Ancillary Information Research Articles

Top-of-the-atmosphere shortwave flux estimation from satellite observations: an empirical neural network approach applied with data from the A-train constellation

Abstract. Estimates of top-of-the-atmosphere (TOA) radiative flux are essential for the understanding of Earth's energy budget and climate system. Clouds, aerosols, water vapor, and ozone (O3) are among the most important atmospheric agents impacting the Earth's shortwave (SW) radiation budget. There are several sensors in orbit that provide independent information related to these parameters. Having coincident information from these sensors is important for understanding their potential contributions. The A-train constellation of satellites provides a unique opportunity to analyze data from several of these sensors. In this paper, retrievals of cloud/aerosol parameters and total column ozone (TCO) from the Aura Ozone Monitoring Instrument (OMI) have been collocated with the Aqua Clouds and Earth's Radiant Energy System (CERES) estimates of total reflected TOA outgoing SW flux (SWF). We use these data to develop a variety of neural networks that estimate TOA SWF globally over ocean and land using only OMI data and other ancillary information as inputs and CERES TOA SWF as the output for training purposes. OMI-estimated TOA SWF from the trained neural networks reproduces independent CERES data with high fidelity. The global mean daily TOA SWF calculated from OMI is consistently within ±1 % of CERES throughout the year 2007. Application of our neural network method to other sensors that provide similar retrieved parameters, both past and future, can produce similar estimates TOA SWF. For example, the well-calibrated Total Ozone Mapping Spectrometer (TOMS) series could provide estimates of TOA SWF dating back to late 1978.

Comprehensive profiling and quantitation of oncogenic mutations in non small-cell lung carcinoma using single molecule amplification and re-sequencing technology.

Activating and resistance mutations in the tyrosine kinase domain of several oncogenes are frequently associated with non-small cell lung carcinoma (NSCLC). In this study we assessed the frequency, type and abundance of EGFR, KRAS, BRAF, TP53 and ALK mutations in tumour specimens from 184 patients with early and late stage disease using single molecule amplification and re-sequencing technology (SMART). Based on modelling of EGFR mutations, the detection sensitivity of the SMART assay was at least 0.1%. Benchmarking EGFR mutation detection against the gold standard ARMS-PCR assay, SMART assay had a sensitivity and specificity of 98.7% and 99.0%. Amongst the 184 samples, EGFR mutations were the most prevalent (59.9%), followed by KRAS (16.9%), TP53 (12.7%), EML4-ALK fusions (6.3%) and BRAF (4.2%) mutations. The abundance and types of mutations in tumour specimens were extremely heterogeneous, involving either monoclonal (51.6%) or polyclonal (12.6%) mutation events. At the clinical level, although the spectrum of tumour mutation(s) was unique to each patient, the overall patterns in early or advanced stage disease were relatively similar. Based on these findings, we propose that personalized profiling and quantitation of clinically significant oncogenic mutations will allow better classification of patients according to tumour characteristics and provide clinicians with important ancillary information for treatment decision-making.

RS-BASED WATER RESOURCES INVENTORY OF THE PHILIPPINES: CAPACITY BUILDING EFFORTS FOR NATIONWIDE IMPLEMENTATION

Considering that the Philippines is archipelagic in nature and is exposed to disasters accentuated by climate change, water resource monitoring and management has been an important concern in the country. The design and implementation of an effective management scheme relies heavily on accurate, complete, and updated water resource inventories, usually in the form of maps and geodatabases. With the aim of developing a detailed and comprehensive database of all water resources in the Philippines, a 3-year project entitled “Development of the Philippine Hydrologic Dataset (PHD) for Watersheds from LiDAR Surveys”, has been initiated by the University of the Philippines Diliman (UPD) and the Department of Science and Technology (DOST). Various workflows has been developed to extract inland hydrologic features in the Philippines using accurate Light Detection and Ranging (LiDAR) Digital Terrain Models (DTMs) and LiDAR point cloud data obtained through other government-funded programs, supplemented with other remotely-sensed imageries and ancillary information. Since the project covers national-scale mapping and inventory, the implementation was structured to be a collaborative effort between fifteen (15) State Universities/Colleges (SUCs) and Higher Education Institutes (HEIs), along with multiple National Government Agencies (NGAs) and Local Government Units (LGUs). This paper presents the project’s general structure, focusing mainly on its attempts and accomplishments in strengthening individual capacities of all involved SUCs, HEIs, and stakeholders utilizing hydrologic data for different applications.

