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Research Papers

Select this Article		Forest leaf area index in an Alpine valley from medium resolution satellite imagery and in situ data Daniela Stroppiana, Mirco Boschetti, Pietro A. Brivio, Luca Nizzetto, and Antonio Di Guardo J. Appl. Remote Sens. 6, 063528 (Apr 23, 2012); http://dx.doi.org/10.1117/1.JRS.6.063528 Online Publication Date: Apr 23, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Leaf area index (LAI) is a key variable for modeling the interaction between vegetation and the atmosphere. We collected field LAI measurements over 12 sites in 2005 in the Lys valley, Northern Italy, to calibrate regressive models using normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and wide dynamic range vegetation index (WDRVI) products derived from 250-m moderate resolution imaging spectroradiomete (MODIS) imagery. Field data were compared to the 1 km MODIS leaf area index—fraction of photosynthetically active radiation (LAI/fAPAR) product to show that regressive techniques are better suited for local applications. We investigated these LAI-vegetation index (VI) regressive models to 1. test the sensitivity of the model to forest type and phenology, 2. identify the most suitable VI for LAI retrieval, and 3. verify the feasibility of using a linear model. Results show that in our experimental conditions the LAI-VI relationship is primarily influenced by phenology and that the leaf constant period (maximum LAI) is significantly different compared to the other phenological phases. Among the indices, EVI yielded the poorest performance (R²<0.41) and the linear regressive models for NDVI and WDRVI derived by pooling together data from different phenological phases show a good correlation with field data (R²>0.65); the use of a logarithmic model does not improve the performance. The LAI-WDRVI and LAI-NDVI models were inverted and applied to 2004 MODIS data and model performance was assessed by comparing predicted and measured LAI. Results show that WDRVI performs best in a linear regressive model, yielding a relative root mean square error <23%.

Select this Article		Forest and deforestation identification based on multitemporal polarimetric RADARSAT-2 images in Southwestern China Fengli Zhang, Maosong Xu, Chou Xie, Zhongsheng Xia, Kun Li, and Xuejun Wang J. Appl. Remote Sens. 6, 063527 (Apr 27, 2012); http://dx.doi.org/10.1117/1.JRS.6.063527 Online Publication Date: Apr 27, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract In the southwest of China, it is anticipated that synthetic aperture radar (SAR) will become an important tool for forest inventory because of its all-weather capabilities. The Zhazuo area in Guizhou Province of southwest China, with a typical Karst landform, was selected as the test site. Six RADARSAT-2 polarimetric images were acquired in order to analyze polarimetric backscattering behavior and temporal variation of forest and deforested area. Polarimetric decomposition was conducted, and Pauli and Freeman-Durden decomposition were demonstrated to be more suitable for identifying forest and deforestation respectively. Finally, a scheme for multitemporal polarimetric SAR data fusion was proposed, which could greatly improve image quality and make forest identification more efficient. Support vector machine classification showed that the overall accuracy for forest identification was 87.63%, and the accuracy could be enhanced to 91.49% after gamma filtering.

Select this Article		Backscattering characteristics of L-band polarimetric and optical satellite imagery over planted acacia forests in Sumatra, Indonesia Shoko Kobayashi, Ragil Widyorini, Shuichi Kawai, Yoshiharu Omura, Kazadi Sanga-Ngoie, and Bambang Supriadi J. Appl. Remote Sens. 6, 063525 (Mar 21, 2012); http://dx.doi.org/10.1117/1.JRS.6.063525 Online Publication Date: Mar 21, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Backscattering characteristics of L-band synthetic aperture radar (SAR) polarization data over industrial plantation forests of the fast-growing Acacia mangium in Sumatra, Indonesia, were investigated by combined analysis of microwave and optical reflectance imagery acquired by the Advanced Land Observation Satellite (ALOS) and ground observation data of forest stand characteristics. Prior to the satellite data analysis, growth curves of the planted trees are analyzed using ground observation data. The results show that tree growth fits a sigmoidal curve with a maximum growth rate between 1 and 2 years of age, which gradually declines after that. Our statistical analyses using satellite and ground observation data provide the following findings: 1. The regression between SAR backscattering intensity and forest stand characteristics fits negative quadratic curves for the cross-polarization HV, which show higher correlation than those obtained for the co-polarizations (HH, VV); 2. A decreasing trend in optical normalized difference vegetation index (NDVI) values with age prevails for the trees 2 years and older; 3. A significant correlation is obtained between SAR backscattering and NDVI for the cross-polarization. These findings suggest that the L-band SAR cross-polarization is strongly affected by the acacia tree foliage.

Select this Article		Effect on specific crop mapping using WorldView-2 multispectral add-on bands: soft classification approach Priyadarshi Upadhyay, Anil Kumar, Partha Sarathi Roy, Sanjay Kumar Ghosh, and Ian Gilbert J. Appl. Remote Sens. 6, 063524 (Apr 11, 2012); http://dx.doi.org/10.1117/1.JRS.6.063524 Online Publication Date: Apr 11, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract In this study, new add-on bands in a multispectral dataset of WorldView-2, DigitalGlobe’s second next-generation satellite, have been evaluated. For extraction of a specific agriculture crop at a time, WorldView-2 multispectral single, as well as two-date data sets, were used. For this purpose, a class-based sensor independent spectral band ratio normalized difference vegetation index (NDVI) (CBSI-NDVI) and its possibilistice fuzzy classification approach was used. Different agriculture crops selected for the study were sugarcane, late wheat, cauliflower, berseem (fodder), early wheat and ratoon. It is found that bands four and eight with temporal data are good for extracting sugarcane, while bands four, eight and five, seven with temporal data are suitable for late wheat and bands four and eight work well for cauliflower. Similarly, bands five, seven and five, eight with temporal data are good for extracting berseem (fodder), bands four, eight work for early wheat with temporal data and for ratoon four, six single date or four, six and four, eight temporal data. This suitability of bands has been observed with respect to a maximum membership value difference, as well as maximum entropy difference, between the two closest agriculture crops. Thus, it can be concluded that existing bands five, seven and new bands four, six, eight in WorldView-2 are important for identifying and mapping crops mentioned in this study. This indicates new bands, especially four, six, eight introduced in WorldView-2, are more effective than existing bands in QuickBird for mapping specific crops.

