Browsing by Subject "airborne laser scanning"

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  • Zhu, L (Finnish Geospatial Research Institute FGI, NLS, 2015)
    FGI Publications 157
    3D technologies are becoming increasingly popular as their applications in industrial, consumer, entertainment, healthcare, education, and governmental increase in number. According to market predictions, the total 3D modeling and mapping market is expected to grow from $1.1 billion in 2013 to $7.7 billion by 2018. Thus, 3D modeling techniques for different data sources are urgently needed. This thesis addresses techniques for automated point cloud classification and the reconstruction of 3D scenes (including terrain models, 3D buildings and 3D road networks). First, georeferenced binary image processing techniques were developed for various point cloud classifications. Second, robust methods for the pipeline from the original point cloud to 3D model construction were proposed. Third, the reconstruction for the levels of detail (LoDs) of 1-3 (CityGML website) of 3D models was demonstrated. Fourth, different data sources for 3D model reconstruction were studied. The strengths and weaknesses of using the different data sources were addressed. Mobile laser scanning (MLS), unmanned aerial vehicle (UAV) images, airborne laser scanning (ALS), and the Finnish National Land Survey’s open geospatial data sources e.g. a topographic database, were employed as test data. Among these data sources, MLS data from three different systems were explored, and three different densities of ALS point clouds (0.8, 8 and 50 points/m2) were studied. The results were compared with reference data such as an orthophoto with a ground sample distance of 20cm or measured reference points from existing software to evaluate their quality. The results showed that 74.6% of building roofs were reconstructed with the automated process. The resulting building models provided an average height deviation of 15 cm. A total of 6% of model points had a greater than one-pixel deviation from laser points. A total of 2.5% had a deviation of greater than two pixels. The pixel size was determined by the average distance of input laser points. The 3D roads were reconstructed with an average width deviation of 22 cm and an average height deviation of 14 cm. The results demonstrated that 93.4% of building roofs were correctly classified from sparse ALS and that 93.3% of power line points are detected from the six sets of dense ALS data located in forested areas. This study demonstrates the operability of 3D model construction for LoDs of 1-3 via the proposed methodologies and datasets. The study is beneficial to future applications, such as 3D-model-based navigation applications, the updating of 2D topographic databases into 3D maps and rapid, large-area 3D scene reconstruction. 3D-teknologiat ovat tulleet yhä suositummiksi niiden sovellusalojen lisääntyessä teollisuudessa, kuluttajatuotteissa, terveydenhuollossa, koulutuksessa ja hallinnossa. Ennusteiden mukaan 3D-mallinnus- ja -kartoitusmarkkinat kasvavat vuoden 2013 1,1 miljardista dollarista 7,7 miljardiin vuoteen 2018 mennessä. Erilaisia aineistoja käyttäviä 3D-mallinnustekniikoita tarvitaankin yhä enemmän. Tässä väitöskirjatutkimuksessa kehitettiin automaattisen pistepilviaineiston luokittelutekniikoita ja rekonstruoitiin 3D-ympäristöja (maanpintamalleja, rakennuksia ja tieverkkoja). Georeferoitujen binääristen kuvien prosessointitekniikoita kehitettiin useiden pilvipisteaineistojen luokitteluun. Työssä esitetään robusteja menetelmiä alkuperäisestä pistepilvestä 3D-malliin eri CityGML-standardin tarkkuustasoilla. Myös eri aineistolähteitä 3D-mallien rekonstruointiin tutkittiin. Eri aineistolähteiden käytön heikkoudet ja vahvuudet analysoitiin. Testiaineistona käytettiin liikkuvalla keilauksella (mobile laser scanning, MLS) ja ilmakeilauksella (airborne laser scanning, ALS) saatua laserkeilausaineistoja, miehittämättömillä lennokeilla (unmanned aerial vehicle, UAV) otettuja kuvia sekä Maanmittauslaitoksen avoimia aineistoja, kuten maastotietokantaa. Liikkuvalla laserkeilauksella kerätyn aineiston osalta tutkimuksessa käytettiin kolmella eri järjestelmällä saatua dataa, ja kolmen eri tarkkuustason (0,8, 8 ja 50 pistettä/m2) ilmalaserkeilausaineistoa. Tutkimuksessa saatuja tulosten laatua arvioitiin vertaamalla niitä referenssiaineistoon, jona käytettiin ortokuvia (GSD 20cm) ja nykyisissä ohjelmistoissa olevia mitattuja referenssipisteitä. 74,6 % rakennusten katoista saatiin rekonstruoitua automaattisella prosessilla. Rakennusmallien korkeuksien keskipoikkeama oli 15 cm. 6 %:lla mallin pisteistä oli yli yhden pikselin poikkeama laseraineiston pisteisiin verrattuna. 2,5 %:lla oli yli kahden pikselin poikkeama. Pikselikoko määriteltiin kahden laserpisteen välimatkan keskiarvona. Rekonstruoitujen teiden leveyden keskipoikkeama oli 22 cm ja korkeuden keskipoikkeama oli 14 cm. Tulokset osoittavat että 93,4 % rakennuksista saatiin luokiteltua oikein harvasta ilmalaserkeilausaineistosta ja 93,3 % sähköjohdoista saatiin havaittua kuudesta tiheästä metsäalueen ilmalaserkeilausaineistosta. Tutkimus demonstroi 3D-mallin konstruktion toimivuutta tarkkuustasoilla (LoD) 1-3 esitetyillä menetelmillä ja aineistoilla. Tulokset ovat hyödyllisiä kehitettäessä tulevaisuuden sovelluksia, kuten 3D-malleihin perustuvia navigointisovelluksia, topografisten 2D-karttojen ajantasaistamista 3D-kartoiksi, ja nopeaa suurten alueiden 3D-ympäristöjen rekonstruktiota.
