Triangulated Irregular Network Research Articles

ArcGIS-mapping to sustainable wastewater management in Greece: siting artificial wetlands systems in a biosystem

<p>In this work, the ArcGIS technology combines analogue and digital geospatial data to derive multiple resolution meshes with a triangulated irregular networks (TINs) approach that serves to integrate the geospatial data such as surface topography, hydro graphic features and land surface characteristics into an adaptive representation of a basin biosystem. The ArcGIS model that has been developed is applied at the municipal level to a small remote settlement with less than 2000 people in Northern Greece. The aim was a site assessment for constructing an artificial wetland (ATW) system as a viable solution to the wastewater management problem and protection of biosystems. This study demonstrates that there are discrepancies in Greece between the existing open geospatial data and on the basis of the results from our study we can conclude that this combination of local maps and geographic information in ArcGIS with a TIN approach increases our knowledge of the physical terrain. It accordingly facilitates the analysis and implementation of action plans by selecting suitable sites for construction of ATW systems in small remote settlements. We moreover discuss problems regarding spatial data quality and scale and provide suggestions for improvement while the desktop classification steps can be easily reproduced for other data-similar countries.</p>

Catchworks: A Historical Water-Distribution System on Mountain Meadows in Central Slovakia

Water meadows or flooded meadows are known from many European countries. A historical irrigation system—catchworks—was identified in only one locality in Slovakia. This article brings a methodical approach to the identification of catchworks on mountain slopes. The main aim was to delineate catchworks using terrain and land use geospatial data intended to supplement existing data on catchworks from the field survey. The identification of shallow and narrow channels in the field is difficult, and their detection in a digital terrain model (DTM) and orthomosaic photos is also challenging. A detailed DTM elaborated from laser scanning data was not available. Therefore, we employed break lines of a Triangulated Irregular Network (TIN) model created by EUROSENSE Ltd. 2017, Bratislava, Slovakia. to determine microtopographic features on mountain slopes. Orthomosaics with adjusted red (R) green (G) and blue (B) band thresholds (digital numbers) in a time sequence of 16 years (2002–2018) and the Normalized Green-Red Difference Index (NGRDI) (2018) determined vital herbaceous vegetation and higher biomass. In both cases, the vegetation inside wet functional catchworks was differently coloured from the surroundings. In the case of dry catchworks, the identification relied only on microtopography features. The length of catchworks mapped in the field (1939.12 m; 2013) was supplied with potential catchworks detected from geospatial data (2877.18; 2018) and their total length in the study area increased above 59.74% (4816.30 m). Real and potential catchworks predominantly occupied historical grassland (meadows and pastures) (1952–1957) (4430.31; 91.99%). This result corresponds with the findings of foreign studies referring that catchworks on mountain slopes were related to livestock activities. They are important elements of sustainable land use with a water retention function in traditional agricultural landscapes.

The Effect of Vegetation on the Propagation Loss of V2I Network in High Altitude and Mountainous Area

With the gradual formation of the commercialization of the fifth-generation communication technology, the Internet of Vehicles technology has developed rapidly, and the vehicle to infrastructure (V2I) network technology is an important part of the Internet of Vehicles. In high-altitude and mountainous areas, the communication quality of the V2I network is not only affected by terrain, but forest vegetation will also cause greater propagation loss. Aiming at the influence of vegetation in high-altitude and mountainous complex environment on the propagation loss of V2I network, this paper establishes an equivalent model of vegetation in high-altitude and mountainous areas, and analyzes the relationship between propagation loss, propagation distance and frequency of transmitting antenna. At the same time, through digital elevation model (DEM) data and triangulated irregular network (TIN), MATLAB was used to simulate the communication coverage of V2I network in the complex area covered by vegetation, and the influence of vegetation on the propagation loss of V2I network in high altitude and mountainous areas was obtained. In the future, constructing the Internet of Vehicles in a complex environment, the results of research in this article will provide a scientific basis for network quality with high reliability and low latency.

