98.5%, respectively, with corresponding user accuracy was 98.8% and 97.7%. The overall model accuracy was 98.3%, with a Kappa coefficient of 0.97, reflecting minimal misclassification and omission errors. Through practical verification, NN exhibited good stability. In conclusion, the neural network method was suitable for identifying rodent-damaged bald spots within alpine meadows.

The Yumenkou tunnel is located on the left bank of the Yellow River on the border between Shanxi and Shaanxi provinces, China. It is connected to the Longmen Yellow River Bridge of the Menghua railway. Many unstable rock masses have developed on the cliff above the tunnel entrance, threatening the construction and operation of the tunnel and the bridge. Based on a high-precision three-dimensional (3D) digital terrain model obtained by unmanned air vehicle (UAV) photogrammetry, information of the structural surface which controls the stability of the rock mass in the study area was obtained by manual identification and extraction. We combined the UAV imaging with field survey data to obtain the spatial distribution, shape, scale, and potential failure modes of the unstable rock masses. A 3D numerical model of the unstable rock mass and surface morphology was constructed using a 3D discrete element method. The 3D path and impact energy of the moving unstable rock mass were analyzed. Based on the lithology, topography, structural surface, and other factors, three types of potential rock failure modes were identified in the study area: sliding, toppling, and falling. Differential weathering and the structure plane and stress-unloading cracks are two main factors affecting the stability of the unstable rock mass. According to the result of 3D discrete element method, once the unstable rock masses fail, about 50% of which might hit the portal of the tunnel, and the maximum rebound height at the portal would be about 1.2 m. To prevent such catastrophic failure, active protective net was recommend at each unstable rock zone, and retaining wall with a height of 5 m behind the portal. The results of this study confirm the effectiveness of a UAV photogrammetry-based method for reliable and cost-effective engineering geological surveying of unstable rock mass in complex terrain. [ABSTRACT FROM AUTHOR]

Wheat is one of the main food sources in China, and wheat yield is very important to China's food security. Lodging is a common agricultural disaster in wheat production. Lodging often causes changes in the population structure of wheat, affects the protein synthesis and nutrient transport of wheat, and greatly influences the quality and yield of wheat. To extract the lodging area of wheat quickly and accurately, this study adopted the Unmanned Aerial Vehicle (UAV) remote sensing platform to obtain the visible light images (including the red band, the green band, and the blue band) and the Digital Surface Model (DSM) images of the wheat canopy at the late stage of grain filling. The Excess Green (EXG) index was also calculated. Fusion images of DSM+RGB as well as fusion images of DSM+EXG were created by ArcGIS image mosaic tools. By using the maximum likelihood method and random forest method, the two feature fusion images were supervised and classified for extracting the wheat lodging area, and the results were also compared with the extracted results only based on the RGB images and the DSM images. The results of using four images to extract the lodging area of wheat were evaluated by the confusion matrix and Kappa coefficient. The results showed that the two methods had the same overall trend of wheat lodging area extraction on four images, and the extraction effect of the maximum likelihood method was generally excellent. Based on the random forest method, the overall accuracy of RGB image, DSM image, DSM+RGB fusion image, DSM+EXG fusion image to extract the lodging wheat area was 77.21%, 93.37%, 93.75%, 81.78%, and the Kappa coefficients were 0.54, 0.86, 0.87, 0.64, respectively. According to comparative analysis, the image data extraction accuracy after fusion of the DSM and RGB parameters was the highest, and the overall accuracy was up to 93.75%, the Kappa coefficient was 0.87. The difference between the two images was improved due to added elevation information. The extraction effect of the lodging area was the worst which was only based on the RGB images with an overall accuracy of 77.21% and a Kappa coefficient of 0.54. The reason was that the wheat with normal plants but yellow leaves were misclassified. Compared with the fusion images of DSM+RGB, the classification accuracy based on the DSM images alone was not much different, indicating that DSM effectively reflected the difference between lodging wheat and normal wheat. It was an effective feature, while the accuracy of the latter was higher than that of the former, indicating that the fusion of unreasonable image features effectively improved the image difference and the extraction accuracy of the lodging area of wheat. In this study, the fusion of different image features preserved more detailed information of the image, which enhanced the difference of image features and made the fused image suitable for image classification. This research had shown that the method of image feature fusion could effectively extract the information of lodging wheat and provide a method for accurate identification of wheat lodging. [ABSTRACT FROM AUTHOR]

