Developing a Guideline of Unmanned Aerial Vehicle’s Acquisition Geometry for Landslide Mapping and Monitoring

Abstract

Remote sensing data and techniques are widely used for monitoring and managing natural or man-made disasters, due to their timeliness and their satisfactory accuracy. A key stage in disaster research is the detailed and precise mapping of an affected area. The current work examines the relationship that may exist between the acquisition geometry of Unmanned Aerial Vehicle (UAV) campaigns and the topographic characteristics of an investigated area, toward landslide mapping and monitoring that is as accurate as possible. In fact, this work, concerning the systematic research of the acquisition geometry of UAV flights over multiple active landslides, is conducted for the first time and is focused on creating a guideline for any researcher trying to follow the UAV photogrammetric survey during landslide mapping and monitoring. In particular, UAV flights were executed over landslide areas with different characteristics (land cover, slope, etc.) and the collected data from each area were classified into three groups depending on UAV acquisition geometry, i.e., nadir imagery, oblique imagery, and an integration of nadir and oblique imagery. High-resolution orthophotos and Digital Surface Models (DSMs) emerged from the processing of the UAV imagery of each group through structure-from-motion photogrammetry (SfM). Accuracy assessment was carried out using quantitative and qualitative comparative approaches, such as root mean square error calculation, length comparison, and mean center estimation. The evaluation of the results revealed that there is a strong relationship between UAV acquisition geometry and landslide characteristics, which is evident in the accuracy of the generated photogrammetric products (orthophotos, DSMs). In addition, it was proved that the synergistic processing of nadir and oblique imagery increased overall centimeter accuracy.

1. Introduction

Remote sensing has emerged as an important and valuable tool for various earth observation applications. In particular, remote sensing data and techniques are widely used for monitoring and managing natural or man-made disasters, due to their timeliness and their satisfactory accuracy [1]. The development of UAVs has opened up new possibilities in hazard assessment and disaster risk management [2,3,4]. In fact, these low-cost remote sensors have proven their effectiveness in mapping hazards and disasters (earthquakes, floods, landslides, etc.), monitoring human activity during emergencies, and protecting and preserving cultural heritage sites affected by geo-hazards [5,6,7,8,9,10].

A key stage in disaster research is the detailed and precise mapping of an affected area. In this framework, scientists have developed several new processing methodologies, in which different parameters were assessed in achieving the most efficient results. Specifically, the selection of an appropriate number of ground control points (GCPs) during the georeferencing procedure of the obtained UAV imagery was one of the primary parameters under investigation. Thus, various photogrammetric campaigns, consisting of different combinations of GCPs (varying from 4 to up to 20), were evaluated using root mean square error (RMSE) values extracted from the respective DSMs [11,12,13]. The results demonstrated a clear influence of the number of GCPs on the accuracy of UAV photogrammetry. Although the selection of about 20 GCPs (defined according to the study area) managed to reduce the RSME by 50%, it was proved that a higher number of GCPs slightly improved the accuracy. Moreover, other studies dealt with analyzing the distribution of GCPs within the study area and the effect of such distribution on the accuracy of the derived DSMs, which decreases as the distance to the nearest GCP increases [14,15]. In general, the accuracy of photogrammetric products increases asymptotically as the number of GCPs increase, until an optimal GCP density is reached.

The collection of GCPs is a time-consuming task. Thus, the development of UAVs with onboard Global Navigation Satellite System Real Time Kinematic (GNSS RTK) positioning—in which the georeferencing of the images does not require GCPs—constitutes a promising solution [16,17]. The main disadvantage of the specific approach is the presence of systematic elevation errors, which emerge from the incorrect determination of the interior orientation parameters estimated during bundle adjustment [18]. A recent study proposed that a combination of two UAV flights at the same altitude, consisting of nadir and oblique imagery, was able to reduce the systematic elevation error to less than 0.03 m [19].

