Wetlands systems are at the risk to changes in hydrological regime under climatic variation. Wetland habitat is sensitive and responses quick to climate changes. Wetlands cover 6% of the world’s land surface and contain about 12% of the global carbon pool, play an important role in the global carbon cycle (International Panel on Climate Change (IPCC); In a time of global climate change, wetlands conditions are not known well enough. That’s why the research that we have been carried out since couple of years which are based on satellite data and in-situ data are important for depicting the changes in hydrological and vegetation parameters. Biebrza Wetlands are our main area of research, because they are: • one of the largest area in Europe covered with marshes, swamps, and wet meadows • 60 000 ha of flat river valley covered with hydrogenic soils such as peat in various stages of mouldering • habitat of 271 bird species • protected as a National Park, Natura 2000 and RAMSAR sites The results are also connected to serve as an indicator of UN Sustainable Development Goal 6.6.1 - Change in the extent of water-related ecosystems over time.
The main objective of GrasSat project is a fully operational system in form of desktop and mobile application, which provides a complementary tool for managing grassland production, mainly for medium and large farms in Poland and Norway. Combining the effectiveness of the application with the support of external advisors is the key to improve grass production management. Experience of the team of remote sensing and grassland specialists will be the firm foundation of the tools to be prepared within the project.
The results of application of microwave and optical satellite data for soil moisture (SM) assessment are presented. The research has been carried out from 2015 to 2016 at Biebrza Wetlands test site located in North-East Poland, designated by Ramsar Convention as Wetlands of International Importance. A regression models based on Sentinel-1 backscattering coefficients (σ°) have been developed to generate the soil moisture (SM) maps over Biebrza Wetlands. The optical data from Sentinel-2 have been used for the classification of wetlands vegetation habitats to improve SM predictions. The wetland vegetation differed, there were reeds, sedge-moss, sedges, grass-herbs, and grass. The majority of the changes occurred in moist habitats, while anthropogenic appeared more stable during study period. The observed changes were referred to moving/grazing changes and weather effects causing droughts/floods. SM differed from 30% during the drought season in 2015 to 95% in the wet season in 2016. It has been examined the impact of biomass and SM on microwave signal under changing soil moisture and vegetation growth conditions. Vegetation biomass has been characterized by measured in-situ LAI and by vegetation indices calculated from Sentinel-2, Terra MODIS data. The impact of SM and LAI on σ° calculated from Sentinel-1 data showed that LAI dominates the influence on σ° when SM is low. The analysis have been done to estimate the threshold of the SM values which dominate the backscatter. This study demonstrates the capability of Sentinel-1/2 data to estimate SM, offering an important advantage for wetlands monitoring.
The paper describes experimental L-band ground reflectivity measurement using noise radar demonstrator working as a
scatterometer. The radar ground return is usually described with a scattering coefficient, a quantity that is independent
from the scatterometer system. To calculate the coefficient in a function of incidence angle, range profile values obtained
after range compression were used. In order to improve dynamic range of the measurement, antenna cross-path
interference was removed using lattice filter. The ground return was measured at L band both for HH and VV
polarizations of radar wave as well as for HV and VH crosspolarizations using log-periodic antennas placed at a 10 m
high mast directed towards a meadow surface. In the paper the theoretical considerations, noise radar setup,
measurement campaign and the results are described.
The drought affects agricultural crops by diminishing amount of water
necessary for vegetation. Deficit of soil moisture in specific vegetation growth stage
causes the reduction in crop yield.
The research, which has been carried out in Poland, gives consistent
information on soil and vegetation growth over agricultural regions using various
satellite-derived soil - vegetation indices. A wide range of ground-based
measurements such as soil moisture, leaf area, and biomass were collected on nearly
same dates of satellite overpass.
The different soil moisture indices have been calculated on the basis of
evapotranspiration derived from the surface temperature obtained from
NOAA/AVHRR and meteorological data. The temperature condition index (TCI)
characterising the status of crop development has been obtained from Global Area
Coverage (GAC) data derived from NOAA images. Furthermore, latent heat fluxes
and NDVI values have been calculated and implemented as the input to the models.
This paper describes the analysis and results of a study to improve the detection and
monitoring of drought conditions.
An approach to classification of satellite images aimed at vegetation mapping in a wetland ecosystem has been presented. The wetlands of the Biebrza Valley located in the NE part of Poland has been chosen as a site of interest. The difficulty of using satellite images for the classification of a wetland land cover lies in the strong variability of the hydration state of such ecosystem in time. Satellite images acquired by optical or microwave sensors depend heavily on the current water level which often masks the most interesting long-time scale features of vegetation. Therefore the images have to be interpreted in the context of various ancillary data related to the investigated site. In the case of Biebrza Valley the most useful information was obtained from the soil and hydration maps as well as from the old vegetation maps. The object oriented classification approach applied in eCognition software enabled simultaneous use of satellite images together with the additional thematic data. Some supplementary knowledge concerning possible plant cover changes was also introduced into the process of classification. The accuracy of the classification was assessed versus ground-truth data and results of visual interpretation of aerial photos. The achieved accuracy depends on the type of vegetation community in question and is better for forest or shrubs than for meadows.
Information based on satellite data is used for evaluation of crop growth conditions what is essential for proper management of agricultural fields. The database of satellite data used for this application consists of optical and radar data from ERS. Soil moisture has been assessed using two different approaches. First one concerned the application of soil moisture index based on sensible and latent heat calculated from surface temperature (ATSR) and meteorological data (H/LE) and backscattering coefficient calculated from SAR data. Second one concerned the application of modified semiemperical water-cloud model to simulate backscattering coefficients of C-VV of ERS and L-HH of JERS as a function of LAI, Leaf Water Area Index and Vegetation Water Content. The final results gave the possibilities of comparison of the modeled soil moisture values with field measurements. The two-way attenuation of vegetation in three models for C-VV band and L-HH band has been examined.
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