Description: This paper describes a method of combined ultra-high resolution satellite imaging, unmanned aerial vehicle (UAV) photography, and sub-surface geophysical investigation for anomaly detection, which was employed in a non-invasive survey of three archaeological sites in Northern Mongolia. The surveyed sites were a Bronze Age burial mound, a Turkish period tomb, and a steppe city fortification of unknown origin. For the satellite survey, 50 cm resolution pan-sharpened imagery was generated through a combination of multispectral and panchromatic data, collected from the GeoEye-1 earth-sensing satellite. The imagery was then used to identify the location of the aforementioned sites in an approximate area of 3000 ${hbox{km}}^{2}$ . Aerial photographs of the sites were obtained with two customized electric-powered UAVs: a fixed flying wing rear-propulsion plane and a multi-propeller “oktokopter” helicopter system. Finally, geophysical investigation was conducted with a GSM-19 Overhouser gradiometer, an EM38 electromagnetometer, and an IDS Detector Duo ground penetrating radar. The satellite imagery and aerial photographs were combined with the geophysical survey results and on-site surface observations to provide insight and contextual information about anomalies in multiple layers of data. The results highlight the effectiveness and robustness of the employed method for archaeological investigation in large, rugged and scarcely populated areas.

Description: Knowledge of error characteristics of high resolution satellite rainfall data at different spatial/temporal scales is useful, especially when the scheduled Global Precipitation Mission (GPM) plans to provide High Resolution Precipitation Products (HRPPs) at global scales. Satellite rainfall data contain errors which need ground validation (GV) data for characterization, while satellite rainfall data will be most useful in the regions that are lacking in GV data. Therefore, a critical step to bridge this gap is to assess spatial interpolation schemes for transfer of the error characteristics from GV regions to non-GV regions. In this study, a comprehensive assessment of kriging methods for spatial transfer (interpolation) of error metrics is performed. Three kriging methods for spatial interpolation are compared, which are: ordinary kriging (OK), indicator kriging (IK) and disjunctive kriging (DK). Additional comparison with the simple inverse distance weighting (IDW) method is also performed to quantify the added benefit (if any) of using geostatistical methods. Four commonly used satellite rainfall error metrics are assessed for transfer to non-GV satellite gridboxes: Probability of Detection (POD) for rain, False Alarm Ratio (FAR), bias (BIAS), and Root Mean Squared Error (RMSE). Results show that performance of a kriging scheme is strongly sensitive to the timescale for which the errors are interpolated (monthly and weekly) wherein the extent of coverage by GV data plays an equally sensitive role. While most kriging techniques perform well according to correlation measure at climatologic timescales for a range of GV data coverage, only DK and OK appear to retain accuracy at the shorter timescales (monthly and weekly). However, scalar assessment metrics such as mean and standard deviation of error (i.e., difference between true and interpolated errors) reveal a completely different picture of accuracy of each interpolation method. In terms of such assessment measu- - res, the overall performance ranking of the kriging methods is as follows: ${rm OK}={rm DK}〉{rm IDW}〉{rm IK}$ . Assessment of kriging methods also revealed that the transfer accuracy is sensitive to error metric type. The ranking of error metrics with highest accuracy in transfer is: ${rm POD}〉{rm FAR}〉{rm RMSE}〉{rm BIAS}$ . Overall, the assessment of kriging methods revealed that these best linear unbiased spatial estimators may not be appropriate transfer methods for transfer of satellite rainfall error metrics at time scales shorter than a week. It is worthwhile now to pursue more non-linear transfer methods (such as neural networks) and other kriging methods that use additional spatial information on the rainfall process (such as co-kriging) to further constrain the interpolation uncertainty.