RS-BASED WATER RESOURCES INVENTORY OF THE PHILIPPINES: CAPACITY BUILDING EFFORTS FOR NATIONWIDE IMPLEMENTATION

Abstract. Considering that the Philippines is archipelagic in nature and is exposed to disasters accentuated by climate change, water resource monitoring and management has been an important concern in the country. The design and implementation of an effective management scheme relies heavily on accurate, complete, and updated water resource inventories, usually in the form of maps and geodatabases. With the aim of developing a detailed and comprehensive database of all water resources in the Philippines, a 3-year project entitled “Development of the Philippine Hydrologic Dataset (PHD) for Watersheds from LiDAR Surveys”, has been initiated by the University of the Philippines Diliman (UPD) and the Department of Science and Technology (DOST). Various workflows has been developed to extract inland hydrologic features in the Philippines using accurate Light Detection and Ranging (LiDAR) Digital Terrain Models (DTMs) and LiDAR point cloud data obtained through other government-funded programs, supplemented with other remotely-sensed imageries and ancillary information. Since the project covers national-scale mapping and inventory, the implementation was structured to be a collaborative effort between fifteen (15) State Universities/Colleges (SUCs) and Higher Education Institutes (HEIs), along with multiple National Government Agencies (NGAs) and Local Government Units (LGUs). This paper presents the project’s general structure, focusing mainly on its attempts and accomplishments in strengthening individual capacities of all involved SUCs, HEIs, and stakeholders utilizing hydrologic data for different applications.

SPICE TOOLS SUPPORTING PLANETARY REMOTE SENSING

Abstract. NASA's "SPICE"* ancillary information system has gradually become the de facto international standard for providing scientists the fundamental observation geometry needed to perform photogrammetry, map making and other kinds of planetary science data analysis. SPICE provides position and orientation ephemerides of both the robotic spacecraft and the target body; target body size and shape data; instrument mounting alignment and field-of-view geometry; reference frame specifications; and underlying time system conversions. SPICE comprises not only data, but also a large suite of software, known as the SPICE Toolkit, used to access those data and subsequently compute derived quantities–items such as instrument viewing latitude/longitude, lighting angles, altitude, etc. In existence since the days of the Magellan mission to Venus, the SPICE system has continuously grown to better meet the needs of scientists and engineers. For example, originally the SPICE Toolkit was offered only in Fortran 77, but is now available in C, IDL, MATLAB, and Java Native Interface. SPICE calculations were originally available only using APIs (subroutines), but can now be executed using a client-server interface to a geometry engine. Originally SPICE "products" were only available in numeric form, but now SPICE data visualization is also available. The SPICE components are free of cost, license and export restrictions. Substantial tutorials and programming lessons help new users learn to employ SPICE calculations in their own programs. The SPICE system is implemented and maintained by the Navigation and Ancillary Information Facility (NAIF)–a component of NASA's Planetary Data System (PDS). * Spacecraft, Planet, Instrument, Camera-matrix, Events

