Aerial Photography Data Research Articles

Estimation of dendrometric characteristics in city parks according to data from UAVs and ground-based LiDAR

The possibilities of using ground-based LiDAR and processing of aerial photography data to assess the characteristics of individual trees in urban parks are shown. The actual data obtained during the examination of the object by these methods made it possible to analyze the state of the plantation and identify dendro parameters. It has been established that the methods of automatic search for trees using 3D point clouds can be successfully used in artificial plantations. The UAV data processing method made it possible to correctly detect in automatic mode about 64% of the park’s trees (343 trees), while the number of false positives and the number of missed trees was quite high (111 and 195 trees, respectively), which was associated with a large proportion deciduous trees in the park. The weighted average of the quality of automatic tree detection in the park was 0.69. Ground-based LiDAR data in manual mode made it possible to detect all trees, determine their heights, crown diameter and trunk diameter at a height of 1.3 m, as well as identify tree species and condition category (using panoramic images). To increase the correctness of tree detection by 3D point clouds, the methods used need to be improved.

Tree attribute assessment in urban greenwood using ground-based LiDAR and multiseasonal aerial photography data

Advances in LiDAR and unmanned aerial vehicle technology have made high-resolution data available, which can be used for individual tree detection and assessing tree attributes. The accuracy of these assessments is still not clear for stands with high tree species diversity as well as leaf-off and leaf-on conditions. The aim of this study was to assess the quality of tree top detection and individual tree heights extracted from photogrammetric point clouds and canopy height models as well as ground-based LiDAR clouds in mixed and coniferous forest stands depending on the phenological stage. The study has been carried out in the Botanical Garden of the Petrozavodsk State University (Republic of Karelia, Russia). Four flight missions (in 2019–2021) using Phantom 4 Pro quadcopter were conducted in the arboretum (> 200 tree species) during periods of leafless, leaf biomass growth, full foliage and autumn leaf colouration. A single ground-based laser scanning was performed using a Leica BLK 360. Multiseasonal ultra-high resolution orthophoto mosaics (1.1–2.8 cm/pixel), photogrammetric point clouds (average density is 4200 points/m2), as well as LiDAR clouds (11 600 points/m2) were obtained. Further analysis was performed on three sites differing in tree species composition, tree density and site area. Tree tops were automatically detected from photogrammetric point clouds and their heights were estimated using R environment software. We found that most of the trees (78.9%) were correctly detected by algorithms based on photogrammetric data collected in periods of full foliage and autumn colouration. We also found that the number of false positive (FP) and false negative (FN) cases increased with decreasing in green biomass on deciduous trees. Compared with an average value, tree detection quality increased by 9.4% for coniferous trees with cone-shaped crowns (Abies sibirica, A. balsamea, A. fraseri, Picea abies, P. pungens, P. omorika, Pseudotsuga menziesii, Larix sibirica) regardless of the tree density, and tree detection quality decreased by 10% for coniferous trees with an ellipsoidal-shaped crowns (e.g. Thuja occidentalis, genus Pinus) or in cases for broad-leaved trees with high tree density. The lowest value of tree detection quality (F = 0.49) was found for the leafless period. High values (F = 0.84) obtained for periods of full foliage and autumn colouration indicates that tree detection quality was well in general. For the biomass growth period, this value (F = 0.69) also indicates a high quality of tree detection results. We also found that tree heights estimated using photogrammetric data well matched with tree heights measured on LiDAR clouds (R2 = 0.99). The highest accuracy was obtained for coniferous trees with cone-shaped crowns. We also estimated the height increments of different tree species between 2019 and 2021 based on photogrammetric point clouds. The highest annual height increment was obtained for Pinus sibirica (52 cm), and the lowest for Pseudotsuga menziesii (32 cm). Overall, our results have shown the potential to use photogrammetric and LiDAR data for tree mapping and estimating tree attributes in multi-species forest stands of arboretums or urban parks, as well as in natural forests.

