Biophysical Parameters Assessed from Microwave and Optical Data

Dąbrowska-Zielińska, K.; Budzynska, Maria; Kowalik, W.; Malek, Iwona; Gatkowska, Martyna; Bartold, Maciej; Turlej, Konrad

doi:10.2478/v10177-012-0013-7

Cited by 10 publications

(8 citation statements)

References 3 publications

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“…Table 7 (Figure 12 presents this relationship). The obtained relationship shows that with the increase of LAI there is an increase in the backscattering coefficient, which indicates the strong possibilities to monitor the increase of biomass using the long L wave of PALSAR data [10,36]. The relationship between LAI and biomass, which is strong for wetland vegetation habitats, has been elaborated by authors in previous studies and is described in papers [36][37][38].…”

Section: Prediction Of Leaf Area Indexmentioning

confidence: 79%

“…However, these satellite images have a spatial resolution of 1100 m, which was too low to classify the vegetation communities. We used high spatial resolution (but low temporal resolution) Landsat +ETM (Enhanced Thematic Mapper Plus), ALOS AVNIR-2 (Advanced Land Observing Satellite, Advanced Visible and Near Infrared Radiometer) and Terra ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), as well as medium resolution ENVISAT MERIS (ENVIronmental SATellite MEdium Resolution Imaging Spectrometer) satellite images, for the classification of wetland's vegetation habitats in our previous study [11,36,37]. The results were satisfying as satellite images registered in the optical spectrum are widely used for land use/land cover mapping.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2014

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Abstract: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 . Differences of almost double T s 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.

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Section: Prediction Of Leaf Area Indexmentioning

confidence: 79%

Section: Discussionmentioning

confidence: 99%

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

et al. 2014

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“…Dorigo et al [64] presented the evaluation of CCI (Climate Change Initiative) soil moisture product using ground based observations. Soil moisture for the Biebrza Wetland has been investigated by authors in a previous study using microwave data acquired by ERS-1/2 SAR VV, ENVISAT ASAR IS2 HH, and ALOS PALSAR HH [25,43,44,65,66]. In the present study Sentinel-1 IW VV and VH data have been applied for the assessment of soil moisture (there was no S1 acquisition in HH polarisation).…”

Section: Soil Moisturementioning

confidence: 99%

“…This was done for the wetland habitats distinguished from the classification of MERIS image. LAI prediction, applying ALOS PALSAR HV data, was presented by Dabrowska-Zielinska et al in [43,65,66]. The obtained results showed that with the increase of LAI there was an increase in the backscattering coefficient, which indicates the low signal attenuation by vegetation in L-band, contrary to results obtained in C-and X-band.…”

Section: Influence Of Lai and Soil Moisture On σ° Calculated From Senmentioning

confidence: 99%

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

Dąbrowska-Zielińska¹,

Budzynska²,

Tomaszewska³

et al. 2016

Preprint

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Abstract: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 (R 2 = 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 (R 2 = 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.

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“…Thus, mapping various biophysical parameters of wetlands has attracted interest in a variety of ecological, environmental studies in recent years, including their distribution, leaf area index, biomass, etc. [3][4][5]. Above ground biomass (AGB) has been frequently analyzed among these parameters, which is defined as the total biomass of foliage and woody components of a vegetation canopy above the ground level [6].…”

Section: Introductionmentioning

confidence: 99%

Mapping Above-Ground Biomass by Integrating Optical and SAR Imagery: A Case Study of Xixi National Wetland Park, China

Huang

Deng

et al. 2016

Remote Sensing

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Abstract:Wetlands are important ecosystems as they are known as the "kidney of the earth". Particularly, urban wetlands play an important role in providing both natural and social beneficial services. However, urban wetlands are suffering from various human impacts, such as excessive land use conversion, air and water pollution, especially those in developing countries undergoing rapid industrialization and urbanization. Therefore, it is of great necessity to derive timely biomass information for optimal design, management and protection of urban wetlands. In this paper, we develop a set of models for estimating above ground biomass (AGB) in Xixi National Wetland Park in Hangzhou, China by using optical images and Synthetic Aperture Radar (SAR) images. A series of vegetation indices (VIs) derived from optical data is introduced along with spectral data. The modeling methods consist of (1) curve estimation; (2) linear regression for multivariable model; (3) Back Propagation Neural Network (BPNN) modeling. Curve estimation is a combination of linear and nonlinear regressions. It is applied to generate AGB models from a single variable including Normalized Difference Vegetation Index (NDVI) and radar backscatter coefficient. The models are then compared via three accuracy metrics. According to the results, SAR models generally show better accuracy than optical models and BPNN models show the greatest accuracy among all the models. The BPNN model from the combination of Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and European Remote-Sensing Satellite-2 (ERS-2) SAR (Synthetic Aperture Radar) image has the least root mean square (RMSE, 0.396 kg/m 2 ), least mean absolute error (MAE, 0.256 kg/m 2 ) and the greatest correlation coefficient (0.974). The results indicate that AGB can be estimated by integrating optical and SAR imagery. Four maps of AGB are derived to illustrate the distribution of AGB in the study area. The total AGB in the study area is estimated to be between 165,000 and 210,000 kg/m 2 .

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Biophysical Parameters Assessed from Microwave and Optical Data

Cited by 10 publications

References 3 publications

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

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

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

Mapping Above-Ground Biomass by Integrating Optical and SAR Imagery: A Case Study of Xixi National Wetland Park, China

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