Tree Species Classification with Multiple Source Remote Sensing Data

Remote sensing is a study that provides information on targets of interest without direct interaction with them. Generally, the term is used for measurement techniques that detect electro-magnetic radiation emitted or reflected from the targets. Commonly used wavelength ranges include visible, infra-red, microwaves, and thermal bands. This information can be exploited to determine the structural and spectral properties of targets. Remote sensing techniques are typically utilized in mapping solutions, environment monitoring, target recognition, change detection, and in creation of physical models. In Finland, remote sensing research is of specific importance in forest sciences and industry as they need precise information on tree quantity and quality over large forest ranges. Tree species information on individual tree level is an important parameter to achieve this goal. The aim of this thesis is to study how individual tree species information can be extracted with multiple source remote sensing data. The aim is achieved by combining spatial and spectral remote sensing data. Structural properties of individual trees are determined from three dimensional point clouds collected with laser scanners. Spectral properties of trees are collected with cameras or spectrometers. The thesis consists of four separate studies. The first study examined how shading information of trees canopies could be exploited to improve tree species classification in data collected with airborne sensors. The second study examined the classification performance of a low-cost, multi-sensor, mobile mapping system. The third study investigated the classification performance and accuracy of a novel, active hyperspectral laser scanner. Finally, the fourth study evaluated the suitability of artificial surfaces as on-site intensity calibration targets. The results of the three classification studies showed that the use of combined point cloud and spectral information yielded the best classification results in all study cases when compared against classification results obtained with only structural or spectral information. Moreover, the studies showed that the improved results could be achieved with a low total number of mixed structural and spectral classification parameters. The fourth study showed that the artificial surfaces work as calibration surfaces only in limited cases. The main outcome of the thesis was that the active remote sensing systems measuring multiple wavelengths simultaneously should be promoted. They have a significant potential to improve tree species classification performance even with a few application-specific wavelengths. Kaukokartoitus on tutkimusala, jossa tutkittavia kohteita havainnoidaan ilman suoraa vuorovaikutusta. Yleisimmin kaukokartoituksella tarkoitetaan mittaustekniikoita, joilla havaitaan kohteiden lähettämää tai heijastamaa elektromagneettista säteilyä. Havainnointi tapahtuu tavallisesti näkyvän valon, infrapunan, mikroaaltojen ja lämpösäteilyn aallonpituusalueilla. Havaittua säteilyä voidaan hyödyntää kohteiden rakenteellisten ja spektraalisten ominaisuuksien määrittämisessä. Kaukokartoitusmenetelmiä käytetään tyypillisesti kartoitussovelluksissa, ympäristönseurannassa, kohde- ja muutostulkinnassa sekä fysikaalisten ilmiöiden mallinnuksessa. Kaukokartoitustutkimuksella on tärkeä osa suomalaisessa metsäntutkimuksessa ja -teollisuudessa. Molemmat tarvitsevat tarkkaa tietoa puuston määrästä ja laadusta suurilta metsäalueilta. Yksittäisten puiden lajitulkinta on tärkeä parametri tavoitteen saavuttamisessa. Väitöskirjatutkimuksen tarkoituksena on selvittää, kuinka yksittäisten puiden lajitieto voidaan määrittää eri mittauslaitteilla kerätystä kaukokartoitusaineistosta käyttämällä samanaikaisesti puustoa kuvaavia muotopiirteitä ja spektrivastetta. Muotopiirteiden keräys tehdään laserkeilaimilla. Spektrivasteet kerätään kameroilla tai spektrometreillä. Väitöskirjan sisältö koostuu neljästä erillisestä tutkimuksesta. Ensimmäisessä tutkimuksessa selvitetään, kuinka ilmasta kerättyä tietoa puiden latvustojen varjostumisesta voidaan hyödyntää puulajitulkinnassa. Toisessa tutkimuksessa arvioidaan puulajitulkinnan toteutettavuutta aineistosta, joka on kerätty edullisista komponenteista kootulla liikkuvalla kaukokartoituslaitteistolla. Kolmas tutkimus tarkastelee uuden, aktiivisesti mittaavan hyperspektrilaserin suorituskykyä ja tarkkuutta puulajitulkinnassa. Neljännessä tutkimuksessa selvitetään voidaanko rakennettuja pintoja hyödyntää intensiteetin maastokalibrointikohteina. Kaikki kolme luokittelututkimusta osoittivat yhdistetyn pistepilvi- ja spektriaineiston suoriutuvan parhaimmin lajitulkinnasta, kun tuloksia verrataan pelkästä rakenne- tai spektrisestä aineistosta laskettuihin tuloksiin. Lisäksi parantuneet tulokset saavuttiin yhdistämällä vain muutamaa rakenne- ja spektri-luokitteluparametria kerrallaan. Neljännen tutkimuksen tulokset osoittivat, että rakennetut pinnat soveltuvat kalibraatiokohteiksi vain rajatuissa tapauksissa. Väitöskirjan tärkein johtopäätös on, että aktiivisten, useaa aallonpituutta samanaikaisesti mittaavien kaukokartoituslaitteistojen kehitystä tulisi edistää. Tällaiset laitteistot voisivat parantaa puuston lajitulkintaa huomattavasti jo muutamaa sovellukseen sopivinta aallonpituutta käyttämällä

