Advanced Land Observing Satellite Phased Array Type L-Band SAR (ALOS PALSAR) to Inform the Conservation of Mangroves: Sundarbans as a Case Study

Cornforth, William A.; Fatoyinbo, Lola; Freemantle, Terri; Pettorelli, Nathalie

doi:10.3390/rs5010224

Cited by 66 publications

(52 citation statements)

References 36 publications

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“…Santoro et al [51] who studied ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar) data found that better classification results were obtained from HV than from HH polarization. Dabrowska-Zielinska et al [43] and Conforth et al [52] also presented wetland classification using ALOS PALSAR data acquired in HV polarization. Flores De Santiago et al [53] found that a combination of HH and HV polarization modes from ALOS PALSAR data was better at separating forested wetlands using an object-based classification approach.…”

Section: Classification Of Wetland Vegetation Habitat Typesmentioning

confidence: 99%

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

et al. 2016

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Abstract:The objectives of the study were to determine the spatial rate of CO 2 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 CO 2 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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Section: Classification Of Wetland Vegetation Habitat Typesmentioning

confidence: 99%

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

et al. 2016

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“…According to Santoro et al [21], who studied ALOS PALSAR data, better classification results are obtained from HV-than from HH-polarization. Cornforth et al [22] illustrated the detection and mapping of mangroves based on ALOS PALSAR data. They found that HV polarization is more sensitive to vegetation structure and biomass than HH polarization.…”

Section: Classification Of Wetlands Vegetation Habitatsmentioning

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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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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“…SAR data have also shown potential in conservation planning. TerraSAR-X time series identified swath events in protected semi-natural grasslands within 11-day intervals (Schuster et al 2011), whereas changes in PALSAR backscatter data mapped coastline retreat and health degradation in a large mangrove forest (Cornforth et al 2013). …”

Section: Regulating Servicesmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.

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Advanced Land Observing Satellite Phased Array Type L-Band SAR (ALOS PALSAR) to Inform the Conservation of Mangroves: Sundarbans as a Case Study

Cited by 66 publications

References 36 publications

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

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

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

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

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