The recent advances of low-cost GNSS receivers have broadened their application field not only in positioning and navigation, but also in deformation monitoring of civil engineering structures and geohazards. Even though some consumer-grade low-cost GNSS receivers can achieve cm-level accuracy, their lower performance compared to the dual-frequency high-end GNSS receivers restricts its systematic application of GNSS technology in monitoring projects. In this study, the noise level and performance of the low-cost GNSS receivers are assessed against geodetic receivers in terms of precision and availability when subjected to different measurements conditions, such as antenna grade, satellite constellation, and base station (antenna-receiver), based on zero- and short-baseline measurements. Furthermore, a new method is developed where a dual low-cost GNSS rover-system is formed by deploying two closely spaced low-cost GNSS receivers (30 cm apart), aiming to model their common error (multipath, satellite constellation, etc.) and reduce their noise level. The analysis of the zero- and short-baseline measurements reveals the potential improvement of the precision of the low-cost receiver by using multi-GNSS measurements and the importance of using a GNSS base station with geodetic antenna. However, development of a methodology which is based on adopting the sidereal filtering and the common mode error technique for the two closely spaced low-cost GNSS receivers may lead to precision of mm-level. The proposed methodology may broaden the application of low-cost GNSS receivers in monitoring networks and mainly for slowly developed deformations.

Improving the accuracy of gravity models and their derived products such as Geoid models requires not only accurate airborne, shipborne, and satellite gravity measurements but also a good distribution of ground-based gravity measurements. Since terrestrial gravity surveys are limited by access and may entail a great cost, scientists rely on historical data generated by past surveys. Such datasets are heterogeneous and often lack meta-data. This work achieved a homogenized gravity dataset for Costa Rica and characterizes the spatial distribution of existing, good-quality data in order to consider the focus of terrestrial gravity surveys. First, the gravity value quality control removed duplicates and identified outliers through a process of leave one out cross-validation. Then, we compared terrestrial gravity disturbance values with data from Combined Global Geopotential Models such as EGM2008, GECO, EIGEN-6C4, and the SGG-UGM-1 for quality assessment and consistency of the data. Furthermore, we evaluated a 5′ grid for Costa Rica considering the best spatial resolution expected for the current Combined Global Geopotential Models. Taking into account the spatial distribution of the validated historical dataset, we surveyed new gravity points to significantly improve the coverage, reducing the 5′ × 5′ cells lacking data from 30% to 15%. This resulted in a complete dataset on a 5′ grid except for areas that are exceedingly difficult to access such as national parks, indigenous areas, and restricted private properties.

The present study deals with hyperspectral species mapping, utilization of optimal bands, and studying the spectral separability of vegetation species using AVIRIS-NG data. To reduce data redundancy, a wide range of optimal spectral bands (491 nm, 541 nm, 641 nm, 722 nm, 772 nm, 852 nm, 942 nm, 1047 nm, 1132 nm, 1443 nm, and 2475 nm) were selected and simulated for classification using artificial neural network (ANN) and support vector machine (SVM) techniques. Band-to-band correlative plots demonstrated huge data redundancy (correlation as high as R2 = 0.99, at 682 nm and 687 nm) owing to the need of spectral sub-setting. ANN classification exhibited dominance of low-elevation (0–800 m) evergreen forest (44.81%) followed by Tectona grandis plantation (25.97%). Mid-elevation (800–1450) evergreen forest contributes ~ 16.79% of area, and primarily consists of Coffee arabica (3.14%) and Camelia sinensis (0.7%). Spectrally, each vegetation species exhibited distinct spectral characteristics plotted against different wavelengths and unveiled unique spectral signatures and their classification provided comparable accuracy in species identification in ANN (86.45%) as well as in SVM (86.75%). With immense potential and applicability, spectroscopic AVIRIS-NG data is highly recommended for characterizing, quantifying, modeling, and mapping vegetation.

Earth observation (EO) technologies and products are traditionally providing an extremely important contribution to a more efficient management of built areas and people during emergencies. Their employment could be extremely useful in monitoring and risk assessment of protected monument and sites situated in potentially vulnerable areas such as costal zones. Change detection has a crucial role in such activities, especially in case of hazardous events—on one side, this method can provide inputs in the phases of risk assessment and rapid mapping for immediate response; on the other side, over prolonged periods of time after the event, change detection can be used for purposes of option analysis of technical solutions and for overall recovery planning of the site. Workflow proposed is based on the use of Copernicus Sentinel-2 data to provide the comparison of changes occurring during extreme flooding events of river Centa, Liguria. Firstly, an investigation of NDVI and NDWI of the extreme flooding event occurring in November 2019 was conducted. The event was then put in correlation with another previously studied flooding hazard occurring in 2016 in the same area that has caused severe damages to the archaeological remains of the medieval church of San Clemente, situated within the Centa riverbed. In conclusion, the results from both years have been compared with ground truth data of topographic database in order to observe the extension of the flooded area and to provide contribution to the monitoring activities. The aim of the research was to offer some novel insight for a more informed decision-making during the risk assessment, risk management and resilience phases regarding the vulnerable built heritage sites found in coastal areas.

The term atmospheric or geodetic refraction is the main factor that needed to be known in order to correct the vertical angles in high-precision geodetic applications. This term is found in the relevant surveying literature as mean to describe the alteration in the direction of the light curve as it propagates through the different layers of the lower part of the Earth’s atmosphere in the frame of standard surveying applications. So, what are the new things that the new article deals with? First substantial add is the analysis of the adjustment results of three more 3D geodetic networks with the TG method (totally, four 3D geodetic networks) in the TT1 tunnel at CERN as well as the comparisons of these results with the HLS measurements. However, the main task of this research work is the processing of the same geodetic data with the trigonometrical levelling network adjustment method (TLNA method) in order to detect and eliminate the effect of the refraction in the zenith angle measurements. A significant part of this research work after the result analysis is the comparison of the effectiveness of these two methods. Finally, after the analysis of the results of the two methodologies is proven that the TG method is more adequate than the TLNA method for such accurate measurements. With the TG method, the maximum difference between the nominal height differences of HLSystems and the calculated height differences after the 3D network adjustments with the corrected zenith angles is very promising and approaches the value of 50 μm.

