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"en": "The terrestrial laser scanner (TLS) has become standard technology for vegetation dynamics monitoring. TLS time series have significant underlying application in investigating structural development and dynamics on a daily and seasonal scale. However, the high potential of TLS for the monitoring of long-term temporal phenomena in fully grown trees with high spatial and temporal resolution has not yet been fully explored. Automated TLS platforms for long-term data collection and monitoring of forest dynamics are rare; and long-term TLS time series data is not yet readily available to potential end-user, such as forestry researchers and plant biologists. This work presents an automated and permanent TLS measurement station that collects high frequency and high spatial resolution TLS time series, aiming to monitor short- and long-term phenological changes at a boreal forestry field station (0.006° angular resolution, one scan per hour). The measurement station is the first of its kind considering the scope, accuracy, and length of the time series it produces. The TLS measurement station provides a unique dataset to monitor the 3D physical structure of a boreal forest, enabling new insights into forest dynamics. For instance, the information collected by the TLS station can be used to accurately detect structural changes in tree crowns surrounding the station. These changes and their timing can be linked with the phenological state of plants, such as the start of leaf-out during spring growing season. As the first results of this novel station, we present time series data products collected with the station and what detailed information it provides about the phenological changes in the test site during the leaf sprout in spring.",
"fi": "<jats:p>The terrestrial laser scanner (TLS) has become standard technology for vegetation dynamics monitoring. TLS time series have significant underlying application in investigating structural development and dynamics on a daily and seasonal scale. However, the high potential of TLS for the monitoring of long-term temporal phenomena in fully grown trees with high spatial and temporal resolution has not yet been fully explored. Automated TLS platforms for long-term data collection and monitoring of forest dynamics are rare; and long-term TLS time series data is not yet readily available to potential end-user, such as forestry researchers and plant biologists. This work presents an automated and permanent TLS measurement station that collects high frequency and high spatial resolution TLS time series, aiming to monitor short- and long-term phenological changes at a boreal forestry field station (0.006° angular resolution, one scan per hour). The measurement station is the first of its kind considering the scope, accuracy, and length of the time series it produces. The TLS measurement station provides a unique dataset to monitor the 3D physical structure of a boreal forest, enabling new insights into forest dynamics. For instance, the information collected by the TLS station can be used to accurately detect structural changes in tree crowns surrounding the station. These changes and their timing can be linked with the phenological state of plants, such as the start of leaf-out during spring growing season. As the first results of this novel station, we present time series data products collected with the station and what detailed information it provides about the phenological changes in the test site during the leaf sprout in spring.</jats:p>",
"und": "<jats:p>The terrestrial laser scanner (TLS) has become standard technology for vegetation dynamics monitoring. TLS time series have significant underlying application in investigating structural development and dynamics on a daily and seasonal scale. However, the high potential of TLS for the monitoring of long-term temporal phenomena in fully grown trees with high spatial and temporal resolution has not yet been fully explored. Automated TLS platforms for long-term data collection and monitoring of forest dynamics are rare; and long-term TLS time series data is not yet readily available to potential end-user, such as forestry researchers and plant biologists. This work presents an automated and permanent TLS measurement station that collects high frequency and high spatial resolution TLS time series, aiming to monitor short- and long-term phenological changes at a boreal forestry field station (0.006° angular resolution, one scan per hour). The measurement station is the first of its kind considering the scope, accuracy, and length of the time series it produces. The TLS measurement station provides a unique dataset to monitor the 3D physical structure of a boreal forest, enabling new insights into forest dynamics. For instance, the information collected by the TLS station can be used to accurately detect structural changes in tree crowns surrounding the station. These changes and their timing can be linked with the phenological state of plants, such as the start of leaf-out during spring growing season. As the first results of this novel station, we present time series data products collected with the station and what detailed information it provides about the phenological changes in the test site during the leaf sprout in spring.</jats:p>"
