Rainfall intensity plays an important role in landslide prediction especially in mountain areas. However, the rainfall intensity of a location is usually interpolated from rainfall recorded at nearby gauges without considering any possible effects of topographic slopes. In order to obtain reliable rainfall intensity for disaster mitigation, this study proposes a rainfall-vector projection method for topographic-corrected rainfall. The topographic-corrected rainfall is derived from wind speed, terminal velocity of raindrops, and topographical factors from digital terrain model. In addition, scatter plot was used to present landslide distribution with two triggering factors and kernel density analysis is adopted to enhance the perception of the distribution. Numerical analysis is conducted for a historic event, typhoon Mindulle, which occurred in 2004, in a location in central Taiwan. The largest correction reaches 11%, which indicates that topographic correction is significant. The corrected rainfall distribution is then applied to the analysis of landslide triggering factors. The result with corrected rainfall distribution provides better agreement with the actual landslide occurrence than the result without correction. Full article

Earthquake is one of the dominant triggering factors of landslides. Given the wide areas covered by mega earthquake-triggered landslides, their inventory requires development of automatic or semi-automatic methods applied to satellite imagery. A detection method is here proposed for this purpose, to fit with simple datasets; SPOT5 panchromatic images of 5 m resolution coupled with a freely and globally available DEM. The method takes advantage of multi-temporal images to detect changes based on radiometric variations after precise coregistration/orthorectification. Removal of false alarms is then undertaken using shape, orientation and radiometric properties of connected pixels defining objects. 80% of the landslides and 93% of the landslide area are detected indicating small omission errors but 50% of false alarms remain. They are removed using expert based analysis of the inventory. The method is applied to realize the first comprehensive inventory of landslides triggered by the Pisco earthquake (Peru, 15/08/2007, Mw 8.0) over an area of 27,000 km2. 866 landslides larger than 100 m2 are detected covering a total area of 1.29 km2. The area/number distribution follows a power-law with an exponent of 1.63, showing a very particular regime of triggering in this arid environment compared to other areas in the world. This specific triggering can be explained by the little soil cover in the coastal and forearc regions of Peru. Analysis of this database finally shows a major control of the topography (both orientation and inclination) on the repartition of the Pisco-triggered landslides. Full article

The monitoring of herbaceous fuel moisture content is a crucial activity in order to assess savanna fire risks. Faced with the difficulty of managing wide areas of vegetated surfaces, remote sensing appears an attractive alternative for terrestrial measurements because of its advantages related to temporal resolution and spatial coverage. Earth observation (EO)-based vegetation indices (VIs) and the ratio between Normalized Difference Vegetation Index (NDVI) and surface temperature (ST) were used for assessment of herbaceous fuel moisture content estimates and validated against herbaceous data collected in 2010 at three open savanna sites located in Senegal, West Africa. EO-based estimates of water content were more consistent with the use of VI as compared to the ratio NDVI/ST. Different VIs based on near-infrared (NIR) and shortwave infrared (SWIR) reflectance were tested and a consistent relationship was found between field measurements of leaf equivalent water thickness (EWT) from all test sites and Normalized Difference Infrared Index (NDII), Global Vegetation Moisture Index (GVMI) and Moisture Stress Index (MSI). Also, strong relationships were found between fuel moisture content (FMC) and VIs for the sites separately; however, they were weaker for the pooled data. The correlations between EWT/FMC and VIs were found to decrease progressively as the woody cover increased. Although these results suggest that NIR and SWIR reflectance can be used for the estimation of herbaceous water content, additional validation from an increased number of study sites is necessary to study the robustness of such indices for a larger variety of savanna vegetation types. Full article

