Next Article in Journal
Using the Spatial Knowledge of Map Users to Personalize City Maps: A Case Study with Tourists in Madrid, Spain
Next Article in Special Issue
Generating a High-Precision True Digital Orthophoto Map Based on UAV Images
Previous Article in Journal
A Segmented Processing Approach of Eigenvector Spatial Filtering Regression for Normalized Difference Vegetation Index in Central China
Previous Article in Special Issue
Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures

ISPRS Int. J. Geo-Inf. 2018, 7(8), 331;

Use of Unmanned Aerial Vehicles (UAVs) for Updating Farmland Cadastral Data in Areas Subject to Landslides
AGH University of Science and Technology, Faculty of Mining Surveying and Environmental Engineering, 30-059 Cracow, Poland
Author to whom correspondence should be addressed.
Received: 13 July 2018 / Accepted: 15 August 2018 / Published: 19 August 2018


The purpose of this study was to verify the applicability of unmanned aerial vehicles (UAVs) to update cadastral records in areas affected by landslides. Its authors intended to compare the accuracy of coordinates determined using different UAV data processing methods for points which form the framework of a cadastral database, and to find out whether products obtained as a result of such UAV data processing are sufficient to define the extent of changes in the cadastral objects. To achieve this, an experiment was designed to take place at the site of a landslide. The entire photogrammetry mission was planned to cover an area of more than 70 ha. Given the steep grade of the site, the UAV was flown over each line at a different, individually preset altitude, such as to ensure consistent mean shooting distance (height above ground level), and thus, appropriate ground sample distance (GSD; pixel size). The results were analyzed in four variants, differing from each other in terms of the number of control points used and the method of their measurement. This allowed identification of the factors that affect surveying accuracy and the indication of the cadastral data updatable based on an UAV photogrammetric survey.
unmanned aerial vehicle (UAV); cadastre; cadastral records; landslides; mass wasting

1. Introduction

In recent years, the use of unmanned aerial vehicles (UAVs) steadily grew in many countries, including Poland, primarily as a result of rapid developments in the field of unmanned flight and drone-borne sensors for geospatial data collection. One impediment to the wider spread of UAVs is airspace regulation [1,2]. Currently, legislative and regulatory authorities worldwide are faced with the challenge of striking a balance between different UAV-related economic, information, and safety needs [3,4]. If regulatory constraints are adjusted to respond better to actual social needs, UAVs may find use in a greater number of industries [5].
Studies were already published regarding applications of UAV-collected data in areas such as forestry and agriculture. Guerra-Hernández et al. [6] tested the applicability of this technology to the stock-taking of forests. In Japan, UAV-based remote sensing was successfully used to monitor real-time wheat growth status, and to map within-field spatial variations of wheat yield for smallholder wheat growers [7]. In Brazil, UAVs were deployed over sugarcane fields. Wachholz de Souza et al. [8] described an object-based image analysis (OBIA) procedure for UAV images, designed to map and extract information about skips in sugarcane planting rows. German researchers examined the prospects of monitoring biophysical parameters and nitrogen content in wheat crops using images taken from UAVs [9], while their Swedish colleagues researched ways to identify aquatic plants which serve important environmental functions, and thus, should be monitored to detect changes in ecosystems [10]. In Reference [11], high-resolution thermal imagery acquired by an unmanned aerial vehicle was used to map plant water stress and its spatial variability. This technology can also be applied widely in engineering [12,13] and environmental protection. An interesting application is presented in Reference [14], which describes the integration of an off-the-shelf laser-based methane detector into a multi-rotor UAV, and demonstrates its efficacy in generating an aerial methane concentration map of a landfill. In order to perform plant protection operations, an automatic spraying system based on unmanned aerial vehicles (UAVs) was designed in China [15].
Nevertheless, the authors of this article were mostly interested in landslides. Most publications focus on applying UAVs to monitoring and assessing landslide dynamics [16,17,18,19,20,21]. The UAV demonstrated its capability for producing valuable landslide data, but improvements are required to reduce data processing time for the efficient generation of ortho-mosaics based on photogrammetric digital terrain models (DTMs), in order to minimize geo-referencing errors.
This study, however, addresses a different issue, namely the collection of spatial data for creating and updating cadastral databases with respect to landslide sites. The potential applicability of UAVs to the acquisition of such data was already indicated in the literature [22,23,24,25]. According to Reference [26], with the exception of Reference [27], cadastral mapping is not mentioned in review papers on application fields of UAVs [3,28,29]. As is suggested in Reference [26], this might be due to the small number of case studies within this field, the often highly prescribed legal regulations relating to cadastral surveys, and the general novelty of UAV use in mapping. Nevertheless, all existing case studies underline the high potential of UAVs for cadastral mapping, in both urban and rural contexts, for developing and developed countries.
This study was designed to answer the question of whether the statutorily required accuracy is attainable for difficult-to-access landslide sites where considerable differences in terrain elevation may hinder the use of photogrammetric data for updating cadastral databases. Analyses were conducted to compare the accuracy of different UAV data processing methods in determining the coordinates of points which form the framework of a cadastral database. Relevant tests were carried out with a varying number of ground control points (GCP) used for developing an orthophoto map and a digital surface model (DSM), as well as with varying accuracy of determining the coordinates of such points (static or kinematic Global Navigation Satellite Systems (GNSS) survey of control points).

2. Materials and Methods

2.1. Cadastral Data Requirements

As Reference [30] points out, many countries around the world recognize and appreciate the value of accurate digital cadastral databases. Theoretically, an accurate, efficient, and up-to-date cadastral database offers a better basis for the planning and implementation of a variety of real estate applications. Poland is no exception in this regard. As defined in Polish law, the cadastre is a uniform, nation-wide, and importantly, regularly updated set of data on land, buildings, and building units, as well as on their owners and users [31]. The profile of the Polish cadastre is presented in Figure 1.
The cadastral database, which is maintained by means of an information technology (IT) system, is part of the European Union’s spatial information infrastructure, established under Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community (INSPIRE). In Poland, the directive was implemented by the Act on Spatial Information Infrastructure of 4 March 2010 [32,33].
Regardless of the purpose of creating the spatial information infrastructure in the European Union, in Poland, the real estate cadastre provides information that is necessary for economic planning, spatial planning, tax and benefit assessment, property denotation in land and mortgage registers, public statistics, real estate management, and agricultural farm records [31]. Therefore, the data that are entered into the database must be absolutely reliable [34]. This means that any entry into the cadastral database can only be made based on relevant documents. These include, e.g., notarial deeds, final and legally binding court decisions, final administrative decisions, official copies of land and mortgage registers, etc. Technical surveys, although essential, cannot form the basis for entering data into the real estate cadastre [34].