Water erosion is a serious problem that threatens more and more soils in northern Algeria and particularly in Jijel region. The potential of its natural and tourist resources makes this region an important ecological and socio-economic area that deserves to be preserved. The main objective of this work is to apply a weighted-sum model to water erosion sensitivity mapping in Jijel region by integrating geographical information system (GIS). Four layers of data modeling factors responsible for soil erosion vulnerability were combined: precipitation, topography, lithology, and vegetation cover. The resulting water-erosion sensitivity map enabled distinguishing areas most threatened by erosion; 77% of the total area was found to be highly susceptible to water erosion, mainly caused by steep slopes and vegetation cover degraded by summer fires. The sensitivity map was validated by the high statistically significant value of chi-square test and is, therefore, a solid tool on which local decision-makers can rely to define priority protection areas.

The purpose of the study is to develop a method for analyzing, from satellite imagery and GIS tools, the impact of shading from surrounding buildings on open public squares in a subtropical location in Brazil, in order to assist and provide subsidies for urban planning. The method integrates open data, spatial data, and GIS simulation tools for the analysis of shading profiles on winter and summer solstices, and spring equinox. Different types of spaces were analyzed for the current configuration of adjacent buildings, starting with a historic square, Japan Square, in Curitiba. The analysis for 25 urban public squares with the current height configuration showed that four of them had more than 50% of their surface area shaded more than 3 h in winter solstice. The detailed analysis of a possible future scenario for one of the squares evaluated showed that the study of the impact of future constructions on open-air public spaces is important and necessary, especially when vertical growth is allowed in cities such as Curitiba, characterized by a cold winter, when solar access is largely recommended. The method proved to be applicable and reliable, and can be useful for the initial stages of urban planning, as well as for readjustments of existing spaces. Furthermore, it is a tool to analyze the impact of new constructions of surrounding buildings or even for studies of boundary height parameters of buildings around these sites.

Gully erosion, in the fringe areas of the Chotanagpur, is one of the important environmental issues from the perspective of agricultural sustainability, economic framework and land-resource management. The spatial gully erosion risk map (SGERM) can afford to identify the locales sensitive to gully erosion hazard. The present work intends toward modelling spatial gully erosion risk using the new ensemble model in a plateau fringe semi-agricultural watershed of Jainti River (542.69 km2) in Jharkhand, India. Coupling field operation (gully boundary and area measurement) and geostatistical models, the study was accomplished. Field gullies were split into training and validation sets using random partitioning method. Selected predisposing variables for gully erosion based on physical appearances of this basin and literature survey are slope steepness, length-slope factor, soil type, geomorphology, rainfall erosivity, distance from river, stream power index, topographical position index, topographical wetness index, altitude, slope aspect, land use/land cover, normalised difference vegetation index and geology. To prepare the statistical database, information regarding the number of pixel of individual parameter was extracted using GIS software and for tracing the response of each variable for gully erosion, entropy values were calculated against each of them using IoE. While the probability of gullying within each sub-class of each variable was computed using the CP model. In case of ensemble of CP-IoE, variable weights were assigned through IoE and weights to the sub-classes were assigned through CP. The results of CP, IoE and ensemble models that quantified 10.58%, 7.73% and 8.25% areas of the basin were under the very high-risk category respectively. Based upon the results of ROC curves, the predicting accuracy of ensemble model with the highest area under curve (AUC) is better in comparison to individual CP and IoE models.

Geographical Information System (GIS) and remote sensing have become necessary tools in finding out land use land cover (LULC) change integrated with their associated driving factors. The utilization of satellite imagery made it easy to interpret the highly urbanized Warangal City that has experienced a lot of change in LULC during the last few decades. This paper discusses the ability of the integration of cellular automata (CA) and Markov chain–based 2D land use simulation module conjunction with GIS techniques. Markov chain algorithm is used for calibration and optimization by considering LULC of appropriate set of images for the years 2004, 2006, and 2018. Transitional change in LULC from one class to another is simulated using an artificial neural network (ANN) while cellular automata simulation is carried out to predict the plausible future LULC for the year 2052 after validating the model using the LULC of the year 2018. Analysis of the multi-temporal LULC maps indicated that the biophysical and socio-economic factors have greatly influenced the rise in built up while a decrease in agriculture in the year 2052. In conclusion, this technique is a powerful tool for monitoring and modeling change in land cover. Further suggestions for government officials are provided for an effective policymaking and to protect the land resource.

Urbanization associates physical expansion of built up land which influences rapid change of land use/landcover (LULC) pattern of an area. LULC is one of the most visible results of modification of the natural features by human interventions which leads to encroachment of natural land and related degradation. The present paper relates to the exploration of remote sensing and geographic data to study the land use patterns, land cover classification, relationship between different LULC parameters and their impacts. GIS and remote sensing have been integrated as vital tool for the analysis and investigation of spatio temporal data. Siliguri Jalpaiguri planning region has been growing very rapidly over the last decades that brings great changes in the nature of land use pattern and related transformation. The result indicates 0.34 °C increase of land surface temperature per year and its association with spatial indices such as Normalized Difference Vegetation Index, Normalized Difference Water Index, and Normalized Difference Built Up Index.