Showing papers in "International Journal of Digital Earth in 2016"

A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems

Abstract: Remote sensing-based methods of aboveground biomass (AGB) estimation in forest ecosystems have gained increased attention, and substantial research has been conducted in the past three decades. This paper provides a survey of current biomass estimation methods using remote sensing data and discusses four critical issues – collection of field-based biomass reference data, extraction and selection of suitable variables from remote sensing data, identification of proper algorithms to develop biomass estimation models, and uncertainty analysis to refine the estimation procedure. Additionally, we discuss the impacts of scales on biomass estimation performance and describe a general biomass estimation procedure. Although optical sensor and radar data have been primary sources for AGB estimation, data saturation is an important factor resulting in estimation uncertainty. LIght Detection and Ranging (lidar) can remove data saturation, but limited availability of lidar data prevents its extensive application. This...

A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm

Abstract: The science and management of terrestrial ecosystems require accurate, high-resolution mapping of surface water. We produced a global, 30-m-resolution inland surface water dataset with an automated algorithm using Landsat-based surface reflectance estimates, multispectral water and vegetation indices, terrain metrics, and prior coarse-resolution water masks. The dataset identified 3,650,723 km2 of inland water globally – nearly three quarters of which was located in North America (40.65%) and Asia (32.77%), followed by Europe (9.64%), Africa (8.47%), South America (6.91%), and Oceania (1.57%). Boreal forests contained the largest portion of terrestrial surface water (25.03% of the global total), followed by the nominal ‘inland water’ biome (16.36%), tundra (15.67%), and temperate broadleaf and mixed forests (13.91%). Agreement with respect to the Moderate-resolution Imaging Spectroradiometer water mask and Landsat-based national land-cover datasets was very high, with commission errors <4% and omission er...

Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring

Abstract: Free and open access to the Landsat archive has enabled the implementation of national and global terrestrial monitoring projects. Herein, we summarize a project characterizing the change history of Canada’s forested ecosystems with a time series of data representing 1984–2012. Using the Composite2Change approach, we applied spectral trend analysis to annual best-available-pixel (BAP) surface reflectance image composites produced from Landsat TM and ETM+ imagery. A total of 73,544 images were used to produce 29 annual image composites, generating ∼400 TB of interim data products and resulting in ∼25 TB of annual gap-free reflectance composites and change products. On average, 10% of pixels in the annual BAP composites were missing data, with 86% of pixels having data gaps in two consecutive years or fewer. Change detection overall accuracy was 89%. Change attribution overall accuracy was 92%, with higher accuracy for stand-replacing wildfire and harvest. Changes were assigned to the correct year w...

Big Data Analytics for Earth Sciences: the EarthServer approach

Abstract: Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.

Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization: a case study in Central Vietnam

Abstract: This study represents a hybrid intelligence approach based on the differential evolution optimization and Least-Squares Support Vector Machines for shallow landslide prediction, named as DE–LSSVMSLP. The LSSVM is used to establish a landslide prediction model whereas the DE is adopted to search the optimal tuning parameters of the LSSVM model. In this research, a GIS database with 129 historical landslide records in the Quy Hop area (Central Vietnam) has been collected to establish the hybrid model. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess the performance of the newly constructed model. Experimental results show that the proposed model has high performances with approximately 82% of AUCs on both training and validating datasets. The model’s results were compared with those obtained from other methods, Support Vector Machines, Multilayer Perceptron Neural Networks, and J48 Decision Trees. The result comparison demonstrates that the DE–LSSV...

A framework for a microscale flood damage assessment and visualization for a building using BIM–GIS integration

Abstract: Flood damage assessment (FDA) is a key component of risk-based method for flood management. In the current FDA approaches, generally the uniqueness of the building is disregarded in the analysis. Therefore, they are unfit for detailed applications in which case-by-case analysis of building damage is an essential requirement. This limitation is compounded by the use of incomplete and often low-quality data inputs about the building and the assumptions and approximations made regarding the geometry and materials of its components. Such shortcomings may result in incomplete and uncertain outcomes. Considering the benefits and increasing use of three-dimensional (3D) urban modeling and Building Information Model in various urban management processes, in this paper, an integrated framework for utilization of detailed 3D building models for the assessment and 3D visualization of flood damage to building according to its distinct behavior against flood is presented. A proof-of-concept demonstration of the framew...