Select this Article		Intersatellite bias of the high-resolution infrared radiation sounder water vapor channel determined using ISCCP B1 data Kenneth R. Knapp J. Appl. Remote Sens. 6, 063523 (Mar 22, 2012); http://dx.doi.org/10.1117/1.JRS.6.063523 Online Publication Date: Mar 22, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Information on the distribution and transport of upper tropospheric humidity helps our understanding of atmospheric circulation. However, efforts to develop a dataset that is not diurnally biased—that is, one that is based on geostationary data—have been hindered by a lack of intercalibration of the historical geostationary satellite water vapor channels. Toward this end, the high-resolution infrared radiation sounder (HIRS) intersatellite bias is determined by using collocated observations of geostationary and HIRS to compute monthly biases between HIRS instruments on different satellites. The resulting HIRS intersatellite bias then allows the construction of a temporally consistent HIRS record, from which geostationary satellite water vapor channels can be intercalibrated. This intercalibration will allow a uniform analysis of upper tropospheric water vapor.

Select this Article		Novel approach for computing photosynthetically active radiation for productivity modeling using remotely sensed images in the Great Plains, United States Ramesh K. Singh, Shuguang Liu, Larry L. Tieszen, Andrew E. Suyker, and Shashi B. Verma J. Appl. Remote Sens. 6, 063522 (Mar 09, 2012); http://dx.doi.org/10.1117/1.JRS.6.063522 Online Publication Date: Mar 09, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Gross primary production (GPP) is a key indicator of ecosystem performance, and helps in many decision-making processes related to environment. We used the Eddy covariancelight use efficiency (EC-LUE) model for estimating GPP in the Great Plains, United States in order to evaluate the performance of this model. We developed a novel algorithm for computing the photosynthetically active radiation (PAR) based on net radiation. A strong correlation (R² = 0.94,N = 24) was found between daily PAR and Landsat-based mid-day instantaneous net radiation. Though the Moderate Resolution Spectroradiometer (MODIS) based instantaneous net radiation was in better agreement (R² = 0.98,N = 24) with the daily measured PAR, there was no statistical significant difference between Landsat based PAR and MODIS based PAR. The EC-LUE model validation also confirms the need to consider biological attributes (C₃ versus C₄ plants) for potential light use efficiency. A universal potential light use efficiency is unable to capture the spatial variation of GPP. It is necessary to use C₃ and C₄ based land use/land cover map for using EC-LUE model for estimating spatiotemporal distribution of GPP.

Select this Article		Shadow detection for color remotely sensed images based on multi-feature integration Jiahang Liu, Deren Li, and Tao Fang J. Appl. Remote Sens. 6, 063521 (Apr 23, 2012); http://dx.doi.org/10.1117/1.JRS.6.063521 Online Publication Date: Apr 23, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract A novel shadow detection method for color remotely sensed images that satisfies requirements for both high accuracy and wide adaptability in applications is presented. This method builds on previously reported work investigating the shadow properties in both red/green/blue (RGB) and hue saturation value (HSV) color spaces. The method integrates several shadow features for modeling and uses a region growing (RG) algorithm and a perception machine (PM) of a neural network (NN) to identify shadows. To ensure efficiency of the parameters, first the proposed method uses a small number of shadow samples manually obtained from an input image to automatically estimate the necessary parameters. Then, the method uses the estimated threshold to binarize the hue map of the input image for obtaining possible shadow seeds and applies the RG algorithm to produce a candidate shadow map from the intensity channel. Subsequently, all of the hue, saturation, and intensity maps from the candidate shadow map are filtered with a corresponding band-pass filter, and the filtered results are input into the PM algorithm for the final shadow segmentation. Experiments indicate that the proposed algorithm has better performance in multiple cases, providing a new and practical shadow detection method.

Select this Article		Sensor reduction technique using Bellman optimal estimates of target agent dynamics Brian J. Goode, Philip A. Chin, and Michael J. Roan J. Appl. Remote Sens. 6, 063520 (Apr 20, 2012); http://dx.doi.org/10.1117/1.JRS.6.063520 Online Publication Date: Apr 20, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract A generalized sensor reduction technique is developed for a sensor network used in surveillance and target tracking operations. Reducing the number of sensors in the network leads to addressing immediate threats more quickly and lowering costs for acquiring and processing data. The methods in this work use Bellman optimality principles to estimate possible paths of an agent given an assumed environment model. These paths are then used to determine causal relationships between states in a surveillance field. By using this approach, a capture set is defined where the final states of trajectories are known using information from sensors located in other states. Sensors can then be removed from the network based on this capture set. This method is applied to a crowded hallway surveillance scenario where an agent may choose between two possible exits. The sensor network in this scenario determines if a target deviates from the crowd and moves toward an alternate exit. A proximity sensor grid is placed above the crowd to record the number of people that pass through the hallway. Our result shows that the Bellman optimal approximation of the capture set for the alternate exit identifies the region of the surveillance field where sensors are needed, allowing the others to be removed. Using the reduced sensor network, results are given that show the probability of a deviating agent becoming more distinct with respect to normal motion of the crowd. Therefore, we conclude that by incorporating a dynamic model of the agents’ motion into the sensor network, sensors can be reduced, and increased detectability is noticed when sensors are removed early in a trajectory of interest.