Other approaches focused on evaluating the effect of different scenarios, consisting of multi-view camera combinations, on the accuracy of UAV photogrammetry. Specifically, the combination of non-metric oblique and vertical views, along with an appropriate collection of GCPs, has significantly enhanced the accuracy of the photogrammetric procedure, extracting DSMs comparable to those derived by Light Detection and Ranging (LiDAR) [20]. Indeed, this integration of nadir and oblique imagery has proven its effectiveness in numerous studies, including the reconstruction of the surfaces and the determination of the main geometries in a quarry environment, the geomorphological mapping of landslides, and geotechnical/ hazard mapping [21,22,23]. In addition, it has been proven that the reconstruction of a topographically complex terrain requires the synergy of oblique and facade images to complement nadir views [24]. This synergistic use significantly improved the geometric accuracy of topographic reconstruction, by approximately 35%.

Although the combination of multi-view UAV imagery is a very successful approach in a variety of applications, nadir images combined with a dense distribution of GCPs constituted an ideal solution for the reconstruction of 3D agricultural surfaces [25]. On the other hand, oblique UAV imagery was considered particularly suitable for the three-dimensional modeling of buildings, cities, or urban settlements, presenting an enhanced achievable accuracy [26]. Moreover, oblique-viewing images can be utilized effectively for the 3D modelling of historical architectures and cultural heritage research, especially in areas characterized by limited accessibility [27]. In addition, oblique images increased by 50% the accuracy of 3D representations of topography in areas with high and steep slopes [28].

The current study aims to create a guideline for researchers who try to follow the UAV photogrammetric survey method for landslide mapping and monitoring, by exploring derived data quality (orthophotos and DSMs) in reference to data acquisition geometry. There are previous studies on the influence of the ground control number to the horizontal or vertical accuracy of orthophotos and DSMs, respectively [12,13,14,15]. However, the current study is the first to conduct systematic research on UAV data acquisition methodology applied on multiple active landslides. UAV flights were performed over four landslide areas with different characteristics (land cover, slope, volume, dimensions, etc.), and the collected data from each area were classified into three groups, depending on UAV acquisition geometry: (a) nadir-only images, (b) oblique-only images, and (c) an integration of nadir and oblique images. Moreover, a flat urban area was also surveyed for validation purposes. The processing of UAV imagery in each category was based on structure-from-motion (SfM) photogrammetry, leading to the creation of high resolution orthophotos and digital surface models (DSMs). Accuracy assessment was carried out using quantitative and qualitative comparative approaches, such as root mean square error calculation, length comparison, and mean center estimation. The derived results demonstrate the benefits of combining oblique and nadir images in order to reduce systematic errors and increase the overall accuracy of orthophotos and Digital Surface Models.

2. Materials and Methods

2.1. Case Studies

The selection of research areas was based on the criterion of heterogeneity in terms of slope, land cover, vegetation coverage, etc. Thus, four different landslides were set as case studies. We also surveyed a flat industrial area for validation purposes (Figure 1) (

Table 1. Selected case studies and characteristics (topography, vegetation).

Case Study ID	Locations	Topography Description	Vegetation
1	Moira	Steep slope, large extent, grassland 1	Low and sparse vegetation
2	Egkremni	Steep slope, coastal area, grassland	Very sparse vegetation
3	Zachlorou	Steep slope, narrow gorge, grassland	Low vegetation
4	Messarista	Steep slope, urban settlement, narrow roads, densely build-up area	Low vegetation
5	Patras Port	Flat, industrial environment	No vegetation

¹ Based on coordination of information on the environment (CORINE) land cover classification.

). In order to eliminate the influence of vegetation height on DSM production, the study areas presented, in general, with low and sparse vegetation, as described in Table 1.

The first landslide area is located close to the village of Moira, within Western Greece. The landslide occurred on 20 January 2017 on a mountainous, steep slope, covering an area of approximately 65,569.20 m². The landslide material was spread 300 m in length and 300 m in width and it was geologically structured from flysch, limestone, and loose cherts. It was characterized as a complex slide, based on the different lithologies—silicate and/or silicate lithology was presented in the northwestern part, while limestones were detected in the southeast. The occurrence of the landslide was related to geological factors (flysch lithology), as well as to the rapid snow melting. It has been demonstrated that the increase of the water content of clay soils (flysch) reduces the shear strength of soils, acting as a triggering factor for the occurrence of landslides [29].

The destruction of the road connecting the village of Moira with the city of Patras, as well as a significant change in the local landscape, were the main consequences of the landslide.

