The tree crown, with its functionality of assimilation, respiration, and transpiration, is a key forest ecosystem structure, resulting in high demand for characterizing tree crown structure and growth on a spatiotemporal scale. Airborne laser scanning (ALS) was found to be useful in measuring the structural properties associated with individual tree crowns. However, established ALS-assisted monitoring frameworks are still limited. The main objective of this study was to investigate the feasibility of detecting species-specific individual tree crown growth by means of airborne laser scanning (ALS) measurements in 2009 (T1) and 2014 (T2). Our study was conducted in southern Finland over 91 sample plots with a size of 32 × 32 m. The ALS crown metrics of width (WD), projection area (A2D), volume (V), and surface area (A3D) were derived for species-specific individually matched trees in T1 and T2. The Scots pine (Pinus sylvestris), Norway spruce (Picea abies (L.) H. Karst), and birch (Betula sp.) were the three species groups that studied. We found a high capability of bi-temporal ALS measurements in the detection of species-specific crown growth (Δ), especially for the 3D crown metrics of V and A3D, with Cohen’s D values of 1.09–1.46 (p-value < 0.0001). Scots pine was observed to have the highest relative crown growth (rΔ) and showed statistically significant differences with Norway spruce and birch in terms of rΔWD, rΔA2D, rΔV, and rΔA3D at a 95% confidence interval. Meanwhile, birch and Norway spruce had no statistically significant differences in rΔWD, rΔV, and rΔA3D (p-value < 0.0001). However, the amount of rΔ variability that could be explained by the species was only 2–5%. This revealed the complex nature of growth controlled by many biotic and abiotic factors other than species. Our results address the great potential of ALS data in crown growth detection that can be used for growth studies at large scales.

An account of widespread degradation and deforestation in Nepal has been noticed in various literature sources. Although the contribution of community forests (CF) on the improvement of forest cover and condition in the Mid-hill of Nepal is positive, detailed study to understand the current situation seems important. The study area (Tanahun District) lies in the Gandaki Province of western Nepal. The objective of this study was to estimate the forest cover change over the specified period and to identify factors influencing the change. We used Landsat images from the years 1976, 1991, and 2015 to classify land use and land cover. We considered community perception in addition to the forest cover map to understand the different causes of forest cover change. Forest cover decreased from 1976 to 1991 annually at a rate of 0.96%. After 1991, the forest increased annually at a rate of 0.63%. The overall forest cover in the district regained its original status. Factors related to increasing forest cover were emigration, occupation shift, agroforestry practices, as well as particularly by plantation on barren lands, awareness among forest users, and conservation activities conducted by local inhabitants after the government forest was handed over to community members as a community forest management system.

Forests are dynamic ecosystems that are constantly changing. The most common natural reasons for change in forests are the growth and death of trees, as well as the damage occurring to them. Tree growth appears as an increment of its structural dimensions, such as stem diameter, height, and crown volume, which all affect the structure of a tree. Repeated measurements of tree characteristics enable observations of the respective increments indicating tree growth. According to current knowledge, the tree growth process follows the priority theory, where trees aim to achieve sufficient lightning conditions for the tree crown through primary growth, whereas increment in diameter results from the secondary growth. Tree growth is known to have an effect on the carbon sequestration potential of trees as well as on the quality of timber. To improve the understanding of the underlying cause–effect relations driving tree growth, methods to quantify structural changes in trees and forests are needed. The use of terrestrial laser scanning (TLS) has emerged during the recent decade as an effective tool to determine attributes of individual trees. However, the capacity of TLS point cloud-based methods to measure tree growth remains unexplored. This thesis aimed at developing new methods to measure tree growth in boreal forest conditions by utilizing two-date TLS point clouds. The point clouds were also used to investigate how trees allocate their growth and how the stem form of trees develops, to deepen the understanding of tree growth processes under different conditions and over the life cycle of a tree. The capability of the developed methods was examined during a five- to nine-year monitoring period with two separate datasets consisting of 1315 trees in total. Study I demonstrated the feasibility of TLS point clouds for measuring tree growth in boreal forests. In studies II and III, an automated point cloud-based method was further developed and tested for measuring tree growth. The used method could detect trees from two-date point clouds, with the detected trees representing 84.5% of total basal area. In general, statistically significant changes in the examined attributes, such as diameter at breast height, tree height, stem volume, and logwood volume, were detected during the monitoring periods. Tree growth and stem volume allocation seemed to be more similar for trees growing in similar structural conditions. The findings obtained in this thesis demonstrate the capabilities of repeatedly acquired TLS point clouds to be used for measuring the growth of trees and for characterizing the structural changes in forests. This thesis showed that TLS point cloud-based methods can be used for enhancing the knowledge of how trees allocate their growth, and thus help discover the underlying reasons for processes driving changes in forests, which could generate benefits for ecological or silvicultural applications where information on tree growth and forest structural changes is needed.

