A Comparative Analysis of Phytovolume Estimation Methods Based on UAV-Photogrammetry and Multispectral Imagery in a Mediterranean Forest

Management and control operations are crucial for preventing forest fires, especially in Mediterranean forest areas with dry climatic periods. One of them is prescribed fires, in which the biomass fuel present in the controlled plot area must be accurately estimated. The most used methods for estimating biomass are time-consuming and demand too much manpower. Unmanned aerial vehicles (UAVs) carrying multispectral sensors can be used to carry out accurate indirect measurements of terrain and vegetation morphology and their radiometric characteristics. Based on the UAV-photogrammetric project products, four estimators of phytovolume were compared in a Mediterranean forest area, all obtained using the difference between a digital surface model (DSM) and a digital terrain model (DTM). The DSM was derived from a UAV-photogrammetric project based on the structure from a motion algorithm. Four different methods for obtaining a DTM were used based on an unclassified dense point cloud produced through a UAV-photogrammetric project (FFU), an unsupervised classified dense point cloud (FFC), a multispectral vegetation index (FMI), and a cloth simulation filter (FCS). Qualitative and quantitative comparisons determined the ability of the phytovolume estimators for vegetation detection and occupied volume. The results show that there are no significant differences in surface vegetation detection between all the pairwise possible comparisons of the four estimators at a 95% confidence level, but FMI presented the best kappa value (0.678) in an error matrix analysis with reference data obtained from photointerpretation and supervised classification. Concerning the accuracy of phytovolume estimation, only FFU and FFC presented differences higher than two standard deviations in a pairwise comparison, and FMI presented the best RMSE (12.3 m) when the estimators were compared to 768 observed data points grouped in four 500 m2 sample plots. The FMI was the best phytovolume estimator of the four compared for low vegetation height in a Mediterranean forest. The use of FMI based on UAV data provides accurate phytovolume estimations that can be applied on several environment management activities, including wildfire prevention. Multitemporal phytovolume estimations based on FMI could help to model the forest resources evolution in a very realistic way

Co-registration of multi-sensor UAV imagery. Case study: Boreal forest areas

Monitoring the regeneration process of a forest is an important part of forestry management. Compared to traditional methods of counting tree species, UAVs have been a revolutionary means of saving time and costs due to the temporal and spatial flexibility of data collection. In turn, the integration of multispectral cameras allows the traditional vegetation indices that have been used with satellite imagery to be obtained. However, data from multispectral cameras must be combined with data from other types of sensors, such as RGB. It is therefore necessary to co-register all the information in order to obtain combined vegetation indices and carry out segmentation processes that allow the identification of the different tree species. In this study, the coordinate transformation methods available in QGIS software through the georeferencer plugin are evaluated. It also studies the influence of the number and distribution of control points on the accuracy of the transformation. It is concluded that of the transformation methods studied, TPS transformation has the highest accuracy with an MAE of 0.9 pixels and a deviation of 0.6 pixels, providing a minimum of 10 control points and a stratified or edge distribution

Editorial for Special Issue “UAV Photogrammetry and Remote Sensing”

The concept of Remote Sensing as a way of capturing information from an object without making contact with it has, until recently, been exclusively focused on the use of earth observation satellites [...

Combination of nadiral and oblique UAV photogrammetry and HBIM for the virtual reconstruction of cultural heritage. Case study of Cortijo del Fraile in Níjar, Almería (Spain)

Historic Building Information Modelling (HBIM) is the most effective method of rebuilding virtual 3D models of heritage buildings, and constitutes a new information management system in the field of cultural heritage interventions. In this study, photogrammetry based on Unmanned Aerial Vehicles (UAV photogrammetry) was applied as an alternative to Terrestrial Laser Scanning (TLS) for the development of HBIM for historical buildings in a ruinous state, analysing as a case study the Cortijo del Fraile, in Níjar, Almería (Spain). Based on the analysis of the historical information of the building, a photogrammetric survey was carried out with UAV by means of a combination of nadiral and oblique photographs. In this way, a precise characterization of the object was obtained, avoiding the grey areas that are characteristic of TLS. The generated 3D point cloud served as the basis for the virtual reconstruction of an HBIM model focused on both the exterior and interior. In order to ensure reasonable agreement between the parametric model and the ground truth, a validation procedure has been established that restricts the deviations between the two. Finally, a texturizing process is applied to the HBIM model to achieve a photorealistic finish for purposes of visualization, archiving, and recording

Combination of HBIM and UAV photogrammetry for modelling and documentation of forgotten heritage. Case study: Isabel II dam in Níjar (Almería, Spain)