Description: This study develops a theoretical model to estimate the scaling error of the two-band ratio of red to near-infrared (TBRRN) in an inhomogeneous pixel. Three different imageries and a 3 $times$ 3 moving window are used to verify and approximately estimate the scaling error of the TBRRN for remote-sensing imageries. The datasets are Landsat Thematic Mapper (Landsat/TM) imagery taken on 15 October 2005, satellite probatoire d'Observation de la terre (SPOT) imagery taken on 7 September 2005, and moderate-resolution imaging spectroradiometer (MODIS) imagery taken on 5 October 2005 of the Yellow River Estuary. It is found that 1) about 15.70%, 17.24% and 26.52% of SPOT, Landsat/TM and MODIS pixels have relative scaling error higher than 2% respectively, 2) the average relative scaling error increases with increasing scale of the image pixel, and 3) it is difficult to achieve the goal of the National Aeronautics and Space Administration of obtaining valid ocean-color data of the world's oceans for estimating the chlorophyll-a concentration with uncertainty of less than 35% if the scaling error cannot be effectively reduced. Our results suggest the need for an in-depth study of scaling errors in water-color remote sensing.

Description: In this study, an atmospheric correction algorithm is designed for Landsat/TM imagery. The lookup tables with multiple scattering and polarization correction are used to remove the reflectance resulting from Rayleigh scattering. The Landsat/TM imageries collected on October 28, 2003, in Taihu Lake, water-leaving reflectance measured by synchronized experiments and the aerosol lookup table are used to estimate the aerosol optical thickness (AOT) and atmospheric diffuse transmittance from Landsat/TM imagery at 15 experimental stations. The inverse distance spatial interpolation algorithm (IDSIA) is used to improve the uncertainty produced by the non-homogeneous distribution of AOT. According to the study results carried out by this paper, it is found that using IDSIA to improve the spatial changes of AOT at least decreases 4.5% uncertainty at TM3 and 16.4% uncertainty at TM4 from non-homogeneous distribution of AOT. The improved performance of IDSIA is fairly obviously. Additionally, water-leaving reflectance of Landsat imageries is estimated by this atmospheric correction algorithm. The stability and accuracy validation results show that the estimation accuracy of water-leaving reflectance is 8.31% at TM1 and 9.56% at TM2, respectively.

Description: Avoiding spatial autocorrelation is the key to many research questions especially for field design, remote sensing data selection, and maximum spatial variation caption. Spatial variation across land cover types as well as the gradients inherent in ecotones can be captured in reflectance which is a spatially continuous variable. The spatial variation between reflectance values of any two pixels will depend on the lag distance beyond which pixels are no longer spatially autocorrelated. This paper demonstrates the utility of semivariogram for determining the lag distance in which pixels will be spatially autocorrelated. According to sampling theorem, objects should be sampled at half their width such that spatial resolution should be half of the semivariogram lag distance. As object-oriented classification is now the most broadly accepted classification method, scale parameter determination is the foremost important decision for determining the size of image objects. The scale parameter was adjusted during image segmentation to test how the size of image objects changed. The optimal scale parameter was chosen when the average distance between neighbouring image object centroids was near to the lag distance of the semivariogram. Results showed that the size of image objects reached a scaling threshold as the scale parameter was increased. When the scale parameter was adjusted to create image objects that exceeded this threshold, the segmentation was not able to accurately represent the spatial variation observed on the ground.

Description: As the number of operational wind scatterometers is getting smaller, other sources of spaceborne sensors are now included in global wind mapping. One of the prominent sensors is the Synthetic Aperture Radar (SAR). Besides serving as a generic scatterometer, SAR systems are the only type of radar systems that can provide sub-km resolution sea surface wind data and offers near shore mapping capability. This unique feature is important for assessing the offshore wind resources. As an important source of renewable energy, offshore wind farms are growing rapidly. Furthermore, recent research shows that the cross-polarization radar backscatter does not seem to saturate in high winds, and provides an excellent supplement for scatterometer wind sensing in storm conditions. The saturation issues of co-polarization radar returns have so far made it difficult to resolve wind speeds beyond roughly 20 m/s, or even less for lower incidence angles. The scope of this paper is to show the potential of RADARSAT-2's polarimetric modes for wind speed retrieval. RADARSAT-2 is the first operational fully polarimetric ( ${rm HH ~VV ~HV ~VH}$ ) C-band satellite. Standard Quad-pol images have been collected in the St. Lawrence Gulf and compared against the Mont-Louis buoy and QuikSCAT scatterometer data. Co-polarization wind speeds were computed with CMOD-5 algorithms. A few polarization ratios were tested to determine the most suitable one for RADARSAT-2's ${rm HH}$ polarization mode. For Cross-polarization, two different models were compared. Cross-polarization gives excellent results when wind exceeds 5 m/s. In general, SAR wind retrieval is suitable for resolution of 400 m.