Loess landslides are a severe engineering geological problem on the Loess Plateau of China. This study relies on interferometric synthetic aperture radar (InSAR) and unmanned aerial vehicle (UAV) photogrammetry techniques to characterise a large-scale loess landslide deformation process in Northwest China. First, a total of 85 Sentinel-1 SAR images acquired from March 2015 to March 2019 were used to detect the spatial displacement and characterise the pattern of deformation from presliding to postsliding. Second, three UAV flight surveys were conducted to reconstruct landslide morphology, identify scarps, cracks and fissures; and generate elevation differences. By comparing these two technologies, we found that InSAR has more advantages in the retrieval of creeping deformation, while UAV data is valuable for detecting large sudden slides. Dynamic deformation zonation maps were generated based on comprehensive analysis, and the deformation pattern of the large loess landslide was deduced. The Hongheyan landslide was induced by heavy rainfall and then gradually became suspended owing to the topography effect. Recently, the western part of the slope showed a clear accelerating trend. This evolution provides comprehensive kinematic information for local governments to make further intensive observations or take effective precautions. [ABSTRACT FROM AUTHOR]

With high spatial resolution, on-demand-flying ability, and the capacity for obtaining three-dimensional measurements, unmanned aerial vehicle (UAV) photogrammetry is widely used for detailed investigations of single landslides, but its effectiveness for landslide detection and monitoring in a large area needs to be investigated. The Heifangtai terrace in the Loess Plateau of China is a loess terrace that is extremely susceptible to irrigation-induced loess landslides. This paper used UAV-based photogrammetry for a series of highresolution images spanning over 30 months for landslide detection and monitoring of the terrace with an area of 32 km2. Dense and evenly distributed ground control points were established and measured to ensure the high accuracy of the photogrammetry results. The structure-from-motion (SfM) technique was used to convert overlapping images into orthographic images, 3D point clouds, digital surface models (DSMs) and mesh models. Using multitemporal differential mesh models, landslide vertical movements and potential landslides were detected and monitored. The results indicate that a combination of UAV-based orthophotos and differential mesh models can be used for flexible and accurate detection and monitoring of potential loess landslides in a large area. [ABSTRACT FROM AUTHOR]

In recent years, low-altitude and low-volume plant protection operations using unmanned aerial vehicle (UAV) sprayer developed rapidly in China with the advantages of high efficiency, labour saving, high safety, high terrain adaptability, high flexibility, water and chemicals saving, and high intelligence. With the UAV application technology in field crops is becoming more and more mature, aerial spraying operations in orchards are promising and in the ascendant, but a high risk of UAV spray drift is appearing due to high working height and fine droplets sprayed in slope orchards, highlighting the necessity of the study on the spray drift characteristics of UAV chemicals application for fruit trees. Therefore, based on previous research, a novel type of measuring method of spray drift for UAV chemicals application in orchard was proposed in this study and an artificial orchard test stand (vineyard) and 3 airborne drift frame collectors were designed and built, and a set of field drift test bench was firstly used to collect aerial spray drift droplets at different downwind distances, together with ground drift collectors and canopy deposition collectors. An airborne drift index (ADX) of UAV’s spray was initially applied for quantitative analysis to compare spray drift characteristics of different models of unmanned aircrafts and variable operation parameters. Fluorescence tracer Pyranine water solution was prepared at the concentration of 0.1% as the spray liquid. Four typical types of plant protection UAV (a single-rotor oil-powered helicopter, a 6-rotor motor drone and two models of 8-rotor motor drones) equipped with conventional hollow cone nozzle ‘TR 80-0067’ and air-induction anti-drift nozzle ‘IDK 120-015’were tested in the artificial vineyard, and results of canopy deposition distribution, ground sediment drift, near-ground drift, and airborne drift were obtained and analysed, and different sampling collectors for spray drift were evaluated and compared. The results showed that: Under the environmental conditions that the nominal crosswind speed was 2.4-3.6 m/s, the temperature was 29.8-34.3 ℃ and relative humidity was 10.7%-30.6%, at the flight height of 1.5 m (3.5 m from the ground) and the speed of 2.0 m/s the air-induction nozzle IDK can significantly reduce the level of downwind spray drift of UAV, optimize the uniformity of deposition distribution and increase the effective utilization rate of chemicals; There was no significant difference in the drift characteristics of the 4 types of unmanned aircraft, and the vortex generated by the combination of the rotor’s downwash airflow and the external wind was an important factor on spray drift; Buffer zone of UAV aerial spraying operation in vineyards should be set at at least 15 m; The lower the canopy deposition rate (P<0.05, r<0), the worse the uniformity of deposition distribution (P<0.01, r>0), the larger the average average drift rate (AADR) and 90% cumulative drift distancex90% of the field drift test bench (P<0.01, r> 0), the greater the ADX value (P<0.01, r> 0) all indicated the higher spray drift risk, respectively; Both these sampling collectors and their evaluation index could assess the downwind drift characteristics effectively; the relationship between the UAV spray drift rate βdep% and the downwind distance x was described by the exponential function. The results of this study are expected to provide references and data supports for the R&D of UAV dedicated for orchard spraying, the formulation of standards on spray drift field measuring method for UAV orchard operations and the selection of aerial application working parameters in orchards. [ABSTRACT FROM AUTHOR]

