bluuebunny/crossref_metadata_2025 huggingface dataset:
REMOTE SURVEY OF TRADITIONAL DWELLINGS USING ADVANCED PHOTOGRAMMETRY INTEGRATED WITH ARCHIVAL DATA: THE CASE OF LISBON
Authors: Stellacci S. , Condorelli F.
Published: May 2022

Abstract. The application of advanced photogrammetry and the integration of cross-sourced maps of historic cityscapes contribute to a thorough understanding of our surrounding, enabling the comparison between present physical layout/use of the built environment and its changes over time. Remote surveys integrated with the analysis of building dossiers, stored in municipal archives, and onsite surveys are carried out in this work for providing insight into a traditional building typology in Portugal, which is so far overlooked in heritage studies. Two examples of humble multi-family dwellings with overhanging timber-framed façades (16th century) are virtually reconstructed using structure-from-Motion (SfM) of selected historic photographs. During a first phase, a photogrammetric reconstruction of the state of the buildings, as conceived originally, was implemented by reconstructing two parts (due to the number of images available for each case study), and then a metric evaluation of the models was conducted comparing past and current state of the buildings. This research shows the importance of integrating distinct tools and databases to foster truly tourists' perception of authenticity in cultural heritage sites and towards a more sensitive heritage management. Potential interactive applications capable of increasing the visibility and impact of these virtual reconstructions, such as GIS data enrichment or digital travel apps, are also briefly discussed.
MIXED REALITY CONTENT ALIGNMENT IN MONUMENTAL ENVIRONMENTS
Authors: Teruggi S. , Fassi F.
Published: May 2022

Abstract. Mixed reality provides on-the-spot and real-time data access capabilities by making virtual models and information more intuitive and accessible. Furthermore, allowing the operator to perceive 3D as holograms would allow for a more natural and straightforward manipulation of the perceived 3D content by permitting the augmentation of real objects with various levels of data. This can be accomplished by appropriately registering and superimposing the presented 3D models with the surrounding environment. This work aims to provide a quantitative evaluation of HoloLens 2 capabilities in registering virtual content inside monumental spaces. Two different methodologies are evaluated: Vuforia image targets and Microsoft World Locking Tools (WLTs). Tests have been performed inside Milan Cathedral's monumental spaces. Here, ambience dimensions, single architectural element repetition and non-uniform lighting conditions push out-of-the-box methods to their limits. Results show that WLTs with their space pins API can correctly reference virtual content keeping deviations in the order of 15 cm coping with the scale error produced from sensors’ drifts.
A COMBINED APPROACH FOR LONG-TERM MONITORING OF BENTHOS IN ANTARCTICA WITH UNDERWATER PHOTOGRAMMETRY AND IMAGE UNDERSTANDING
Authors: Menna F. , Nocerino E. , Malek S. , Remondino F. , Schiaparelli S.
Published: May 2022

Abstract. Long-term monitoring projects are becoming more than ever crucial in assessing the effects of climate change on marine communities, especially in Antarctica, where these changes are expected to be particularly dramatic. Detailed studies of the Antarctic benthos are in fact particularly important for a better understanding of benthos dynamics and potential climate-driven shifts. Here, due to the extreme fragility of benthic communities, non-destructive techniques represent the best solution in long-term monitoring programs. In this paper we report new results from 2017, 2018, 2019 photogrammetric campaigns within the Italian National Antarctic Research Program (PNRA). A new protocol of data acquisition and multi-temporal processing that provides co-registered 3D point clouds between the three years without control points nor direct georeferencing methods is presented. This is achieved by adding a level of image understanding leveraging semantic segmentation with convolutional neural network (CNN) of the benthic features. Slow growing (estimated less than a mm per year) organisms, such as Corallinales (Rhodophyta algae), represent a natural stable pattern, leveraged to automatically orient in the same reference system the photogrammetric surveys of different epochs. This approach is also proved to be effective in improving the orientation of adjacent strips acquired within the same campaign. Within the paper an in depth analysis of the achieved results shows the effectiveness of the implemented procedure.
SEMI-AUTOMATED PRODUCTION AND FILTERING OF SATELLITE DERIVED WATER QUALITY PARAMETERS
Authors: Toro Herrera J. F. , Carrion D. , Bresciani M. , Bratić G.
Published: May 2022

Abstract. This paper describes the semi-automated procedure implemented for the production of Water Quality Parameters (WQP) maps obtained processing Sentinel-3 and Landsat-8 imagery in the framework of SIMILE Interreg project. The processing chain includes the use of the C2RCC processor to obtain Chl-a (Chlorophyll-a) and TSM (Total Suspended Matter) and the Barsi method to produce maps of water surface temperature. The maps were filtered to exclude anomalous values due for example to clouds, water reflection (such as glint), or mixed pixels and compared to in-situ data. The filtering included an outlier rejection performed with the 3σ rule. The values singled out as local anomalies where checked with respect to possible local behaviours, such as the presence of very small gulfs and inflow/outflow streams and providing guidelines with visual examples, to support the operator. The idea of a procedure as much as possible automated and guided is to foster the WQP maps production after the end of SIMILE project.
SNOW AVALANCHE SUSCEPTIBILITY MAPPING FOR DAVOS, SWITZERLAND
Authors: Cetinkaya S. , Kocaman S.
Published: May 2022