There is a growing demand for high-quality spatial data and for efficient methods of updating spatial databases. In the present study, automated object-based interpretation methods were developed and tested for coarse land use mapping, detailed land cover and building mapping, and change detection of buildings. Various modern remotely sensed datasets were used in the study. An automatic classification tree method was applied to building detection and land cover classification to automate the development of classification rules. A combination of a permanent land cover classification test field and the classification tree method was suggested and tested to allow rapid analysis and comparison of new datasets. The classification and change detection results were compared with up-to-date map data or reference points to evaluate their quality. The combined use of airborne laser scanner data and digital aerial imagery gave promising results considering topographic mapping. In automated building detection using laser scanner and aerial image data, 96% of all buildings larger than 60 m2 were correctly detected. This accuracy level (96%) is compatible with operational quality requirements. In automated change detection, about 80% of all reference buildings were correctly classified. The overall accuracy of a land cover classification into buildings, trees, vegetated ground and non-vegetated ground using laser scanner and aerial image data was 97% compared with reference points. When aerial image data alone were used, the accuracy was 74%. A comparison between first pulse and last pulse laser scanner data in building detection was also carried out. The comparison showed that the use of last pulse data instead of first pulse data can improve the building detection results. The results yielded by automated interpretation methods could be helpful in the manual updating process of a topographic database. The results could also be used as the basis for further automated processing steps to delineate and reconstruct objects. The synthetic aperture radar (SAR) and optical satellite image data used in the study have their main potential in land cover monitoring applications. The coarse land use classification of a multitemporal interferometric SAR dataset into built-up areas, forests and open areas lead to an overall accuracy of 97% when compared with reference points. This dataset also appeared to be promising for classifying built-up areas into subclasses according to building density. Important topics for further research include more advanced interpretation methods, new and multitemporal datasets, optimal combinations of the datasets, and wider sets of objects and classes. From the practical point of view, work is needed in fitting automated interpretation methods in operational mapping processes and in further testing of the methods. Laadukkaan paikkatiedon tarve kasvaa jatkuvasti, ja paikkatietokantojen ajantasaistukseen tarvitaan tehokkaita menetelmiä. Tässä tutkimuksessa käytettiin useita uudenaikaisia kaukokartoitusaineistoja. Niiden pohjalta kehitettiin ja testattiin automaattisia, objektipohjaisia tulkintamenetelmiä yleispiirteiseen maankäytön luokitteluun, yksityiskohtaiseen maanpeitteen ja rakennusten kartoitukseen sekä rakennusten muutostulkintaan. Rakennusten tulkintaan ja maanpeiteluokitteluun sovellettiin automaattista luokittelupuumenetelmää, jonka avulla voidaan automatisoida luokittelusääntöjen kehittäminen. Uusia aineistoja voidaan analysoida ja vertailla nopeasti, kun luokittelupuumenetelmää käytetään yhdessä pysyvän maanpeiteluokittelutestikentän kanssa. Luokittelu- ja muutostulkintatuloksia verrattiin niiden laadun arvioimiseksi ajantasaiseen kartta-aineistoon tai referenssipisteisiin. Ilmalaserkeilausaineisto ja digitaalinen ilmakuva-aineisto yhdessä antoivat lupaavia tuloksia maastotietojen kartoitusta ajatellen. Automaattisessa rakennusten tulkinnassa 96 % kaikista yli 60 m2:n rakennuksista tunnistettiin oikein. Tämä tarkkuustaso (96 %) vastaa käytännön laatuvaatimuksia. Automaattisessa muutostulkinnassa noin 80 % kaikista referenssirakennuksista luokiteltiin oikein. Maanpeiteluokittelussa neljään luokkaan saavutettiin laserkeilaus- ja ilmakuva-aineistoa käyttäen 97 %:n kokonaistarkkuus referenssipisteisiin verrattuna. Pelkkää ilmakuva-aineistoa käytettäessä tarkkuus oli 74 %. Tutkimuksessa verrattiin myös ensimmäiseen ja viimeiseen paluupulssiin perustuvia laserkeilausaineistoja rakennusten tulkinnassa. Vertailu osoitti, että viimeisen paluupulssin käyttö ensimmäisen sijasta voi parantaa tulkintatuloksia. Automaattisten tulkintamenetelmien tuloksista voisi olla hyötyä maastotietojen manuaalisessa ajantasaistusprosessissa tai lähtötietoina kohteiden automaattisessa rajauksessa ja mallinnuksessa. Tutkimuksessa käytettyjen synteettisen apertuurin tutkan (SAR) tuottamien kuvien ja optisen satelliittikuvan tärkeimmät hyödyntämismahdollisuudet liittyvät maanpeitteen kartoitukseen. Yleispiirteisessä maankäyttöluokittelussa kolmeen luokkaan saavutettiin moniaikaista interferometrista SAR-aineistoa käyttäen 97 %:n kokonaistarkkuus referenssipisteisiin verrattuna. Aineisto osoittautui lupaavaksi myös rakennettujen alueiden jatkoluokitteluun rakennustiheyden perusteella. Jatkotutkimusten kannalta tärkeitä aiheita ovat edistyneemmät tulkintamenetelmät, uudet ja moniaikaiset aineistot, eri aineistojen optimaalinen yhdistäminen sekä useampien kohteiden ja luokkien tarkastelu. Käytännön näkökulmasta työtä tarvitaan automaattisten tulkintamenetelmien sovittamiseksi operatiivisiin kartoitusprosesseihin. Myös menetelmien testausta on jatkettava.

