Identification of groundwater potential in hard rock aquifer systems using Remote Sensing, GIS and Magnetic Survey in Veppanthattai, Perambalur, Tamilnadu

Water is an essential natural resource without which life wouldn’t exist.The study aims to identify groundwater potential areas in Vepapanthattai taluk of Perambalur district,Tamil Nadu,India,using analytic hierarchy process(AHP)model.Remote sensing and magnetic parameters have been used to determine the evaluation indicators for groundwater occurrence under the ArcGIS environment.Groundwater occurrence is linked to structural porosity and permeability over the predominantly hard rock terrain,making magnetic data more relevant for locating groundwater potential zones in the research area.NE-SW and NW-SE trending magnetic breaks derived from reduction to pole map are found to be more significant for groundwater exploration.The lineaments rose diagram indicates the general trend of the fracture to be in the NE-SW direction.Assigned normalised criteria weights acquired using the AHP model was used to reclassify the thematic layers.As a result,the taluk’s low,moderate,and high potential zones cover 25.08%,25.68%and 49.24%of the study area,respectively.The high potential zones exhibit characteristics favourable for groundwater infiltration and storage,with factors as gentle slope of<3°,high lineament densities,magnetic breaks,magnetic low zones as indicative of dykes and cracks,lithology as colluvial deposits and land surface with dense vegetation.The depth of the fracture zones was estimated using power spectrum and Euler Deconvolution method.The groundwater potential mapping results were validated using groundwater level data measured from the wells,which indicated that the groundwater potential zoning results are consistent with the data derived from the real world.

Mapping of Flood Risk Zones Using Multi-Criteria Approach and Radar a Case Study of Ala and Akure-Ofosu Communities, Ondo State, Nigeria

Floods are among the worst natural catastrophes, devastating homes, businesses, public buildings, farms, and crops. Studies show that it’s not the flood itself that’s deadly but people’s vulnerability. This study investigates the Ala and Akure-Ofosu flood-prone zones;identifies elements that cause flooding in the study area;classifies each criterion by its effect;develops a flood risk map;estimates flood damage using Sentinel-1A SAR data;compares AHP results. Literature study and GIS-computer database georeferenced fieldwork data. Photos from the 2020 Sentinel 2A satellite have been organized. Built-up area, cropland, rock, the body of water, and forest Land use and cover, slope, rainfall, soil, Euclidean River Distance, and flow accumulation were mapped. These variables were integrated into a Multi-Criteria Analysis (MCA) using GIS tools, resulting in the creation of a flood risk map that categorizes the region into five risk zones: 5% of the area is identified as high-risk, 21% as low-risk, and 74% as moderate-risk. Copernicus SAR data from before and after the flood were processed on Google Earth Engine to map flood extent and ensured that the MCA map accurately reflected flood-prone areas. Periodic review, real-time flood susceptibility monitoring, early warning, and quick damage assessment are suggested to avoid flood danger and other environmental problems.

Watershed Characteristics and Landuse Analysis of Govindsagar Catchment, Lalitpur (UP) India Using Remote Sensing and GIS Techniques

Watershed characteristics and land use/land cover study is necessary, for improved decision-making and for the resource management strategies. The methodology necessitates the provision of the base map from SOI toposheet, delineation of drainage, preparation of slope and flow direction map using ASTER data and for the land use/land cover change detection, visual interpretation has been carried out using IRSP6-LISS-III data of 2005 and 2015. The land use/land cover analysis discloses several categories of land cover as well as land use present in Govindsagar variation from 2005 to 2015. The study area is mainly cramped to cultivated land and uncultivated land which show changes since last decade, there is an increase in cultivated land of about 4.86% of the geographical area where as uncultivated land (fallow land) shows a decline of 1.61% of the total geographical area, morphometric analysis reveals that area has impermeable subsurface materials and mountainous relief with dendritic drainage pattern with low surface runoff.