Accurate in-season yield forecasts for field-scale crops are crucial for both farmers and decision-makers. Common methods for yield prediction are limited by the availability of unknown weather data (process-based crop models) and the failure to consider yield formation processes (statistical models based on unmanned aerial vehicle (UAV) images), respectively. Furthermore, previous studies focused only on crops without mulching, yet mulching is an important agronomic approach to increase grain yield in the arid areas of northwest China. We aim to develop a hybrid approach coupling crop model and UAV data through ensemble learning to achieve in-season yield forecasts for film-mulched wheat. A four-year field experiment was constructed (2018–2020 and 2021–2023). We first calibrated AquaCrop using data from 2018 to 2020, and historical weather data were employed to drive AquaCrop for predicting yields in 2021–2023. Next, statistical models were constructed to predict yields based on spectral and textural indices calculated from UAV images. Finally, a hybrid approach coupling the AquaCrop model and remote-sensing data was developed using ensemble learning technique. Quantifying the relative contribution of features used SHapley Additive exPlanations values. The results indicated that AquaCrop yield forecasts exhibited considerable uncertainties (R2: 0.53–0.63; NRMSE: 16.54%–14.83%). The interpretation of yield for remote-sensing data was influenced by background and saturation effects, reaching its highest accuracy at the heading stage (R2 was 0.80, NRMSE was 11.88%). Ensemble learning demonstrated strong performance compared to machine learning algorithms. The coupling model combined the advantages of crop and statistical models by the ensemble learning algorithm, achieving accurate yield predictions more than 40 days before harvest (heading stage) based on AdaBoost regression (R2 was 0.88, NRMSE was 8.40%). The most important forecasting factors affecting yield prediction were the textural indices, followed by the AquaCrop simulated values. Overall, the coupled model showed good performance in predicting the in-season yield of film-mulched wheat, which provided new insights into farm-scale yield prediction. Further validation of the generalizability of the coupled model in different scenarios is required in the future to improve the applicability of the model in actual production practice. • AquaCrop model and UAV data were coupled by the ensemble learning. • AdaBoost regression in Boosting was the optimal yield prediction algorithm. • TIs and AquaCrop simulated values were all important yield forecasting factors. [ABSTRACT FROM AUTHOR]

As the most dominant fast-growing timber species in China, exploring the response of aboveground biomass to stand spatial structure parameters in Chinese fir (Cunninghamia lanceolate) is of great significance for improving forest quality and carbon sequestration capacity. In this study, we used unmanned aerial vehicle (UAV) point cloud data to extract forest parameters and constructed weighted Voronoi diagrams to determine the spatial structure units of the forest to realize rapid acquisition of the spatial structure parameters of the forest stand. Combined with the biomass model constructed using 20 harvested logs, we used stepwise regression to explore the key factors of the spatial structure of Chinese fir that affect biomass accumulation at different age stages. The results showed that: (1) the angle competition index should be kept in the range of (0,0.25] for the biomass of the Chinese fir to be maximized at the young, near-mature, mature, and overmature forest stages; (2) the openness ratio affects the accumulation of the Chinese fir biomass, and its optimal range changes continuously during the growth process from (0.25,0.5]- (0.75,1]-(0.5,0.75] and subsequently has been at (0.5,0.75]; (3) in the mature and overripe forest stages, the forest layer index further affects biomass accumulation, and its optimal range varies from (0.25,0.5] to (0.5,0.75]; (4) in the five Chinese fir growth stages, biomass accumulation was more favorable when the number of nearest trees change was 7, 6, 6, 6, and 5; (5) adopting reasonable measures to regulate the spatial structure of forest stands at different age stages can effectively enhance the biomass of Chinese fir. At the young, middle-aged, and mature forest stages, ensuring the optimal number of neighboring trees through reasonable dense planting and nursery thinning is essential; at the near-mature and mature forest stages, enhancing the use of vertical space of the forest trees by trimming the branches and trunks is crucial. • The spatial structure units of the forest stand were determined using UAV point cloud data. • Utilize weighted Voronoi diagram and Delaunay triangulation to quickly calculate forest stand spatial structure indices. • The angle competition index, openness ratio and forest layer index had an effect on the accumulation of Chinese fir biomass. [ABSTRACT FROM AUTHOR]

