New Methodologies for Grasslands Monitoring

Monitoring grassland areas to assess changes in their condition over time has been the subject of a lot of research at different scales. Initially the techniques focused on field-based measurements, and modelling. However, several obtained data were site specific. Based on the increase in availability of remote sensing data and products, there is an expectation that remote sensing can provide rapid and definite answers to the challenges of detecting and monitoring grassland conditions and associated changes in productivity. At the time of European Copernicus Programme, the new possibilities of satellite data from the group of Sentinel satellites give the new perspective for grasslands monitoring. The Finegrass Polish – Norwegian Project have been set to detect the biomass and its changes for grasslands in Poland and Norway applying different approaches due to different specific of the area. The results have been verified by ground measurements

Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data

The objectives of the study were to determine the spatial rate of CO2 flux (Net Ecosystem Exchange) and soil moisture in a wetland ecosystem applying Sentinel-1 IW (Interferometric Wide) data of VH (Vertical Transmit/Horizontal Receive—cross polarization) and VV (Vertical Transmit/Vertical Receive—like polarization) polarization. In-situ measurements of carbon flux, soil moisture, and LAI (Leaf Area Index) were carried out over the Biebrza Wetland in north-eastern Poland. The impact of soil moisture and LAI on backscattering coefficient (σ°) calculated from Sentinel-1 data showed that LAI dominates the influence on σ° when soil moisture is low. The models for soil moisture have been derived for wetland vegetation habitat types applying VH polarization (R2 = 0.70 to 0.76). The vegetation habitats: reeds, sedge-moss, sedges, grass-herbs, and grass were classified using combined one Landsat 8 OLI (Operational Land Imager) and three TerraSAR-X (TSX) ScanSAR VV data. The model for the assessment of Net Ecosystem Exchange (NEE) has been developed based on the assumption that soil moisture and biomass represented by LAI have an influence on it. The σ° VH and σ° VV describe soil moisture and LAI, and have been the input to the NEE model. The model, created for classified habitats, is as follows: NEE = f (σ° Sentinel-1 VH, σ° Sentinel-1 VV). Reasonably good predictions of NEE have been achieved for classified habitats (R2 = 0.51 to 0.58). The developed model has been used for mapping spatial and temporal distribution of NEE over Biebrza wetland habitat types. Eventually, emissions of CO2 to the atmosphere (NEE positive) has been noted when soil moisture (SM) and biomass were low. This study demonstrates the importance of the capability of Sentinel-1 microwave data to calculate soil moisture and estimate NEE with all-weather acquisition conditions, offering an important advantage for frequent wetlands monitoring

Monitoring Wetlands Ecosystems Using ALOS PALSAR (L-Band, HV) Supplemented by Optical Data: A Case Study of Biebrza Wetlands in Northeast Poland

The aim of the study was to elaborate the remote sensing methods for monitoring wetlands ecosystems. The investigation was carried out during the years 2002–2010 in the Biebrza Wetlands. The meteorological conditions at the test site varied from extremely dry to very wet. The authors propose applying satellite remote sensing data acquired in the optical and microwave spectrums to classify wetlands vegetation habitats for the assessment of vegetation changes and estimation of wetlands’ biophysical properties to improve monitoring of these unique, very often physically impenetrable, areas. The backscattering coefficients (σ°) calculated from ALOS PALSAR FBD (Advanced Land Observing Satellite, Phased Array type L-band Synthetic Aperture Radar, Fine Beam Dual Mode) images registered at cross polarization HV on 12 May 2008 were used to classify the main wetland communities using ground truth observations and the visual interpretation method. As a result, the σ° values were distributed among the six wetlands’ vegetation classes: scrubs, sedges-scrubs, sedges, reeds, sedges-reeds, rushes, and the areas of each community and changes were assessed. Also, the change in the biophysical variable as Leaf Area Index (LAI) is described using the information from PALSAR data. Strong linear relationships have been found between LAI and σ° derived for particular wetland classes, which then were applied to elaborate the maps of LAI distribution. The other variables used to characterize the changing environmental conditions are: surface temperature (Ts) calculated from NOAA AVHRR (National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer) and Normalized Difference Vegetation Index (NDVI) from ENVISAT MERIS (ENVIronmental SATellite MEdium Resolution Imaging Spectrometer). Differences of almost double Ts between “dry” and “wet” years were noticed that reflect observed weather conditions. The highest values of NDVI occurred in years with a sufficient amount of precipitation with the lowest in “dry” years. NDVI values variances within the same wetlands class resulted mainly from the differences in soil moisture. The results of this study show that the satellite data from microwave and optical spectrum gave the repetitive spatial information about vegetation growth conditions and could be used for monitoring wetland ecosystems