Skip to main content
Login | Suomeksi | På svenska | In English

Browsing by Subject "GAM"

Sort by: Order: Results:

  • Modelling dry season grass biomass in savanna rangelands in Kenya using imaging spectroscopy 
    Pesonen, Linda (2024)
    Grass biomass has many important and diverse roles for ecosystems functioning, the carbon cycle, rangeland productivity and local livelihoods. Quantifying and understanding grass biomass in dynamic savanna ecosystems during dry season is important for sustainable land management and monitoring grazing pressures, especially amidst climate change. Traditional ground-based methods to assess vegetation are subjective and time consuming, while remote sensing provides efficiency in monitoring grass biomass at large scales. Grass biomass assessments using remote sensing data have been extensively conducted worldwide, but such research in African savannas remains rare. This study aimed to study connections between dry season grass biomass measured in savanna rangelands and airborne hyperspectral imagery data obtained simultaneously in LUMO Conservancy area of South-Eastern Kenya. Two modelling techniques were compared: averaged plot values (n=24) and individual sample values (n=96). Three vegetation indices (RSI, NDSI, RDSI) were computed and Generalised Additive Models (GAM) were applied to portray the relationship between measured grass biomass and VIs. The highest explanatory power for both modelling techniques was found with RSI and NDSI indices with averaged plot level values having the highest performance (D2 = 0.79, RMSE = 40.15 g/m2), with the band combination of B78 and B43 (908 nm / 667 nm). The best performing vegetation index (RSI) was used to predict grass biomass in the study area, which indicated a biomass range of 0 to 2894 g/m2. The study highlights the potential of using hyperspectral imagery to assess grass biomass in the savanna environments. However, challenges and limitations were faced related to the heterogeneous nature of savannas, varying weather conditions affected by rainfall, the temporal limits of the study, and disturbances in spectral information caused by heavily grazed areas, dead material, and preprocessing techniques. It is suggested that future research considers these factors by incorporating a broader set of variables, extending the duration of the study, exploring various preprocessing techniques, increasing the sample size, and employing additional data sources, such as active sensors and hyperspectral satellite imagery, to enhance model performance and improve accuracy.
  • Topografian, maaperän ja kasvillisuuden vaikutus maaperän kosteuden alueelliseen ja ajalliseen vaihteluun boreaalisella vyöhykkeellä 
    Suppula, Meri (2023)
    Soil moisture plays a key role in ecosystems. Soil moisture varies spatially and temporally, and the variations are influenced by many different factors. On a large scale, climate has a large effect on soil moisture, but more locally, especially topography, soil and vegetation become important factors. In addition, mean soil moisture content affects whether soil moisture variations are greatest when soil is dry or moist. Climate change will significantly affect soil moisture around the world, and effects will also be visible in the boreal forest. Therefore, it is important to study soil moisture more comprehensively in boreal environment. The purpose of this thesis is to find out how soil moisture varies spatially and temporally, and how topography, soil and vegetation explain this variation in different parts of the boreal forest and in different environments. Study area covers eight areas of varying size (3,5–37 km2) in the boreal forest around Finland. Each study area has 44–96 study points from which soil moisture has been measured every 15 minutes during July 2020. Mean and standard deviation of soil moisture (response variables) of each research point were calculated from the measurements, of which the mean describes the spatial variation of soil moisture and the standard deviation the temporal variation. The response variables were explained by environmental variables. Variables explaining topography’s effect were altitude, SAGA wetness index (SWI), topographic position index (TPI) and radiation. Vegetation was explained by canopy cover and site fertility class, and soil was explained by soil class. The effect of environmental variables on spatial and temporal variations of soil moisture was analysed using a generalized additive model (GAM), which was fitted for each study area and for both response variables separately. Explanatory power of the models was examined with an adjusted R2 value using the bootstrap method. Relative importance of the individual environmental variables in the model was examined by randomizing the variables, and the direction of the effect of the environmental variables was examined using response curves. Soil moisture varied considerably spatially and temporally within the study areas and between the areas. Soil moisture was generally high in study areas with a lot of peatlands, and moisture varied most spatially usually in topographically heterogeneous areas. Often, the temporal variation of moisture was highest on dryer study areas and lowest on moister areas. Indeed, it was found that when the mean soil moisture was high, the standard deviation was often small and vice versa. Topographic factors influenced to the mean and standard deviation of soil moisture more in the northern than in the southern regions, while the role of canopy cover was emphasized in the southern regions when explaining the mean moisture. Soil influenced to the moisture more in the northern than in the southern areas. From all variables, SWI clearly explained the best both the mean and standard deviation of moisture. Canopy cover and radiation also explained well the mean moisture in a part of the study areas. In addition to SWI, the standard deviation of moisture was best explained by site fertility class and soil class. Altitude and TPI rarely explained the mean and standard deviation of moisture well. SWI often increased moisture and decreased moisture’s temporal variability in the study areas, but the directions of the effects of other environmental variables varied a lot between areas. This study shows that the large spatial and temporal variability of soil moisture in different environments of the boreal forest is dominated by different factors, and even the same environmental factors affect soil moisture in very different ways between areas. Research must be continued to get a better general picture of the factors affecting soil moisture variations in the boreal forest, and to be able to prepare better for the environmental changes caused by climate change.

Yhteystiedot

HELSINGIN YLIOPISTO