Wheat and rice are the main food crops in China. Appropriate nitrogen (N) fertilizer can effectively promote the crop growth, whereas excessive use has repercussions on yield formation and environmental preservation. Therefore, timely assessment of crop N status and precise N application management are of paramount importance. The study aims to assess the impacts of N fertilizer on N accumulation and spectral dynamics in wheat and rice, and investigate the feasibility of real-time crop N status diagnosis using unmanned aerial vehicle (UAV) spectra and devise subsequent managements. Ten experiments were conducted in Xinghua City and Lianyungang City from 2017 to 2020, involving different N fertilizer rates (0–405 kg N ha−1) and various cultivars. Field sampling was carried out simultaneously with UAV image acquisition, and the crop dry matter and N concentration were obtained by indoor analysis. Normalized difference red-edge index (NDRE) and N nutrition index (NNI) demonstrated a robust power function relationship (R2 > 0.70). The time series N diagnosis curves established by critical NDRE values achieved recognition accuracy of over 89%. The validation accuracy of critical NDRE values achieved 93.84%. The probability of the calculated topdressing rate (N) falling between the agronomic optimal N rate (AONR) and economic optimal N rate (EONR) is 86%. The time series N diagnosis curve offered possibility to real-time judgement of plant N status. In addition, the subsequent N topdressing design was proved through the improvement of existing sufficiency index (SI) algorithm. The time series N diagnosis curve will provide valuable decision support for the optimal N fertilizer management of wheat and rice. Moreover, the UAV platform holds promising potential for regional-scale application in the future. • N fertilizer significantly affected dynamics of N accumulation and canopy spectra. • NDRE had a good correlation with PNA and NNI of wheat and rice. • RAGDD-based time series N diagnosis curve was established and verified. • N diagnosis curve combined well with SINR to guide field topdressing in real time. [ABSTRACT FROM AUTHOR]

Aquatic plants play important roles in the productivity of intensive Chinese mitten crab (CMC) rearing ponds. This study aimed to evaluate the lower limit of aquatic plant coverage (APC) in CMC ponds. A field investigation was conducted and unmanned aerial vehicle was used to estimate APCs. A follow‐on single factor experiment was performed to verify the results and evaluated the efficacy of APC values with 15%, 45%, 65% and 85% APC treatments. Average total nitrogen (0.81 mg/L), total phosphorus (0.06 mg/L), ammonium‐N (0.12 mg/L) and nitrate‐N (0.10 mg/L) met the grade II–IV standards for surface water quality in China in the investigations. Further, the use of an exponential decay model for water qualities and APCs indicated that an effective APC threshold was around 30%, and crab yields (1,536 kg ha–1 year–1) significantly decreased by 19.2% if the pond APCs could not reach this threshold. Crab yields were also correlated with crab feed characteristics, and especially the level of fish meat that was used. The single factor experiment for APC again confirmed our APC value threshold via exponential model. Meanwhile, the water quality measurements of the 15% APC treatment were significantly larger than the other APC treatments, but the crab yield, individual weight and carapace length indeed decreased in this single factor experiment for APC. We therefore conclude that the empirical APC level of 60% is preferred, but that smaller APCs could be acceptable in intensive CMC ponds if an APC of at least 30% is maintained. [ABSTRACT FROM AUTHOR]

