Browsing by Subject "biomass"

Biomass is an important parameter for crop monitoring and management, as well as for assessing carbon cycle. In the field, allometric models can be used for non-destructive biomass assessment, whereas remote sensing is a convenient method for upscaling the biomass estimations over large areas. This study assessed the dry leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre and biofuel production in tropical and subtropical regions. First, an allometric model was developed for predicting the leaf biomass. Then, Sentinel-2 multispectral satellite imagery was used to model the leaf biomass at 8851 ha plantation in South-Eastern Kenya. For the allometric model 38 leaves were sampled and measured. Plant height and leaf maximum diameter were combined into a volume approximation and the relation to biomass was formalised with linear regression. A strong log-log linear relation was found and leave-one-out cross-validation for the model showed good prediction accuracy (R2 = 0.96, RMSE = 7.69g). The model was used to predict biomass for 58 field plots, which constituted a sample for modelling the biomass with Sentinel-2 data. Generalised additive models were then used to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (D2 = 74%, RMSE = 4.96 Mg/ha) was achieved with VIs based on the red-edge (R740 and R783), near-infrared (R865) and green (R560) spectral bands. Highly heterogeneous growing conditions, mainly variation in the understory vegetation seemed to be the main factor limiting the model performance. The best performing VI (R740/R783) was used to predict the biomass at plantation level. The leaf biomass ranged from 0 to 45.1 Mg/ha, with mean at 9.9 Mg/ha. This research resulted a newly established allometric equation that can be used as an accurate tool for predicting the leaf biomass of sisal. Further research is required to account for other parts of the plant, such as the stem and the roots. The biomass-VI modelling results showed that multispectral data is suitable for assessing sisal leaf biomass over large areas, but the heterogeneity of the understory vegetation limits the model performance. Future research should address this by investigating the background effects of understory and by looking into complementary data sources. The carbon stored in the leaf biomass at the plantation corresponds to that in the woody aboveground biomass of natural bushlands in the area. Future research is needed on soil carbon sequestration and soil and plant carbon fluxes, to fully understand the carbon cycle at sisal plantation.

The world population is growing and is expected to reach over 9 billion in about 30 years. Climate change is also widely expected to worsen famines in certain regions of the world. This will drastically increase global food demand. Food security efforts should be therefore be geared towards promoting food crops that can thrive in these regions and can withstand the condition likely to be brought about by changing climate. Cassava is a typical example of such a crop. This study investigated the use of digital images to estimate growth parameters of young cassava plants. Cassava was cultivated in pots at the University of Helsinki greenhouse at Viikki. The plants were given different water level (100%, 60% and 30% saturation) and potassium (0.1, 1.0, 4.0, 16.0 and 32.0mM) treatments. Digital red-green-blue (RGB) and multispectral images were taken every other week for 5 consecutive times. The images were processed to obtain leaf area, Normalized Difference Vegetation Index (NDVI), and Crop Senescence Index (CSI) and correlated with directly measured growth parameters of the young cassava crops. It was observed that leaf area that was computed from images, and NDVI which was computed from the multispectral images have significant positive correlations with the growth parameters, ie, actual leaf area, chlorophyll content, and plant biomass. CSI however showed weak a correlation between the growth parameters of the young cassava plants. Images leaf area and NDVI were then used to identify the changes in the effects of the water and potassium treatments.

Arctic ecosystems face drastic changes in community structure due to warming, shrubification, permafrost loss, and other environmental changes. Due to the spatial heterogeneity of these ecosystems, understanding such changes on a local scale requires high-resolution data. Earth observation using satellite imagery and aerial photography has become a staple in mapping large areas and general patterns. Advances in sensor technology, the proliferation of unmanned aerial vehicles (UAVs), and increases in processing capacity enable the use of higher spatial and spectral resolutions. As a result, more detailed ecological observations can be made using remote sensing methods. In this thesis, I assess how increased spectral resolution affects the remote-sensing based modelling of plant communities in low-growth oroarctic tundra heaths. Based on a large field observation dataset, I estimate biomass, leaf area index, species richness, Shannon's biodiversity index, and fuzzy community clusters. I then build random forest models of these with image data of varying spectral, spatial, and temporal specifications and topographical data. Finally, I create maps of the vegetation. Leaf area index and biomass are best estimated of the response variables, with R2 values of 0.64 and 0.59, respectively, with multispectral data proving the most important explanatory dataset. Biodiversity metrics are best estimated with R2 values of 0.40–0.50 with the most important explanatory variables being topographical and hyperspectral, and community cluster with R2 values of 0.27–0.53, with the importance of various explanatory variables depending on the cluster being estimated. These results can help choose a suitable high-resolution remote sensing approach for modelling plant communities in similar conditions.

Cyanobacteria are an important part of the phytoplankton community and aquatic ecosystems. Cyanobacteria can form large mass occurrences, i.e. blooms, which can be toxic or cause other harm. Research and monitoring of cyanobacteria has been based on microscopy analysis. However, molecular-based methods, such as 16S rRNA sequencing are replacing microscopy analyses in the near future. The Finnish Environment Institute has stated that molecular methods are part of environmental monitoring before 2030. In this Master’s thesis the aim was to determine whether conventional microscopy analyses and 16S rRNA sequencing differ when comparing nano- and micro-sized cyanobacteria. The material was collected from a laboratory experiment of the Finnish Environment Institute’s (SYKE) MiDAS project, which was conducted in the summer of 2020. The results of the microscopy and 16S rRNA analyses differed from each other. The relative abundances of the cyanobacteria genera differed between sample types. Microscopy analyses estimated that the alpha diversity was higher compared to the results of the sequencing analyses. The main reason for the difference between the types of analyses was due to the differences in cyanobacteria belonging to the order of Synechococcales. Some of the Synechococcales species were observed only by the sequencing analyses, e.g. Snowella and some of the Synechococcales species were only observed by the microscopy analyses, e.g. Romeria and Woronichinia. It was observed that both methods are prone to identification errors. The differences between the 16S rRNA sequencing and the microscopy analyses are vastly different. It may affect on the review of long-term data of the phytoplankton community. Therefore, it is important to examine the differences between the types of analyses. Studying the dissimilarities between the types of analyses should be focused on the research of the small cell-sized colonial cyanobacteria, i.e. the species of Chroococcales and Synechococcales.