  • Amara, Edward; Adhikari, Hari; Heiskanen, Janne; Siljander, Mika; Munyao, Martha; Omondi, Patrick; Pellikka, Petri (2020)
    Savannahs provide valuable ecosystem services and contribute to continental and global carbon budgets. In addition, savannahs exhibit multiple land uses, e.g., wildlife conservation, pastoralism, and crop farming. Despite their importance, the effect of land use on woody aboveground biomass (AGB) in savannahs is understudied. Furthermore, fences used to reduce human-wildlife conflicts may affect AGB patterns. We assessed AGB densities and patterns, and the effect of land use and fences on AGB in a multi-use savannah landscape in southeastern Kenya. AGB was assessed with field survey and airborne laser scanning (ALS) data, and a land cover map was developed using Sentinel-2 satellite images in Google Earth Engine. The highest woody AGB was found in riverine forest in a conservation area and in bushland outside the conservation area. The highest mean AGB density occurred in the non-conservation area with mixed bushland and cropland (8.9 Mg center dot ha(-1)), while the lowest AGB density (2.6 Mg center dot ha(-1)) occurred in overgrazed grassland in the conservation area. The largest differences in AGB distributions were observed in the fenced boundaries between the conservation and other land-use types. Our results provide evidence that conservation and fences can create sharp AGB transitions and lead to reduced AGB stocks, which is a vital role of savannahs as part of carbon sequestration.
  • Vastaranta, Mikko; Yrttimaa, Tuomas; Saarinen, Ninni; Yu, Xiaowei; Karjalainen, Mika; Nurminen, Kimmo; Karila, Kirsi; Kankare, Ville; Luoma, Ville; Pyörälä, Jiri; Junttila, Samuli; Tanhuanpaa, Topi; Kaartinen, Harri; Kukko, Antero; Honkavaara, Eija; Jaakkola, Anttoni; Liang, Xinlian; Wang, Yunsheng; Vaaja, Matti; Hyyppä, Hannu; Katoh, Masato; Wulder, Michael A.; Holopainen, Markus; Hyyppä, Juha (2018)
    The objective of this study is to better understand the relationship between forest structure and point cloud features generated from certain airborne and space borne sensors. Point cloud features derived from airborne laser scanning (ALS), aerial imagery (AI), WorldView-2 imagery (WV2), TerraSAR-X, and Tandem-X (TDX) data were classified as features characterizing forest height and density as well as variation in tree height. Correlations between these features and field-measured attributes describing forest height, density and tree height variation were investigated at plot scale. From the field-measured attributes, basal area (G) and the number of trees per unit area (N) were used as forest density indicators whereas maximum tree height (H-max) and standard deviation in tree height (H-std) were used as indicators for forest height and tree height variation, respectively. In the analyses, field observations from 91 sample plots (32 m x 32 m) located in southern Finland were used. Even though ALS was found to be the most accurate data source in characterizing forest structure, AI, WV2, and TDX were also capable of characterizing forest height at plot scale with correlation coefficients stronger than 0.85. However, ALS was the only data source capable of providing separate features for characterizing also the variation in tree height and forest density. Features related to forest height, generated from the other data sources besides ALS, also provided strongest correlation with the forest density attributes and variation in tree height, in addition to H-max. Due to these more diverse characterization capabilities, forest structural attributes can be predicted more accurately by using ALS, also in the areas where the relation between the attributes of interest is not solely dependent on forest height, compared to the other investigated 3D remote sensing data sources.
