Biofizinių augalijos parametrų vertinimas remiantis palydovo „Sentinel 2“ duomenimis

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Sezoninė chlorofilo a koncentracijos kaita eutrofiniuose ežeruose Lietuvoje naudojant palydovinius duomenis

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Sezoninė ir daugiametė NDVI kaita skirtingų tipų žemės naudmenose rytinėje Baltijos jūros regiono dalyje

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Machine learning algorithms for biophysical classification of Lithuanian lakes based on remote sensing data

Inland waters are dynamic systems that are under pressure from anthropogenic activities, thus constant observation of these waters is essential. Remote sensing provides a great opportunity to have frequent observations of inland waters. The aim of this study was to create a data-driven model that uses a machine learning algorithm and Sentinel-2 data to classify lake observations into four biophysical classes: Clear, Moderate, Chla-dominated, and Turbid. We used biophysical variables such as water transparency, chlorophyll concentration, and suspended matter to define these classes. We tested six machine learning algorithms that use spectral features of lakes as input and chose random forest classifiers, which yielded the most accurate results. We applied our two-step model on 19292 lake spectra for the years 2015–2020, from 226 lakes. The prevalent class in 67% of lakes was Clear, while 19% of lakes were likely affected by strong algal blooms (Chla-dominated class). The models created in this study can be applied to lakes in other regions where similar lake classes are found. Biophysical lake classification using Sentinel-2 MSI data can help to observe long-term and short-term changes in lakes, thus it can be a useful tool for water management experts and for the public

Uncertainty of atmospheric correction algorithms for chlorophyll α concentration retrieval in lakes from Sentinel-2 data

One of the largest uncertainties in remote sensing data comes from atmospheric influence. This research aims to explain the uncertainties emanating from atmospheric correction (AC) product selection and how they influence chlorophyll α concentration retrieval in lakes in eastern Lithuania. We tested seven products from six AC processors (Acolite, Acolite Rayleigh, iCOR, Sen2Cor, C2RCC, C2X, and POLYMER) and 10 chlorophyll α retrieval algorithms with different architectures. The uncertainty of AC products transferred to chlorophyll α concentrations, and large differences in the chlorophyll α concentrations retrieved using different AC products were observed. The match-up analysis showed that chlorophyll α algorithms based on band difference performed best in terms of a high coefficient of determination and the lowest median bias when used with image-based, Sen2Cor, and TOA data. The results of this study highlight the uncertainties of AC products as well as how the selection of the chlorophyll α retrieval algorithm can mitigate the influence of AC selection

Seasonality and long-term trends of NDVI values in different land use types in the eastern part of the Baltic Sea basin

This study analyses changes in Normalized Difference Vegetation Index (NDVI) values in the eastern Baltic region. The main aim of the work is to evaluate changes in growing season indicators (onset, end time, time of maximum greenness and duration) and their relationship with meteorological conditions (air temperature and precipitation) in 1982–2015. NDVI seasonality and long-term trends were analysed for different types of land use: arable land, pastures, wetlands, mixed and coniferous forests. In the southwestern part of the study area, the growing season lasts longest, while in the northeast, the growing season is shorter on average by 10 weeks than in the other parts of the analysed territory. The air temperature in February and March is the most important factor determining the start of the growing season and the air temperature in September and October determines the end date of the growing season. Precipitation has a much smaller effect, especially at the beginning of the growing season. The effect of meteorological conditions on peak greenness is weak and, in most cases, statistically insignificant. At the end of the analysed period (1982–2015), the growing season started earlier and ended later (in both cases the changes were 3–4 weeks) than at the beginning of the study period. All these changes are statistically significant. The duration of the growing season increased by 6–7 weeks

Long-term precipitation events in the eastern part of the Baltic Sea region

Precipitation anomalies have a significant impact on both natural environmental and human activity. Long lasting drought analysis has received great attention on a global and regional scale while prolonged rainy periods so far have been much less studied. However, long-term precipitation events are also important and threatening. The situation around the Baltic Sea in 2017 revealed that such periods could cause significant losses in agriculture. The rainy periods of 30, 60, and 90 consecutive days in a given year during which the maximum precipitation amount was recorded in the eastern part of the Baltic Sea region were analysed in this study. Daily precipitation amount data from the E-OBS gridded dataset was used. The investigation covered a period from 1950 to 2019. The changes in magnitude and timing of such rainy periods were evaluated. It was found that the annual precipitation in the eastern part of the Baltic Sea region increased significantly during the analysed period. Positive changes were observed throughout the year except during April and September. The amounts of precipitation during rainy periods of different duration also increased in most of the investigated areas but changes were mostly insignificant. Consequently, a decrease in the ratio of precipitation amount during the rainy period to annual precipitation was observed. It was also found that the rainy periods occurred earlier, especially in the case of the rainy periods of 60- and 90-days durations. Such tendencies pose an increasing threat to agriculture