Abstract. Snow avalanches are among destructive hazards occurring in mountainous regions and spatial distribution (susceptibility) of their occurrences needs to be considered for spatial planning and disaster risk mitigation efforts. The susceptibility assessment is the first step in avalanche disaster management and can be carried out using high resolution geospatial data and machine learning (ML) algorithms. In this study, we have assessed the snow avalanche susceptibility in Davos, Switzerland using an inventory delineated on satellite imagery in a previous study. The conditioning factors used for the avalanche susceptibility assessment include elevation, slope, plan curvature, profile curvature, aspect, topographic position index, topographic ruggedness index, topographic wetness index, land use and land cover, lithology, distance to road, and distance to the river. Two ML algorithms, the logistic regression (LR) and the random forest (RF), were comparatively assessed using validation data split from the training data (30/70). The prediction performances of both models were assessed based on the area under the receiver operating characteristic curve (ROC-AUC) value. Although the AUC value obtained from the LR method was relatively low (0.74), the value obtained from the RF (0.96) demonstrated high performance and usability of this approach. The results indicate that the RF method can successfully produce an avalanche susceptibility map for the region, although potential improvements may be possible by investigating various input features and ML algorithms as well as by classifying the starting and runout zones of the avalanche data separately. Furthermore, the accuracy is expected to increase by using a larger training dataset.
IMPACT OF FOREST FIRE EMISSIONS ON AIR QUALITY OVER WESTERN HIMALAYA REGION
Authors: Murmu M. , Roy A. , Karnatak H. C. , Chauhan P.
Published: May 2022

Abstract. Emissions from forest fires give out huge amounts of greenhouse gases into the atmosphere degrading the surrounding air quality. Climate change results in prolonged summer days and less precipitation thus increasing fuel accumulation facilitating recurring forest fires in the Western Himalayan regions. Most of the forest fire cases are human ignited that damaging the forest irreparably. This study attempts to analyse the impact of the forest fire events on the tropospheric concentrations of five major gaseous pollutants aerosols, Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2) and Formaldehyde (HCHO). A clear increase in daily average of the air pollutants considered in the study was observed as the number of forest fires were also increasing. The gases responded inversely with precipitation. Precipitation washed away majority of the air pollutants. The high-resolution air quality data were retrieved from the Google Earth Engine platform using Sentinel 5 Precursor datasets, daily active fire points from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument of Suomi National Polar-orbiting Partnership (SNPP) and daily precipitation data from CHIRPS Daily Version 2.0 Final. Four out of five gases i.e., CO, aerosols, NO2, HCHO pollutants gave moderate to high correlation values with the forest fire incidences. Air pollutants responded inversely with the precipitation pattern.
3DCD: A NEW DATASET FOR 2D AND 3D CHANGE DETECTION USING DEEP LEARNING TECHNIQUES
Authors: Coletta V. , Marsocci V. , Ravanelli R.
Published: May 2022

Abstract. Change detection is one of the main topics in Earth Observation, due to its wide range of applications, varying from urban development monitoring to natural disaster management. Most of the recently developed change detection methodologies rely on the use of deep learning algorithms. These kinds of algorithms are generally focused on generating two-dimensional (2D) change maps, thus they are only able to detect horizontal changes in land use/land cover, not considering nor returning any information on the corresponding elevation changes. Our work proposes a step forward, creating and sharing a dataset where two optical images acquired in different epochs are provided together with both the related 2D change maps containing land use/land cover variations and the three-dimensional (3D) maps containing elevation changes. Particularly, our aim is to provide a dataset useful to address and possibly solve the change detection task in 3D. Indeed, the proposed dataset, on the one hand, can empower a further development of 2D change detection algorithms, and, on the other hand, can allow to develop algorithms able to provide 3D change detection maps from two optical images captured in different epochs, without the need to rely directly on elevation data as input. The proposed dataset is publicly available at the following link: https://bit.ly/3wDdo41.
HOW SMART GROWTH CAN CURB URBAN SPRAWL: A CASE STUDY OF A RAPIDLY URBANIZING CITY IN KERALA, INDIA
Authors: Krishnaveni K. S. , Anilkumar P. P.
Published: May 2022

Abstract. Cities begin to spread at an alarming rate as the world's urban population grows, resulting in a slew of socioeconomic, environmental, and ecological challenges. Urban sprawl, defined as low-density, non-contiguous, unplanned, unregulated, and uncoordinated outer expansion of cities, is associated with various negative consequences, including loss of arable land, environmental destruction, increased transportation costs, deterioration of the city center, and many others. Smart growth can be considered a potential remedy for the negative impacts of urbanization. The smart growth idea is centered on creating more lively, competitive, and habitable cities to address the deterioration and decay in urban systems caused by urban land sprawl. This article discusses the possibilities of smart Growth approaches as a tool for reducing undesirable urban sprawl. Furthermore, this study aims to capture the land use land cover (LULC) dynamics of Kozhikode Urban Agglomeration (KUA) from 1991 to 2021 using remote sensing and GIS techniques. This study aims to identify potential smart growth locations in KUA using Geographic Information System (GIS)-based modeling methods to mitigate the effects of uncontrolled urban sprawl. To get the most out of smart growth possibilities and effective sprawl reduction, future development activities should focus more on the identified potential hotspots of Smart Growth. The study demonstrated the application of the Smart Growth techniques in a sprawling tier-2 city of Kerala. The city planning authorities can use this technique for effective sprawl management and sustainable city planning.
SENTINEL-1 SAR FOR OPERATIONAL MONITORING OF SOUTHERN AFRICA OCEANS
Authors: Mdakane L. W.
Published: May 2022