Select this Article		Water quality monitoring in Basque coastal areas using local chlorophyll-a algorithm and MERIS images Stefani Novoa, Guillem Chust, Jean M. Froidefond, Caroline Petus, Javier Franco, Emma Orive, Sergio Seoane, and Angel Borja J. Appl. Remote Sens. 6, 063519 (Apr 03, 2012); http://dx.doi.org/10.1117/1.JRS.6.063519 Online Publication Date: Apr 03, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Accurate estimation of chlorophyll-a (chl-a), a proxy of the eutrophication risk, is necessary in coastal areas for the assessment of water quality in accordance with European Directives. Local parameterization of remote sensing algorithms is useful to cope with the variability and specificity of optically-active in-water constituents. Using the Bay of Biscay coastal waters, affected by Basque river runoffs, as a case study, the objectives of this investigation are to: 1. develop an empirical algorithm to estimate water surface chl-a for the optically-complex Basque coastal waters; 2. explore the influence of suspended matter, phytoplankton species, and pigment content on the algorithm developed for medium resolution imaging spectrometer instrument (MERIS) imagery; 3. compare the local algorithm to three ocean color algorithms (OC4v6, Gitelson’s algorithm, and the OC5); and 4. apply the local algorithm to the MERIS images. For this purpose, two surveys were undertaken within the study area, the Batel-1 survey in 2007, and the Batel-2, in 2009. The empirical algorithm was developed with remote sensing reflectances (R_rs), undertaken with a TriOS field spectrometer, and chl-a measured in situ from the Batel-2 survey. The algorithm was not affected by different concentrations of suspended matter in surface waters, within the range from 0.0 to 6.6 g·m⁻³. There was no significant effect of 23 accessory pigments found in the area on the algorithm. Eighty-four R_rs and chl-a measurements from the Batel-1 survey were used to validate the local algorithm and to compare it with output of the other algorithms. The local algorithm provided the lowest root-mean-square difference (RMS = 1.7 mg·m⁻³), the best correlation with the observed data (R = 0.8), together with the best slope-intercept combination between predicted and observed chl-a (slope = 0.5, intercept = 0.6). The chl-a algorithm developed here for MERIS imagery can assist in the assessment of water ecological status in the southeastern part of the Bay of Biscay, in a cost-effective manner.

Select this Article		Influence of topographic normalization on the vegetation index–surface temperature relationship Jasper Van doninck, Jan Peters, Bernard De Baets, Eva M. De Clercq, Els Ducheyne, and Niko E. C. Verhoest J. Appl. Remote Sens. 6, 063518 (Mar 20, 2012); http://dx.doi.org/10.1117/1.JRS.6.063518 Online Publication Date: Mar 20, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract The estimation of surface soil moisture status and evapotranspiration from optical remote sensing using the vegetation index–surface temperature (VI-T_S) relationship is severely hampered in regions with strong topography, due to the influence of altitude and terrain orientation on surface temperature. In our study, a new empirical approach to normalize surface temperature for terrain elevation—a stratified linear regression model—is presented and is applied on moderate-resolution imaging spectroradiometer (MODIS) data over Calabria, Italy. The method incorporates remotely sensed land surface temperature, a vegetation index, and a digital elevation model. The influence of the newly developed normalization on the VI-T_S relationship and on a soil dryness index is compared to the influence of two existing normalization methods: one using a standard lapse rate of 0.65 K per 100 m and one using a lapse rate derived through simple linear regression between elevation and surface temperature. Stratified linear regression adequately corrects surface temperature while the two other normalization techniques seem to overestimate the actual temperature lapse rate during certain periods of the year. Comparison of a soil dryness index derived using the three different normalization methods with limited in situ soil moisture data results in a slightly stronger correlation for the stratified linear regression model than for the two other normalization methods. VI-T_S–based soil wetness estimation in mountainous terrains remains, however, limited by other spatially varying factors, including terrain orientation and atmospheric conditions.

Select this Article		Automated two-dimensional–three-dimensional registration using intensity gradients for three-dimensional reconstruction Prakash Duraisamy, Yassine Belkhouche, Stephen Jackson, Kamesh Namuduri, and Bill Buckles J. Appl. Remote Sens. 6, 063517 (Apr 23, 2012); http://dx.doi.org/10.1117/1.JRS.6.063517 Online Publication Date: Apr 23, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract We develop a robust framework for the registration of light detection and ranging (LiDAR) images with 2-D visual images using a method based on intensity gradients. Our proposed algorithm consists of two steps. In the first step, we extract lines from the digital surface model (DSM) given by the LiDAR image, then we use intensity gradients to register the extracted lines from the LiDAR image onto the visual image to roughly estimate the extrinsic parameters of the calibrated camera. In our approach, we overcome some of the limitations of 3-D reconstruction methods based on the matching of features between the two images. Our algorithm achieves an accuracy for the camera pose recovery of about 98% for the synthetic images tested, and an accuracy of about 95% for the real-world images we tested, which were from the downtown New Orleans area.

