Digital classification of the contact metamorphic aureole along the Los Pedroches batholith,south-central Spain,using Landsat Thematic Mapper data

Visible and near-infrared laboratory reflectance spectra of soils sampled over the southern contact metamorphic aureole of the Los Pedroches granodiorite north of Cordoba in the southern-central part of the Iberian Peninsula are characterized in terms of relative albedo, depth, width, asymmetry, and wavelength position of absorption features. A decrease in albedo and relative depth of the Al-OH absorption feature (near 2.2μm), the H₂0 feature (near 1.9μm), and the OH feature (near 1.4μm) is observed with distance to the contact metamorphic domain. This effect is attributed to spectra quenching related to the absorbing capacity of finely disseminated carbonaceous material in soils which opacity increases with increasing temperature related to metamor-phism. Fe⁺² and Fe⁺³ cause absorption features near l.0μm. 0.55μm, and 0.46 μm and also, a decrease of reflectivity in the 0.8-0.4μm wavelength region. These absorption features, however, are unaffected by the amount of carbonaceous material. Landsat-TM images were used for mapping variations in iron, water, and hydroxyl absorption features over the contact zone using ratio and normalized difTerence images with TM-3:1 for Fe and TM-5:7 for H₂0. TM-5 was added as blue component in a colour ratio component image mapping the effect of spectral quenching. Ratios enhanced the spectral variability between individual bands resulting in correlation coefficients of the order of (plus or minus) 0-2.     

Geological controls of land degradation as detected by remote sensing: A case study in Los Monegros,north-east Spain

The focus of the work reported in this paper is on the way in which land degradation processes affecting semi-arid Mediterranean environments are enhanced by the operation of external (human-induced) factors. A study of landscape change in the Los Monegros area of Aragon, north-east Spain, over the period 1984-1997 has been undertaken in order to evaluate the effects of the extension of irrigation on the expansion of arable agriculture, and to estimate the consequential effects on the landscape. Radiometrically-calibrated Landsat TM data were combined with ground-based observations (soil and geology maps, plus hydrogeological data) with the aim of analysing temporal change in land cover. A combination of remote sensing methods (linear spectral unmixing and principal components analysis) was used to determine the proportions of individual soil types. Change detection techniques were employed to pick out areas at risk from land degradation processes (increased soil erosion and soil salinization) and to explain the ways in which agricultural land-use practices interact with the geological and hydrogeological characteristics of the study area.     

Attention-based multi-modal fusion for improved real estate appraisal: a case study in Los Angeles

Multimedia Tools and Applications - The geographical presentation of a house, which refers to the sightseeing and topography near the house, is a critical factor to a house buyer. The street map is...     

Mapping of heavy metal pollution in stream sediments using combined geochemistry, field spectroscopy, and hyperspectral remote sensing: A case study of the Rodalquilar mining area, SE Spain

The aim of this study is to derive parameters from spectral variations associated with heavy metals in soil and to explore the possibility of extending the use of these parameters to hyperspectral images and to map the distribution of areas affected by heavy metals on HyMAP data. Variations in the spectral absorption features of lattice OH and oxygen on the mineral surface due to the combination of different heavy metals were linked to actual concentrations of heavy metals. The ratio of 610 to 500 nm (R_610,500 nm) in the visible and near-infrared (VNIR) range, absorption area at 2200 nm (Area_2200 nm), and asymmetry of the absorption feature at 2200 nm (Asym_2200 nm) showed significant correlations with concentrations of Pb, Zn, and As, respectively. The resulting spectral gradient maps showed similar spatial patterns to geochemical gradient maps. The ground-derived spectral parameters showed a reliable quantitative relationship with heavy metal levels based on multiple linear regression. To examine the feasibility to applying these parameters to a HyMAP image, image-derived spectral parameters were compared with ground-derived parameters in terms of R², one-way ANOVA, and spatial patterns in the gradient map. The R_1344,778 nm and Area_2200 nm parameters showed a weak relationship between the two datasets (R² > 0.5), and populations of spectral parameter values, Depth_500 nm, R_1344,778 nm, and Area_2200 nm derived from the image pixels were comparable with those of ground-derived spectral parameters along a section of the stream channel. The pixels classified in the rule image of Depth_500 nm, R_1344,778 nm, and Area_2200 nm derived from a HyMAP image showed similar spatial patterns to the gradient maps of ground-derived spectral parameters. The results indicate the potential applicability of the parameters derived from spectral absorption features in screening and mapping the distribution of heavy metals. Correcting for differences in spectral and spatial resolution between ground and image spectra should be considered for quantitative mapping and the retrieval of heavy metal concentrations from HyMAP images.     