Tree Species Classification with Multiple Source Remote Sensing Data

Remote sensing is a study that provides information on targets of interest without direct interaction with them. Generally, the term is used for measurement techniques that detect electro-magnetic radiation emitted or reflected from the targets. Commonly used wavelength ranges include visible, infra-red, microwaves, and thermal bands. This information can be exploited to determine the structural and spectral properties of targets. Remote sensing techniques are typically utilized in mapping solutions, environment monitoring, target recognition, change detection, and in creation of physical models. In Finland, remote sensing research is of specific importance in forest sciences and industry as they need precise information on tree quantity and quality over large forest ranges. Tree species information on individual tree level is an important parameter to achieve this goal. The aim of this thesis is to study how individual tree species information can be extracted with multiple source remote sensing data. The aim is achieved by combining spatial and spectral remote sensing data. Structural properties of individual trees are determined from three dimensional point clouds collected with laser scanners. Spectral properties of trees are collected with cameras or spectrometers. The thesis consists of four separate studies. The first study examined how shading information of trees canopies could be exploited to improve tree species classification in data collected with airborne sensors. The second study examined the classification performance of a low-cost, multi-sensor, mobile mapping system. The third study investigated the classification performance and accuracy of a novel, active hyperspectral laser scanner. Finally, the fourth study evaluated the suitability of artificial surfaces as on-site intensity calibration targets. The results of the three classification studies showed that the use of combined point cloud and spectral information yielded the best classification results in all study cases when compared against classification results obtained with only structural or spectral information. Moreover, the studies showed that the improved results could be achieved with a low total number of mixed structural and spectral classification parameters. The fourth study showed that the artificial surfaces work as calibration surfaces only in limited cases. The main outcome of the thesis was that the active remote sensing systems measuring multiple wavelengths simultaneously should be promoted. They have a significant potential to improve tree species classification performance even with a few application-specific wavelengths. Kaukokartoitus on tutkimusala, jossa tutkittavia kohteita havainnoidaan ilman suoraa vuorovaikutusta. Yleisimmin kaukokartoituksella tarkoitetaan mittaustekniikoita, joilla havaitaan kohteiden lähettämää tai heijastamaa elektromagneettista säteilyä. Havainnointi tapahtuu tavallisesti näkyvän valon, infrapunan, mikroaaltojen ja lämpösäteilyn aallonpituusalueilla. Havaittua säteilyä voidaan hyödyntää kohteiden rakenteellisten ja spektraalisten ominaisuuksien määrittämisessä. Kaukokartoitusmenetelmiä käytetään tyypillisesti kartoitussovelluksissa, ympäristönseurannassa, kohde- ja muutostulkinnassa sekä fysikaalisten ilmiöiden mallinnuksessa. Kaukokartoitustutkimuksella on tärkeä osa suomalaisessa metsäntutkimuksessa ja -teollisuudessa. Molemmat tarvitsevat tarkkaa tietoa puuston määrästä ja laadusta suurilta metsäalueilta. Yksittäisten puiden lajitulkinta on tärkeä parametri tavoitteen saavuttamisessa. Väitöskirjatutkimuksen tarkoituksena on selvittää, kuinka yksittäisten puiden lajitieto voidaan määrittää eri mittauslaitteilla kerätystä kaukokartoitusaineistosta käyttämällä samanaikaisesti puustoa kuvaavia muotopiirteitä ja spektrivastetta. Muotopiirteiden keräys tehdään laserkeilaimilla. Spektrivasteet kerätään kameroilla tai spektrometreillä. Väitöskirjan sisältö koostuu neljästä erillisestä tutkimuksesta. Ensimmäisessä tutkimuksessa selvitetään, kuinka ilmasta kerättyä tietoa puiden latvustojen varjostumisesta voidaan hyödyntää puulajitulkinnassa. Toisessa tutkimuksessa arvioidaan puulajitulkinnan toteutettavuutta aineistosta, joka on kerätty edullisista komponenteista kootulla liikkuvalla kaukokartoituslaitteistolla. Kolmas tutkimus tarkastelee uuden, aktiivisesti mittaavan hyperspektrilaserin suorituskykyä ja tarkkuutta puulajitulkinnassa. Neljännessä tutkimuksessa selvitetään voidaanko rakennettuja pintoja hyödyntää intensiteetin maastokalibrointikohteina. Kaikki kolme luokittelututkimusta osoittivat yhdistetyn pistepilvi- ja spektriaineiston suoriutuvan parhaimmin lajitulkinnasta, kun tuloksia verrataan pelkästä rakenne- tai spektrisestä aineistosta laskettuihin tuloksiin. Lisäksi parantuneet tulokset saavuttiin yhdistämällä vain muutamaa rakenne- ja spektri-luokitteluparametria kerrallaan. Neljännen tutkimuksen tulokset osoittivat, että rakennetut pinnat soveltuvat kalibraatiokohteiksi vain rajatuissa tapauksissa. Väitöskirjan tärkein johtopäätös on, että aktiivisten, useaa aallonpituutta samanaikaisesti mittaavien kaukokartoituslaitteistojen kehitystä tulisi edistää. Tällaiset laitteistot voisivat parantaa puuston lajitulkintaa huomattavasti jo muutamaa sovellukseen sopivinta aallonpituutta käyttämällä