Abstract. The introduction of Synthetic Aperture Radar (SAR) through the Oceans and Coasts Monitoring Information system (OCIMS) has therefore become a critical tool for operational coastal and ocean monitoring in South Africa. Natural/man-made ocean phenomena can be observed using satellite Synthetic Aperture Radar (SAR) techniques, making it an integral part of large ocean area monitoring for maritime domain awareness. SAR is an active instrument on-board a satellite platform that transmits electromagnetic waves and measures the returning signal to infer what is happening on the ocean surface. The objective of this paper was to provide information and present options to the decision-makers around sustainability of commercial SAR data acquisition in support of OCIMS IVT. The OCIMS IVT tool identifies ships from multiple types of SAR data, which can provide locations of vessels that have switched off their AIS and oil slicks caused by vessels. Although open-data offerings, such as Sentinel-1, can be considered alternatives to commercial sensors, they present severe limitations for an operational service. To meet OCIMS requirements, and address the imperative to reduce costs, a hybrid of commercial acquisition being used in conjunction with an open data source such as Sentinel-1 is recommended. This would ensure that an operational service with the desired latency as well as increasing the revisit time over the EEZ.
INTEGRATED MINING IMPACT MONITORING (EU-PROJECT I2MON) FOR OPEN-PIT AND UNDERGROUND MINES
Authors: Yang C. H. , Stemmler C. , Pakzad K. , Zimmermann K. , Müterthies A.
Published: May 2022

Abstract. Monitoring mining impact has become increasingly important as the awareness of safety and environmental protection is rising. Our project Integrated Mining Impact Monitoring (i2Mon), funded by European Commission – Research Fund for Coal and Steel, intends to monitor the mining-induced impact, in particular, ground movement. The monitoring system comprises terrestrial measurement and remote sensing: levelling, GPS, LiDAR scanning, UAV survey, and SAR interferometry. The aim is to launch an interactive GIS-based platform as an early warning and decision making service for mining industry. This study has developed a scheme based on advanced SAR interferometry to monitor the ground movement over an extensive area at millimetre level. The first test site is a deactivated open-pit mine in Cottbus, Germany owned by Lausitz Energie Bergbau AG. The whole area was reclaimed into a post-mining lake and must be monitored for the safety. The second test site is located in Poland, where the underground mining operated by POLSKA GRUPA GÓRNICZA began in June 2021. We have monitored the in-situ ground movement carefully as part of the influenced area covers the human settlement. The ground movement of our test sites was analysed from Sentinel-1 images. The crucial parameters include stepwise movement series, instantaneous velocities and accelerations, and significance index. In addition, six corner reflectors along with sensors like GPS were installed across the region in Cottbus. They were observed in the Sentinel-1 series and the GPS readings will be used for validation. Finally, all the data will be integrated into DMT’s platform – SAFEGUARD.
BRDF LABORATORY MEASUREMENTS USING A CAMERA-AIDED SPECTRORADIOMETER
Authors: Roitberg E. , Malgeac I. , Weil-Zattelman S. , Kizel F.
Published: May 2022

Abstract. Numerous natural surfaces observed using Remote Sensing do not reflect light as Lambertian surfaces. Instead, their reflection is highly dependent on two main directions: the direction of the light source and the observation viewing angle, which characterize the Bidirectional Reflectance Distribution Function (BRDF). The BRDF is one of the challenging main effects of remote sensing. Thus, studying the BRDF of various land cover surfaces is essential, and researchers invest many efforts to fulfill this objective. However, measuring the BRDF is tricky and requires unique instruments, e.g., the Gonioreflectometer. Unfortunately, the availability of such instruments is deficient, and they are costly and hard to maintain. Considering these limitations, we present a study and a new approach for measuring the BRDF of surfaces with a camera-aided spectroradiometer that simultaneously acquires an RGB image from the sensor location beside the spectral measurement. Then, we feed the Structure From Motion (SFM) process with the RGM images to retrieve the sensor locations. Next, we convert the sensor locations into the quantities needed for the BRDF measurement, i.e., zenith angles and distances relative to the measured sample. Finally, we apply a set of measurements under controlled conditions in a dark room designed for hyperspectral remote sensing studies to evaluate the proposed methodology. In particular, we experimented with three different material surfaces. The results clearly show the highly accurate sensor position derived by SFM, providing zenith angles and distance from the scene’s center with mean errors around one degree and 2.5 centimeters, respectively. In addition, the obtained spectra tell that the proposed approach is suitable for multiangular measurements of reflected light and studying the BRDF.
DROACOR®-THERMAL: AUTOMATED TEMPERATURE / EMISSIVITY RETRIEVAL FOR DRONE BASED HYPERSPECTRAL IMAGING DATA
Authors: Schläpfer D. , Richter R. , Popp C. , Nygren P.
Published: May 2022

