Retrieval of EVI from Oceansat 2 Data and Comparison with MODIS Derived EVI

The vegetation index is derived using many remote sensing sensors. Vegetation Index is extensively used and remote sensing has become the primary data source. Number of vegetation indices (VIs) have been developed during the past decades in order to assess the state of vegetation qualitatively and quantitatively. Analysis of vegetation indices has been carried out by many investigators scaling from regional level to global level using the remote sensing data of varying spatial, temporal and radiometric resolutions. There are as many as 14 VIs in use. Globally operational algorithms for generation of NDVI have utilized digital counts, at sensor radiances, ‘normalized’ reflectance (top of the atmosphere), and more recently, partially atmospheric corrected (ozone absorption and molecular scattering) reflectance. Presently NDVI and EVI are standard MODIS data products which are widely used by the scientific community for environmental studies. The OCM sensor in Oceansat 2 is designed for ocean colour studies. The OCM sensor has been used for studying ocean phytoplankton, suspended sediments and aerosol optical depth by many investigators. In addition to its capability of studying the ocean surface, OCM sensor has also the potential to study the land surface features. In a past EVI has been retrieved using OCM sensor of Oceansat 1. However, there is slight change in the band width of Oceansat 2—OCM sensor compared with OCM of Oceansat 1 sensor. In the present paper an attempt has been made to derive EVI using Oceansat 2 OCM sensor and the results have been compared with MODIS data. The enhanced vegetation index (EVI) is calculated using the reflectance values obtained after removing molecular scattering and ozone absorption component from the total radiance detected by the sensor. The band-2, Band-3, band-6 and band-8 corresponding to Blue, Red and Infrared part of the visible spectrum have been used to determine EVI. The result shows that Oceansat 2 derived EVI and MODIS derived EVI are well correlated.     

联合HJ-1/CCD和Landsat8/OLI数据反演黑河中游叶面积指数

目前制约30 m分辨率地表参数遥感提取的主要因素是有限的观测个数,而联合多传感器观测是提高单位时间观测频次的一个有效途径。本文以黑河中游为研究区,利用HJ-1/CCD和Landsat 8/OLI传感器构建多传感器观测数据集。对多传感器观测数据集在观测周期内的有效观测个数、观测角度和双向反射分布函数BRDF分布特征、以及经过预处理后的多传感器数据一致性等问题进行分析。不同传感器观测数据质量差异是多传感器联合反演的主要问题,因此本文首先制定了多传感器数据质量控制方案,然后利用统一模型查找表反演单传感器叶面积指数LAI结果,对10天观测周期内经过质量筛选的单传感器反演结果采用平均方法合成LAI产品。结果表明,LAI有效反演像元占总反演像元比例由单传感器的6.4%—49.7%提高到多传感器的75.9%。利用地面测量数据进行验证分析,LAI反演结果与地面实测数据的均方根误差RMSE均值为0.71。利用30 m分辨率的HJ-1/CCD和Landsat 8/OLI传感器数据可以生产精度可信、时间分辨率连续的LAI产品。     

Consistency of vegetation index seasonality across the Amazon rainforest

Vegetation indices (VIs) calculated from remotely sensed reflectance are widely used tools for characterizing the extent and status of vegetated areas. Recently, however, their capability to monitor the Amazon forest phenology has been intensely scrutinized. In this study, we analyze the consistency of VIs seasonal patterns obtained from two MODIS products: the Collection 5 BRDF product (MCD43) and the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC). The spatio-temporal patterns of the VIs were also compared with field measured leaf litterfall, gross ecosystem productivity and active microwave data. Our results show that significant seasonal patterns are observed in all VIs after the removal of view-illumination effects and cloud contamination. However, we demonstrate inconsistencies in the characteristics of seasonal patterns between different VIs and MODIS products. We demonstrate that differences in the original reflectance band values form a major source of discrepancy between MODIS VI products. The MAIAC atmospheric correction algorithm significantly reduces noise signals in the red and blue bands. Another important source of discrepancy is caused by differences in the availability of clear-sky data, as the MAIAC product allows increased availability of valid pixels in the equatorial Amazon. Finally, differences in VIs seasonal patterns were also caused by MODIS collection 5 calibration degradation. The correlation of remote sensing and field data also varied spatially, leading to different temporal offsets between VIs, active microwave and field measured data. We conclude that recent improvements in the MAIAC product have led to changes in the characteristics of spatio-temporal patterns of VIs seasonality across the Amazon forest, when compared to the MCD43 product. Nevertheless, despite improved quality and reduced uncertainties in the MAIAC product, a robust biophysical interpretation of VIs seasonality is still missing.     