A Fast Progressive TIN Densification Filtering Algorithm for Airborne LiDAR Data Using Adjacent Surface Information

Point cloud filtering is a preliminary and essential step in various applications of airborne LiDAR (light detection and ranging) data, with progressive triangulated irregular network (TIN) densification (PTD) being one of the classic methods for filtering LiDAR point clouds. The PTD algorithm densifies ground points through iteration operation based on initial ground seed points. However, the poor performance in steeply sloped areas and time-consuming processing are serious drawbacks for PTD algorithms. In this paper, we propose a fast progressive TIN densification (FPTD) filtering algorithm for airborne LiDAR data using adjacent surface information. After carefully establishing parameters and removing outliers, our improved FPTD uses a sliding window to obtain significantly more initial ground seed points. And we modified some iterative determination criterion, including the definition of maximum relative elevation threshold and the introduction of signed computation, to eliminate avoidable non-ground points. Then adjacent surface information was utilized to iterate each point cloud block, which is the smallest unit that point cloud can be segmented. Additionally, the algorithm is easily run in a multi-threaded environment, further accelerating the filtering process to some extent. Experiments show that our proposed FPTD filtering algorithm is fast and robust. Compared to the PTD, the FPTD algorithm yields better error rates and kappa coefficients in 1/12 of the time required by the PTD.

Attitude Mounting Misalignment Estimation Method for the Calibration of UAV LiDAR System by using a TIN-based Corresponding Model

In traditional attitude mounting misalignment estimation methods for the calibration of unmanned autonomous vehicle (UAV) based light detection and ranging (LiDAR) system, signalized targets and iterative corresponding models are required, which makes it highly cost and computationally time-consuming. This paper presents an attitude mounting misalignment estimation (AMME) method for the calibration of UAV LiDAR system. The proposed method is divided into the coarse registration of LiDAR strips and the estimation of the attitude mounting misalignment. Firstly, 3D keypoints are extracted in the point clouds using the scale-invariant feature transform (SIFT) algorithm. Afterwards, the point feature transform (PFH) descriptor is used for 3D keypoint matching. Then, the coarse registration is executed. In the second part of the contribution, the systematic errors in the attitude mounting misalignment are estimated by incorporating the proposed triangular irregular network (TIN) corresponding model into the calibration modelling. Using the TIN-based corresponding model saves time and cost for AMME method. Furthermore, it provides two important effects: practical and computational, as no designed calibration boards, segmentation and iterative matching are needed. The performance of the proposed method is demonstrated under an UAV LiDAR data onboarded with lightweight navigation sensors. The experimental results show the efficacy of the method in comparison with a state-of-the-art method.

Attitude Mounting Misalignment Estimation Method for the Calibration of UAV LiDAR System by using a TIN-based Corresponding Model

In traditional attitude mounting misalignment estimation methods for the calibration of unmanned autonomous vehicle (UAV) based light detection and ranging (LiDAR) system, signalized targets and iterative corresponding models are required, which makes it highly cost and computationally time-consuming. This paper presents an attitude mounting misalignment estimation (AMME) method for the calibration of UAV LiDAR system. The proposed method is divided into the coarse registration of LiDAR strips and the estimation of the attitude mounting misalignment. Firstly, 3D keypoints are extracted in the point clouds using the scale-invariant feature transform (SIFT) algorithm. Afterwards, the point feature transform (PFH) descriptor is used for 3D keypoint matching. Then, the coarse registration is executed. In the second part of the contribution, the systematic errors in the attitude mounting misalignment are estimated by incorporating the proposed triangular irregular network (TIN) corresponding model into the calibration modelling. Using the TIN-based corresponding model saves time and cost for AMME method. Furthermore, it provides two important effects: practical and computational, as no designed calibration boards, segmentation and iterative matching are needed. The performance of the proposed method is demonstrated under an UAV LiDAR data onboarded with lightweight navigation sensors. The experimental results show the efficacy of the method in comparison with a state-of-the-art method.

Procjena prostorne raspodjele oborina u GIS okruženju

Oborine su izrazito varijabilna komponenta bilance voda, koja ovisi o nizu faktora, kao što su geografski položaj, udaljenost od mora i nadmorska visina, a ključne su u razumijevanju hidroloških procesa nekog područja. Mjerenja oborina provode se na diskretnim lokacijama meteoroloških postaja (osim u slučaju radarskih opažanja oborina) te su saznanja o njihovoj varijabilnosti u prostoru rezultat primjene različitih metoda interpolacije izmjerenih vrijednosti u točki na analiziranom prostoru. U GIS okruženju, oborina može biti prikazana u formi diskretnog ili kontinuiranog polja pa će o tome ovisiti i odabir metode interpolacije. U radu su, na primjeru srednjih godišnjih količina oborina za razdoblje 1961. – 1990. na području Istre, prikazane i uspoređene tri najčešće primjenjivane metode prostorne interpolacije: Thiessenovi poligoni, TIN (Triangular Irregular Network) te VLR (metoda višestruke linearne regresije). Prve dvije metode ne uzimaju u obzir faktore koji utječu na količinu palih oborina, već su procijenjene vrijednosti u funkciji udaljenosti promatrane točke od oborinskih postaja. Za razliku od njih, metoda višestruke linearne regresije omogućuje određivanje vrijednosti oborina u prostoru u ovisnosti o drugim čimbenicima; u ovome slučaju to su geografski položaj, udaljenost od mora te nadmorska visina.

