The Hidden Cairns : A Case Study of Drone-Based ALS as an Archaeological Site Survey Method

Conducting archaeological site surveys is time consuming, and large sites may have many small features or structures that are difficult to locate and interpret. Vegetation cover and dense forest hide small structures, like cairns, while at the same time forest cover can cause problems for LiDAR tools. In this case study, drone-based ALS (airborne laser scanning) was tested as an archaeological site survey tool. The research site was complex and located partially in a forested area, which made it possible to evaluate how forest cover affects data. The survey methods used were rather simple: visual analysis, point density calculations in the forest area, and, for site interpretation purposes, digitizing observations and viewshed analysis. Using straightforward methods allowed us to evaluate the minimum time and skills needed for this type of survey. Drone-based ALS provided good results and increased knowledge of the site and its structures. Estimates of the number of cairns interpreted as graves more than doubled as a result of the high-accuracy ALS data. Based on the results of this study, drone-based ALS could be a suitable high-accuracy survey method for large archaeological sites. However, forest cover affects the accuracy, and more research is needed

The Hidden Cairns—A Case Study of Drone-Based ALS as an Archaeological Site Survey Method

Conducting archaeological site surveys is time consuming, and large sites may have many small features or structures that are difficult to locate and interpret. Vegetation cover and dense forest hide small structures, like cairns, while at the same time forest cover can cause problems for LiDAR tools. In this case study, drone-based ALS (airborne laser scanning) was tested as an archaeological site survey tool. The research site was complex and located partially in a forested area, which made it possible to evaluate how forest cover affects data. The survey methods used were rather simple: visual analysis, point density calculations in the forest area, and, for site interpretation purposes, digitizing observations and viewshed analysis. Using straightforward methods allowed us to evaluate the minimum time and skills needed for this type of survey. Drone-based ALS provided good results and increased knowledge of the site and its structures. Estimates of the number of cairns interpreted as graves more than doubled as a result of the high-accuracy ALS data. Based on the results of this study, drone-based ALS could be a suitable high-accuracy survey method for large archaeological sites. However, forest cover affects the accuracy, and more research is needed.Peer reviewe

Airborne laser scanning reveals large tree trunks on forest floor

Fallen trees decompose on the forest floor and create habitats for many species. Thus, mapping fallen trees allows identifying the most valuable areas regarding biodiversity, especially in boreal forests, enabling well-focused conservation and restoration actions. Airborne laser scanning (ALS) is capable of characterizing forests and the underlying topography. However, its potential for detecting and characterizing fallen trees under varying boreal forest conditions is not yet well understood. ALS-based fallen tree detection methods could improve our understanding regarding the spatiotemporal characteristics of dead wood over large landscapes. We developed and tested an automatic method for mapping individual fallen trees from an ALS point cloud with a point density of 15 points/m2. The presented method detects fallen trees using iterative Hough line detection and delineates the trees around the detected lines using region growing. Furthermore, we conducted a detailed evaluation of how the performance of ALS-based fallen tree detection is impacted by characteristics of fallen trees and the structure of vegetation around them. The results of this study showed that large fallen trees can be detected with a high accuracy in old-growth forests. In contrast, the detection of fallen trees in young managed stands proved challenging. The presented method was able to detect 78% of the largest fallen trees (diameter at breast height, DBH > 300 mm), whereas 30% of all trees with a DBH over 100 mm were detected. The performance of the detection method was positively correlated with both the size of fallen trees and the size of living trees surrounding them. In contrast, the performance was negatively correlated with the amount of undergrowth, ground vegetation, and the state of decay of fallen trees. Especially undergrowth and ground vegetation impacted the performance negatively, as they covered some of the fallen trees and lead to false fallen tree detections. Based on the results of this study, ALS-based collection of fallen tree information should be focused on old-growth forests and mature managed forests, at least with the current operative point densities.Peer reviewe

Modeling of Dead Wood Potential Based on Tree Stand Data

Here we present a framework for identifying areas with high dead wood potential (DWP) for conservation planning needs. The amount and quality of dead wood and dying trees are some of the most important factors for biodiversity in forests. As they are easy to recognize on site, it is widely used as a surrogate marker for ecological quality of forests. However, wall-to-wall information on dead wood is rarely available on a large scale as field data collection is expensive and local dead wood conditions change rapidly. Our method is based on the forest growth models in the Motti forest simulator, taking into account 168 combinations of tree species, site types, and vegetation zones as well as recommendations on forest management. Simulated estimates of stand-level dead wood volume and mean diameter at breast height were converted into DWP functions. The accuracy of the method was validated on two sites in southern and northeastern Finland, both consisting of managed and conserved boreal forests. Altogether, 203 field plots were measured for living and dead trees. Data on living trees were inserted into corresponding DWP functions and the resulting DWPs were compared to the measured dead wood volumes. Our results show that DWP modeling is an operable tool, yet the accuracy differs between areas. The DWP performs best in near-pristine southern forests known for their exceptionally good quality areas. In northeastern areas with a history of softer management, the differences between near-pristine and managed forests is not as clear. While accurate wall-to-wall dead wood inventory is not available, we recommend using DWP method together with other spatial datasets when assessing biodiversity values of forests

