Ground Truth Observations Research Articles

Mapping paddy rice areas based on vegetation phenology and surface moisture conditions

Accurate and timely rice mapping is important for food security and environmental sustainability. We developed a novel approach for rice mapping through Combined Consideration of Vegetation phenology and Surface water variations (CCVS). Variation of the Land Surface Water Index (LSWI) in rice fields was relatively smaller than that in other crops fields during the period from tillering to heading dates. Therefore, the ratios of change amplitude of LSWI to 2-band Enhanced Vegetation Index 2 (EVI2) during that period were utilized as the primary metric for paddy rice mapping. This algorithm was applied to map paddy rice fields in southern China using an 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) in 2013. The resultant rice cropping map was consistent with the agricultural census data (r2=0.8258) and ground truth observations (overall accuracy=93.4%). Validation with Landsat Thematic Mapper images in test regions also revealed its high accuracy (with overall accuracy of 94.3% and kappa coefficient of 0.86). The proposed CCVS method was more robust to intra-class variability and other related uncertainties compared with other related methods in rice mapping. Its successful application in southern China revealed its efficiency and great potential for further utilization.

Seafloor geomorphic manifestations of gas venting and shallow subbottom gas hydrate occurrences

High-resolution multibeam bathymetry data collected with an autonomous underwater vehicle (AUV) complemented by compressed high-intensity radar pulse (Chirp) profiles and remotely operated vehicle (ROV) observations and sediment sampling reveal a distinctive rough topography associated with seafloor gas venting and/or near-subsurface gas hydrate accumulations. The surveys provide 1 m bathymetric grids of deep-water gas venting sites along the best-known gas venting areas along the Pacific margin of North America, which is an unprecedented level of resolution. Patches of conspicuously rough seafloor that are tens of meters to hundreds of meters across and occur on larger seafloor topographic highs characterize seepage areas. Some patches are composed of multiple depressions that range from 1 to 100 m in diameter and are commonly up to 10 m deeper than the adjacent seafloor. Elevated mounds with relief of >10 m and fractured surfaces suggest that seafloor expansion also occurs. Ground truth observations show that these areas contain broken pavements of methane-derived authigenic carbonates with intervening topographic lows. Patterns seen in Chirp profiles, ROV observations, and core data suggest that the rough topography is produced by a combination of diagenetic alteration, focused erosion, and inflation of the seafloor. This characteristic texture allows previously unknown gas venting areas to be identified within these surveys. A conceptual model for the evolution of these features suggests that these morphologies develop slowly over protracted periods of slow seepage and shows the impact of gas venting and gas hydrate development on the seafloor morphology.

NASA ROVER, Tackling Citizen Science With Grand Challenges and Everyday Problems

ROVER is the Citizen Science arm of the NASA Clouds and the Earth's Radiant Energy System (CERES) Students' Cloud Observations On-Line (S'COOL) Project. Since 2007, participants around the world have been making and reporting ground truth observations of clouds to assist in the validation of the NASA CERES satellite instrument. NASA scientists are very interested in learning how clouds affect our atmosphere, weather, and climate (relating to climate change). It is the clouds, in part, that affect the overall temperature and energy balance of the Earth. The more we know about clouds, the more we will know about our Earth as a system and citizen scientists are an important piece of that puzzle! As a ROVER cloud observer, all participants follow simple online tutorials to collect data on cloud type, height, cover and related conditions. Observations are sent to NASA to be matched to similar information obtained from satellites and sent back to participants for comparison and analysis. The supporting ROVER website houses a searchable database archiving all participant reports and matching satellite data. By involving Citizen Scientists in cloud observations and reporting we can gain a valuable set of data that would have been previously unavailable to science teams due to funding, manpower, and resource limitations or would have taken an unreasonable amount of time to collect. Reports from a wide range of Citizen Scientist locations are helpful to assess the satellite data under different conditions. With nothing more than their eyes and an internet connection participants provide a different perspective and analysis of clouds, adding to a more complete picture of what's happening in the atmosphere in which we live.

