Resolution Digital Surface Models Research Articles

Photovoltaic generation on vertical façades in urban context from open satellite-derived solar resource data

Solar radiation incident at building façades and elements in an urban context is essential in determining the energy production on building rooftops and vertical façades. The proper determination of solar irradiance incident on a vertical façade needs quality input of the components of solar radiation and a high resolution digital surface model with the heights of buildings and other elements in the urban area of study. In this work a thorough methodology for modeling PV generation, in hourly basis, at building façades with open available data and methods is presented. Hourly data of satellite-derived solar irradiance is used with high resolution digital model from LIDAR information to estimate with the Sandia model the PV generation of five small arrays at west, south and east façades of a building in Madrid. PV output modeled for west and south arrays are in rather good agreement with the monitored experimental data of the production. RMSE of 8% and 12% was observed for the monthly power predicted for west and south facades, respectively. The east façade case was much more challenging due to variability of shadows it receives from the nearby large deciduous trees throughout the year, which results in high uncertainty in the shading influence estimation. Anyway, the methodology proves the benefits and possibilities of detailed estimation of PV production in building façades from open available information regarding both solar resource, open modeling tools and urban topography, even in a very challenging conditions associated to the variability of trees canopy.

High resolution mapping and analysis of shore platform morphology in Galicia, northwestern Spain

Most research on shore platforms has been at fairly large spatial scales over distances ranging from hundreds to thousands of metres. Other work at much smaller scales, ranging from one to several decimetres, often corresponds to the dimensions of micro-erosion meter stations. Few studies have been concerned with platform morphology in which the basic data are acquired at intermediate or meso-scales ranging up to a few metres. This is due, in part, to terrestrial surveying at meso-scales being time-consuming while aerial surveys using LiDAR are expensive. A meso-scale study was made on three shore platforms in western Galicia, northwestern Spain using data from an Unmanned Aerial Vehicle (UAV) to produce high resolution Digital Surface Models (DSMs) and to calculate local surface elevation, roughness, slope, and joint density at a 0.5 m pixel scale, and joint orientation and length. This was supplemented by Equotip field measurements of rock hardness. A granitic platform was higher and had rougher surfaces and steeper slopes than two platforms in schist. The relationship between platform morphology and rock hardness and joint density was complex, however, reflecting in part the role of schistosity in accounting for the formation of low, regular platforms in hard schists with low joint density. The study suggested that while tidal range, inheritance, and other environmental and evolutionary factors can be dominant in determining platform morphology at the macro- or regional scale, geological, and particularly structural, factors become increasingly important in Galicia as the scale diminishes, and they are generally dominant at the local or meso-scale.

Evidence of Surface Rupture Associated With Historical Earthquakes in the Lower Tagus Valley, Portugal. Implications for Seismic Hazard in the Greater Lisbon Area

The Lower Tagus Valley Fault, Portugal, has long been associated with the damaging earthquakes that affected the Greater Lisbon Area in historical times. These include a poorly documented earthquake that occurred in 1344, the relatively well-documented 1531 earthquake, and the most recent M6.0 1909 earthquake. In this work, we use a 0.5 m resolution LiDAR-based digital elevation model and a 0.5 cm resolution digital surface model based on UAV photogrammetry to accurately locate the fault scarps in the northernmost portion of the western fault strand and to select sites to perform paleoseimolological investigations. The paleoseismological and geochronological analysis performed in the Alviela trench site document the fault activity in the last 3000 years, including two earthquakes during historical times. We performed ground motion scenarios for 20 km, 40 km, and 60 km ruptures including the trench site. The ground motion fields obtained for the 40 km and 60 km ruptures are in agreement with most macroseismic intensity data available for the 1531 earthquake, implying a magnitude in the range M6.8–7.4. However, the degree of deformation preserved in the trench suggests a value closer to the lower magnitude bound. The intensity level observed in Lisbon in 1531 (IX) is lower than the modeled intensities for all considered scenarios and could be related to a particularly high level of vulnerability of the building stock.