  • Hovi, Aarne (Helsingfors universitet, 2011)
    Understory trees often emerging beneath dominant tree layer in even-aged stands have significance for timber harvesting operations, forest regeneration, landscape and visibility analysis, biodiversity and carbon balance. Airborne laser scanning (ALS) has proven to be an efficient remote sensing method in inventory of mature forest stands. Recent introduction of ALS to operational forest inventory systems could potentially enable cost-efficient acquisition of information on understory tree layer. In this study, accurate field reference and discrete return LiDAR data (1–2 km flying altitude, 0.9–9.7 pulses m-2) were used. The LiDAR data were obtained with Optech ALTM3100 and Leica ALS50-II sensors. The field reference plots represented typical commercially managed, even-aged pine stands in different developmental stages. Aims of the study were 1) to study the LiDAR signal from understory trees at pulse level and the factors affecting the signal, and 2) to explore what is the explanatory power of area-based LiDAR features in predicting the properties of understory tree layer. Special attention was paid in studying the effect of transmission losses to upper canopy layers on the obtained signal and possibilities to make compensations for transmission losses to the LiDAR return intensity. Differences in intensity between understory tree species were small and varied between data sets. Thus, intensity is of little use in tree species classification. Transmission losses increased noise in intensity observations from understory tree layer. Compensations for transmission losses were made to the 2nd and 3rd return data. The compensations decreased intensity variation within targets and improved classification accuracy between targets. In classification between ground and most abundant understory tree species using 2nd return data, overall classification accuracies were 49.2–54.9 % and 57.3–62.0 %, and kappa values 0.03–0.13 and 0.10–0.22, before and after compensations, respectively. The classification accuracy improved also in 3rd return data. The most important variable explaining the transmission losses was the intensity from previous echoes and pulse intersection geometry with upper canopy layer had a minor effect. The probability of getting an echo from an understory tree was studied, and differences between tree species were observed. Spruce produced an echo with a greater probability than broadleaved trees. If the pulse was subject to transmission losses, the differences were increased. The results imply that area-based LiDAR height distribution metrics could depend on tree species. There were differences in intensity data between sensors, which are a problem if multiple LiDAR data sets are used in inventory systems. Also the echo probabilities differed between sensors, which caused minor changes in LiDAR height distribution metrics. Area-based predictors for stem number and mean height of understory trees were detected if trees with height < 1 m were not included. In general, predictions for stem number were more accurate than for mean height. Explanatory power of the studied features did not markedly decrease with decreasing pulse density, which is important for practical applications. Proportion of broadleaved trees could not be predicted. As a conclusion, discrete return LiDAR data could be utilized e.g. in detecting the need for initial clearings before harvesting operations. However, accurate characterization of understory trees (e.g. detection of tree species) or detection of the smallest seedlings seems to be out of reach. Additional research is needed to generalize the results to different forests.
  • Ahokas, Eero (Finnish Geodetic Institute, 2013)
    Publications of the Finnish Geodetic Institute No 150
    Airborne laser scanning is a technique that produces three-dimensional coordinates of the Earth’s surface as well as generating intensity values. Nationwide airborne laser scanning was launched in Finland in 2008 and some 180 000 km2 had been scanned by the end of 2012. While the main goal in this endeavour is to produce an accurate digital elevation/terrain model (2 x 2 m2 grid size) of the whole of the country, other applications, e.g. forestry, will benefit from the data as well. This study deals with the accuracy of airborne laser scanning, the optimization of the scanning angle, and the calibration of intensity. Accuracy assessments of airborne laser scanning have shown that the geometric accuracy of the method can fulfill the accuracy requirements for producing a nationwide digital elevation model with a grid of 2 x 2 m2. When studying the effect of scanning angle and biomass on elevation modeling capability, it was found that it would be possible to increase the scanning angle applied in Finland’s nationwide laser scanning. Even though the accuracy of the elevation model in the conditions prevailing in Finland allows increasing of the scanning angle, other applications would most probably not benefit from this. For example, these same data are sometimes used in nationwide forest inventory in Finland. A method for relative and absolute calibration of airborne laser scanning intensity was developed. The portable reference targets have proved their usefulness for calibration