Prediction of Radio Wave Propagation Loss in Ultra-Rugged Terrain Areas

When designing and maintaining a wireless network, engineers need to first evaluate the impact of a specific propagation environment on radio wave propagation. Currently, although many popular propagation models (PMs) are available for reference, their analysis and validation in ultrarugged terrain areas (uRTAs) are still underexplored. This article aims to implement an uRTA PM (uRTAPM), which consists of two modes, i.e., the point-to-point (uRTAPM-P2P) and the point-to-area (uRTAPM-P2A) modes, to meet the requirements of practical applications. First, we consider the digital elevation model (DEM) data sets and three types of propagation, i.e., line of sight (LOS), non-LOS (NLOS), and multiobstacle propagation. Second, we propose a cone multipath model to simulate the NLOS diffraction behavior caused by mountains. Third, we utilize a triangulated irregular network (TIN) to finely divide the terrestrial surface of uRTAs. Finally, based on numerical calculations, compared with the existing commonly used PMs, the uRTAPM is found to reveal more details about the propagation loss (PL) caused by the terrain in uRTAs. The measurements demonstrate that the mean absolute error (MAE), root-mean-square error (RMSE), and standard deviation (SD) between the uRTAPM predicted values and the actual measured values are lower than those of other methods.

Base Point Split Algorithm for Generating Polygon Skeleton Lines on the Example of Lakes

This article presents the Base Point Split (BPSplit) algorithm to generate a complex polygon skeleton based on sets of vector data describing lakes and rivers. A key feature of the BPSplit algorithm is that it is dependent on base points representing the source or mouth of a river or a stream. The input values of base points determine the shape of the resulting skeleton of complex polygons. Various skeletons can be generated with the use of different base points. Base points are applied to divide complex polygon boundaries into segments. Segmentation supports the selection of triangulated irregular network (TIN) edges inside complex polygons. The midpoints of the selected TIN edges constitute a basis for generating a skeleton. The algorithm handles complex polygons with numerous holes, and it accounts for all holes. This article proposes a method for modifying a complex skeleton with numerous holes. In the discussed approach, skeleton edges that do not meet the preset criteria (e.g., that the skeleton is to be located between holes in the center of the polygon) are automatically removed. An algorithm for smoothing zigzag lines was proposed.

Digital photogrammetric analysis approaches for the realization of detailed terrain models

Digital Surface Models (DSMs) with a high spatial resolution and accuracy are of increasing importance for many applications. Recent advances in data acquisition technologies, such as those related to the use of Unmanned Aerial Vehicles (UAVs), enabled the acquisition of images suitable to generate high-resolution DSMs. The present study aims to provide useful indications for data acquisition and processing to obtain DSMs with small errors by using the Structure from Motion (SfM) algorithm. We changed three parameters during data acquisition (flight height, camera type and acquisition mode) and used four different interpolation methods in the elaboration phase (Inverse Distance Weighted - IDW, IDW ArcGIS, Triangular Irregular Network – TIN, and Topo to Raster). Obtained results highlight the advantage to use the IDW method for data interpolation and for areas of various surface in combination with different flight heights as follows: i) for areas smaller than 0.1 ha, a flight altitude of 5 meters is advisable; ii) for areas between 0.1 and 1 ha, a flight altitude of 15 meters is advisable; iii) for areas larger than 1 ha, the most convenient flight altitude is 30 meters. Our workflow can also be followed by different tools (RGB cameras, light detection and ranging - LIDAR) to obtain the best DSMs accuracy.