Horizontal accuracy assessment of very high resolution Google Earth images in the city of Rome, Italy

Abstract: Google Earth (GE) has recently become the focus of increasing interest and popularity among available online virtual globes used in scientific research projects, due to the free and easily accessed satellite imagery provided with global coverage. Nevertheless, the uses of this service raises several research questions on the quality and uncertainty of spatial data (e.g. positional accuracy, precision, consistency), with implications for potential uses like data collection and validation. This paper aims to analyze the horizontal accuracy of very high resolution (VHR) GE images in the city of Rome (Italy) for the years 2007, 2011, and 2013. The evaluation was conducted by using both Global Positioning System ground truth data and cadastral photogrammetric vertex as independent check points. The validation process includes the comparison of histograms, graph plots, tests of normality, azimuthal direction errors, and the calculation of standard statistical parameters. The results show that GE VHR imageries o...

Global mapping of urban built-up areas of year 2014 by combining MODIS multispectral data with VIIRS nighttime light data

Abstract: An improved methodology for the extraction and mapping of urban built-up areas at a global scale is presented in this study. The Moderate Resolution Imaging Spectroradiometer (MODIS)-based multispectral data were combined with the Visible Infrared Imager Radiometer Suite (VIIRS)-based nighttime light (NTL) data for robust extraction and mapping of urban built-up areas. The MODIS-based newly proposed Urban Built-up Index (UBI) was combined with NTL data, and the resulting Enhanced UBI (EUBI) was used as a single master image for global extraction of urban built-up areas. Due to higher variation of the EUBI with respect to geographical regions, a region-specific threshold approach was used to extract urban built-up areas. This research provided 500-m-resolution global urban built-up map of year 2014. The resulted map was compared with three existing moderate-resolution global maps and one high-resolution map in the United States. The comparative analysis demonstrated finer details of the urban built...

Rapid, high-resolution detection of environmental change over continental scales from satellite data – the Earth Observation Data Cube

Abstract: The effort and cost required to convert satellite Earth Observation (EO) data into meaningful geophysical variables has prevented the systematic analysis of all available observations. To overcome these problems, we utilise an integrated High Performance Computing and Data environment to rapidly process, restructure and analyse the Australian Landsat data archive. In this approach, the EO data are assigned to a common grid framework that spans the full geospatial and temporal extent of the observations – the EO Data Cube. This approach is pixel-based and incorporates geometric and spectral calibration and quality assurance of each Earth surface reflectance measurement. We demonstrate the utility of the approach with rapid time-series mapping of surface water across the entire Australian continent using 27 years of continuous, 25 m resolution observations. Our preliminary analysis of the Landsat archive shows how the EO Data Cube can effectively liberate high-resolution EO data from their complex s...

Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data

Abstract: The goal of this study was to map rainfed and irrigated rice-fallow cropland areas across South Asia, using MODIS 250 m time-series data and identify where the farming system may be intensified by the inclusion of a short-season crop during the fallow period. Rice-fallow cropland areas are those areas where rice is grown during the kharif growing season (June–October), followed by a fallow during the rabi season (November–February). These cropland areas are not suitable for growing rabi-season rice due to their high water needs, but are suitable for a short -season (≤3 months), low water-consuming grain legumes such as chickpea (Cicer arietinum L.), black gram, green gram, and lentils. Intensification (double-cropping) in this manner can improve smallholder farmer’s incomes and soil health via rich nitrogen-fixation legume crops as well as address food security challenges of ballooning populations without having to expand croplands. Several grain legumes, primarily chickpea, are increasingly grown across Asia as a source of income for smallholder farmers and at the same time providing rich and cheap source of protein that can improve the nutritional quality of diets in the region. The suitability of rainfed and irrigated rice-fallow croplands for grain legume cultivation across South Asia were defined by these identifiers: (a) rice crop is grown during the primary (kharif) crop growing season or during the north-west monsoon season (June–October); (b) same croplands are left fallow during the second (rabi) season or during the south-east monsoon season (November–February); and (c) ability to support low water-consuming, short-growing season (≤3 months) grain legumes (chickpea, black gram, green gram, and lentils) during rabi season. Existing irrigated or rainfed crops such as rice or wheat that were grown during kharif were not considered suitable for growing during the rabi season, because the moisture/water demand of these crops is too high. The study established cropland classes based on the every 16-day 250 m normalized difference vegetation index (NDVI) time series for one year (June 2010–May 2011) of Moderate Resolution Imaging Spectroradiometer (MODIS) data, using spectral matching techniques (SMTs), and extensive field knowledge. Map accuracy was evaluated based on independent ground survey data as well as compared with available sub-national level statistics. The producers’ and users’ accuracies of the cropland fallow classes were between 75% and 82%. The overall accuracy and the kappa coefficient estimated for rice classes were 82% and 0.79, respectively. The analysis estimated approximately 22.3 Mha of suitable rice-fallow areas in South Asia, with 88.3% in India, 0.5% in Pakistan, 1.1% in Sri Lanka, 8.7% in Bangladesh, 1.4% in Nepal, and 0.02% in Bhutan. Decision-makers can target these areas for sustainable intensification of short-duration grain legumes.

Rainfall erosivity in Italy: a national scale spatio-temporal assessment

Abstract: Soil erosion by water is a serious threat for the Mediterranean region. Raindrop impacts and consequent runoff generation are the main driving forces of this geomorphic process of soil degradation. The potential ability for rainfall to cause soil loss is expressed as rainfall erosivity, a key parameter required by most soil loss prediction models. In Italy, rainfall erosivity measurements are limited to few locations, preventing researchers from effectively assessing the geography and magnitude of soil loss across the country. The objectives of this study were to investigate the spatio-temporal distribution of rainfall erosivity in Italy and to develop a national-scale grid-based map of rainfall erosivity. Thus, annual rainfall erosivity values were measured and subsequently interpolated using a geostatistical approach. Time series of pluviographic records (10-years) with high temporal resolution (mostly 30-min) for 386 meteorological stations were analysed. Regression-kriging was used to interpol...

Assessing quality of volunteer crowdsourcing contributions: lessons from the Cropland Capture game

Abstract: Volunteered geographic information (VGI) is the assembly of spatial information based on public input. While VGI has proliferated in recent years, assessing the quality of volunteer-contributed data has proven challenging, leading some to question the efficiency of such programs. In this paper, we compare several quality metrics for individual volunteers’ contributions. The data were the product of the ‘Cropland Capture’ game, in which several thousand volunteers assessed 165,000 images for the presence of cropland over the course of 6 months. We compared agreement between volunteer ratings and an image's majority classification with volunteer self-agreement on repeated images and expert evaluations. We also examined the impact of experience and learning on performance. Volunteer self-agreement was nearly always higher than agreement with majority classifications, and much greater than agreement with expert validations although these metrics were all positively correlated. Volunteer quality showed a broad...

The ADV/ASV AATSR aerosol retrieval algorithm: current status and presentation of a full-mission AOD dataset

Abstract: An advanced along-track scanning radiometer (AATSR) global multi-year aerosol retrieval algorithm is described. Over land, the AATSR dual-view (ADV) algorithm utilizes the measured top of the atmosphere (TOA) reflectance in both the nadir and forward views to decouple the contributions of the atmosphere and the surface to retrieve aerosol properties. Over ocean, the AATSR single-view (ASV) algorithm minimizes the discrepancy between the measured and modelled TOA reflectances in one of the views to retrieve the aerosol information using an ocean reflectance model. Necessary steps to process global, multi-year aerosol information are presented. These include cloud screening, the averaging of measured reflectance, and post-processing. Limitations of the algorithms are also discussed. The main product of the aerosol retrieval is the aerosol optical depth (AOD) at visible/near-infrared wavelengths. The retrieved AOD is validated using data from the surface-based AERONET and maritime aerosol network (MA...