Abstract. Thermal remote sensing from unmanned aerial vehicles is a slowly but steadily growing field of application. New hyperspectral systems operating in the thermal infrared are deployable on such systems and are also usable for ground based monitoring, such as in mining applications. Temperature/emissivity retrieval methods have to be adapted for these new situations. This contribution presents an extension of the Drone Atmospheric Correction method (DROACOR®) for thermal infrared imaging spectroscopy. The method includes an implementation of the semi-automatic normalized emissivity mapping (NEM) method for temperature/ emissivity separation. Furthermore, an extension of the method for correction of low emissivity targets, appearing as cold targets in the temperature mapping is introduced. Two examples of DROACOR-thermal processing are presented for a nadir looking drone based and a horizontal ground based data acquisitions are shown. The resulting spectral emissivitiy distributions and temperature mappings are plausible. They are well comparable to spectral library references and allow for the detection of materials only visible in the thermal infrared range.
USING BISPECTRAL FULL-WAVEFORM LIDAR TO MAP SEAMLESS COASTAL HABITATS IN 3D
Authors: Letard M. , Collin A. , Lague D. , Corpetti T. , Pastol Y. , Ekelund A.
Published: May 2022

Abstract. Mapping coastal habitats is essential to their preservation, but the presence of water hinders seamless data collection over land-water interfaces. Thanks to its dual-wavelength and optical properties, topo-bathymetric lidar can address this task efficiently. Topo-bathymetric lidar waveforms contain relevant information to classify land and water covers automatically but are rarely analysed for both infrared and green wavelengths. The present study introduces a point-based approach for the classification of coastal habitats using bispectral waveforms of topo-bathymetric lidar surveys and machine learning. Spectral features and differential elevations are fed to a random forest algorithm to produce three-dimensional classified point clouds of 17 land and sea covers. The resulting map reaches an overall accuracy of 86%, and 65% of the prediction probabilities are above 0.60. Using this prediction confidence, it is possible to map coastal habitats and eliminate the classification errors due to noise in the data, that generate a clear tendency of the algorithm to over-estimate some classes at the expense of some others. By filtering out points with a low prediction confidence (under 0.7), the classification can be highly improved and reach an overall accuracy of 97%.
DECADAL TRANSFORMATION OF LAND USE - LAND COVER AND FUTURE SPATIAL EXPANSION IN BANGALORE METROPOLITAN REGION, INDIA: OPEN-SOURCE GEOSPATIAL MACHINE LEARNING APPROACH
Authors: Dehingia H. , Das R. R. , Abdul Rahaman S. , Surendra P. , Hanjagi A. D.
Published: May 2022

Abstract. Land use and land cover (LULC) are the terms by which we can denote how the people are utilising the land to serve different purposes and what covers the earth's surface, respectively. This study will present LULC changes and future spatial expansion of the Bangalore Metropolitan Region (BMR), which comprises of Bangalore urban district, Bangalore rural district, and Ramanagara district. The Bangalore metropolitan region is spread across an area of 8005 sq. km and is undergoing a phase of rapid population expansion, which causes increasing concrete space and decreasing green space. This study will focus on achieving the following objectives: Comparing the Spatio-temporal changes, accessing the pattern of expansion, future projection of LULC pattern in Bangalore Metropolitan Region, and finding how the population growth impacts the land use changes. The study is based on an urban expansion model for the years 2030 and 2050 in BMR. The study has used two add-on tools, i.e., the Dzetsaka classification tool and Modules for Land Use Change Simulations (MOLUSCE) in QGIS, to generate the land cover transformation. The core upshots of this technique show the land use and land cover transformation, and urban expansion pattern in the Bangalore Metropolitan Region. It also illustrates the Spatio-temporal changes and how dispersion or expansion has occurred in the last five decades and predicts the future three decades changes. The results are expected to aid society and several organisations such as city planners and policymakers.
ON THE APPLICATION OF REMOTE SENSING TIME SERIES ANALYSIS FOR LAND COVER MAPPING: SPECTRAL INDICES FOR CROPS CLASSIFICATION
Authors: Collu C. , Dessì F. , Simonetti D. , Lasio P. , Botti P. , Melis M. T.
Published: May 2022