Validation of COSMIC values of foF2 and M(3000)F2 using ground-based ionosondes

The COSMIC radio occultation values of the ionospheric F2 region peak parameters are a potential source of ground-truth data for validation of global ionospheric models. They should therefore be validated against reliable ground-truth observations such as manually scaled ionograms from ground-based vertical incidence (VI) ionosondes. We have therefore used the manually scaled hourly ionospheric characteristics from the Australian ionosonde network for this purpose.A comparison of the COSMIC F2 peak density, NmF2, against the values of the maximum plasma frequency, foF2, scaled from the Australian ionosondes showed very good agreement, with an RMS error in foF2 at mid latitudes of ∼0.5MHz.The ionograms from the Australian ionosondes are manually processed so the plasma frequency profile and the value of hmF2, the altitude corresponding to foF2, are not routinely provided. However, the ionograms do provide observations of M(3000)F2, the obliquity factor for a 3000km communications circuit, which is well correlated with hmF2. We have therefore compared observed and COSMIC values of M(3000)F2. The latter are derived from numerical ionograms calculated for the COSMIC profiles.Because of the known uncertainties in the COSMIC profiles, we have applied some stringent acceptance procedures to them, especially for the M(3000)F2 analysis. These acceptance procedures are all automated, reducing the possibility of biased data selection.

Mapping of Land Use/Land Cover Changes in Upper Ponnaiyar Watershed, Tamil Nadu: A Remote Sensing and GIS Approach

Land use and land cover is an important element in understanding the interactions of human activities with the environment and thus it is necessary to assess changes. Experiential observation revealed that changes have occurred in land use/land cover and hence an attempt is made to study the changes in land use and land cover in upper Ponnaiyar river basin over a period of 40 years (1973–2013). The study carried out using LAND SAT imageries of Thematic Mapper of 1973, Thematic Mapper of 1992, Enhanced Thematic Mapper (ETM) of 2006 and Cartosat image of 2013. Ground truth observations were also done to improve the accuracy of the classification. Finally, it was observed that the area under crop land was decreased due to quarry, industrial and real estate activities in the study area. Keywords: Land use/land cover, thematic mapper, visual image interpretation, GIS

Landsat TM imagery and NDVI differencing to detect vegetation change: assessing natural forest expansion in Basilicata, southern Italy

The NDVI (Normalized Difference Vegetation Index) differencing method using Landsat Thematic Mapping images was implemented to assess natural expansion of forests in the Basilicata region (southern Italy) for the period 1984 through 2010. Two Landsat TM (Thematic Mapper) images (1984-2010) were georeferenced and geographically corrected using the first order polynomial transformation, and the nearest neighbour method for resampling. The images were radiometrically corrected using the dark object subtraction model. The pre-processed Landsat TM images were used to calculate NDVI, and subsequently for NDVI differencing. Finally, a threshold for vegetation change detection was identified by visual analysis of Landsat TM RGB band composition, and ratios and visual comparison of digital aerial orthophotos. The methodology was validated using ground-truth observations over the study area. The applied method showed 91.8% accuracy in detection of natural forest expansion. During the examined period, total regional forest cover increased by 19.7% (70154 ha), consistent with National Forest Inventory data (1984-2005). The observed forest expansion was also examined in relationship with landscape physical characteristics and distribution of vegetation types in the Basilicata region. Surprisingly, considerable forest expansion also occurred on degraded soils in drought-prone Mediterranean areas.

The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data

We consider the problem of reconstructing vehicle trajectories from sparse sequences of GPS points, for which the sampling interval is between 1 s and 2 min. We introduce a new class of algorithms, which are altogether called the path inference filter (PIF), that maps GPS data in real time, for a variety of tradeoffs and scenarios and with a high throughput. Numerous prior approaches in map matching can be shown to be special cases of the PIF presented in this paper. We present an efficient procedure for automatically training the filter on new data, with or without ground-truth observations. The framework is evaluated on a large San Francisco taxi data set and is shown to improve upon the current state of the art. This filter also provides insights about driving patterns of drivers. The PIF has been deployed at an industrial scale inside the Mobile Millennium traffic information system, and is used to map fleets of data in San Francisco and Sacramento, CA, USA; Stockholm, Sweden; and Porto, Portugal.