The second landslide took place in November 2015 on the Egkremni beach on the island of Lefkada; it was classified as a debris slide . The Ionian Islands constitute the northwestern part of the Hellenic arc, which is considered as a site of complex continent-continent to continent-ocean convergent plate margins. The Cephalonia Transform Fault Zone (CTFZ) is recognized as the major tectonic structure of the site and is responsible for the particularly active seismicity in the wider area. In addition, another fault, named Athani, is detected ashore, shaping the west coast of Lefkada island [30,31]. The Athani fault, along with smaller, parallel faults with similar kinematics, form steep slopes, which are an ideal site for the occurrence of landslides during seismic events [32]. On 17 November 2015, a 6.5 magnitude earthquake struck the west coast of the island, causing a series of landslides on the cliffs [33,34].

Another active continental region, in terms of tectonics and seismicity, is Northern Peloponnese, due to the fast-extending rift of the Gulf of Corinth. This third area of interest is located close to the village of Kato Zachlorou within Western Greece. The first mass movements at the specific area occurred in April 2019, while the main event, consisting of rock falls and earth flows, took place on 4 April 2020. The repetitive sliding episodes were strongly associated with the increase of the water content of the clay soils, due to intense rainfall [29].

The fourth area of interest includes a landslide located in a semi-mountainous village of Western Greece named Messarista. Extremely heavy and prolonged rains hit the wider region on 11 December 2021, acting as a main triggering factor for the occurrence of a series of landslides in different places throughout the region, as well as within the village of Messarista. The phenomenon is still ongoing and is being monitored by local authorities, while the landslides were classified as earth slides and earth flows.

Finally, a flat industrial area, covering part of the new port of the city of Patras, was chosen for the validation of the results, due to the past view on photogrammetry, in which flat areas were surveyed using nadir imagery solely.

2.2. Data Collection

UAV imagery was obtained using a DJI Phantom 4, which is equipped with a built-in GNSS system and a CMOS camera (12.4 MP) with 4000 × 3000 resolution. Each case study consisted of three different flight grids (nadir, oblique, and nadir and oblique). During these distinct campaigns, flight characteristics were kept the same (

Table 2. Characteristics and parameters of UAV campaigns.

Case Study ID	Acquisition Geometry	Number of Photos	Average Flight Altitude (m)	Average GSD (cm)	Along the Track Overlap %	Along the Track Overlap %
Moira	Nadir	189	110	3.5	90	75
	Oblique	174
	Synergistic use	363
Egremni	Nadir	97	60	2.3	90	75
	Oblique	84
	Synergistic use	181
Zachlorou	Nadir	60	70	2.5	90	75
	Oblique	210
	Synergistic use	286
Messarista	Nadir	65	110	3.5	90	75
	Oblique	70
	Synergistic use	135
Patras Port	Nadir	96	60	2.3	90	75
	Oblique	85
	Synergistic use	181

). Specifically, flights were executed with a 90% along-track overlap and a 75% cross-track overlap. However, the flight acquisition altitude was adapted to the topography of each study area, with the aim of extracting photogrammetric products with spatial resolution between 2.3 and 3.5 cm (Table 2). The collected UAV imagery of each area was classified into three groups of data. The first group included nadir-only images with a 90-degree gimbal pitch angle; the second group consisted of oblique-only images with a 65-degree gimbal pitch angle; and a synergistic use of nadir and oblique imagery was selected for the third group.

In addition, GCPs were distributed throughout the survey areas in order to orient and match the aerial imagery to data measured terrestrially. GCPs were collected using a Leica GS08 GNSS receiver. Coded targets were created according to the general recommendations of Agisoft Metashape software [35], and were printed on matte finish plastic boards. These targets were used as GCPs (Figure 2). Furthermore, large rectangular aluminum targets were placed in the survey areas for comparison and validation of the photogrammetric outputs (Figure 3). These aluminum targets had specific dimensions and a hole in each corner, in order to be accurately measured with a GNSS sensor.

2.3. Methodology

The aim of the current study was to examine whether the geometry of UAV acquisition plays a key role in the accuracy of derived photographic products that are subsequently used as high-precision data for landslide mapping and monitoring. A schematic illustration of the applied methodology is displayed in Figure 4.