Drone-Based Remote Sensing for Research on Wind Erosion in Drylands: Possible Applications

With rapid innovations in drone, camera, and 3D photogrammetry, drone-based remote sensing can accurately and efficiently provide ultra-high resolution imagery and digital surface model (DSM) at a landscape scale. Several studies have been conducted using drone-based remote sensing to quantitatively assess the impacts of wind erosion on the vegetation communities and landforms in drylands. In this study, first, five difficulties in conducting wind erosion research through data collection from fieldwork are summarized: insufficient samples, spatial displacement with auxiliary datasets, missing volumetric information, a unidirectional view, and spatially inexplicit input. Then, five possible applications—to provide a reliable and valid sample set, to mitigate the spatial offset, to monitor soil elevation change, to evaluate the directional property of land cover, and to make spatially explicit input for ecological models—of drone-based remote sensing products are suggested. To sum up, drone-based remote sensing has become a useful method to research wind erosion in drylands, and can solve the issues caused by using data collected from fieldwork. For wind erosion research in drylands, we suggest that a drone-based remote sensing product should be used as a complement to field measurements.

Generation of Digital Surface Model (DSM) USING UAV/ QUADCOPTER

Satellite imageries are being used as primary source of information due to their vast coverage and high temporal resolution. Unnamed Aerial Vehicle (UAV) is being used these days because of its accuracy, autonomous flights, cost effectiveness and rapid overview of data. UAV provides a fully or partially autonomous image acquiring platform which is devoid of any manned flight controller. In this research Phantom 3 advanced Quadcopter was used for an image acquisition plan for generation of Digital Surface Model (DSM). Two designs were drawn through this workflow for the reconstruction of Department of Space Science and technology in university of Punjab. For the first design 3D quadcopter is hovered at the height of 120 feet (37 meters) which covered an area of 83 x 130 meter, frontal and sidewise overlapping is 80%, and the camera is kept at an angle of 70° for the double grid type pattern. For second mission design a circular flight is taken to obtain images at the height of 27meters with coverage area of (107 x 106) meter, 45° camera angle and 10° circular angle. For reconstruction of urban area, quadcopter is hovered at relatively greater height of 210 feet (64 meters), following the double grid pattern. In order to attain desired GST, the camera is flown at a constant height over the Area of Interest (AOI). The highly overlapped images obtained using Phantom 3, advanced Quadcopter are then processed using Pix4d software for processing of images. Initially, the common points of adjacent images are matched automatically. After matching similar points, additional geographic information of coordinates and z-value of elevation associated with it is generated in 3D space by sparse point cloud. Then a detailed 3D model along the precise geo location is obtained using dense point cloud. A study surface area and its texture are generated using 3D mesh. Finally, a desired 3D surface model is accurately generated containing desired AOI. The results are analyzed using UAV imagery to generate high resolution DSM. DSM for construction of urban area of Department of Space Science was generated at a very high resolution of 3.55cm and 1.8cm respectively. The accuracy of geo locations can be improved by using GPS loggers or by taking the GCPs. It is suggested by many authors that 3D surface model of reconstruction of building is quite accurate geographically and geometrically, after the comparison of bundle block adjustments, Ground Sampling Distance (GSD) value, 3D matching and average point cloud density of DSM. Thus, the 3D surface models are used in parameters, features extraction and estimation of values including depth and elevation values, in texturing, 3D data collection for 3D visualizations, 3D roof tops and building facades and contour maps and orthodox photos.

UAV-based high spatial and temporal resolution monitoring and mapping of surface moisture status in a vineyard

Drone-based RGB and thermal infrared remote sensing are rapidly becoming useful tools for monitoring crop and soil status for precision farming. The objectives of this study are as follows: (1) flying UAV or drone to collect large spatial scale RGB and infrared aerial images during the growing season and (2) producing high spatial–temporal resolution digital surface models and imagery mosaics to help farmers monitor surface moisture status in the study vineyard. A total of four field aerial surveys were conducted during the growing season of 2018 from May to August. A surface moisture mapping index (SMMI) model was proposed based on modified normalized difference water index and topographic wetness index. This model combines the factors of both radiant reflection properties by moisture bearing surfaces of a farming field, and the slope gradient and micro-topographic positions in the field. The results indicate that the spatial pattern of SMMI, or the relative quantities of surface moisture contents, across the vineyard was maintained the same during the entire growing season. This might be impacted by localized factors of surface slope gradient and topographic positions. Meanwhile, temporal variations of surface moisture status are mainly influenced by frequency and intensity of the precipitations.