The need for an efficient and standard technique for optimal spectral sampling of hyperspectral data during the inversion of canopy reflectance models has been the subject of many studies. The objective of this study was to investigate the utility of the discrete wavelet transform (DWT) for extracting useful features from hyperspectral data with which forest LAI can be estimated through inversion of a three dimensional radiative transfer model, the Discrete Anisotropy Radiative Transfer (DART) model. DART, coupled with the leaf optical properties model PROSPECT, was inverted with AVIRIS data using a look-up-table (LUT)-based inversion approach. We used AVIRIS data and in situ LAI measurements from two different hardwood forested sites in Wisconsin, USA. Prior to inversion, model-simulated and AVIRIS hyperspectral data were transformed into discrete wavelet coefficients using Haar wavelets. The LUT inversion was performed with three different datasets, the original reflectance bands, the full set of wavelet extracted features, and two wavelet subsets containing 99.99% and 99.0% of the cumulative energy of the original signal. The energy subset containing 99.99% of the cumulative signal energy provided better estimates of LAI (RMSE = 0.46, R² = 0.77) than the original spectral bands (RMSE = 0.60, R² = 0.47). The results indicate that the discrete wavelet transform can increase the accuracy of LAI estimates by improving the LUT-based inversion of DART (and, potentially, by implication, other terrestrial radiative transfer models) using hyperspectral data. The improvement in accuracy of LAI estimates is potentially due to different properties of wavelet analysis such as multi-scale representation, dimensionality reduction, and noise removal. Full article

Recent satellite data and modeling studies indicate a pronounced role Tropical Instability Waves (TIW)-induced wind feedback plays in the tropical Pacific climate system. Previously, remotely sensed data were used to derive a diagnostic model for TIW-induced wind stress perturbations (τ_TIW), which was embedded into an ocean general circulation model (OGCM) to take into account TIW-induced ocean-atmosphere coupling in the tropical Pacific. While the previous paper by Zhang (2013) is concerned with the effect on the mean ocean state, the present paper is devoted to using the embedded system to examine the effects on TIW activity in the ocean, with τ_TIW being interactively determined from TIW-scale sea surface temperature (SST_TIW) fields generated in the OGCM, written as τ_TIW = α_TIW·F(SST_TIW), where α_TIW is a scalar parameter introduced to represent the τ_TIW forcing intensity. Sensitivity experiments with varying α_TIW (representing TIW-scale wind feedback strength) are performed to illustrate a negative feedback induced by TIW-scale air-sea coupling and its relationship with TIW variability in the ocean. Consistent with previous modeling studies, TIW wind feedback tends to have a damping effect on TIWs in the ocean, with a general inverse relationship between the τ_TIW intensity and TIWs. It is further shown that TIW-scale coupling does not vary linearly with α_TIW: the coupling increases linearly with intensifying τ_TIW forcing at low values of α_TIW (in a weak τ_TIW forcing regime); it becomes saturated at a certain value of α_TIW; it decreases when α_TIW goes above a threshold value as the τ_TIW forcing increases further. This work presents a clear demonstration of using satellite data to effectively represent TIW-scale wind feedback and its multi-scale interactions with large-scale ocean processes in the tropical Pacific. Full article

WorldView commercial imaging satellites comprise a constellation developed by DigitalGlobe Inc. (Longmont, CO, USA). Worldview-3 (WV-3), currently planned for launch in 2014, will have 8 spectral bands in the Visible and Near-Infrared (VNIR), and an additional 8 bands in the Short-Wave-Infrared (SWIR); the approximately 1.0–2.5 μm spectral range. WV-3 will be the first commercial system with both high spatial resolution and multispectral SWIR capability. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data collected at 3 m spatial resolution with 86 SWIR bands having 10 nm spectral resolution were used to simulate the new WV-3 SWIR data. AVIRIS data were converted to reflectance, geographically registered, and resized to the proposed 3.7 and 7.5 m spatial resolutions. WV-3 SWIR band pass functions were used to spectrally resample the data to the proposed 8 SWIR bands. Characteristic reflectance signatures extracted from the data for known mineral locations (endmembers) were used to map spatial locations of specific minerals. The WV-3 results, when compared to spectral mapping using the full AVIRIS SWIR dataset, illustrate that the WV-3 spectral bands should permit identification and mapping of some key minerals, however, minerals with similar spectral features may be confused and will not be mapped with the same detail as using hyperspectral systems. The high spatial resolution should provide detailed mapping of complex alteration mineral patterns not achievable by current multispectral systems. The WV-3 simulation results are promising and indicate that this sensor will be a significant tool for geologic remote sensing. Full article