Risk assessment for wildfire occurrence in high-voltage power line corridors by using remote-sensing techniques: a case study in Hubei Province,China

Wildfires are a significant problem for power line maintenance that can lead to widespread power outages and economic loss in China. Remote sensing (RS) and geographic information system (GIS) technologies and their derived applications are effective tools for power line surveillance and disaster prevention. Using multi-source data and remote-sensing techniques, a risk assessment of wildfire occurrence in high-voltage power line corridors in Hubei Province, China, is presented in this study. Both natural and human causes are considered in the assessment, including a traditional Chinese spring custom. Historical ignition points along power line corridors in Hubei Province that occurred from 2009 to 2014 were collected as training data to create a quantitative analysis. Next, a logistic regression model was applied, and ignition probability maps were produced for power lines. Several wildfire accidents that occurred in 2015 were used as validation data to evaluate the performance of the proposed method. The experimental results indicated that the drought conditions significantly influenced wildfires as natural cause, and human activities play important roles in causing wildfires during specific time periods. The effectiveness and robustness of our method is thereby demonstrated, and our method can be used to provide valuable suggestions for wildfire management for the electrical department in Hubei Province.     

Mapping tree distribution and LAI in peatlands using field methods and imaging spectroscopy: a case study for the Haaksbergerveen,the Netherlands

Leaf area index (LAI) is among the vegetation parameters that play an important role in climate, hydrological and ecological studies, and is used for assessing growth and expansion of vegetation. The main objective of this study was to develop a methodology to map the LAI distribution of birch trees (Betula pendula) in peatland ecosystems using field-based instruments and airborne-based remote-sensing techniques. The developed mapping method was validated using field-based LAI measurements using the LAI-2000 instrument. First vegetation indices, including simple ratio (SR), normalized difference vegetation index (NDVI), and reduced simple ratio (RSR), were derived from HyMap data and related to ground-based measurements of LAI. LAI related better with RSR (R² = 0.68), followed by NDVI (R² = 0.63) and SR (R² = 0.58), respectively. Areas with birch were identified using Spectral Angle Mapper (SAM) to classify the image into 11 end members of dominant species including bare soil and open water. Next, the relationship between LAI and RSR was applied to areas with birch, yielding a birch LAI map. Comparison of the map of the birch trees and field-based LAI data was done using linear regression, yielding an R² = 0.38 and an RMSE = 0.25, which is fairly accurate for a structurally highly diverse field situation. The method may prove an invaluable tool to monitor tree encroachment and assess tree LAI in these remote and poorly accessible areas.     

Mapping of coral reefs using hyperspectral CASI data; a case study: Fordata,Tanimbar, Indonesia

Airborne remote sensing with a CASI‐550 sensor has been used to map the benthic coverage and the bottom topography of the Pulau Nukaha coral reef located in the Tanimbar Archipelago (Southeast Moluccas, Eastern Indonesia). The image classification method adopted was performed in three steps. Firstly, five geomorphological reef components were identified using a supervised spectral angle mapping algorithm in combination with data collected during the field survey, i.e. benthic cover type, percentage cover and depth. Secondly, benthic cover mapping was performed for each of the five geomorphological components separately using an unsupervised hierarchical clustering algorithm followed by class aggregation using both spectral and spatial information. Finally, 16 benthic cover classes could be labelled using the benthic cover data collected during the field survey. The overall classification accuracy, calculated on the biological diverse fore reef, was 73% with a kappa coefficient of 0.63. A reliable bathymetric model (up to a depth of 15 m) of the Pulau Nukaha reef was also obtained using a semi‐analytical radiative transfer model. When compared with independent in‐situ depth measurements, the result proved relatively accurate (mean residual error: ?0.9 m) and was consistent with the seabed topography (Pearson correlation coefficient: 86%).     

Classification management for grassland using MODIS data: a case study in the Gannan region,China

Classification of grasslands is a convenient method to measure and manage the sustainability of Chinese grasslands. In this study, a timely and reliable procedure was examined using remote-sensing (RS) techniques. Linear regression analysis between field survey data and Moderate-Resolution Imaging Spectroradiometer (MODIS) data showed that among 17 vegetation indices (VIs) evaluated, the enhanced vegetation index (EVI) was the best VI to simulate forage dry biomass and cover in the Gannan region. The results of precision estimation of the models showed that power and logarithm regression satisfactorily simulated grassland dry biomass and grassland cover, respectively. The index of classification management of grasslands (ICGs) was used to subdivide grasslands into conservation grasslands and moderately productive grasslands in the Gannan region, where no grasslands fell into intensively productive grasslands. Conservation grasslands accounted for 2.04% of the available grasslands, whereas moderately productive grasslands were 97.96% of the available grasslands, and this is related to the history of the grasslands’ use and the per capita income in the Gannan region. This study proposes that the area of conservation grasslands and that of moderately productive grasslands are determined by increases in per capita income and changes in the human use of grasslands.     

