Imaging ionospheric inhomogeneities using spaceborne synthetic aperture radar

We present a technique and results of 2-D imaging of Faraday rotation and total electron content using spaceborne L band polarimetric synthetic aperture radar (PolSAR). The results are obtained by processing PolSAR data collected using the Phased Array type L-band Synthetic Aperture Radar (PALSAR) on board the Advanced Land Observation Satellite. Distinguished ionospheric inhomogeneities are captured in 2-D images from space with relatively high resolutions of hundreds of meters to a couple of kilometers in auroral-, middle-, and low-latitude regions. The observed phenomena include aurora-associated ionospheric enhancement arcs, the middle-latitude trough, traveling ionospheric disturbances, and plasma bubbles, as well as ionospheric irregularities. These demonstrate a new capability of spaceborne synthetic aperture radar that will not only provide measurements to correction of ionospheric effects in Earth science imagery but also significantly benefit ionospheric studies

Minimizing the residual topography effect on interferograms to improve DInSAR results: estimating land subsidence in Port-Said City, Egypt

The accurate detection of land subsidence rates in urban areas is important to identify damage-prone areas and provide decision-makers with useful information. Meanwhile, no precise measurements of land subsidence have been undertaken within the coastal Port-Said City in Egypt to evaluate its hazard in relationship to sea-level rise. In order to address this shortcoming, this work introduces and evaluates a methodology that substantially improves small subsidence rate estimations in an urban setting. Eight ALOS/PALSAR-1 scenes were used to estimate the land subsidence rates in Port-Said City, using the Small BAse line Subset (SBAS) DInSAR technique. A stereo pair of ALOS/PRISM was used to generate an accurate DEM to minimize the residual topography effect on the generated interferograms. A total of 347 well distributed ground control points (GCP) were collected in Port-Said City using the leveling instrument to calibrate the generated DEM. Moreover, the eight PALSAR scenes were co-registered using 50 well-distributed GCPs and used to generate 22 interferogram pairs. These PALSAR interferograms were subsequently filtered and used together with the coherence data to calculate the phase unwrapping. The phase-unwrapped interferogram-pairs were then evaluated to discard four interferograms that were affected by phase jumps and phase ramps. Results confirmed that using an accurate DEM (ALOS/PRISM) was essential for accurately detecting small deformations. The vertical displacement rate during the investigated period (2007–2010) was estimated to be −28 mm. The results further indicate that the northern area of Port-Said City has been subjected to higher land subsidence rates compared to the southern area. Such land subsidence rates might induce significant environmental changes with respect to sea-level rise

Mapping forests in monsoon Asia with ALOS PALSAR 50-m mosaic images and MODIS imagery in 2010.

Extensive forest changes have occurred in monsoon Asia, substantially affecting climate, carbon cycle and biodiversity. Accurate forest cover maps at fine spatial resolutions are required to qualify and quantify these effects. In this study, an algorithm was developed to map forests in 2010, with the use of structure and biomass information from the Advanced Land Observation System (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) mosaic dataset and the phenological information from MODerate Resolution Imaging Spectroradiometer (MOD13Q1 and MOD09A1) products. Our forest map (PALSARMOD50 m F/NF) was assessed through randomly selected ground truth samples from high spatial resolution images and had an overall accuracy of 95%. Total area of forests in monsoon Asia in 2010 was estimated to be ~6.3 × 10(6 )km(2). The distribution of evergreen and deciduous forests agreed reasonably well with the median Normalized Difference Vegetation Index (NDVI) in winter. PALSARMOD50 m F/NF map showed good spatial and areal agreements with selected forest maps generated by the Japan Aerospace Exploration Agency (JAXA F/NF), European Space Agency (ESA F/NF), Boston University (MCD12Q1 F/NF), Food and Agricultural Organization (FAO FRA), and University of Maryland (Landsat forests), but relatively large differences and uncertainties in tropical forests and evergreen and deciduous forests

Forest cover estimation in Ireland using radar remote sensing: a comparative analysis of forest cover assessment methodologies

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting

Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches

Accurate inventories of grasslands are important for studies of carbon dynamics, biodiversity conservation and agricultural management. For regions with persistent cloud cover the use of multi-temporal synthetic aperture radar (SAR) data provides an attractive solution for generating up-to-date inventories of grasslands. This is even more appealing considering the data that will be available from upcoming missions such as Sentinel-1 and ALOS-2. In this study, the performance of three machine learning algorithms; Random Forests (RF), Support Vector Machines (SVM) and the relatively underused Extremely Randomised Trees (ERT) is evaluated for discriminating between grassland types over two large heterogeneous areas of Ireland using multi-temporal, multi-sensor radar and ancillary spatial datasets. A detailed accuracy assessment shows the efficacy of the three algorithms to classify different types of grasslands. Overall accuracies ≥ 88.7% (with kappa coefficient of 0.87) were achieved for the single frequency classifications and maximum accuracies of 97.9% (kappa coefficient of 0.98) for the combined frequency classifications. For most datasets, the ERT classifier outperforms SVM and RF