DETECTION OF UNDOCUMENTED BUILDINGS USING CONVOLUTIONAL NEURAL NETWORK AND OFFICIAL GEODATA

Abstract. Undocumented buildings are buildings which were built years ago, but were never recorded in official digital cadastral maps. Detection of undocumented buildings is of great importance for urban planning and monitoring. The state of Bavaria, Germany, pursues this task based on high resolution optical data and digital surface models, using semi-automatic detection methods, which suffer from a high false alarm rate. In order to study the influence of sampling strategies on the performance of building detection, we have firstly designed a transferability analysis experiment, which has not been adequately addressed in the current literature. In this experiment, we test whether the trained model from a district contains valuable information for building detection in a different district. It was found that the large-scale building detection results can be considerably improved when training samples are collected from different districts. Based on the building detection results, we propose a novel framework for the detection of undocumented buildings using Convolutional Neural Network (CNN) and official geodata. More specifically, buildings are identified as undocumented, when their pixels in the output of the CNN are predicted as “building”, whereas they belong to the “non-building” in the Digital Cadastral Map (DFK). The detected undocumented building pixels are subsequently divided into the class of old or new undocumented building with the aid of a Temporal Digital Surface Model (tDSM) in the stage of decision fusion. By doing so, a seamless map of undocumented buildings is generated for 1/4th of the state of Bavaria, Germany at a spatial resolution of 0.4 m, which has demonstrated the use of CNN for the robust detection of undocumented buildings at large-scale.

AUTOMATIC BENGGANG RECOGNITION BASED ON LATENT SEMANTIC FUSION OF UHR DOM AND DSM FEATURES

Abstract. Benggang are characterized by deep-cut slopes with various shapes and depressions on the vast weathered crust slopes in southern China. The gully heads have been continuously collapsed and eroded to form a chair-like erosion landforms. It develops rapidly, and leads to large amounts of erosion, with the hazards of damaging land resources, destroying basic farmland, and deteriorating ecological environment. To study and manage Benggang, the primary task is to discover it. Traditional methods based on local in-situ investigations, which are not only labour-consuming but also inefficient. These methods are difficult to meet the needs of large-scale investigations of Benggang. This paper proposes a method for automatic Benggang recognition based on Ultra-High Resolution (UHR) DOM (Digital Orthophoto Map) and DSM (Digital Surface Model) obtained from UAV (Unmanned Aerial Vehicle) survey. This method adopts a Bag of Visual-Topographical Words (BoV-TW) model. The local features extracted from DOM and DSM are represented based on BoV-TW, and fused by Latent Dirichlet Allocation (LDA). Finally Support Vector Machine (SVM) is adopted as a supervised classifier to achieve high-precision automatic Benggang recognition. Experimental results prove that the total accuracy of our method can be maintained at about 95%, with recall and precision above 80% (the highest are 97.22% and 94.44%, respectively), which are significantly higher than the methods of using only DOM local features and using only BoV-TW.

Assessing the impact of topography and land cover data resolutions on two-dimensional HEC-RAS hydrodynamic model simulations for urban flood hazard analysis

This study assesses the effects of topography and land cover data resolutions on the estimates of flood extent, inundation depths, flow velocities, and arrival times of a two-dimensional (2D) hydrodynamic HEC-RAS model under differently sized mesh structures, with the example of the urban floodplain of Kilicozu Creek (Kirsehir, Turkey). To analyse these effects under a wide range of data conditions, seven different resolution digital surface models (DSMs) (from 0.0432 to 10 m/pixel) and Manning’s roughness layers (MRLs) (from 2 to 25 m/pixel) are produced for the subject floodplain by processing the high-quality DSM and orthophoto of the Kirsehir city centre. Additionally, seven different computational point spacings (CPSs) (from 2 m × 2 m to 25 m × 25 m) are tested to evaluate changes in the model outputs depending on the dimensions of mesh grids. Simulations are carried out for 19 different DSM, MRL, and CPS configurations under the 500-year flood scenario. The simulation performed for the most detailed model configuration is utilised as the base model simulation to compare the performances of other simulations. The model simulation configurated with the 2 m cell size DSM, 10 m cell size MRL, and 10 m × 10 m CPS shows comparable performance to the base model simulation with a small loss in the accuracy of the estimates, indicating that very-fine-resolution (less than 2 m) topography and high-resolution (less than 10 m) land cover data may not be indispensable to produce reliable simulations with 2D urban flood modelling using HEC-RAS software.