2.2. Accuracy Requirements

As UAV data acquisition is a universal method, the study of whether it can be applied to obtain landslide data with sufficient accuracy was preceded by research into accuracy requirements in other countries. The first conclusion of the research was that not all applicable laws and regulations define the accuracy of positioning boundary points.
According to Reference [36], in Australia, the accuracy required for determining boundaries depends on the needs and nature of the site being surveyed. In this case, accuracy levels achievable thanks to modern technologies are balanced with user requirements. According to Reference [37], the recommended accuracy for rural areas is 0.15 m. However, Reference [36] points out that setting a single value for all kinds of land areas could be impractical.
An interesting solution was adopted in the Netherlands. According to Reference [38], when ownership of one or more parts of a parcel changes, buyers and sellers are legally obliged to identify the new boundaries. After verification, the new cadastral situation is created through surveying and cartographical activities. As a result, the new situation is represented in the cadastral registration with an accuracy of 0.06 m or greater [39].
According to Reference [30], in Malaysia, the accuracy of determining boundary points is regulated by law. The permissible tolerance is 0.05 m for urban areas and 0.10 m for farmland.
Reference [40] concludes that, in different countries, the standards of accuracy documented in manuals of survey instruction provide for varying “classes of surveys” or zones. The class of survey addresses issues, such as the difficulty of terrain and the value of the land being surveyed, and influences the expected standard of accuracy. The author also indicates that the point position as determined on the ground from any of the control points should be within a given tolerance (for example, in Zone 1—0.03 m, Zone 2—0.07 m). Accordingly, in Germany, the statutorily required accuracy depends on the zone in which the property is located.
A similar solution was adopted in Switzerland. According to Reference [22], the statutorily required accuracy for cadastral surveying is defined in the technical ordinances on official cadastral surveying [41,42]. In Swiss cadastral surveying, the territory is divided into five zones with different levels of surveying tolerances, specified in Article 3 of Reference [41]. The accuracies for points (e.g. building points, boundary points, and land cover) for the different tolerance levels are listed in Articles 27–32 [41]. For instance, the accuracy of a boundary point in zone TS2 (built-up areas and construction zones) is 0.035 m, while it is only 0.07 m in zone TS3 (intensively used agricultural and forested areas).
According to Reference [43], the accuracy of boundaries in the Polish land cadastre is determined by the mean error of the boundary point position. This error is related to the fundamental geodetic control network of class one. This horizontal control network is composed of ASG-EUPOS (active geodetic network) reference stations, which belong to the EPN (EUREF permanent network). Ultimately, the error should not exceed 0.10 m and 0.30 m, respectively [44]. The horizontal survey of points which provide numerical descriptions of plot boundaries, structure boundaries, or building outlines must be conducted in such way as to ensure that the positions of such points are measured in relation to the nearest control network points with an accuracy of no less than 0.10 m. For boundaries of land use classes and soil quality classes, the accuracy is 0.50 m [45]. With regards to surface area measurements, the prescribed accuracy for plots, buildings, and boundaries of land use classes and soil quality classes is 1 m2 [46].

2.3. Updating the Cadastre with Respect to Landslide Sites

A cadastral database can become out of date owing to a wide range of factors, including natural causes. Obviously, the elements most prone to becoming out of date are those which tend to change substantially over time [47]. Factors which may outdate information stored in a cadastral database include mass wasting, defined as natural or human-caused sliding, creeping, or falling of superficial layers or rock, weathered material, or soil [48]. Landslides usually have catastrophic consequences, including degradation of land and the razing of buildings and infrastructure. On farmland and in forests, crops and vegetation are destroyed. Restoring land hit by such a disaster to its previous condition is extremely difficult or even impossible. Long-term consequences of a landslide must be recorded in relevant spatial databases, including the cadastre.
The relevant cadastral database may require both its land and building records to be updated with respect to the site where the landslide occurred.
The landslide does not directly affect the extent of rights to real estate, i.e. it does not change the owner by virtue of law, but it may be related to the following changes [31,49]: of property rights to real estate and entities of these rights, configuration of boundaries, land uses and contours of soil quality classes, as well as of statuses of buildings and premises.
Changes in holders of the rights may occur exclusively in the areas where the principles of the reconstruction, renovation, and demolition of building structures, as well as special principles of land development and the procedure for real estate acquisition related to landslides, are specified by the Prime Minister in the form of a regulation [49]. This is the case when the Commune Council, guided by the need to ensure the safety of people and property, enacts a total ban on development in areas threatened by mass wasting and areas where mass wasting occurs, or where the reconstruction of building structures is subject to special conditions. Owners of the properties covered by such a restriction or limitation may either claim compensation for the damage they suffered, or demand that the commune purchase their property.
In order to allow reconstruction works under special conditions, the so-called local recovery plan is adopted. This plan includes, e.g., decisions on the method and implementation of investments related to linear infrastructure that is contained in this plan, as well as decisions regarding the purchase or expropriation of real estate that is essential for the implementation of investments included in the local recovery plan.
Real properties covered by the local recovery plan may also be sold, let into perpetual usufruct, lease or lending, without a tender procedure, to owners and perpetual usufructiaries of the real properties covered by the ban or restriction of development. Any change in the rights to a real property entails the necessity of introducing new data into the cadastral database (title transfer).
The configuration of boundaries are subject to change when the title transfer relates only to parts of the real estate [50] that cannot be utilized in the current manner, or which are necessary to eliminate the consequences of the natural disaster. Such a fragment of the plot is parceled out through a surveying legal procedure of plot division.
Areas with newly formed natural landmarks such as escarpments, steep slopes, faults, rocks, debris, sinkholes, landslide scars, or screes should be marked as wasteland [44]. Such sites may require changes in land use classes and soil quality classes, and marking of individual areas. In addition, the surface area of new land use classes must be determined, based on their numerical description (change in land use classes). Table 1 lists selected land plot attributes recorded in the Polish cadastre, which may change following a landslide.
For buildings, the easiest changes to record are the complete destruction or destruction of an independent part of a building. If a building is partially destroyed, its data may change as a result of later reconstruction, extension, redevelopment, conversion, etc. [51]. If only one part of a building is destroyed while the other remains usable, it seems reasonable to adjust all geometric data of such a building. It must also be borne in mind that new buildings may be erected on a landslide site. In such a case, which is rather exceptional, an entirely new structure, with all its attributes and a new classification for the plot it would be erected on, would be entered into the relevant cadastral database. Complete destruction or construction of a new building may also lead to changes in land use classes and assigned soil quality classes. Table 2 presents selected attributes which are recorded in the Polish cadastre with respect to buildings and building units, and which may change as a result of their complete or partial destruction.
All the changes listed above must be recorded under separate administrative procedures regulated in References [31,43], and are discussed, e.g., in References [52,53,54], and each affects the value of the property, which also needs to be included in the cadastre. A landslide may result in a situation where changes must be made to reflect more than one of the events described above (e.g., destruction of a building combined with a change in land use classes and soil quality classes, as well as in their respective identification symbols). Unfortunately, not all of the attributes listed in the tables above can be changed on the basis of UAV-collected data. Those that can be updated based on a photogrammetric survey are written in bold letters. Also, if a destroyed building included any units recorded in the cadastre, their destruction can be easily updated in the relevant database. However, no details of such units can generally be inferred from UAV-acquired data.