Abstract. This study aims to introduce a semi-automatic classification workflow for the production of a land use/land cover (LULC) map of the island of Sardinia (Italy) following the CORINE legend schema, and a ground spatial resolution compatible with a scale of 1:25.000. The classification is based on free high-resolution satellite imagery from Sentinel-1 and Sentinel-2 collected in 2020, ancillary data derived from Sardinian Geoportal, Joint Research Centre (JRC) and OpenStreetMap. The LULC map production includes three steps: 1) pixel-based classification, realized with two different approaches, that use i) information derived from existing thematic maps eventually re-coded in case of incoherencies observed between datasets and/or satellite data products, and ii) spectral indices and parameter thresholds defined on the basis of multitemporal analysis; 2) segmentation of Sentinel-1 and 2 annual composites, and pre-labelling of segments with the pixel-based classified map, obtaining the preliminary map; 3) visual inspection procedure in order to confirm, or re-assign, classes to polygons. The accuracy of the preliminary map was tested in a sample area and on specific class of non-irrigated crops through ground truth data collected from a detailed photo-interpretation, estimating 97% of overall accuracy. The results show a great improvement from existing thematic maps in terms of detail, with the possibility of a yearly updating of the map via automatic processes. However, some limitations were found, due to the high fragmentation of Sardinian landscape and the high variety of crop types and agricultural practices, that could affect the efficiency of the classifier.
A COMPARISON OF CLOUD REMOVAL METHODS FOR DEFORESTATION MONITORING IN AMAZON RAINFOREST
Authors: Martinez J. A. C. , Adarme M. X. O. , Turnes J. N. , Costa G. A. O. P. , De Almeida C. A. , Feitosa R. Q.
Published: May 2022

Abstract. Deforestation in tropical rainforests is a major source of carbon dioxide emissions, an important driver of climate change. For decades, the Brazilian government has maintained monitoring programs for deforestation detection in the Brazilian Legal Amazon area based on remotely sensed optical images in a protocol that involves considerable efforts of visual interpretation. However, the Amazon region is covered with clouds for most of the year, and deforestation assessment can rely only on images acquired in the dry season when cloud-free images are more likely to capture. One possibility to lessen that restriction and enable deforestation detection throughout the year is to synthesize cloud-free optical images from corresponding SAR images, which are only marginally influenced by atmospheric conditions. This work compares a set of such image synthesis methods, considering deforestation detection in the Amazon forest as the target application. Specifically, we evaluate three deep learning methods for cloud removal in Sentinel-2 images: a conditional Generative Adversarial Network (cGAN) based on the pix2pix architecture; an extension of that method, which uses atrous convolutions (Atrous cGAN) to enhance fine image details; and a non-generative method (DSen2-CR) based on residual networks. In the evaluation, we assess both the quality of the generated images and the accuracy obtained when performing deforestation detection from those images. We further compare those methods with an image aggregation tool available in Google Earth Engine (GEE Tool), which creates cloud-free mosaics from sequences of images acquired at nearby dates. In this study, we considered two sites in the Brazilian Amazon, characterized by distinct vegetation and deforestation patterns. In terms of the quality metrics and classification accuracy, the Atrous cGAN was the best performing deep learning method. The GEE Tool outperformed all those methods when dealing with images from the dry season but turned out to be the poorest performing method in the wet season.
COMPARISON OF MACHINE LEARNING CLASSIFIERS FOR MULTITEMPORAL AND MULTISENSOR MAPPING OF URBAN LULC FEATURES
Authors: Ouma Y. , Nkwae B. , Moalafhi D. , Odirile P. , Parida B. , Anderson G. , Qi J.
Published: May 2022

Abstract. This study compares four machine-learning algorithms comprising of Classification And Regression Trees (CART), Random Forest (RF), Gradient Tree Boosting (GTB) and Support Vector Machine (SVM) for the classification of urban land-use and land-cover (LULC) features. Using multitemporal and multisensor Landsat data from 1984-2020 at 5-year intervals for the Greater Gaborone Planning Area (GGPA) in Botswana, the aim of the study is to determine the performance of the classifiers in the extraction of different urban LULC features as built-up, bare-soil, water, grass, shrubs and forest. The results show that for mapping built-up areas, RF and SVM presented the best results with overall accuracy of 85%. Bare soil is best mapped using RF and CART with accuracy of up to 98%, while SVM and GTB were most suitable for mapping water bodies. The suitable classifiers for mapping the vegetation classes were RF for grass (94.5%), SVM for shrubland (81.5%) and GTB for forest (84.3%). In terms of class specific accuracy, RF achieved the highest performance with average overall accuracy (OA) of 95.9%, SVM (95.8%), GTB (95.6%) and CART (95.1%). The same performance pattern was observed from the F1-score, True Positive Rate (TPR), False Positive Rate (FPR) and Area under ROC curve (AUC) metrices for the class classification accuracies. The overall accuracy for the eight-epoch years were RF (87.8%), SVM (87.5%), GTB (86.4%) and CART (85.3%). To improve on the urban LULC mapping, the study proposes the post-classification feature fusion of the best classifier results.
DYNAMIC UPDATING METHOD OF GEOSPATIAL DATABASE WITH INCREMENTAL DATA
Authors: Zhai Z. K. , Liu J. W. , Liu J. J. , Zhao W. H. , Gao Y. , Che J.
Published: May 2022

Abstract. With the rapid development of computer technology and Geographic Information System (GIS), geospatial data are commonly stored and managed in the geospatial database. And geospatial data are widely used in government decision-making, environmental protection, scientific research, and national defence construction. The frequency of geospatial database updating is getting higher and higher due to economic and social development. In this paper, dynamic updating method of geospatial database with the incremental data is proposed, which means only changed features are updated into the geospatial database. Features that have not changed are not updated. This method can greatly improve the efficiency of database update. It has been applied into dynamic updating of national fundamental geographic information database of China. This database has been updated and published once a year. The current situation of data is improved continuously. It can provide more reliable data guarantee for national economic construction and social development. In the future, there will be further optimization of the dynamic updating method. We also hope more and more geospatial database can be updated by this method.
INTEGRATION OF GEOMATIC TECHNIQUES FOR THE 3D REPRESENTATION AND MONITORING OF A VETERAN CHESTNUT TREE
Authors: Balestra M. , Chiappini S. , Vitali A. , Tonelli E. , Malandra F. , Galli A. , Urbinati C. , Malinverni E. S. , Pierdicca R.
Published: May 2022