Sea Level Rise Scenario for 2100 A.D. in the Heritage Site of Pyrgi (Santa Severa, Italy)

Sea level rise is one of the main risk factors for the preservation of cultural heritage sites located along the coasts of the Mediterranean basin. Coastal retreat, erosion, and storm surges are posing serious threats to archaeological and historical structures built along the coastal zones of this region. In order to assess the coastal changes by the end of 2100 under the expected sea level rise of about 1 m, we need a detailed determination of the current coastline position based on high resolution Digital Surface Models (DSM). This paper focuses on the use of very high-resolution Unmanned Aerial Vehicles (UAV) imagery for the generation of ultra-high-resolution mapping of the coastal archaeological area of Pyrgi, Italy, which is located near Rome. The processing of the UAV imagery resulted in the generation of a DSM and an orthophoto with an accuracy of 1.94 cm/pixel. The integration of topographic data with two sea level rise projections in the Intergovernmental Panel on Climate Change (IPCC) AR5 2.6 and 8.5 climatic scenarios for this area of the Mediterranean are used to map sea level rise scenarios for 2050 and 2100. The effects of the Vertical Land Motion (VLM) as estimated from two nearby continuous Global Navigation Satellite System (GNSS) stations located as close as possible to the coastline are included in the analysis. Relative sea level rise projections provide values at 0.30 ± 0.15 cm by 2050 and 0.56 ± 0.22 cm by 2100 for the IPCC AR5 8.5 scenarios and at 0.13 ± 0.05 cm by 2050 and 0.17 ± 0.22 cm by 2100, for the IPCC Fifth Assessment Report (AR5) 2.6 scenario. These values of rise correspond to a potential beach loss between 12.6% and 23.5% in 2100 for Representative Concentration Pathway (RCP) 2.6 and 8.5 scenarios, respectively, while, during the highest tides, the beach will be provisionally reduced by up to 46.4%. In higher sea level positions and storm surge conditions, the expected maximum wave run up for return time of 1 and 100 years is at 3.37 m and 5.76 m, respectively, which is capable to exceed the local dune system. With these sea level rise scenarios, Pyrgi with its nearby Etruscan temples and the medieval castle of Santa Severa will be exposed to high risk of marine flooding, especially during storm surges. Our scenarios show that suitable adaptation and protection strategies are required.

FTF: a quick surveying approach for constructing high resolution digital surface model for road elements

Digital Surface Models (DSMs) are commonly built using data collected via remote sensing techniques such as aerial photogrammetry, LiDAR, and InSAR. DSM accuracy mainly depends upon the type of data used beside the methodology followed. The highest accuracy can be achieved when using a land surveying data which takes more time and costs a lot. The objective of this study is to present a quick approach for constructing high resolution DSM for road elements based on land surveying data. This approach is called Field-To-Finish (FTF), it is a process of converting surveying data into final graphical files based on the characteristics of the points and the accompanying code. To illustrate the proposed approach and assess the accuracy comparing with the traditional method, a case study was presented. We found that the FTF is superior to the traditional method on more than one level. The number of observed points, the time for surveying field work and for drafting were reduced. The methodology adopted in this study for obtaining DSM has proven to produce a digital model which is suitable for production engineering charts of scale 1: 100 that can be used in many scientific and practical applications related to civil infrastructure.