Sea level rise impact on compound coastal river flood risk in Klaipėda city (Baltic Coast, Lithuania)

Due to climate change, extreme floods are projected to increase in the 21st century in Europe. As a result, flood risk and flood-related losses might increase. It is therefore essential to simulate potential floods not only relying on historical but also future projecting data. Such simulations can provide necessary information for the development of flood protection measures and spatial planning. This paper analyzes the risk of compound flooding in the Danė River under different river discharge and Klaipėda Strait water level probabilities. Additionally, we examine how a water level rise of 1 m in the Klaipėda Strait could impact Danė River floods in Klaipėda city. Flood extent was estimated with the Hydrologic Engineering Center’s River Analysis System (HEC-RAS) and visualized with ArcGIS Pro. Research results show that a rise in the water level in the Klaipėda Strait has a greater impact on the central part of Klaipėda city, while that of the maximum discharge rates of the river affected the northern upstream part of the analyzed river section. A sea level rise of 1 m could lead to an increase in areas affected by Danė floods by up to three times. Floods can cause significant damage to the infrastructure of Klaipėda port city, urbanized territories in the city center, and residential areas in the northern part of the city. Our results confirm that, in the long run, sea level rise will significantly impact the urban areas of the Klaipėda city situated near the Baltic Sea coast

Drought identification in the eastern Baltic region using NDVI

The droughts are the phenomena which affect large areas. Remote sensing data covering large territory can be used to assess the droughts’ impact and theirs extent. Drought effect on vegetation was determined using Normalized Difference Vegetation Index (NDVI) in the east Baltic Sea region located between 53–60 N and 20–30 E. The effect of precipitation deficit on vegetation in arable land, broad–leaved and coniferous forest was analysed using the Standardized Precipitation Index (SPI) calculated for 1 to 9 month time scales. The vegetation has strong seasonality in the analysed area. The beginning and the end of vegetation season depends on the distance to the Baltic Sea which affects temperature and precipitation patterns. The vegetation season duration in the south-eastern part of the region is 5–6 weeks longer than in the north-western part. The early spring air temperature, snowmelt water storage in the soil and precipitation has the largest influence on NDVI values in the first half of the growing season. The precipitation deficit in the first part of the vegetation season has a significant impact only on the vegetation in the arable land. The vegetation in the forests is less sensitive to moisture deficit. The positive correlation between 3 and 6 month SPI and vegetation condition was observed in the arable land and both types of forests in the second half of the vegetation season. The precipitation deficit is only one of the vegetation condition drivers and NDVI cannot be used universally to identify droughts, but it may be applied to better assess the effect of droughts on vegetation in the eastern Baltic Sea region

Dynamics of meteorological and hydrological droughts in the Neman river basin

The analysis of drought dynamics in the Neman river basin allows an assessment of extreme regional climate changes. Meteorological and hydrological warm period droughts were analyzed in this study. Meteorological droughts were identified using the standardized precipitation index, and hydrological droughts using the streamflow drought index. The whole river basin was analyzed over the period from 1961 to 2010. Precipitation data from Vilnius meteorological station (from 1887) and discharge data from Smalininkai (Neman) hydrological station (from 1811) were used for an evaluation of meteorological and hydrological drought recurrence over a long-term period. It was found that the total area dryness has decreased over the last 50 years. A statistically significant increase in standardized precipitation index values was observed in some river sub-basins. An analysis of drought recurrence dynamics showed that there was no indication that the number of dangerous drought was increased. It was determined that the standardized precipitation index cannot successfully identify the hydrological summer droughts in an area where the spring snowmelt forms a large part of the annual flow. In particular, the weak relationship between the indices was recorded in the first half of the summer, when a large part of the river runoff depends on accumulated water during the spring thaw