Aims Remote sensing technology has been proved useful in mapping grassland vegetation properties. Spectral features of vegetation cover can be recorded by optical sensors on board of different platforms. With increasing popularity of applying unmanned aerial vehicle (UAV) to mapping plant cover, the study aims to investigate the possible applications and potential issues related to mapping leaf area index (LAI) through integration of remote sensing imagery collected by multiple sensors. Methods This paper applied the collected spectral data through field-based (FLD) and UAV-borne spectroradiometer to map LAI in a Sino–German experiment pasture located in the Xilingol grassland, Inner Mongolia, China. Spectroradiometers on FLD and UAV platforms were taken to measure spectral reflectance related to the targeted vegetation properties. Based on eight vegetation indices (VIs) computed from the collected hyperspectral data, regression models were used to inverse LAI. The spectral responses between FLD and UAV platforms were compared, and the regression models relating LAI with VIs from FLD and UAV were established. The modeled LAIs by UAV and FLD platforms were analyzed in order to evaluate the feasibility of potential integration of spectra data for mapping vegetation from the two platforms. Important Findings Results indicated that the spectral reflectance between FLD and UAV showed critical gaps in the green and near-infrared regions of the spectrum over densely vegetated areas, while the gaps were small over sparsely vegetated areas. The VI values from FLD spectra were greater than their UAV-based counterparts. Out of all the VIs, broadband generalized soil-adjusted vegetation index (GESAVI) and narrow-band nNDVI2 were found to achieve the best results in terms of the accuracy of the inversed LAIs for both FLD and UAV platforms. We conclude that GESAVI and nNDVI2 are the two promising VIs for both platforms and thus preferred for LAI inversion to carry spectra integration of the two platforms. We suggest that accuracy on the LAI inversion could be improved by applying more advanced functions (e.g. non-linear) considering the observed bias for the difference between the UAV- and FLD-inversed LAIs, especially when LAI was low. [ABSTRACT FROM AUTHOR]

• Development of a novel registration method combining ULS and TLS LiDAR data. • Hierarchical clustering of trunk point clouds generates accurate tree maps. • Validated in 40 sample plots in subtropical forests. • Achieved registration accuracy is better compared to existing methods. • Significantly reduced registration time to approximately 50 s per 30 × 20 m plot. Unmanned aerial vehicle laser scanning (ULS) and terrestrial laser scanning (TLS) provide complementary, non-destructive approaches to acquire three-dimensional forest structure information. Registration of their point clouds enables the reconstruction of complete vertical structure of forests. Current registration methods are primarily designed to register different TLS scans and thus are not applicable to ULS-TLS registration directly. In this study, the proposed method first generated multi-layer tree maps from ULS and TLS data using hierarchical clustering, then extracted Fast Point Feature Histograms (FPFH) features for each cluster point based on spatial relationships in the tree maps. After that, a point-to-point matching strategy was used to obtain the transformation matrix of each layer between ULS and TLS trunk point clouds and the best matrix from all layers was finally selected for fine registration. The algorithm was validated in 40 sample plots in Guangxi province of China. Our findings indicated that both high-density ULS and TLS data generate accurate tree maps compared to manually counted tree number, with a Concordance Correlation Coefficient (CCC) of 0.961 and 0.973, respectively. The proposed method performed well in registration accuracy and time efficiency, and achieved a higher matching score (0.945 > 0.928) and lower RMSE (0.144 < 0.151) than manual registration. The average registration time per sample plot of 600 m2 was 48.9 s, with 19.4 s dedicated to coarse registration. This research highlights the potential of clustering-based registration methods for effectively aligning ULS-TLS point cloud data in forests, laying the foundation for further technological advancements in forest vertical structure reconstruction. [ABSTRACT FROM AUTHOR]

Highlights • The cultural heritage – Huanxiu Shanzhuang – were surveyed via multiple sensors based on optics and lacer from ground and air. • The site data acquired via the techniques of terrestrial laser scanning and terrestrial and unmanned aerial vehicle digital photogrammetry were accurately integrated. • The landscape elements in Huanxiu Shanzhuang were analyzed, 3D models and digitally documented with the integration data from terrestrial laser scanning and terrestrial and unmanned aerial vehicle digital photogrammetry. Abstract Chinese classical gardens, which contain various landscape elements and complicated spaces, are an important component of both gardens worldwide and culture heritage sites. However, there is a research gap regarding surveys and the digital documentation of Chinese classical gardens. In this study, we take one of the most famous and important Chinese classical gardens – Huanxiu Shanzhuang (HXSZ) in Suzhou, China – as an example and apply multiple techniques including terrestrial laser scanning (TLS) and terrestrial and unmanned aerial vehicle digital photogrammetry (TDP and UAVDP) to acquire comprehensive data of its diverse elements. These multi-source data were integrated on the basis of point clouds via two steps – fusing the TLS and UAVDP point clouds firstly and integrating the TDP point clouds with the already-merged TLS and UAVDP point clouds. Both these two integrations included coarse and fine registration. With the integrated point clouds of the garden, 3D models and 2D fine drawings of the landscape elements and the garden were constructed to document the study site and show its complicated characteristics. The purpose of this study is to: survey, measure and display the rich and complicated landscape elements and spatial characteristics of HXSZ, and make 3D models and digitally document for HXSZ. The high-accuracy 3D models and fine drawings obtained in this study are crucial for the cultural heritage preservation of HXSZ. Our multiple surveying methods, data processing of multi-source data and presentation and documentation of the data are not only useful for Chinese classical garden preservation, but also could be applied to other researches of cultural heritage sites that contain various elements and complicated space characteristics. [ABSTRACT FROM AUTHOR]