Characterizing degradation of palm swamp peatlands from space and on the ground: an exploratory study in the Peruvian Amazon

Peru has the fourth largest area of peatlands in the Tropics. Its most representative land cover on peat is a Mauritia flexuosa dominated palm swamp (thereafter called dense PS), which has been under human pressure over decades due to the high demand for the M. flexuosa fruit often collected by cutting down the entire palm. Degradation of these carbon dense forests can substantially affect emissions of greenhouse gases and contribute to climate change. The first objective of this research was to assess the impact of dense PS degradation on forest structure and biomass carbon stocks. The second one was to explore the potential of mapping the distribution of dense PS with different degradation levels using remote sensing data and methods. Biomass stocks were measured in 0.25 ha plots established in areas of dense PS with low (n = 2 plots), medium (n = 2) and high degradation (n = 4). We combined field and remote sensing data from the satellites Landsat TM and ALOS/PALSAR to discriminate between areas typifying dense PS with low, medium and high degradation and terra firme, restinga and mixed PS (not M. flexuosa dominated) forests. For this we used a Random Forest machine learning classification algorithm. Results suggest a shift in forest composition from palm to woody tree dominated forest following degradation. We also found that human intervention in dense PS translates into significant reductions in tree carbon stocks with initial (above and below-ground) biomass stocks (135.4 ± 4.8 Mg C ha−1) decreased by 11 and 17% following medium and high degradation. The remote sensing analysis indicates a high separability between dense PS with low degradation from all other categories. Dense PS with medium and high degradation were highly separable from most categories except for restinga forests and mixed PS. Results also showed that data from both active and passive remote sensing sensors are important for the mapping of dense PS degradation. Overall land cover classification accuracy was high (91%). Results from this pilot analysis are encouraging to further explore the use of remote sensing data and methods for monitoring dense PS degradation at broader scales in the Peruvian Amazon. Providing precise estimates on the spatial extent of dense PS degradation and on biomass and peat derived emissions is required for assessing national emissions from forest degradation in Peru and is essential for supporting initiatives aiming at reducing degradation activities

Damage mapping after the 2017 Puebla Earthquake in Mexico using high-resolution Alos2 Palsar2 data

On September 19, 2017, the Mw7.1 Puebla Earthquake caused significant destruction in several cities in central Mexico. In this paper, two pre- and one post-event ALOS2-PALSAR2 data were used to detect the damaged area around Izucar de Matamoros town in Mexico. First, we identify the built-up areas using pre-event data. Second, we evaluate the earthquake-induced damage areas using an RGB color-coded image constructed from the pre- and co-event coherence images. Our analysis showed that the green and red bands display a great potential to discriminate the damaged areas.Accepted manuscrip

Using GPS as a reference frame for SAR images applied to a post eruptive period for Okmok Volcano, Aleutian Islands, Alaska

While high spatial coverage makes InSAR a popular tool to study active volcanoes its use can possess challenges for certain environments. Volcanoes along Alaska's Aleutian chain are difficult targets for InSAR as their seasonal snow cover causes decorrelation close to the volcanic caldera, their exposed location in the North Pacific renders them prone to severe atmospheric phase artifacts, and their location on small islands prevents the selection of suitable reference points necessary for deformation analysis. Existing GPS networks define a known reference frame in which SAR is better understood. Okmok volcano is one of the most active volcanoes in the Aleutian Island Chain and shows significant non-linear deformation behavior as it progresses through its eruption cycles. A stack of L-band imagery acquired by the SAR sensor PALSAR on board the JAXA Advanced Land Observing Satellite produced a post eruption deformation time series between August 2008 and October 2010. This data along with a merged DEM comprised of AirSAR SRTM and Worldview-1 stereo pair data, and GPS data from 3 continuous and 3 post eruption campaign sites was used for this study. In this research, a comparison and combination of InSAR and GPS time-series data will be presented aimed at the following research goals: 1) What is the accuracy and precision of InSAR-derived deformation estimates in such challenging environments; 2) How accurate can the deformation of the InSAR reference point be estimated from a joint analysis of InSAR and GPS deformation signals; 3) How non-linear volcanic deformation can be constrained by the measurements of a local GPS network and support the identification of residual atmospheric signals in InSAR-derived deformation time series. Further research into the combination of GPS and InSAR applied to the nonlinear aspect of volcanic deformation can enhance geodetic modeling of the volcano and associated eruption processes