Tree growth is a multidimensional process that is affected by several factors. There is a continuous demand for improved information on tree growth and the ecological traits controlling it. This study aims at providing new approaches to improve ecological understanding of tree growth by the means of terrestrial laser scanning (TLS). Changes in tree stem form and stem volume allocation were investigated during a five-year monitoring period. In total, a selection of attributes from 736 trees from 37 sample plots representing different forest structures were extracted from taper curves derived from two-date TLS point clouds. The results of this study showed the capability of point cloud-based methods in detecting changes in the stem form and volume allocation. In addition, the results showed a significant difference between different forest structures in how relative stem volume and logwood volume increased during the monitoring period. Along with contributing to providing more accurate information for monitoring purposes in general, the findings of this study showed the ability and many possibilities of point cloud-based method to characterize changes in living organisms in particular, which further promote the feasibility of using point clouds as an observation method also in ecological studies.

The rapid development of remote sensing technologies pro-vides interesting possibilities for the further development of nationwide mapping procedures that are currently based mainly on passive aerial images. In particular, we assume that there is a large undiscovered potential in multitemporal airborne laser scanning (ALS) for topographic mapping. In this study, automated change detection from multitemporal multispectral ALS data was tested for the first time. The results showed that direct comparisons between height and intensity data from different dates reveal even small chang-es related to the development of a suburban area. A major challenge in future work is to link the changes with objects that are interesting in map production. In order to effectively utilize multisource remotely sensed data in mapping in the future, we also investigated the potential of satellite images and ground-based data to complement multispectral ALS. A method for continuous change monitoring from a time series of Sentinel-2 satellite images was developed and tested. Finally, a high-density point cloud was acquired with terres-trial mobile laser scanning and automatically classified into four classes. The results were compared with the ALS data, and the possible roles of the different data sources in a fu-ture map updating process were discussed.