Agricultural robots, or agrobots, have been increasingly adopted in every aspect of farming from surveillance to fruit harvesting in order to improve the overall productivity over the last few decades. Motivated by the compelling growth of the agricultural robots in modern farms, in this work, an autonomous quality inspection over rice farms is proposed by employing quadcopters. Real-time control of these vehicles, however, is still challenging as they exhibit a highly nonlinear behavior especially for agile maneuvers. What is more, these vehicles have to operate under uncertain working conditions such as wind and gust disturbances as well as positioning errors caused by inertial measurement units and global positioning system. To handle these difficulties, as a model-free and learning control algorithm, type-2 fuzzy neural networks (T2-FNNs) are designed for the control of a quadcopter. The novel particle swarm optimization-sliding mode control (PSO-SMC) theory-based hybrid algorithm is proposed for the training of the T2-FNNs. In particular, the continuous version of PSO is adopted for the identification of the antecedent part of the T2-FNNs while the SMC-based update rules are utilized for the online learning of the consequent part during control. In the virtual environment, the quadcopter is expected to perform an autonomous flight including agile maneuvers such as steep turning and sudden altitude changes over a rice terrace farm in Longsheng, China. The simulation results for the T2-FNNs are compared with the outcome of conventional proportional-derivative (PD) controllers for different case studies. The results show that our method decreases the trajectory tracking integral squared error by %26 over PD controllers in the ideal case, while this ratio goes up to %95 under uncertain working conditions. [ABSTRACT FROM AUTHOR]

Above-bottom biomass (ABB) is considered as an important parameter for measuring the growth status of aquatic plants, and is of great significance for assessing health status of wetland ecosystems. In this study, Structure from Motion (SfM) technique was used to rebuild the study area with high overlapped images acquired by an unmanned aerial vehicle (UAV). We generated orthoimages and SfM dense point cloud data, from which vegetation indices (VIs) and SfM point cloud variables including average height (H AVG ), standard deviation of height (H SD ) and coefficient of variation of height (H CV ) were extracted. These VIs and SfM point cloud variables could effectively characterize the growth status of aquatic plants, and thus they could be used to develop a simple linear regression model (SLR) and a stepwise linear regression model (SWL) with field measured ABB samples of aquatic plants. We also utilized a decision tree method to discriminate different types of aquatic plants. The experimental results indicated that (1) the SfM technique could effectively process high overlapped UAV images and thus be suitable for the reconstruction of fine texture feature of aquatic plant canopy structure; and (2) an SWL model based on point cloud variables: H AVG , H SD, H CV and two VIs: NGRDI, ExGR as independent variables has produced the best predictive result of ABB of aquatic plants in the study area, with a coefficient of determination of 0.84 and a relative root mean square error of 7.13%. In this analysis, a novel method for the quantitative inversion of a growth parameter (i.e., ABB) of aquatic plants in wetlands was demonstrated. [ABSTRACT FROM AUTHOR]