Solar Glare Vulnerability Analysis of Urban Road Networks—A Methodology

Solar glare on roads is responsible for momentaneous deterioration of a driver’s view, interfering with driving capacity and causing accidents. The situation of a driver being affected by solar glare on a road is a spatial-temporal variable, since it depends not only on the time of the day and the day of the year, which are determinant for the local Sun position (temporal factors), but also on the local slope and azimuth of the road in the driven direction (spatial factors). The present work describes a method for producing road glare maps along a road network, as well as retrieving glare information from urban roads so that solar glare vulnerability can be easily accessed. Input data are a 1m resolution Digital Surface Model from Light Detection and Ranging data and the road network. Spatial parameters are processed in a Geographic Information System environment. The Urban Glare Algorithm detects glare and outputs temporal matrices and glare maps. Shadows cast by buildings and trees are considered as well as the driver’s eyes height. The method is tested in an area of Lisbon (Portugal). This work is a contribution to road safety systems implementation and constitutes a relevant basis for warning drivers of glare through car navigation systems.

Sugarcane canopy detection using high spatial resolution UAS images and digital surface model

The use of an unmanned aerial system (UAS) equipped with multispectral cameras is a potential approach to acquire canopy reflectance to make various correlations with desired crop parameters. However, the acquired reflectance data are mixed with unwanted data, such as reflectance from soil, which significantly affects some commonly used vegetation indices, such as the NDVI. This study compares the performance of three methods for detecting the canopy area of 3-month-old sugarcane crops. These methods extract the canopy areas using 5 NDVI thresholds (0.2, 0.3, 0.4, 0.5, and 0.6), a principal component analysis (PCA) threshold, and a digital surface model (DSM) threshold. The performance assessment will deliberately consider the quality percentage (QP) of each method to correctly detect the canopy area of short sugarcane crops in 10 selected images. The results show that filtration by the PCA threshold method provides the best result with a QP of 65.89-78.72%. The NDVI threshold method at levels of 0.3 and 0.4 follow with QPs of 58.42-68.81% and 40.80-70.81%, respectively, and the lowest accuracy is obtained by the DSM threshold method, which has QPs of 14.80-30.78%.

BUILDING OUTLINE EXTRACTION FROM AERIAL IMAGES USING CONVOLUTIONAL NEURAL NETWORKS

Abstract. Automatic detection and extraction of buildings from aerial images are considerable challenges in many applications, including disaster management, navigation, urbanization monitoring, emergency responses, 3D city mapping and reconstruction. However, the most important problem is to precisely localize buildings from single aerial images where there is no additional information such as LiDAR point cloud data or high resolution Digital Surface Models (DSMs). In this paper, a Deep Learning (DL)-based approach is proposed to localize buildings, estimate the relative height information, and extract the buildings’ boundaries using a single aerial image. In order to detect buildings and extract the bounding boxes, a Fully Connected Convolutional Neural Network (FC-CNN) is trained to classify building and non-building objects. We also introduced a novel Multi-Scale Convolutional-Deconvolutional Network (MS-CDN) including skip connection layers to predict normalized DSMs (nDSMs) from a single image. The extracted bounding boxes as well as predicted nDSMs are then employed by an Active Contour Model (ACM) to provide precise boundaries of buildings. The experiments show that, even having noises in the predicted nDSMs, the proposed method performs well on single aerial images with different building shapes. The quality rate for building detection is about 86% and the RMSE for nDSM prediction is about 4 m. Also, the accuracy of boundary extraction is about 68%. Since the proposed framework is based on a single image, it could be employed for real time applications.

Deep Learning for Soil and Crop Segmentation from Remotely Sensed Data

One of the most challenging problems in precision agriculture is to correctly identify and separate crops from the soil. Current precision farming algorithms based on artificially intelligent networks use multi-spectral or hyper-spectral data to derive radiometric indices that guide the operational management of agricultural complexes. Deep learning applications using these big data require sensitive filtering of raw data to effectively drive their hidden layer neural network architectures. Threshold techniques based on the normalized difference vegetation index (NDVI) or other similar metrics are generally used to simplify the development and training of deep learning neural networks. They have the advantage of being natural transformations of hyper-spectral or multi-spectral images that filter the data stream into a neural network, while reducing training requirements and increasing system classification performance. In this paper, to calculate a detailed crop/soil segmentation based on high resolution Digital Surface Model (DSM) data, we propose the redefinition of the radiometric index using a directional mathematical filter. To further refine the analysis, we feed this new radiometric index image of about 3500 × 4500 pixels into a relatively small Convolution Neural Network (CNN) designed for general image pattern recognition at 28 × 28 pixels to evaluate and resolve the vegetation correctly. We show that the result of applying a DSM filter to the NDVI radiometric index before feeding it into a Convolutional Neural Network can potentially improve crop separation hit rate by 65%.

