Abstract. Better monitoring, reporting and verification (MRV) of the amount, additionality and persistence of the sequestered soil carbon is needed to understand the best carbon farming practices for different soils and climate conditions, as well as their actual climate benefits or cost-efficiency in mitigating greenhouse gas emissions. This paper presents our Field Observatory Network (FiON) of researchers, farmers, companies and other stakeholders developing carbon farming practices. FiON has established a unified methodology towards monitoring and forecasting agricultural carbon sequestration by combining offline and near real-time field measurements, weather data, satellite imagery, modeling and computing networks. FiON’s first phase consists of two intensive research sites and 20 voluntary pilot farms testing carbon farming practices in Finland. To disseminate the data, FiON built a web-based dashboard called Field Observatory (v1.0, fieldobservatory.org). Field Observatory is designed as an online service for near real-time model-data synthesis, forecasting and decision support for the farmers who are able to monitor the effects of carbon farming practices. The most advanced features of the Field Observatory are visible on the Qvidja site which acts as a prototype for the most recent implementations. Overall, FiON aims to create new knowledge on agricultural soil carbon sequestration and effects of carbon farming practices, and provide an MRV tool for decision-support.
The relative neighhourhood graph (RNG) of a set of n points in a d-dimensional space contains an edge between a particular pair (v, w) of points if in the point set there is no other point for which the larger of the distances from v and w is smaller than the distance of v and w.Let U d p dénote the d-dimensional space with the L p metric, 1 Sp = °o« A new simple algorithm for Computing the RNG in U d p is given.It is an improved version oj the Q(dn 2 + n 3 ) algorithm proposed by R. B. Urquhart.In U 2 the worst case running time of our algorithm is O (n 25 ) for 1 < p < co, for p-\ or p= oo the worst case time is 0 (n 3 ), and for point sets uniformly distributed in a unit square the average time is 6(n 2 ) for 1 ^ p ^ oo.The demand for the storage space is $(dn + n 2 ) and it can be reduced to Q(dn+ |RNG|), where |RNG| stands for the cardinality of the output, but this réduction manifolds the.observed running time by a constant factor. Resumé. -Le graphe du voisinage relatif d'un ensemble de n points dans un espace de dimension d contient une arête (v, w) s'il existe un point tel que le maximum des distances de ce point à v et à w est inférieur à la distance entre v et w.Soit U d p l'espace de dimension d muni de la distance de L p (1^/7^ oo).On donne un algorithme simple pour calculer le graphe du voisinage relatif.C'est une version améliorée de l'algorithme en G (dn z + n 2 ) proposé par Urquhart.Dans U p notre algorithme est en O (n 2 ' 3 ) dans le pire des cas pour 1
Abstract. Vegetation phenology, which refers to the seasonal changes in plant physiology, biomass and plant cover, is affected by many abiotic factors, such as precipitation, temperature and water availability. Phenology is also associated with the carbon dioxide (CO2) exchange between ecosystems and the atmosphere. We employed digital cameras to monitor the vegetation phenology of three northern boreal peatlands during five growing seasons. We derived a greenness index (green chromatic coordinate, GCC) from the images and combined the results with measurements of CO2 flux, air temperature and high-resolution satellite data (Sentinel-2). From the digital camera images it was possible to extract greenness dynamics on the vegetation community and even species level. The highest GCC and daily maximum gross photosynthetic production (GPPmax) were observed at the site with the highest nutrient availability and richest vegetation. The short-term temperature response of GCC depended on temperature and varied among the sites and months. Although the seasonal development and year-to-year variation in GCC and GPPmax showed consistent patterns, the short-term variation in GPPmax was explained by GCC only during limited periods. GCC clearly indicated the main phases of the growing season, and peatland vegetation showed capability to fully compensate for the impaired growth resulting from a late growing season start. The GCC data derived from Sentinel-2 and digital cameras showed similar seasonal courses, but a reliable timing of different phenological phases depended upon the temporal coverage of satellite data.
This package contains the data used in the research article: "Partial cutting of a boreal nutrient-rich peatland forest causes radically less on-site CO2 emissions than clear-cutting" published in Agricultural and Forest Meteorology. LAI_data.xlsx - Contains Leaf Area Index data and their standard deviations for all the measured areas WTL_data.csv - Contains the mean water table level data for pre-harvest, partial harvest and clearcut areas. Lettosuo_2010-2015_Section_A_fluxes.csv - Contains the pre-harvest (2010-2015) carbon flux data for Section A. Lettosuo_2010-2015_Section_BCD_fluxes.csv - Contains the pre-harvest carbon flux data for Section BCD. Lettosuo_2016-2021_Section_AB_(partialcut).csv - Contains the carbon flux data for the partial cut area (2016-2021, Section AB). Lettosuo_2016-2021_Section_D_(Clearcut).csv - Contains the carbon flux data for the clear-cut area (2016-2021, Section D) The carbon flux data files contain the following columns: Gapfilled PAR - Gapfilled photsynthetically active radiation Gapfilled air temperature - Gapfilled air temperature Measured NEE - Filtered NEE data Modelled TER - Modelled total ecosystem respiration Modelled GPP - Modelled gross primary production Modelled NEE - Modelled NEE calculated from the modelled TER and GPP Gapfilled NEE - A combination of measured and modelled NEE. Gaps in the measured data are filled with modelled NEE Modelling uncertainty - Uncertainty of the modelled NEE Measurement uncertainty - An estimation of the uncertainty of the measured NEE
Abstract Uncrewed Aerial Systems (UAS) offer a versatile solution for monitoring forest ecosystems. This study aimed to develop and assess an individual tree-based methodology using multi-temporal, multispectral UAS images to track changes caused by the European spruce bark beetle ( Ips typographus L.). The approach encompassed four key steps: (1) individual tree detection using structure-from-motion point clouds, (2) tree species classification, (3) health classification of spruce trees as healthy, declined, or dead, and (4) change detection, identifying fallen/removed trees and alterations in tree health status. The developed methodology was employed to quantify changes in a bark beetle outbreak area covering 215 hectares in southeastern Finland during 2019–2021. The dataset included two managed and two conserved forest areas. The uncertainty estimation demonstrated the overall accuracies ranging from 0.58 to 0.91 for individual tree detection, 0.84 for species classification, and 0.83–0.96 for health classification, and a F1-score of 0.91 for the fallen or removed tree detection. Maps and statistics were produced, containing information on the health of the spruce trees in the area and information on changes, including trees that died during monitoring and those that fell or were removed from the forest. The results demonstrated successful control of the outbreak in the managed stands, evidenced by moderate tree mortality. Conversely, in the conserved stands, the outbreak resulted in dramatic tree mortality. This method serves stakeholders by enabling large-scale outbreak impact monitoring, facilitating timely risk assessment, and validating bark beetle outbreak management strategies.