The Isabel II dam is a monumental hydraulic structure built in the middle of the nineteenth century in Spain. In this study, unmanned aerial vehicle (UAV) photogrammetry was used as a data acquisition technique to carry out a survey of the dam’s current state and its surrounding constructions. The point cloud obtained by the photogrammetric process, together with the collected historical in-formation, served as the basis to generate an historic building information model (HBIM) that is the central core containing all the graphical, structural and archaeological information. The HBIM was validated by means of the As-Built for Autodesk Revit®-FARO® plug-in, and shows the high accuracy obtained with respect to the point cloud. The results show that with this methodology it is possible to obtain models representative of reality with an accuracy of ± 0.05 m. In addition, in order to improve the visualization, texture adjustments are made to obtain a photorealistic rendering of the model

Effects of point cloud density, interpolation method and grid size on derived Digital Terrain Model accuracy at micro topography level

The objective of this study was to evaluate the effects of the three dimensional (3D) point cloud density derived from Unmanned Aerial Vehicle (UAV) photogrammetry (using Structure from Motion (SfM) and Multi-View Stereopsis (MVS) techniques), the interpolation method for generating a digital terrain model (DTM), and the resolution (grid size (GS)) of the derived DTM on the accuracy of estimated heights in small areas, where a very accurate high spatial resolution is required. A UAV-photogrammetry project was carried out on 13 m × 13 m bare soil with a rotatory wing UAV at 10 m flight altitude (equivalent ground sample distance = 0.4 cm), and the 3D point cloud was derived. A stratified random sample (200 points in each square metre) was extracted and from the rest of the cloud, 15 stratified random samples representing 1, 2, 3, 4, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, and 90% were extracted. Five replications of each percentage were extracted to analyse the effect of cloud density on DTM accuracy. For each of these 15 × 5 = 75 samples, DTMs were derived using four different interpolation methods (Inverse Distance Weighted (IDW), Multiquadric Radial Basis Function (MRBF), Kriging (KR), and Triangulation with Linear Interpolation (TLI)) and 15 DTM GS values (20, 15, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0.67, 0.50, and 0.40 cm). Then, 75 × 4 × 15 = 4500 DTMs were analysed. The results showed an optimal GS value for each interpolation method and each density (most of the cases were equal to 1 cm) for which the Root Mean Square Error (RMSE) was the minimum. IDW was the interpolator that yielded the best accuracies for all combinations of densities and GS. Its RMSE when considering the raw cloud was 1.054 cm and increased by 3% when a point cloud with 80% extracted from the raw cloud was used to generate the DTM. When the point cloud included 40% of the raw cloud, RMSE increased by 5%. For densities lower than 15%, RMSE increased exponentially (45% for 1% of raw cloud). The GS minimizing RMSE for densities of 20% or higher was 1 cm, which represents 2.5 times the ground sample distance of the pictures used for developing the photogrammetry project

A Comparative Analysis of Phytovolume Estimation Methods Based on UAV-Photogrammetry and Multispectral Imagery in a Mediterranean Forest

Management and control operations are crucial for preventing forest fires, especially in Mediterranean forest areas with dry climatic periods. One of them is prescribed fires, in which the biomass fuel present in the controlled plot area must be accurately estimated. The most used methods for estimating biomass are time-consuming and demand too much manpower. Unmanned aerial vehicles (UAVs) carrying multispectral sensors can be used to carry out accurate indirect measurements of terrain and vegetation morphology and their radiometric characteristics. Based on the UAV-photogrammetric project products, four estimators of phytovolume were compared in a Mediterranean forest area, all obtained using the di erence between a digital surface model (DSM) and a digital terrain model (DTM). The DSM was derived from a UAV-photogrammetric project based on the structure from a motion algorithm. Four di erent methods for obtaining a DTM were used based on an unclassified dense point cloud produced through a UAV-photogrammetric project (FFU), an unsupervised classified dense point cloud (FFC), a multispectral vegetation index (FMI), and a cloth simulation filter (FCS). Qualitative and quantitative comparisons determined the ability of the phytovolume estimators for vegetation detection and occupied volume. The results show that there are no significant di erences in surface vegetation detection between all the pairwise possible comparisons of the four estimators at a 95% confidence level, but FMI presented the best kappa value (0.678) in an error matrix analysis with reference data obtained from photointerpretation and supervised classification. Concerning the accuracy of phytovolume estimation, only FFU and FFC presented di erences higher than two standard deviations in a pairwise comparison, and FMI presented the best RMSE (12.3 m) when the estimators were compared to 768 observed data points grouped in four 500 m2 sample plots. The FMI was the best phytovolume estimator of the four compared for low vegetation height in a Mediterranean forest. The use of FMI based on UAV data provides accurate phytovolume estimations that can be applied on several environment management activities, including wildfire prevention. Multitemporal phytovolume estimations based on FMI could help to model the forest resources evolution in a very realistic way

Virtual reconstruction of damaged archaeological sites based on Unmanned Aerial Vehicle Photogrammetry and 3D modelling. Study case of a southeastern Iberia production area in the Bronze Age