2.4. Survey Site

In May and June 2010, heavy precipitation triggered landslides which caused extensive damage across large areas of Poland [55]. One such landslide was selected for the UAV survey in question. It occurred in June 2010 in Kłodne, Poland (Municipality of Limanowa, County of Limanowa, the Lesser Poland Voivodship), and covers part of the southern slope of the Chełm Mountain in the Beskid Wyspowy mountain range. It was one of the largest and most dangerous landslides activated in the Polish Carpathians in recent years. Its site is classified as farmland, and therefore, should be afforded special protection as postulated by References [56,57,58,59,60,61]. The site before and after the landslide is presented in Figure 2.
Given the size of the area affected by mass wasting (approximately 50 ha), the extent of the damage caused, and the threat to the remaining buildings and infrastructure, the landslide was studied by many scientific and research institutions, including the Polish Geological Institute (National Research Institute) since 2010 [64], and students and employees of the Faculty of Geology, Geophysics, and Environmental Protection and of the Faculty of Mining, Surveying, and Environmental Engineering of the AGH University of Science and Technology of Kraków since 2013 [65,66]. All the previous research was conducted to determine the current external boundaries of the landslide, as well as its stability, and found that the landslide did not remain active. Since 2013, practically no movement of soil is noticeable.

2.5. Study Methods

Case studies discussed in the literature are mostly focused on the compliance of UAV-collected data with applicable local standards and requirements [22,24,38]. Reference [22] compares cadastral data from UAV-borne sensors with data collected using conventional methods. The study concludes that the factors that reduce the attainable accuracy of UAV-collected data are the quality and calibration of the camera, image quality, and the definition of ground control points (whether natural or artificial). It also points to the high flexibility of UAV systems, which enable additional information to be obtained easily, including elevation models and three-dimensional (3D) objects. The article emphasizes that UAV-based methods have enormous data collection potential, and, in areas with limited accessibility, such as those following a natural disaster, offer a valuable alternative to traditional survey methods (such as tacheometry or GNSS).
The study presented in Reference [38] was conducted in 2012 after the Dutch Product Innovation Department of Kadaster decided to seriously investigate the suitability of aerial images captured by UAV for the identification of property boundaries by executing a practical evaluation. The first experiment was carried out at the Pyramid of Austerlitz, a flat sandy area surrounded by forest with a 30-m-high pyramid, built as a victory monument by troops of Napoleon. This was the learning phase of the study, aimed at becoming acquainted with the technology. Although the target accuracy of 0.06 m was not attained, enough experience was gained to identify the factors affecting the accuracy of measurements taken using UAVs, including the quality of the camera, the camera calibration, the number and location of ground control points, and the processing software used. With modified equipment parameters and increased density of control points, the other two experiments yielded sufficiently accurate results. It must, however, be borne in mind that, except for the Pyramid of Austerlitz, the other two survey sites in the above study were flat areas of developed land, while the experiment discussed in this article was conducted at the site of a landslide with considerable differences in terrain elevation, crags, and ridges. Other research involving UAV surveying includes studies conducted to record narrow tourist trails in areas with large difference in terrain elevation in the Polish Tatra Mountains [67], which partly comprised the analysis of the accuracy of UAV-based photogrammetric products. This process involved all points of the photogrammetric control (both control points and check points). The following mean errors for point coordinates were obtained: mx = 29 mm, my = 29 mm, and mh = 31 mm, which correspond to the error of the horizontal point position, mxy = 41 mm, and the error of spatial position, mxyh = 51 mm. The worst results of root-mean-squared errors were obtained for areas with a small number of control points, which were difficult to set up due to field conditions (steep slopes and exposure).
Figure 3 shows a general processing diagram for typical aerial photogrammetry products developed from UAV-collected data.
The UAV selected for conducting the photogrammetric flight was a DJI S1000 octocopter with a maximum take-off weight of 11 kg. The platform was fitted with a Sony ILCE A7R camera equipped with a Sony Zeiss Sonnar T* FE 35 mm F2,8 ZA lens whose position was stabilized with a Zenmuse Z15-A7 gimbal. The size of the sensor used in the digital camera was 35.9 mm × 24.0 mm.
The photogrammetric mission plan was prepared, taking into account the specification of the surveying equipment used, the characteristics of the site, and the target ground sample distance (GSD) of 20 mm. On the basis of these parameters, the flight altitude was preset at 145 m. The assumed forward overlap was 80%, while the assumed side overlap was 60%. Given the UAV flight duration, the mission was divided into three parts. The measurements were taken along a total of 22 flight lines, covering a total area of more than 70 ha.
Given the steep grade of the site, the UAV was flown over each line at a different, individually preset altitude, such as to ensure consistent mean shooting distance (height above ground level), and thus, appropriate GSD (pixel size) (Figure 4). During the survey, 465 photographs were taken, and 388 of those were selected for further processing. The key flight parameters are presented in Table 3.
A total of 33 points, comprising 15 control points and 18 check points, were established throughout the survey site. Their locations are presented in Figure 5. The coordinates of the points were determined using two methods, static GNSS and real time kinematic (RTK) GNSS, both with reference to an ASG-EUPOS reference station.
The static survey was conducted at the Kłodne landslide together with members of the Dahlta Student Club of Surveyors as part of their seventh measurement cycle. Two types of GNSS receivers were employed for the survey, namely a Leica GPS500 and a Leica GPS1200. Measurements were taken during 11 sessions, each lasting 40–45 min and together covering 43 points (including 33 points used for this study). Static GNSS data were referenced to the ASG-EUPOS Nowy Sącz (NWSC) reference station and post-processed using the Leica Geo Office 8.0 software. The RTK measurements were taken with a Leica Viva CS10 receiver using an RTK correction signal from the Nowy Sącz reference station. The logging interval was 1 s, and the measurement of each point included no fewer than 30 epochs.