A Deep Learning Semantic Segmentation Method for Landslide Scene Based on Transformer Architecture

Semantic segmentation technology based on deep learning has developed rapidly. It is widely used in remote sensing image recognition, but is rarely used in natural disaster scenes, especially in landslide disasters. After a landslide disaster occurs, it is necessary to quickly carry out rescue and ecological restoration work, using satellite data or aerial photography data to quickly analyze the landslide area. However, the precise location and area estimation of the landslide area is still a difficult problem. Therefore, we propose a deep learning semantic segmentation method based on Encoder-Decoder architecture for landslide recognition, called the Separable Channel Attention Network (SCANet). The SCANet consists of a Poolformer encoder and a Separable Channel Attention Feature Pyramid Network (SCA-FPN) decoder. Firstly, the Poolformer can extract global semantic information at different levels with the help of transformer architecture, and it greatly reduces computational complexity of the network by using pooling operations instead of a self-attention mechanism. Secondly, the SCA-FPN we designed can fuse multi-scale semantic information and complete pixel-level prediction of remote sensing images. Without bells and whistles, our proposed SCANet outperformed the mainstream semantic segmentation networks with fewer model parameters on our self-built landslide dataset. The mIoU scores of SCANet are 1.95% higher than ResNet50-Unet, especially.

Potential analysis of Nipa Palm (Nypa Fruticans) in Singkil

Nipa palm (Nypa fruticans) is the plant that grouped in mangrove forest ecosystems which has a great economic potential when developed with a good strategy. The potential of nipa palm in Singkil has not been mapped properly, so that the objective of this research was to analyze the potential of nipa palm in Singkil. Data in this research were obtained directly from observations in the form of ground checks at several research locations and aerial photo data taken directly in the field using UAV vehicles. The results of data obtained through observation and documentation are then processed before being used to validate the results of image data processing. The use of UAV data provides a solution for mapping and monitoring nipa palm with high accuracy as well as a solution to the limited availability of nipa palm data in Singkil. Nipa palm was found spread along the estuary of the Lae Singkil River with total area 768.08 Ha. In the aerial photo taken in Kampong Pulosarok covering an area of 37.01 Ha, it was found that the land covering of nipa palm was 20.12 Ha or 54%. Whereas, in the aerial photo taken in Kampong Kilangan covering an area of 34.41 ha, it was found that the land covering of nipa palm was 22.16 ha or 64%. From the data, it can conclude that the nipa palm potential in Singkil is large.

Pine pest detection using remote sensing satellite images combined with a multi-scale attention-UNet model

Pest monitoring of forest areas is essential to pest control. The existing remote sensing satellite image methods have been widely used in detecting pine wilt disease due to their low cost and large detection range. However, most existing methods for pine wilt disease detection are based on multi-phase remote sensing satellite imagery and use manually designed features or machine learning-based algorithms. This makes these methods time-consuming and does not allow early detection of pest-infested forests and can also lead to further spread of the disease. In addition, machine learning-based algorithms can have poor detection performance and generalization ability. To address these shortcomings, this paper uses the pine forest in the Qingyuan area of Liaoning Province in China as a study area to analyze the physiological characteristics of pine pests based on the aerial photography data collected by a Quadrotor-type unmanned aerial vehicle (UAV). By combining these data with the artificial field survey data, the pest-infested areas of forest are marked in the Landsat 8 satellite remote sensing (SRS) images. Further, an end-to-end automatic pest detection framework is designed based on a multi-scale attention-UNet (MA-UNet) model and monophasic images. In addition, the detection performance of the developed model is further optimized using the data augmentation technique to extend the labeled dataset. Compared with the traditional model, the proposed model achieves a much better recall rate of 57.38% in detecting pest-infested forest areas, while the recall rates of the Support Vector Machine (SVM), UNet, attention-UNet, and MedT models are 14.38%, 49.33%, 48.02%, and 33.64%, respectively. According to the results, the proposed model can achieve timely detection and screening of pest-infested forest areas, improving forest management efficiency.

Identification of oil mining technogenesis based on aerial photography data

Identification of oil mining technogenesis based on aerial photography data

Combining aerial photos and LiDAR data to detect canopy cover change in urban forests.