Monitoring Wetlands Ecosystems Using ALOS PALSAR (L-Band, HV) Supplemented by Optical Data: A Case Study of Biebrza Wetlands in Northeast Poland

The aim of the study was to elaborate the remote sensing methods for monitoring wetlands ecosystems. The investigation was carried out during the years 2002–2010 in the Biebrza Wetlands. The meteorological conditions at the test site varied from extremely dry to very wet. The authors propose applying satellite remote sensing data acquired in the optical and microwave spectrums to classify wetlands vegetation habitats for the assessment of vegetation changes and estimation of wetlands’ biophysical properties to improve monitoring of these unique, very often physically impenetrable, areas. The backscattering coefficients (σ°) calculated from ALOS PALSAR FBD (Advanced Land Observing Satellite, Phased Array type L-band Synthetic Aperture Radar, Fine Beam Dual Mode) images registered at cross polarization HV on 12 May 2008 were used to classify the main wetland communities using ground truth observations and the visual interpretation method. As a result, the σ° values were distributed among the six wetlands’ vegetation classes: scrubs, sedges-scrubs, sedges, reeds, sedges-reeds, rushes, and the areas of each community and changes were assessed. Also, the change in the biophysical variable as Leaf Area Index (LAI) is described using the information from PALSAR data. Strong linear relationships have been found between LAI and σ° derived for particular wetland classes, which then were applied to elaborate the maps of LAI distribution. The other variables used to characterize the changing environmental conditions are: surface temperature (Ts) calculated from NOAA AVHRR (National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer) and Normalized Difference Vegetation Index (NDVI) from ENVISAT MERIS (ENVIronmental SATellite MEdium Resolution Imaging Spectrometer). Differences of almost double Ts between “dry” and “wet” years were noticed that reflect observed weather conditions. The highest values of NDVI occurred in years with a sufficient amount of precipitation with the lowest in “dry” years. NDVI values variances within the same wetlands class resulted mainly from the differences in soil moisture. The results of this study show that the satellite data from microwave and optical spectrum gave the repetitive spatial information about vegetation growth conditions and could be used for monitoring wetland ecosystems.

New Retrieval Algorithm for Deriving Land Surface Temperature From Geostationary Orbiting Satellite Observations

Accurate derivations of land surface temperature (LST) and land surface emissivity (LSE) from satellite measurements are difficult because the two variables are closely coupled. Features of significant/insignificant temporal variations in LST/LSE are recognized to de-couple both values using multiple-temporal satellite observations over the same geolocation. In this paper, a new approach is presented for deriving LST and LSE simultaneously by using multiple-temporal satellite observations. Two split-window regression formulas are carefully selected for the approach, and two satellite observations over the same geolocation within a certain time interval are utilized. The method is particularly applicable to geostationary satellite missions from which qualified multiple-temporal observations are available. The approach is designed and implemented for generating the LST and LSE values from the U.S. geostationary operational environmental satellite (GOES) eight imager data and the european meteosat second generation (MSG) mission spinning enhanced visible and infrared imager (SEVIRI) data. The performance of the algorithm is evaluated in terms of both accuracy and sensitivity. The retrieval results are compared against ground-truth observations from the U.S. Atmospheric radiation measurement facility and six surface radiation budget network (SURFRAD) stations. The validation results show the LST retrieval accuracy is around 1.95 K with good correlations of up to 0.9038. The method is applicable to the future U.S. GOES-R mission as well as the MSG mission considering that the advanced baseline imager (ABI) onboard the GOES-R satellites and the SEVIRI onboard the MSG satellite have similar split-window bands.