Select this Article		Near-surface soil moisture estimation by combining airborne L-band brightness temperature observations and imaging hyperspectral data at the field scale Marion Pause, Karsten Schulz, Steffen Zacharias, and Angela Lausch J. Appl. Remote Sens. 6, 063516 (Apr 27, 2012); http://dx.doi.org/10.1117/1.JRS.6.063516 Online Publication Date: Apr 27, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract The observation of spatially distributed soil moisture fields is an essential component for a large range of hydrological, climate, and agricultural applications. While direct measurements are expensive and limited to small spatial domains, the inversion of airborne and satellite L-band radiometer data has shown the potential to provide spatial estimates of near surface soil moisture from the local up to the global scale. When using L-band radiometer observations for soil moisture retrieval, a major limitation is the attenuation of the microwave signal by the vegetation, hampering the signal inversion and thereby making spatially distributed plant information necessary. Usually vegetation types are considered with a vegetation type specific global parameterization, e.g., for leaf area index (LAI). Within this study we evaluate and address the effect of spatially varying LAI on high spatial resolution (pixel size 50 m) airborne L-band brightness temperature of crop canopies that are usually regarded homogeneous. To account for within field variations of LAI we used airborne imaging spectrometer data (pixel size 1.5 m) to empirically create maps of LAI using spectral greenness vegetation indices. We found clear (R²<0.90) functional relationships between spatially varying L-band brightness temperature and LAI variations within crop canopies that in literature are usually assumed homogeneous. Very good (R² = 0.93) near surface soil moisture estimates were achieved using multi-variate regression and adding plant specific spectral information to the independent variable set for final soil moisture retrieval. The study shows that a multi-sensor campaign using airborne L-band radiometer and imaging spectrometers provide a powerful data set for monitoring patterns of near surface soil moisture and vegetation canopy at the field scale with high accuracy.

Select this Article OPEN ACCESS		Hyperspectral image classification using an unsupervised neuro-fuzzy system Caiyun Zhang and Fang Qiu J. Appl. Remote Sens. 6, 063515 (Apr 06, 2012); http://dx.doi.org/10.1117/1.JRS.6.063515 Online Publication Date: Apr 06, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract An unsupervised neuro-fuzzy system, Gaussian fuzzy self-organizing map (GFSOM), is proposed for hyperspectral image classification. This algorithm operates by integrating an unsupervised neural network with a Gaussian function-based fuzzy system. We also explore the potential for hyperspectral image analysis of three other artificial intelligence (AI)-based unsupervised techniques popular for multispectral image analysis: self-organizing map (SOM), fuzzy c-mean (FCM), and descending fuzzy learning vector quantization (DFLVQ). To apply these methods effectively and efficiently to hyperspectral imagery, an optimal learning sample selection strategy and a prototype initialization system are developed. An experimental study on classifying an EO-1/Hyperion hyperspectral image illustrates that GFSOM achieves the best accuracy, since it can model both the central tendency characteristics of input samples and capture the dispersion characteristics of data within a cluster. By adopting the system initialization approach developed here, all the AI-based techniques have the capability to classify hyperspectral images and can deliver acceptable accuracy, which could consequently accelerate their transitions from the multispectral to the hyperspectral field.

Select this Article		Analysis of United States Geological Survey spectral library of silicate minerals: implication for remote sensing applications Chaojun Fan, Hongjie Xie, Joan Wu, and Stuart Birnbaum J. Appl. Remote Sens. 6, 063514 (Mar 20, 2012); http://dx.doi.org/10.1117/1.JRS.6.063514 Online Publication Date: Mar 20, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Hyperspectral imaging is broadly used in the identification of hydroxylated and hydrated silicate minerals, especially at the wavelengths of 1.0 to 2.5 µm. The vibrations of the hydroxyl (OH) molecule, water molecule (H−O−H), and metal−OH bonds, or their combinations, produce prominent absorption features at ∼ 1.40, ∼ 1.91, and 2.20 to 2.40 μm wavelengths. In remote sensing applications, minerals showing one or two of these absorption features are usually categorized as hydroxylated and/or hydrated, and minerals showing all three absorption features are usually categorized as hydrated phyllosilicates. We examine the USGS spectral library of common silicate minerals and find that some showing these absorption features are not hydroxylated and/or hydrated but with slight alteration or impurities. This implies that the identification of hydroxylated and/or hydrated silicate minerals may not be merely based on these absorption features. Rather, incorporation of geological and environmental background is also important. We further calculate the absorption depths and silicate mineral ratios in these absorption features as references for discrimination. This study provides a cautionary perspective on the identification of hydrated phyllosilicate minerals with respect to mineralogical interpretations of Mars and other planets.

Select this Article		Lidar and microwave radiometer observations of planetary boundary layer structure under light wind weather Ning Zhang, Yan Chen, and Wenjing Zhao J. Appl. Remote Sens. 6, 063513 (Apr 11, 2012); http://dx.doi.org/10.1117/1.JRS.6.063513 Online Publication Date: Apr 11, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract The planetary boundary layer (PBL) structures on four light wind days at Suzhou city, China were analyzed with both micropulse lidar (MPL) and passive microwave radiometer (MWR). The planetary boundary layer height (PBLH) was estimated with the first-derivation method from the lidar observations. The lidar-observed PBLH and microwave radiometer observations were used to estimate the lapse rate in a simple encroachment model. Under light wind conditions in this area, the nocturnal PBLH is about 300 to 400 m. The maximum of PBLH was about 800 to 1000 m. The daytime PBL development could be divided into three periods: growing, maintenance, and decay. The PBLH is nearly linear in the growing period with a growth ratio of about 110 to 120 m/h. The encroachment model lapse rate was calculated from the MPL and MWR observations. The lapse rate γ is estimated in the four days and the average is about 0.0010 ?startKend?/m. The vertical structure of air temperature and humidity were also analyzed with the microwave radiometer observations. Diurnal variations of relative humidity in the boundary layer partly explain the light fog appearing at night during this period.