Assessing the suitability of GlobeLand30 for mapping land cover in Germany

Abstract: Global land cover (LC) maps have been widely employed as the base layer for a number of applications including climate change, food security, water quality, biodiversity, change detection, and environmental planning. Due to the importance of LC, there is a pressing need to increase the temporal and spatial resolution of global LC maps. A recent advance in this direction has been the GlobeLand30 dataset derived from Landsat imagery, which has been developed by the National Geomatics Center of China (NGCC). Although overall accuracy is greater than 80%, the NGCC would like help in assessing the accuracy of the product in different regions of the world. To assist in this process, this study compares the GlobeLand30 product with existing public and online datasets, that is, CORINE, Urban Atlas (UA), OpenStreetMap, and ATKIS for Germany in order to assess overall and per class agreement. The results of the analysis reveal high agreement of up to 92% between these datasets and GlobeLand30 but that large...

On predicting and improving the quality of Volunteer Geographic Information projects

Abstract: Initiatives that rely upon the contributions of volunteers to reach a specific goal are growing more and more with the success of Web 2.0–interactive applications. Also scientific projects are testing and exploiting volunteers' collaboration, but the quality of information obtained with this approach is often puzzling. This paper offers a rich overview of many scientific projects where geographic contributions are committed to volunteers, to the aim of defining strategies to improve information quality. By describing real examples of Volunteer Geographic Information (VGI), the contribution establishes a categorization based on the characteristics of the information, tasks, and scopes of the projects. After a discussion on the relationships of categories and VGI quality, the paper analyses techniques to improve the quality of volunteered information according to the moment of its assessment (i.e., ex ante, ex post, or both with respect to information creation). The paper outlines the main limitations of th...

Digital Earth and Future Earth

Abstract: Future Earth, a 10-year international research programme jointly initiated by the ICSU (International Council for Science) and the ISSC (International Social Science Council), was launched in June 2012 at the UN conference on Sustainable Development (Rio+20). Building on existing international global environmental change projects, Future Earth aims to provide a new platform for consolidating the knowledge and support needed to accelerate our transformation to a sustainable world. It will connect the world’s scientists across regions and across disciplines to work together under a unifying framework and will focus on three research themes: Dynamic Planet, Global Sustainable Development and Transformations towards Sustainability (Future Earth 2014). The implementation and success of the Future Earth programme will critically depend on Digital Earth science and technology. Digital Earth integrates the massive amount of multi-spatial, multitemporal and multi-mode Earth observation and socioeconomic data into a virtual representation of the planet. After near twenty years of development, the science and engineering of Digital Earth fully encompass the 4V (volume, variety, veracity and velocity) and 3H (high dimension, high complexity and high uncertainty) features of scientific big data, thereby driving the new Digital Earth vision with ‘Big Earth Data’ (Guo et al. 2014). In the ‘Big Data Age’, the amount of Digital Earth data, with geospatial data at its core, has reached the exabyte (EB) level. The new generation of Digital Earth systems, which comprise analysis algorithms and simulation models, as well as platforms for data acquisition, management, computation, distribution and visualization, have the ability to handle big Earth data, and to monitor and predict the dynamics of Earth systems, recognize socio-environmental interactions and address the challenges related to global environmental change. Undoubtedly, big data challenges Digital Earth but also paves the way for Digital Earth to effectively and efficiently extract information and to extract knowledge from big Earth data and ultimately support decision-making and sustainable development. Given all this, Digital Earth will definitely improve Future Earth’s cross-cutting capabilities with respect to observing systems, Earth system models and research infrastructure in order to facilitate integration across its research themes. Meanwhile, Digital Earth will enhance the confidence that Future Earth can meet the great challenges indicated by Reid et al. (2010) by providing robust data and information systems. In addition, sustainable development has long been a major issue to be addressed among the goals and activities of the International Society for Digital Earth (ISDE) and within the aims and scope of the International Journal of Digital Earth (IJDE), which are broad platforms that promote international collaboration for creating the future of Digital Earth. The ISDE and IJDE will make continuous efforts to co-design, co-produce and co-develop solutions-oriented science, knowledge and innovation for global sustainability. As one pillar of Digital Earth, geospatial data are increasingly generated at local, regional and global scales by Earth observation, web-sensors, crowd-sourcing and other technologies. To promote multi-disciplinary collaboration aimed at providing reliable global geo-information to support Future Earth, an ISPRS international workshop has selected papers for submission to an IJDE special issue on ‘Supporting Future Earth with Global Geospatial Information’. This special issue will present original, state-of-the-art research that will assist the development of technologies for producing, sharing and analyzing global geospatial information and also the development of applications to