In the areas with steep topography and vulnerable geological condition, landslide deformation monitoring is an important task for risk assessment and management. Differential Synthetic-Aperture Radar interferometry (D-InSAR) and Persistent Scatterer Interferometry (PS-InSAR) are two advanced SAR Interferometry techniques for detection, analysis and monitoring of slow moving landslides. The techniques can be used to identify wide displacement areas and measure displacement rates over long time series with millimeter-level accuracy. In this paper, multiple SAR datasets of Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) and Environmental Satellite (ENVISAT) C-band Advanced Synthetic Aperture Radar (ASAR) are used for landslide monitoring with both D-InSAR and PS-InSAR techniques in Badong at the Three Gorges area in China. Two areas of significant deformation along the southern riverbank of Yangtze River in Badong are identified by joint analyses of PS-InSAR results from different data stacks. Furthermore, both qualitative and quantitative evaluations of the PS-InSAR results are carried out together with preliminary correlation analysis between the time series deformation of a PS point in high risk location and the temporal variation of water level in the Three Gorges Reservoir. Full article

An active landslide in Doren, Austria, has been studied by multitemporal airborne and terrestrial laser scanning from 2003 to 2012. To evaluate the changes, we have determined the 3D motion using the range flow algorithm, an established method in computer vision, but not yet used for studying landslides. The generated digital terrain models are the input for motion estimation; the range flow algorithm has been combined with the coarse-to-fine resolution concept and robust adjustment to be able to determine the various motions over the landslide. The algorithm yields fully automatic dense 3D motion vectors for the whole time series of the available data. We present reliability measures for determining the accuracy of the estimated motion vectors, based on the standard deviation of components. The differential motion pattern is mapped by the algorithm: parts of the landslide show displacements up to 10 m, whereas some parts do not change for several years. The results have also been compared to pointwise reference data acquired by independent geodetic measurements; reference data are in good agreement in most of the cases with the results of range flow algorithm; only some special points (e.g., reflectors fixed on trees) show considerably differing motions. Full article

Remotely sensed imagery is proving to be a useful tool in estimating water depths in coastal zones. On the other hand, many coastal zone studies in the southern part of the Caspian Sea are only concerned with areas of shallow water and would benefit from easily updated bathymetric estimates. In this study, we tested three different methods for extracting bathymetry information from Landsat 5 data in the southeastern Caspian Sea, Iran. The first method used was a single band algorithm (SBA), utilizing either blue or red bands. The second method was principal components analysis (PCA), and the third method was the multi-layer perceptron (back propagation) neural network between visible bands and one output neuron (bathymetry). This latter MLP-ANNs method produced the best depth estimates (r = 0.94). The single band algorithm utilizing a red band also produced reasonably accurate results (r = 0.66), while the blue band algorithm and PCA did not perform (correlation between the estimated and measured depths 0.49 and 0.21, respectively). Furthermore, the shallow waters have negative influences on the accuracy of bathymetric modeling, thus the correction of data in these shallow waters is challenged by the presence of continental aerosols, bottom reflectance, and adjacency of land. Full article

Radiometric precision is difficult to maintain in orbital images due to several factors (atmospheric conditions, Earth-sun distance, detector calibration, illumination, and viewing angles). These unwanted effects must be removed for radiometric consistency among temporal images, leaving only land-leaving radiances, for optimum change detection. A variety of relative radiometric correction techniques were developed for the correction or rectification of images, of the same area, through use of reference targets whose reflectance do not change significantly with time, i.e., pseudo-invariant features (PIFs). This paper proposes a new technique for radiometric normalization, which uses three sequential methods for an accurate PIFs selection: spectral measures of temporal data (spectral distance and similarity), density scatter plot analysis (ridge method), and robust regression. The spectral measures used are the spectral angle (Spectral Angle Mapper, SAM), spectral correlation (Spectral Correlation Mapper, SCM), and Euclidean distance. The spectral measures between the spectra at times t1 and t2 and are calculated for each pixel. After classification using threshold values, it is possible to define points with the same spectral behavior, including PIFs. The distance and similarity measures are complementary and can be calculated together. The ridge method uses a density plot generated from images acquired on different dates for the selection of PIFs. In a density plot, the invariant pixels, together, form a high-density ridge, while variant pixels (clouds and land cover changes) are spread, having low density, facilitating its exclusion. Finally, the selected PIFs are subjected to a robust regression (M-estimate) between pairs of temporal bands for the detection and elimination of outliers, and to obtain the optimal linear equation for a given set of target points. The robust regression is insensitive to outliers, i.e., observation that appears to deviate strongly from the rest of the data in which it occurs, and as in our case, change areas. New sequential methods enable one to select by different attributes, a number of invariant targets over the brightness range of the images. Full article