3. Results and Discussion

The respective mean errors of determining the coordinates of the control points and the check points were as follows:
  • mxy = 30 mm and mh = 50 mm for RTK GNSS;
  • mxy = 5 mm and mh = 15 mm for static GNSS.
These values were determined in relation to the base control network, which includes ASG-EUPOS reference stations.
A comparison of coordinates determined using two independent methods (Table 4) shows that the obtained values were free of gross errors, and that the maximum difference in the coordinates did not exceed 0.050 m.
The UAV-collected data were processed using the Agisoft PhotoScan Professional software [69] in four variants (Table 5), differing from each other in terms of the number of control points used (Figure 5) and the method of their measurement.
The first step of image processing was to align the images. At this stage, the images were uploaded to the software and were given the initial orientation by adding approximate coordinates of image projection centers. The alignment was competed using an accuracy parameter set to “high”. It ensured the use of the original image resolution. Additionally, control points were indicated on the images. This was preceded by uploading coordinates of the terrestrial photogrammetric control to the software. Each marker was indicated on all photos where it was visible. The block of photographs for each variant was finally aligned, and, at the same time, the camera calibration parameters were determined. In this process, the following values were determined: the principal distance (c), the location of the principal point (cx and cy), and the distortion parameters (k1, k2, k3, and p1 and p2). As a result of the alignment, the root-mean-squared errors of the control points were obtained (Table 6).
The assumed control point identification error at the data processing stage (interpretation error) was 1 pix (20 mm). Accordingly, the ultimate marker (control point) mean spatial error was 60 mm for RTK GNSS, and 25 mm for static GNSS.
It must be noted that both the number of photogrammetric control points used, as well as the survey method applied to measure them, affects the obtained error values. The control point mean-squared error for Variant 1, which was considered to be the least accurate (eight control points surveyed using RTK GNSS), was more than twice the analogous parameter determined for Variant 4 (15 control points surveyed using static GNSS). However, none of the errors was greater than 0.100 m. The obtained values of spatial errors were considerably affected by altitude errors, which were negligible for the purposes of updating a cadastral database. The mean-squared errors of the planar coordinates showed a similar tendency and range from 0.026 m (Variant 4) to 0.048 m (Variant 1).
The final step of image processing in the Agisoft PhotoScan software was to create dense point clouds with the method of dense matching. Then, DSMs were created. These were used as a basis to conduct orthorectification of images and to create orthophoto maps. As a result of the data processing conducted, four orthophoto maps with a GSD of 19.7 mm and four DSMs with a resolution of 39.5 mm were generated in the PL-2000/7 rectangular planar coordinate system.
In order to determine the accuracy of the obtained photogrammetric products and their suitability for updating a cadastral database, the positions of 18 check points on the orthophoto map were measured, with their altitude coordinates read from the DSM. The obtained coordinates were compared with the data received by means of the direct static GNSS and RTK GNSS surveys. The comparison of the coordinates was carried out for the datasets marked by (+) in Table 7. The computed accuracy assessment parameters for check points surveyed using static GNSS, compared to an aligned block of photographs, are presented in Table 8. Similar calculations for check points surveyed using RTK GNSS are presented in Table 9.
It must be noted that, regardless of the check point survey method, the standard deviation of the differences in spatial coordinates did not exceed 0.100 m for any of the calculation variants, or 0.050 m for the planar coordinates. Such values met the accuracy requirements for determining the coordinates of boundary points laid down in applicable Polish laws and regulations [45]. The mean difference in the coordinates is also worth noting, as it may indicate the occurrence of a systematic error. In Variants 2, 3, and 4, this parameter was near 0 for each coordinate, while, in Variant 1, it was −0.050 m for the altitude coordinate, which is a significant value. Analysis of the maximum and minimum differences in the coordinates shows that they ranged from −0.100 m to 0.100 m except for Variant 1, for which the obtained results were the most unsatisfactory (as much as −0.200 m for ΔH).
When studying the obtained results, one must consider the locations of the check points in relation to the control points (Figure 5). Some of the check points were located on or even outside the external boundary of the survey area as defined by the extreme control points (points 606, 609, 613, 513, 507, 305, 306, 311, and 312). Such locations were chosen intentionally, in order to see how the corresponding deviations would trend. It was at precisely those points that the maximum/minimum differences in coordinates were recorded for Figure 6.
For planar coordinates, the greatest east–west deviation was recorded for point 606. Depending on the calculation variant, it ranged from −0.087 m to −0.052 m. The greatest north–south deviations occurred for points 507, 311, and 312, from 0.062 m to 0.049 m for point 507, from 0.024 m to 0.071 m for point 311, and from 0.034 m to 0.071 m for point 312. For point 305, similar results (0.067 m and 0.060 m) were obtained only in Variants 3 and 4.
When ready, the orthophoto map was compared with the cadastral database. In order to carry out relevant spatial analysis, the boundary of the landslide was outlined on the orthophoto map. It was established in line with the landslide visible in the photographs. If the boundary of the landslide ran close to cadastral boundaries, it was drawn over the boundaries of relevant plots in order to prevent the creation of new objects with a small surface area in the cadastre.
It should be emphasized here that any interference with the cadastral database must be preceded by appropriate settlements that are carried out in the field with the owners of the subject properties. Entities of the real estate cadastre express their consent for entering new objects into the database or updating the existing ones, by signing relevant documents that are necessary to carry out surveying and legal procedures.
Given the fact that the survey was conducted following the demolition of all the designated buildings, this study focused solely on plots of land. Once the vectorized outline of the landslide was imposed on the cadastral map (Figure 7), it was found that the landslide covered 25 plots in whole and 34 plots in part. These analyses were carried out manually. There are studies aimed at exploring options for automatically delineating boundaries for UAV-based cadastral mapping [26,70]. However, in the opinion of the authors, the algorithms described in Reference [70] to extract boundaries automatically would be ineffective in the case of areas subject to landslides. Cadastral boundaries in areas affected by landslides are assumed to be not visible, as they may not coincide with natural or manmade object contours.
For the plots which were entirely covered by the landslide, data should be changed with respect to land use classes and soil quality classes, i.e., such plots should be marked as wasteland, excluded from soil classification. With regards to the other plots, their land use classes and soil quality classes should be properly adjusted. Under applicable Polish laws and regulations [45], this means that the boundary of the landslide running over relevant plots should be surveyed with an accuracy of at least 0.50 m in relation to the nearest points of the geodetic control network and the survey network, which can be attained with a large margin of error on the basis of UAV-acquired data.
Unfortunately, this process cannot be automated due to the fact that surveys using UAV technology are only a part of the surveying and legal procedures that lead to the introduction of changes in the cadastral database.
If the landslide is declared undevelopable land area, it will be possible to register future changes with respect to all or part of the properties covered by the landslide. In such a case, the outline of the landslide, which would become a new boundary, would have to be surveyed with an accuracy of 0.10 m in relation to the nearest points of the geodetic control network and the survey network [45], which is also attainable, as proven above.
Given the accuracy of the generated orthophoto map, it can be concluded that UAV-collected data may be sufficiently accurate to be used in surveying and legal procedures aimed at updating the cadastral database.

