Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review

The coastal zone offers among the world’s most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land‐ and water‐related applications in coastal zones. Compared to optical satellites, cloud‐cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all‐weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud‐prone tropical and sub‐tropical climates. The canopy penetration capability with long radar wavelength enables L‐band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change‐induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L‐band SAR data for geoscientific analyses that are relevant for coastal land applications

HEIGHT MODEL INTEGRATION USING ALOS PALSAR, X SAR, SRTM C, AND ICESAT/GLAS

The scarcity of height models is one of the important issues in Indonesia. ALOS PALSAR, X SAR, SRTM C, and ICESAT/GLAS are free available global height models. Four data can be integrated the height models. Integration takes advantage of each characteristic data. The spatial resolution uses ALOS PALSAR. ICESAT/GLAS has a minimal height error because it is DTM. SAR has advantages of minimal error in the highland and need a low pass filter on the lowland. DSM uses X SAR and DEM from ALOS PALSAR. Characteristics and penetration of vegetation objects can be seen from the wavelength type of SAR data. This research aims to make height model integration in order to get the vertical accuracy better than vertical accuracy of global height models and minimum height error. The study area is located in Karo Regency. The first process is to crop the height models into Karo Regency, geoid undulation correction using EGM 2008. The next step is to detect pits and spires by using radius value 1000 m and depth +1.96σ (+5 m) with uncertainty 95,45%. Then generate HEM and height model integration. To know the accuracy of this height model, 100 reference points measured using GNSS, altimeter, and similar point observed on the height model integration are selected. The accuracy test covers RMSE, accuracy (z), and height difference test. The result of this study shows that the height model integration has a vertical accuracy in 1.14 m. This height model integration can be used for mapping scale 1: 10.0000

Feasibility Study for the Application of Synthetic Aperture Radar for Coastal Erosion Rate Quantification Across the Arctic

The applicability of optical satellite data to quantify coastal erosion across the Arctic is limited due to frequent cloud cover. Synthetic Aperture Radar (SAR) may provide an alternative. The interpretation of SAR data for coastal erosion monitoring in Arctic regions is, however, challenging due to issues of viewing geometry, ambiguities in scattering behavior and inconsistencies in acquisition strategies. In order to assess SAR applicability, we have investigated data acquired at three different wavelengths (X-, C-, L-band; TerraSAR-X, Sentinel-1, ALOS PALSAR 1/2). In a first step we developed a pre-processing workflow which considers viewing geometry issues (shoreline orientation, incidence angle relationships with respect to different landcover types). We distinguish between areas with foreshortening along cliffs facing the sensor, radar shadow along cliffs facing away and traditional land-water boundary discrimination. Results are compared to retrievals from Landsat trends. Four regions which feature high erosion rates have been selected. All three wavelengths have been investigated for Kay Point (Canadian Beaufort Sea Coast). C- and L-band have been studied at all sites, including also Herschel Island (Canadian Beaufort Sea Coast), Varandai (Barents Sea Coast, Russia), and Bykovsky Peninsula (Laptev Sea coast, Russia). Erosion rates have been derived for a 1-year period (2017–2018) and in case of L-band also over 11 years (2007–2018). Results indicate applicability of all wavelengths, but acquisitions need to be selected with care to deal with potential ambiguities in scattering behavior. Furthermore, incidence angle dependencies need to be considered for discrimination of the land-water boundary in case of L- and C-band. However, L-band has the lowest sensitivity to wave action and relevant future missions are expected to be of value for coastal erosion monitoring. The utilization of trends derived from Landsat is also promising for efficient long-term trend retrieval. The high spatial resolution of TerraSAR-X staring spot light mode (<1 m) also allows the use of radar shadow for cliff-top monitoring in all seasons. Derived retreat rates agree with rates available from other data sources, but the applicability for automatic retrieval is partially limited. The derived rates suggest an increase of erosion at all four sites in recent years, but uncertainties are also high