SPICE TOOLS SUPPORTING PLANETARY REMOTE SENSING

NASA's "SPICE"<sup>*</sup> ancillary information system has gradually become the de facto international standard for providing scientists the fundamental observation geometry needed to perform photogrammetry, map making and other kinds of planetary science data analysis. SPICE provides position and orientation ephemerides of both the robotic spacecraft and the target body; target body size and shape data; instrument mounting alignment and field-of-view geometry; reference frame specifications; and underlying time system conversions. <br><br> SPICE comprises not only data, but also a large suite of software, known as the SPICE Toolkit, used to access those data and subsequently compute derived quantities–items such as instrument viewing latitude/longitude, lighting angles, altitude, etc. <br><br> In existence since the days of the Magellan mission to Venus, the SPICE system has continuously grown to better meet the needs of scientists and engineers. For example, originally the SPICE Toolkit was offered only in Fortran 77, but is now available in C, IDL, MATLAB, and Java Native Interface. SPICE calculations were originally available only using APIs (subroutines), but can now be executed using a client-server interface to a geometry engine. Originally SPICE "products" were only available in numeric form, but now SPICE data visualization is also available. <br><br> The SPICE components are free of cost, license and export restrictions. Substantial tutorials and programming lessons help new users learn to employ SPICE calculations in their own programs. The SPICE system is implemented and maintained by the Navigation and Ancillary Information Facility (NAIF)–a component of NASA's Planetary Data System (PDS). <br><br> <sup>*</sup> Spacecraft, Planet, Instrument, Camera-matrix, Events

Profiling of aerosol microphysical properties at several EARLINET/AERONET sites during the July 2012 ChArMEx/EMEP campaign

Abstract. The simultaneous analysis of aerosol microphysical properties profiles at different European stations is made in the framework of the ChArMEx/EMEP 2012 field campaign (9–11 July 2012). During and in support of this campaign, five lidar ground-based stations (Athens, Barcelona, Bucharest, Évora, and Granada) performed 72 h of continuous lidar measurements and collocated and coincident sun-photometer measurements. Therefore it was possible to retrieve volume concentration profiles with the Lidar Radiometer Inversion Code (LIRIC). Results indicated the presence of a mineral dust plume affecting the western Mediterranean region (mainly the Granada station), whereas a different aerosol plume was observed over the Balkans area. LIRIC profiles showed a predominance of coarse spheroid particles above Granada, as expected for mineral dust, and an aerosol plume composed mainly of fine and coarse spherical particles above Athens and Bucharest. Due to the exceptional characteristics of the ChArMEx database, the analysis of the microphysical properties profiles' temporal evolution was also possible. An in-depth analysis was performed mainly at the Granada station because of the availability of continuous lidar measurements and frequent AERONET inversion retrievals. The analysis at Granada was of special interest since the station was affected by mineral dust during the complete analyzed period. LIRIC was found to be a very useful tool for performing continuous monitoring of mineral dust, allowing for the analysis of the dynamics of the dust event in the vertical and temporal coordinates. Results obtained here illustrate the importance of having collocated and simultaneous advanced lidar and sun-photometer measurements in order to characterize the aerosol microphysical properties in both the vertical and temporal coordinates at a regional scale. In addition, this study revealed that the use of the depolarization information as input in LIRIC in the stations of Bucharest, Évora, and Granada was crucial for the characterization of the aerosol types and their distribution in the vertical column, whereas in stations lacking depolarization lidar channels, ancillary information was needed. Results obtained were also used for the validation of different mineral dust models. In general, the models better forecast the vertical distribution of the mineral dust than the column-integrated mass concentration, which was underestimated in most of the cases.

Precipitation From the Advanced Microwave Scanning Radiometer 2

The Goddard Profiling Algorithm 2010 Version 2 (GPROF2010V2) was adapted for use for the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission-Water satellite. This study presents the validation and evaluation of the National Oceanic and Atmospheric Administration's AMSR2 precipitation product. Rain rates calculated by GROF2010V2 meet mission accuracy requirements, with root-mean-squared error of 1.3 mm·h <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> and 3.6 mm·h <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> over ocean and land, respectively. In addition to meeting instantaneous rain rate requirements, GPROF2010V2 captures monthly and seasonal precipitation patterns relative to rain gauge analysis. Recurring problems with the algorithm over land surfaces, such as false alarms due to snow cover and surface contamination, are identified. These deficiencies can be mitigated using separate day/night screening procedures and ancillary information on daily snow cover.