Estimation of vegetation LAI from hyperspectral reflectance data: Effects of soil type and plant architecture

The retrieval of canopy biophysical variables is known to be affected by confounding factors such as plant type and background reflectance. The effects of soil type and plant architecture on the retrieval of vegetation leaf area index (LAI) from hyperspectral data were assessed in this study. In situ measurements of LAI were related to reflectances in the red and near-infrared and also to five widely used spectral vegetation indices (VIs). The study confirmed that the spectral contrast between leaves and soil background determines the strength of the LAI–reflectance relationship. It was shown that within a given vegetation species, the optimum spectral regions for LAI estimation were similar across the investigated VIs, indicating that the various VIs are basically summarizing the same spectral information for a given vegetation species. Cross-validated results revealed that, narrow-band PVI was less influenced by soil background effects (0.15 ≤ RMSE_cv ≤ 0.56). The results suggest that, when using remote sensing VIs for LAI estimation, not only is the choice of VI of importance but also prior knowledge of plant architecture and soil background. Hence, some kind of landscape stratification is required before using hyperspectral imagery for large-scale mapping of vegetation biophysical variables.     

Indoor localization for pedestrians with real-time capability using multi-sensor smartphones

ABSTRACT

The localization of persons or objects usually refers to a position determined in a spatial reference system. Outdoors, this is usually accomplished with Global Navigation Satellite Systems (GNSS). However, the automatic positioning of people in GNSS-free environments, especially inside of buildings (indoors) poses a huge challenge. Indoors, satellite signals are attenuated, shielded or reflected by building components (e.g. walls or ceilings). For selected applications, the automatic indoor positioning is possible based on different technologies (e.g. WiFi, RFID, or UWB). However, a standard solution is still not available. Many indoor positioning systems are only suitable for specific applications or are deployed under certain conditions, e.g. additional infrastructures or sensor technologies. Smartphones, as popular cost-effective multi-sensor systems, is a promising indoor localization platform for the mass-market and is increasingly coming into focus. Today’s devices are equipped with a variety of sensors that can be used for indoor positioning. In this contribution, an approach to smartphone-based pedestrian indoor localization is presented. The novelty of this approach refers to a holistic, real-time pedestrian localization inside of buildings based on multi-sensor smartphones and easy-to-install local positioning systems. For this purpose, the barometric altitude is estimated in order to derive the floor on which the user is located. The 2D position is determined subsequently using the principle of pedestrian dead reckoning based on user's movements extracted from the smartphone sensors. In order to minimize the strong error accumulation in the localization caused by various sensor errors, additional information is integrated into the position estimation. The building model is used to identify permissible (e.g. rooms, passageways) and impermissible (e.g. walls) building areas for the pedestrian. Several technologies contributing to higher precision and robustness are also included. For the fusion of different linear and non-linear data, an advanced algorithm based on the Sequential Monte Carlo method is presented.     