Select this Article OPEN ACCESS		Evaluating the use of remote sensing data in the U.S. Agency for International Development Famine Early Warning Systems Network Molly E. Brown and Elizabeth B. Brickley J. Appl. Remote Sens. 6, 063511 (Mar 23, 2012); http://dx.doi.org/10.1117/1.JRS.6.063511 Online Publication Date: Mar 23, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract The U.S. Agency for International Development (USAID)’s Famine Early Warning System Network (FEWS NET) provides monitoring and early warning support to decision makers responsible for responding to food insecurity emergencies on three continents. FEWS NET uses satellite remote sensing and ground observations of rainfall and vegetation in order to provide information on drought, floods, and other extreme weather events to decision makers. Previous research has presented results from a professional review questionnaire with FEWS NET expert end-users whose focus was to elicit Earth observation requirements. The review provided FEWS NET operational requirements and assessed the usefulness of additional remote sensing data. We analyzed 1342 food security update reports from FEWS NET. The reports consider the biophysical, socioeconomic, and contextual influences on the food security in 17 countries in Africa from 2000 to 2009. The objective was to evaluate the use of remote sensing information in comparison with other important factors in the evaluation of food security crises. The results show that all 17 countries use rainfall information, agricultural production statistics, food prices, and food access parameters in their analysis of food security problems. The reports display large-scale patterns that are strongly related to history of the FEWS NET program in each country. We found that rainfall data were used 84% of the time, remote sensing of vegetation 28% of the time, and gridded crop models 10% of the time, reflecting the length of use of each product in the regions. More investment is needed in training personnel on remote sensing products to improve use of data products throughout the FEWS NET system.

Select this Article		Eye-safe, 243-mJ, rapidly tuned by injection-seeding, near-infrared, optical, parametric, oscillator-based differential-absorption light detection and ranging transmitter Robert J. Foltynowicz and Michael D. Wojcik J. Appl. Remote Sens. 6, 063510 (Mar 08, 2012); http://dx.doi.org/10.1117/1.JRS.6.063510 Online Publication Date: Mar 08, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Here, we demonstrate and characterize a high-energy, eye-safe, spectrally narrow, and frequency-agile near-IR optical parametric oscillator (OPO). The injection-seeded, noncritical phase-matched (NCPM) potassium titanyle arsenate (KTA) and ring-cavity OPO was pumped in single longitudinal mode (SLM) at 7 ns full width at half maximum FWHM and 30 Hz, neodymium-doped yttrium aluminum garnet (Nd:YAG), generating 243 mJ per pulse OPO signal output with a conversion efficiency of 27%, spectral linewidth of 157 MHz, and approximately M² of 29. Also, we demonstrate a nonmechanical method to switch the frequency of the OPO at a rate of 2 Hz from 1535.036 to 1535.195 nm, which represents the on/off resonances of carbon dioxide, respectively. However, the switching rate can be extended into the MHz range and is limited by the electronics driving the diode laser. Given the performance results of our frequency-agile OPO, this transmitter has great potential as a source in DIAL applications.

Select this Article		Automatic algorithm for relative radiometric normalization of data obtained from Landsat TM and HJ-1A/B charge-coupled device sensors Changmiao Hu and Ping Tang J. Appl. Remote Sens. 6, 063509 (Mar 08, 2012); http://dx.doi.org/10.1117/1.JRS.6.063509 Online Publication Date: Mar 08, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract The HJ-1 A and HJ-1 B satellites were launched on September 6, 2008 from China. The radiometric normalization of charge coupled device (CCD) images is still challenging work. In this paper, an automatic algorithm for relative radiometric normalization between HJ-1A/B CCD images and Landsat TM is presented. This method directly normalizes the digital numbers (DN) of HJ-1A/B CCD images, band by band, to surface reflectance. A united linear relationship between the DN of the target images and the surface reflectance of the referenced images was derived, and the applicable conditions are described here. The iteratively reweighted modification of the multivariate alteration detection (IR-MAD) transformation was used to automatically select pseudoinvariant features (PIFs). This procedure is simple, fast, and completely automatic. The algorithm was applied to normalize three subregions of different HJ-1A/B CCD images. The results show that the retrieval quality of the surface reflectance does meet the requirements of quantitative remote sensing.

Select this Article		Subpixel hyperspectral target detection using local spectral and spatial information Yuval Cohen, Dan G. Blumberg, and Stanley R. Rotman J. Appl. Remote Sens. 6, 063508 (Mar 07, 2012); http://dx.doi.org/10.1117/1.JRS.6.063508 Online Publication Date: Mar 07, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract We present two methods to improve three hyperspectral stochastic algorithms for target detection; the algorithms are the constrained energy minimization, the generalized likelihood ratio test, and the adaptive coherence estimator. The original algorithms rely solely on spectral information and do not use spatial information; this usage is normally justified in subpixel target detection, since the target size is smaller than the size of a pixel. However, we found that since the background (and the false alarms) may be spatially correlated and the point spread function can distribute the energy of a point target between several neighboring pixels, the implementation of spatial filtering algorithms considerably improved target detection. Our first improvement used the local spatial mean and covariance matrices, which take into account the spatial local mean instead of the global mean. While this concept has been found in the literature, the effect of its implementation in both the estimated mean and the covariance matrix is examined quantitatively here. The second was based on the fact that the effect of a target of physical subpixel size will extend to a cluster of pixels. We tested our algorithms by using the data set and scoring methodology of the Rochester Institute of Technology Target Detection Blind Test project. The results showed that both spatial methods independently improved the basic spectral algorithms mentioned above, and when the two methods were used together, the results were even better.

