Tree Species Classification : Analyzing Multitemporal Satellite Imagery and Multispectral Airborne Laser Scanning Data

Tree species composition of forests affects the whole ecosystem and is part of the information needed for an efficient planning of forest management. This thesis explores how recent developments in remote sensing can provide more accurate tree species mapping. I try to answer the question of how the properties of these data can be used to derive more information on tree species. Out of the four papers in this thesis, two papers examine how multitemporal satellite imagery from the Sentinel-2 mission can be of use, and the other two papers investigate what properties of multispectral airborne laser scanning (MSALS) data that contain the most information on tree species. We applied a Bayesian method to multitemporal satellite imagery for tree species classification of pixels in the hemiboreal forest of Remningstorp in southwestern Sweden. The Bayesian method was applied to 142 Sentinel-2 images, and to a subset of images ranked and selected by the separability of tree species classes. The method was also compared to a Random Forest classifier for 45 Sentinel-2 images of boreal forest in mid-Sweden. The Bayesian method performed better for homogeneous tree species classes, while Random Forest performed better for heterogeneous classes. Data from two MSALS systems were used for classifying the tree species of individual trees. Optech Titan-X data were used to classify free-standing trees of nine species in Remningstorp. By using Riegl VQ-1560i-DW data, we performed a tree species classification in a more operational setting for three tree species in closed-canopy hemiboreal forest in Asa in southern Sweden. Multispectral intensity features provided a great improvement in classification accuracy in both cases, compared to using only structural features or combining them with monospectral intensity features. For Optech Titan-X, the green wavelength performed poorly, but for Riegl VQ-1560i-DW, the green wavelength provided the most information for separability, especially for birch (Betula spp.). There are two main conclusions in this thesis. The first is that Bayesian methods that updates probabilities as new observations are made provides an opportunity to automate the addition of satellite images for an updated classification. The second is that MSALS data provides more information on tree species than monospectral data and tree crown structure do, with the most information coming from the upper parts of the canopy. Nonetheless, what wavelengths of light that contribute most to tree species classification accuracy is highly dependent on what MSALS system that is used

Using multispectral ALS for tree species identification

Accurate and large area tree species classification is an important subject with problems that have not yet been completely solved. For both nature conservation and wood production purposes, a detailed description of tree species composition would be useful. The objective of this master’s thesis is to explore how tree species differ in spectral and structural properties using multispectral airborne laser scanning data from the Optech Titan X system. Remote sensing data was gathered from Remningstorp, Västra Götaland in Sweden on 21st July 2016. Field data contained 179 solitary trees from nine species. Two new methods for feature extraction are tested and compared to features of height and intensity distributions. The features that were most important for tree species classification were those from the upper part of the crown. Spectral features provided a better basis for tree species classification than structural features. Using single, first or all returns gave only a small difference in cross-validation correctness rate. The best classification model was created using multispectral distribution features of all returns, with an correctness rate of 77.09 %. Spruce and pine had a 100 % overall classification accuracy and were not confused with any other species. Linden was the deciduous species with a large sample that was most frequently confused with many other deciduous species

Tree species classification using Sentinel-2 imagery and Bayesian inference

The increased temporal frequency of optical satellite data acquisitions provides a data stream that has the potential to improve land cover mapping, including mapping of tree species. However, for large area operational mapping, partial cloud cover and different image extents can pose challenges. Therefore, methods are needed to assimilate new images in a straightforward way without requiring a total spatial coverage for each new image. This study shows that Bayesian inference applied sequentially has the potential to solve this problem. To test Bayesian inference for tree species classification in the boreo-nemoral zone of southern Sweden, field data from the study area of Remningstorp (58?27?18.35?N, 13?39?8.03?E) were used. By updating class likelihood with an increasing number of combined Sentinel-2 images, a higher and more stable cross-validated overall accuracy was achieved. Based on a Mahalanobis distance, 23 images were automatically chosen from the period of 2016 to 2018 (from 142 images total). An overall accuracy of 87% (a Cohen?s kappa of 78.5%) was obtained for four tree species classes: Betula spp., Picea abies, Pinus sylvestris, and Quercus robur. This application of Bayesian inference in a boreo-nemoral forest suggests that it is a practical way to provide a high and stable classification accuracy. The method could be applied where data are not always complete for all areas. Furthermore, the method requires less reference data than if all images were used for classification simultaneously

Två höjdtilldelningsmetoder för trädhöjder

Detta kandidatarbete avsåg att utvärdera två metoder för höjdtilldelning av enskilda träd och hur dessa påverkade kvalitén på skattningar av grundytevägd medelhöjd gjord med hjälp av laserdata. De metoder som utvärderades var Söderbergs funktioner och en ny metod som Riksskogstaxeringen nyligen tagit fram. Studien skedde i tre delar, först en jämförelse mellan de värden för varje provyta som tilldelats enligt Söderbergs funktioner respektive Riksskogstaxeringens metod, sedan en regressionsanalys av mängden värden från respektive metod och laserdata, och slutligen en granskning av regressionsfunktionernas noggrannhet gentemot fältinventerade provytor som inte användes i framtagandet av funktionerna. För att göra dessa analyser användes Riksskogstaxeringens provytor, Lantmäteriets laserdata samt provytor tillhandahållna av Bergvik Skog.Resultatet visade på att den nya metoden gav systematiskt högre skattningar än Söderbergs funktioner. Skattningar gjorda utifrån laserdata baserade på den nya metoden hamnade närmare det fältinventerade värdet, jämfört med skattningar grundade på Söderbergs funktioner.The aim of this bachelors degree thesis was to analyze how estimations of basal area weighted mean height made with airborne laser scanning (ALS) data is affected by the tree wise height estimation method used for the plots before regression analysis. The methods compared are Söderberg’s functions and a new method produced by the Swedish National Forest Inventory (NFI). This study was performed in three parts. First an initial comparison between the values assigned to every plot was made. Then a regression analysis between these values and laser data was completed. Finally an evaluation of estimations made with the computed functions versus third party inventoried values was performed. To make this analysis, data was used that came from the NFI, Lantmäteriet’s (the Swedish national land survey) national laser scanning, and Bergvik Skog. The result showed that the new method developed by the NFI systematically produced a higher value than Söderberg’s functions. It was found that the estimates, made with ALS data, based on the new height estimation method showed better agreement with the evaluation data from Bergvik Skog than the estimates based on Söderberg’s functions