Characteristic properties of seabed fluvial-marine sediments in front of Damietta promontory, Nile Delta, Egypt

Acoustic bottom classification utilities have been developed in favor of routine investigation of the seabed texture and grain size, as well as type of sediments. Single beam echosounder, seabed classification systems and QTC VIEW series V were used to map out and identify sediment gradients offshore Nile Delta in front of Damietta promontory. The survey area has an asymmetric shape and of very gentle slope parallel to the shore line at Damietta promontory. Ground truth observation analyses were carried out for 24 grab samples using the classification technique. The acoustic survey data were analyzed with the QTC IMPACT software and classified into five acoustic classes namely (silty clay, clayey silt, silt, sandy silt and very fine sand). The fine sediments (clay and silt) cover the front area of Damietta outlet, forming a stream like shape cutting the center of the area of study, which composed mainly of fluvial sediments. While the sandy sediments, appear as small patches to the east and west. The dominant of the small mean size (fine fractions) is corresponding to the fluvial depositions that form the Nile Delta fan of mainly mud sediments. Actually, since the building of Aswan High Dam in 1964, sediment discharge at the Nile promontories has diminished to almost zero, the recent situation of seabed characteristics is related mainly to the effect of the oceanographic circulation regime dominating the study area.

Characterizing and Visualizing Predictive Uncertainty in Numerical Ensembles Through Bayesian Model Averaging

Numerical ensemble forecasting is a powerful tool that drives many risk analysis efforts and decision making tasks. These ensembles are composed of individual simulations that each uniquely model a possible outcome for a common event of interest: e.g., the direction and force of a hurricane, or the path of travel and mortality rate of a pandemic. This paper presents a new visual strategy to help quantify and characterize a numerical ensemble's predictive uncertainty: i.e., the ability for ensemble constituents to accurately and consistently predict an event of interest based on ground truth observations. Our strategy employs a Bayesian framework to first construct a statistical aggregate from the ensemble. We extend the information obtained from the aggregate with a visualization strategy that characterizes predictive uncertainty at two levels: at a global level, which assesses the ensemble as a whole, as well as a local level, which examines each of the ensemble's constituents. Through this approach, modelers are able to better assess the predictive strengths and weaknesses of the ensemble as a whole, as well as individual models. We apply our method to two datasets to demonstrate its broad applicability.

Statistical Modeling for Spectrum Usage Characterizing Wireless Fading Channels and Mobile Service Dynamics

Cognitive radio (CR) is considered a promising technology for addressing the issue of spectrum scarcity and has been receiving increasing attention in recent years. One of its “cognitive” capabilities is environment awareness, which gathers useful information from the radio environment, such that CR devices can accordingly change the operating (e.g., spectrum sensing) strategies. Spectrum measurement is an effective means for radio environment awareness since ground-truth observation of spectrum usage is more convincing than any theoretical assumption. In this paper, we perform in-depth statistical analysis on wireless signal strength in mobile service (MS) bands and propose a novel statistical model based on characteristic function (CF), which characterizes both wireless fading channels and MS dynamics. We validate the proposed model through empirical studies on the spectrum measurement data previously collected in Guangdong Province of China. We statistically fit the CF of the overall signal strength in MS (GSM uplink) band and estimate both Nakagami fading parameters of wireless channels and the arrival rate of MS. Then, we further utilize the estimated information to optimize the spectrum sensing strategy, which aims at maximizing the secondary user's (SU's) expected achievable throughput. The simulation results show that the optimal sensing strategy that we propose significantly improves the long-term average achievable throughput since it fully utilizes the estimated information on wireless fading channels and MS dynamics.

The possibility of using high resolution satellite images for detection of marine mammals

The possibility of using modern systems of remote sensing in the optical range from high spatial resolution satellites for detection of marine mammals and traces of their activity is investigated. An image obtained by the GeoEye satellite within the FEAC project was used for the analysis. The image covers Herald Island and adjacent waters, which are a part of the Wrangel Island Reserve, during the seasonal thaw (June 2009). It is shown that marine mammals (polar bears, walruses, and whales) can be identified on such images. The absence of synchronous ground truth observations reduces the reliability of the results.