purposes. An intensity correction method should be used in pre-processing the airborne laser data. As a result of this, the usability of the intensity values may increase in practical applications, such as in classification. The studies constituting this dissertation have already impacted on the practical aspects of the nationwide airborne laser scanning dealing with accuracy assessment, the work done in the field of intensity calibration, and scanning angle analysis may have a further impact on nationwide laser scanning in the coming years. The optimization of airborne laser scanning flight parameters for multi-use nationwide laser scanning is a topic deserving further research. Ilmasta tehtävä laserkeilaus tuottaa 3D-koordinaatteja maan pinnalta sekä intensiteettiarvoja. Suomen valtakunnallinen laserkeilaus aloitettiin vuonna 2008 ja noin 180000 km2 oli keilattu vuoden 2012 loppuun mennessä. Vaikka päätarkoituksena on tuottaa tarkka digitaalinen korkeus/maastomalli (2 x 2 m2 ruutukoko) koko maasta, muutkin sovellukset, kuten metsätalous, hyötyvät tästä aineistosta. Tämä tutkimus käsittelee ilmasta tehtävän laserkeilauksen tarkkuutta, keilauskulman optimointia sekä intensiteetin kalibrointia. Laserkeilauksen tarkkuusarviointi on osoittanut, että menetelmän geometrinen tarkkuus täyttää valtakunnallisen digitaalisen korkeusmallin tuottamisen tarkkuusvaatimukset. Kun tutkittiin keilauskulman ja biomassan vaikutusta korkeusmallin tuottamiseen, huomattiin että olisi mahdollista kasvattaa valtakunnallisen laserkeilauksen havaintokulmaa. Vaikka korkeusmallin tarkkuus mahdollistaisi Suomen oloissa keilauskulman kasvattamisen, muut sovellukset eivät luultavasti hyötyisi tästä. Esimerkiksi tätä samaa aineistoa käytetään Suomen valtakunnallisessa metsien inventoinnissa. Laserkeilauksen intensiteetin suhteellista ja absoluuttista kalibrointia varten kehitettiin menetelmä. Siirrettävät referenssikohteet osoittivat käyttökelpoisuutensa intensiteetin kalibroinnissa. Intensiteetin kalibrointimenetelmää tulisi käyttää laserkeilausaineiston esikäsittelyssä. Tämän tuloksena intensiteettiarvojen käyttökelpoisuus kasvaisi käytännön sovelluksissa, kuten luokittelussa. Tämän väitöskirjan muodostaneet tutkimukset ovat jo käytännössä vaikuttaneet valtakunnallisen laserkeilauksen tarkkuusarvioinnissa. Intensiteetin kalibrointityö ja keilauskulman analysointi vaikuttanevat valtakunnalliseen laserkeilaukseen tulevina vuosina. Lisätutkimusta tarvitaan ilmasta tehtävän laserkeilauksen lentoparametrien optimoimiseksi monikäyttöistä valtakunnallista laserkeilausta varten.
  • Aalto, Iris (Helsingin yliopisto, 2020)
    Global warming is expected to have detrimental consequences on fragile ecosystems in the tropics and to threaten both the global biodiversity as well as food security of millions of people. Forests have the potential to buffer the temperature changes, and the microclimatic conditions below tree canopies usually differ substantially from the ambient macroclimate. Trees cool down their surroundings through several biophysical mechanisms, and the cooling benefits occur also with trees outside forest. Remote sensing technologies offer new possibilities to study how tree cover affects temperatures both in local and regional scales. The aim of this study was to examine canopy cover’s effect on microclimate and land surface temperature (LST) in Taita Hills, Kenya. Temperatures recorded by 19 microclimate sensors under different canopy covers in the study area and LST estimated by Landsat 8 thermal infrared sensor (TIRS) were studied. The main interest was in daytime mean and maximum temperatures measured with the microclimate sensors in June-July 2019. The Landsat 8 imagery was obtained in July 4, 2019 and LST was retrieved using the single-channel method. The temperature records were combined with high-resolution airborne laser scanning (ALS) data of the area from years 2014 and 2015 to address how topographical factors and canopy cover affect temperatures in the area. Four multiple regression models were developed to study the joint impacts of topography and canopy cover on LST. The results showed a negative linear relationship between daytime mean and maximum temperatures and canopy cover percentage (R2 = 0.6–0.74). Any increase in canopy cover contributed to reducing temperatures at all microclimate measuring heights, the magnitude being the highest at soil surface level. The difference in mean temperatures between 0% and 100% canopy cover sites was 4.6–5.9 ˚C and in maximum temperatures 8.9–12.1 ˚C. LST was also affected negatively by canopy cover with a slope of 5.0 ˚C. It was found that canopy cover’s impact on LST depends on altitude and that a considerable dividing line existed at 1000 m a.s.l. as canopy cover’s effect in the highlands decreased to half compared to the lowlands. Based on the results it was concluded that trees have substantial effect on both microclimate and LST, but the effect is highly dependent on altitude. This indicates trees’ increasing significance in hot environments and highlights the importance of maintaining tree cover particularly in the lowland areas. Trees outside forests can increase climate change resilience in the area and the remaining forest fragments should be conserved to control the regional temperatures.