In more detail, UAV surveys were conducted over five study areas, presenting different characteristics (topography, land cover, etc.). Three UAV flights, consisting of: (a) nadir-only acquisitions, (b) oblique-only acquisitions, and (c) an integration of nadir and oblique acquisitions, were executed for each case study. The different UAV acquisition trajectories of nadir- and oblique-viewing campaigns over the landslide of Messarista (case study) are depicted in the Figure 5. It is worth mentioning that the collection of the oblique-viewing imagery was implemented as the UAV moved forward and backward, following line paths that were perpendicular to the slope of each study area. Furthermore, photogrammetric GCPs were collected during the UAV surveys.

The processing of the collected UAV imagery was carried out through SfM photogrammetry. The specific technique contributes to the three-dimensional reconstruction of the topography, using the basic principles of photogrammetry along with computer vision algorithms [36,37,38,39]. The main advantage of this functional and low-cost method is that it allows the automatic and simultaneous determination of scene geometry, camera positions, and orientation, without requiring pre-existing known points. Thus, multiple, overlapped and shifted 2D images are transformed into 3D representations using an automatic, feature-matching algorithm.

The photogrammetric processing of UAV imagery took place in Agisoft Metashape software. The calibration and optimization of the camera took place in accordance with Agisoft’s default values, as set for the DJI Phantom 4 camera. Internal orientation parameters were estimated automatically, due to the ability of the software to recognize the model of the camera and to specify the appropriate settings.

The high-quality option was selected for the alignment of the images, aiming at a more precise estimation of the camera positions [40]. At the same time, the processing of UAV imagery was implemented using the original image size. The quality option was closely linked to the quality of the topographic reconstruction. Moreover the, ultra-high setting was defined as the parameter during “build dense cloud” and “build mesh” procedures.

Orthophotos and DSMs emerged from the processing of the collected UAV imagery. These products were projected into the Hellenic Geodetic Reference System 1987. Specifically, three orthophotos and three DSMs were created for each study area, corresponding to the three different acquisition geometries (i.e., nadir-viewing images, oblique-viewing images, and an integration of nadir and oblique imagery). The evaluation of the accuracy of the generated orthophotos and DSMs was based on qualitative and quantitative comparative approaches, including RMSE calculation, length comparison, and mean center estimation.

3. Results

3.1. Accuracy Asssessment of Orthophotos

Three high resolution orthophotos were extracted from the photogrammetric processing for each case study, consisting of UAV data from the three different acquisition groups, i.e., (a) nadir-viewing imagery, (b) oblique-viewing imagery, and (c) the integration of nadir and oblique imagery. The aforementioned orthophotos, covering the area of case study 4 (Messarista), are depicted in Figure 6. As can be observed, a visual comparison between the orthophotos created by data for the different groups of geometry acquisitions is a particularly difficult task, due to apparent similarity.

In this context, the evaluation of the accuracy of the derived orthophotos was performed using comparison approaches in an ArcGIS environment. GIS applications have been utilized in respective studies for the characterization of geological features and geological mapping [41]. In this study, GCPs were utilized as precise reference positions. Then, two geodetic lines were shaped from the x-y coordinates of the GCPs of each study area. Figure 7 displays the reference geodetic lines, as formed for each case study. It is important to highlight that we tried to create the geodetic lines in different topographic reliefs, varying from steep slopes to flat regions. Subsequently, we digitized the same lines in all orthophotos, derived from the processing of the UAV imagery of all groups of acquisition geometry. The digitization of the lines was based on the visual identification of the respective GCPs, which were used to shape the reference geodetic lines. The length of each digitized and reference geodetic line were calculated and compared (

Table 3. Comparison of the length of the geodetic lines of each case study in accordance with the respective reference length.

