Environmental 3D photogrammetric hyperspectral and RGB measurements on lightweight remotely piloted aircraft system’s

Kauko-ohjattavien ilma-alusjärjestelmien (RPAS) käyttö kaukokartoituksessa on lisääntynyt räjähdysmäisesti viime vuosina. Niiden etuna on lentokoneisiin ja helikoptereihin verrattuna niiden pieni koko ja edullisuus. RPAS-laitteistoille on kehitelty useita uusia hyperspektrikameroita, jotka sopivat pienen kokonsa ja painonsa puolesta keveille lentoalustoille. Yksi tällainen on Suomessa kehitetty Fabry-Pérot –interferometriin (FPI) perustuva kokonaisia 2D kuvamatriiseja keräävä hyperspektrikamera. Toisin kuin perinteisillä hyperspektriskannereilla tallennetut yksittäiset rivit, nämä kuvamatriisit mahdollistavat fotogrammetristen tekniikoiden käytön 3D-pistepilvien, ortomosaiikkien ja korkeusmallien luomiseen. Tässä työssä kehitettiin FPI-kameran ja RGB-kameran kuville geometrinen prosessointiketju ja selvitettiin kuinka eri ympäristöt vaikuttavat näiden kuvien geometriseen prosessointiin. Työssä tehtiin FPI- ja RGB-kameran kuvilta fotogrammetrisesti Structure-from-Motion (SfM) tekniikalla tuotettujen 3D-pistepilvien, ortomosaiikkien ja korkeusmallien virheen mittausta. Lisäksi tutkimuksessa selvitettiin voidaanko FPI- ja RGB-kameroiden kuvista tehtyjä kasvillisuuden korkeusmalleja (CHM) hyödyntää biomassojen estimoinnissa manuaalisen mittauksen sijasta. Tutkimuksessa käytettiin Paikkatietokeskuksen RPAS-laitteistolla kerättyjä FPI- ja RGB-kuvia Vihdissä sijaitsevista pelloista ja Mustila Arboretumin kansallismetsästä. Kerätyistä aineistoista muodostettiin SfM-tekniikalla 3D-pistepilvet, joista laskettiin digitaalinen korkeusmalli (DSM) ja digitaalinen maanpinnankorkeusmalli (DTM) sekä ortomosaiikit. Viljakasvien CHM laskettiin DSM:n ja DTM:n erotuksella ja tästä irrotettiin referenssien näytteenottoruutuja vastaavat keskiarvokorkeudet, joita verrattiin referensseihin. Peltoaineistosta tuotetuiden korkeusmallien ja ortomosaiikkien absoluuttisen virheen mittaus toteutettiin tunnettujen tarkistuspisteiden avulla ja näiden virheitä verrattiin muissa tutkimuksissa saatuihin tuloksiin. Metsäaineistoista eri kuvapeitoilla tuotettuja FPI-korkeusmalleja vertailtiin parhaalla peitolla tuotettuun FPI-korkeusmalliin. Tämä työ osoittaa, että eri ympäristöjen FPI- ja RGB-kuvaukset ja geometrinen prosessointi sisältävät omat haasteensa. Peltoaineistojen korkeusmallit ja ortomosaiikit olivat absoluuttisilta tarkkuuksiltaan hyviä (FPI RMSEx = 11,9 cm, RMSEy = 11,9 cm ja RMSEz = 13,0 cm; RGB RMSEx = 4,0 cm, RMSEy = 4,0 cm ja RMSEz = 5,4 cm) verrattaessa muihin tutkimuksiin. Viljakasvien CHM korreloi parhaassa tapauksessa referenssien kanssa hyvin (R2 = 0,75 – 0,87) ja näin ollen sen hyödyntäminen biomassojen estimoinnissa on mahdollista. Metsäaineistojen FPI-korkeusmallit olivat tarkkuudeltaan huonompia (RMSEz = 31,0 – 309,5 cm), mihin osaltaan vaikutti haastava maasto.The use of Remotely Piloted Aircraft System’s (RPAS) in Remote Sensing has increased rapidly in recent years. Their advantage compared to airplanes and helicopters is their small size and cheap price. A number of new hyperspectral instruments, suitable for light aircraft platforms due to their small size and light weight, have been developed for RPAS. One of these is a hyperspectral camera developed in Finland that utilises the Fabry-Pérot inferometer to measure a number of different wavelength ranges and collect whole image arrays. Unlike the old hyperspectral scanners that recorded only individual lines, these image arrays enable the creation 3D point clouds, orthorectified images and surface models using photogrammetric techniques. This work developed a geometric processing chain for FPI and RBG camera images and examined how different environments and parameters affect the geometric processing of these images. In the work the measurement of coordinate errors of 3D point clouds, orthorectified images and surface models, created from FPI and RGB camera images with photogrammetric Structure-from-Motion (SfM) technique, was carried out. In addition, the work investigated if canopy height models (CHM) created from FPI and RGB images could be utilized to estimate biomass of vegetation instead of manual field measurement. This study utilized FPI and RGB images collected by the RPAS of Paikkatietokeskus in fields located in Vihti and forest located in Mustila Arboretum National Forest. The collected data was processed with the SfM technique to create 3D point clouds, which were used to calculate a digital elevation model (DSM) and a digital terrain model (DTM) as well as orthorectified images. CHM was calculated by subtracting DTM from DSM, and from this the average heights corresponding with the sampling frames of reference were extracted and compared. The measurement of absolute error of the field surface models and orthorectified images was carried out using reference control points, and these errors were compared with the results obtained in other studies. The assessment of the forest FPI-surface models was carried out using FPI-surface model which had highest overlaps. This work shows that FPI and RGB imaging and the geometric processing of images of different environments pose their own challenges. The absolute accuracies of the field surface models and the orthomosaic coordinates were good (FPIs RMSEx = 11.9 cm, RMSEy = 11.9 cm ja RMSEz = 13.0 cm; RGBs RMSEx = 4.0 cm, RMSEy = 4.0 cm ja RMSEz = 5.4 cm) when compared to the other studies. In the best case the crops CHM correlated with the references well (R2 = 0.75 – 0.87), and thus its utilization in biomass estimation is possible. The accuracies of the forest FPI-surface models were worse (RMSEz = 31.0 – 309.5 cm)