Cross-sensor calibration between Ikonos and Landsat ETM+ for spectral mixture analysis

Spectral mixture analysis is an algorithm that is developed to overcome the weakness in traditional land-use/land-cover (LULC) classification where each picture element (pixel) from remote sensing is assigned to one and only one LULC type. In reality, a remotely sensed signal from a pixel is often a spectral mixture from several LULC types. Spectral mixture analysis can derive subpixel proportions for the endmembers from remotely sensed data. However, one frequently faces the problem in determining the spectral signatures for the endmembers. This study provides a cross-sensor calibration algorithm that enables us to obtain the endmember signatures from an Ikonos multispectral image for spectral mixture analysis using Landsat ETM+ images. The calibration algorithm first converts the raw digital numbers from both sensors into at-satellite reflectance. Then, the Ikonos at-satellite reflectance image is degraded to match the spatial resolution of the Landsat ETM+ image. The histograms at the same spatial resolution from the two images are matched, and the signatures from the pure pixels in the Ikonos image are used as the endmember signatures. Validation of the spectral mixture analysis indicates that the simple algorithm works effectively. The algorithm is not limited to Ikonos and Landsat sensors. It is, in general, applicable to spectral mixture analysis where a high spatial resolution sensor and a low spatial resolution sensor with similar spectral resolutions are available as long as images collected by the two sensors are close in time over the same place.     

Exploring the use of vegetation indices to sense canopy nitrogen to phosphorous ratio in grasses

Reduced availability of plant nutrients such as nitrogen (N) and phosphorous (P) has detrimental effects on plant growth. Plant N:P ratio, calculated as the quotient of N and P concentrations, is an ecological indicator of relative N and P limitation. Remote sensing has already been widely used to detect plant traits in foliage, particularly canopy N and P concentrations and could be used to detect canopy N:P faster and at lower cost than traditional destructive methods. Despite the potential opportunity of applying remote sensing techniques to detect canopy N:P, studies investigating canopy N:P remote detection are scarce. In this study, we examined if vegetation indices developed for canopy N or P detection can also be used for canopy N:P detection. Using in situ spectrometry, we measured the reflectance of a common grass species, Yorkshire fog (Holcus lanatus L.), grown under different nutrient ratios and levels. We calculated 60 VIs found in literature and compared them to optimized VIs developed specifically for this study. The VIs were calculated using both the original narrow band spectra and the spectra resampled to the band properties of six satellite sensors (MSI – Sentinel 2, OLCI – Sentinel 3, MODIS – Terra/Aqua, OLI – Landsat 8, WorldView 4 and RapidEye) to investigate the influence of bandwidths and band positions. The results showed that canopy N:P was significantly related to both existing VIs (r² = 0.16 - 0.48) and optimized VIs (r² = 0.59 – 0.72) with correlations similar to what was observed for canopy N or canopy P. Existing VIs calculated with MSI and OLI sensors bands showed higher correlation with canopy N:P compared to the other sensors while the correlation with optimized VIs was not affected by the differences in sensors’ bands. This study might lead to future practical applications using in situ reflectance measurements to sense canopy N:P in grasslands.     

A novel method for enhancement of radiometric resolution using image fusion

The Resourcesat-I satellite is equipped with different types of sensors with varied characteristics. For the effective utilization of the available multi-sensor, multi-temporal, multi-spectral and multi-radiometric data from these sensors, fusion of digital image data has become a valuable technique. Image fusion enhances the information content and helps in better discrimination of various land cover types. The Resourcesat-1 has equipped with three sensors, AWiFS, LISS-III and LISS-IV, which are having identical spectral resolutions, with different spatial, radiometric and temporal resolutions. The spatial resolutions ratio of the data set for merging are required to be maximum of 1:6, where as the data sets (AWiFS and LISS-III) that are used in the current study are having the ratio of 1:2.5 approximately. A novel merging technique is designed, which retains the multi-spectral response of the input data in the output data. The merged data set provides the higher spatial and radiometric resolutions. In order to evaluate the fusion merits quantitatively, all the data sets are digitally classified and studied the output classes for homogeneity and clear discrimination. A comprehensive comparative study is carried out between the fused image and the LISS-III image based on the contingency matrix and the scatter plots, which demonstrates the strength of fused image for discriminating the object classes at 23.5 m spatial and 10-bit radiometric resolutions. The merged data set gives the improved classification accuracy.     