The science of fish stock assessment is one that is very resource and labor intensive, with stock assessment models historically being based on data that causes a model to overestimate the strength of a population, sometimes with drastic consequences. The need of cost-effective assessment models and approaches increases, which is why I looked into using Bayesian modeling and networks as an approach not often used in fisheries science. I wanted to determine if it could be used to predict both recruitment and spawning stock biomass of four fish species in the north Atlantic, cod, haddock, pollock and capelin, based on no other evidence other than the recruitment or biomass data of the other species and if these results could be used to lower the uncertanties of fish stock models. I used data available on the RAM legacy database to produce four different models with the statistical software R, based on four different Bayes algorithms found in the R-package bnlearn, two based on continuous data and two based on discrete data. What I found was that there is much potential in the Bayesian approach to stock prediction and forecasting, as our prediction error percentage ranged between 1 and 40 percent. The best predictions were made when the species used as evidence had a high correlation coefficient with the target species, which was the case with cod and haddock biomass, which had a unusually high correlation of 0.96. As such, this approach could be used to make preliminary models of interactions between a high amount of species in a specific area, where there is data abundantly available and these models could be used to lower the uncertanties of the stock assessments. However, more research into the applicability for this approach to other species and areas needs to be conducted.

The natural carbon cycle is affected by human activity. Terrestrial carbon stocks have been decreasing as at the same time carbon dioxide concentration in the atmosphere has increased causing climate change. The Paris Agreement sets the target to limit climate change to 1.5°C and to reach that goal, all possible mitigation practises should be included into global framework to avoid the most serious consequences of warming. Carbon sequestration into natural soil and biomass could be one mitigation practice. To enhance carbon sequestration activities and to include natural carbon stocks into to the EU climate policy, it would be necessary to quantify stock sizes and changes in those stocks. For developing carbon trading markets, the quantification methods should provide accurate results and at the same time be practical and financially achievable. Used research method in this thesis was comparatively literature survey and aim was to gather and compere information about currently used carbon stock quantification methods against developing carbon trading markets. Soil carbon stocks can be quantified with direct soil sampling, spectroscopic sensing methods or by mathematical models. Biomass carbon stocks can be quantified with inventory-based field measurements and modelling and by remote sensing. The full carbon budget on the ecosystem level can be achieved with carbon flux measurements. Quantification of different terrestrial carbon stocks and their changes is not a simple task. There is a lot of variation between different stocks and in some cases, the stock changes occur slow. Cost of carbon stock quantification depends on the accuracy, size of the area under focus and frequency of the measures. Methods for terrestrial carbon stock quantification are dependent on high quality data and there is demand for research considering carbon sequestration. For carbon offsetting purposes of developing carbon markets, the modelling approach is achievable, cost efficient, repeatable and transparent. There is no perfect model or one universal model that would fit to every situation and thus the differences must be known. At this stage, this approach could be one possibility to include small scale projects and enhance climate actions. Different quantification methods provide information which can be used to different method developments and to increase accuracies. It’s important to know, how all information can be effectively utilized.

Improving land management to mitigate climate change is important, especially in agriculture on soils with high organic content. Many studies have found evidence that increasing diversity can help to improve plant biomass production and soil carbon storage. This is attributed to complementarity which consists of more efficient resource use due to niche differences and facilitative interactions. For the total climate impact, the effect of greenhouse gas emissions from the soil needs to be considered. To find out if adding more species to a grass mixture could have similar benefits in boreal zone grass cultivation in Finland, an experiment was set up with four different species mixtures, and three levels of species richness were established under a nurse crop. It was additionally of interest if these effects can counter the emissions of cultivation on organic soils. Biomass samples were collected both before the nurse crop was removed and at the end of the growing season. Both species richness and Shannon diversity index were considered as explanatory factors. Carbon exchange, divided into respiration and photosynthetic capacity, as well as nitrous oxide and methane fluxes, were monitored monthly. There was no strong evidence that species richness affects biomass or greenhouse gas fluxes during the first year. The effect of species richness on the biomass was clearer when the diversity index was considered. These results were significant when the lowest biomass values were excluded from the analysis, probably because complementary resource use needs enough biomass to have an effect. The differences in carbon flux measurements may be sensitive to timing within the growing season since the results closest to significant were obtained at the start of the season. At the time, the measurement conditions were good and the nurse crop biomass was small enough not to obscure the effects of grass mixture. When it comes to other greenhouse gases, species richness had most impact on early nitrous oxide emissions, while methane flux probably needs significantly more time for any changes to appear. Overall, the effect of species richness needs to be studied over the full grass cultivation cycle to find out the full effect. Based on current results, increasing species richness may be an option when other methods cannot be used to reduce emissions and improve carbon sink of agriculture.