Mapping and monitoring of vegetation using airborne laser scanning

In this thesis, the utility of airborne laser scanning (ALS) for monitoring vegetation of relevance for the environmental sector was investigated. The vegetation characteristics studied include measurements of biomass, biomass change and vegetation classification in the forest-tundra ecotone; afforestation of grasslands; and detection of windthrown trees. Prediction of tree biomass for mountain birch (Betula pubescens ssp. czerepanovii) using sparse (1.4 points/m²) and dense (6.1 points/m²) ALS data was compared for a site at the forest-tundra ecotone near Abisko in northern Sweden (Lat. 68° N, Long. 19° E). The predictions using the sparse ALS data provided almost as good results (RMSE 21.2%) as the results from the dense ALS data (18.7%) despite the large difference in point densities. A new algorithm was developed to compensate for uneven distribution of the laser points without decimating the data; use of this algorithm reduced the RMSE for biomass prediction from 19.9% to 18.7% for the dense ALS data. Additional information about vegetation height and density from ALS data improved a satellite data classification of alpine vegetation, in particular for the willow and mountain birch classes. Histogram matching was shown to be effective for relative calibration of metrics from two ALS acquisitions collected over the same area using different scanners and flight parameters. Thus the difference between histogram-matched ALS metrics from different data acquisitions can be used to locate areas with unusual development of the vegetation. The height of small trees (0.3–2.6 m tall) in former pasture land near the Remnings¬torp test site in southern Sweden (Lat. 58° N, Long. 13° E) could be measured with high precision (standard deviation 0.3 m) using high point density ALS data (54 points/m2). When classifying trees taller than 1 m into the two classes of changed and unchanged, the overall classification accuracy was 88%. A new method to automatically detect windthrown trees in forested areas was developed and evaluated at the Remningstorp test site. The overall detection rate was 38% on tree-level, but when aggregating to 40 m square grid cells, at least one windthrown tree was detected in 77% of the cells that according to field data contained windthrown trees. In summary, this thesis has shown the high potential for ALS to be a future tool to map and monitor vegetation for several applications of interest for the environmental sector

The Canterbury Hinterland Project: understanding dynamic rural landscapes through the semi-automated interpretation of geophysical survey results

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Individual tree detection using template matching of multiple rasters derived from multispectral airborne laser scanning data

Multispectral airborne laser scanning (MS-ALS) provides information about 3D structure as well as the intensity of the reflected light and is a promising technique for acquiring forest information. Data from MS-ALS have been used for tree species classification and tree health evaluation. This paper investigates its potential for individual tree detection (ITD) when using intensity as an additional metric. To this end, rasters of height, point density, vegetation ratio, and intensity at three wavelengths were used for template matching to detect individual trees. Optimal combinations of metrics were identified for ITD in plots with different levels of canopy complexity. The F-scores for detection by template matching ranged from 0.94 to 0.73, depending on the choice of template derivation and raster generalization methods. Using intensity and point density as metrics instead of height increased the F-scores by up to 14% for the plots with the most understorey trees

Application of Black-Bridge Satellite Imagery for the Spatial Distribution of Salvage Cutting in Stands Damaged by Wind

Salvage logging is performed to remove the fallen and damaged trees after a natural disturbance, e.g., fire or windstorm. From an economic point of view, it is desirable to remove the most valuable merchantable timber, but usually, the process depends mainly on topography and distance to forest roads. The objective of this study was to evaluate the suitability of the Black-Bridge satellite imagery for the spatial distribution of salvage cutting in southern Poland after the severe windstorm in July 2015. In particular, this study aimed to determine which factors influence the spatial distribution of salvage cutting. The area of windthrow and the distribution of salvage cutting (July–August 2015 and August 2015–May 2016) were delineated using Black-Bridge satellite imagery. The distribution of the polygons (representing windthrow and salvage cutting) was verified with maps of aspect, elevation and slope, derived from the Digital Terrain Model and the distance to forest roads, obtained from the Digital Forest Map. The analysis included statistical modelling of the relationships between the process of salvage cutting and selected geographical and spatial features. It was found that the higher the elevation and the steeper the slope, the lower the probability of salvage cutting. Exposure was also found to be a relevant factor (however, it was difficult to interpret) as opposed to the distance to forest roads

Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future

Maalahopuun kartoitus maastolaserkeilauksella

Decaying dead wood is a key factor for forest biodiversity. In boreal forests, many threatened and specialised species are dependent on dead wood. Therefore, information on quantity and quality of dead wood is needed. Conventionally, the inventory of dead wood is based on measurements and observations done in the field with traditional forest measurement tools, which, however, could be replaced by terrestrial laser scanning (TLS). TLS provides a dense point cloud on its surroundings with a millimetre-level of detail enabling versatile measurements at the levels from an individual tree to an entire sample plot. Previous studies have proven TLS to efficiently provide information for mapping standing trees, but the feasibility of TLS for dead wood inventory has not yet examined. The objective of this study was to develop an automatic method for mapping downed dead wood using TLS. TLS data were collected from 20 sample plots (32 m x 32 m in size) using the multi-scan approach with five scanning positions on each plot. All downed dead tree trunks with a diameter exceeding 5 cm at the middle of the trunk were measured in the field and considered as the field reference. Cylinder fitting and surface model segmentation were utilised when the downed dead wood trunks were automatically detected from the point clouds. The trunks were also detected visually to reveal all the potential of the use of a dense point cloud in mapping downed dead wood from a sample plot. Dimensions, volume, geometry-related quality attributes and position in the sample plot were automatically determined for each trunk detected from the point cloud. Based on trunk attributes, a map representing the spatial distribution of downed dead wood, as well as estimates for attributes describing the quantity and quality of downed dead wood at the plot level, were constructed. Finally, a diameter distribution for downed dead wood in the study area was comprised. This study revealed that TLS is a valid method for mapping downed dead wood from sample plots. By utilising the TLS point clouds, 68 % of the downed dead wood volume was detected automatically, while the total volume of downed dead wood was estimated with an RMSE of 15,0 m3/ha. The mapping accuracy could be improved with the visual interpretation of the point cloud, in which case 83 % of the dead wood volume was detected, and the estimate for the total volume of downed dead wood was determined with an accuracy of 6,4 m3/ha. On average, the length of the detected tree trunk was underestimated while the diameter was overestimated since the trunk was not able to be detected entirely from the point cloud. According to the results, the reliability of TLS based dead wood mapping increases alongside the dimensions of the dead wood trunks. The density of plot vegetation, however, causes shading and reduces the trunk detection accuracy. Therefore, when collecting the data, extra attention must be paid to the quality of the point cloud.Lahopuu ylläpitää metsäluonnon monimuotoisuutta, sillä se on välttämätön elinympäristö monille uhanalaisille eliölajeille. Tietoa lahopuun määrästä ja laadusta tarvitaan, jotta voidaan arvioida lahopuun vaikutusta metsäekosysteemin erilaisiin toimintoihin. Lahopuun kartoitus perustuu yhä maastoinventointiin, jossa perinteiset mittavälineet voitaisiin korvata maastolaserkeilauksella. Maastolaserkeilain tuottaa ympäristöstään tiheän pistepilven, jonka millimetritason tarkkuutta voidaan hyödyntää puu- ja koealatason mittauksissa. Maastolaserkeilaus on osoittautunut tehokkaaksi tiedonkeruumenetelmäksi elävän puuston koealamittaukseen, mutta sen soveltuvuutta lahopuun kartoitukseen ei ole vielä tutkittu. Tämän tutkielman tavoitteena oli kehittää maastolaserkeilaukseen perustuva automaattinen menetelmä maalahopuun määrän ja laadun kartoitukseen. Maalahopuun kartoitusta varten kerättiin maastolaserkeilausaineisto 20 metsikkökoealalta (32 m x 32 m). Maastossa koealoilta kartoitettiin vähintään 5 cm järeät maalahopuurungot kartoitusmenetelmän kehitystä ja tarkkuuden arviointia varten. Maalahopuurungot tunnistettiin koealojen pistepilvistä automaattisesti runkojen geometristen muotojen perusteella sylinterisovitusta ja pintamallien segmentointia käyttäen. Rungot tunnistettiin myös pistepilven visuaaliseen tulkintaan perustuvalla menetelmällä, jotta voitiin tarkastella, miten hyvin maalahopuut on mahdollista kartoittaa koealaa kuvaavan tiheän pistepilven avulla. Pistepilvistä tunnistetuille rungoille määritettiin dimensiot, joiden perusteella laskettiin runkojen tilavuus- ja järeystunnukset. Runkojen ominaisuus- ja sijaintitietojen avulla muodostettiin kartta maalahopuun jakautumisesta koealalle, estimaatit maalahopuun määrää ja laatua koealatasolla kuvaaville tunnuksille sekä edelleen maalahopuun järeysjakauma koko tutkimusalueelle. Tulokset osoittivat, että maastolaserkeilaus soveltuu tiedonkeruumenetelmäksi maalahopuun kartoitukseen metsikkökoealoilta. Metsikkökoealaa kuvaavasta pistepilvestä voitiin tunnistaa automaattisesti 68 % maalahopuun tilavuudesta, jolloin maalahopuun kokonaistilavuus määritettiin 15,0 m3/ha tarkkuudella (RMSE). Pistepilven visuaalisella tulkinnalla kartoitusta voitiin edelleen tarkentaa: maalahopuun tilavuudesta tunnistettiin 83 %, ja kokonaistilavuusestimaatti määritettiin lähes harhattomasti 6,4 m3/ha tarkkuudella. Keskimäärin maalahopuurungon pituus aliarvioitiin ja järeys yliarvioitiin, koska runkoa ei pystytty tunnistamaan pistepilvestä koko pituudeltaan. Tulosten perusteella maastolaserkeilaukseen perustuva maalahopuun kartoitus on sitä luotettavampaa, mitä järeämmästä lahopuusta ollaan kiinnostuneita. Puuston ja aluskasvillisuuden tiheys aiheuttaa kuitenkin pistepilveen katvealueita, joilta runkoja ei voida tunnistaa. Siksi maastolaserkeilaukseen perustuvassa maalahopuun kartoituksessa on kiinnitettävä huomiota pistepilven laatuun