The welfare benefit of a home’s location: an empirical comparison of spatial and non-spatial model estimates

This article compares results from non-spatial and new spatial methods to examine the reliability of welfare estimates (direct and multiplier effects) for locational housing attributes in Seattle, WA. In particular, we assess if OLS with spatial fixed effects is able to account for the spatial structure in a way that represents a viable alternative to spatial econometric methods. We find that while OLS with spatial fixed effects accounts for more of the spatial structure than simple OLS, it does not account for all of the spatial structure. It thus does not present a viable alternative to the spatial methods. Similar to existing comparisons between results from non-spatial and established spatial methods, we also find that OLS generates higher coefficient and direct effect estimates for both structural and locational housing characteristics than spatial methods do. OLS with spatial fixed effects is closer to the spatial estimates than OLS without fixed effects but remains higher. Finally, a comparison of the direct effects with locally weighted regression results highlights spatial threshold effects that are missed in the global models. Differences between spatial estimators are almost negligible in this study.     

Comprehensive study of the biophysical parameters of agricultural crops based on assessing Landsat 8 OLI and Landsat 7 ETM+ vegetation indices

For three agricultural crop types, winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.), we estimated biophysical parameters including fresh and dry biomass, leaf area index (LAI), and vegetation water content, for which we found the equivalent water thickness (EWT), fuel moisture content per fresh weight (FMC_FW), and fuel moisture content per dry weight (FMC_DW). We performed these estimations using data from the newly launched Landsat 8 Operational Land Imager (OLI) sensor, as well as its predecessor the Landsat 7 Enhanced Thematic Mapper Plus (ETM+). Progress in the design of the new sensor (i.e., Landsat 8), including narrower near-infrared (NIR) wavebands, higher signal-to-noise ratio (SNR), and greater radiometric resolution highlights the necessity to investigate the biophysical parameters of agricultural crops, especially compared to data from its predecessor. This study aims to evaluate vegetation indices (VIs) derived from the Landsat 8 OLI and the Landsat 7 ETM+. Both the Landsat 8 OLI and Landsat 7 ETM+ VIs agreed well with in-situ data measurements. However, the Landsat 8 OLI-derived VIs were generally more consistent with in situ data than the Landsat 7 ETM+ VIs. We also note that the Landsat 8 OLI is better able to capture the small variability of the VIs because of its higher SNR and wider radiometric range; in addition, the saturation phenomenon occurred earlier for the Landsat 7 ETM+ than for the Landsat 8 OLI. This indicates that the new sensor is better able to estimate the biophysical parameters of crops.     

基于激光跟踪仪的传感器坐标标定应用研究

传感器空间位置标定是测量系统集成与安装的关键技术之一。根据激光跟踪仪的特点与优势,探讨了激光跟踪仪应用于移动测量多传感器坐标转换关系标定的可行性与适用性;重点探索了基于激光跟踪仪的传感器标定测量与数据处理方法,并分析了标定误差及其影响因素。针对船载多波束单体标定需求,基于Leica AT930型激光跟踪仪和经纬仪工业测量系统展开对比分析和标定对比实验,从而证明了采用接触测量为手段的激光跟踪仪能满足部分传感器标定任务需求,能极大提升便捷性与标定效率。     

Very high resolution Earth Observation features for testing the direct and indirect effects of landscape structure on local habitat quality