Fundamentals of satellite remote sensing: an environmental approach

Abstract: This is a very useful textbook for university teachers giving undergraduate courses in remote sensing, and for those giving courses on environmental issues. It is written by one of the best known E...

A novel water index for urban high-resolution eight-band WorldView-2 imagery

Abstract: Land surface water mapping is one of the most important remote-sensing applications. However, water areas are spectrally similar and overlapped with shadow, making accurate water extraction from remote-sensing images still a challenging problem. This paper develops a novel water index named as NDWI-MSI, combining a new normalized difference water index (NDWI) and a recently developed morphological shadow index (MSI), to delineate water bodies from eight-band WorldView-2 imagery. The newly available bands (e.g. coastal, yellow, red-edge, and near-infrared 2) of WorldView-2 imagery provide more potential for constructing new NDWIs derived from various band combinations. Through our testing, a new NDWI is defined in this study. In addition, MSI, a recently developed automatic shadow extraction index from high-resolution imagery can be used to indicate shadow areas. The NDWI-MSI is created by combining NDWI and MSI, which is able to highlight water bodies and simultaneously suppress shadow areas. In e...

An effective Building Neighborhood Green Index model for measuring urban green space

Abstract: Urban green space forms an integral part of urban ecosystems. Quantifying urban green space is of substantial importance for urban planning and development. Considering the drawbacks of previous urban green space index models, which established either through a grid method or green distribution, and the difficulty of the validation process of earlier urban green space index models, this study exploits the advantages of multisource high-resolution remote sensing data to establish a Building Neighborhood Green Index (BNGI) model. The model which analyzes the spatial configuration of built-up areas and the vegetation is based on the building-oriented method and considers four parameters – Green Index (GI), proximity to green, building sparsity, and building height. Comparing BNGI with GI in different types of characteristic building regions, it was found that BNGI evaluates urban green space more sensitively. It was also found that high-rise low-sparsity area has a lower mean value of BNGI (0.56) as compared...

Mapping floods due to Hurricane Sandy using NPP VIIRS and ATMS data and geotagged Flickr imagery

Abstract: In this study, we present an approach to estimate the extent of large-scale coastal floods caused by Hurricane Sandy using passive optical and microwave remote sensing data. The approach estimates the water fraction from coarse-resolution VIIRS and ATMS data through mixed-pixel linear decomposition. Based on the water fraction difference, using the physical characteristics of water inundation in a basin, the flood map derived from the coarse-resolution VIIRS and ATMS measurements was extrapolated to a higher spatial resolution of 30 m using topographic information. It is found that flood map derived from VIIRS shows less inundated area than the Federal Emergency Management Agency (FEMA) flood map and the ground observations. The bias was mainly caused by the time difference in observations. This is because VIIRS can only detect flood under clear conditions, while we can only find some clear-sky data around the New York area on 4 November 2012, when most flooding water already receded. Meanwhile, microwave...

Expanding the SDI environment: comparing current spatial data infrastructure with emerging indoor location-based services

Abstract: The authors compare key elements of the emerging field of Indoor Location-Based Services (Indoor LBS) to those currently found in spatial data infrastructure (SDI) programs. After a brief review of SDIs and Location-Based Services, the corresponding drivers, characteristics and emerging issues within the field of Indoor LBS are introduced and discussed. A comparative framework relates the two in terms of the criteria ‘People’, ‘Data', ‘Technologies', ‘Standards' and ‘Policies/Institutional Arrangements'. After highlighting key similarities and differences, the authors suggested three areas – definition of common framework datasets in Indoor LBS, more effective use of volunteered geographic information by SDI programs and development of appropriate privacy policies by both communities – that may benefit from sharing ‘lessons learned'.