Abstract. This study concerns the application of different geomatic techniques in forestry sector. An accurate 3D model of a veteran chestnut tree (Castanea sativa Mill.) has been realised integrating point clouds by Mobile Laser Scanner (MLS) and photogrammetry techniques. The surveys were conducted in two seasons (summer time: July 22nd 2021; autumn time: December 10th 2021), since chestnut is a deciduous tree and thus taking the chance to detect it also without leaves. Therefore, it was obtained tree’s metric data such as the diameter at breast height (DBH), the total height (TH), the crown basal area, the wood and crown volume. Annual surveys will result in a dataset of virtual trees that will be used to monitor the evolution of these noteworthy plants through time, which are expected to be considered as cultural heritage, given their story and their importance at regional and landscape level. The 3D model was then released for virtualization, making it accessible and available as a data container for different users. Such data will also provide historical memory about the presence of these veteran trees that might be lost in the future if affected by species-specific phytopathologies such as Ink Disease (Phytophthora spp.).
MULTISPECTRAL AERIAL IMAGES TO SUPPORT BIOTOPE INFORMATION SYSTEMS FOR MIDGE INFESTATION AND BARK BEETLE MONITORING
Authors: Kemper L. , Kemper H. , Kemper G.
Published: May 2022

Abstract. Monitoring environmental phenomena with assistance of remotely captured data is increasingly important in environmental studies. The observation of midge infestation or bark beetles can be considered a non-direct approach to their environments or in some cases to their impact on the ecosystem. Multispectral or hyper spectral data captured by aircrafts or UAVs assist in the observation of such areas. We want to describe the technology of the sensors, the way to process the data, extract related areas and correlate those with the ground truth situation. The examples chosen are the midge infestation in the river Rhine ecosystem and the bark beetle invasion in a forest in Saxony in Germany. Special attention is put on the consequences of such extreme sprawls for the environment and the population close to these areas.
DRONE REMOTE SENSING FOR THE CONTROLLED CAPTURE OF SIKA DEER (CERVUS NIPPON): CASE STUDY IN VILLAGE OF YAMANAKAKO
Authors: Miura N. , Saito H. , Hada T.
Published: May 2022

Abstract. In recent years, the population of sika deer in Japan has been increasing, causing animal damage. As for sika deer in the vicinity of the village of Yamanakako, Yamanashi Prefecture, although the total number of individuals is on a downward trend due to the controlled capture that has been conducted throughout the year since 2009, the number of individuals (population) that stay in the vicinity of residential areas (villa areas) has been increasing in recent years. Unmanned aerial vehicle (UAV) or drone has been proven as an effective tool in wildlife research. In particular, thermal images are useful for relatively large mammals such as deer. In this paper, the utility of drone remote sensing using RGB and thermal video is tested for controlled capture of sika deer as a case study in the village of Yamanakako. We develop a methodology to acquire data and to visually identify sika deer specifically for this mission. As a result, the drone remote sensing was considered successful completing within 1 hour and identifying 25 sika deer individuals. Thermal video format was effective to visually identify sika deer on site in terms of time savings, compared to RGB video or RGB/thermal still image format. To have flight area grided and hovering 3 seconds at the centre of a grid during the flight was essential both for visually identifying the number and location of sika deer and for sharing such information later with beaters and shooters using a map.
UNSUPERVISED SEGMENTATION OF SMALLHOLDER FIELDS IN MOZAMBIQUE USING PLANETSCOPE IMAGERY
Authors: Picoli M. C. A. , Radoux J. , Tong X. , Bey A. , Rufin P. , Brandt M. , Fensholt R. , Meyfroidt P.
Published: May 2022

Abstract. Smallholders produce about a third of the global crop production. Supporting these smallholder farms is an important lever for poverty alleviation. Farm and field sizes are key indicators of many smallholder dynamics, including fragmentation, farm consolidation, and interactions between smallholders, medium-scale commercial farming, and large enterprises. Despite the socio-economic, environmental, and political importance of these dynamics, spatially explicit data on farms and field sizes are still lacking. Identifying small-scale agriculture using satellite imagery is challenging due to the heterogeneity in the crop types and management practices.This study compared three unsupervised segmentation approaches that have not been widely explored for delineating smallholder fields: mean shift, multiresolution segmentation, and simple non-iterative clustering (SNIC), using PlanetScope imagery. The study area is located in northern Mozambique, where 71% of the farms cover less than 2 ha. The results were evaluated using four segmentation accuracy metrics based on object geometries: Area Fit Index (AFI), Quality Rate (QR), Oversegmentation (OS), and Undersegmentation (US). The results showed that the multiresolution segmentation algorithm outperformed the other methods to delineate smallholder fields. This work will support future regional-scale mapping efforts.
Recent contrasting behaviour of mountain glaciers across the European High Arctic revealed by ArcticDEM data
Authors: Małecki Jakub
Published: May 2022