A Learning Approach to Evaluate the Quality of 3D City Models

The automatic generation of three-dimensional (3D) building models from geospatial data is now a standard procedure. An abundance of literature covers the last two decades, and several solutions are now available. However, urban areas are very complex environments. Inevitably, practitioners still have to visually assess, at a city-scale, the correctness of these models and detect frequent reconstruction errors. Such a process relies on experts and is highly time-consuming, with approximately two hours/km 2 per expert. This work proposes an approach for automatically evaluating the quality of 3D building models. Potential errors are compiled in a novel hierarchical and versatile taxonomy. This allows, for the first time, to disentangle fidelity and modeling errors, whatever the level of details of the modeled buildings. The quality of models is predicted using the geometric properties of buildings and, when available, Very High Resolution images and Digital Surface Models. A baseline of handcrafted, yet generic, features is fed into a Random Forest classifier. Both multiclass and multilabel cases are considered: due to the interdependence between classes of errors, it is possible to retrieve all errors at the same time while simply predicting correct and erroneous buildings. The proposed framework was tested on three distinct urban areas in France with more than 3000 buildings. 80%–99% F-score values are attained for the most frequent errors. For scalability purposes, the impact of the urban area composition on the error prediction was also studied, in terms of transferability, generalization, and representativeness of the classifiers. It showed the necessity of multimodal remote sensing data and mixing training samples from various cities to ensure a stability of the detection ratios, even with very limited training set sizes.

SIMULATING UNMANNED-AERIAL-VEHICLE BASED LASER SCANNING DATA FOR EFFICIENT MISSION PLANNING IN COMPLEX TERRAIN

Abstract. In complex mountainous terrain the mapping efficiency is a crucial factor. Unmanned aerial vehicle (UAV) based laser scanning (ULS) has the capability for efficient mapping, as it allows realizing higher flight velocities, higher flying altitude above ground level (AGL) and larger distances between neighbouring flight strips, compared to image based techniques. However, fully utilising the efficiency of the system in mission planning (especially for complex terrain projects, where occlusions and differently inclined surfaces are present) is prone to miss the project requirements in terms of point density and strip overlap. Therefore, the numerical simulation of point densities is a helpful tool for realizing a reliable planning of scan coverage. We implemented a ray-tracing-based ULS-simulator, specifically designed for emulating the mechanism of a Riegl VUX-1LR laser scanner carried by a Riegl RiCOPTER. The simulator can consider copter and scanner motion, which makes it possible to generate synthetic scan data excluding or including the aircraft movement due to aerodynamics by using either planned trajectories from a flight planning software or recorded and post-processed trajectories from an inertial measurement unit (IMU). Laser shots are simulated by intersecting rays from the virtual scanner with a mesh-based digital surface model (DSM). The results show that the tool generates plausible synthetic laser point distributions. However, this is only the case, when aircraft aerodynamics are considered, as the effect of striping due to flight control corrections during the flight is very prominent. It can be shown that applying the presented tool for mission planning (without knowing the actual flight movements) has to consider an error margin of ±50pts/m2 in order to guarantee a compliance with the planned project requirements. Nevertheless, the consideration of terrain by a high resolution DSM, especially in complex terrain, improves the correlation between simulated and real point densities significantly.