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"fi": "<jats:p>Abstract. Silver birch (Betula pendula Roth) is a deciduous pioneer tree species with significant economic and ecological importance due to its rapid growth, high genetic variability and adaptability to diverse climates and environments. In this regard, understanding the factors that influence silver birch tree growth variability and its seasonal patterns has been a subject of research interests, which aim at effective forest management and ecological analyses. Tree size, competition, light availability, and topography has been considered significant factors affecting tree growth patterns. However, their relative contributions are not well understood. This is because, to study the interactions between neighbor trees and their competitive responses requires complex measurements. Accurately measuring tree attributes, such as 3D canopy shape and arrangement, is challenging but has been made possible through advancements in high-resolution 3D remote sensing, specifically laser scanning technology. This study shows the potential of high-spatial and temporal resolution LiDAR time-series from a permanent laser scanning setup to detect detailed structural changes and timing in individual tree canopies, focusing on assessment of the structural canopy growth characteristics of silver birch trees. We first investigate how silver birch trees respond to competition and neighboring species. Our results focusing on canopy height increment show that tree size, competition, neighboring species, and water availability affect the rate of vertical (height) growth of the studied silver birch trees. Further, we detect the timing in canopy vertical and horizontal growth using LiDAR time-series. Significant variations of up to one week were detected among trees, providing insights for future studies on growth dynamics of silver birch in coniferous-dominant forests.\n </jats:p>",
"und": "<jats:p>Abstract. Silver birch (Betula pendula Roth) is a deciduous pioneer tree species with significant economic and ecological importance due to its rapid growth, high genetic variability and adaptability to diverse climates and environments. In this regard, understanding the factors that influence silver birch tree growth variability and its seasonal patterns has been a subject of research interests, which aim at effective forest management and ecological analyses. Tree size, competition, light availability, and topography has been considered significant factors affecting tree growth patterns. However, their relative contributions are not well understood. This is because, to study the interactions between neighbor trees and their competitive responses requires complex measurements. Accurately measuring tree attributes, such as 3D canopy shape and arrangement, is challenging but has been made possible through advancements in high-resolution 3D remote sensing, specifically laser scanning technology. This study shows the potential of high-spatial and temporal resolution LiDAR time-series from a permanent laser scanning setup to detect detailed structural changes and timing in individual tree canopies, focusing on assessment of the structural canopy growth characteristics of silver birch trees. We first investigate how silver birch trees respond to competition and neighboring species. Our results focusing on canopy height increment show that tree size, competition, neighboring species, and water availability affect the rate of vertical (height) growth of the studied silver birch trees. Further, we detect the timing in canopy vertical and horizontal growth using LiDAR time-series. Significant variations of up to one week were detected among trees, providing insights for future studies on growth dynamics of silver birch in coniferous-dominant forests.\n </jats:p>"
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"en": "The terrestrial laser scanner (TLS) has become standard technology for vegetation dynamics monitoring. TLS time series have significant underlying application in investigating structural development and dynamics on a daily and seasonal scale. However, the high potential of TLS for the monitoring of long-term temporal phenomena in fully grown trees with high spatial and temporal resolution has not yet been fully explored. Automated TLS platforms for long-term data collection and monitoring of forest dynamics are rare; and long-term TLS time series data is not yet readily available to potential end-user, such as forestry researchers and plant biologists. This work presents an automated and permanent TLS measurement station that collects high frequency and high spatial resolution TLS time series, aiming to monitor short- and long-term phenological changes at a boreal forestry field station (0.006° angular resolution, one scan per hour). The measurement station is the first of its kind considering the scope, accuracy, and length of the time series it produces. The TLS measurement station provides a unique dataset to monitor the 3D physical structure of a boreal forest, enabling new insights into forest dynamics. For instance, the information collected by the TLS station can be used to accurately detect structural changes in tree crowns surrounding the station. These changes and their timing can be linked with the phenological state of plants, such as the start of leaf-out during spring growing season. As the first results of this novel station, we present time series data products collected with the station and what detailed information it provides about the phenological changes in the test site during the leaf sprout in spring.",
"fi": "<jats:p>The terrestrial laser scanner (TLS) has become standard technology for vegetation dynamics monitoring. TLS time series have significant underlying application in investigating structural development and dynamics on a daily and seasonal scale. However, the high potential of TLS for the monitoring of long-term temporal phenomena in fully grown trees with high spatial and temporal resolution has not yet been fully explored. Automated TLS platforms for long-term data collection and monitoring of forest dynamics are rare; and long-term TLS time series data is not yet readily available to potential end-user, such as forestry researchers and plant biologists. This work presents an automated and permanent TLS measurement station that collects high frequency and high spatial resolution TLS time series, aiming to monitor short- and long-term phenological changes at a boreal forestry field station (0.006° angular resolution, one scan per hour). The measurement station is the first of its kind considering the scope, accuracy, and length of the time series it produces. The TLS measurement station provides a unique dataset to monitor the 3D physical structure of a boreal forest, enabling new insights into forest dynamics. For instance, the information collected by the TLS station can be used to accurately detect structural changes in tree crowns surrounding the station. These changes and their timing can be linked with the phenological state of plants, such as the start of leaf-out during spring growing season. As the first results of this novel station, we present time series data products collected with the station and what detailed information it provides about the phenological changes in the test site during the leaf sprout in spring.</jats:p>",