Mapping growing stock volume and biomass carbon storage of larch plantations in Northeast China with L-band ALOS PALSAR backscatter mosaics

Reliable spatial information on growing stock volume (GSV) and biomass is critical for creating management strategies for plantation forests. This study developed empirical models to map the GSV and biomass of larch plantations (LPs) in Northeast China (1.25 million km(2) total area) by integrating L-band synthetic aperture radar (SAR) data with ground-based survey data. The best correlation model was used to map the GSVs and biomasses of LPs. The total GSV and biomass carbon storage were estimated at 224.3 +/- 59.0 million m(3) and 113.0 +/- 29.7 x 10(12) g C with average densities of 85.1 m(3) ha(-1) and 42.9 10(6) g x C ha(-1), respectively, over a total area of 2.64 million ha. The saturation effect of SAR was determined beyond 260 m(3) ha(-1), which was expected to influence the estimations for a small proportion of the study area. The accuracy of the estimations has limitations mainly due to the uncertainties in the GSV inventories, discrimination of natural larch and the SAR dataset. Based on the mapping results of the GSVs of LPs, a planning strategy for multipurpose management was tentatively proposed. This study can inform policies and management practices to assure broader and sustainable benefits from plantation forests in the future.ArticleINTERNATIONAL JOURNAL OF REMOTE SENSING.39(22):7978-7997(2018)journal articl

Multi-temporal landslide activity investigation by spaceborne SAR interferometry: The case study of the Polish Carpathians

The main goal of this research is to verify the activity state of landslides provided by an existing landslide inventory map using Persistent Scatterers (PS) Interferometry (PSInSAR). The study was conducted in the Małopolskie municipality, a rural setting with sparse urbanization in the Polish Flysch Carpathians. PSInSAR has been applied using Synthetic Aperture Radar (SAR) data from ALOS PALSAR and Sentinel 1A/B with different acquisition geometries (ascending and descending orbit) to increase PS coverage and mitigate the geometric effects due to layover and shadowing. The Line-Of-Sight PSInSAR measurements were projected to the steepest slope, which allowed to homogenize the results from diverse acquisition modes and to compare the displacement velocities with different slope orientations. Additionally, landslide intensity (motion rate) and expected damage maps were generated and verified during field investigations. A high correlation between PSInSAR results and in-situ damage observations was confirmed. The activity state and landslide-related expected damage maps have been confirmed for 43 out of a total of 50 landslides investigated in the field. The short temporal baseline provided by both Sentinel satellites (1A/B data) increases the PS density significantly. The study substantiates the usefulness of SAR based landslide activity monitoring for land use and land development, even in rural areas

Assessing the performance of random forest regression for estimating canopy height in tropical dry forests

Accurate estimation of forest canopy height is essential for monitoring forest ecosystems and assessing their carbon storage potential. This study evaluates the effectiveness of different remote sensing techniques for estimating forest canopy height in tropical dry forests. Using field data and remote sensing data from airborne lidar and polarimetric synthetic aperture radar (SAR), a random forest (RF) model was developed to estimate canopy height based on different indices. Results show that the normalize difference build-up index (NDBI) has the highest correlation with canopy height, outperforming other indices such as relative vigor index (RVI) and polarimetric vertical and horizontal variables. The RF model with NDBI as input showed a good fit and predictive ability, with low concentration of errors around 0. These findings suggest that NDBI can be a useful tool for accurately estimating forest canopy height in tropical dry forests using remote sensing techniques, providing valuable information for forest management and conservation efforts

An evaluation of the ALOS PALSAR L-band backscatter—Above ground biomass relationship Queensland, Australia: Impacts of surface moisture condition and vegetation structure