Modelling the empirical relationships between habitat quality and species distribution patterns is the first step to understanding human impacts on biodiversity. It is important to build on this understanding to develop a broader conceptual appreciation of the influence of surrounding landscape structure on local habitat quality, across multiple spatial scales. Traditional models which report that ‘habitat amount’ in the landscape is sufficient to explain patterns of biodiversity, irrespective of habitat configuration or spatial variation in habitat quality at edges, implicitly treat each unit of habitat as interchangeable and ignore the high degree of interdependence between spatial components of land-use change. Here, we test the contrasting hypothesis, that local habitat units are not interchangeable in their habitat attributes, but are instead dependent on variation in surrounding habitat structure at both patch- and landscape levels. As the statistical approaches needed to implement such hierarchical causal models are observation-intensive, we utilise very high resolution (VHR) Earth Observation (EO) images to rapidly generate fine-grained measures of habitat patch internal heterogeneities over large spatial extents. We use linear mixed-effects models to test whether these remotely-sensed proxies for habitat quality were influenced by surrounding patch or landscape structure. The results demonstrate the significant influence of surrounding patch and landscape context on local habitat quality. They further indicate that such an influence can be direct, when a landscape variable alone influences the habitat structure variable, and/or indirect when the landscape and patch attributes have a conjoined effect on the response variable. We conclude that a substantial degree of interaction among spatial configuration effects is likely to be the norm in determining the ecological consequences of habitat fragmentation, thus corroborating the notion of the spatial context dependence of habitat quality.     

Mapping natural and urban environments using airborne multi-sensor ADS40–MIVIS–LiDAR synergies

The recent and forthcoming availability of high spatial resolution imagery from satellite and airborne sensors offers the possibility to generate an increasing number of remote sensing products and opens new promising opportunities for multi-sensor classification. Data fusion strategies, applied to modern airborne Earth observation systems, including hyperspectral MIVIS, color-infrared ADS40, and LiDAR sensors, are explored in this paper for fine-scale mapping of heterogeneous urban/rural landscapes. An over 1000-element array of supervised classification results is generated by varying the underlying classification algorithm (Maximum Likelihood/Spectral Angle Mapper/Spectral Information Divergence), the remote sensing data stack (different multi-sensor data combination), and the set of hyperspectral channels used for classification (feature selection). The analysis focuses on the identification of the best performing data fusion configuration and investigates sensor-derived marginal improvements. Numerical experiments, performed on a 20-km stretch of the Marecchia River (Italy), allow for a quantification of the synergies of multi-sensor airborne data. The use of Maximum Likelihood and of the feature space including ADS40, LiDAR derived normalized digital surface, texture layers, and 24 MIVIS bands represents the scheme that maximizes the classification accuracy on the test set. The best classification provides high accuracy (92.57% overall accuracy) and demonstrates the potential of the proposed approach to define the optimized data fusion and to capture the high spatial variability of natural and human-dominated environments. Significant inter-class differences in the identification schemes are also found by indicating possible sub-optimal solutions for landscape-driven mapping, such as mixed forest, floodplain, urban, and agricultural zones.     

Evaluation and use of Resourcesat-I data for agricultural applications

Resourcesat-1 satellite offers a unique opportunity of simultaneous observations at three different spatial scales through LISS-IV, LISS-III^* (improved LISS-III) and AWiFS sensors from a common platform. The sensors have enhanced capabilities in terms of spectral, spatial and radiometric resolution as compared to earlier Indian Remote sensing Satellite sensors. This paper summarizes the results of various studies such as evaluation of sensor characteristics, inter-sensor comparison studies, derivation and validation of surface reflectance measurements, quantification of improvements due to Resourcesat-1 sensors, and their use for various agricultural applications. The studies presented in this paper demonstrate that suit of sensors onboard Resourcesat-1 satellite provides better prospects for several agricultural applications like crop identification, discrimination and crop inventory for some major Indian crops, than its predecessors on IRS satellites.     

On the generation of ERS/ENVISAT DInSAR time-series via the SBAS technique

We exploit the small baseline subset (SBAS) algorithm for generating deformation time-series from SAR data acquired by sensors with different characteristics but with the same illumination geometry. In particular, our approach is focused on the use of European Remote Sensing (ERS) and ENVISAT satellite data, the latter acquired by the Advanced Synthetic Aperture Radar sensor on the IS2 swath. The proposed solution is oriented to investigate large-scale displacements with a relatively low spatial resolution (about 100/spl times/100 m) and implements an easy but effective combination of ERS and ENVISAT multilook interferograms which benefits of the temporal overlap between the acquisitions of the two sensors. Moreover, the algorithm does not rely on specific hypothesis on the spatial or temporal characteristics of the investigated deformations. Presented results, achieved on a synthetic aperture radar dataset relevant to the Napoli city area (Italy), confirm the validity of the approach.     