Developing a 3D cadastre for the administration of urban land use: A case study of Shenzhen,China

Fast economic growth and rapid urbanization present a challenge to the limited urban land resources of China. Specifically, they encourage a shift in the concentration on the surface of land parcels to the space above and below them. Recent development in land utilization in developed cities has broken the traditional frame of the parcel-based or 2D cadastre. The three-dimensional (3D) cadastre is now accepted by researchers as an effective technical means to support the administration of space. Taking Shenzhen, a quite developed city in China as a case study, this paper presents an ad hoc paradigm to develop a 3D cadastral system for the management of land space use in it. The demand for a 3D cadastre is shown through two actual instances of land space exploitation. Relying on current legislation, administration, and 3D technology, we design a full 3D cadastral system and embed it in the current cadastral system so that the administration of land space can be realized without an alteration in the frame of cadastral data or in current administrative procedures. Presenting an application example, we demonstrate the easy implementation and practicability of our 3D cadastral system. We find, however, that current institutions require some but not excessive modification when fully implementing it for land administration.     

Developing an intelligent expert system for streamflow prediction,integrated in a dynamic decision support system for managing multiple reservoirs: A case study

Since fresh water is limited while agricultural and human water demands are continuously increasing, optimal prediction and management of streamflows as a source of fresh water is crucially important. This study investigates and demonstrates how data preprocessing and data mining techniques would improve the accuracy of streamflow predictive models. Based on easily accessible Snow Telemetry data (SNOTEL), four streamflow prediction models – autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs), a hybrid-model of ANN and ARIMA (ANN-ARIMA), and an adaptive neuro fuzzy inference system (ANFIS) – were developed and utilized in a streamflow prediction process on Elephant Butte Reservoir. Utilizing the statistical correlation analysis and the extracting importance degrees of predictors led to efficiently select the most effective predictors for daily and monthly streamflow to Elephant Butte Reservoir. For the daily prediction time step, by preprocessing the historical data and extracting and utilizing the extracted climate variability indices through data mining techniques, the ANFIS model achieved a superior streamflow prediction performance for Elephant Butte Reservoir compared to the other three evaluated prediction models. Additionally, for predicting monthly streamflow to the Elephant Butte Reservoir, ANFIS showed significantly higher accuracy than the ANNs. As an optimal application of the developed predictive expert systems, successful integrating the prediction models in integrated reservoir operations balanced the need for a reliable supply of irrigation water against losses through evaporation. The optimal operation plan significantly minimizes the total evaporation loss from both reservoirs by providing the optimal storage levels in both reservoirs. This study provides the conceptual procedures of non-seasonal (ARIMA) model, and since the model is univariate, it demonstrates a strongly-reliable inflow prediction when existing information is limited to streamflow data as a predictor.     

Monitoring subway construction using Sentinel-1 data: a case study in Bucharest,Romania

ABSTRACT

In Bucharest, new subway tunnels are under construction since 2011. The whole project of extending Bucharest underground infrastructure is due to be finished by 2030. The M-5 artery has been under construction since 2011 and ongoing. In December 2015, dewatering procedures in one of the metro stations caused serious damage to the surface infrastructure above, which subsided and partially collapsed in some locations. This paper relies on multi-temporal interferometry techniques to characterize displacement patterns of the ground surface above M-5 beltway between 2014 and 2018, and presents the capacity of Sentinel-1 satellite imagery to detect potential hazards in urban environments. Two stacks of Synthetic Aperture Radar (SAR) images acquired between October 2014 and October 2018 by the Sentinel-1 A and B satellites on ascending and descending orbits have been processed using the Delft Persistent Scatterer Interferometry algorithm (DePSI). The results indicate atypical acceleration in velocities starting in November 2015, prior to the described events, and manifesting until April 2016, when the ground was completely stabilized. The displacement trends and velocity values in the area of the Eroilor underground station were successfully validated using the ground-based Global Navigation Satellite Systems (GNSS) technique.     