Description: This study presents the possible application of the airborne Light Detection And Ranging (LiDAR) survey technique to extract a cadastral shoreline in South Korea for the first time. This paper summarizes the results of two case studies to compare the airborne LiDAR-derived shoreline with other data sources, such as the digital topographic map, the seamless cadastral map, and the cadastral surveying. In this current study, the well-known contouring method is used to extract shorelines in combination with LiDAR-derived Digital Elevation Models (DEMs) and digital aerial images. Approximate Highest High Water Level (AHHWL) published by the Korea Hydrographic and Oceanographic Administration (KHOA) is introduced as the tidal reference elevation. This is close to a legally defined shoreline for the cadastral shoreline mapping in South Korea. The comparison results show that some discrepancies are found between the applied methods mentioned due to the inconsistent tidal data references and the heterogeneous data sources. However, it is verified that the airborne LiDAR surveying can lead to this method's increasing applications in the cadastral shoreline mapping. This is only possible under conditions of a new guideline for LiDAR surveying and re-establishment of the tidal reference.

Description: When using dual-orthogonal frequency modulation continuous wave (FMCW) polarimetric radar, the polarimetric range-Doppler spectra of the atmospheric targets are contaminated by noise, clutter, and artificial signals that disturb the analysis of atmospheric target parameters, such as reflectivity, mean Doppler velocity, and Doppler width. This paper describes an improved clutter suppression method that is able to detect most atmospheric targets while suppressing clutter, noise, and artificial signals as well as possible. By using logical decisions that are based on the spectral linear depolarization ratios (SLDRs), a binary mask matrix is constructed, and the characteristics of this original mask matrix are improved via mathematical morphology methods. To suppress clutter, noise, and artificial signals and retain atmospheric targets, we multiply the range-Doppler spectra by the improved mask matrix. The raw atmospheric data are acquired using the Polarimetric Agile Radar S- And X-band (PARSAX) radar, which is able to measure the backscattering matrix of atmospheric targets in one sweep. After calibration, the range-Doppler polarimetric spectra are processed using the proposed clutter suppression method, and the atmospheric targets are successfully detected. Compared with the unprocessed data and the data processed by noise clipping and double SLDR filtering, the data processed by the proposed method show improvement in the ability to determine the atmospheric targets in the range-Doppler spectrogram, the reflectivity, the mean Doppler velocity, and the Doppler width.

Description: The work presented here tests an automatic procedure able to recognize the presence of built-up areas in the satellite images with the output nominal scale of 1:50,000. The input data is a set of 54 Ikonos and Quick Bird scenes considered as representative of the variety of human settlement patterns in large cities at global level. The methodology for automatic image information extraction is based on calculation of anisotropic rotation-invariant textural grey-level co-occurrence measures, also called PANTEX methodology. The total area analyzed covers 35,000 $hbox{km}^{2}$ . The data under test shows high variety in latitude, season, sun elevation and sun azimuth at the time of image data collection. The output of the automatic image information retrieval is evaluated by comparison with a collection of reference information visually interpreted from the same satellite data input. Two complementary evaluation strategies are presented here: i) interactive selection of one threshold level in the textural measurement and then unsupervised application of the same threshold level to all the datasets under test, and ii) per-scene optimization of the threshold based on the available reference samples. This work briefly summarizes the nature of the errors and implications for global settlement classification.

Description: Destructive earthquakes challenge Earth Observation (EO) systems to demonstrate their usefulness in supporting intervention and relief actions. The use of EO data in a disaster context has been widely investigated from a theoretical point of view, but only recently the developed methods seem to have reached near to the operational use. In this paper a case study on the April 6th, 2009 earthquake ( $M _{w} = 6.3$ ) event, which stroke L'Aquila, Italy, is presented and commented. Although damage to the city was not extremely extensive, the case is interesting because it was handled by the authors in a real-time, emergency context. A new data fusion approach, between SAR and optical data, has been proposed. It shows that optical data are more suitable to distinguish between damage and non-damage classes, while SAR textures features allow to better distinguishing different classes of damages at block scale such as low and heavy damage.