Case Study ID	Acquisition Geometry	Length Line 1 (m)	Difference (m)	Difference %	Length Line 2 (m)	Difference (m)	Difference %
Reference line		226.676			129.881
Moira	Nadir	226.933	−0.257	−0.113%	129.550	0.331	0.254%
	Oblique	226.745	−0.069	−0.030%	129.937	−0.056	−0.043%
	Synergistic use	226.734	−0.058	−0.029%	129.725	0.156	0.120%
Reference line		32.898			23.221
Egremni	Nadir	32.861	0.038	0.112%	23.274	−0.054	−0.228%
	Oblique	32.906	−0.007	−0.024%	23.229	−0.008	−0.034%
	Synergistic use	32.868	0.030	0.091%	23.229	−0.008	−0.034%
Reference line		8.938			16.152
Zachlorou	Nadir	8.921	0.017	0.190%	16.179	−0.027	−0.167%
	Oblique	8.944	−0.006	−0.067%	16.179	−0.027	−0.167%
	Synergistic use	8.938	0.000	0.000%	16.177	−0.025	−0.154%
Reference line		60.524			23.064
Messarista	Nadir	60.577	−0.053	−0.088%	23.149	−0.085	−0.368%
	Oblique	61.002	−0.478	−0.790%	23.299	−0.235	−1.018%
	Synergistic use	60.539	−0.015	−0.014%	23.097	−0.033	−0.143%
Reference line		84.240			74.220
Patras Port	Nadir	84.559	−0.319	−0.379%	74.492	−0.272	−0.366%
	Oblique	84.117	0.123	0.146%	73.988	0.232	0.312%
	Synergistic use	84.228	0.012	0.012%	74.208	0.012	0.016%

). The length variations were significantly small, which proved the high accuracy of the photogrammetric products. In addition, the integration of nadir and oblique viewing acquisitions displayed the smallest length variations in eight out of ten comparisons, demonstrating the positive influence of the specific acquisition geometry on the enhancement of the accuracy of the derived orthophotos. Moreover, oblique-viewing geometry showed a better performance, compared to nadir-viewing acquisitions. In particular, the difference in the length of line 1, in comparison to the corresponding reference line in the case study of Zachlorou, was zero for the orthophoto resulting from the integration of nadir and oblique imagery. In addition, the variation for the nadir-viewing and oblique-viewing orthophotos was estimated at 0.190% and 0.067%, respectively. In the case of Egnemni, the percentage variations in line 2 were calculated at about 0.034% for the orthophotos that emerged from either the synergistic use of nadir and oblique images or the nadir-viewing imagery. In contrast, the corresponding difference for the nadir-viewing orthophoto was obviously larger (i.e., 0.228%).

The processing of nadir-viewing images, along with oblique imagery, enhanced the overall accuracy in all the investigated areas. In more detail, for nine of ten cases (Table 3) the length difference between the GNSS measurements and the measurements on the orthophotos was less than 0.05 m (5 cm). Taking into consideration the terrain steepness and the dense vegetation, we assume that the overall accuracy is excellent.

It is worth mentioning that even in the fifth test area (Patras Port), the combined processing of nadir and oblique images gave results that were more accurate than the single processing of nadir or oblique data. In detail, the length difference of the geodetic lines for the common data set was only 0.012 m (centimeter accuracy), while the respective accuracies for the nadir or oblique data set ranged between 0.12 m and 0.31 m (Table 3).

Another method, utilized for the evaluation of the accuracy of orthophotos resulted from the photogrammetric processing of each acquisition geometry, was based on the comparison of the mean center of the reference geodetic lines and the respective digitized ones. The mean center constitutes the geographical center of a set of features resulting from average x and y coordinates [42,43], which is calculated as:

$$\overline{X}=\frac{{{\displaystyle \sum }}_{i=1}^n{x}_i}{n}\mathrm{and}\overline{Y}=\frac{{{\displaystyle \sum }}_{i=1}^n{y}_i}{n}$$

(1)

where x_i and y_i are the coordinates for a feature i and n is the total number of features. This approach is widely used for the identification of distribution changes or for the comparison of the distributions of features. The mean centers of the reference geodetic lines of each case study are depicted in Figure 7. The Near tool [44] was applied for the determination of the distance between the reference mean centers and the respective mean center of the digitized lines. Distance variations are displayed in

Table 4. Near distances of mean centers resulting from the reference mean centers of geodetic lines 1 and 2.

Case Study ID	Acquisition Geometry	Near Distance (m) Mean Center of Line 1	Near Distance (m) Mean Center of Line 2
Moira	Nadir	0.023	0.095
	Oblique	0.015	0.049
	Synergistic use	0.013	0.012
Egkremni	Nadir	0.020	0.022
	Oblique	0.025	0.014
	Synergistic use	0.038	0.021
Zachlorou	Nadir	0.006	0.016
	Oblique	0.070	0.068
	Synergistic use	0.023	0.010
Messarista	Nadir	0.064	0.036
	Oblique	0.477	0.305
	Synergistic use	0.082	0.034
Patras Port	Nadir	0.700	0.812
	Oblique	0.946	0.890
	Synergistic use	0.024	0.009

. As can be noted, the multi-acquisition geometry (nadir and oblique imagery) presented the shortest distances from the reference mean centers in 50% of the UAV campaigns.