Select this Article		Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model Mingquan Wu, Zheng Niu, Changyao Wang, Chaoyang Wu, and Li Wang J. Appl. Remote Sens. 6, 063507 (Mar 07, 2012); http://dx.doi.org/10.1117/1.JRS.6.063507 Online Publication Date: Mar 07, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Due to cloud coverage and obstruction, it is difficult to obtain useful images during the critical periods of monitoring vegetation using medium-resolution spatial satellites such as Landsat and Satellite Pour l’Observation de la Terre (SPOT), especially in pluvial regions. Although high temporal resolution sensors, such as the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS), can provide high-frequency data, the coarse ground resolutions of these sensors make them unsuitable to quantify the vegetation growth processes at fine scales. This paper introduces a new data fusion model for blending observations of high temporal resolution sensors (e.g., MODIS) and moderate spatial resolution satellites (e.g., Landsat) to produce synthetic imagery with both high-spatial and temporal resolutions. By detecting temporal change information from MODIS daily surface reflectance images, our algorithm produced high-resolution temporal synthetic Landsat data based on a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) image at the beginning time (T₁). The algorithm was then tested over a 185×185 km² area located in East China. The results showed that the algorithm can produce high-resolution temporal synthetic Landsat data that were similar to the actual observations with a high correlation coefficient (r) of 0.98 between synthetic imageries and the actual observations.

Select this Article		Adaptive regional feature extraction for very high spatial resolution image classification Leiguang Wang, Qinling Dai, Liang Hong, and Guoying Liu J. Appl. Remote Sens. 6, 063506 (Mar 07, 2012); http://dx.doi.org/10.1117/1.JRS.6.063506 Online Publication Date: Mar 07, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract An object-oriented, multiscale feature extraction approach is proposed for the land-cover classification of high spatial resolution images. The approach provides more discriminative features by considering the spatial context information from different segmentation levels. It consists of three successive substeps: segmentation by mean-shift algorithm, an iteratively merging process controlled by merging cost function and range-of-scale parameter, and feature extraction from linked multilevel image partitions. The mean-shift method is to get boundary-preserved and spectrally homogeneous over-segmentation regions. Then, a family of nested image partitions is constructed by a merging procedure. Meanwhile, every region of the finest scale is linked to image objects of its superlevels. Finally, every region in the finest scale is treated as a basic analysis unit, and the feature vectors are created by stacking statistics from the region and their superlevels. A support vector machine is used as a classifier and the method on two widely used high spatial resolution data sets over Pavia City, Italy, are evaluated. Compared with results reported in many papers, the result indicates superior accuracy.

Select this Article		Content security protection for remote sensing images integrating selective content encryption and digital fingerprint Yanyan Xu, Zhengquan Xu, and Yuxia Zhang J. Appl. Remote Sens. 6, 063505 (Mar 07, 2012); http://dx.doi.org/10.1117/1.JRS.6.063505 Online Publication Date: Mar 07, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Content security protection methods should be adopted to guarantee the security of highly sensitive remote sensing images during their transmission and usage. The joint fingerprinting and decryption (JFD) framework, which integrates encryption and fingerprinting, can provide comprehensive, effective content security protection for remote sensing information. However, several challenges need to be overcome. In order to solve the contradictory relationship between encryption security and fingerprinted image quality in JFD, a selective content encryption strategy is proposed to enhance encryption security and a new method for choosing fingerprint embedding area is proposed to reduce partial decryption’s influence on image quality. Furthermore, a new method for generating and controlling fingerprints is introduced to JFD to eliminate its vulnerability to collusion attacks. The experiment results show that the proposed techniques are highly effective. Because of its high security, good fingerprinted image quality and high data processing and transmission speed, the new JFD scheme is an effective content security protection approach for remote sensing images, especially suitable for massive remote sensing images.

Select this Article		Assessment of shuttle radar topographic mission performance over the Kuwait desert terrain Kota S. Rao and Hala K. Al Jassar J. Appl. Remote Sens. 6, 063504 (Mar 05, 2012); http://dx.doi.org/10.1117/1.JRS.6.063504 Online Publication Date: Mar 05, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Forty-three digital elevation models (DEMs) of the Managesh oil field, Kuwait desert study area, are derived from 28 advanced synthetic aperture radar (ASAR) images using radar interferometry (InSAR) technique. Weighted-average technique is used to reduce the noise in the integrated DEM. The final DEM is compared with DEM of Shuttle Radar Topography Mission (SRTM) and found to have a general agreement. The standard deviation (STD) of individual DEMs with reference to DEM of SRTM is in the range 10 to 40 m. The results indicate that the 90 m spatial resolution DEM of SRTM is noisier over the Managesh oil field as it shows drastic changes in elevations of neighboring pixels which is not expected for a plain desert like the Managesh oil field. Using mean filter, the noise level is estimated as one meter. The reasons for high noise in the DEM of SRTM may be due to uneven distribution of soil moisture leading to uneven penetration of microwaves. The results are in confirmation with the earlier investigators as explained in the text.

Select this Article		Aerosol-cloud-precipitation relationships from satellite observations and global climate model simulations Bingqi Yi, Ping Yang, Kenneth P. Bowman, and Xiaodong Liu J. Appl. Remote Sens. 6, 063503 (Mar 07, 2012); http://dx.doi.org/10.1117/1.JRS.6.063503 Online Publication Date: Mar 07, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Substantial uncertainties exist in the current knowledge of aerosol-cloud-precipitation relationships and stem from the complicated interactions among the atmospheric constituents. We use a straightforward statistical method, the regression analysis technique, to examine the aerosol-cloud-precipitation relationships from satellite observational data sets, including the aqua moderate resolution imaging spectroradiometer (MODIS) aerosol and cloud products and the tropical rainfall measuring mission (TRMM) precipitation rate. Furthermore, the conventional MODIS aerosol product is combined with the Deep Blue algorithm product to reconstruct a complete global map of aerosol optical depth. Numerical simulations using the latest version of the community earth system model (CESM) are also carried out. Globally, distinct statistically significant relationships between aerosol optical depth, cloud fraction, and precipitation rate are obtained over both land and ocean. Signals agreeing with the first and second indirect effects of aerosols are detected, but other factors are likely contributors. The modeling results are found to generally agree with satellite observations, but the model usually overestimates the aerosol-cloud-precipitation relationship. An increasing trend in cloud fraction with the increase of aerosol optical depth (AOD) over ocean regions is found in the observations, while the reverse is true in the model simulation. It is mostly consistent that the model and observation both show a negative relationship between AOD and precipitation rate over land and a positive relationship over ocean.