We developed and evaluated a new approach for mapping rubber plantations and natural forests in one of Southeast Asia’s biodiversity hot spots, Xishuangbanna in China. We used a one-year annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS), Enhanced Vegetation Index (EVI) and short-wave infrared (SWIR) reflectance data to develop phenological metrics. These phenological metrics were used to classify rubber plantations and forests with the Random Forest classification algorithm. We evaluated which key phenological characteristics were important to discriminate rubber plantations and natural forests by estimating the influence of each metric on the classification accuracy. As a benchmark, we compared the best classification with a classification based on the full, fitted time series data. Overall classification accuracies derived from EVI and SWIR time series alone were 64.4% and 67.9%, respectively. Combining the phenological metrics from EVI and SWIR time series improved the accuracy to 73.5%. Using the full, smoothed time series data instead of metrics derived from the time series improved the overall accuracy only slightly (1.3%), indicating that the phenological metrics were sufficient to explain the seasonal changes captured by the MODIS time series. The results demonstrate a promising utility of phenological metrics for mapping and monitoring rubber expansion with MODIS. Full article

Terrestrial laser scanners (TLS) allow large and complex landforms to be rapidly surveyed at previously unattainable point densities. Many change detection methods have been employed to make use of these rich data sets, including cloud to mesh (C2M) comparisons and Multiscale Model to Model Cloud Comparison (M3C2). Rather than use simulated point cloud data, we utilized a 58 scan TLS survey data set of the Selawik retrogressive thaw slump (RTS) to compare C2M and M3C2. The Selawik RTS is a rapidly evolving permafrost degradation feature in northwest Alaska that presents challenging survey conditions and a unique opportunity to compare change detection methods in a difficult surveying environment. Additionally, this study considers several error analysis techniques, investigates the spatial variability of topographic change across the feature and explores visualization techniques that enable the analysis of this spatiotemporal data set. C2M reports a higher magnitude of topographic change over short periods of time (~12 h) and reports a lower magnitude of topographic change over long periods of time (~four weeks) when compared to M3C2. We found that M3C2 provides a better accounting of the sources of uncertainty in TLS change detection than C2M, because it considers the uncertainty due to surface roughness and scan registration. We also found that localized areas of the RTS do not always approximate the overall retreat of the feature and show considerable spatial variability during inclement weather; however, when averaged together, the spatial subsets approximate the retreat of the entire feature. New data visualization techniques are explored to leverage temporally and spatially continuous data sets. Spatially binning the data into vertical strips along the headwall reduced the spatial complexity of the data and revealed spatiotemporal patterns of change. Full article

Mapping and monitoring forest extent is a common requirement of regional forest inventories and public land natural resource management, including in Australia. The state of Victoria, Australia, has approximately 7.2 million hectares of mostly forested public land, comprising ecosystems that present a diverse range of forest structures, composition and condition. In this paper, we evaluate the performance of the Random Forest (RF) classifier, an ensemble learning algorithm that has recently shown promise using multi-spectral satellite sensor imagery for large area feature classification. The RF algorithm was applied using selected Landsat Thematic Mapper (TM) imagery metrics and auxiliary terrain and climatic variables, while the reference data was manually extracted from systematically distributed plots of sample aerial photography and used for training (75%) and accuracy (25%) assessment. The RF algorithm yielded an overall accuracy of 96% and a Kappa statistic of 0.91 (confidence interval (CI) 0.909–0.919) for the forest/non-forest classification model, given a Kappa maximised binary threshold value of 0.5. The area under the receiver operating characteristic plot produced a score of 0.91, also indicating high model performance. The framework described in this study contributes to the operational deployment of a robust, but affordable, program, able to collate and process large volumes of multi-sourced data using open-source software for the production of consistent and accurate forest cover maps across the full spectrum of Victorian sclerophyll forest types. Full article