Abstract. Small land-terminating mountain glaciers are a widespread
and important element of Arctic ecosystems, influencing local hydrology,
microclimate, and ecology. Due to their relatively small ice volumes, this
class of ice mass is particularly sensitive to the significant ongoing
climate warming in the European sector of the Arctic, i.e. in the Barents
Sea area. Archipelagos surrounding the Barents Sea, i.e. Svalbard (SV),
Novaya Zemlya (NZ), and Franz Josef Land (FJ), host numerous populations of
mountain glaciers, but their response to recent strong warming remains
understudied in most locations. This paper aims to obtain a snapshot of
their state by utilizing high-resolution elevation data (ArcticDEM) to
investigate the recent (ca. 2011–2017) elevation and volume changes of 382
small glaciers across SV, NZ, and FJ. The study concludes that many mountain
glacier sites across the Barents Sea have been in a critical imbalance with
the recent climate and might melt away within the coming several decades.
However, deviations from the general trend exist; e.g. a cluster of small
glaciers in north SV has been experiencing thickening. The findings reveal
that near-stagnant glaciers might exhibit contrasting behaviours (fast
thinning vs. thickening) over relatively short distances, which is a
challenge for glacier mass balance models but also an opportunity to test
their reliability.
The stability of present-day Antarctic grounding lines – Part B: Possible commitment of regional collapse under current climate
Authors: Reese Ronja , Garbe Julius , Hill Emily A. , Urruty Benoît , Naughten Kaitlin A. , Gagliardini Olivier , Durand Gael , Gillet-Chaulet Fabien , Chandler David , Langebroek Petra M. , Winkelmann Ricarda
Published: May 2022

Abstract. Observations of ocean-driven grounding line retreat in the Amundsen Sea Embayment in Antarctica give rise to the question of a collapse of the West Antarctic Ice Sheet. Here we analyse the committed evolution of Antarctic grounding lines under present-day climate conditions to locate the underlying steady states that they are attracted to and understand the reversibility of large-scale changes. To this aim, we first calibrate the sub-shelf melt module PICO with observed and modelled melt sensitivities to ocean temperature changes. Using the new calibration, we run an ensemble of historical simulations from 1850 to 2015 with the Parallel Ice Sheet Model to create model instances of possible present-day ice sheet configurations. Then, we extend a subset of simulations best representing the present-day ice sheet for another 10,000 years to investigate their evolution under constant present-day climate forcing. We test for reversibility of grounding line movement if large-scale retreat occurs. While we find parameter combinations for which no retreat happens in the Amundsen Sea Embayment sector, we also find admissible model parameters for which an irreversible retreat takes place. Hence, it cannot be ruled out that the grounding lines – which are not engaged in an irreversible retreat at the moment as shown in our companion paper (Part A, Urruty et al., subm.) – will evolve towards such a retreat under current climate conditions. Importantly, an irreversible collapse in the Amundsen Sea Embayment sector evolves on millennial timescales and is not inevitable yet, but could become so if forcing on the climate system is not reduced in the future. In contrast, we find that allowing ice shelves to regrow to their present geometry means that large-scale grounding line retreat into marine basins upstream of Filchner-Ronne and Ross ice shelves is reversible. Other grounding lines remain close to their current positions in all configurations under present-day climate.
Un tremissis inédito de Iliorice/Eliocroca (Lorca) acuñado por Sisebuto
Authors: Martínez Chico David
Published: May 2022

En 2021, durante la construcción de un porche situado en un jardín particular de la calle Rodríguez del Valle, en pleno casco urbano de León, se halló un tremissis visigodo acuñado en Eliocroca por Sisebuto (612-621 d. C.). El ejemplar, depositado hoy día en el Museo de León, es la tercera moneda conocida para esta rara ceca visigoda del sureste. La peculiaridad más notoria de este nuevo ejemplar es la leyenda del reverso: Iliorice. Frente a las dos formas conocidas anteriormente (Ilocrici e Iliocri[ce/i]), Iliorice constituye en realidad una variante lingüística más cercana al original topónimo ibérico. En este sentido, deducimos el nombre original de la Lorca ibérica como Iliori-ci/ILIORIKi, con el importante elemento autóctono Ili-. Esto nos lleva a confirmar, como creyó Un-termann, la existencia de un topónimo genuinamente ibérico, ahora evidenciado mediante una mone-da visigoda como un excelente e indirecto testimonio escrito. Nada debería sorprendernos de este notable hecho, pues como ha puesto de relieve la moderna investigación filológica, los topónimos monetarios visigodos testimonian un habla coloquial. Al mismo tiempo, el nuevo Iliorice de la mone-da visigoda se relaciona con el Ilorcire de una copia manuscrita latina del siglo VIII d. C. de Nort-humbria, que reproduce el pasaje de Plinio (Nat.: 3, 9), suscitando un problema controvertido durante siglos: Cneo Cornelio Escipión Calvo sí pudo fallecer en el territorio de la actual Lorca (211 a. C.) durante la Segunda Guerra Púnica.
La génesis documental en la administración de correos de Cartagena de Indias
Authors: Moreno Cabanillas Rocío
Published: May 2022