Developing a cellular automata model of urban growth to inform spatial policy for flood mitigation: A case study in Kampala,Uganda

Urban growth may intensify local flooding problems. Understanding the spatially explicit flood consequences of possible future land cover patterns contributes to inform policy for mitigating these impacts. A cellular automata model has been coupled with the openLISEM integrated flood modeling tool to simulate scenarios of urban growth and their consequent flood; the urban growth model makes use of a continuous response variable (the percentage of built-up area) and a spatially explicit simulation of supply for urban development. The models were calibrated for Upper Lubigi (Kampala, Uganda), a sub-catchment that experienced rapid urban growth during 2004–2010; this data scarce environment was chosen in part to test the model's performance with data inputs that introduced important uncertainty. The cellular automata model was validated in Nalukolongo (Kampala, Uganda). The calibrated modeling ensemble was then used to simulate urban growth scenarios of Upper Lubigi for 2020. Two scenarios, trend conditions and a policy of strict protection of existing wetlands, were simulated. The results of simulated scenarios for Upper Lubigi show how a policy of only protecting wetlands is ineffective; further, a substantial increase of flood impacts, attributable to urban growth, should be expected by 2020. The coupled models are operational with regard to the simulation of dynamic feedbacks between flood and suitability for urban growth. The tool proved useful in generating meaningful scenarios of land cover change and comparing their policy drivers as flood mitigation measures in a data scarce environment.     

Spatial prediction of shallow landslide using Bat algorithm optimized machine learning approach: A case study in Lang Son Province,Vietnam

This study develops a machine learning method that hybridizes the Least Squares Support Vector Classification (LSSVC) and Bat Algorithm (BA), named as BA-LSSVC, for spatial prediction of shallow landslide. To construct and verify the hybrid method, a Geographic Information System (GIS) database for the study area of Lang Son province (Vietnam) has been employed. LSSVC is used to separate data samples in the GIS database into two categories of non-landslide (negative class) and landslide (positive class). The BA metaheuristic is employed to assist the LSSVC model selection process by fine-tuning its hyper-parameters: the regularization coefficient and the kernel function parameter. Experimental results point out that the hybrid BA-LSSVC can help to achieve a desired prediction with an accuracy rate of more than 90%. The performance of BA-LSSVC is also better than those of benchmark methods, including the Convolutional Neural Network, Relevance Vector Machine, Artificial Neural Network, and Logistic Regression. Hence, the newly developed model is a capable tool to assist local authority in landslide hazard mitigation and management.     

Developing IntegrityCatalog,a software system for managing integrity‐related metadata in digital repositories

Digital repositories must periodically check the integrity of stored objects to assure users of their correctness. Prior solutions calculate integrity metadata and require the repository to store it alongside the actual data objects. To safeguard and detect damage to this metadata, prior solutions rely on widely visible media (unaffiliated third parties) to store and provide back digests of the metadata to verify it is intact. However, they do not address recovery of the integrity metadata in case of damage or adversarial attack. We introduce IntegrityCatalog, a novel software system that can be integrated into any digital repository. It collects all integrity‐related metadata in a single component and treats them as first class objects, managing both their integrity and their preservation. We introduce a treap‐based persistent authenticated dictionary managing arbitrary length key/value pairs, which we use to store all integrity metadata, accessible simply by object name. Additionally, IntegrityCatalog is a distributed system that includes a network protocol that manages both corruption detection and preservation of this metadata, using administrator‐selected network peers with 2 possible roles. Verifiers store and offer attestations on digests and have minimal storage requirements, while preservers efficiently synchronize a complete copy of the catalog to assist in recovery in case of a detected catalog compromise on the local system. We present our approach in developing the prototype implementation, measure its performance experimentally, and demonstrate its effectiveness in real‐world situations. We believe the implementation techniques of our open‐source IntegrityCatalog will be useful in the construction of next‐generation digital repositories.     

Comparing keyword search to semantic search: a case study in solving crossword puzzles using the Google™ API

Keyword‐based search engines such as Google? index Web pages for human consumption. Sophisticated as such engines have become, surveys indicate almost 25% of Web searchers are unable to find useful results in the first set of URLs returned (Technology Review, March 2004). The lack of machine‐interpretable information on the Web limits software agents from matching human searches to desirable results. Tim Berners‐Lee, inventor of the Web, has architected the Semantic Web in which machine‐interpretable information provides an automated means to traversing the Web. A necessary cornerstone application is the search engine capable of bringing the Semantic Web together into a searchable landscape. We implemented a Semantic Web Search Engine (SWSE) that performs semantic search, providing predictable and accurate results to queries. To compare keyword search to semantic search, we constructed the Google CruciVerbalist (GCV), which solves crossword puzzles by reformulating clues into Google queries processed via the Google API. Candidate answers are extracted from query results. Integrating GCV with SWSE, we quantitatively show how semantic search improves upon keyword search. Mimicking the human brain's ability to create and traverse relationships between facts, our techniques enable Web applications to ‘think’ using semantic reasoning, opening the door to intelligent search applications that utilize the Semantic Web. Copyright © 2007 John Wiley & Sons, Ltd.     

