Katarzyna Dabrowska-Zielinska

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.

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.

Mapping vegetation of a wetland ecosystem by fuzzy classification of optical and microwave satellite images supported by various ancillary data

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.

Various approaches for soil moisture estimates using remote sensing

Three approaches using optical and microwave remotely sensed data were considered for soil moisture assessment. The first was based on NOAA/AVHRR images and meteorological data, the second on synergy of NOAA and ERS-2.SAR data, and the third one on the ERS-2 SAR and JERS SAR data. For the test area the classification of crop type has been carried out using Thematic Mapper images.

Retrieval of crop parameters and soil moisture from ENVISAT ASAR based on model analysis

Katarzyna Dabrowska-Zielinska, Maria Gruszczynska, Wanda Kowalik, Y. Inoue, Agata Hoscilo, Institute of Geodesy and Cartography, Remote Sensing Centre, Modzelewskiego 27, 02-679 Warsaw, Poland, phone: +4822 3291974, www.igik.edu.pl , kasia@igik.edu.pl National Institute for Agro-Environmental Sciences Tsukuba, Ibaraki 305-8604, Japan; yinoue@niaes.affrc.go.jp Abstract:The aim of the project was to examine the impact of soil-vegetation parameters on backscattering coefficient calculated from ASAR data under various polarization and incidence angle. Usage of VV/HV IS4 and HV/HH IS2 data gave the possibility of obtaining Leaf Area Index, Leaf Water Area Index, soil moisture. Extensive field measurements were conducted at the test site simultaneously to satellite overpasses. The applicability of three different vegetation descriptors to the semi-empirical water-cloud model was investigated. The following article covers part of gathered material and concerns only winter wheat and in some part corn and sugar beet. The study has been realized under ESA CAT-1 1427 project.

Examination of crop characteristics using microwave data

The numeric inversion of water-cloud model of synchronized microwave bands of ERS-2 and JERS satellites gave the possibility of obtaining crop characteristics. Model performance was validated by comparison between backscattering coefficients simulated and measured by satellites. The contribution of various crop characteristics was presented and compared to measured soil-vegetation parameters at the ground level during satellite overpasses.

European Area Frame Sampling Based on Very High Resolution Images

Initiated in 2007, the Area Frame Sampling Europe subtask of the Seasonal and Annual Change Monitoring Service (SATChMo) Core Service in the geoland2 project delivered its final products in 2012. Three of these are described in this paper: (i) an Area Frame Sampling scheme design that aims at optimizing the statistical accuracy when extrapolating a land cover classification at reasonable cost, (ii) a semi-automatic classification tool that is able to discriminate 10 land cover classes on VHR samples with 0.25ha minimum mapping unit (MMU), and (iii) a highly automatized change detection tool based on Multivariate Alteration Detection (MAD) approach that additionally employs Normalised Difference Vegetation Index (NDVI) and texture characteristics. This later step, as well as giving change/no-change mask provides directions of changes in three main categories, artificialization, revegetation, and devegetation. The algorithms were cast into the form of production chains starting with data acquisition and processing, through the main processing to validation and product dissemination via Spatial Data Infrastructure servers. The whole process was tested on representative set of 114 sites from across the European Union (EU).

Study in Biebrza Wetlands using optical and microwave satellite data

This study was conducted during 2003-2009 in Biebrza Wetlands, a NATURA 2000 and Ramsar Convention test site situated in Northeast Poland. It is one of the largest in Europe natural rich biotope with the large amount of unique spices of flora and important zone for nesting and wintering for fauna. Protection of wetlands that are very sensitive ecosystems are of great importance in nature conservation for carbon and water cycles. Changes of soil water content affect plant cover and lead to elimination or preference of certain species. Controlling soil moisture is essential for protection of peat-forming plant communities and slow down drying processes against mineralization and carbon exhaust. Data from optical and microwave satellite images and soil-vegetation ground measurements were analyzed to develop methods for monitoring and mapping soil-vegetation parameters over wetlands. This study was conducted in the framework of national grant N N526021733 and ESA projects AOID.122 and AOALO.3742.

Biophysical properties of wetlands vegetation retrieved from satellite images

The investigation carried out at wetlands in Biebrza Basin, the biggest area of the marshes and swamps in Central Europe, aimed at finding the best biophysical properties of wetlands vegetation to characterise marshland habitats. The various soil-vegetation indices on the basis of all considered spectral bands of satellites as Landsat +ETM, SPOT, ERS-2, NOAA, ENVISAT have been calculated. The GEMI and EVI index calculated from SPOT VEGETATION was the best for distinguishing vegetation classes. Significant correlation between LAI measured at the ground and the indices was with GEMI and EVI index. Soil moisture values calculated from ERS-2 and ENVISAT microwave data well characterise marshland humidity classes. For the retrieval of the biophysical parameters as LAI (Leaf Area Index), vegetation moisture (VM) and soil moisture (SM), ERS-2.SAR and ENVISAT ASAR data acquired at VV, HV, VH and HH polarisations at two different viewing angles (IS2, IS4) have been applied. Evapotranspiration was assessed using NOAA AVHRR and meteorological data. ERS-2 and ENVISAT images have been obtained from ESA for AO-ID122 project

Interactions between multi-frequency microwave backscatters and rice canopy variables

Microwave backscatter coefficients at all combinations of five frequencies (Ka, Ku, X, C, and L), all polarizations (HH, VH, HV, and VV) and four incident angles (25/spl deg/, 35/spl deg/, 45/spl deg/, and 55/spl deg/) have been collected on nearly a daily basis before transplanting to after the harvesting period of the rice paddy. Analysis based on a backscattering model clearly showed unique interactions between each microwave backscattering signature and vegetation variables such as LAI, biomass, and grain yield.