Calibration of DART Radiative Transfer Model with Satellite Images for Simulating Albedo and Thermal Irradiance Images and 3D Radiative Budget of Urban Environment

Remote sensing is increasingly used for managing urban environment. In this context, the H2020 project URBANFLUXES aims to improve our knowledge on urban anthropogenic heat fluxes, with the specific study of three cities: London, Basel and Heraklion. Usually, one expects to derive directly 2 major urban parameters from remote sensing: the albedo and thermal irradiance. However, the determination of these two parameters is seriously hampered by complexity of urban architecture. For example, urban reflectance and brightness temperature are far from isotropic and are spatially heterogeneous. Hence, radiative transfer models that consider the complexity of urban architecture when simulating remote sensing signals are essential tools. Even for these sophisticated models, there is a major constraint for an operational use of remote sensing: the complex 3D distribution of optical properties and temperatures in urban environments. Here, the work is conducted with the DART (Discrete Anisotropic Radiative Transfer) model. It is a comprehensive physically based 3D radiative transfer model that simulates optical signals at the entrance of imaging spectro-radiometers and LiDAR scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental (atmosphere, topography,…) and instrumental (sensor altitude, spatial resolution, UV to thermal infrared,…) configuration. Paul Sabatier University distributes free licenses for research activities. This paper presents the calibration of DART model with high spatial resolution satellite images (Landsat 8, Sentinel 2, etc.) that are acquired in the visible (VIS) / near infrared (NIR) domain and in the thermal infrared (TIR) domain. Here, the work is conducted with an atmospherically corrected Landsat 8 image and Bale city, with its urban database. The calibration approach in the VIS/IR domain encompasses 5 steps for computing the 2D distribution (image) of urban albedo at satellite spatial resolution. (1) DART simulation of satellite image at very high spatial resolution (e.g., 50cm) per satellite spectral band. Atmosphere conditions are specific to the satellite image acquisition. (2) Spatial resampling of DART image at the coarser spatial resolution of the available satellite image, per spectral band. (3) Iterative derivation of the urban surfaces (roofs, walls, streets, vegetation,…) optical properties as derived from pixel-wise comparison of DART and satellite images, independently per spectral band. (4) Computation of the band albedo image of the city, per spectral band. (5) Computation of the image of the city albedo and VIS/NIR exitance, as an integral over all satellite spectral bands. In order to get a time series of albedo and VIS/NIR exitance, even in the absence of satellite images, ECMWF information about local irradiance and atmosphere conditions are used. A similar approach is used for calculating the city thermal exitance using satellite images acquired in the thermal infrared domain. Finally, DART simulations that are conducted with the optical properties derived from remote sensing images give also the 3D radiative budget of the city at any date including the date of the satellite image acquisition

Morphology-based landslide monitoring with an unmanned aerial vehicle

PhD ThesisLandslides represent major natural phenomena with often disastrous consequences. Monitoring landslides with time-series surface observations can help mitigate such hazards. Unmanned aerial vehicles (UAVs) employing compact digital cameras, and in conjunction with Structure-from-Motion (SfM) and modern Multi-View Stereo (MVS) image matching approaches, have become commonplace in the geoscience research community. These methods offer a relatively low-cost and flexible solution for many geomorphological applications. The SfM-MVS pipeline has expedited the generation of digital elevation models at high spatio-temporal resolution. Conventionally ground control points (GCPs) are required for co-registration. This task is often expensive and impracticable considering hazardous terrain. This research has developed a strategy for processing UAV visible wavelength imagery that can provide multi-temporal surface morphological information for landslide monitoring, in an attempt to overcome the reliance on GCPs. This morphological-based strategy applies the attribute of curvature in combination with the scale-invariant feature transform algorithm, to generate pseudo GCPs. Openness is applied to extract relatively stable regions whereby pseudo GCPs are selected. Image cross-correlation functions integrated with openness and slope are employed to track landslide motion with subsequent elevation differences and planimetric surface displacements produced. Accuracy assessment evaluates unresolved biases with the aid of benchmark datasets. This approach was tested in the UK, in two sites, first in Sandford with artificial surface change and then in an active landslide at Hollin Hill. In Sandford, the strategy detected a ±0.120 m 3D surface change from three-epoch SfM-MVS products derived from a consumer-grade UAV. For the Hollin Hill landslide six-epoch datasets spanning an eighteen-month duration period were used, providing a ± 0.221 m minimum change. Annual displacement rates of dm-level were estimated with optimal results over winter periods. Levels of accuracy and spatial resolution comparable to previous studies demonstrated the potential of the morphology-based strategy for a time-efficient and cost-effective monitoring at inaccessible areas