Select this Article		Accurate detection of tree apexes in coniferous canopies from airborne scanning light detection and ranging images based on crown-extraction filtering Fumiki Hosoi, Hiroaki Matsugami, Kenichi Watanuki, Yo Shimizu, and Kenji Omasa J. Appl. Remote Sens. 6, 063502 (Mar 12, 2012); http://dx.doi.org/10.1117/1.JRS.6.063502 Online Publication Date: Mar 12, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract We describe crown-extraction (CE) filtering to accurately determine tree apex positions for various coniferous species using an airborne light detection and ranging–derived digital canopy height model (DCHM). This method uses a square mask, with a frame at the edges, that overlaps pixels within the DCHM image; when no pixels touch the frame, the pixel at the center is extracted as a tree-crown pixel. The apex of each tree is determined by choosing the pixel with maximum height from the pixels in the crown. We compared the performance of this method and of two other methods (local-maximum filtering and canopy-segmentation method) for several species. The CE filtering had the most accurate results for most tree species with appropriate mask size selection. The mean omission, commission, and total errors for all tree species were 8.1%, 1.6%, and 9.7%, respectively, for CE filtering. Comparing mask sizes and canopy diameters estimated from the DCHM for each species revealed that the smallest canopy diameter of each species was close to the most appropriate mask size for that species in CE filtering. We also confirmed that the smoothing process used in the DCHM has little effect on the accuracy of CE filtering.

Select this Article OPEN ACCESS		Spectral response to varying levels of leaf pigments collected from a degraded mangrove forest Chunhua Zhang, Yali Liu, John M. Kovacs, Francisco Flores-Verdugo, Francisco Flores de Santiago, and Ke Chen J. Appl. Remote Sens. 6, 063501 (Mar 12, 2012); http://dx.doi.org/10.1117/1.JRS.6.063501 Online Publication Date: Mar 12, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Mangrove forests are being removed or degraded at an alarming rate, even though they play a vital role in the sustainability of tropical coastal communities. Many of these forests are identified as degraded based on observable changes in their leaves (e.g., density, size, color, etc.). Of these, color can be considered one of the most important indicators of degradation because changes in the spectral response may be indicative of changes in the leaf pigment content. In this investigation, hyperspectral laboratory techniques were applied to examine potential relationships between the mangrove leaf spectral response and three leaf pigments: chlorophyll a, chlorophyll b, and total carotenoid content. Using an ASD spectroradiometer, the spectral reflectance of leaf samples were collected from poor condition, dwarf and healthy black (Avicennia germinans) and from healthy and poor condition red (Rhizophora mangle) mangroves located in a degraded mangrove system of the Mexican Pacific. A subset of 150 representational leaves was then used for pigment content analysis. The results indicate significant relationships between the spectral response and the levels of chlorophyll a, b, and total carotenoid content contained in the leaves. In particular, wavebands at the red edge position were shown to be the best predictors of the pigment contents. The results also indicate that vegetation indices do not necessarily improve the ability to predict these constituents. Finally, the red edge position was found to be significantly different between the healthy and poor condition mangroves (P = 0), with the healthy mangroves having longer wavelengths associated with the red edge position.

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Review Papers

Select this Article OPEN ACCESS		Digital high spatial resolution aerial imagery to support forest health monitoring: the mountain pine beetle context Michael A. Wulder, Joanne C. White, Sam Coggins, Stephanie M. Ortlepp, Nicholas C. Coops, Jamie Heath, and Brice Mora J. Appl. Remote Sens. 6, 062527 (Apr 06, 2012); http://dx.doi.org/10.1117/1.JRS.6.062527 Online Publication Date: Apr 06, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract We summarize the capacity of high spatial resolution (<1 m) digital aerial imagery to support forest health monitoring. We review the current use of digital aerial imagery in the context of the recent mountain pine beetle epidemic in western Canada. Supported by this review, we posit that high spatial resolution digital aerial imagery can play at least two critical roles in forest health monitoring. First, the capacity to characterize damage at the individual tree level directly supports a broad range of forest health information needs (e.g., tree-level attributes for estimating the population at risk and for inputs to models, estimates of mortality, rates of population growth). Second, the level of detail afforded by the digital high spatial resolution aerial imagery provides critical calibration and validation data for lower spatial resolution remotely sensed imagery (e.g., QuickBird, Landsat) for large-area detection and mapping of forest damage and can be used in a double sampling scheme as a bridge between detailed field measures and landscape-level estimates of mortality. In an era with increasing numbers of commercially deployed sensors capable of acquiring high spatial resolution satellite imagery, the flexibility and cost-effectiveness of aerial image options should not be disregarded. Moreover, experiences with airborne imagery can continue to inform applications using high spatial resolution satellite imagery for forest health information needs.