Focusing on woody vegetation in Queensland, Australia, the study aimed to establish whether the relationship between Advanced Land Observing Satellite (ALOS) Phased Array L-band SAR (PALSAR) HH and HV backscattering coefficients and above ground biomass (AGB) was consistent within and between structural formations (forests, woodlands and open woodlands, including scrub). Across these formations, 2781 plot-based measurements (from 1139 sites) of tree diameters by species were collated, from which AGB was estimated using generic allometric equations. For Queensland, PALSAR fine beam dual (FBD) 50 m strip data for 2007 were provided through the Japanese Space Exploration Agency’s (JAXA) Kyoto and Carbon (K&C) Initiative, with up to 3 acquisitions available for each Reference System for Planning (RSP) paths. When individual strips acquired over Queensland were combined, ‘banding’ was evident within the resulting mosaics, with this attributed to enhanced L-band backscatter following rainfall events in some areas. Reference to Advanced Microwave Scanning Radiometer-EOS (AMSR-E) data indicated that strips with enhanced L-band backscatter corresponded to areas with increased effective vegetation water content kg m and, to a lesser extent, soil moisture g cm . Regardless of moisture conditions, L-band HV topographically normalized backscattering intensities backscatter increased asymptotically with AGB, with the saturation level being greatest for forests and least for open woodlands. However, under conditions of relative maximum surface moisture, L-band HV and HH was enhanced by as much as 2.5 and 4.0 dB respectively, particularly for forests of lower AGB, with this resulting in an overall reduction in dynamic range. The saturation level also reduced at L-band HH for forests and woodlands but remained similar for open woodlands. Differences in the rate of increase in both L-band HH and HV with AGB were observed between forests and the woodland categories (for both relatively wet and dry conditions) with these attributed, in part, to differences in the size class distribution and stem density between non-remnant (secondary) forests and remnant woodlands of lower AGB. The study concludes that PALSAR data acquired when surface moisture and rainfall are minimal allow better estimation of the AGB of woody vegetation and that retrieval algorithms ideally need to consider differences in surface moisture conditions and vegetation structure

Quantifying the aboveground biomass stock changes associated with oil palm expansion on tropical peatlands using plot-based methods and L-band radar

The recent rapid expansion of oil palm (OP, Elaeis guineensis) plantations into tropical forest peatlands has resulted in net ecosystem carbon emissions. However, quantifications of the net carbon flux from biomass changes require accurate estimates of the above ground biomass (AGB) accumulation rate of OP on peat in working plantations. Current efforts that aim to reduce the emissions from OP expansion would also benefit from the development of economically viable remote sensing approaches that enable the detection of OP plantation expansion and monitoring of AGB stocks across at a fine spatial and temporal resolution. Here, destructive harvest and non-destructive plot inventories are conducted across a chronosequence of OP planting blocks (3 to 12 years after planting (YAP)) in plantations on drained peat in Sarawak, Malaysia. The effectiveness of using a timeseries of L-band synthetic aperture radar (SAR) scenes (ALOS PALSAR-1/2) and a novel ‘biomass matching’ approach to detect, quantify and map the AGB stock changes associated with OP establishment and growth was then assessed. Peat specific allometric equations for palm (9 palms, R2 = 0.92) and frond biomass are developed and upscaled to estimate AGB at the plantation block-level (902 palms). Aboveground biomass stocks on peat accumulated at ~6.39 ± 1.12 Mg ha-1 per year in the first 12 years after planting. However, high inter-palm and inter-block AGB variability was observed in mature classes as a result of variations in palm leaning and mortality. The ‘biomass matching’ approach detected statistically significant deforestation associated with OP establishment. OP growth was well estimated between 4 and 10 YAP, however sensitivity to increases in AGB was lost at ~ 45 - 60 Mg ha. Validation of the allometric equations defined and expansion of non-destructive inventories across alternative plantations and age classes on peat would further strengthen our understanding of OP AGB accumulation rates. With further investigation into the relationship between OP structural characteristics and L-band radar cross section (RCS) in the HV and HH polarisations, ‘biomass matching’ could be a feasible tool for monitoring AGB stock changes to inform carbon emission mitigation strategies