Wetland Classification with Multi-Angle/Temporal SAR Using Random Forests

To better understand and mitigate threats to the long-term health and functioning of wetlands, there is need to establish comprehensive inventorying and monitoring programs. Here, remote sensing data and machine learning techniques that could support or substitute traditional field-based data collection are evaluated. For the Bay of Quinte on Lake Ontario, Canada, different combinations of multi-angle/temporal quad pol RADARSAT-2, simulated compact pol RADARSAT Constellation Mission (RCM), and high and low spatial resolution Digital Elevation and Surface Models (DEM and DSM, respectively) were used to classify six land cover classes with Random Forests: shallow water, marsh, swamp, water, forest, and agriculture/non-forested. Results demonstrate that high accuracies can be achieved with multi-temporal SAR data alone (e.g., user’s and producer’s accuracies ≥90% for a model based on a spring image and a summer image), or via fusion of SAR and DEM and DSM data for single dates/incidence angles (e.g., user’s and producer’s accuracies ≥90% for a model based on a spring image, DEM, and DSM data). For all models based on single SAR images, simulated compact pol data generally achieved lower accuracies than quad pol RADARSAT-2 data. However, it was possible to compensate for observed differences through either multi-temporal/angle data fusion or the inclusion of DEM and DSM data (i.e., as a result, there was not a statistically significant difference between multiple models). With a higher repeat-pass cycle than RADARSAT-2, RCM is expected to be a reliable source of C-band SAR data that will contribute positively to ongoing efforts to inventory wetlands and monitor change in areas containing the same land cover classes evaluated here.

Mapping of River Terraces with Low-Cost UAS Based Structure-from-Motion Photogrammetry in a Complex Terrain Setting

River terraces are the principal geomorphic features for unraveling tectonics, sea level, and climate conditions during the evolutionary history of a river. The increasing availability of high-resolution topography data generated by low-cost Unmanned Aerial Systems (UAS) and modern photogrammetry offer an opportunity to identify and characterize these features. In this paper, we assessed the capabilities of UAS-based Structure-from-Motion (SfM) photogrammetry, coupled with a river terrace detection algorithm for mapping of river terraces over a 1.9 km2 valley of complex terrain setting, with a focus on the performance of this latest technology over such complex terrains. With the proposed image acquisition approach and SfM photogrammetry, we constructed a 3.8 cm resolution orthomosaic and digital surface model (DSM). The vertical accuracy of DSM was assessed against 196 independent checkpoints measured with a real-time kinematic (RTK) GPS. The results indicated that the root mean square error (RMSE) and mean absolute error (MAE) were 3.1 cm and 2.9 cm, respectively. These encouraging results suggest that this low-cost, logistically simple method can deliver high-quality terrain datasets even in the complex terrain, competitive with those obtained using more expensive laser scanning. A simple algorithm was then employed to detect river terraces from the generated DSM. The results showed that three levels of river terraces and a high-level floodplain were identified. Most of the detected river terraces were confirmed by field observations. Despite the highly erosive nature of fluvial systems, this work obtained good results, allowing fast analysis of fluvial valleys and their comparison. Overall, our results demonstrated that the low-cost UAS-based SfM technique could yield highly accurate ultrahigh-resolution topography data over complex terrain settings, making it particularly suitable for quick and cost-effective mapping of micro to medium-sized geomorphic features under such terrains in remote or poorly accessible areas. Methods discussed in this paper can also be applied to produce highly accurate digital terrain data over large spatial extents for some other places of complex terrains.

Using Digital Surface Models from UAS Imagery of Fire Damaged Sphagnum Peatlands for Monitoring and Hydrological Restoration

The sub-alpine and alpine Sphagnum peatlands in Australia are geographically constrained to poorly drained areas c. 1000 m a.s.l. Sphagnum is an important contributor to the resilience of peatlands; however, it is also very sensitive to fire and often shows slow recovery after being damaged. Recovery is largely dependent on a sufficient water supply and impeded drainage. Monitoring the fragmented areas of Australia’s peatlands can be achieved by capturing ultra-high spatial resolution imagery from an unmanned aerial systems (UAS). High resolution digital surface models (DSMs) can be created from UAS imagery, from which hydrological models can be derived to monitor hydrological changes and assist with rehabilitation of damaged peatlands. One of the constraints of the use of UAS is the intensive fieldwork required. The need to distribute ground control points (GCPs) adds to fieldwork complexity. GCPs are often used for georeferencing of the UAS imagery, as well as for removal of artificial tilting and doming of the photogrammetric model created by camera distortions. In this study, Tasmania’s northern peatlands were mapped to test the viability of creating hydrological models. The case study was further used to test three different GCP scenarios to assess the effect on DSM quality. From the five scenarios, three required the use of all (16–20) GCPs to create accurate DSMs, whereas the two other sites provided accurate DSMs when only using four GCPs. Hydrological maps produced with the TauDEM tools software package showed high visual accuracy and a good potential for rehabilitation guidance, when using ground- controlled DSMs.