As a final comparison of the orthophotos derived from different acquisition geometries, we utilized the variations of the dimensions of the aluminum targets. These dimensional variations, regarding the orthophotos covering the landslide of Zachlorou, are presented in

Table 5. Comparison of the dimensions of the aluminum target between the different acquisition geometries.

Case Study ID	Acquisition Geometry	Length (m)	Difference (m)	Difference %	Area (m2)	Difference (m2)	Difference %
Reference		0.538			0.150
Zachlorou	Nadir	0.546	−0.008	−1.486%	0.149	0.001	0.666%
	Oblique	0.548	−0.010	−1.858%	0.148	0.002	1.332%
	Synergistic use	0.542	−0.004	−0.743%	0.151	−0.001	−0.666%

. As can be observed, the synergistic use of nadir and oblique photos increase the accuracy of the orthophoto. The true length of the aluminum target is 0.538 m and the respective measured value from the oblique and nadir imagery is 0.542 m, meaning that the overall difference is only 4 mm. The difference value for the nadir imagery is 8 mm and for the oblique imagery is 10 mm.

3.2. Accuracy Asssessment of DSMs

The evaluation of the accuracy of the extracted DSMs was executed through the computation of RMSE, which estimates the differences between the values of a DMS and the reference high-precision values. The computation is performed by the following equation:

$$RMSE=\sqrt{\frac{1}{n}{{\displaystyle \sum }}_{i=n}^n{\left(h_{ref}-h_i\right)}^2}$$

(2)

where h_ref is the reference elevation, h_i is the DSM elevation at point i, and n is the number of GCPs. A basic condition for the performance of the calculation is that the reference data should be an order better than the data to be evaluated. Thus, GCPs with sub-millimeter accuracy were utilized as reference points in the current study.

The variations in the elevation values between the reference data and the respective photogrammetric DSMs, acquired by different viewing geometries over all case studies, are presented in

Table 6. RMSE values emerging from the generated DSMs.

Case Study ID	Acquisition Geometry	RMSE (m)
Moira	Nadir	0.380
	Oblique	0.260
	Synergistic use	0.140
Egkremni	Nadir	0.092
	Oblique	0.085
	Synergistic use	0.083
Zachlorou	Nadir	0.506
	Oblique	0.498
	Synergistic use	0.475
Messarista	Nadir	0.162
	Oblique	0.357
	Synergistic use	0.090
Patras Port	Nadir	0.159
	Oblique	0.236
	Synergistic use	0.047

. It is obvious that the synergistic use of nadir- and oblique- viewing images during UAV campaigns managed to minimize the elevation errors in most DSMs. In particular, the RMSE for the DSM arising from the combined use of nadir and oblique imagery in the case study of Egnemni, was calculated at 0.083 m, while the respective errors for the nadir-viewing and oblique-viewing DSMs were estimated at 0.092 m and 0.085 m, respectively. Furthermore, in the case study of Messarista, the RMSE for the DSM that emerged from the simultaneous processing of nadir and oblique imagery was calculated at 0.090 m. The respective RMSE for the nadir-viewing DSM was computed at 0.162 m, while the error of the oblique-viewing DSM was notably larger (i.e., 0.357 m).

Although oblique-viewing images demonstrated an overall good performance in the generation of DSMs, we noticed a correlation between the UAV acquisition geometry and the topographic characteristics of the survey area. Specifically, nadir-viewing geometry was considered more suitable for DSM generation in flat urban (case study 5) or densely built-up (case study 4) areas, since it revealed smaller values in elevation variations than oblique imagery.

4. Discussion

The capability of performing a high accuracy field campaign with excellent repeatability is the basic demand [45,46,47] for landslide mapping and monitoring for assessing any deformation, usually within a steep and possibly dangerous environment. Sub-centimeter accuracy is a prerequisite in both horizontal and vertical axes in order to assess the activity of a landslide. Aiming to develop a guideline for accurate landslide mapping, we conducted four repeated tests within four active landslides with diverse characteristics. Both the horizontal and the vertical accuracy of the orthophotos and the DSMs were examined.