4. Conclusions

As UAV photogrammetry became immensely popular, it is unsurprising that attempts were also made to apply it to the maintenance of a cadastre. The research described in this article proved that an orthophoto map and a DSM developed with due care based on UAV-acquired data may be used to update cadastral data, including those with respect to areas affected by landslides. To enable that, a photogrammetric control network needs to be established over the entire site of the survey. The coordinates of the control points in the case under discussion were determined using two independent methods, static GNSS and RTK GNSS. Analysis of the obtained results showed that the accuracy of UAV photogrammetry products did not, in practice, depend on which of these data collecting and processing methods were used. Results were slightly improved in Variants 3 and 4, where the coordinates of the control points were established using static GNSS. However, given the amount of time and labor required for conducting such a survey, this method cannot be considered to produce significantly better results than RTK GNSS. A more important factor contributing to the accuracy of photogrammetry products was the number of control points used. Although for Variants 1 and 3, where only eight control points were used, the attained accuracy of the orthophoto map and the DSM satisfied the applicable Polish legal requirements [45], i.e., the maximum deviations for individual check points did not exceed 0.100 m. Such a level of accuracy may be insufficient to ensure compliance with legal requirements imposed in other countries, e.g., Switzerland or the Netherlands. It is, therefore, advised that a greater number of photogrammetry control points be used in order to increase the certainty that the photogrammetric products generated are correct and as accurate as is required.

Author Contributions

Conceptualization, A.B. and P.Ć. Methodology, P.Ć. Data collection and processing, P.Ć. and E.P. Data validation, P.Ć. and E.P. Investigation, A.B., P.Ć., E.P., and P.P. Literature surveys, A.K.-P. and P.P. Writing—original draft preparation, A.B., P.Ć., A.K.-P., and E.P. Writing—review and editing, A.K.-P., E.P., and P.P. Visualization, A.B. and E.P.


This research received no external funding.