Bathymetry and Siltation of Oyan Dam, Ogun State, Nigeria

Dams are reservoirs established for different reasons. Oyan dam, Ogun State, Nigeria was established and commissioned on the 29th March 1983 to supply water to Lagos State and Abeokuta for municipal uses, with power generation potentials to support Lower Ogun Irrigation Project. However, flooding has become an annual experience of downstream communities along Ogun river especially when the Oyan dam is opened; it has really becomes remarkable since the flood events of 2012. This project investigates the level of siltation and floods menace adjoining Oyan dam and its environ. An integrated methodology of bathymetric survey, total station traversing and satellite imagery were used to acquire geospatial locations of the dam features and other details within the dam through the process of traversing, heightening and detailing. The field investigation was conducted between January 2018 and January 2020 to determine dam bed topography with the deployment of integrated approach. The collected data were processed using Hydrologic Engineering Center’s River Analysis System (HEC-RAS), HYPACK software and ArcGIS 10.6 software. The maximum and minimum depth within the dam are -4.072m and -21.588m respectively. The cross sections are represented for each 200m length of the dam. Furthermore, volumetric analysis of sediment budget was computed to be 251.7x106m3 and compared with designed reservoir capacity of 270x106m3. From the study, a loss of about 18.2x106m3 approximately 6.7% was recorded. Satellite imagery shows the rate of change within Oyan dam and its catchment area downstream based on Triangulated Irregular Network (TIN) generated from Shuttle Radar Topography Mission (SRTM) the maximum and minimum elevation in the catchment are 29m and 182m respectively. The study recommends that deepen and training of Ogun river and all adjoining drainages system within the study corridor to retain more water when peak rainfall is recorded.

Morphologically iterative triangular irregular network for airborne LiDAR filtering

Morphological and triangular irregular network (TIN) ground filters require setting up different parameters to achieve high accuracy for different terrains. A proposed morphologically iterative TIN (MIT) ground filter only requires maximum building size in the processing of raw light detection and ranging (LiDAR) data. This approach applies morphological and TIN densification in an iterative way for separating ground points from off-ground ones. A radial nearest neighbor is designed to select the surrounding nearest neighbors for each point, and these neighbors are analyzed to define the parameters of a local translational 3D plane surface. Experimental results using ISPRS benchmark datasets show that MIT achieves an average total error of 85 % . Further experimental validation with Hong Kong LiDAR datasets reveals that MIT is effective in detecting dense ground points and robust in various terrain situations.

Towards a revised framework of modified time series InSAR for mapping land deformation

Interferometric synthetic aperture radar (InSAR) time series technique has been widely applied to map the ground deformation over the past two decades. One of the key steps of InSAR time series approach is to select appropriate measurement scatterer (MS) pixels to build the triangulated irregular network (TIN). In order to build a stable TIN, there are four criteria need to be satisfied: (1) working from whole to the part, (2) hierarchical network, step-by-step control, (3) with sufficient accuracy, and (4) with appropriate spatial density. The effectiveness of Geodesy and Earth Observing Systems-PSI (GEOS-PSI) and GEOS-Advance Time Series Analysis (GEOS-ATSA) approaches have met the first three criteria above. Nevertheless, it is noteworthy to mention that higher sparseness of the TIN may lead to unwrapping failures, while having more evenly distributed of MS offers a higher chance to eliminate such effect and include more additional pixels during the adaptive processing. In this work, we proposed a revised MS pixels selection approach to satisfy the fourth criterion, in combination with the GEOS-PSI and GEOS-ATSA processing chain: the modified GEOS-ATSA (GEOS-MATSA). As compared to the conventional MS pixels selection criterion, the proposed approach has three advantages: (1) the MS pixels are added into the main network with control through an adaptive estimation strategy, (2) isolated sub-networks have better chances of including into the main network, and (3) the computing workload can be decreased by reducing the iteration times. The case study area is selected in Beijing–Langfang region, while the ideal threshold settings for the initial reference network are discussed for both persistent scatterer and distributed scatterer pixels in this work.

Impact of Catchment Discretization and Imputed Radiation on Model Response: A Case Study from Central Himalayan Catchment