Light Field Coding Using Panoramic Projection

A new generation of 3d displays provides depth perception without the need for glasses and allows the viewer to see content from many different directions. Providing video for these displays requires capturing the scene by several cameras at different viewpoints, the data from which together forms light field video. To encode such video with existing video coding requires a large amount of data and it increases quickly with a higher number of views, which this application needs. One such coding is the multiview extension of High Efficiency Video Coding (mv-hevc), which encodes a number of similar video streams as different layers. A new coding scheme for light field video, called Panoramic Light Field (plf), is implemented and evaluated in this thesis. The main idea behind the coding is to project all points in a scene that are visible from any of the viewpoints to a single, global view, similar to how texture mapping maps a texture onto a 3d model in computer graphics. Whereas objects ordinarily shift position in the frame as the camera position changes, this is not the case when using this projection. A visible point in space is projected to the same image pixel regardless of viewpoint, resulting in large similarities between images from different viewpoints. The similarity between the layers in light field video helps to achieve more efficient compression when the projection is combined with existing multiview coding. In order to evaluate the scheme, 3d content was created and software was developed to encode it using plf. Video using this coding is compared to existing technology: a straightforward encoding of the views using mvhevc. The results show that the plf coding performs better on the sample content at lower quality levels, while it is worse at higher bitrate due to quality loss from the projection procedure. It is concluded that plf is a promising technology and suggestions are given for future research that may improve its performance further.Nya tekniker är under utveckling för 3D-bildskärmar kan visa light field: bilder och video som spelas in med arrayer av kameror. Sådan video kräver stora datamängder. En ny kodning av light field, syftande till att uppnå ett bättre förhållande mellan bildkvalitet och bitrate, utvärderas i det här examensarbetet

Exploring Multispectral ALS Data for Tree Species Classification

Multispectral Airborne Laser Scanning (ALS) is a new technology and its output data have not been fully explored for tree species classification purposes. The objective of this study was to investigate what type of features from multispectral ALS data (wavelengths of 1550 nm, 1064 nm and 532 nm) are best suited for tree species classification. Remote sensing data were gathered over hemi-boreal forest in southern Sweden (58 degrees 2718.35N, 13 degrees 398.03E) on 21 July 2016. The field data consisted of 179 solitary trees from nine genera and ten species. Two new methods for feature extraction were tested and compared to features of height and intensity distributions. The features that were most important for tree species classification were intensity distribution features. Features from the upper part of the upper and outer parts of the crown were better for classification purposes than others. The best classification model was created using distribution features of both intensity and height in multispectral data, with a leave-one-out cross-validated accuracy of 76.5%. As a comparison, only structural features resulted in an highest accuracy of 43.0%. Picea abies and Pinus sylvestris had high user's and producer's accuracies and were not confused with any deciduous species. Tilia cordata was the deciduous species with a large sample that was most frequently confused with many other deciduous species. The results, although based on a small and special data set, suggest that multispectral ALS is a technology with great potential for tree species classification

Training of frequency discrimination – is it meaningful?

Denna experimentella studie syftar till att undersöka huruvida en eventuell träningseffekt i  frekvensdiskrimination kvarstår efter en tids träningsuppehåll. Tidigare forskning har visat  tydliga samband mellan frekvensdiskriminering och talutveckling, kortikal signalbehandling  och dyslexi. Det har klargjorts genom tidigare studier att förmågan att diskriminera frekvenser  är möjligt att träna upp. Därmed är träning i frekvensdiskrimination en lämplig del i en  behandlingsmetod med syfte att utveckla den auditiva perceptionen. Denna studie undersöker  de praktiska förutsättningarna till denna behandlingsmetod genom att utreda varaktigheten av  den eventuella träningseffekten 14 dagar efter träningen. 24 normalhörande personer deltog i  studien där försökspersonernas förmåga att frekvensdiskriminera undersöktes med hjälp av ett  egenutvecklat mjukvaruprogram. Först uppmättes försökspersonernas förmåga när de var  otränade (mätning 1). Sedan genomfördes ett träningspass som följdes av en mätning  (mätning 2). Efter 14 dagar genomfördes ytterligare en mätning för att undersöka den  bestående effekten (mätning 3).  Resultatet visade (när tre outliers exkluderats) en signifikant skillnad mellan mätning 1 och 2  (p=0,034) vilket innebär att en omedelbar träningseffekt erhölls. Ingen signifikant skillnad  kunde påvisas mellan mätning 2 och 3 (p=0,952) men en signifikant skillnad erhölls mellan  mätning 1 och 3 (p=0,031) vilket påvisar att den omedelbara träningseffekten kvarstod efter  de gångna 14 dagarna.