Knowledge about cultural and archaeological heritage can dissolve or even disappear with the passage of time, especially when the protection level of the archaeological site is weak. The recent development of techniques based on remote sensing from remotely controlled light platforms, so-called Unmanned Aerial Vehicles (UAV) or drones, carrying sensors in visible and other spectral ranges, allows to measure efficiently the current surface morphology of a damaged archaeological site. In this work, a deteriorated and unique archaeological site due its chronological and functional singularity was chosen as the study case. Mound structures made of stone, interconnected one to each other in a regular network, covered by vaults, and well adapted to a smooth slope topography have no known precedents in the Iberian Peninsula in the Bronze Age. Nowadays, this site is seriously damaged, and its protection level by the administrations is weak. Nevertheless, two archaeological campaigns were carried out recording interesting information. A UAV-Photogrammetry project based on Structure from Motion (SfM) and Multi-View Stereopsis (MVS) algorithms was applied to model the surface terrain where the structural basis and connection channels were built, obtaining a dense point cloud, an orthoimage and a Digital Surface Model (DSM). The topographic data covering altered areas were removed from the dense point cloud, and then a new interpolated surface was obtained representing the unaltered morphology. Finally, the information recorded in the archaeological campaigns was materialised in a virtual reconstruction located between both surfaces, measured by UAV-Photogrammetry and interpolated, and then the original architectural complex in its context was recreated and shared with the scientific community through Google Earth, contributing to recovering and preserving this cultural heritage, even after its disappearance

Multi-sensor imagery rectification and registration for herbicide testing

The use of multi-spectral sensors has been focused on several agricultural tasks, yet it is necessary to further assess this approach to achieve sufficient precision to carry out adequately these. Metric information from these images is traditionally derived by photogrammetric techniques, but with a major limitation: photographed objects should be static while the photographs are being taken, but plants are generally in movement because of wind and this causes the photogrammetric process to be unable to generate the necessary information to make any metric measurement. To bypass this, metric information can be derived via rectification, using only one photograph. This work aims to develop a band co-registration method with agricultural purposes, based on rectified images taken from different sensors usually mounted on UAVs or terrestrial vehicles, studying its accuracy in a quantitative way. All multispectral information co-registered in a precise way will allow the calculation or development of new radiometric and even geometric indices that will help to improve efficiency in many tasks related to agriculture. Images taken from a multi-spectral (green, near infra-red, red and red edge) and a thermal camera were used to apply the developed methodology. First, a digital elevation model describing the displacement produced by distortion due to the sensor lens was obtained and applied to each of the studied pictures to correct this distortion. Then, distortion due to conic perspective present in the photographs was corrected, taking into account the homology relationship between the photographed object and the picture. To carry out these tasks, several computers programs were developed. Subsequently, the edges of the five bands corresponding to 250 plants were digitalised and their areas were measured. Furthermore, the intersection of the five bands of each plant was calculated, and an index (AI) indicating the fraction of the area of each band, which was out of the common area edge of the five bands, was calculated for each plant. The average value of this index for each band ranged from 0.22 to 0.24, with no statistically significant differences between them, indicating a high accuracy of the proposed methodology

Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV

Fire severity is a key factor for management of post-fire vegetation regeneration strategies because it quantifies the impact of fire, describing the amount of damage. Several indices have been developed for estimation of fire severity based on terrestrial observation by satellite imagery. In order to avoid the implicit limitations of this kind of data, this work employed an Unmanned Aerial Vehicle (UAV) carrying a high-resolution multispectral sensor including green, red, near-infrared, and red edge bands. Flights were carried out pre- and post-controlled fire in a Mediterranean forest. The products obtained from the UAV-photogrammetric projects based on the Structure from Motion (SfM) algorithm were a Digital Surface Model (DSM) and multispectral images orthorectified in both periods and co-registered in the same absolute coordinate system to find the temporal di erences (d) between pre- and post-fire values of the Excess Green Index (EGI), Normalized Di erence Vegetation Index (NDVI), and Normalized Di erence Red Edge (NDRE) index. The di erences of indices (dEGI, dNDVI, and dNDRE) were reclassified into fire severity classes, which were compared with the reference data identified through the in situ fire damage location and Artificial Neural Network classification. Applying an error matrix analysis to the three di erence of indices, the overall Kappa accuracies of the severity maps were 0.411, 0.563, and 0.211 and the Cramer’s Value statistics were 0.411, 0.582, and 0.269 for dEGI, dNDVI, and dNDRE, respectively. The chi-square test, used to compare the average of each severity class, determined that there were no significant di erences between the three severity maps, with a 95% confidence level. It was concluded that dNDVI was the index that best estimated the fire severity according to the UAV flight conditions and sensor specifications