As a consequence of population aging and shrinkage in China, the trend of labor shortage for agricultural production has grown and will continue in future. Acceleration of agricultural mechanization has been proposed as a developmental strategy in support of steady and sustainable development of agriculture in China. In alignment with this national developmental plan, the use of unmanned aerial vehicle (UAV) for pesticide application contributes to the advancement of plant protection mechanization by providing improved ability to apply pesticide in a time sensitive manner with enhanced safety and efficiency. The flight parameter of UAV was one kind of the most important indices affecting the quality of aerial pesticide application. To explore the distribution characteristics of pesticide spraying droplets deposition of UAV and the effect of flight parameters (flight altitude and velocity) and parameter accuracy on the deposition distribution characteristics, the spraying droplets depositions of 4 models of typical Chinese domestic UAVs (3WQF120-12, Mercury Ⅰ, 3WCJ8-10 and 3WQFTX-10) were tested in the wheat field in Xinxiang City, Henan Province in April, 2017. The high-precision Beidou navigation satellite system was used for recording the accurate working altitude, velocity and track of UAV, and the tartrazine tracer water solution of 0.5% was adopted to replace pesticide liquid for spraying. The filter paper with the diameter of 7 mm was used for collecting spraying droplets from UAV at the height of 0.7 m on the top of the wheat canopy, and the sensor of Zeno-3200 weather station was set at the height of 6 m to record the environmental parameters at test site. The size of the testing field was 50 m × 100 m and there were 15 sampling points in a straight line with 3 duplications for longitudinal droplet deposition distribution and 11 sampling points also with 3 duplications for crosswise one. During every test, the operator controlled the UAV remotely to take off, when the UAV reached the required height, then open the spraying system and make the unmanned aircraft fly over the experimental area according to the planned flight. After tests, the eluent from the filter paper was used to measure the absorbance of the tracer tartrazine by the 722 type spectrophotometer and the droplet deposition in every test was figured out. Combining the flight parameters with their coefficient of variation (CV) and root mean square error (RMSE), the impact factors on deposition distribution characteristics were studied and the results were analyzed by significance and regression analysis via SPSS 20 software. The results showed: The deposition in the area of both ends of flight route varied violently (30%＜CV＜70%) and there were the maximal values, while the deposition in the area of the middle of route was relatively steady (CV＜20%); the main factor impacting the crosswise deposition distribution was the overlap ratio of droplets of adjacent nozzles and spraying swath, and too high or too low overlap ratio both led to the poor uniformity of droplets distribution; with regard to pesticide application operation of single-rotor UAV, at the altitude of 1.1-1.2 m, and the velocity of 4.2-4.9 m/s, there was a high significant positive correlation between average droplets deposition and the RMSE of flight velocity (P＜0.01，r=0.952) and between the CV of the longitudinal droplets deposition and the CV of flight velocity (P＜0.01，r=0.963), and the CV of the total deposition was significantly negatively correlated with the average flight altitude (P＜0.01，r=-0.888). So, the method that the droplets deposition and distribution uniformity were analyzed and predicted by the flight parameters and their accuracy, such as the RMSE and CV of flight velocity and flight altitude, was reasonable, effective and feasible. The results of this study provide some theory reference for the determination of the ideal spraying parameters of UAV pesticide application work, the choice of environmental conditions of UAV operation and the formulation of standard working practice of UAV aerial spraying. [ABSTRACT FROM AUTHOR]

Urban green space (UGS), which plays an important role in reducing the problems associated with urbanization, needs to be evaluated by metrics. Three-dimension green quantity (3DGQ), a quantitative index that measures the crown space occupied by a growing plant, is often used to evaluate the extent, and the environmental and climatic benefits of UGS. The objective of this study was to measure the 3DGQ of Paotaiwan Wetland Park (PWP) in Shanghai, China. Implementation of the 3DGQ index was supported by remote sensing (RS) images taken by an unmanned aerial vehicle (UAV). The 3DGQ calculations for 100 species of trees were used to calculate the 3DGQ of the UGS in PWP. The environmental and climatic benefits of UGS in PWP were also evaluated. The 3DGQ for the whole PWP was 668,624.13 m 3 . The mixed woods in the PWP annually absorbed 1,635.57 t CO 2 , 2.03 kg SO 2 , 735.48 t dust, and 2,254.49 t of O 2 . There was 367.74 t of diurnal transpiration. The lowered temperature of the PWP in the transpiration scope at 100 m altitude was 1.8 °C. The use of a UAV to assess UGS could help planners and policy makers to improve the environmental and climatic benefits of UGS. [ABSTRACT FROM AUTHOR]