  • Yu, Xiaowei; Hyyppä, Juha; Karjalainen, Mika; Nurminen, Kimmo; Karila, Kirsi; Vastaranta, Mikko; Kankare, Ville; Kaartinen, Harri; Holopainen, Markus; Honkavaara, Eija; Kukko, Antero; Jaakkola, Anttoni; Liang, Xinlian; Wang, Yunsheng; Hyyppä, Hannu; Katoh, Masato (2015)
    It is anticipated that many of the future forest mapping applications will be based on three-dimensional (3D) point clouds. A comparison study was conducted to verify the explanatory power and information contents of several 3D remote sensing data sources on the retrieval of above ground biomass (AGB), stem volume (VOL), basal area (G), basal-area weighted mean diameter (D-g) and Lorey's mean height (H-g) at the plot level, utilizing the following data: synthetic aperture radar (SAR) Interferometry, SAR radargrammetry, satellite-imagery having stereo viewing capability, airborne laser scanning (ALS) with various densities (0.8-6 pulses/m(2)) and aerial stereo imagery. Laser scanning is generally known as the primary source providing a 3D point cloud. However, photogrammetric, radargrammetric and interferometric techniques can be used to produce 3D point clouds from space- and air-borne stereo images. Such an image-based point cloud could be utilized in a similar manner as ALS providing that accurate digital terrain model is available. In this study, the performance of these data sources for providing point cloud data was evaluated with 91 sample plots that were established in Evo, southern Finland within a boreal forest zone and surveyed in 2014 for this comparison. The prediction models were built using random forests technique with features derived from each data sources as independent variables and field measurements of forest attributes as response variable. The relative root mean square errors (RMSEs) varied in the ranges of 4.6% (0.97 m)-13.4% (2.83 m) for H-g, 11.7% (3.0 cm)-20.6% (5.3 cm) for D-g, 14.8% (4.0 m(2)/ha)-25.8% (6.9 m(2)/ha) for G, 15.9% (43.0 m(3)/ha)-31.2% (84.2 m(3)/ha) for VOL and 14.3% (19.2 Mg/ha)-27.5% (37.0 Mg/ha) for AGB, respectively, depending on the data used. Results indicate that ALS data achieved the most accurate estimates for all forest inventory attributes. For image-based 3D data, high-altitude aerial images and WorldView-2 satellite optical image gave similar results for H-g and D-g, which were only slightly worse than those of ALS data. As expected, spaceborne SAR data produced the worst estimates. WorldView-2 satellite data performed well, achieving accuracy comparable to the one with ALS data for G, VOL and AGB estimation. SAR interferometry data seems to contain more information for forest inventory than SAR radargrammetry and reach a better accuracy (relative RMSE decreased from 13.4% to 9.5% for H-g, 20.6% to 19.2% for D-g, 25.8% to 20.9% for G, 31.2% to 22.0% for VOL and 27.5% to 20.7% for AGB, respectively). However, the availability of interferometry data is limited. The results confirmed the high potential of all 3D remote sensing data sources for forest inventory purposes. However, the assumption of using other than ALS data is that there exist a high quality digital terrain model, in our case it was derived from ALS.
  • Seitsonen, Oula; Ikäheimo, Janne (2021)
    Open access airborne laser scanning (ALS) data have been available in Finland for over a decade and have been actively applied by the Finnish archaeologists in that time. The low resolution of this laser scanning 2008-2019 dataset (0.5 points/m(2)), however, has hindered its usability for archaeological prospection. In the summer of 2020, the situation changed markedly, when the Finnish National Land Survey started a new countrywide ALS survey with a higher resolution of 5 points/m(2). In this paper we present the first results of applying this newly available ALS material for archaeological studies. Finnish LIDARK consortium has initiated the development of semi-automated approaches for visualizing, detecting, and analyzing archaeological features with this new dataset. Our first case studies are situated in the Alpine tundra environment of Sapmi in northern Finland, and the assessed archaeological features range from prehistoric sites to indigenous Sami reindeer herding features and Second Word War-era German military structures. Already the initial analyses of the new ALS-5p data show their huge potential for locating, mapping, and assessing archaeological material. These results also suggest an imminent burst in the number of known archaeological sites, especially in the poorly accessible and little studied northern wilderness areas, when more data become available.
  • Viinikka, Arto; Hurskainen, Pekka; Keski-Saari, Sarita; Kivinen, Sonja; Tanhuanpää, Topi; Mäyrä, Janne; Poikolainen, Laura; Vihervaara, Petteri; Kumpula, Timo (MDPI, 2020)
    Remote Sensing 12 16 (2020)
    Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. European aspen (Populus tremula L.) is one key feature in boreal forests contributing significantly to the biological diversity of boreal forest landscapes. However, due to their sparse and scattered occurrence in northern Europe, the explicit spatial data on aspen remain scarce and incomprehensive, which hampers biodiversity management and conservation efforts. Our objective was to study tree-level discrimination of aspen from other common species in northern boreal forests using airborne high-resolution hyperspectral and airborne laser scanning (ALS) data. The study contained multiple spatial analyses: First, we assessed the role of different spectral wavelengths (455–2500 nm), principal component analysis, and vegetation indices (VI) in tree species classification using two machine learning classifiers—support vector machine (SVM) and random forest (RF). Second, we tested the effect of feature selection for best classification accuracy achievable and third, we identified the most important spectral features to discriminate aspen from the other common tree species. SVM outperformed the RF model, resulting in the highest overall accuracy (OA) of 84% and Kappa value (0.74). The used feature set affected SVM performance little, but for RF, principal component analysis was the best. The most important common VI for deciduous trees contained Conifer Index (CI), Cellulose Absorption Index (CAI), Plant Stress Index 3 (PSI3), and Vogelmann Index 1 (VOG1), whereas Green Ratio (GR), Red Edge Inflection Point (REIP), and Red Well Position (RWP) were specific for aspen. Normalized Difference Red Edge Index (NDRE) and Modified Normalized Difference Index (MND705) were important for coniferous trees. The most important wavelengths for discriminating aspen from other species included reflectance bands of red edge range (724–727 nm) and shortwave infrared (1520–1564 nm and 1684–1706 nm). The highest classification accuracy of 92% (F1-score) for aspen was achieved using the SVM model with mean reflectance values combined with VI, which provides a possibility to produce a spatially explicit map of aspen occurrence that can contribute to biodiversity management and conservation efforts in boreal forests.