The study was carried out with financial support from the statutory research No. and No. from the AGH University of Science and Technology in Krakow.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Puniach, E.; Kwartnik-Pruc, A.; Ćwiąkała, P. Use of unmanned aerial vehicles in Poland. In Proceedings of the GIS ODYSSEY 2016, Geographic Information Systems Conference and Exhibition, Perugia, Italy, 5–9 September 2016; pp. 207–217. [Google Scholar]
  2. Stöcker, C.; Bennett, R.; Nex, F.; Gerke, M.; Zevenbergen, J. Review of the Current State of UAV Regulations. Remote Sens. 2017, 9, 459. [Google Scholar] [CrossRef]
  3. Watts, A.C.; Ambrosia, V.G.; Hinkley, E.A. Unmanned Aircraft Systems in Remote Sensing and Scientific Research: Classification and Considerations of Use. Remote Sens. 2012, 4, 1671–1692. [Google Scholar] [CrossRef][Green Version]
  4. Cramer, M.; Bovet, S.; Gültlinger, M.; Honkavaara, E.; McGill, A.; Rijsdijk, M.; Tabor, M.; Tournadre, V. On the use of RPAS in national mapping—The EuroSDR point of view. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2013, XL-1/W2, 93–99. [Google Scholar] [CrossRef]
  5. Haarbrink, R. UAS for geo-information: Current status and perspectives. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2011, XXXVIII-1/C22, 207–212. [Google Scholar] [CrossRef]
  6. Guerra-Hernández, J.; González-Ferreiro, E.; Monleón, V.J.; Faias, S.P.; Tomé, M.; Díaz-Varela, R.A. Use of Multi-Temporal UAV-Derived Imagery for Estimating Individual Tree Growth in Pinus pinea Stands. Forests 2017, 8, 300. [Google Scholar] [CrossRef]
  7. Du, M.; Noguchi, N. Monitoring of Wheat Growth Status and Mapping of Wheat Yield’s within-Field Spatial Variations Using Color Images Acquired from UAV-camera System. Remote Sens. 2017, 9, 289. [Google Scholar] [CrossRef]
  8. Wachholz de Souza, C.H.; Lamparelli, R.A.C.; Rocha, J.V.; Magalhães, P.S.G. Mapping skips in sugarcane fields using object-based analysis of unmanned aerial vehicle (UAV) images. Comput. Electron. Agric. 2017, 143, 49–56. [Google Scholar] [CrossRef]
  9. Schirrmann, M.; Giebel, A.; Gleiniger, F.; Pflanz, M.; Lentschke, J.; Dammer, K.-H. Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery. Remote Sens. 2016, 8, 706. [Google Scholar] [CrossRef]
  10. Husson, E.; Ecke, F.; Reese, H. Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images. Remote Sens. 2016, 8, 724. [Google Scholar] [CrossRef]
  11. Park, S.; Ryu, D.; Fuentes, S.; Chung, H.; Hernández-Montes, E.; O’Connell, M. Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV). Remote Sens. 2017, 9, 828. [Google Scholar] [CrossRef]
  12. Zhang, Y.; Yuan, X.; Fang, Y.; Chen, S. UAV Low Altitude Photogrammetry for Power Line Inspection. ISPRS Int. J. Geo-Inf. 2017, 6, 14. [Google Scholar] [CrossRef]
  13. Vacca, G.; Dessì, A.; Sacco, A. The Use of Nadir and Oblique UAV Images for Building Knowledge. ISPRS Int. J. Geo-Inf. 2017, 6, 393. [Google Scholar] [CrossRef]
  14. Emran, B.J.; Tannant, D.D.; Najjaran, H. Low-Altitude Aerial Methane Concentration Mapping. Remote Sens. 2017, 9, 823. [Google Scholar] [CrossRef]
  15. Xue, X.; Lan, Y.; Sun, Z.; Chang, Ch.; Hoffmann, W.C. Develop an unmanned aerial vehicle based automatic aerial spraying system. Comput. Electron. Agric. 2016, 128, 58–66. [Google Scholar] [CrossRef]
  16. Niethammer, U.; James, M.R.; Rothmunda, S.; Travelletti, J.; Joswiga, M. UAV-based remote sensing of the Super-Sauze landslide: Evaluation and results. Eng. Geol. 2012, 128, 2–11. [Google Scholar] [CrossRef]
  17. Turner, D.; Lucieer, A.; de Jong, S.M. Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV). Remote Sens. 2015, 7, 1736–1757. [Google Scholar] [CrossRef][Green Version]
  18. Al-Rawabdeh, A.; He, F.; Moussa, A.; El-Sheimy, N.; Habib, A. Using an Unmanned Aerial Vehicle-Based Digital Imaging System to Derive a 3D Point Cloud for Landslide Scarp Recognition. Remote Sens. 2016, 8, 95. [Google Scholar] [CrossRef]
  19. Fernández, T.; Pérez, J.L.; Cardenal, J.; Gómez, J.M.; Colomo, C.; Delgado, J. Analysis of Landslide Evolution Affecting Olive Groves Using UAV and Photogrammetric Techniques. Remote Sens. 2016, 8, 837. [Google Scholar] [CrossRef]
  20. Rothmund, S.; Vouillamoz, N.; Joswig, M. Mapping slow-moving alpine landslides by UAV—Opportunities and limitations. Lead Edge 2017, 36, 571–579. [Google Scholar] [CrossRef]
  21. Casagli, N.; Frodella, W.; Morelli, S.; Tofani, V.; Ciampalini, A.; Intrieri, E.; Raspini, F.; Rossi, G.; Tanteri, L.; Lu, P. Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning. Geoenviron. Disasters 2017, 4, 9. [Google Scholar] [CrossRef]
  22. Manyoky, M.; Theiler, P.; Steudler, D.; Eisenbeiss, H. Unmanned aerial vehicle in cadastral applications. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2011, XXXVIII-1/C22, 57–62. [Google Scholar] [CrossRef]
  23. Jazayeri, I.; Rajabifard, A.; Kalantari, M. A geometric and semantic evaluation of 3D data sourcing methods for land and property information. Land Use Policy 2014, 36, 219–230. [Google Scholar] [CrossRef]
  24. Mesas-Carrascosa, F.J.; Notario-García, M.D.; de Larriva, J.E.M.; de la Orden, M.S.; Porras, A.G.-F. Validation of measurements of land plot area using UAV imagery. Int. J. Appl. Earth Obs. Geoinf. 2014, 33, 270–279. [Google Scholar] [CrossRef]
  25. Stępień, G.; Sanecki, J.; Klewski, A.; Beczkowski, K. Wyznaczanie granic użytków rolnych z wykorzystaniem bezzałogowych systemów latających (Determination of agricultural land borders using unmanned aerial systems). Infrastruct. Ecol. Rural Areas 2014, III/2, 1011–1024. [Google Scholar] [CrossRef]
  26. Crommelinck, S.; Bennett, R.; Gerke, M.; Nex, F.; Yang, M.Y.; Vosselman, G. Review of Automatic Feature Extraction from High-Resolution Optical Sensor Data for UAV-Based Cadastral Mapping. Remote Sens. 2016, 8, 689. [Google Scholar] [CrossRef]
  27. Colomina, I.; Molina, P. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 2014, 92, 79–97. [Google Scholar] [CrossRef]
  28. Everaerts, J. The use of unmanned aerial vehicles (UAVs) for remote sensing and mapping. Proc. ISPRS 2008, 37, 1187–1192. [Google Scholar]
  29. Remondino, F.; Barazzetti, L.; Nex, F.; Scaioni, M.; Sarazzi, D. UAV photogrammetry for mapping and 3D modeling: Current status and future perspectives. Proc. ISPRS 2011, 38/C22, 25–31. [Google Scholar] [CrossRef]
  30. Hashim, N.M.; Omar, A.H.; Ramli, S.N.M.; Omar, K.M.; Din, N. Cadastral database positional accuracy improvement. Proc. ISPRS 2017, 42, 91–96. [Google Scholar] [CrossRef]
  31. Geodetic and Cartographic Law of 17 May 1989. Journal of Laws of 2016, Item 1629 (as Amended), Poland. Available online: (accessed on 9 February 2018).
  32. Bieda, A.; Bydłosz, J.; Parzych, P. Actualization of data concerning surface flowing waters, based on INSPIRE directive requirements. Geom. Environ. Eng. 2013, 7, 25–36. [Google Scholar] [CrossRef]
  33. Balawejder, M.; Adamczyk, T.; Cygan, M. The problem of adjusting polish spatial information resources to the standards of the inspire. In Proceedings of the GIS ODYSSEY 2016, Geographic Information Systems Conference and Exhibition, Perugia, Italy, 5–9 September 2016; pp. 14–24. [Google Scholar]
  34. Hycner, R. Ewidencja gruntów i budynków w Polsce jako kataster (Registration of land and buildings in Poland as a cadastre). Acta Sci. Acad. Ostroviensis 2006, 23, 19–43. (In Polish) [Google Scholar]
  35. Bydłosz, J. The application of the Land Administration Domain Model in building a country profile for the Polish cadastre. Land Use Policy 2015, 49, 598–605. [Google Scholar] [CrossRef]
  36. Stock, K.M. Accuracy Requirements for Rural Land Parcel Boundaries. J. Aust. Surv. 1998, 43, 165–171. [Google Scholar] [CrossRef]
  37. Nisbet, K.A. Procedures for the South Australian Legal Coordinated Cadastre. In Proceedings of the International Conference on Cadastral Reform, Melbourne, Australia, 29 June–1 July 1992; Hunter, G.J., Williamson, I.P., Eds.; Department of Surveying and Land Information, University of Melbourne: Melbourne, Australia, 1992. [Google Scholar]
  38. Rijsdijk, M.; van Hinsbergh, W.H.M.; Witteveen, W.; Buuren, G.H.M.; Schakelaar, G.A.; Poppinga, G.; Persie, M.V.; Ladige, R. Unmanned aerial systems in the process of juridical verification of cadastral border. Proc. ISPRS 2013, 61, 325–331. [Google Scholar] [CrossRef]
  39. Handleiding Technische Werkzaamheden (HTW) Kadaster; Kadaster: Apeldoorn, The Netherlands, 1994.
  40. Siriba, D. Positional Accuracy Assessment of a Cadastral Dataset based on the Knowledge of the Process Steps used. In Proceedings of the 12th AGILE International Conference on Geographic Information Science, Hannover, Germany, 2–5 June 2009. [Google Scholar]
  41. Das Eidgenössische Departement für Verteidigung, Bevölkerungsschutz und Sport, Technische Verordnung des VBS über die Amtliche Vermessung, 10. June 1994 (Stand 1. Juli 2008). pp. 2–16. Available online: (accessed on 9 February 2018).
  42. Der Schweizerische Bundesrat, Verordnung über die Amtliche Vermessung, 18. November 1992 (Stand 1. July 2008). pp. 2–3. Available online: (accessed on 9 February 2018).
  43. Hanus, P.; Pęska-Siwik, A.; Szewczyk, R. Spatial analysis of the accuracy of the cadastral parcel boundaries. Comput. Electron. Agric. 2018, 144, 9–15. [Google Scholar] [CrossRef]
  44. Regulation of the Minister of Regional Development and Construction of 29 March 2001 on the Register of Land and Buildings. Available online: (accessed on 9 February 2018).
  45. Regulation of the Minister of Internal Affairs and Administration of 9 November 2011 on the Technical Standards for the Performance of Surveying Detailed Measurements, as Well as the Preparation and Transfer of These Measurement Results to the National Cartographic Information Center Database. Available online: (accessed on 9 February 2018).