"und": "<jats:p>The terrestrial laser scanner (TLS) has become standard technology for vegetation dynamics monitoring. TLS time series have significant underlying application in investigating structural development and dynamics on a daily and seasonal scale. However, the high potential of TLS for the monitoring of long-term temporal phenomena in fully grown trees with high spatial and temporal resolution has not yet been fully explored. Automated TLS platforms for long-term data collection and monitoring of forest dynamics are rare; and long-term TLS time series data is not yet readily available to potential end-user, such as forestry researchers and plant biologists. This work presents an automated and permanent TLS measurement station that collects high frequency and high spatial resolution TLS time series, aiming to monitor short- and long-term phenological changes at a boreal forestry field station (0.006° angular resolution, one scan per hour). The measurement station is the first of its kind considering the scope, accuracy, and length of the time series it produces. The TLS measurement station provides a unique dataset to monitor the 3D physical structure of a boreal forest, enabling new insights into forest dynamics. For instance, the information collected by the TLS station can be used to accurately detect structural changes in tree crowns surrounding the station. These changes and their timing can be linked with the phenological state of plants, such as the start of leaf-out during spring growing season. As the first results of this novel station, we present time series data products collected with the station and what detailed information it provides about the phenological changes in the test site during the leaf sprout in spring.</jats:p>"
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"en": "Silver birch (Betula pendula Roth) is a deciduous pioneer tree species with significant economic and ecological importance due to its rapid growth, high genetic variability and adaptability to diverse climates and environments. In this regard, understanding the factors that influence silver birch tree growth variability and its seasonal patterns has been a subject of research interests, which aim at effective forest management and ecological analyses. Tree size, competition, light availability, and topography has been considered significant factors affecting tree growth patterns. However, their relative contributions are not well understood. This is because, to study the interactions between neighbor trees and their competitive responses requires complex measurements. Accurately measuring tree attributes, such as 3D canopy shape and arrangement, is challenging but has been made possible through advancements in high-resolution 3D remote sensing, specifically laser scanning technology. This study shows the potential of high-spatial and temporal resolution LiDAR time-series from a permanent laser scanning setup to detect detailed structural changes and timing in individual tree canopies, focusing on assessment of the structural canopy growth characteristics of silver birch trees. We first investigate how silver birch trees respond to competition and neighboring species. Our results focusing on canopy height increment show that tree size, competition, neighboring species, and water availability affect the rate of vertical (height) growth of the studied silver birch trees. Further, we detect the timing in canopy vertical and horizontal growth using LiDAR time-series. Significant variations of up to one week were detected among trees, providing insights for future studies on growth dynamics of silver birch in coniferous-dominant forests.",
"fi": "<jats:p>Abstract. Silver birch (Betula pendula Roth) is a deciduous pioneer tree species with significant economic and ecological importance due to its rapid growth, high genetic variability and adaptability to diverse climates and environments. In this regard, understanding the factors that influence silver birch tree growth variability and its seasonal patterns has been a subject of research interests, which aim at effective forest management and ecological analyses. Tree size, competition, light availability, and topography has been considered significant factors affecting tree growth patterns. However, their relative contributions are not well understood. This is because, to study the interactions between neighbor trees and their competitive responses requires complex measurements. Accurately measuring tree attributes, such as 3D canopy shape and arrangement, is challenging but has been made possible through advancements in high-resolution 3D remote sensing, specifically laser scanning technology. This study shows the potential of high-spatial and temporal resolution LiDAR time-series from a permanent laser scanning setup to detect detailed structural changes and timing in individual tree canopies, focusing on assessment of the structural canopy growth characteristics of silver birch trees. We first investigate how silver birch trees respond to competition and neighboring species. Our results focusing on canopy height increment show that tree size, competition, neighboring species, and water availability affect the rate of vertical (height) growth of the studied silver birch trees. Further, we detect the timing in canopy vertical and horizontal growth using LiDAR time-series. Significant variations of up to one week were detected among trees, providing insights for future studies on growth dynamics of silver birch in coniferous-dominant forests.\n </jats:p>",
