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)

A Novel Machine Learning Method for Estimating Bio-mass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone

-Agriculture, vol. 8, issue 5, 2018. - This article belongs to the Special Issue Remote Sensing in Agricultural System

Examining Structural Complexity of Scots Pine Trees – A Comparison between Terrestrial Laser Scanning and Photogrammetric Point Clouds

Peer reviewe

Geometric and Reflectance Signature Characterization of Complex Canopies Using Hyperspectral Stereoscopic Images from Uav and Terrestrial Platforms

Peer reviewe

Accurate Calibration Scheme for a Multi-Camera Mobile Mapping System

Mobile mapping systems (MMS) are increasingly used for many photogrammetric and computer vision applications, especially encouraged by the fast and accurate geospatial data generation. The accuracy of point position in an MMS is mainly dependent on the quality of calibration, accuracy of sensor synchronization, accuracy of georeferencing and stability of geometric configuration of space intersections. In this study, we focus on multi-camera calibration (interior and relative orientation parameter estimation) and MMS calibration (mounting parameter estimation). The objective of this study was to develop a practical scheme for rigorous and accurate system calibration of a photogrammetric mapping station equipped with a multi-projective camera (MPC) and a global navigation satellite system (GNSS) and inertial measurement unit (IMU) for direct georeferencing. The proposed technique is comprised of two steps. Firstly, interior orientation parameters of each individual camera in an MPC and the relative orientation parameters of each cameras of the MPC with respect to the first camera are estimated. In the second step the offset and misalignment between MPC and GNSS/IMU are estimated. The global accuracy of the proposed method was assessed using independent check points. A correspondence map for a panorama is introduced that provides metric information. Our results highlight that the proposed calibration scheme reaches centimeter-level global accuracy for 3D point positioning. This level of global accuracy demonstrates the feasibility of the proposed technique and has the potential to fit accurate mapping purposes

Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (Pinus sylvestris L.) Stands

Terrestrial laser scanning (TLS) provides a detailed three-dimensional representation of surrounding forest structures. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees, and especially upper parts of forest canopy, is often limited. In this study, we investigated how much forest characterization capacity can be improved in managed Scots pine (Pinus sylvestris L.) stands if TLS point clouds are complemented with photogrammetric point clouds acquired from above the canopy using unmanned aerial vehicle (UAV). In this multisensorial (TLS+UAV) close-range sensing approach, the used UAV point cloud data were considered especially suitable for characterizing the vertical forest structure and improvements were obtained in estimation accuracy of tree height as well as plot-level basal-area weighted mean height (Hg) and mean stem volume (Vmean). Most notably, the root-mean-square-error (RMSE) in Hg improved from 0.8 to 0.58 m and the bias improved from −0.75 to −0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands, the mere TLS also captured the upper parts of the forest canopy rather well. Both approaches were capable of deriving stem number, basal area, Vmean, Hg, and basal area-weighted mean diameter with the relative RMSE less than 5.5% for all the sample plots. Although the multisensorial close-range sensing approach mainly enhanced the characterization of the forest vertical structure in single-species, single-layer forest conditions, representation of more complex forest structures may benefit more from point clouds collected with sensors of different measurement geometries

Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (Pinus sylvestris L.) Stands

Terrestrial laser scanning (TLS) provides a detailed three-dimensional representation of surrounding forest structures. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees, and especially upper parts of forest canopy, is often limited. In this study, we investigated how much forest characterization capacity can be improved in managed Scots pine (Pinus sylvestris L.) stands if TLS point clouds are complemented with photogrammetric point clouds acquired from above the canopy using unmanned aerial vehicle (UAV). In this multisensorial (TLS+UAV) close-range sensing approach, the used UAV point cloud data were considered especially suitable for characterizing the vertical forest structure and improvements were obtained in estimation accuracy of tree height as well as plot-level basal-area weighted mean height (Hg) and mean stem volume (Vmean). Most notably, the root-mean-square-error (RMSE) in Hg improved from 0.8 to 0.58 m and the bias improved from −0.75 to −0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands, the mere TLS also captured the upper parts of the forest canopy rather well. Both approaches were capable of deriving stem number, basal area, Vmean, Hg, and basal area-weighted mean diameter with the relative RMSE less than 5.5% for all the sample plots. Although the multisensorial close-range sensing approach mainly enhanced the characterization of the forest vertical structure in single-species, single-layer forest conditions, representation of more complex forest structures may benefit more from point clouds collected with sensors of different measurement geometries