Evaluating Factors Impacting Fallen Tree Detection from Airborne Laser Scanning Point Clouds

Fallen tree mapping provides valuable information regarding the ecological value of boreal forests. Airborne laser scanning (ALS) enables mapping fallen trees on a large scale. We compared the performance of line-detection-based individual fallen tree detection when using moderate point density ALS data (15 points/m2) and high-point-density unmanned aerial vehicle-based laser scanning (ULS) data (285 points/m2). Furthermore, we inspected the dataset and detection methodology-related factors impacting performance in each case. The results of this study showed that increasing the point density of the laser scanning dataset enables the detection of a larger proportion of fallen trees. However, based on our experiment, a line-detection-based fallen tree detection approach is sensitive to noise, thus generating a large number of false detections, especially with high-point-density data. Different types of filters, such as a simple height-based filter and machine-learning-based filters, can be used for reducing noise. However, using such filters is always a compromise, as in addition to reducing noise and thus false detections, they also reduce the number of true detections. Hence, a less noise-sensitive fallen tree detection method utilizing the finer details visible in high-density point clouds could be more suitable for high-point-density laser scanning data

Kolmiulotteinen kaupunkipuiden yksinpuintulkinta ilmalaserkeilausaineistosta

Urban trees are a valuable resource, as they affect the climate of cities, provide aesthetic and recreational value and maintain the biodiversity in the cities. Thus, cities and municipalities often keep tree registers for monitoring the condition of urban trees. Updating these registers with field measurements is laborious and time-consuming and thus there is a need to automate the updating process. Airborne laser scanning (ALS) provides an efficient option for the tree registry updating process, as it enables acquiring detailed three-dimensional (3D) data from large areas at once. This thesis studied the ALS-based urban tree monitoring process starting from the extraction of vegetation points from the ALS point cloud and ending in the detection and delineation of individual trees. One method was developed and tested for removing falsely classified vegetation points from a pre-classified point cloud. In addition, three individual tree detection (ITD) methods were developed and tested. Method 1 detected trees using region growing, method 2 divided the point cloud into horizontal slices and delineated the trees by merging clusters of each slice, and method 3 detected trees from a surface model. The method for removing falsely classified vegetation points produced varying results. Some false vegetation points originating from flat man-made objects were detected rather well, whereas the detection of vertical and narrow objects was very poor. In conclusion, the method by itself was not sufficient, but it could be used as a part of the vegetation point extraction process. The accuracy of the ITD methods was assessed by calculating the tree detection rates with distance thresholds ranging from 0.5 m to 6 m. The distance threshold determined the maximum locational difference between a delineated tree and a reference tree for these trees to be matched. The detection rates of ITD methods 1,2 and 3 ranged from 0.09 to 0.79, 0.14 to 0.79 and 0.11 to 0.50, respectively. The study showed that none of the tested methods perform sufficiently well by themselves, but a combination of methods 1 and 3 could be a suitable method for detecting urban trees.Kaupunkipuut ovat tärkeitä, sillä ne vaikuttavat kaupunkien ilmastoon ja biodiversiteettiin sekä tuottavat virkistysarvoa ja esteettistä arvoa. Tämän vuoksi monet kunnat ja kaupungit ylläpitävät puurekisteriä kaupunkipuiden kunnon valvomiseksi. Näitä rekistereitä päivitetään maastomittauksin, mikä on varsin työlästä ja näin ollen rekisterien päivittämistä pyritään automatisoimaan. Ilmalaserkeilaus mahdollistaa rekisterien tehokkaan päivittämisen, sillä ilmalaserkeilaamalla voi kerätä tarkkaa kolmiulotteista informaatiota laajoilta alueilta. Tässä työssä tutustuttiin ilmalaserkeilaukseen perustuvaan kaupunkipuiden monitorointiin alkaen kasvillisuuspisteiden luokittelusta ja päättyen yksittäisten puiden tunnistukseen ja rajaamiseen. Esiluokitellussa pistepilvessä virheellisesti kasvillisuudeksi luokiteltujen laserpisteiden poistamiseksi kehitettiin menetelmä. Tämän lisäksi työssä tutkittiin kolmen yksinpuintulkintamenetelmän toimintaa kaupunkiympäristössä. Menetelmä 1 tunnisti puita alueen kasvatusta hyödyntämällä, menetelmä 2 jakoi laserpistepilven vaakasuoriin liuskoihin ja mallinsi puut yhdistelemällä kunkin liuskan klustereita ja menetelmä 3 tunnisti puita pintamallilta. Virheellisesti kasvillisuuspiteiksi luokiteltujen laserpisteiden tunnistamiseen kehitetty menetelmä toimi vaihtelevasti. Tasapintaisista kohteista syntyneet laserpisteet tunnistettiin melko hyvin, mutta kapeat ja pystysuorat kohteet tunnistettiin varsin huonosti. Näin ollen menetelmä ei yksinään ole riittävä virheellisten kasvillisuuspisteiden poistamiseen, mutta sitä voisi käyttää osana kasvillisuuspisteiden luokitteluprosessia. Yksinpuintulkintamenetelmien tarkkuutta mitattiin laskemalla puiden tunnistusaste, eli kuinka suuri osa koealueiden puista kyettiin tunnistamaan kullakin menetelmällä. Referenssipuun ja yksinpuintulkintamenetelmällä mallinnetun puun yhdistämiseen käytettiin etäisyyden kynnysarvoja, jotka vaihtelivat 0,5 metristä 6 metriin. Menetelmien 1, 2 ja 3 tunnistusasteet vaihtelivat välillä 0,09-0,79, 0,14-0,79 ja 0,11-0,50. Tutkimus osoitti, että yksikään menetelmistä ei yksinään toimi riittävän hyvin. Sen sijaan menetelmien 1 ja 3 yhdistelmä voisi olla sopiva menetelmä kaupunkipuiden yksinpuintulkintaan