This paper examines a land surface solar radiation partitioning scheme, i.e., that of the Community Land Model version 4 (CLM4) with coupled carbon and nitrogen cycles. Taking advantage of a unique 30-year fraction of absorbed photosynthetically active radiation (FPAR) dataset, derived from the Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) data set, multiple other remote sensing datasets, and site level observations, we evaluated the CLM4 FPAR’s seasonal cycle, diurnal cycle, long-term trends, and spatial patterns. Our findings show that the model generally agrees with observations in the seasonal cycle, long-term trends, and spatial patterns, but does not reproduce the diurnal cycle. Discrepancies also exist in seasonality magnitudes, peak value months, and spatial heterogeneity. We identify the discrepancy in the diurnal cycle as, due to, the absence of dependence on sun angle in the model. Implementation of sun angle dependence in a one-dimensional (1-D) model is proposed. The need for better relating of vegetation to climate in the model, indicated by long-term trends, is also noted. Evaluation of the CLM4 land surface solar radiation partitioning scheme using remote sensing and site level FPAR datasets provides targets for future development in its representation of this naturally complicated process. Full article

Offshore wind resource maps for the coastal waters off Shirahama, Japan were made based on 104 images of the Advanced Synthetic Aperture Radar (ASAR) onboard the ENVISAT satellite. Wind speed fields were derived from the SAR images with the geophysical model function CMOD5.N. Mean wind speed and energy density were estimated using the Weibull distribution function. These accuracies were examined in comparison with in situ measurements from the Shirahama offshore platform and the Southwest Wakayama buoy (SW-buoy). Firstly, it was found that the SAR-derived 10 m-height wind speed had a bias of 0.52 m/s and a RMSE of 2.33 m/s at Shirahama. Secondly, it was found that the mean wind speeds estimated from SAR images and the Weibull distribution function were overestimated at both sites. The ratio between SAR-derived and in situ measured mean wind speeds at Shirahama is 1.07, and this value was used for a long-term bias correction in the SAR-derived wind speed. Finally, mean wind speed and wind energy density maps at 80 m height were made based on the corrected SAR-derived 10 m-height wind speeds and the ratio U₈₀/U₁₀ calculated from the mesoscale meteorological model WRF. Full article

In the Sudano-Sahelian areas of Africa droughts can have serious impacts on natural resources, and therefore land surface moisture is an important factor. Insufficient conventional sites for monitoring land surface moisture make the use of Earth Observation data for this purpose a key issue. In this study we explored the potential of using reflectance data in the Red, Near Infrared (NIR), and Shortwave Infrared (SWIR) spectral regions for detecting short term variations in land surface moisture in the Sahel, by analyzing data from three test sites and observations from the geostationary Meteosat Second Generation (MSG) satellite. We focused on responses in surface reflectance to soil- and surface moisture for bare soil and early to mid- growing season. A method for implementing detrended time series of the Shortwave Infrared Water Stress Index (SIWSI) is examined for detecting variations in vegetation moisture status, and is compared to detrended time series of the Normalized Difference Vegetation Index (NDVI). It was found that when plant available water is low, the SIWSI anomalies increase over time, while the NDVI anomalies decrease over time, but less systematically. Therefore SIWSI may carry important complementary information to NDVI in terms of vegetation water status, and can provide this information with the unique combination of temporal and spatial resolution from optical geostationary observations over Sahel. However, the relation between SIWSI anomalies and periods of water stress were not found to be sufficiently robust to be used for water stress detection. Full article

The analysis of experimental data collected by X-band SAR of COSMO-SkyMed (CSK®) and TerraSAR-X (TSX) images on the same surface types has shown significant differences in the signal level of the two sensors. In order to investigate the possibility of combining data from the two instruments, a study was carried out by comparing images collected with similar orbital and sensor parameters (e.g., incidence angle, polarization, look angle) at approximately the same date on two Italian agricultural test sites. Several homogenous agricultural fields within the observed area common to the two sensors were selected. Some forest plots have also been considered and used as a reference target). Direct comparisons were then performed between CSK and TSX images in different acquisition modes. The analysis carried out on the agricultural fields showed that, in general, the backscattering coefficient is higher in TSX Stripmap images with respect to CSK-Himage (about 3 dB), while CSK-Ping Pong data showed values lower than TSX of about 4.8 dB. Finally, a difference in backscattering of about 2.5 dB was pointed out between CSK-Himage and Ping-Pong images on agricultural fields. These results, achieved on bare soils, have also been compared with simulations performed by using the Advanced Integral Equation Model (AIEM). Full article