A Bayesian Network for Flood Detection Combining SAR Imagery and Ancillary Data

Accurate flood mapping is important for both planning activities during emergencies and as a support for the successive assessment of damaged areas. A valuable information source for such a procedure can be remote sensing synthetic aperture radar (SAR) imagery. However, flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground. For this reason, a data fusion approach of remote sensing data with ancillary information can be particularly useful. In this paper, a Bayesian network is proposed to integrate remotely sensed data, such as multitemporal SAR intensity images and interferometric-SAR coherence data, with geomorphic and other ground information. The methodology is tested on a case study regarding a flood that occurred in the Basilicata region (Italy) on December 2013, monitored using a time series of COSMO-SkyMed data. It is shown that the synergetic use of different information layers can help to detect more precisely the areas affected by the flood, reducing false alarms and missed identifications which may affect algorithms based on data from a single source. The produced flood maps are compared to data obtained independently from the analysis of optical images; the comparison indicates that the proposed methodology is able to reliably follow the temporal evolution of the phenomenon, assigning high probability to areas most likely to be flooded, in spite of their heterogeneous temporal SAR/InSAR signatures, reaching accuracies of up to 89%.

Identifying and removing micro-drift in ground-based electromagnetic induction data

As the application of ground-based frequency domain electromagnetic induction (FDEM) surveys is on the rise, so increases the need for processing strategies that allow exploiting the full potential of these often large survey datasets. While a common issue is the detection of baseline drift affecting FDEM measurements, the impact of residual corrugations present after initial drift removal is less documented. Comparable to the influence of baseline drift, this ‘micro-drift’ introduces aberrant data fluctuations through time, independent of the true subsurface variability. Here, we present a method to detect micro-drift in drift-corrected FDEM survey data, therefore allowing its removal. The core of the procedure lies in approaching survey datasets as a time series. Hereby, discrete multi-level wavelet decomposition is used to isolate micro-drift in FDEM data. Detected micro-drift is then excluded in subsequent signal reconstruction to produce a more accurate FDEM dataset. While independently executed from ancillary information, tie-line measurements are used to evaluate the reliability and pitfalls of the procedure. This demonstrates how data levelling without evaluation data can increase subjectivity of the procedure, and shows the flexibility and efficiency of the approach in detecting minute drift effects. We corroborated the method through its application on three experimental field datasets, consisting of both quadrature and in-phase measurements gathered with different FDEM instruments. Through a 1D assessment of micro-drift, we show how it impacts FDEM survey data, and how it can be identified and accounted for in straightforward processing steps.

Evaluation of Radiometric and Atmospheric Correction Algorithms for Aboveground Forest Biomass Estimation Using Landsat 5 TM Data

Solar radiation is affected by absorption and emission phenomena during its downward trajectory from the Sun to the Earth’s surface and during the upward trajectory detected by satellite sensors. This leads to distortion of the ground radiometric properties (reflectance) recorded by satellite images, used in this study to estimate aboveground forest biomass (AGB). Atmospherically-corrected remote sensing data can be used to estimate AGB on a global scale and with moderate effort. The objective of this study was to evaluate four atmospheric correction algorithms (for surface reflectance), ATCOR2 (Atmospheric Correction for Flat Terrain), COST (Cosine of the Sun Zenith Angle), FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) and 6S (Second Simulation of Satellite Signal in the Solar), and one radiometric correction algorithm (for reflectance at the sensor) ToA (Apparent Reflectance at the Top of Atmosphere) to estimate AGB in temperate forest in the northeast of the state of Durango, Mexico. The AGB was estimated from Landsat 5 TM imagery and ancillary information from a digital elevation model (DEM) using the non-parametric multivariate adaptive regression splines (MARS) technique. Field reference data for the model training were collected by systematic sampling of 99 permanent forest growth and soil research sites (SPIFyS) established during the winter of 2011. The following predictor variables were identified in the MARS model: Band 7, Band 5, slope (β), Wetness Index (WI), NDVI and MSAVI2. After cross-validation, 6S was found to be the optimal model for estimating AGB (R2 = 0.71 and RMSE = 33.5 Mg·ha−1; 37.61% of the average stand biomass). We conclude that atmospheric and radiometric correction of satellite images can be used along with non-parametric techniques to estimate AGB with acceptable accuracy.

Accidental Trauma Mimicking Homicidal Violence.