  • Viinikka, Arto; Hurskainen, Pekka; Keski-Saari, Sarita; Kivinen, Sonja; Tanhuanpää, Topi; Mäyrä, Janne; Poikolainen, Laura; Vihervaara, Petteri; Kumpula, Timo (2020)
    Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. European aspen (Populus tremulaL.) is one key feature in boreal forests contributing significantly to the biological diversity of boreal forest landscapes. However, due to their sparse and scattered occurrence in northern Europe, the explicit spatial data on aspen remain scarce and incomprehensive, which hampers biodiversity management and conservation efforts. Our objective was to study tree-level discrimination of aspen from other common species in northern boreal forests using airborne high-resolution hyperspectral and airborne laser scanning (ALS) data. The study contained multiple spatial analyses: First, we assessed the role of different spectral wavelengths (455-2500 nm), principal component analysis, and vegetation indices (VI) in tree species classification using two machine learning classifiers-support vector machine (SVM) and random forest (RF). Second, we tested the effect of feature selection for best classification accuracy achievable and third, we identified the most important spectral features to discriminate aspen from the other common tree species. SVM outperformed the RF model, resulting in the highest overall accuracy (OA) of 84% and Kappa value (0.74). The used feature set affected SVM performance little, but for RF, principal component analysis was the best. The most important common VI for deciduous trees contained Conifer Index (CI), Cellulose Absorption Index (CAI), Plant Stress Index 3 (PSI3), and Vogelmann Index 1 (VOG1), whereas Green Ratio (GR), Red Edge Inflection Point (REIP), and Red Well Position (RWP) were specific for aspen. Normalized Difference Red Edge Index (NDRE) and Modified Normalized Difference Index (MND705) were important for coniferous trees. The most important wavelengths for discriminating aspen from other species included reflectance bands of red edge range (724-727 nm) and shortwave infrared (1520-1564 nm and 1684-1706 nm). The highest classification accuracy of 92% (F1-score) for aspen was achieved using the SVM model with mean reflectance values combined with VI, which provides a possibility to produce a spatially explicit map of aspen occurrence that can contribute to biodiversity management and conservation efforts in boreal forests.
  • Junttila, Samuli (Helsingin yliopisto, 2014)
    The effect of forest health and structure to the relative surface temperature captured by airborne thermal imagery was investigated in Norway Spruce-dominated stands in Southern Finland. Canopy surface temperature has long been recognized useful in monitoring vegetation water status. Recent studies have shown also its potential in monitoring vegetation health. Airborne thermal imagery, Airborne Light Detection and Ranging (LiDAR) and field measurements were acquired from the area of interest (AOI). The relative surface temperature correlated most negatively with the logarithm of stem volume, Lorey’s height and logarithm of basal area at resolution of 254m2 (9-m radius). In other words, taller and older stands had colder surface temperatures. In addition, LiDAR metrics, such as height percentiles and canopy cover percentage, were compared with surface temperature. Standard deviation of canopy height model, height features (H90, CHM_max) and canopy cover percentage were most strongly negatively correlated with the surface temperature. On average, higher surface temperatures were detected in defoliated canopies indicating that thermal images may provide some additional information for classifying forests health status. However, the surface temperature of defoliated plots varied considerably. It was also found that surface temperature differences between canopy and ground responses were higher in defoliated plots. Based on the results, forest health and structure affect to the surface temperature captured by airborne thermal imagery and these effects should be taken into account when developing forest health mapping applications using thermal imagery.
  • Junttila, Oula Samuli; Vastaranta, Mikko Antero; Hämäläinen, Jarno; Latva-käyrä, Petri; Holopainen, Markus Edvard; Hernandez-Clemente, Rocio; Hyyppä, Hannu; Navarro-Cerrillo, Rafael (2017)
    The effect of forest structure and health on the relative surface temperature captured by airborne thermal imagery was investigated in Norway Spruce-dominated stands in Southern Finland. Airborne thermal imagery, airborne scanning light detection and ranging (LiDAR) data and 92 field-measured sample plots were acquired at the area of interest. The surface temperature correlated most negatively with the logarithm of stem volume, Lorey’s height and the logarithm of basal area at a resolution of 254 m2 (9-m radius). LiDAR-derived metrics: the standard deviations of the canopy heights, canopy height (upper percentiles and maximum height) and canopy cover percentage were most strongly negatively correlated with the surface temperature. Although forest structure has an effect on the detected surface temperature, higher temperatures were detected in severely defoliated canopies and the difference was statistically significant. We also found that the surface temperature differences between the segmented canopy and the entire plot were greater in the defoliated plots, indicating that thermal images may also provide some additional information for classifying forests health status. Based on our results, the effects of forest structure on the surface temperature captured by airborne thermal imagery should be taken into account when developing forest health mapping applications using thermal imagery.