La administración de correos de Cartagena de Indias, creada en 1765 bajo el marco de la reforma borbónica del correo, se concibió como una institución productora de documentos en torno a la materia de la circulación de la comunicación postal hispanoamericana. En este artículo examinamos la génesis de la documentación producida por esta oficina postal, abordando las diferentes fases por las que atraviesa la elaboración de sus documentos tanto en la actio como en la conscriptio. Esta investigación revela cómo la estafeta cartagenera contaba con un procedimiento de confección documental normalizado y racional, en consonancia con las necesidades burocráticas del momento.
Աշխատավարձի մակարդակը պայմանավորող գործոնների գնահատումը ՀՀ օրինակով/Evaluation of Factors Influencing Salary by the Example of the RA
Authors: GHAZARYAN ROMIK
Published: May 2022

In a market economy in general, wage differences
are inevitable, but the question arises as to what factors in
particular affect wage differences. Those factors may vary in
every country or organization, but there are key factors, the
impact of which should always be assessed in this kind of
research. The aim of this research paper is to assess the wage
differences in Armenia depending on various economic and
social factors. To meet the objectives of the paper, a regression
analysis was performed based on the Mincer model of salaries.
The research was conducted on the basis of three databases,
which allowed to compare the effects of factors determining the
salaries of workers with different levels of education.
Contemporary Issues In Management & Psychology
Authors:   Sharda University, Greater Noida, Roy Prof.(Dr.) S.K.
Published: May 2022

The book comprises of 20 chapters. Some chapters are related to management and can be easily understood by a layman. Other chapters are related to psychology which requires little more background knowledge of the subject. One of the important features of this book is that it can be read in any order. Each chapter has its own message attached to it. The beneficiaries will be everyone who can have access to this book- ourselves, our children, our beloved ones, the society, as a whole; and professionals from the industry, academia, and government agencies, as well as consumer protection and advocacy groups.
Stomata
Authors: Bassiouni Maoya , Guo Xiaoyu , Vialet-Chabrand Silvere
Published: May 2022

Hosted by New Phytologist Advisor, Rob Roelfsema, University of Würzburg

Featuring

- Maoya Bassiouni, author of [Parsimony versus predictive and functional performance of three stomatal optimization principles in a big-leaf framework](https://nph.onlinelibrary.wiley.com/doi/10.1111/nph.17392)

- Xiaoyu Guo, author of [Establishing asymmetry: stomatal division and differentiation in plant](https://nph.onlinelibrary.wiley.com/doi/10.1111/nph.17613)

- Silvere Vialet-Chabrand, author of [Light, power, action! Interaction of respiratory energy- and blue light-induced stomatal movements](https://nph.onlinelibrary.wiley.com/doi/10.1111/nph.17538)
THE PERCEPTION ON THE TRADITIONAL METHODS OF STIMULATING GROUP COHESION AND WORK EFFICIENCY WITHIN MILITARY INSTITUTIONS IN OLTENIA
Authors:   University of Craiova, Romania, PÎRȘOI Teodora
Published: May 2022

The present paper contributes in a significant way to enriching the information accumulated so far on the managerial models adopted within the military institutions situated in the South-Western part of the country as well as the opportunities of improving their efficiency. Specifically, the main objective of the scientific research in question is to highlight how traditional managerial means and methods of increasing the group cohesion and the institutional efficiency are perceived within military units in the Oltenia region. The research methodology is represented by the questionnaire method, the answers given by the members of the studied sample providing a solid basis for the conclusions adopted on the optimal way to create a homogeneous and efficient collective. The results of the study carried out between October 2021 and February 2022 reveal that in military-type organisations, brainstorming and scenario-based analyses are considered the most appropriate methods of increasing group cohesion and organisational efficiency.
Internal Audit and Sustainable Quality Assurance Practices in Zimbabwean Tertiary Institutions: A Literature Perspective
Authors: Pinias Chikuvadze , Munyaradzi Chidarikire
Published: May 2022

In the recent years the issue of quality of service delivery in Zimbabwean tertiary institutions has been at the centre of discussion at different fora. It is against this background that this paper sought to explore the role of internal audit in fostering sustainable quality assurance practices in Zimbabwean tertiary institutions. Thus, the discussion on interaction between internal audit with the view to create a sustainable quality assurance culture in tertiary institutions was viewed through a lens comprised the agency, and stakeholders theories. The methodology was qualitative in nature, grounded in content analysis, and interpretation with the view to provide a rich and thick inquiry into the issue. Hence, from the discussion it was noted that in tertiary education, and training, the production of goods, and services involves the interaction of mutually exclusive facets such as institutional management, resource mobilization, teaching-learning, and monitoring practices. This discussion came to a conclusion that the interaction of different facets of tertiary institutions based on control, compliance, accountability, and transparency thereby creating sustainable quality assurance practices that are vital in enhancing the right to use, and the quality of teaching-learning in tertiary institutions. Researchers recommends for an improvement of the existing quality assurance in teaching-learning, as a way to guarantee quality culture in tertiary institutions.