Homicide investigations represent an important function of death investigators. Although recognizing nonobvious homicides is crucial, an equally important role involves the identification of cases that initially present as possible homicides, but are ultimately discovered to not represent homicides. Failure to recognize such cases results in wasted time, squandered resources, false allegations, and potential life-altering consequences. The authors review a series of cases wherein initial investigation suggested a possibility that the deaths represented homicides. By carefully considering additional information, including scene findings, history, and postmortem examination, each was determined to represent an accidental traumatic death. In addition to highlighting the importance of recognizing accidental traumatic deaths that initially present as homicides, the cases serve to highlight the fact that forensic pathology cannot be practiced without knowledge of appropriate ancillary information. Although guarding against cognitive bias is important in all forensic disciplines, including forensic pathology, access to vital case-related ancillary information is an essential component of practicing medicine as a forensic pathologist.

The role of European National Forest Inventories for international forestry reporting

Despite agreements on definitions, the national data provided for international reporting are lacking comparability. To address this limitation the European National Forest Inventory Network has established criteria to harmonise definitions and to provide tools to transform national data into internationally comparable data. Forest reporting presents a series of challenges for countries, owing to diverse processes at international level such as the Global Forest Resources Assessment (FRA), Convention on Biological Diversity, United Nations Framework Convention on Climate Change and its Kyoto Protocol. Further challenges are faced at European level with Forest Europe and policy needs. The aim of this paper is to provide a comprehensive review of the national and international forest reporting processes and of the role of the National Forest Inventories (NFIs) and the long-associated challenges resulting from a lack of comparability in definitions used. In addition, there is a discussion on the role of the European National Forest Inventory Network (ENFIN) as a facilitator for enhancing harmonization and comparability of national data and the ancillary information required to monitor European forestry-related policies. NFIs take part to international reporting processes as providers of information. They are correspondent to the FRA process, and then they know very well the context of harmonization. Participating in the ENFIN research projects, NFIs, and particularly authors, conducted a screening exercise on harmonization status at European and World level. This review article is a synthesis of the main findings of the abovementioned screening exercise. It highlights the main gaps in terms of comparability of result in international reporting. Thanks to ENFIN harmonization research project, it gives same ways of working as a possible benchmark for the rest of the world. Based on the international reporting exercises, their interactions, and impacts on new forestry policy requirements, the need for a strengthened harmonization process can clearly be demonstrated. Due to European policy needs, research work within ENFIN has been initiated to develop tools for building comparable results at international level. This work is an important benchmark particularly for countries outside Europe from which to base future harmonization work.

Comprehensive patient-specific information preprocessing for cardiac surgery simulations.

Patient-specific biomechanical simulations of the behavior of soft tissue gain importance in current surgery assistance systems as they can provide surgeons with valuable ancillary information for diagnosis and therapy. In this work, we aim at supporting minimally invasive mitral valve reconstruction (MVR) surgery by providing scenario setups for FEM-based soft tissue simulations, which simulate the behavior of the patient-individual mitral valve subject to natural forces during the cardiac cycle after an MVR. However, due to the complexity of these simulations and of their underlying mathematical models, it is difficult for non-engineers to sufficiently understand and adequately interpret all relevant modeling and simulation aspects. In particular, it is challenging to set up such simulations in automated preprocessing workflows such that they are both patient-specific and still maximally comprehensive with respect to the model. In this paper, we address this issue and present a fully automated chain of preprocessing operators for setting up comprehensive, patient-specific biomechanical models on the basis of patient-individual medical data. These models are suitable for FEM-based MVR surgery simulation. The preprocessing methods are integrated into the framework of the Medical Simulation Markup Language and allow for automated information processing in a data-driven pipeline. We constructed a workflow for holistic, patient-individual information preprocessing for MVR surgery simulations. In particular, we show how simulation preprocessing can be both fully automated and still patient-specific, when using a series of dedicated MVR data analytics operators. The outcome of our operator chain is visualized in order to help the surgeon understand the model setup. With this work, we expect to improve the usability of simulation-based MVR surgery assistance, through allowing for fully automated, patient-specific simulation setups. Combined visualization of the biomechanical model setup and of the corresponding surgery simulation results fosters the understandability and transparency of our assistance environment.