Detecting individual dead trees using airborne laser scanning

The global crises – climate change and biodiversity loss – have created a need for precise and wide-scale information of forests. Airborne laser scanning (ALS) provides a means for collecting such information, as it enables mapping large areas efficiently with a resolution sufficient for object-level information extraction. Deadwood is an important component of the forest environment, as it stores carbon and provides a habitat for a wide variety of species. Mapping deadwood provides information about the valuable areas regarding biodiversity, which can be used in, e.g., conservation and restoration planning. The aim of this thesis was to develop automated methodology for detecting individual fallen and standing dead trees from ALS data. Studies I and II presented a line detection based method for detecting fallen trees and evaluated its performance on a moderate-density ALS dataset (point density approx. 15 points/m2) and a high point density unmanned aerial vehicle borne laser scanning (ULS) dataset (point density approx. 285 points/m2). In addition, the studies inspected the dataset, methodology, and forest structure related factors affecting the performance of the method. The studies found that the length and diameter of fallen trees significantly impact their detection probability, and that the majority of large fallen trees can be identified from ALS data automatically. Furthermore, study I found that the amount and type of undergrowth and ground vegetation, as well as the size of surrounding living trees determine how accurately fallen trees can be mapped from ALS data. Moreover, study II found that increasing the point density of the laser scanning dataset does not automatically improve the performance of fallen tree detection, unless the methodology is adjusted to consider the increase in noise and detail in the point cloud. Study III inspected the feasibility of high-density discrete return ULS data for mapping individual standing dead trees. The individual tree detection method developed in the study was based on a three-step process consisting of individual tree segmentation, feature extraction, and machine learning based classification. The study found that, while some of the large standing dead trees could be identified from the ULS dataset, basing detection on discrete return data and the geometrical properties of trees did not suffice for acquiring applicable deadwood information. Thus, spectral information acquired with multispectral laser scanners or aerial imaging, or full-waveform laser scanning is necessary for detecting individual standing dead trees with a sufficient accuracy. The findings of this thesis contribute to the existing deadwood detection methodology and improve the understanding of factors to take into account when utilizing ALS for detecting dead trees at a single-tree-level. Although remotely sensed deadwood mapping is still far from a resolved topic, these contributions are a step towards operationalizing remotely sensed biodiversity monitoring.Metsien kestävä käyttö vaatii tarkkaa metsävaratietoa, sillä tarkka metsävaratieto mahdollistaa metsiin kohdistuvien toimenpiteiden optimaalisen sijoittelun ja ajoittamisen. Perinteisesti metsävaratietoa on kerätty ensisijaisesti metsätalouden tarpeisiin. Luonnon monimuotoisuuden vähenemisen aikakautena tärkeää olisi kuitenkin kerätä tietoa myös ekologisesti merkittävistä kohteista, sillä tällainen tieto mahdollistaisi entisöinti- ja suojelutoimenpiteiden optimaalisen kohdentamisen. Metsien monimuotoisuuden suora mittaaminen on kuitenkin hankalaa ja työlästä, ja tämän vuoksi alueiden ekologista merkittävyyttä arvioidaankin usein monimuotoisuuden indikaattorien avulla. Lahopuu on yksi merkittävimmistä metsien monimuotoisuuden indikaattoreista, sillä useat eliö- ja eläinlajit ovat riippuvaisia lahopuusta joko suoraan tai välillisesti muiden lajien kautta. Lahopuun etuna monimuotoisuuden kartoittamisessa on myös se, että kohtalaisen suuren kokonsa ansiosta lahopuita voidaan havaita myös ilmasta käsin. Kaukokartoitus on metsävaratiedon keräämiseen yleisesti käytetty menetelmä, sillä se mahdollistaa suurien alueiden tehokkaan monitoroinnin. Lentolaserkeilaus on kaukokartoituksen muoto, jossa lentokoneeseen asennettu laserkeilain kerää kohteesta tietoa lähettämällä kohteeseen laserpulsseja ja mittaamalla pulssien kulkuajan kohteeseen ja takaisin keilaimeen. Laserkeilauksen lopputuloksena on kohdetta esittävä kolmiulotteinen pistepilvi. Laserkeilauksen etu metsien monitoroinnissa on se, että sen avulla voidaan kerätä tietoa latvuston alapuolelta, mihin esimerkiksi ilmakuvaus ei kykene. Tämän väitöskirjan tavoitteena oli tarkastella lentolaserkeilauksen soveltuvuutta lahopuun kartoitukseen. Väitöskirjan osatutkimuksissa kehitettiin automaattisia menetelmiä kaatuneiden puiden ja pystyssä olevien kuolleiden puiden havaitsemiseksi yksittäisten puiden tasolla. Tämän lisäksi osatutkimuksissa tarkasteltiin, miten lahopuiden ja niitä ympäröivän metsän piirteet, sekä käytetyn laserkeilausaineiston ominaisuudet vaikuttavat lahopuukartoituksen tarkkuuteen. Väitöskirjan osatutkimukset osoittivat, että merkittävä osa suurista maalahopuista voidaan havaita automaattisesti lentolaserkeilausaineistosta, etenkin, jos elävä puusto lahopuiden ympärillä on kookasta ja aluskasvillisuus harvaa. Sen sijaan pystylahopuiden havaitseminen ainoastaan lentolaserkeilausta käyttäen osoittautui ongelmalliseksi, sillä elävien ja kuolleiden pystypuiden kolmiulotteinen muoto ei ole riittävän erilainen, jotta nämä puut voitaisiin erotella toisistaan. Tämän vuoksi väri-informaatiota sisältävien ilmakuvien yhdistäminen laserkeilauksen kanssa voisi parantaa pystylahopuiden havaitsemista merkittävästi. Tämä väitöskirja edistää olemassa olevaa kaukokartoituspohjaiseen monimuotoisuuden kartoitukseen liittyvää metodologiaa. Tämän lisäksi väitöskirja lisää ymmärrystämme laserkeilauspohjaisen lahopuukartoituksen eduista ja haasteista, sekä kartoituksen tarkkuuteen vaikuttavista tekijöistä