In this study, two different methods were applied to derive daily and monthly sunshine duration based on high-resolution satellite products provided by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring using data from Meteosat Second Generation (MSG) SEVIRI (Spinning Enhanced Visible and Infrared Imager). The satellite products were either hourly cloud type or hourly surface incoming direct radiation. The satellite sunshine duration estimates were not found to be significantly different using the native 15-minute temporal resolution of SEVIRI. The satellite-based sunshine duration products give additional spatial information over the European continent compared with equivalent in situ-based products. An evaluation of the satellite sunshine duration by product intercomparison and against station measurements was carried out to determine their accuracy. The satellite data were found to be within ±1 h/day compared to high-quality Baseline Surface Radiation Network or surface synoptic observations (SYNOP) station measurements. The satellite-based products differ more over the oceans than over land, mainly because of the treatment of fractional clouds in the cloud type-based sunshine duration product. This paper presents the methods used to derive the satellite sunshine duration products and the performance of the different retrievals. The main benefits and disadvantages compared to station-based products are also discussed. Full article

This study presents a method for the reconstruction of pixels contaminated by optical thick clouds in multi-spectral Landsat images using multi-frequency SAR data. A number of reconstruction techniques have already been proposed in the scientific literature. However, all of the existing techniques have certain limitations. In order to overcome these limitations, we expose the Closest Spectral Fit (CSF) method proposed by Meng et al. to a new, synergistic approach using optical and SAR data. Therefore, the term Closest Feature Vector (CFV) is introduced. The technique facilitates an elegant way to avoid radiometric distortions in the course of image reconstruction. Furthermore the cloud cover removal is independent from underlying land cover types and assumptions on seasonality, etc. The methodology is applied to mono-temporal, multi-frequency SAR data from TerraSAR-X (X-Band), ERS (C-Band) and ALOS Palsar (L-Band). This represents a way of thinking about Radar data not as foreign, but as additional data source in multi-spectral remote sensing. For the assessment of the image restoration performance, an experimental framework is established and a statistical evaluation protocol is designed. The results show the potential of a synergistic usage of multi-spectral and SAR data to overcome the loss of data due to cloud cover. Full article

Estimates of above ground biomass density in forests are crucial for refining global climate models and understanding climate change. Although data from field studies can be aggregated to estimate carbon stocks on global scales, the sparsity of such field data, temporal heterogeneity and methodological variations introduce large errors. Remote sensing measurements from spaceborne sensors are a realistic alternative for global carbon accounting; however, the uncertainty of such measurements is not well known and remains an active area of research. This article describes an effort to collect field data at the Harvard and Howland Forest sites, set in the temperate forests of the Northeastern United States in an attempt to establish ground truth forest biomass for calibration of remote sensing measurements. We present an assessment of the quality of ground truth biomass estimates derived from three different sets of diameter-based allometric equations over the Harvard and Howland Forests to establish the contribution of errors in ground truth data to the error in biomass estimates from remote sensing measurements. Full article

Terrestrial laser scanners (TLS) have the potential to revolutionise measurement of the three-dimensional structure of vegetation canopies for applications in ecology, hydrology and climate change. This potential has been the subject of recent research that has attempted to measure forest biophysical variables from TLS data, and make comparisons with two-dimensional data from hemispherical photography. This research presents a systematic comparison between forest canopy gap fraction estimates derived from TLS measurements and hemispherical photography. The TLS datasets used in the research were obtained between April 2008 and March 2009 at Delamere Forest, Cheshire, UK. The analysis of canopy gap fraction estimates derived from TLS data highlighted the repeatability and consistency of the measurements in comparison with those from coincident hemispherical photographs. The comparison also showed that estimates computed considering only the number of hits and misses registered in the TLS datasets were consistently lower than those estimated from hemispherical photographs. To examine this difference, the potential information available in the intensity values recorded by TLS was investigated and a new method developed to estimate canopy gap fraction proposed. The new approach produced gap fractions closer to those estimated from hemispherical photography, but the research also highlighted the limitations of single return TLS data for this application. Full article