Automatic determination of 3D orientations of fossilized oyster shells from a densely packed Miocene shell bed

Shell beds represent a useful source of information on various physical processes that cause the depositional condition. We present an automated method to calculate the 3D orientations of a large number of elongate and platy objects (fossilized oyster shells) on a sedimentary bedding plane, developed to support the interpretation of possible depositional patterns, imbrications, or impact of local faults. The study focusses on more than 1900 fossil oyster shells exposed in a densely packed Miocene shell bed. 3D data were acquired by terrestrial laser scanning on an area of 459 m2 with a resolution of 1 mm. Bivalve shells were manually defined as 3D-point clouds of a digital surface model and stored in an ArcGIS database. An individual shell coordinate system (ISCS) was virtually embedded into each shell and its orientation was determined relative to the coordinate system of the entire, tectonically tilted shell bed. Orientation is described by the rotation angles roll, pitch, and yaw in a Cartesian coordinate system. This method allows an efficient measurement and analysis of the orientation of thousands of specimens and is a major advantage compared to the traditional 2D approach, which measures only the azimuth (yaw) angles. The resulting data can variously be utilized for taphonomic analyses and the reconstruction of prevailing hydrodynamic regimes and depositional environments. For the first time, the influence of possible post-sedimentary vertical displacements can be quantified with high accuracy. Here, the effect of nearby fault lines—present in the reef—was tested on strongly tilted oyster shells, but it was found out that the fault lines did not have a statistically significant effect on the large tilt angles. Aside from the high reproducibility, a further advantage of the method is its non-destructive nature, which is especially suitable for geoparks and protected sites such as the studied shell bed

Automatic determination of 3D orientations of fossilized oyster shells from a densely packed Miocene shell bed

Shell beds represent a useful source of information on various physical processes that cause the depositional condition. We present an automated method to calculate the 3D orientations of a large number of elongate and platy objects (fossilized oyster shells) on a sedimentary bedding plane, developed to support the interpretation of possible depositional patterns, imbrications, or impact of local faults. The study focusses on more than 1900 fossil oyster shells exposed in a densely packed Miocene shell bed. 3D data were acquired by terrestrial laser scanning on an area of 459 m2 with a resolution of 1 mm. Bivalve shells were manually defined as 3D-point clouds of a digital surface model and stored in an ArcGIS database. An individual shell coordinate system (ISCS) was virtually embedded into each shell and its orientation was determined relative to the coordinate system of the entire, tectonically tilted shell bed. Orientation is described by the rotation angles roll, pitch, and yaw in a Cartesian coordinate system. This method allows an efficient measurement and analysis of the orientation of thousands of specimens and is a major advantage compared to the traditional 2D approach, which measures only the azimuth (yaw) angles. The resulting data can variously be utilized for taphonomic analyses and the reconstruction of prevailing hydrodynamic regimes and depositional environments. For the first time, the influence of possible post-sedimentary vertical displacements can be quantified with high accuracy. Here, the effect of nearby fault lines—present in the reef—was tested on strongly tilted oyster shells, but it was found out that the fault lines did not have a statistically significant effect on the large tilt angles. Aside from the high reproducibility, a further advantage of the method is its non-destructive nature, which is especially suitable for geoparks and protected sites such as the studied shell bed

The effect of seasonal variation on automated land cover mapping from multispectral airborne laser scanning data