It was proved that the combination of nadir and oblique imagery produced more accurate results in all the studied cases. This output was in full accordance with the results of a previous study [28] that mentioned that overall accuracy is increased by 50% when nadir and oblique imagery are combined. The statistical comparison of the length of the digitized geodetic lines demonstrated that the integration of nadir and oblique images provides orthophotos with higher accuracy. Oblique imagery seems to provide more accurate results than nadir imagery. The only exception was the Messarista case study, where the processing of the oblique images yielded the worst results, compared to the respective result from the nadir images. This can be justified, as Messarista is a densely built-up village on a steep hill with very narrow backstreets. These narrow side streets cannot be clearly mapped in oblique imagery. Furthermore, the houses are built side-by-side and the front buildings hide the back ones. As a result, nadir imagery provides useful information that cannot be derived from oblique imagery alone.

Similar results were extracted from the measurements of the near distances of the mean centers that were calculated in the ArcMap environment. In almost all the cases, the simultaneous processing of nadir and oblique imagery provided more accurate results. The same combination produced more accurate DSMs in all the study areas. As presented in Table 4, in very steep environments (the four landslide areas) and in a flat area (Patras Port), the vertical accuracy of the oblique-nadir DSM was higher in comparison to the accuracies of either nadir or oblique DSMs.

Summarizing the overall assessment of the results, based on the application of simple statistical calculations, it was proved that the synergistic use of nadir and oblique imagery was considered to be the most appropriate geometry at 80% (i.e., in eight cases), according to the comparison of the length of the two lines of the five study areas (i.e., 10 cases) (Figure 8a). Moreover, comparing only nadir-viewing and oblique-viewing geometry, oblique acquisitions provided more accurate results by 70% (Figure 8b). Figure 9 and Figure 10 display the corresponding evaluations using the comparison of near distances and RMSE calculation.

Our findings are in accordance with those of previous studies [20,21,24,25,26]. Specifically, nadir and oblique images were compared with TLS data for the mapping of a fault plane [20]. The tests proved that both precision and accuracy have been increased when the two sets of images were combined. It was marked [21] that the combined use of nadir and oblique imagery offers a valued methodology for open-pit operations and for the continuous monitoring of the exploitation by managers. UAV data acquired from oblique and facade viewing angles were compared with nadir images in a steep and complex mountainous terrain, and the produced point clouds were compared with TLS data [24]. It was proved that the combination of oblique and façade images, compared to nadir data, increased the geometric accuracy of the derived point cloud data [24]. In a respective test [25], it was demonstrated that the combination of nadir and oblique images ameliorates the accuracy of 3D surface models in an agricultural area when no ground control points or a small number of ground control points are used. Nadir and oblique imageries were compared for 3D building representation [26]. The results proved that oblique data ameliorates the obtainable accuracy of the derived point cloud and of the produced 3D model. As described in [28], supplementing nadir image blocks with oblique images consistently mitigates the systematic error patterns within complex topography. The results from many scenarios proved that the combination of nadir and oblique images increased precision and accuracy, and reduced data gaps. Those previous results are in full accordance with our results. In all the studied areas, it was also proved that the simultaneous processing of the nadir and oblique imagery decreases the RMSE error and increases the geometric precision.

5. Conclusions

The objective of the current study was to develop a guidance for accurate landslide mapping in steep terrains. In this context, we performed four identical tests within four active landslides spreading throughout Western Greece. The main conclusions that emerged from this research included the following:

• It was proved that the acquisition of UAV oblique and nadir imagery and the synergistic processing increase overall centimeter accuracy.
• In general, oblique imagery provides more accurate results in steep terrains compared to nadir imagery. However, in areas that combine high slopes, dense urban settlements, and narrow streets (as in Messarista village), nadir imagery could not be omitted.
• Even in flat areas, such as Patras Port, the combined use of oblique and nadir imagery ameliorates the overall accuracy.
• A UAV flight campaign should be adjusted each time to an investigated area’s characteristics and local topography.
• In steep terrains, an average flight altitude between 70 and 110 m above ground level or a ground spatial resolution of around 3 cm are recommended for both nadir and oblique campaigns in order to assess centimeter accuracy.