Historically, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) was the unique satellite sensor used to collect the nighttime light, which is an efficient means to map the global economic activities. Since it was launched in October 2011, the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite has become a new satellite used to monitor nighttime light. This study performed the first evaluation on the NPP-VIIRS nighttime light imagery in modeling economy, analyzing 31 provincial regions and 393 county regions in China. For each region, the total nighttime light (TNL) and gross regional product (GRP) around the year of 2010 were derived, and a linear regression model was applied on the data. Through the regression, the TNL from NPP-VIIRS were found to exhibit R² values of 0.8699 and 0.8544 with the provincial GRP and county GRP, respectively, which are significantly stronger than the relationship between the TNL from DMSP-OLS (F16 and F18 satellites) and GRP. Using the regression models, the GRP was predicted from the TNL for each region, and we found that the NPP-VIIRS data is more predictable for the GRP than those of the DMSP-OLS data. This study demonstrates that the recently released NPP-VIIRS nighttime light imagery has a stronger capacity in modeling regional economy than those of the DMSP-OLS data. These findings provide a foundation to model the global and regional economy with the recently availability of the NPP-VIIRS data, especially in the regions where economic census data is difficult to access. Full article

As geothermal energy is of increasing importance as a renewable energy source, there is a high demand for comprehensive studies to prevent failure during implementation, as is the case in Staufen im Breisgau, Germany. The drilling of seven wells for the geothermal heating of the city hall in 2007 is thought to have disturbed the existing hydro-geological system in the complex structured transition zone of the Upper Rhine Graben and the Schwarzwald massif. This event has led to uplift, related to the transformation of anhydrite to gypsum, which affects the infrastructure of the city centre via the generation of large cracks. This study focuses on the application of the InSAR Small Baseline Subset (SBAS) approach using 50 X-band radar images from the German TerraSAR-X satellite (TSX) to map the spatial and temporal patterns of the deformation field in detail. X-band InSAR time series analysis for the three-year time period from July 2008 through May 2011 indicates maximum velocities of ~12 cm/yr in the line of sight (LOS) direction, from the ground to the satellite, approximately 50 m northeast of the drilling field. In comparison with leveling data for the same time period, TSX data analysis better delineates the border of the deformation area, and it is able to map the amount of deformation associated with different parts of the city. Moreover, this comparison indicates contributions of horizontal motion, as is expected for uplift patterns. Full article

This study evaluates the potential of C- and L-band polarimetric SAR data for the discrimination of iron-mineralized laterites in the Brazilian Amazon region. The study area is the N1 plateau located on the northern border of the Carajás Mineral Province, the most important Brazilian mineral province which has numerous mineral deposits, particularly the world’s largest iron deposits. The plateau is covered by low-density savanna-type vegetation (campus rupestres) which contrasts visibly with the dense equatorial forest. The laterites are subdivided into three units: chemical crust, iron-ore duricrust, and hematite, of which only the latter two are of economic interest. Full polarimetric data from the airborne R99B sensor of the SIVAM/CENSIPAM (L-band) system and the RADARSAT-2 satellite (C-band) were evaluated. The study focused on an assessment of distinct schemes for digital classification based on decomposition theory and hybrid approach, which incorporates statistical analysis as input data derived from the target decomposition modeling. The results indicated that the polarimetric classifications presented a poor performance, with global Kappa values below 0.20. The accuracy for the identification of units of economic interest varied from 55% to 89%, albeit with high commission error values. In addition, the results using L-band were considered superior compared to C-band, which suggest that the roughness scale for laterite discrimination in the area is nearer to L than to C-band. Full article

We develop an assimilation method of high horizontal resolution sea surface temperature data, provided from the Moderate Resolution Imaging Spectroradiometer (MODIS-SST) sensors boarded on the Aqua and Terra satellites operated by National Aeronautics and Space Administration (NASA), focusing on the reproducibility of the Kuroshio front variations south of Japan in February 2010. Major concerns associated with the development are (1) negative temperature bias due to the cloud effects, and (2) the representation of error covariance for detection of highly variable phenomena. We treat them by utilizing an advanced data assimilation method allowing use of spatiotemporally varying error covariance: the Local Ensemble Transformation Kalman Filter (LETKF). It is found that the quality control, by comparing the model forecast variable with the MODIS-SST data, is useful to remove the negative temperature bias and results in the mean negative bias within −0.4 °C. The additional assimilation of MODIS-SST enhances spatial variability of analysis SST over 50 km to 25 km scales. The ensemble spread variance is effectively utilized for excluding the erroneous temperature data from the assimilation process. Full article