Browsing by Subject "maankäyttö"
Now showing items 1-3 of 3
-
(2019)Soil carbon (C) is a key part of the global C cycle. Agricultural soils can be both source and sink of the atmospheric carbon dioxide (CO2). In the Mid-Hill region of Nepal, a lot of the historical soil C has been lost in consequence of the conversion of forests into agricultural lands. However, there is huge potential to increase the soil C sink through appropriate farming practices. The region is characterized by mountainous topography with various microclimates found within a short distance. Thus, also the farming systems differ from each other, which further contributes to the altering soil C accumulation in the region. This Master's thesis is linked to the project Building Climate Resilience in Farming Systems in Sloping Lands of South Asia, supported by Asia-Pacific Network for Global Change Research (APN). The aim of the study was to find out what is the present soil carbon status in two predominant farming systems in the study site in Kavre, located in the Mid-Hill region, and what are the farming practices contributing to soil C. Soil samples from upland and lowland were taken into the analysis complemented with the interviews of the farmers and the field observations. The effect of the farming practices was investigated in three systems, including the both farming systems, upland solely, and lowland solely. The analysis of variance (ANOVA) was applied for studying the effects of the categorically measured farming practices. The effects of the farming practices classified as continuous variables were measured with the analysis of covariance (ANCOVA). The results showed that the soil C content was significantly higher in the upland system compared with lowlands. Vegetation cover, agroforestry, and the weed management with weed residues left to the fields were associated with higher soil C stocks. Negative relationship between the chemical fertilizer use and soil C sequestration was found. The use of organic fertilizers, tillage method, tilling intensity, crop residue management and irrigation did not show significant effect on soil C. This study suggests that the aboveground vegetation cover is an integral part of the soil C sequestration in the sloping agricultural lands in the Mid-Hill region of Nepal. Nonetheless, further research with replication and a larger sample size is needed in order to fully investigate the farming practices contributing to the greater soil C contents in the region.
-
(2022)Remote sensing brings new potential to complement environmental sampling and measuring traditionally conducted in the field. Satellite images can bring spatial coverages and accurately repeated time-series data collection to a whole new level. While developing methos for doing ecological assessment from space in situ sampling is still in key role. Satellite images of relatively coarser pixel size where individual plants or trees are not possible to separate usually utilize vegetation indices as proxies for environmental qualities and measures. One of the most extensively used and studied vegetation index is Natural Difference Vegetation Index (NDVI). It is calculated as normalized ratio between red light and near-infra-red radiation with formula: NDVI=NIR- RED/NIR+RED. Index functions as a measure for plant productivity, that has also been linked to species-level diversity. In this thesis MODIS NDVI (MOD13Q1, 250 m x 250 m resolution) and selected additional variables were examined through their predictive power for explaining variation in tree species richness in six different types of moist tropical evergreen forests in the province of West Kalimantan, on the island Borneo in Indonesia. Simple and multiple regression models were built and tested with main focus on 20- year mean-NDVI. Additional variables used were aboveground carbon, elevation stem count, tree height and DBH. Additional variables were examined initially on individual basis and subsequently potential variables were then combined with NDVI. Results indicate statistically significant, but not very strong predictable power for NDVI (R2=0.25, p-value=2.11e-07). Elevation and number of stems outperformed NDVI in regression analyses (R2=0.64, p-value=2.2e-16 and R2=0.36, p-value=4.5e-11, respectively). Aboveground biomass carbon explained 19% of the variation in tree species richness (p-value=6.136e-06) and thus was the worst predictor selected for multiple regression models. Tree height (R2=0.062, p-value=0.0137) and DBH (R2=0.003, p-value=0.6101) did not show any potential in predicting tree species richness. Best variable combination was NDVI, elevation and stem count (R2=0.71, p-value=2.2e-16). Second best was NDVI, elevation and aboveground biomass carbon (R2=0.642, p-value=2.2e-16), which did not promote for biomass carbon as a potential predictor as model including only NDVI and elevation resulted nearly identically (R2=0.639, p-value=2.2e-16). Model including NDVI and stem count explained 54% of the variation in tree species richness (p-value=2.2e-16) suggesting elevation and stem count being potential variables combined with NDVI for this type of analysis. Problems with MODIS NDVI are mostly linked to the relatively coarse spectral scale which seems to be too coarse for predicting tree species richness. Spectral scale also caused spatial mismatch with field plots as being significantly of different sizes. Applicability in other areas is also limited due to the narrow ecosystem spectrum covered as only tropical evergreen forests were included in this study. For future research higher resolution satellite data is a relevant update. In terms methodology, alternative approach known as Spectral Variability Hypothesis (SVH), which takes into account heterogeneity in spectral reflectance, seems more appropriate method for relating spectral signals to tree species richness.
-
(2018)Tropical forests, which hold significant carbon reserves mostly in the soil and trees, are under constant land-use change pressure. At the same time, there is an increasing demand for carbon sinks, as atmospheric CO2 levels keep increasing. Protecting the carbon sinks is crucial in curbing the further heating of the atmosphere. Tropical forests are also the mayor biodiversity hotspots. Conservation of biodiversity is important for keeping the flora and fauna populations viable. More species on Earth equals to more possibly adapting species in the changing climate. This Master’s thesis investigates the relation between the terrestrial carbon and biodiversity in two study areas in Peruvian regions Madre de Dios and San Martín and estimates the Biodiversity Conservation Value (BCV) for different land-use scenarios for future. The studied areas are located in the Tropical Andes and the Amazon rainforest and are therefore key biodiversity hotspots on a global scale. This thesis also discusses other environmental issues threatening the biodiversity in the studied areas. These environmental issues include illegal gold mining in Madre de Dios, impacts of selective logging, oil exploration, illegal logging, habitat fragmentation and agricultural expansion. The data was collected in 26 face-to-face interviews with biodiversity experts in Peru. The data was then entered into an Excel-based tool called CarboScen, which created estimations of the future BCV values in the studied landscapes. The land-use scenarios used in this study are based on the research of Larjavaara et. al. (2018) about the cost of increasing ecosystem carbon in different locations around the world. The main results of this thesis are that the payments for extra carbon ton stocked in the landscape would increase the BCV in the studied landscapes in Peru and be beneficial for the biodiversity. The environmental issues such as tackling illegal logging and gold mining and decreasing the use of mercury in gold extraction require changes in legislation, monitoring and transparency. Curbing the agricultural expansion requires global action in decreasing the consumption.
Now showing items 1-3 of 3