The advancement and accessibility of high-resolution remotely sensed data has made it feasible to detect tree canopy cover (TCC) changes over small spatial scales. However, the short history of these high-resolution collection techniques presents challenges when assessing canopy changes over longer time scales (> 50 years). This research shows how using high-resolution LiDAR data in conjunction with historical aerial photos can overcome this limitation. We used the University of British Columbia's Point Grey campus in Vancouver, Canada, as a case study, using both historical aerial photographs from 1949 and 2015 LiDAR data. TCC was summed in 0.05 ha analysis polygons for both the LiDAR and aerial photo data, allowing for TCC comparison across the two different data types. Methods were validated using 2015 aerial photos, the means (Δ 0.24) and a TOST test indicated that the methods were statistically equivalent (±5.38% TCC). This research concludes the methods outlined is suitable for small scale TCC change detection over long time frames when inconsistent data types are available between the two time periods.

Monitoring lava dome extrusion of Merapi Volcano during 2018-2019, using low-cost UAV application

Merapi volcano has a well-known eruption type, namely Merapi type, in which an extruded lava dome collapses and is accompanied by pyroclastic density current (PDC). This type of eruption makes morphological monitoring of the lava dome crucial in the hazard mitigation process. After the VEI 4 eruption in 2010, a new lava dome of Merapi appeared on top of the 2010 lava dome in August 2018 and continuously grew. In November 2019, the lava dome started to collapse outward the crater area. We reported the lava dome morphological monitoring using a UAV (Unmanned Aerial Vehicle) photogrammetry conducted from August 2018 to February 2019. This UAV monitoring provides processed aerial photo data in Digital Terrain Model (DTM) and orthophoto with low operating costs and short data acquisition time. The lava domes erupted from the same eruptive canter within this period and grew evenly in all directions. The 2018-2019 Merapi lava dome has basal ratio of 0.183 to 0.290 with height of 11 to 41 m, respectively. Volume changed from 33,623 m3 in August 2018 to 658,075 m3 in February 2019, suggesting growth rate at ~3,500 m3/day. The lava base filled the crater base area (0.21 km2) and started to collapse outward in November 2019.

Implementation of Building Information Modelling for Road Rehabilitation and Reconstruction Project: Liquefaction Disaster of Palu Area

Purpose: Utilization of Building Information Modeling (BIM) is believed to improve planning accuracy, mitigate construction risks, and better plan the entire life cycle of infrastructure asset projects. The application of BIM technology in post-disaster management can combine GIS technology (Geographic Information System) with BIM technology itself, or commonly referred to as geospatial BIM. The purpose of this paper is to apply BIM technology, explain the stages of road planning and modeling with BIM, and identify the advantages of using BIM compared to conventional methods. The road planning location is the location of the Jono Oge liquefaction disaster, Palu, Indonesia.
 Design/methodology/approach: Data collection, data processing with Agisoft Metashape, DEM data input, and orthophoto image data processed by Agisoft Metashape to Autodesk® InfraWorks®, road planning with Autodesk® InfraWorks®, road planning model output and field data presentation. 3D models, IMX format files, FGDB format files, and animation videos.
 Findings: The application of BIM technology in planning and modeling road projects at liquefaction locations can be made using Agisoft Metashape software to process aerial photo data taken using drones and using Autodesk® InfraWorks® software for planning and modeling of the road.
 Research limitations/implications: Modeling Road design in the application of BIM and the use of Autodesk® InfraWorks® applications on the project. We are not modeling the pavement, just visualizing the geometry of the road.
 Practical implications: BIM implementation can drive time, effort, and realistic 3D visualization.
 Originality/value: Original paper
 Paper type: A case study

3D Topography Modeling from Integrating Bathymetric and Aerial Imagery for Sermo Reservoir Monitoring

Reservoir monitoring is important in maintaining water retention and controlling volume changes as well as sedimentation rates. Reservoir monitoring usually uses conventional means such as recording the pole leveling height at a certain time, using large ships with the sounding method and determining the position and depth by utilizing total stations with intersection method measurements. However, such conventional methods require a lot of manpower, a significant period of time, a lot of equipment, and, more often than not, yielding in results that cannot be used to depict the real condition of the corresponding reservoir. This paper uses bathymetric and aerial photographic data to construct land and water topography, the state of the reservoir, and a 3D model of the reservoir, which later can be used as the basis for volume and sedimentation analyses. An effective way of merging the aforementioned data is by utilizing point cloud data generated from bathymetric surveys and UAVs. The point cloud data was then used as the basic material for creating DEM, land, and water contours. The bathymetric data quality test results meet the SNI 7647:2010 standard tolerance with a 1.96*standard deviation of 0.191. It passes the SNI 8202:2015 photo quality test with CE90/LE90 values of 0.325 and 0.285, respectively. Merging bathymetric and aerial photographic data in the regular reservoir monitoring or shallow waters is proven to be a more efficient, effective, and optimum method compared to the existing conventional means.