Maize (Zea mays L.) is a major global crop, but lodging leads to yield loss. Ethephon can regulate maize growth, reduce plant height, and improve lodging resistance. The application of small crop protection unmanned aerial vehicles (UAV) to replace electric knapsack sprayers (EKS) for the mechanization of agriculture in China has been widely developed. However, there are no reports on the application of ethephon to control maize growth by UAV. In the present study, the effects of spraying by UAV (15 L ha−1 and 30 L ha−1) and EKS (450 L ha−1) on the droplet deposition distribution and dynamic degradation process of ethephon (60 g a.i. ha−1) were studied. The control of maize growth by different ethephon dosages was also investigated, i.e., 48, 60, and 90 g a.i. ha−1 for UAV and 60 g a.i. ha−1 for EKS. The droplet deposition rates of 15 L ha−1 and 30 L ha−1 by UAV were 11.2% and 15.2%, significantly higher than those of EKS. Furthermore, the degradation half-life under UAV application was longer than that under EKS. The 20% dosage reduction of ethephon applied by UAV (48 g a.i. ha−1) had the same maize growth control effect as EKS (60 g a.i. ha−1), and the UAV treatment was better than EKS when the dosage was the same (60 g a.i. ha−1). Considering the input cost and yield, we recommend that 48 a.i. g ha−1 ethephon is applied by UAV when the lodging risk is low and 90 a.i. g ha−1 when the lodging risk is high, at a spray volume of 15 L ha−1. The application cost of ethephon can be reduced by 47.91–71.81% compared to EKS under these conditions. • Application of UAV increased droplet deposition rate than EKS on maize leaves. • Degradation half-life of ethephon applied by UAV was longer than by EKS. • Application of ethephon by UAV had stronger maize lodging resistance than by EKS. • Ethephon applied by UAV increased maize yield when lodging occurred seriously. [ABSTRACT FROM AUTHOR]

This research is focused on gully erosion mapping and monitoring at multiple spatial scales using multi-source remote sensing data of the Sancha River catchment in Northeast China, where gullies extend over a vast area. A high resolution satellite image (Pleiades 1A, 0.7 m) was used to obtain the spatial distribution of the gullies of the overall basin. Image visual interpretation with field verification was employed to map the geometric gully features and evaluate gully erosion as well as the topographic differentiation characteristics. Unmanned Aerial Vehicle (UAV) remote sensing data and the 3D photo-reconstruction method were employed for detailed gully mapping at a site scale. The results showed that: (1) the sub-meter image showed a strong ability in the recognition of various gully types and obtained satisfactory results, and the topographic factors of elevation, slope and slope aspects exerted significant influence on the gully spatial distribution at the catchment scale; and (2) at a more detailed site scale, UAV imagery combined with 3D photo-reconstruction provided a Digital Surface Model (DSM) and ortho-image at the centimeter level as well as a detailed 3D model. The resulting products revealed the area of agricultural utilization and its shaping by human agricultural activities and water erosion in detail, and also provided the gully volume. The present study indicates that using multi-source remote sensing data, including satellite and UAV imagery simultaneously, results in an effective assessment of gully erosion over multiple spatial scales. The combined approach should be continued to regularly monitor gully erosion to understand the erosion process and its relationship with the environment from a comprehensive perspective. [ABSTRACT FROM AUTHOR]

• Soil erosion modulus of the large-scale gullies increased from north to south. • Hydraulic erosion dominated gully development during one-year monitoring period. • Contribution of gravity erosion to the gully was ascribed to hypsometric integral. • Clay in colluvial deposits contributed to regional difference of gully erosion. Gully erosion is the most serious threat to sustainable development of global ecosystem and economy, with declining soil productivity and fertility threatening agricultural production. However, due to limitations in quantifying gully erosion rate, origin of erosion could be hardly distinguished. This study aimed to figure out this problem by developing a soil erosion estimation method based on unmanned aerial vehicles (UAV) monitoring techniques. Specifically, 18 gullies with different terrain characteristics were selected across typical erosion regions in southeast China, and UAV low-altitude photography, weather station monitoring, and field sampling investigation were conducted from 2020 to 2021to monitor their total erosion volume (VE), collapse volume (VC), proportion of collapse volume to total erosion volume (VC%), morphological parameters (e.g., total length of scour channels, L; erosion area, A; residual volume, V; and hypsometric integral) and soil-environmental factors (rainfall, temperature, and soil properties). It showed that 92.65 % of VE was resulted from colluvial deposits, attributing to the variation of L. Additionally, VC quantifying the amount of gravity erosion was determined by soil clay content in the upper catchment (Clay1). And percentage of VC to VE, characterized by VC%, possessed a significant linear correlation with HI (R2 = 0.571, p < 0.001). Moreover, regional differences of VE and VC was mainly attributed to soil clay content in colluvial deposits (Clay3) and HI. Collectively, clay content in the gully system inhibited gully erosion, while all the morphologic factors facilitated the development of gully erosion. Meanwhile, rainfall and temperature had significant impacts on VE and the regional difference of VC, probably due to the limited monitoring period. [ABSTRACT FROM AUTHOR]