  • Korpela, Ilkka; Haapanen, R.; Korrensalo, A.; Tuittila, E-S; Vesala, T. (2020)
    Boreal bogs are important stores and sinks of atmospheric carbon whose surfaces are characterised by vegetation microforms. Efficient methods for monitoring their vegetation are needed because changes in vegetation composition lead to alteration in their function such as carbon gas exchange with the atmosphere. We investigated how airborne image and waveform-recording LiDAR data can be used for 3D mapping of microforms in an open bog which is a mosaic of pools, hummocks with a few stunted pines, hollows, intermediate surfaces and mud-bottom hollows. The proposed method operates on the bog surface, which is reconstructed using LiDAR. The vegetation was classified at 20 cm resolution. We hypothesised that LiDAR data describe surface topography, moisture and the presence and depth of field-layer vegetation and surface roughness; while multiple images capture the colours and texture of the vegetation, which are influenced by directional reflectance effects. We conclude that geometric LiDAR features are efficient predictors of microforms. LiDAR intensity and echo width were specific to moisture and surface roughness, respectively. Directional reflectance constituted 4-34 % of the variance in images and its form was linked to the presence of the field layer. Microform-specific directional reflectance patterns were deemed to be of marginal value in enhancing the classification, and RGB image features were inferior to LiDAR variables. Sensor fusion is an attractive option for fine-scale mapping of these habitats. We discuss the task and propose options for improving the methodology.
  • Drag, Lukas; Burner, Ryan C.; Stephan, Jorg G.; Birkemoe, Tone; Doerfler, Inken; Gossner, Martin M.; Magdon, Paul; Ovaskainen, Otso; Potterf, Maria; Schall, Peter; Snäll, Tord; Sverdrup-Thygeson, Anne; Weisser, Wolfgang; Mueller, Joerg (2023)
    Climate, topography and the 3D structure of forests are major drivers affecting local species communities. However, little is known about how the specific functional traits of saproxylic (wood-living) beetles, involved in the recycling of wood, might be affected by those environmental characteristics. Here, we combine ecological and morphological traits available for saproxylic beetles and airborne laser scanning (ALS) data in Bayesian trait-based joint species distribution models to study how traits drive the distributions of more than 230 species in temperate forests of Europe. We found that elevation (as a proxy for temperature and precipitation) and the proportion of conifers played important roles in species occurrences while variables related to habitat heterogeneity and forest complexity were less relevant. Furthermore, we showed that local communities were shaped by environmental variation primarily through their ecological traits whereas morphological traits were involved only marginally. As predicted, ecological traits influenced species' responses to forest structure, and to other environmental variation, with canopy niche, wood decay niche and host preference as the most important ecological traits. Conversely, no links between morphological traits and environmental characteristics were observed. Both models, however, revealed strong phylogenetic signal in species' response to environmental characteristics. These findings imply that alterations of climate and tree species composition have the potential to alter saproxylic beetle communities in temperate forests. Additionally, ecological traits help explain species' responses to environmental characteristics and thus should prove useful in predicting their responses to future change. It remains challenging, however, to link simple morphological traits to species' complex ecological niches. Read the free Plain Language Summary for this article on the Journal blog.
  • Yu, Xiaowei (Finnish Geodetic Institute, 2007)
    Publications of the Finnish Geodetic Institute No 137
  • Nisula, Kalle (Helsingin yliopisto, 2019)
    In Finland, forest road network has played a significant role in the society throughout history by serving landowners, stakeholders of timber trade, forest management operators, agricultural- and other entrepreneurs. Different forest recreational users such as berry pickers, mushroom pickers and hunters benefit also from good quality forest roads. Wide forest road network help also in preventing forest fires, building fires and it provides help for human and animal rescue missions. In Finland, large number of private forest roads have reached end of their working life and require therefore wide renovations in near future so that the high quality can be maintained. The large-scale determination of forest roads quality is vital so that situation of lower level road network can be followed, and decisions can be maid whether forest roads can be utilized in timber harvesting operations for example. The growing trend in size and weight of timber transport vehicles will cause more careful route planning to the harvest site when forest roads are in bad shape. Good quality forest roads will reduce fuel consumption in timber transport, vehicle damages and road damages. The main objective in this study was to determine the potential of open access geographic information data and especially open access low-density airborne laser scanning data to evaluate the quality of forest roads. Area-based laser scanning inventory method was used with reference data from field plots. Field data was collected from area of research in November 2018 and it consisted from predefined sample plots that were evaluated with the means of traditional forest road quality factors. The aim was to find these quality factors from ALS data and from other open access data and predict forest road quality class using non-parametric k-nearest neighbor method. The results show that metrics calculated from ALS data were quite important in evaluating forest road quality classes. Metrics that illustrate point height distribution, height averages and metrics extracted from digital elevation model which illustrate slope were the most significant in this study. The results show also that the correlation of individual metrics and forest road quality class from reference data was not very high. However, the quality class of forest roads could be predicted correctly at least 69,8 % accuracy when k-nearest neighbor method was used, and all metrics were used. The method used in this study can be utilized to predict forest road quality class relatively accurately, but the accuracy could still be improved. One way to improve this method would be to use high density ALS data and more accurate reference data. It could also be interesting to use this method in another area of research and inspect how the results would differ from this study.