ГЕОДЕЗІЯ, КАРТОГРАФІЯ І АЕРОФОТОЗНІМАННЯ

The aim of this work. This article is devoted to the study of geodynamic processes in the Tysza River basin within the Transcarpathian region with an analysis of geodetic observations obtained over the past decade. Method. Karst monitoring began with the identification of the most dangerous areas of the earth's surface that are subject to vertical displacements. After the detection of the most dangerous areas the local geodetic monitoring was carried out at facilities within the urban settlement to prevent possible accidents: Solotvyno, Dilove and Bila Tserkva. A collection of archival aerial photography was also used to develop a methodology for identifying changes in landscapes and landforms under the influence of geodynamic processes. Results. UAVs were used to remove karsts. On the basis of digital aerial photography data were created: orthophotos and digital terrain models. Digital aerial photography was carried out in accordance with the requirements of regulatory documents. To determine the dynamics of landslides and karst the digital aerial photography must be repeated several times at certain intervals. Aerial photography work was carried out in two stages in 2020 and 2021 Contour points were selected for identification marks. They are recognized on aerial photography and the terrain with an accuracy of at least 0.1 mm on the scale of the created plan. Mathematical processing of geodetic GPS measurements was performed using Trimble Geomatics Office software. After photogrammetric processing, the quality control of the obtained results was performed and digital surface models using DEM and TIN methods. Orthophotomaps on a scale of 1:1000 were made from raster images of aerial photographs, taking into account the created digital terrain model. There is a need for monitoring work to update information on the state of modern karst formations and areas with exogenous processes in Solotvyno and Bila Tserkva, Tyachiv district and the village Dilove, Rakhiv district, Transcarpathian region. The technology of topographic and geodetic works with the use of UAVs and GPS measurements in mountainous areas has been developed and tested. The results of aerial photography were used to visualize the study objects and to convey information regarding the deformation processes to local governments. For processes of natural or man-made nature (displacement, landslides, karst) requires the development of individual approaches to the use of UAVs. With the mass use of UAV images, a data bank is formed, which cannot be obtained by other methods. The study made it possible to create the method of complex determination of movements in exogenous and technogenic areas in mountainous areas with the use of the latest technologies. It allows quick establishing a plan-altitude basis of the required accuracy in the reference coordinate system in solving a number of applied geodesy problems using satellite technologies and UAVs for observations by objects.

Developing a More Reliable Aerial Photography-Based Method for Acquiring Freeway Traffic Data

Due to the widespread use of unmanned aerial vehicles (UAVs) in remote sensing, there are fully developed techniques for extracting vehicle speed and trajectory data from aerial video, using either a traditional method based on optical features or a deep learning method; however, there are few papers that discuss how to solve the issue of video shaking, and existing vehicle data are rarely linked to lane lines. To address the deficiencies in current research, in this study, we formulated a more reliable method for real traffic data acquisition that outperforms the traditional methods in terms of data accuracy and integrity. First, this method implements the scale-invariant feature transform (SIFT) algorithm to detect, describe, and match local features acquired from high-altitude fixed-point aerial photographs. Second, it applies “you only look once” version 5 (YOLOv5) and deep simple online and real-time tracking (DeepSORT) to detect and track moving vehicles. Next, it leverages the developed Python program to acquire data on vehicle speed and distance (to the marked reference line). The results show that this method achieved over 95% accuracy in speed detection and less than 20 cm tolerance in vehicle trajectory mapping. This method also addresses common problems involving the lack of quality aerial photographic data and accuracy in lane line recognition. Finally, this approach can be used to establish a Frenet coordinate system, which can further decipher driving behaviors and road traffic safety.