MODIS observations of water color of the largest 10 lakes in China between 2000 and 2012

Abstract: Forel-Ule (FU) index of water color is an important parameter in traditional water quality investigations. We retrieved the FU index of the largest 10 lakes in China during 2000-2012 from MODerate-resolution Imaging Spectroradiometer surface reflectance product (MOD09) images. Since FU index is an optical parameter, it can be derived from optical remote sensing data by direct formulas, which is invariant with region and season. Based on validation by in situ measured reflectance data, the FU index products are reliable, with average relative error of 7.7%. FU index can be used to roughly assess water clarity: the clearer a water body is, and the bluer it is in color, the smaller its FU index is. FU index can also be used to roughly classify trophic state into three classes: oligotrophic, mesotrophic, and eutrophic. We analyzed the spatial, interannual, and seasonal variations of the FU index and its implications for water clarity and trophic state, and the findings are mostly consistent with the r...

Multi-frequency satellite radar imaging of cultural heritage: the case studies of the Yumen Frontier Pass and Niya ruins in the Western Regions of the Silk Road Corridor

Abstract: Synthetic Aperture Radar (SAR) remote sensing is increasingly favoured in archaeological applications. However, the effectiveness of this technology for archaeological prospection has so fa...

Vertical accuracy assessment of freely available digital elevation models over low-lying coastal plains

Abstract: The frequency of coastal flood damages is expected to increase significantly during the twenty-first century as sea level rises in the coastal floodplain. Coastal digital elevation model (DEM) data describing coastal topography are essential for assessing future flood-related damages and understanding the impacts of sea-level rise. The Shuttle Radar Topography Mission (SRTM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) are currently the most accurate and freely available DEM data. However, an accuracy assessment specifically targeted at DEMs over low elevation coastal plains is lacking. The present study focuses on these areas to assess the vertical accuracy of SRTM and ASTER GDEM using Ice, Cloud, and land Elevation Satellite, Geoscience Laser Altimeter System (ICESat/GLAS) and Real Time Kinematic (RTK) Global Positioning System (GPS) field survey data. The findings show that DEM accuracy is much better than the mission specifications over...

Coastal upwelling off the southwest coast of India: observations and simulations

Abstract: Spatial hydrographic data collected from August 2007 through May 2008 and the Princeton Ocean Model (POM) are utilized to explain and document the upwelling in the south eastern Arabian Sea (SEAS). The decrease in the magnitude of winds towards the coast favors local anti-cyclonic vorticity, resulting in the formation of cyclonic eddy and reversal of coastal currents. The Ekman transport due to alongshore winds, Ekman pumping due to wind stress curl, cyclonic eddy and southward West Indian Coastal Currents play different roles in the SEAS upwelling. In the offshore, wind stress curl leads to the formation of meso-scale eddies, resulting in Ekman pumping at the center and consequent upwelling. The rapid depth variation on the western side of Quilon Mount intensifies upwelling in the offshore. The upwelling Kelvin waves generated at the equator terminates in the Bay of Bengal and do not contribute to the SEAS upwelling. The possible role of local and remote winds, especially around Sri Lanka on the ...

Modelling monthly soil losses and sediment yields in Cyprus

Abstract: The aim of this study was to map soil erosion on the Mediterranean island of Cyprus. The G2 model, an empirical model for month-time step erosion assessments, was used. Soil losses in Cyprus were mapped at a 100 m cell size, while sediment yields at a sub-basin scale of 0.62 km2 mean size. The results indicated a mean annual erosion rate of 11.75 t ha−1 y−1, with October and November being the most erosive months. The 34% of the island's surface was found to exceed non-sustainable erosion rates (>10 t ha−1 y−1), with sclerophyllous vegetation, coniferous forests, and non-irrigated arable land being the most extensive non-sustainable erosive land covers. The mean sediment delivery ratio (SDR) was found to be 0.26, while the mean annual specific sediment yield (SSY) value for Cyprus was found to be 3.32 t ha−1 y−1. The annual sediment yield of the entire island was found to be 2.746 Mt y−1. This study was the first to provide complete and detailed erosion figures for Cyprus at a country scale. The g...