"und": "<jats:p>Abstract. Silver birch (Betula pendula Roth) is a deciduous pioneer tree species with significant economic and ecological importance due to its rapid growth, high genetic variability and adaptability to diverse climates and environments. In this regard, understanding the factors that influence silver birch tree growth variability and its seasonal patterns has been a subject of research interests, which aim at effective forest management and ecological analyses. Tree size, competition, light availability, and topography has been considered significant factors affecting tree growth patterns. However, their relative contributions are not well understood. This is because, to study the interactions between neighbor trees and their competitive responses requires complex measurements. Accurately measuring tree attributes, such as 3D canopy shape and arrangement, is challenging but has been made possible through advancements in high-resolution 3D remote sensing, specifically laser scanning technology. This study shows the potential of high-spatial and temporal resolution LiDAR time-series from a permanent laser scanning setup to detect detailed structural changes and timing in individual tree canopies, focusing on assessment of the structural canopy growth characteristics of silver birch trees. We first investigate how silver birch trees respond to competition and neighboring species. Our results focusing on canopy height increment show that tree size, competition, neighboring species, and water availability affect the rate of vertical (height) growth of the studied silver birch trees. Further, we detect the timing in canopy vertical and horizontal growth using LiDAR time-series. Significant variations of up to one week were detected among trees, providing insights for future studies on growth dynamics of silver birch in coniferous-dominant forests.\n </jats:p>"
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"fi": "FIN2023N2000 geoidimalli kattaa koko Suomen alueen. Mallia suositellaan käytettäväksi Suomen rajojen sisällä, myös Suomen aluevesillä. Malli sisältää kvasigeoidikorkeuksia, ζ, joilla EUREF-FIN-datumissa olevat ellipsoidiset korkeudet, h, voidaan muuntaa Suomen N2000-korkeusjärjestelmässä oleviin normaalikorkeuksiin, H*.\n\nFIN2023N2000-mallin sisältämät kvasigeoidikorkeudet ovat annettu metreinä millimetrin tarkkuudella. Testeissä on todettu, että mallilla voidaan ennustaa kvasigeoidi-korkeuksia ±1.38 cm tarkkuudella. Suurimmat erot ennustettujen (FIN2023N2000) ja havaittujen (GPS-vaaitus) kvasigeoidi-korkeuksien välillä ovat 3–4 cm.\n\nKorkeuksien muuntaminen tapahtuu etsimällä neljä geoidihilan pistettä, jotka ovat lähimpänä muunnettavan pisteen maantieteellisiä EUREF-FIN -koordinaatteja, ts. mitkä neljä pistettä rajaavat haettavan pisteen muodostamansa suorakulmion sisään. Tämän jälkeen bilineaarisesti interpoloidaan hilapisteiden korkeuksista muunnettavan pisteen sijaintiin kvasigeoidikorkeus. Normaalikorkeus saadaan vähentämällä laskettu kvasigeoidikorkeus ellipsoidisesta EUREF-FIN-korkeudesta (H* = h - ζ).\n\nGeoidimalli on saatavissa kolmessa eri ASCII-muodossa (laatikko, hila ja lista). Laatikko- ja hilamuodossa on ensimmäisellä rivillä otsake, mikä sisältää tiedon mallin ulottuvuuksista leveys- ja pituusasteiden (latitudi ja longitudi) avulla. Otsakkeen arvot on erotettu toisistaan välilyönnein ja niiden yksikkönä on aste. Mallin ulottuvuudet ovat seuraavat:\n\n| **Kuvaus** | **Arvo** |\n|---|---|\n| Latitudin minimi | 58.80° | \n| Latitudin maksimi | 70.19° | \n| Longitudin minimi | 19.00° | \n| Longitudin maksimi | 31.98° | \n| Pisteväli latitudin suunnassa | 0.01° | \n| Pisteväli longitudin suunnassa | 0.02° | \n| Pisteiden lukumäärä | 741 000 |",
"sv": "Geoidmodellen FIN2023N2000 täcker hela Finland. Modellen rekommenderas för bruk inom Finlands gränser inklusive Finlands territorialvatten. Modellen innehåller kvasigeoidhöjder, ζ, med vilka höjder över ellipsoiden i EUREF-FIN-datum, h, kan omvandlas till det finländska höjdsystemets (N2000) normalhöjder, H*.\n\nKvasigeoidhöjderna i FIN2023N2000 anges i meter med en mm noggrannhet. Tester har visat att modellen kan förutse kvasigeoidhöjder med ±1.38 cm noggrannhet. De största skillnaderna mellan predikterade (FIN2023N2000) och observerade (GPS-avvägning) kvasigeoidhöjder är 3–4 cm.\n\nVid omvandlingen ska man hitta de fyra punkter i geoidrutnätet som ligger närmast de geografiska EUREF-FIN-koordinaterna för den punkt som ska omvandlas, m.a.o. fyra punkter som bildar en rektangel och innesluter den sökta punkten. Därefter görs en bilinjär interpolering av rutnätspunkternas höjder så att man får en kvasigeoidhöjd för den punkt som omvandlas. Normalhöjden fås genom att minska höjden över ellipsoiden i EUREF-FIN med den beräknade kvasigeoidhöjden (H* = h - ζ).\n\nGeoidmodellen är tillgänglig i tre olika ASCII-format (box, rutnät och lista). I box- och rutnätsformatet anger den inledande raden modellens dimensioner med hjälp av bredd- och längdgrader (latitud och longitud). Värdena på den inledande raden är i grader och åtskiljs med mellanslag. Modellen har följande dimensioner:\n\n| **Beskrivning** | **Värde** |\n|---|---|\n| Lägsta latitud | 58.80° | \n| Högsta latitud | 70.19° | \n| Lägsta longitud | 19.00° | \n| Högsta longitud | 31.98° | \n| Punktintervall i latitudens riktning | 0.01° | \n| Punktintervall i longitudens riktning | 0.02° | \n| Antal punkter | 741 000 |"
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