Estimation of Seaweed Biomass Based on Multispectral UAV in the Intertidal Zone of Gouqi Island

UAV remote sensing inversion is an efficient and accurate method for obtaining information on vegetation coverage, biomass and other parameters. It is widely used on forest, grassland and other terrestrial vegetation. However, it is rarely used on aquatic vegetation, especially in intertidal zones and other complex environments. Additionally, it is mainly used for inversion of coverage, and there have been few studies thus far on biomass assessment. In this paper, we applied multispectral UAV aerial photography data to evaluate the biomass of seaweed in an intertidal zone. During the ebb tide, UAV aerial photography and in situ sampling data were collected in the study area. After optimizing the spectral index and performing a multiple linearity test, the spectral parameters were selected as the input of the evaluation model. Combined with two machine learning algorithms, namely random forest (RF) and gradient boosting decision tree (GBDT), the biomasses of three species of seaweed (Ulva pertusa, Sargassum thunbergii and Sargassum fusiforme) in the intertidal zone were assessed. In addition, the input parameters of the machine learning algorithms were optimized by one-way ANOVA and Pearson’s correlation analysis. We propose a method to assess the biomass of intertidal seaweed based on multispectral UAV data combined with statistics and machine learning. The results show that the two machine learning algorithms have different accuracies in terms of biomass evaluation using multispectral images; the gradient boosting decision tree can evaluate the biomass of seaweed in the intertidal zone more accurately.

A reconstruction of the occupation layer of archaeological sites according to a statistical analysis of multispectral imaging

AbstractIn the modern era and especially in the 20th century, the territory with most of the archaeological sites in central Russia and the Urals was used as agricultural land. The history of long‐term studies of medieval Finno‐Ugric settlements (Cis‐Urals) makes it possible to distinguish segments of an occupation layer in various states of preservation: superficially disrupted, replaced and transported. Superficially disrupted and replaced layers are found at the settlement site and are believed to be residential and economic areas​​. The transported layer is mainly transported to the slopes of the terraces to which the sites of the Chepetskaya culture are assigned. Therefore, it is necessary to study not only the site of the settlement itself but also the adjacent territory. To reconstruct the boundaries and structure of medieval settlements, a new methodological approach has been proposed. Initially, a statistical analysis of multispectral aerial photography data was carried out: the calculation of Haralick's textural features; the reduction in the number of features by principal component analysis and the segmentation of images based on the obtained features using the k‐means method. This makes it possible to divide the settlement site and the surrounding area into areas with fundamentally different vegetation intensities. A comparison with the geophysical, soil and archaeological survey data allows the interpretation of the identified areas. The research was carried out at the settlement of Kushman‐3 (9th–13th centuries AD). Two lines of defensive structures, which are not manifested in the relief, were identified; the structural parts of the settlement were determined and an interpretation of the way they were used in the Middle Ages was proposed. As a result of the application of the new statistical algorithm, the locations of occupation layer segments in different states of preservation were determined, and the boundaries of the Kushman‐3 settlement were substantiated.

ASSESSMENT OF ACCURACY LEVELS AND PRECISION BASE MAP GEOMETRY FROM PROCESSING RESULTS AERIAL PHOTOGRAPHY DATA FOR AT MINING SECTOR UTILIZATION

Orthophoto is an aerial photo with a photographic recording process with the support of a detector or a film detector that has corrected its geometric errors using Digital Elevation Model (DEM) data. DEM data is a digital elevation model that represents the surface topography and Ground Control Point (GCP) or ground control points as a location point that is known or identified in real space on the ground surface. This can be used for mapping purposes without any scale inconsistencies throughout the photo coverage, and for getting a high-resolution scale map, that is corrected referring to geometric accuracy, which aims to be utilized in the mining sector as a base map on a large scale. From the results of the aerial photo data processing, the geometric accuracy of class I is at a scale of 1:500, which is used as a base map for the Mining Permit.