Hydrogeomorphological Study in Bamanghaty Subdivision of Mayurbhanj District, Odisha an Integrated Remote Sensing and GIS Approach

Remote Sensing (RS) and geographic information system (GIS) are now very essential tools for efficient planning and management and handling a range of data simultaneously in a time- and cost-efficient manner for targeting of groundwater, which assists in measuring, monitoring, and conserving groundwater resources. Survey of India toposheets, LISS-III and CARTOSAT DEM satellite imageries are used to prepare various thematic layers viz., geology, slope, lineament, drainage, and geomorphology, and were transformed to raster data using feature to raster conversion tool in ArcGIS spatial analysis, then we reclassify each raster map using reclassify tools. By using weight overlay analysis, each weighted thematic layer is statistically computed to get the ground water potential zones. Then, five different groundwater potential zones were identified, namely “very good”, “good”, “moderate”, “poor”, and “very poor”. The villages under poor groundwater potential zone and the villages under very good groundwater potential zone are finding out. The above study has clearly demonstrated the capabilities of Remote Sensing and GIS in demarcation of the different groundwater potential zones in hard rock terrain.

Monitoring urban land cover and vegetation change by multi-temporal remote sensing information

In order to analyze changes in human settlement in Xuzhou city during the past 20 years, changes in land cover and vegetation were investigated based on multi-temporal remote sensing Landsat TM images. We developed a hierarchical classifier system that uses different feature inputs for specific classes and conducted a classification post-processing approach to improve its accuracy. From our statistical analysis of changes in urban land cover from 1987 to 2007, we conclude that built-up land areas have obviously increased, while farmland has seen in a continuous loss due to urban growth and human activities. A NDVI difference approach was used to extract information on changes in vegetation. A false change information elimination approach was developed based on prior knowledge and statistical analysis. The areas of vegetation cover have been in continuous decline over the past 20 years, although some measures have been adopted to protect and maintain urban vegetation. Given the stability of underground coal exploitation since 1990s, urban growth has become the major driving force in vegetation loss, which is different from the vegetation change driven by coal exploitation mainly before 1990.

Encoding Methods of Spectral Vector in Hyperspectral Romote Sensing Image

Taking into account the demands of hyperspectral remote sensing(RS) image retrieval and processing, some encoding methods of spectral vector including direct encoding, feature-based encoding and tree-based encoding methods are proposed and compared. In direct encoding, based on the analysis of binary encoding and quad-value encoding, decimal encoding is proposed. It is proved that quad-value encoding and decimal encoding are suitable to fast processing and retrieval. In absorption feature-based encoding method, five common metrics are compared. Because locations of reflection/absorption features are sensitive to noise, this method is not very effective in retrieval. In tree-based encoding methods, bitree, quadtree, octree and hextree are proposed and discussed. It is proved that 2-level octree and 2-level hextree are more effective than bitree and quadtree. Finally, quad-value encoding, decimal encoding, 2-level octree and 2-level hextree are proposed in spectral vectors encoding, similarity measure and hyperspectral RS image retrieval.

Editorial note for the Geodesy and Geodynamics journal special issue:Remote Sensing and GIS applications in crustal deformation and neotectonics

Crustal deformation and neotectonics are well-understood phenomena that happen almost on every part of the Earth and have some diastrophic effects on the Earth’s surface.To understand these effects,Remote Sensing(RS)and(GIS)have sharpened the human ability to learn about the scientiflc reasons for the Earth’s dynamic activities,including active tectonics and surface landform changes in spatially and temporally.