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

Detection of forest windthrows with bitemporal COSMO-SkyMed and Sentinel-1 SAR data

Wind represents a primary source of disturbances in forests, necessitating an assessment of the resulting damage to ensure appropriate forest management. Remote sensing, encompassing both active and passive techniques, offers a valuable and efficient approach for this purpose, enabling coverage of large areas while being costeffective. Passive remote sensing data could be affected by the presence of clouds, unlike active systems such as Synthetic Aperture Radar (SAR) which are relatively less affected. Therefore, this study aims to explore the utilization of bitemporal SAR data for windthrow detection in mountainous regions. Specifically, we investigated how the detection outcomes vary based on three factors: i) the SAR wavelength (X-band or C-band), ii) the acquisition period of the pre- and post-event images (summer, autumn, or winter), and iii) the forest type (evergreen vs. deciduous). Our analysis considers two SAR satellite constellations: COSMO-SkyMed (band-X, with a pixel spacing of 2.5 m and 10 m) and Sentinel-1 (band-C, with a pixel spacing of 10 m). We focused on three study sites located in the Trentino-South Tyrol region of Italy, which experienced significant forest damage during the Vaia storm from 27th to 30th October 2018. To accomplish our objectives, we employed a detailpreserving, scale-driven approach for change detection in bitemporal SAR data. The results demonstrate that: i) the algorithm exhibits notably better performance when utilizing X-band data, achieving a highest kappa accuracy of 0.473 and a balanced accuracy of 76.1%; ii) the pixel spacing has an influence on the accuracy, with COSMO-SkyMed data achieving kappa values of 0.473 and 0.394 at pixel spacings of 2.5 m and 10 m, respectively; iii) the post-event image acquisition season significantly affects the algorithm’s performance, with summer imagery yielding superior results compared to winter imagery; and iv) the forest type (evergreen vs. deciduous) has a noticeable impact on the results, particularly when considering autumn/winter dat