Mapping forest leaf area index using reflectance and textural information derived from WorldView-2 imagery in a mixed natural forest area in Florida,US

The leaf area index (LAI) of plant canopies is an important structural parameter that controls energy, water, and gas exchanges of plant ecosystems. Remote sensing techniques may offer an alternative for measuring and mapping forest LAI at a landscape scale. Given the characteristics of high spatial/spectral resolution of the WorldView-2 (WV2) sensor, it is of significance that the textural information extracted from WV2 multispectral (MS) bands will be first time used in estimating and mapping forest LAI. In this study, LAI mapping accuracies would be compared from (a) spatial resolutions between 2-m WV2 MS data and 30-m Landsat TM imagery, (b) the nature of variables between spectrum-based features and texture-based features, and (c) sensors between TM and WV2. Therefore spectral/textural features (SFs) were first selected and tested; then a canonical correlation analysis was performed with different data sets of SFs and LAI measurement; and finally linear regression models were used to predict and map forest LAI with canonical variables calculated from image data. The experimental results demonstrate that for estimating and mapping forest LAI, (i) using high resolution data (WV2) is better than using relatively low resolution data (TM); (ii) extracted from the same WV2 data, texture-based features have higher capability than that of spectrum-based features; (iii) a combination of spectrum-based features with texture-based features could lead to even higher accuracy of mapping forest LAI than their either one separately; and (iv) WV2 sensor outperforms TM sensor significantly. However, we need to address the possible overfitting phenomenon that might be brought in by using more input variables to develop models. In addition, the experimental results also indicate that the red-edge band in WV2 was the worst on estimating LAI among WV2 MS bands and the WV2 MS bands in the visible range had a much higher correlation with ground measured LAI than that red-edge and NIR bands did.     

Sensing strides using EMG signal for pedestrian navigation

Navigation applications and location-based services are now becoming standard features in smart phones. However, locating a mobile user anytime anywhere is still a challenging task, especially in GNSS (Global Navigation Satellite System) degraded and denied environments, such as urban canyons and indoor environments. To approach a seamless indoor/outdoor positioning solution, Micro-Electro-Mechanical System sensors such as accelerometers, digital compasses, gyros and pressure sensors are being adopted as augmentation technologies for a GNSS receiver. However, the GNSS degraded and denied environments are typically contaminated with significant sources of error, which disturb the measurements of these sensors. We introduce a new sensor, the electromyography (EMG) sensor, for stride detection and stride length estimation and apply these measurements, together with a digital compass, to a simple pedestrian dead reckoning (PDR) solution. Unlike the accelerometer, which senses the earth gravity field and the kinematic acceleration of the sensor, the EMG sensor senses action potentials generated by the muscle contractions of the human body. The EMG signal is independent of the ambient environment and its disturbance sources. Therefore, it is a good alternative sensor for stride detection and stride length estimation. For evaluating the performance of the EMG sensor, we carried out several field tests at a sports field and along a pedestrian path. The test results demonstrated that the accuracy of stride detection was better than 99.5%, the errors of the EMG-derived travelled distances were less than 1.5%, and the performance of the corresponding PDR solutions was comparable to that of the global positioning system solutions.     