  46. Bieda, A.; Hanus, P.; Jasińska, E.; Preweda, E. Accuracy of Determination of Real Estate Area. International Conference Environmental Engineering. Available online: (accessed on 9 February 2018).
  47. Akińcza, M.; Bieda, A.; Buśko, M.; Hannibal, H.; Hanus, P.; Hycner, R.; Krzyżek, R.; Kwartnik-Pruc, A.; Łuczyński, R.; Przewięźlikowska, A. Aktualne Problemy Katastru w Polsce (Present Cadastral Problems in Poland); Oficyna Wydawnicza Politechniki Warszawskiej: Warszawa, Poland, 2014. (In Polish) [Google Scholar]
  48. Environmental Protection Law of 27 April 2001. Journal of Laws of 2017, Item 519, Poland. Available online: (accessed on 9 February 2018).
  49. Act of 11 August 2001 on Special Principles of the Reconstruction, Renovation and Demolition of Building Structures Destroyed or Damaged as a Result of the Action of the Element. Journal of Laws of 2016, Item 1067 (as Amended), Poland. Available online: (accessed on 9 February 2018).
  50. Bieda, A.; Hanus, P. Subdivision as a tool for regulating and approving legal status of real estate. In Proceedings of the International Conference Environmental Engineering, Vilnius, Lithuania, 22–23 May 2014. [Google Scholar]
  51. Pęska, A.; Benduch, P. Land and Buildings Register Data Change as a Result of Construction Process. Geom. Environ. Eng. 2016, 10, 75–86. [Google Scholar] [CrossRef]
  52. Bieda, A.; Hanus, P. Determination of real estate boundaries for the purposes of subdivision process. Geom. Environ. Eng. 2010, 4, 15–20. [Google Scholar]
  53. Łuczyński, R. Technologiczne i prawne aspekty wznawiania oraz ustalania przebiegu granic działek ewidencyjnych (Technological and legal aspects of marking out and delimitation of borders of lands parcels). Acta Sci. Pol. Geodesia et Descriptio Terrarum 2009, 8, 23–38. (In Polish) [Google Scholar]
  54. Kwartnik-Pruc, A. Assessment of procedures for determining property boundaries in the context of creating cadastre in Poland. In Proceedings of the SGEM2013, GeoConference on Informatics, Geoinformatics and Remote Sensing, Albena, Bulgaria, 16–22 June 2013; pp. 71–78. [Google Scholar]
  55. Front-Dąbrowska, T. The Principles of Changing Land Use Classification under Special Provisions in the Areas of Poland where Landslide Movements and the Risk of Landslide Movements Occur. Geom. Environ. Eng. 2015, 9, 25–38. [Google Scholar] [CrossRef]
  56. Kwartnik-Pruc, A.; Bydłosz, J.; Parzych, P. Analiza procesu przeznaczenia gruntów rolnych i leśnych na cele inwestycyjne (The analysis of the process of destining agricultural and forest land for investment purposes). Studia i Materiały Towarzystwa Naukowego Nieruchomości (J. Pol. Real Estate Sci. Soc.) 2011, 19, 169–179. (In Polish) [Google Scholar]
  57. Kwartnik-Pruc, A.; Szafarczyk, A. Designating agricultural land for investment purposes and the requirements of environmental sustainability. Polish J. Environ. Stud. 2011, 20, 212–216. [Google Scholar]
  58. Bieda, A.; Jasińska, E.; Preweda, E. Surveying Protection of Agricultural Land in Poland. International Conference on Environmental Engineering. Available online: (accessed on 9 February 2018).
  59. Bielska, A.; Turek, A. Analysis of the needs for updates of the land and building register considering the procedure of exclusion of agricultural land from production. Infrastruct. Ecol. Rural Areas 2016, IV/3, 1633–1644. [Google Scholar] [CrossRef]
  60. Cienciała, A. The issue of the legal and surveying division of agricultural land on selected examples in Poland and on the international stage. In Proceedings of the GIS ODYSSEY 2017, Geographic Information Systems Conference and Exhibition, Trento, Vattaro, Italy, 4–8 September 2017; pp. 67–75. [Google Scholar]
  61. Pawlikowska, E.; Popek, P.; Bieda, A.; Moteva, M.; Stoeva, A. Analysis of the Legal Methods of Agricultural Land Protection in Central Europe On the Example of Poland and Bulgaria. Real Estate Manag. Valuat. 2017, 25, 58–71. [Google Scholar] [CrossRef][Green Version]
  62. Geoportal. Available online: (accessed on 9 February 2018).
  63. Google Maps. Available online: (accessed on 9 February 2018).
  64. Wójcik, A.; Perski, Z.; Borkowski, A.; Wojciechowski, T. Zastosowanie Teledetekcji Lotniczej i satelitarnej do badania dynamiki czynnych osuwisk w 2010 r na przykładzie osuwiska w Kłodnem koło Limanowej (Application of aerial and satellite remote sensing to study the dynamics of active landslides in 2010 on the example of a landslide in Klodne near Limanowa); Polski Kongres Drogowy: Zakopane, Poland, 2011. [Google Scholar]
  65. Ćwiąkała, P.; Drwal, P.; Daroch, M.; Grabek, P. Temporary monitoring of areas prone to landslides illustrated with the example of the Kłodne village. Infrastruct. Ecol. Rural Areas 2014, IV/1, 1089–1099. [Google Scholar] [CrossRef]
  66. Ćwiąkała, P.; Stanisz, J.; Wróbel, A.; Kaczmarczyk, R.; Drwal, P.; Grabek, P.; Daroch, M.; Pękala, M.; Świątek, M.; Zierkiewicz, M. Wyznaczenie przemieszczeń powierzchniowych na osuwisku w Kłodnem (gmina Limanowa, południowa Polska) (Determination of surface displacement on the landslide in Kłodne (Limanowa community, southern Poland). Przegląd Geologiczny 2016, 64, 122–130. (In Polish) [Google Scholar]
  67. Ćwiąkała, P.; Kocierz, R.; Puniach, E.; Nędzka, M.; Mamczarz, K.; Niewiem, W.; Wiącek, P. Assessment of the Possibility of Using Unmanned Aerial Vehicles (UAVs) for the Documentation of Hiking Trails in Alpine Areas. Sensors 2018, 18, 81. [Google Scholar] [CrossRef] [PubMed]
  68. Nex, F.; Remondino, F. UAV for 3D mapping applications: A review. Appl. Geom. 2014, 6, 1–15. [Google Scholar] [CrossRef]
  69. Agisoft. Available online: (accessed on 8 August 2018).
  70. Crommelinck, S.; Bennett, R.; Gerke, M.; Yang, M.Y.; Vosselman, G. Contour Detection for UAV-Based Cadastral Mapping. Remote Sens. 2017, 9, 171. [Google Scholar] [CrossRef]
Figure 1. The profile of the Polish cadastre based on the Land Administration Domain Model [35].
Figure 1. The profile of the Polish cadastre based on the Land Administration Domain Model [35].
Ijgi 07 00331 g001
Figure 2. Survey site: (a) April 2010—before landslide [62]; (b) October 2014—after landslide [63].
Figure 2. Survey site: (a) April 2010—before landslide [62]; (b) October 2014—after landslide [63].
Ijgi 07 00331 g002
Figure 3. Processing diagram for typical aerial photogrammetry products developed from unmanned aerial vehicle (UAV)-collected data [68].
Figure 3. Processing diagram for typical aerial photogrammetry products developed from unmanned aerial vehicle (UAV)-collected data [68].
Ijgi 07 00331 g003
Figure 4. Plan of photogrammetric flight over the landslide.
Figure 4. Plan of photogrammetric flight over the landslide.
Ijgi 07 00331 g004
Figure 5. Locations of control points and check points.
Figure 5. Locations of control points and check points.
Ijgi 07 00331 g005
Figure 6. Differences in coordinates for check points compared to an aligned block of photographs: (a) check points surveyed using static GNSS; (b) check points surveyed using RTK GNSS.
Figure 6. Differences in coordinates for check points compared to an aligned block of photographs: (a) check points surveyed using static GNSS; (b) check points surveyed using RTK GNSS.
Ijgi 07 00331 g006
Figure 7. (a) Outline of the landslide over plots of land; (b) plots wholly covered by the landslide.
Figure 7. (a) Outline of the landslide over plots of land; (b) plots wholly covered by the landslide.
Ijgi 07 00331 g007
Table 1. Land plot attributes that may change as a result of a landslide.
Table 1. Land plot attributes that may change as a result of a landslide.
ItemPlot AttributeTitle TransferPlot DivisionChange in Land Use Classes
1Identifier of the parcel +
2Numerical description of boundaries * +
3Surface area * +
4Surface area of land use classes and soil quality classes * ++
5Land value and valuation date ++
6Land registration unit number+
7Land and Mortgage Register reference+
8Reference to documents defining other rights to the plot+
9For public road plots—road numbers +
10For landmark plots such as water courses, reservoirs, parks, or other natural landmarks—names of such landmarks +
* Data obtainable through unmanned aerial vehicle (UAV) survey.
Table 2. Building attributes that may change as a result of a landslide.
Table 2. Building attributes that may change as a result of a landslide.
ItemBuilding AttributeDestructionPartial DestructionReconstruction
1Identifier of the building+
2Building’s status *++
3Numerical description of the building’s outline * +
4Value of the building +
5Date constructed, and, if applicable, date reconstructed +
6Degree of certainty which the dates referred to in item 5 are determined with +
7scope of redevelopment/conversion +
8Number of aboveground/underground stories +
9Building footprint * +
10Usable area of the building determined on the basis of survey or information included in the relevant planning permission +
11Total usable area of the following: +
units which are independent properties, +
units which are not subdivided from the main property, +
rooms comprising units +
12Number of independent units disclosed in the cadastre +
13Information on whether the building has been commissioned in whole or in part +
14Identification of the commissioned part of the building +
15Date the building or part thereof was commissioned +
16Total number of rooms in a residential building +
17Demolition date for the following:
the whole building
part of the building