Distributed and semi-distributed hydrological modeling approaches commonly involve the discretization of a catchment into several modeling elements. Although some modeling studies were conducted using triangulated irregular networks (TINs) previously, little attention has been given to assess the impact of TINs as compared to the standard catchment discretization techniques. Here, we examine how different catchment discretization approaches and radiation forcings influence hydrological simulation results. Three catchment discretization methods, i.e., elevation zones (Hypsograph) (HYP), regular square grid (SqGrid), and TIN, were evaluated in a highly steep and glacierized Marsyangdi-2 river catchment, central Himalaya, Nepal. To evaluate the impact of radiation on model response, shortwave radiation was converted using two approaches: one with the measured solar radiation assuming a horizontal surface and another with a translation to slopes. The results indicate that the catchment discretization has a great impact on simulation results. Evaluation of the simulated streamflow value using Nash–Sutcliffe efficiency (NSE) and log-transformed Nash–Sutcliffe efficiency (LnNSE) shows that highest model performance was obtained when using TIN followed by HYP (during the high flow condition) and SqGrid (during the low flow condition). Similar order of precedence in relative model performance was obtained both during the calibration and validation periods. Snow simulated from the TIN-based discretized models was validated with Moderate Resolution Imaging Spectroradiometer (MODIS) snow products. Critical Success Indexes (CSI) between TIN-based discretized model snow simulation and MODIS snow were found satisfactory. Bias in catchment average snow cover area from the models with and without using imputed radiation is less than two percent, but implementation of imputed radiation into the Statkraft Hydrological Forecasting Toolbox (Shyft) gives better CSI with MODIS snow.

Improved progressive triangular irregular network densification filtering algorithm for airborne LiDAR data based on a multiscale cylindrical neighborhood.

Laser point cloud filtering is a fundamental step in various applications of light detection and ranging (LiDAR) data. The progressive triangulated irregular network (TIN) densification (PTD) filtering algorithm is a classic method and is widely used due to its robustness and effectiveness. However, the performance of the PTD filtering algorithm depends on the quality of the initial TIN-based digital terrain model (DTM). The filtering effect is also limited by the tuning of a number of parameters to cope with various terrains. Therefore, an improved PTD filtering algorithm based on a multiscale cylindrical neighborhood (PTD-MSCN) is proposed and implemented to enhance the filtering effect in complex terrains. In the PTD-MSCN algorithm, the multiscale cylindrical neighborhood is used to obtain and densify ground seed points to create a high-quality DTM. By linearly decreasing the radius of the cylindrical neighborhood and the distance threshold, the PTD-MSCN algorithm iteratively finds ground seed points and removes object points. To evaluate the performance of the proposed PTD-MSCN algorithm, it was applied to 15 benchmark LiDAR datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) commission. The experimental results indicated that the average total error can be decreased from 5.31% when using the same parameter set to 3.32% when optimized. Compared with five other publicized PTD filtering algorithms, the proposed PTD-MSCN algorithm is not only superior in accuracy but also more robust.

Evaluation of potential sites for soil erosion risk in and around Yamuna River flood plain using RUSLE

In general, agricultural intensification and human intervention lead to the soil erosion problem. Remarkably, marginal alluvial plains of vast Indo-Gangetic plain consistently suffer a great peril of land degradation in and around the Yamuna-Chambal valley. This is probably due to the geologic evolution of the landscape. Geomorphology and the land forms developed in and around the studied countryside themselves indicate the erosional processes. However, quantification of erosion rates and mapping of most intense erosion areas in and around Agra, Firozabad and Etawah district of Uttar Pradesh have been carried out using geospatial technology. A Geographic Information System (GIS)-based Revised Universal Soil Loss Equation (RUSLE) model was used to analyse grid cell–based erosion rates by incorporating all the crucial factors, i.e. rainfall, slope, soil, length slope, cover management and practice management factors. An average annual soil loss rate of 445 t ha−1 year−1 was observed in the study area. Moreover, the intensity-based categorization reveals that 64% area is at high erosion risk. The Google Earth imagery along with Triangular Irregular Network (TIN) model presents the erosion-associated features and slope gradient of the Yamuna River watershed further validates the results obtained in the present study.

Bias Correction of RCM Precipitation by TIN-Copula Method: A Case Study for Historical and Future Simulations in Cyprus

Numerical models are being used for the simulation of recent climate conditions as well as future projections. Due to the complexity of the Earth’s climate system and processes occurring at sub-grid scales, model results often diverge from the observed values. Different methods have been developed to minimize such biases. In the present study, the recently introduced “triangular irregular networks (TIN)-Copula” method was used for the bias correction of modelled monthly total and extreme precipitation in Cyprus. The method was applied to a 15-year historical period and two future periods of the same duration. Precipitation time-series were derived from a 12-km resolution EURO-CORDEX regional climate simulation. The results show that the TIN-Copula method significantly reduces the positive biases between the model results and observations during the historical period of 1986–2000, for both total and extreme precipitation (>80%). However, the level of improvement differs temporally and spatially. For future periods, the model tends to project significantly higher total precipitation rates prior to bias correction, while for extremes the differences are smaller. The adjustments slightly affect the overall climate change signal, which tends to be enhanced after bias correction, especially for total precipitation and for the autumn period.