A new approach for thresholding spectral change detection using multispectral and hyperspectral image data,a case study over Sokolov,Czech republic

Change detection and multitemporal analyses aim to detect changes occurring over a specific geographical area using two or more images acquired at two or more different times. In this article, we present a new thresholding approach for unsupervised change detection. This approach focuses on determining the threshold that discriminates between change and no-change pixels. The differences between pixels in the two images are associated with real changes or noise. We propose a thresholding scheme that separates the threshold into two parts: (1) a spectral domain threshold that accounts for errors related to sensor stability, atmospheric conditions, and data-processing variations, and (2) a spatial domain threshold associated with georectification errors. We demonstrate our method using both multispectral Landsat images and airborne imaging spectroscopy HyMap images. The results show that the spectral domain threshold gives high detection capabilities with moderate false-alarm rate. Adding the spatial domain threshold to the spectral domain threshold reduces the false-alarm rates while maintaining good detection capabilities.     

Pre-classification and post-classification change-detection techniques to monitor land-cover and land-use change using multi-temporal Landsat imagery: a case study on Pisa Province in Italy

This article explores the simultaneous use of pre-classification and post-classification change-detection techniques to map and monitor land-cover and land-use change using multi-temporal Landsat Multi-spectral Scanner and Enhanced Thematic Mapper plus data over one of the most important tourism centres of Italy (e.g. Pisa Province) for 1972, 2000 and 2006.

Pre-classification approaches of principal-component analysis and band combination are potentially tailored to reduce data redundancy of the satellite imagery in order to highlight different objects of significance for change-detection analysis across time-series data. In this work, the application of pre-classification techniques could contribute to produce land-cover and land-use maps with higher quality of classification. At this point, the average value of overall classification accuracies for the three classification outputs was an estimated 90%.

Then, ‘from–to’ change information, as well as the area and the type of landscape transformations, are provided through the post-classification technique. The findings of this study show that the province of Pisa has significantly experienced a high rate of deforestation and urban development over past decades. It is revealed that artificial structures (e.g. urban and industrial zones) in Pisa Province increased at a change rate of around 265% and forested land decreased from approximately 45% to 32% of the total area of the province between 1972 and 2006. Likewise, the perceptible growth of built-up structures from about 4% to 10.6% in Pisa City during this 34-year period has imposed a heavy pressure on the landscape of Pisa.     

Building a database for product design knowledge retrieval—A case study in robotic design database

In product design process, when dealing with technical problems or initiating a new design, R&D personnel would often turn to technical database to seek inspiration. The building of a database with such documents has not been systematically dealt with. In this paper, several issues on how to build up a product design database are investigated: input source, sampling scheme and quality control. A case study of building a database for robotic design is used to demonstrate the concept. It is an archive of more than 1500 relevant technical papers. A total of 16 graduates are employed as operators in the labeling process and subsequently the hypothesis tests are utilized to process the labeling results. To ensure this database quality, the labeling consistency of each operator and the understanding of each category are tested. With the use of statistical methods, this work proposes a feasible and practical way to create such a database for product design.     

The identification of pseudo-invariant targets using ground field spectroscopy measurements intended for the removal of atmospheric effects from satellite imagery: a case study of the Limassol area in Cyprus

This study proposes five types of commonly located surfaces that were evaluated to be determined for use as pseudo-invariant targets (PITs) in order to apply the so-called ‘empirical-line’ atmospheric correction method. Spectroradiometric measurements were taken over wet and dry conditions to obtain the spectral signatures of the targets. From the acquired in situ spectroradiometric campaign, it was found that the proposed commonly found PITs (sandy, concrete and asphalt) are suitable non-variant targets. An accuracy assessment of the empirical-line atmospheric correction method using the five PITs was performed using other calibration targets and aerosol optical thickness (AOT) values from MICROTOPS hand-held sun photometers acquired simultaneously with the satellite overpass. The radiative transfer equation was used to determine AOT levels, using the reflectance values derived from the empirical-line method, in order to conduct an accuracy assessment with in situ AOT measurements. It was also shown that precipitation conditions should be considered when using non-variant targets in atmospheric correction methods.