18Reasons why the building or part thereof was demolished++
19Information on whether the building is equipped with high-speed-ready in-building infrastructure +
* Data obtainable through UAV survey.
Table 3. Technical specification of flight planned over the Kłodne landslide.
Table 3. Technical specification of flight planned over the Kłodne landslide.
Ground sample distance (GSD; pixel size) (cm)2.0
Forward overlap (%)80
Side overlap (%)60
Flight altitude (m)145
Distance between photographs (m)20
Distance between lines (m)60
Ground footprint of one image (m)148.3 × 99.0
Total number of lines22
Total survey area (ha)70
Table 4. Comparison of coordinates of points determined using static GNSS and RTK GNSS.
Table 4. Comparison of coordinates of points determined using static GNSS and RTK GNSS.
Average difference (m)0.0200.0220.002
Maximum difference (m)0.0070.0440.046
Minimum difference (m)−0.036−0.009−0.026
Standard deviation (m)0.0100.0140.014
Table 5. Survey data processing variants.
Table 5. Survey data processing variants.
8 control points15 control points
surveyed usingsurveyed using
8 control points15 control points
surveyed usingsurveyed using
static GNSSstatic GNSS
Table 6. Comparison of control point mean-squared errors for all of the data processing variants.
Table 6. Comparison of control point mean-squared errors for all of the data processing variants.
Block TypemX (m)mY (m)mH (m)mXY (m)mXYH (m)
VARIANT 10.0370.0310.0830.0480.096
VARIANT 20.0230.0260.0550.0350.065
VARIANT 30.0210.0200.0380.0290.048
VARIANT 40.0160.0200.0320.0260.042
Table 7. List of data analysis performed on datasets.
Table 7. List of data analysis performed on datasets.
Data SetCoordinates of Check Points Measured on Photogrammetric Products
Coordinates of check points surveyed using static GNSS++++
Coordinates of check points surveyed using RTK GNSS++
Table 8. Average differences in coordinates and their maximum and minimum values for check points surveyed using static GNSS, compared to an aligned block of photographs.
Table 8. Average differences in coordinates and their maximum and minimum values for check points surveyed using static GNSS, compared to an aligned block of photographs.
Average difference (mm)−716−50−618312−4−1411−36
Maximum difference (mm)558082496073673743603895
Minimum difference (mm)−71−67211−71−52−89−34−72−86−39−54−72
Standard deviation (mm)363584342848292644302342
Table 9. Average differences in coordinates and their maximum and minimum values for check points surveyed using RTK GNSS, compared to an aligned block of photographs.
Table 9. Average differences in coordinates and their maximum and minimum values for check points surveyed using RTK GNSS, compared to an aligned block of photographs.
Average difference (mm)10−3−521101
Maximum difference (mm)627178565087
Minimum difference (mm)−51−87−206−47−72−79
Standard deviation (mm)323785303347

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Back to TopTop