Developing Basic Design and Mathematical Framework for a Mobile Mapping System—a Case Study Using Available Sensors

There is a growing demand for technologies that support capturing of comprehensive and good quality 3D spatial data at a faster rate with low investment and minimal effort, while also causing least disturbance to other activities in the area. Mobile mapping systems (MMS), which are being developed in a few western countries, solve this problem but their import is highly expensive. While the components of a MMS are easily available off-the-shelf at lower cost, the main reason for their high cost is the intellectual property involved in the sensor design, integration, calibration, and the related software. Developing the intellectual property locally can bring down the cost of MMS to a large extent. Keeping this in mind, a MMS has been developed in this research using the locally available sensors. This paper describes the methodology to integrate navigation and mapping sensors including the developed calibration procedures. It further describes the time synchronization technique developed for multi-sensor data fusion and algorithms implemented by software package for data processing. The sensors integrated in the MMS include a standard GPS, IMU and a standard laser scanner. A Kalman filter is implemented to integrate the GPS and IMU data, which provides position and orientation information for the sensors. A simulation software package is also developed to verify, understand and develop the equations used in MMS. Field tests have been performed using the developed MMS and the results are shown for a few cases. Results validate the designed algorithms and indicate the successful development of the MMS, which has potential to be further developed and used in field. Though a number of papers are available on MMS, the thrust of this paper is to present a complete methodology for developing a MMS using locally available sensors. Unlike available papers, this paper outlines all aspects of design, calibration and operation, where each of these aspects is handled in a novel way as demanded by the available sensors. This is particularly useful for individuals or organizations interested in procuring sensor components off-the-shelf and develop their own (low cost) Mobile Mapping system.     

Implications of sensor design for coral reef detection: Upscaling ground hyperspectral imagery in spatial and spectral scales

Remote sensing offers a potential tool for large scale environmental surveying and monitoring. However, remote observations of coral reefs are difficult especially due to the spatial and spectral complexity of the target compared to sensor specifications as well as the environmental implications of the water medium above. The development of sensors is driven by technological advances and the desired products. Currently, spaceborne systems are technologically limited to a choice between high spectral resolution and high spatial resolution, but not both. The current study explores the dilemma of whether future sensor design for marine monitoring should prioritise on improving their spatial or spectral resolution. To address this question, a spatially and spectrally resampled ground-level hyperspectral image was used to test two classification elements: (1) how the tradeoff between spatial and spectral resolutions affects classification; and (2) how a noise reduction by majority filter might improve classification accuracy. The studied reef, in the Gulf of Aqaba (Eilat), Israel, is heterogeneous and complex so the local substrate patches are generally finer than currently available imagery. Therefore, the tested spatial resolution was broadly divided into four scale categories from five millimeters to one meter. Spectral resolution resampling aimed to mimic currently available and forthcoming spaceborne sensors such as (1) Environmental Mapping and Analysis Program (EnMAP) that is characterized by 25 bands of 6.5 nm width; (2) VENμS with 12 narrow bands; and (3) the WorldView series with broadband multispectral resolution. Results suggest that spatial resolution should generally be prioritized for coral reef classification because the finer spatial scale tested (pixel size < 0.1 m) may compensate for some low spectral resolution drawbacks. In this regard, it is shown that the post-classification majority filtering substantially improves the accuracy of all pixel sizes up to the point where the kernel size reaches the average unit size (pixel < 0.25 m). However, careful investigation as to the effect of band distribution and choice could improve the sensor suitability for the marine environment task. This in mind, while the focus in this study was on the technologically limited spaceborne design, aerial sensors may presently provide an opportunity to implement the suggested setup.     

#Earthquake: Twitter as a Distributed Sensor System

Social media feeds are rapidly emerging as a novel avenue for the contribution and dissemination of information that is often geographic. Their content often includes references to events occurring at, or affecting specific locations. Within this article we analyze the spatial and temporal characteristics of the twitter feed activity responding to a 5.8 magnitude earthquake which occurred on the East Coast of the United States (US) on August 23, 2011. We argue that these feeds represent a hybrid form of a sensor system that allows for the identification and localization of the impact area of the event. By contrasting this with comparable content collected through the dedicated crowdsourcing ‘Did You Feel It?’ (DYFI) website of the U.S. Geological Survey we assess the potential of the use of harvested social media content for event monitoring. The experiments support the notion that people act as sensors to give us comparable results in a timely manner, and can complement other sources of data to enhance our situational awareness and improve our understanding and response to such events.