Assessing the effects of thinning on stem growth allocation of individual Scots pine trees

Forest management alters the growing conditions and thus further development of trees. However, quantitative assessment of forest management on tree growth has been demanding as methodologies for capturing changes comprehensively in space and time have been lacking. Terrestrial laser scanning (TLS) has shown to be capable of providing three-dimensional (3D) tree stem reconstructions required for revealing differences between stem shapes and sizes. In this study, we used 3D reconstructions of tree stems from TLS and an unmanned aerial vehicle (UAV) to investigate how varying thinning treatments and the following growth effects affected stem shape and size of Scots pine (Pinus sylvestris L.) trees. The results showed that intensive thinning resulted in more stem volume and therefore total biomass allocation and carbon uptake compared to the moderate thinning.Relationship between tree height and diameter at breast height (i.e. slenderness) varied between both thinning intensity and type (i.e. from below and above) indicating differing response to thinning and allocation of stem growth of Scots pine trees. Furthermore, intensive thinning, especially from below, produced less variation in relative stem attributes characterizing stem shape and size. Thus, it can be concluded that thinning intensity,type, and the following growth effects have an impact on post-thinning stem shape and size of Scots pine trees.Our study presented detailed measurements on post-thinning stem growth of Scots pines that have been laborious or impracticable before the emergence of detailed 3D technologies. Moreover, the stem reconstructions from TLS and UAV provided variety of attributes characterizing stem shape and size that have not traditionally been feasible to obtain. The study demonstrated that detailed 3D technologies, such as TLS and UAV, provide information that can be used to generate new knowledge for supporting forest management and silviculture as well as improving ecological understanding of boreal forests.1Peer reviewe

Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft

Climate-related extended outbreaks and range shifts of destructive bark beetle species pose a serious threat to urban boreal forests in North America and Fennoscandia. Recent developments in low-cost remote sensing technologies offer an attractive means for early detection and management of environmental change. They are of great interest to the actors responsible for monitoring and managing forest health. The objective of this investigation was to develop, assess, and compare automated remote sensing procedures based on novel, low-cost hyperspectral imaging technology for the identification of bark beetle infestations at the individual tree level in urban forests. A hyperspectral camera based on a tunable Fabry-Perot interferometer was operated from a small, unmanned airborne vehicle (UAV) platform and a small Cessna-type aircraft platform. This study compared aspects of using UAV datasets with a spatial extent of a few hectares (ha) and a ground sample distance (GSD) of 10-12 cm to the aircraft data covering areas of several km(2) and having a GSD of 50 cm. An empirical assessment of the automated identification of mature Norway spruce (Picea abies L. Karst.) trees suffering from infestation (representing different colonization phases) by the European spruce bark beetle (Ips typographus L.) was carried out in the urban forests of Lahti, a city in southern Finland. Individual spruces were classified as healthy, infested, or dead. For the entire test area, the best aircraft data results for overall accuracy were 79% (Cohen's kappa: 0.54) when using three crown color classes (green as healthy, yellow as infested, and gray as dead). For two color classes (healthy, dead) in the same area, the best overall accuracy was 93% (kappa: 0.77). The finer resolution UAV dataset provided better results, with an overall accuracy of 81% (kappa: 0.70), compared to the aircraft results of 73% (kappa: 0.56) in a smaller sub-area. The results showed that novel, low-cost remote sensing technologies based on individual tree analysis and calibrated remote sensing imagery offer great potential for affordable and timely assessments of the health condition of vulnerable urban forests.Peer reviewe

Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features

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