The mined mass measurement by using drone technology approach in the local scale mining in Tumiyang area, Kebasen District, Banyumas Regency, Central Java, the observation area of CV. Sinergi Karya Solutif

In this study, the objective or focal point is the number of reserves that have been mined and how to calculate the volume mined using the Unmanned Aerial Vehicle (UAV) method. In the evaluation of the volume mined using the UAV approach in Tumiyang village, Kebasen sub-district, Banyumas regency, Central Java province, this research was conducted using qualitative and quantitative methods related to the characteristics and modeling of sediment distribution from prospect block exploration. This research was conducted in the even semester of the 2020-2021 academic year, which is between October 2020 to March 2021. Based on the results of the study, it can be concluded that the data collection process using the UAV method requires a short time and complete initial information regarding the condition of the research area is very important for success. aerial photography data collection. The data obtained is aerial photo data and with supporting data such as topography and geology. Based on data processing, the mined volume obtained using supporting software is 32,313.55 m3, while the volume obtained when we calculate manually is 247,050.65 m3.

IDENTIFICATION OF BUILDING DENSITY USING UAV MAPPING METHOD IN CENTRAL KALIMANTAN REGIONAL SETTLEMENT INFRASTRUCTURE

The increased population density in urban areas has caused buildings density that need to be identified to find out the development and growth of an area that requires clear planning. This research is using aerial mapping method to produce orthophoto area of Garuda - Tingang street. The on-screen digitation was carried out based on the aerial photo data result to determine the number of buildings and the total area of the building. Then, the classification of land cover vegetation and built up land were carried out. From the data processing, 1641 buildings were found on Garuda – Tingang street. The total area of the building is 35,478 hectares. Based on the Decree of the Minister of Public Works No.378/KPTS/1987, Appendix No.22 has a low building density level. The results of the classification state that the vegetation class has a total percentage of 42,969% and the built-up land class has the percentage of 57.031%.

Development of a Software Package for Digital Forest Inventory Based on Aerial Photography Data

When clearing protective strips for linear infrastructure such as power lines, it is important to minimize tree felling. The purpose of the study is to develop and test a software package for digital forest inventory of tree and shrub vegetation in the zones of linear infrastructure facilities. In the study, a software package for digital forest inventory based on aerial photography with UAVs was developed and tested, based on the methods of multilayer perceptron and a self-organizing network based on competition (Kohonen network). The approbation has shown that it allows to accurately identifying the presence and height of trees and shrubs in the protection zones, to obtain precise coordinates for planning clearing works. At present, the areas of the territory where it is necessary to remove trees and shrubs are determined manually with an overestimation of 25-50%. Therefore, the use of a software package for digital forest inventory allows you to save up to 0.8 hectares of trees and shrubs per 1 kilometre of the length of the power transmission line, as well as save about 27 thousand rubles (about 375 US dollars) per 1 kilometre during work.

Integration of land survey data using aerial photogrammetry method on 3 dimensional bim (Building Information Modeling) Modeling

The use of UAV (Unmanned Aerial Vehicle) in land surveys and the implementation of BIM (Building Information Modeling) in the construction world has begun to be used frequently, both in the manufacture of infrastructure and building structures. The purpose of this study was to determine the effectiveness of the use of UAV in land surveys with parameters of cost, quality and time, and to find out whether the land survey can be integrated with a 3-dimensional design with applications related to BIM. This study uses secondary data in the form of aerial photo data and 3-dimensional BIM design data, which produces some data such as 3-dimensional data from the layout of a land using UAV, with a fairly good level of effectiveness compared to previous studies. In addition, the results of the integration of a 3-dimensional map of a land survey using a UAV with a 3-dimensional design of the BIM method can provide a visualization of the design form with actual conditions in the field.

Use of drones to determine potential locations of reservoir to anticipate water crisis in Weninggalih Village, Jonggol District, Bogor Regency, West Java

Weninggalih village experiences drought that keeps recurring every year which has an impact on the people in the area. The purpose of this study was to determine the potential location for the construction of a reservoir to anticipate water crisis in Weninggalih Village, Jonggol Districts, Bogor Regency. The research method used is a survey method to determine the sampling location and quantitative methods are used for analysis. The field survey used a drones for aerial photo data recording. Sampling was determined purposively based on the Digital Elevation Model in three dimensions from the resulting aerial photos. Quantitative methods are used to determine the potential location of the reservoir. The results showed that there were 6.609 photos taken with the drone flying at a height of 150 meters above ground level with 80% overlap. Based on the results show that there are three potential locations of reservoirs. The first potential sites of reservoir is in the area of 423 m2. The second one potential sites of reservoirs 1.415 m2 and the last one potential site of reservoirs are in area three land area of 1.191 m2.