Automated detection of boreal forest deadwood using RGB UAV imagery

Deadwood and decaying wood are the most important components for the biodiversity of boreal forests, and around a quarter of all flora and fauna in Finnish forests depend on it, with third of these species being red-listed. However, there is a severe lack of stand-level deadwood data in Finland and in boreal forest in general, as the operational inventories either focus on the large-scale estimates or omit deadwood altogether. Unoccupied Aerial Vehicles (UAVs) are the only method for remotely detection of small objects, such as fallen deadwood, as even the most spatially accurate commercial satellites and aerial photography provide 30 cm ground sampling distance, compared to less than 2 cm that is easily achievable with UAVs. Interesting question is that can high resolution UAV data replace reliably enough usually lacking deadwood field data? In this work, we utilized Mask R-CNN to detect individual standing and fallen deadwood instances from RGBUAV imagery. We manually annotated over 16 000 deadwood instances from three separate study sites to be used as the training and validation data, and also compared these data to field-measured deadwood data which consisted of 3200 standing and downed deadwood measured with RTK GPS. These three sites represents southern, middle and northern boreal forests. Our models achieved test set Average Precision (AP) of 0.341 for the same geographical area the models were trained on, and AP of 0.236 for geographically distinct area used only for testing. In addition to instance level deadwood maps, we also estimated stand-level deadwood characteristics, such as length and approximate total volume of fallen deadwood based on the annotated polygons. These stand-level features clearly show the borders of the conserved forest, and the volume estimations can distinguish the naturally formed deadwood hotspots from the areas with logging residue. The proposed method enables deadwood mapping for areas consisting of multiple forest compartments, thus complementing the traditional field work

Laser scanning in mapping the quantity and qualities of coarse woody debris

Non peer reviewe