Multispectral airborne laser scanning (MS-ALS) sensors are a new promising source of data for auto-mated mapping methods. Finding an optimal time for data acquisition is important in all mapping applica-tions based on remotely sensed datasets. In this study, three MS-ALS datasets acquired at different times of the growing season were compared for automated land cover mapping and road detection in a suburban area. In addition, changes in the intensity were studied. An object-based random forest classi-fication was carried out using reference points. The overall accuracy of the land cover classification was 93.9% (May dataset), 96.4% (June) and 95.9% (August). The use of the May dataset acquired under leafless conditions resulted in more complete roads than the other datasets acquired when trees were in leaf. It was concluded that all datasets used in the study are applicable for suburban land cover map-ping, however small differences in accuracies between land cover classes exist

Canopy Roughness: A New Phenotypic Trait to Estimate Aboveground Biomass from Unmanned Aerial System

Cost-effective phenotyping methods are urgently needed to advance crop genetics in order to meet the food, fuel, and fiber demands of the coming decades. Concretely, charac-terizing plot level traits in fields is of particular interest. Re-cent developments in high resolution imaging sensors for UAS (unmanned aerial systems) focused on collecting de-tailed phenotypic measurements are a potential solution. We introduce canopy roughness as a new plant plot-level trait. We tested its usability with soybean by optical data collect-ed from UAS to estimate biomass. We validate canopy roughness on a panel of 108 soybean [Glycine max (L.) Merr.] recombinant inbred lines in a multienvironment trial during the R2 growth stage. A senseFly eBee UAS platform obtained aerial images with a senseFly S.O.D.A. compact digital camera. Using a structure from motion (SfM) tech-nique, we reconstructed 3D point clouds of the soybean experiment. A novel pipeline for feature extraction was de-veloped to compute canopy roughness from point clouds. We used regression analysis to correlate canopy roughness with field-measured aboveground biomass (AGB) with a leave-one-out cross-validation. Overall, our models achieved a coefficient of determination (R2) greater than 0.5 in all trials. Moreover, we found that canopy roughness has the ability to discern AGB variations among different geno-types. Our test trials demonstrate the potential of canopy roughness as a reliable trait for high-throughput phenotyping to estimate AGB. As such, canopy roughness provides practical information to breeders in order to select pheno-types on the basis of UAS data

Tree Water Status Affects Tree Branch Position

Physiological processes cause movements of tree stems and branches that occur in a circadian rhythm and over longer time periods, but there is a lack of quantitative understanding of the cause-and-effect relationships. We investigated the movement of tree branches in a long-term drought experiment and at a circadian time scale using time-series of terrestrial laser scanning measurements coupled with measurements of environmental drivers and tree water status. Our results showed that movement of branches was largely explained by leaf water status measured as leaf water potential in a controlled environment for both measured trees (R2 = 0.86 and R2 = 0.75). Our hypothesis is that changes in leaf and branch water status would cause branch movements was further supported by strong relationship between vapor pressure deficit and overnight branch movement (R2 = [0.57–0.74]). Due to lower atmospheric water demand during the nighttime, tree branches settle down as the amount of water in leaves increases. The results indicate that the quantified movement of tree branches could help us to further monitor and understand the water relations of tree communities

Tree Water Status Affects Tree Branch Position

Physiological processes cause movements of tree stems and branches that occur in a circadian rhythm and over longer time periods, but there is a lack of quantitative understanding of the cause-and-effect relationships. We investigated the movement of tree branches in a long-term drought experiment and at a circadian time scale using time-series of terrestrial laser scanning measurements coupled with measurements of environmental drivers and tree water status. Our results showed that movement of branches was largely explained by leaf water status measured as leaf water potential in a controlled environment for both measured trees (R2 = 0.86 and R2 = 0.75). Our hypothesis is that changes in leaf and branch water status would cause branch movements was further supported by strong relationship between vapor pressure deficit and overnight branch movement (R2 = [0.57–0.74]). Due to lower atmospheric water demand during the nighttime, tree branches settle down as the amount of water in leaves increases. The results indicate that the quantified movement of tree branches could help us to further monitor and understand the water relations of tree communities

Scalable Crop Yield Prediction with Sentinel-2 Time Series and Temporal Convolutional Network

One of the precepts of food security is the proper functioning of the global food markets. This calls for open and timely intelligence on crop production on an agroclimatically meaningful territorial scale. We propose an operationally suitable method for large-scale in-season crop yield estimations from a satellite image time series (SITS) for statistical production. As an object-based method, it is spatially scalable from parcel to regional scale, making it useful for prediction tasks in which the reference data are available only at a coarser level, such as counties. We show that deep learning-based temporal convolutional network (TCN) outperforms the classical machine learning method random forests and produces more accurate results overall than published national crop forecasts. Our novel contribution is to show that mean-aggregated regional predictions with histogram-based features calculated from farm-level observations perform better than other tested approaches. In addition, TCN is robust to the presence of cloudy pixels, suggesting TCN can learn cloud masking from the data. The temporal compositing of information do not improve prediction performance. This indicates that with end-to-end learning less preprocessing in SITS tasks seems viable