Satellite‐based global volcanic SO2 emissions and sulfate direct radiative forcing during 2005–2012

AbstractAn 8 year volcanic SO2 emission inventory for 2005–2012 is obtained based on satellite measurements of SO2 from OMI (Ozone Monitoring Instrument) and ancillary information from the Global Volcanism Program. It includes contributions from global volcanic eruptions and from eight persistently degassing volcanoes in the tropics. It shows significant differences in the estimate of SO2 amount and injection height for medium to large volcanic eruptions as compared to the counterparts in the existing volcanic SO2 database. Emissions from Nyamuragira (DR Congo) in November 2006 and Grímsvötn (Iceland) in May 2011 that were not included in the Intergovernmental Panel on Climate Change 5 (IPCC) inventory are included here. Using the updated emissions, the volcanic sulfate (SO42−) distribution is simulated with the global transport model Goddard Earth Observing System (GEOS)‐Chem. The simulated time series of sulfate aerosol optical depth (AOD) above 10 km captures every eruptive volcanic sulfate perturbation with a similar magnitude to that measured by Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The 8 year average contribution of eruptive SO42− to total SO42− loading above 10 km is ~10% over most areas of the Northern Hemisphere, with a maxima of 30% in the tropics where the anthropogenic emissions are relatively smaller. The persistently degassing volcanic SO42− in the tropics barely reaches above 10 km, but in the lower atmosphere it is regionally dominant (60%+ in terms of mass) over Hawaii and other oceanic areas northeast of Australia. Although the 7 year average (2005–2011) of eruptive volcanic sulfate forcing of −0.10 W m−2 in this study is comparable to that in the 2013 IPCC report (−0.09 W m−2), significant discrepancies exist for each year. Our simulations also imply that the radiative forcing per unit AOD for volcanic eruptions can vary from −40 to −80 W m−2, much higher than the −25 W m−2 implied in the IPCC calculations. In terms of sulfate forcing efficiency with respect to SO2 emission, eruptive volcanic sulfate is 5 times larger than anthropogenic sulfate. The sulfate forcing efficiency from degassing volcanic sources is close to that of anthropogenic sources. This study highlights the importance of characterizing both volcanic emission amount and injection altitude as well as the key role of satellite observations in maintaining accurate volcanic emissions inventories.

Plant‐O‐Matic: a dynamic and mobile guide to all plants of the Americas

Summary Advances in both informatics and mobile technology are providing exciting new opportunities for generating, disseminating, and engaging with information in the biological sciences at unprecedented spatial scales, particularly in disentangling information on the distributions and natural history of hyperdiverse groups of organisms. We describe an application serving as a mobile catalog of all of the plants of the Americas developed using species distribution models estimated from field observations of plant occurrences. The underlying data comprise over 3·5 million standardized observations of over 88 000 plant species. Plant‐O‐Matic, a free iOS application, combines the species distribution models with the location services built into a mobile device to provide users with a list of all plant species expected to occur in the 100 × 100 km geographic grid cell corresponding to the user's location. The application also provides ancillary information on species’ attributes (when available) including growth form, reproductive mode, flower color, and common name. Results can be searched and conditionally filtered based on these attributes. Links to externally sourced specimen images further aid in identification of species by the user. The application's ability to assemble locally relevant lists of plant species and their attributes on demand for anywhere in the Americas provides a powerful new tool for identifying, exploring, and understanding plant diversity. Mobile applications such as Plant‐O‐Matic can facilitate dynamic new approaches to science, conservation, and science education.