PMODTRAN: a parallel implementation based on MODTRAN for massive remote sensing data processing

Abstract: MODerate resolution atmospheric TRANsmission (MODTRAN) is a commercial remote sensing (RS) software package that has been widely used to simulate radiative transfer of electromagnetic radiation through the Earth's atmosphere and the radiation observed by a remote sensor. However, when very large RS datasets must be processed in simulation applications at a global scale, it is extremely time-consuming to operate MODTRAN on a modern workstation. Under this circumstance, the use of parallel cluster computing to speed up the process becomes vital to this time-consuming task. This paper presents PMODTRAN, an implementation of a parallel task-scheduling algorithm based on MODTRAN. PMODTRAN was able to reduce the processing time of the test cases used here from over 4.4 months on a workstation to less than a week on a local computer cluster. In addition, PMODTRAN can distribute tasks with different levels of granularity and has some extra features, such as dynamic load balancing and parameter checking.

Measurement of subsidence in the Yangbajing geothermal fields, Tibet, from TerraSAR-X InSAR time series analysis

Abstract: Yangbajain contains the largest geothermal energy power station in China. Geothermal explorations in Yangbajain first started in 1976, and two plants were subsequently built in 1981 and 1986. A large amount of geothermal fluids have been extracted since then, leading to considerable surface subsidence around the geothermal fields. In this paper, InSAR time series analysis is applied to map the subsidence of the Yangbajain geothermal fields during the period from December 2011 to November 2012 using 16 senses of TerraSAR-X stripmap SAR images. In the case of the TerraSAR-X data, most orbital fringes were removed using precise orbits during the interferometric processing. However, residual orbital ramps remain in some interferograms due to the uncertainties in the TerraSAR-X orbits. To remove the residual orbital ramps, we estimated a best-fit ‘twisted plane’ for each epoch interferogram using quadratic polynomial models based on a network approach. This method removes most of the long-wavelength si...

Deriving a minimum set of viewpoints for maximum coverage over any given digital elevation model data

Abstract: This paper introduces how to automatically derive a minimum set of viewpoints for maximum coverage over a large scale of digital terrain data. This is a typical data and computation-intensive research covering a series of geocomputation tasks that have not been implemented efficiently or optimally in prior works. This paper introduces a three-step computational solution to resolve the problem. For any given digital elevation model (DEM) data, automatic generation of control viewpoints is the first step through map algebra calculation and hydrological modeling approaches. For each viewpoint, the viewshed calculation then has to be implemented. The combined viewshed derived from the viewshed of all viewpoints establishes the maximum viewshed coverage of the given DEM. Finally, detecting the minimum set of viewpoints for the maximum coverage is a Non-deterministic Polynomial-time hard problem. The outcome of the computation has broader societal impacts since the research questions and solutions can b...

Fuzzy–frequency ratio model for avalanche susceptibility mapping

Abstract: Avalanche activities in the Indian Himalaya cause the majority of fatalities and responsible for heavy damage to the property. Avalanche susceptibility maps assist decision-makers and planners to execute suitable measures to reduce the avalanche risk. In the present study, a probabilistic data-driven geospatial fuzzy–frequency ratio (fuzzy–FR) model is proposed and developed for avalanche susceptibility mapping, especially for the large undocumented region. The fuzzy–FR model for avalanche susceptibility mapping is initially developed and applied for Lahaul-Spiti region. The fuzzy–FR model utilized the six avalanche occurrence factors (i.e. slope, aspect, curvature, elevation, terrain roughness and vegetation cover) and one referent avalanche inventory map to generate the avalanche susceptibility map. Amongst 292 documented avalanche locations from the avalanche inventory map, 233 (80%) were used for training the model and remaining 59 (20%) were used for validation of the map. The avalanche susce...