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Special Section on High-Performance Computing in Applied Remote Sensing

Select this Article		Fast motion detection from airborne videos using graphics processing unit Kui Liu, Ben Ma, Qian Du, and Genshe Chen J. Appl. Remote Sens. 6, 061505 (May 10, 2012); http://dx.doi.org/10.1117/1.JRS.6.061505 Online Publication Date: May 10, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract In our previous work, we proposed a joint optical flow and principal component analysis (PCA) approach to improve the performance of optical flow based detection, where PCA is applied on the calculated two-dimensional optical flow image, and motion detection is accomplished by a metric derived from the two eigenvalues. To reduce the computational time when processing airborne videos, parallel computing using graphic processing unit (GPU) is implemented on NVIDIA GeForce GTX480. Experimental results demonstrate that our approach can efficiently improve detection performance even with dynamic background, and processing time can be greatly reduced with parallel computing on GPU.

Select this Article		Fast supervised hyperspectral band selection using graphics processing unit Wei Wei, Qian Du, and Nicolas H. Younan J. Appl. Remote Sens. 6, 061504 (May 04, 2012); http://dx.doi.org/10.1117/1.JRS.6.061504 Online Publication Date: May 04, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Band selection is a common technique to reduce the dimensionality of hyperspectral imagery. When the desired object information is known, the reduction process can be achieved by selecting the bands that contain the most object information. It is expected that these selected bands can offer an overall satisfactory detection and classification performance. In this paper, we propose a new particle swarm optimization (PSO) based supervised band-selection algorithm that uses the known class signatures only without examining the original bands or the need of class training samples. Thus, this method requires much less computing time than other traditional methods. However, the PSO process itself may introduce additional computation cost. To tackle this problem, we propose parallel implementations via emerging general-purpose graphics processing units that can provide satisfactory results in speedup when compared to the cluster-based parallel implementation.

Select this Article		Anomaly detection based on a parallel kernel RX algorithm for multicore platforms José M. Molero, Ester M. Garzón, Inmaculada García, and Antonio Plaza J. Appl. Remote Sens. 6, 061503 (May 10, 2012); http://dx.doi.org/10.1117/1.JRS.6.061503 Online Publication Date: May 10, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Anomaly detection is an important task for hyperspectral data exploitation. A standard approach for anomaly detection in the literature is the method developed by Reed and Yu, also called RX algorithm. It implements the Mahalanobis distance, which has been widely used in hyperspectral imaging applications. A variation of this algorithm, known as kernel RX (KRX), consists of applying the same concept to a sliding window centered around each image pixel. KRX is computationally very expensive because, for every image pixel, a covariance matrix and its inverse has to be calculated. We develop an efficient implementation of the kernel RX algorithm. Our proposed approach makes use of linear algebra libraries and further develops a parallel implementation optimized for multi-core platforms, which is a well known, inexpensive and widely available high performance computing technology. Experimental results for two hyperspectral data sets are provided. The first one was collected by NASA’s airborne visible infra-red imaging spectrometer (AVIRIS) system over the World Trade Center (WTC) in New York, five days after the terrorist attacks, and the second one was collected by the hyperspectral digital image collection experiment (HYDICE). Our anomaly detection accuracy, evaluated using receiver operating characteristics (ROC) curves, indicates that KRX can significantly outperform the classic RX while achieving close to linear speedup in state-of-the-art multi-core platforms.

Select this Article		High-performance visual analytics of terrestrial light detection and ranging data on large display wall Tung-Ju Hsieh, Yang-Lang Chang, and Bormin Huang J. Appl. Remote Sens. 6, 061502 (Apr 03, 2012); http://dx.doi.org/10.1117/1.JRS.6.061502 Online Publication Date: Apr 03, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract A typical LIDAR (light detection and ranging) scan contains hundreds of millions of points. As such, the visualization of LIDAR point clouds poses a significant challenge in data analysis. We propose to visualize and process LIDAR point clouds on a large display wall with an array of monitors. This provides researchers with a high-resolution display environment for looking at and studying large data sets. High-resolution large displays offer both global perspectives and local details of point clouds, which is essential in the process of data exploration. The ability to explore, conceptualize, and correlate spatial and temporal changes of topographical records is required for the development of new analytical models that capture the mechanisms contributing toward cliff erosion. Large displays driven by high-performance parallel visualization cluster allow researchers to fully interact with LIDAR point clouds of slopes in Houshanyue mountain and cliff failures observed in Solana Beach in California. In our study, cases studies of visualization based approaches were conducted using large displays in digital immersive environments. Visual analytics techniques such as delineation, segmentation, and classification of features, change detection, and annotation were used to perform erosion assessment. The results showed that the researchers can observe the temporal change of a failure mass effectively in high-resolution large display environments.

Select this Article		High-performance computing and visualization of earthquake simulations and ground-motion sensor network data Tung-Ju Hsieh, Shiann-Jong Lee, Yuan-Sen Yang, Yang-Lang Chang, Bormin Huang, Cheng-Kai Chen, and Kwan-Liu Ma J. Appl. Remote Sens. 6, 061501 (Apr 03, 2012); http://dx.doi.org/10.1117/1.JRS.6.061501 Online Publication Date: Apr 03, 2012 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Comparing numerical simulation results with accelerograph readings is essential in earthquake investigations and discoveries. We provide a case study on the magnitude 7.6 Taiwan Chi-Chi earthquake in 1999. More than 400 seismic sensor stations recorded this event, and the readings from this event increased global strong-motion records fivefold so that the accuracy of the earthquake simulation was enhanced significantly. Direct volume rendering is used to depict the space-time relationships of numerical results and seismic readings. When earthquake simulation data are volume rendered, it reveals the sequence of seismic wave initiation, propagation, attenuation, and energy releasing events of fault ruptures so that the direction of seismic wave propagation can be observed. Both accelerograph readings and earthquake simulation data are used to generate a sequence of ground-motion maps. Stacking these maps up in sequence forms a volume data. Visual analysis of the time-varying component reveals hidden features for better comparison and evaluation. Earthquake scientists are able to obtain insights and evaluate their simulation criteria from volume rendering.

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