  • Vastaranta, Mikko; Saarinen, Ninni; Kankare, Ville; Holopainen, Markus; Kaartinen, Harri; Hyyppa, Juha; Hyyppa, Hannu (2014)
  • Puolakka, Paula (Helsingfors universitet, 2010)
    Leaf and needle biomasses are key factors in forest health. Insects that feed on needles cause growth losses and tree mortality. Insect outbreaks in Finnish forests have increased rapidly during the last decade and due to climate change the damages are expected to become more serious. There is a need for cost-efficient methods for inventorying these outbreaks. Remote sensing is a promising means for estimating forests and damages. The purpose of this study is to investigate the usability of airborne laser scanning in estimating Scots pine defoliation caused by the common pine sawfly (Diprion pini L.). The study area is situated in Ilomantsi district, eastern Finland. Study materials included high-pulse airborne laser scannings from July and October 2008. Reference data consisted of 90 circular field plots measured in May-June 2009. Defoliation percentage on these field plots was estimated visually. The study was made on plot-level and methods used were linear regression, unsupervised classification, Maximum likelihood method, and stepwise linear regression. Field plots were divided in defoliation classes in two different ways: When divided in two classes the defoliation percentages used were 0–20 % and 20–100 % and when divided in four classes 0–10 %, 10–20 %, 20–30 % and 30–100 %. The results varied depending on method and laser scanning. In the first laser scanning the best results were obtained with stepwise linear regression. The kappa value was 0,47 when using two classes and 0,37 when divided in four classes. In the second laser scanning the best results were obtained with Maximum likelihood. The kappa values were 0,42 and 0,37, correspondingly. The feature that explained defoliation best was vegetation index (pulses reflected from height > 2m / all pulses). There was no significant difference in the results between the two laser scannings so the seasonal change in defoliation could not be detected in this study.
  • Soininen, Valtteri; Kukko, Antero; Yu, Xiaowei; Kaartinen, Harri; Luoma, Ville; Saikkonen, Otto; Holopainen, Markus; Matikainen, Leena; Lehtomäki, Matti; Hyyppä, Juha (MDPI AG, 2022)
    Forests
    Reviewing forest carbon sinks is of the utmost importance in efforts to control climate change. This study focuses on reporting the 20-year boreal forest growth values acquired with airborne laser scanning (ALS). The growth was examined on the Kalkkinen research site in southern Finland as a continuation of several earlier growth studies performed in the same area. The data for the study were gathered with three totally different airborne laser scanning systems, namely using Toposys-I Falcon in June 2000 and Riegl VUX-1HA and miniVUX-3UAV in June 2021 with approximate point densities of 11, 1360, and 460 points/m2, respectively. The ALS point cloud was preprocessed to identify individual trees, from each of which different features were extracted either for direct or indirect growth measurement. In the direct method, the growth value is predicted based on differences of features, whereas in the indirect method, the growth value is obtained by subtracting the results of two independent predictions of different years. The growth in individual tree attributes, such as growth in height, diameter at breast height (DBH), and stem volume, were calculated for direct estimation. Field reference campaigns were performed in the summer of 2001 and in November 2021 to validate the obtained growth values. The study showed that long-term series growth of height, DBH, and stem volume are possible to record with a high-to-moderate coefficient of determination (R2) of 0.90, 0.48, and 0.45 in the best-case scenarios. The respective root-mean-squared errors (RMSE) values were 0.98 m, 0.02 m, and 0.17 m3, and the biases were −0.06 m, 0.00 m, and 0.17 m3. The direct method produced better metrics in terms of RMSE-% and bias, but the indirect method produced better best-fit lines. Additionally, the mean growth values for height, diameter, and stem volume intervals were compared, and they are presumed to be usable even for forest modelling.
  • Vastaranta, Mikko; Niemi, Mikko; Karjalainen, Mika; Peuhkurinen, Jussi; Kankare, Ville; Hyyppa, Juha; Holopainen, Markus (2014)