A comparison of seismic and radar methods to establish the thickness and density of glacier snow cover

AbstractWe show that geophysical methods offer an effective means of quantifying snow thickness and density. Opportunistic (efficient but non-optimized) seismic refraction and ground-penetrating radar (GPR) surveys were performed on Storglaciären, Sweden, co-located with a snow pit that shows the snowpack to be 1.73 m thick, with density increasing from ∼120 to ∼500 kg m–3 (with a +50 kg m–3 anomaly between 0.73 and 0.83 m depth). Depths estimated for two detectable GPR reflectors, 0.76 ±0.02 and 1.71 ± 0.03 m, correlate extremely well with ground-truth observations. Refraction seismic predicts an interface at 1.90 ± 0.31 m depth, with a refraction velocity (3730 ± 190 ms–1) indicative of underlying glacier ice. For density estimates, several standard velocity-density relationships are trialled. In the best case, GPR delivers an excellent density estimate for the upper snow layer (observed = 321 ± 74 kg m–3, estimated = 319 ± 10 kgm–3) but overestimates the density of the lower layer by 20%. Refraction seismic delivers a bulk density of 404 ±22 kgm–3 compared with a ground-truth average of 356 ± 22 kg m–3. We suggest that geophysical surveys are an effective complement to mass-balance measurements (particularly for controlling estimates of snow thickness between pits) but should always be validated against ground-truth observations.

Automatic detection of field furrows from very high resolution optical imagery

Surface features such as furrows are here detected using high spatial resolution images such as those provided by IKONOS (pixel size 1 m × 1 m in panchromatic mode) and WorldView-1 instruments (pixel size 0.5 m × 0.5 m), through a new detection methodology based on image analysis. This methodology includes four different steps. First, segmentation of the whole image is performed in order to label each agricultural field individually. Then, for each field, mathematical morphological processes are applied (erosion followed by functional geodesic reconstruction) in order to enhance the image contrast. Then, an automatic thresholding process is applied to construct a binary image of the furrows or other features in the field. In order to reduce the noise due to the overlap between the classes of furrow and background, the a priori information that furrows are straight lines exhibiting regular patterns is introduced. The Hough transform is applied to detect straight lines (represented in polar coordinates) and furrow directions as accumulations on the angle axis in the Hough space. The proposed method was applied on a WorldView-1 image acquired over the Lokna catchment within the Plava watershed in Russia (around 180 km2) and validated against ground truth observations.

An indigenous state-of-the-art Digital Automatic Recording System (DARS) for surface meteorological observatories

The existing self recording instruments of India Meteorological Department (IMD) urgently require modernization by considering the demand for its real time interface with the modern now-casting tools like Doppler Weather Radar (DWR), real time weather models etc. Hence IMD has designed and developed a state-of-the-art Digital Automatic Recording System (DARS) for the automatic measurement, storage, display and communication of meteorological parameters like Atmospheric pressure, Wind speed, Wind direction, Air temperature, Relative humidity and Rainfall. The system has its origin from the existing IMD make High Wind Speed Recording (HWSR) System installed at more than 15 coastal observatories in the eastern coast of India, by suitable integration with different types of electronic meteorological sensors available in the global market. Authors have participated at various stages of design, development, installation, and data validation of the system. This paper highlights the design aspects of indigenous system and its data quality with respect to imported AWS and ground truth observations and the proposal for its installation at selected villages in a district to study the spatial variation of Wind and Rainfall during Monsoon period.

An atmospheric instability derived with MODIS profile using real-time direct broadcast data over the Indian region