Impact of interpolation techniques on the accuracy of large-scale digital elevation model

Abstract There is no doubt that the tremendous development of information technology was one of the driving factors behind the great growth of surveying and geodesy science. This has spawned modern geospatial techniques for data capturing, acquisition, and visualization tools. Digital elevation model (DEM) is the 3D depiction of continuous elevation data over the Earth’s surface that is produced through many procedures such as remote sensing, photogrammetry, and land surveying. DEMs are essential for various surveying and civil engineering applications to generate topographic maps for construction projects at a scale that varies from 1:500 to 1:2,000. GIS offers a powerful tool to create a DEM with high resolution from accurate land survey measurements using interpolation methods. The aim of this research is to investigate the impact of estimation techniques on generating a reliable and accurate DEM suitable for large-scale mapping. As a part of this study, the deterministic interpolation algorithms such as ANUDEM (Topo to Raster), inverse distance weighted (IDW), and triangulated irregular network (TIN) were tested using the ArcGIS desktop for elevation data obtained from real total station readings, with different landforms to show the effect of terrain roughness, data density, and interpolation process on DEM accuracy. Furthermore, comparison and validation of each interpolator were carried out through the cross-validation method and numerous graphical representations of the DEM. Finally, the results of the investigations showed that ANUDEM and TIN models are similar and significantly better than those attained from IDW.

Detecting Matching Blunders of Multi-Source Remote Sensing Images via Graph Theory.

Large radiometric and geometric distortion in multi-source images leads to fewer matching points with high matching blunder ratios, and global geometric relationship models between multi-sensor images are inexplicit. Thus, traditional matching blunder detection methods cannot work effectively. To address this problem, we propose two matching blunder detection methods based on graph theory. The proposed methods can build statistically significant clusters in the case of few matching points with high matching blunder ratios, and use local geometric similarity constraints to detect matching blunders when the global geometric relationship is not explicit. The first method (named the complete graph-based method) uses clusters constructed by matched triangles in complete graphs to encode the local geometric similarity of images, and it can detect matching blunders effectively without considering the global geometric relationship. The second method uses the triangular irregular network (TIN) graph to approximate a complete graph to reduce to computational complexity of the first method. We name this the TIN graph-based method. Experiments show that the two graph-based methods outperform the classical random sample consensus (RANSAC)-based method in recognition rate, false rate, number of remaining matching point pairs, dispersion, positional accuracy in simulated and real data (image pairs from Gaofen1, near infrared ray of Gaofen1, Gaofen2, panchromatic Landsat, Ziyuan3, Jilin1and unmanned aerial vehicle). Notably, in most cases, the mean false rates of RANSAC, the complete graph-based method and the TIN graph-based method in simulated data experiments are 0.50, 0.26 and 0.14, respectively. In addition, the mean positional accuracy (RMSE measured in units of pixels) of the three methods is 2.6, 1.4 and 1.5 in real data experiments, respectively. Furthermore, when matching blunder ratio is no higher than 50%, the computation time of the TIN graph-based method is nearly equal to that of the RANSAC-based method, and roughly 2 to 40 times less than that of the complete graph-based method.

Slope Effect on The Vertical Accuracy of Spatial Information Using Multi Rotor UAV

Unmanned aerial vehicle (UAV) recently has been proved to be used for slope mapping studies. Slope map can be created by gathering spatial information including elevations and surface features of the surrounding terrain. Spatial information on a slope is important to enhance the efficiency of slope modeling. Thus, the spatial information must be very accurate and reliable. This study aims to determine the effect of a slope on the vertical accuracy of spatial information acquired by using multi-rotor UAV. The objectives are to investigate the effect of slope elevation and to identify the effect of slope steepness on the vertical accuracy of spatial information acquired using multi-rotor UAV. Terrain Model (DTM), Contour Map, Triangular Irregular Network (TIN), and Slope Map is produced for analysis purpose. It was found that the vertical accuracy of spatial information increase when the elevation of slope and steepness of slope decrease. In conclusion, slope elevation and slope steepness do have an influence on the vertical accuracy of spatial information captured by UAV.