Impact assessment of climate change on future soil erosion and SOC loss

Soil organic carbon (SOC) is an important component of soil fertility and agricultural production that also controls the atmospheric CO2 which affects the global carbon cycle. Soil erosion is a major hazard which is directly affected by the rainfall change caused by the climate change. SOC is depleted through soil erosion affected by a change in the rainfall pattern. This study aims at quantifying the impact of climate change on future soil erosion and SOC with respect to the different controlling parameters (slope, soil and landuse) of soil erosion. The study area is a part of the Narmada river basin in India. Future rainfall is estimated by least square support vector machine method using Hadley Centre coupled model, version 3 data of A2 scenario. Revised universal soil loss equation has been used to estimate the soil erosion spatially, and field data collection is done to estimate SOC. Regression–kriging (RK) method is used for spatial interpolation of SOC on the top surface considering ancillary information of the land. Results show that sediment load has changed by −5.33, 17.97 and 58.37 % in the 2020s, 2050s and 2080s, respectively, from current erosion rate. Soil erosion and SOC loss rate are higher in a high degree of slope (>20), while SOC stock is low here (5.77 gm/kg). Again, SOC stock (1.27 gm/kg) is high in the clay soil, but soil erosion and SOC loss are less, while sandy loam indicates the opposite. Agricultural land and fallow lands have higher rate of soil erosion and SOC loss, while stock SOC is 12.24 and 9.65 gm/kg, respectively. Results show that soil erosion and SOC loss will be increased in the future and steeper slopes, sandy soil and fallow lands are more vulnerable to the loss.

The Added Value of Stratified Topographic Correction of Multispectral Images

Satellite images in mountainous areas are strongly affected by topography. Different studies demonstrated that the results of semi-empirical topographic correction algorithms improved when a stratification of land covers was carried out first. However, differences in the stratification strategies proposed and also in the evaluation of the results obtained make it unclear how to implement them. The objective of this study was to compare different stratification strategies with a non-stratified approach using several evaluation criteria. For that purpose, Statistic-Empirical and Sun-Canopy-Sensor + C algorithms were applied and six different stratification approaches, based on vegetation indices and land cover maps, were implemented and compared with the non-stratified traditional option. Overall, this study demonstrates that for this particular case study the six stratification approaches can give results similar to applying a traditional topographic correction with no previous stratification. Therefore, the non-stratified correction approach could potentially aid in removing the topographic effect, because it does not require any ancillary information and it is easier to implement in automatic image processing chains. The findings also suggest that the Statistic-Empirical method performs slightly better than the Sun-Canopy-Sensor + C correction, regardless of the stratification approach. In any case, further research is necessary to evaluate other stratification strategies and confirm these results.

Spatial distribution of maize in response to climate change in northeast China during 1980–2010

Based on county-level crop statistics and other ancillary information, spatial distribution of maize in the major maize-growing areas (latitudes 39°–48°N) was modelled for the period 1980–2010 by using a cross-entropy-based spatial allocation model. Maize extended as far north as the northern part of the Lesser Khingan Mountains during the period, and the area sown to maize increased by about 5 million ha. More than half of the increase occurred before 2000, and more than 80% of it in the climate transitional zone, where the annual accumulated temperature (AAT) was 2800–3400 °C·d. Regions with AAT of 3800–4000 °C·d became more important, accounting for more than 25% of the increase after 2000. The expansion of maize was thus closely related to warming, although some variation in the distribution was noticed across zones in relation to the warming, indicating that maize in northeast China may have adapted successfully to the warming by adjusting its spatial distribution to match the changed climate.

A Bayesian machine learning model for estimating building occupancy from open source data

Understanding building occupancy is critical to a wide array of applications including natural hazards loss analysis, green building technologies, and population distribution modeling. Due to the expense of directly monitoring buildings, scientists rely in addition on a wide and disparate array of ancillary and open source information including subject matter expertise, survey data, and remote sensing information. These data are fused using data harmonization methods, which refer to a loose collection of formal and informal techniques for fusing data together to create viable content for building occupancy estimation. In this paper, we add to the current state of the art by introducing the population data tables (PDT), a Bayesian model and informatics system for systematically arranging data and harmonization techniques into a consistent, transparent, knowledge learning framework that retains in the final estimation uncertainty emerging from data, expert judgment, and model parameterization. PDT aims to estimate ambient occupancy in units of people/1000 ft2 for a number of building types at the national and sub-national level with the goal of providing global coverage. We present the PDT model, situate the work within the larger community, and report on the progress of this multi-year project.