In this study, assessing the atmospheric instability, a new index, named here as MODIS (Moderate Resolution Imaging Spectroradiometer) profile index (MPI), has been statistically computed using temperature and moisture profile data from the real-time direct broadcast receiving systems installed at three places of India Meteorological Department. The training dataset has been prepared using MODIS temperature and moisture profile from the Aqua and Terra satellites over the Indian region for clear and convective weather conditions during the period of March to June 2011. The MPI values are produced at 5 × 5 km pixel resolution when at least 6 out of 9 FOVs from MODIS granules are found cloud free. If more than 3 FOVs are cloudy, the MPI has not been computed. The formulation of MPI and its comparison have been examined with well-established traditionally used K index, Lifted Index and total totals index derived from radiosonde profiles of temperature, pressure and humidity. It has been observed that in most of the cases, MPI has well correlated with those derived from ground truth observations. Therefore, spatially interpolated MPI can be utilized as an indicator for regional and location-specific forecast over the areas where radiosonde data are not available. The results also indicated that MPI can be used as a sensitive measure in very early stages of instability developments such as thunderstorm and rainfall because no other single stability index can provide a distinct threshold value for these events. Therefore, a single MPI value at a certain threshold can be treated as a stability index instead of other available indices. It is also being proposed that the inclusion of MPI as a stability parameter in physical or numerical modeling can improve accurate local severe storm predictions as a useful predictor and can also be used as diagnostic tools. The MPI can make a useful simulation using entire temperature and moisture profile data for the assessment of instability significantly to severe weather forecasting since other instability indices are often derived from a fixed pressure level quantity of vertical profile parameters.

Sidescan sonar imagery of widespread fossil and active cold seeps along the central Chilean continental margin

The central Chilean subduction zone between 35°S and 37°S was investigated in order to identify, document and possibly understand fluid flow and fluid venting within the forearc region. Several areas were mapped using multibeam bathymetry and backscatter, high-resolution sidescan sonar, chirp subbottom profiling and reflection seismic data. On a subsequent cruise ground-truthing observations were made using a video sled. In general, this dataset shows surprisingly little evidence of fluid venting along the mid-slope region, in contrast to other subduction zones such as Central America and New Zealand. There were abundant indications of active and predominantly fossil fluid venting along the upper slope between 36.5°S and 36.8°S at the seaward margin of an intraslope basin. Here, backscatter anomalies suggest widespread authigenic carbonate deposits, likely the result of methane-rich fluid expulsion. There is unpublished evidence that these fluids are of biogenic origin and generated within the slope sediments, similar to other accretionary margins but in contrast to the erosional margin off Central America, where fluids have geochemical signals indicating an origin from the subducting plate.

Application of Remote Sensing Techniques in Land Use Pattern Auditing of Ero Dam Catchment’s Basin

There are many situations in which a better data base would improve our ability to audit changes that occur in our environment as a result of human interference. This is important especially when changes is extensive and numerous that their cumulative efforts can produce unforeseen results. Consequently, to have adequate appraisal of land use changes that occur at Ero Dam catchment's basin and to be able to prepare for or modify its diverse moods, remote sensing technique is employed. This involves the examination of physiographic map of 1983 and standard satellite images of Landsat MSS of 2003. The data were processed, enhanced and classified using Maximum Likelihood Classification System. The 4-Quadrant Pixel method of ground-truth and observation was utilized to determine the accuracy of the work. Therefore, map-to-map comparison was carried out using 1983 as a master to detect changes in the classified land use type in the area. The results were presented in maps and tables. In an overview from 1983-2003, the result show that agricultural land witness a reduction of size while water bodies witness a considerable increase of size. Other areas that witness increase are the built-up land, rangeland, wetland and barren land while forestland in addition to the agricultural land witnesses a decrease in size. It is obvious from the result that remote sensing technique is capable of monitoring land use changes that occur over time within a dam catchment's basin, especially when complimented with adequate ground-truth observation.

Evolutionary characteristics of a dust storm over Oman on 2 February 2008

The evolutionary characteristics of a dust storm over Oman on 2 February 2008 were studied by analyzing the weather associated with it. The National Center for Environmental Prediction/National Centre for Atmospheric Research (NCEP/NCAR) reanalysis wind flow pattern at 1,000 hPa showed a clockwise and outward wind flow pattern over the study region, a manifestation of a high-pressure cold air mass. In addition, ground truth observations for surface temperature and surface winds showed cold northerly winds until the early morning of 2 February 2008. A strong wind shear resulted from differences in wind speed between warm air and trapped cold air. This vertical wind shear enhanced instability. Furthermore, the weakening of the inversion in the lower troposphere and the formation of a mixed layer due to transfer of horizontal momentum from upper air towards the surface led to strong surface winds. These strong winds lifted a large amount of dust particles off the ground, resulting in the dust event of 2 February 2008.