Analysis of Mountain Rangeland Changes, Using Remotely-Sensed Data, (Case Study: Dehdez Area, Iran)

The land-cover dynamics has been quite conspicuous over the last three decades in Dehdez area, Iran. Therefore, the present study was undertaken in the Dehdez area to assess the trends of rangelands dynamics in the study area during the period 1990-2006. Two clear, cloud-free Landsat and one ASTER images were selected to classify the study area. All images were rectified to UTM zone 39, WGS84 using at least 25 well distributed ground control points and nearest neighbor resampling. Land-use/cover mapping is achieved through interpretation of Landsat TM satellite images of 1990, 1998 and ASTER image of 2006. Fieldwork was carried out to collect data for training and validating land-use/cover interpretation from satellite image of 2006, and for qualitative description of the characteristics of each land-use/cover class. In order to create a testing sample set, first of all, a set of testing points was selected randomly. A supervised classification technique with Maximum Likelihood Algorithm was applied based on 48 training samples for the image of 2006, and 42 samples for the images of 1990 and 1998 and the land-use/cover maps were produced. Error matrices were used to assess classification accuracy. The results showed rangeland covers about 30.8%, 36.7% and 45% of the total geographical area of the Dehdez area in 1990, 1998 and 2006, respectively. Overall accuracies of land-use/cover classification for 1990, 1998 and 2006 were 89.37%, 75.24% and 71.14%, respectively. Kappa values obtained were of 78.71%, 55.61% and 51.41% of accuracy for the 1990, 1998 and 2006, respectively. During 16 years span period (1990-2006) about 1738.4 ha, 383.7 ha, 32.8 ha and 890.1 ha of rangelands were converted to forest, agriculture, water and settlement. The total rich rangelands in the area, accounted for 38.5%, 44% and 42.2% in 1990, 1998 and 2006, respectively. The total poor rangeland in the area accounted for 61.5%, 56% and 57.8% in 1990, 1998 and 2006, respectively. Satellite Remote Sensing enabled the generation of a detailed rangeland map and the separation of grazing intensity levels in rangelands could be generated with the relatively little effort in areas that were difficult to access.

Detection and Mapping of Violent Crime Hotspots in Southwestern Nigeria

The increasing rate of insecurity in Nigeria, especially the southwest requires a paradigm shift from popular approach to crime hotspots detection. This study employed geospatial technologies to integrate spatio-temporal crime, social media and field observation data from the communities in all the six states in the southwest to develop crime hotspots that can serve as preliminary information to assist in allocating resources for crime control and prevention. Historical crime data from January 1972 to April, 2021 were compiled and updated with rigorous field survey in September, 2021. The field data were encoded, input to the SPSS 17 and analyzed using descriptive statistics and multivariate analysis. A total 936 crime locations data were geolocated and exported to ArcGIS 10.5 for spatial mapping using point map operation and further imported to e-Spatial web-based and QGIS for the generation of hotspot map using heatmap tool. The results revealed that armed robbery, assassination and cultism were more pronounced in Lagos and Ogun States. Similarly, high incidences of farmers/herdsmen conflicts are observed in Oyo and Osun States. Increasing incidences of kidnapping are common in all the south-western states but very prominent in Ondo, Lagos and Oyo States. Most of the violent crime incidents took place along the highways, with forests being their hideouts. Violent crimes are dominantly caused by high rate of unemployment while farmer/herdsmen conflicts were majorly triggered by the scarcity of grazing fields and destruction of arable crops. The conflicts have resulted in the increasing cases of rape and disruption of social group, intake of hard drugs, cult-related activities, low income and revenue generation, and displacement of farmers and infrastructural damages. The study advocates regular retraining and equipping of security agents, establishment of cattle ranch, and installation of sophisticated IP Camera at the crime hotspots to assist in real-time crime monitoring and management.

Feature extraction for target identification and image classification of OMIS hyperspectral image

In order to combine feature extraction operations with specific hyperspectral remote sensing information processing objectives,two aspects of feature extraction were explored. Based on clustering and decision tree algorithm,spectral absorption index (SAI),continuum-removal and derivative spectral analysis were employed to discover characterized spectral features of different targets,and decision trees for identifying a specific class and discriminating different classes were generated. By combining support vector machine (SVM) classifier with different feature extraction strategies including principal component analysis (PCA),minimum noise fraction (MNF),grouping PCA,and derivate spectral analysis,the performance of feature extraction approaches in classification was evaluated. The results show that feature extraction by PCA and derivate spectral analysis are effective to OMIS (operational modular imaging spectrometer) image classification using SVM,and SVM outperforms traditional SAM and MLC classifiers for OMIS data.

Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model

WindSat/Coriolis is the first satellite-borne polarimetric microwave radiometer, which aims to improve the potential of polarimetric microwave radiometry for measuring sea surface wind vectors from space. In this paper, a wind vector retrieval algorithm based on a novel and simple forward model was developed for WindSat. The retrieval algorithm of sea surface wind speed was developed using multiple linear regression based on the simulation dataset of the novel forward model. Sea surface wind directions that minimize the difference between simulated and measured values of the third and fourth Stokes parameters were found using maximum likelihood estimation, by which a group of ambiguous wind directions was obtained. A median filter was then used to remove ambiguity of wind direction. Evaluated with sea surface wind speed and direction data from the U.S. National Data Buoy Center （NDBC）, root mean square errors are 1.2 rn/s and 30~ for retrieved wind speed and wind direction, respectively. The evaluation results suggest that the simple forward model and the retrieval algorithm are practicable for near-real time applications, without reducing accuracy.

Research on spatial calculating analysis model of landuse change

The spatial calculating analysis model is based on GIS overlay. It will compartmentalize the land in research district into three spatial types: unchanged parts, converted parts and increased parts. By this method we can evaluate the numerical model and dynamic degree model for calculating land-use change rates. Furthermore, the paper raises the possibility of revising the calculating analysis model of spatial information in order to predicate more precisely the dynamic changing level of all types of land uses. In the most concrete terms, the model is used mainly to understand changed area and changed rates (increasing or decreasing) of different land types from microcosmic angle and establish spatial distribution and spatio-temporal principles of the changing urban lands. And we will try to find out why the situation can take place by combining social and economic situations. The result indicates the calculating analysis model of spatial information can derive more accurate procedure of spatial transference and increase of all kinds of land from microcosmic angle. By this model and technology we can conduct the research of land-use spatio-temporal structure evolution more systematically and more deeply, and can obtain a satisfactory result. The result will benefit the rational planning and management of urban land use of developed coastal areas in China in the future.

Multi-criteria decision based geospatial mapping of flood susceptibility and temporal hydro-geomorphic changes in the Subarnarekha basin, India

The Subarnarekha River in east India experiences frequent high magnitude flooding in monsoon season.In this study,we present an in-depth analysis of flood hydrology and GIS-based flood susceptibility mapping of the entire catchment.About 40 years of annual peak discharge data,historical cross-sections of different gauging sites,and 12 flood conditioning factors were considered.Our flood susceptibility mapping followed an expert knowledge-based multi-parametric analytical hierarchy process(AHP)and optimized AHP-VIP methods.Peak hydrology data indicated more than 5 times higher discharge contrasted with the mean streamflow of the peak monsoon month in all hydro-monitoring stations that correspond to possible overbank flooding in the shallow semi-alluvial reaches of the Subarnarekha River.Widthdepth ratio revealed continuous changes on the channel cross-sections at decadal scale in all gauging sites.Predicted flood susceptibility map through optimized AHP-VIP method showed a great amount of areas(38%)have a high probability of flooding and demands earnest attention of administrative bodies.The AHP-VIP based flood susceptibility map was theoritically validated through AUC approach and it showed fairly high accuracy(AUC=0.93).Our study offers an exceptionally cost and time effective solution to the flooding issues in the Subarnarekha basin.

Quaternary tectonic history of seismically active intraplate Kachchh Rift Basin,western India:A review

Since the recorded historical period,the Kachchh Rift Basin(KRB)has encountered numerous moderate to large magnitude earthquakes.According to the series of seismicity research so far,there are several important points of debate regarding the tectonic history and evolution of the KRB,especially during the Quaternary period.Therefore,the main objective of the present research is to inspect and perceive the association amongst the strain build-up,earthquake provenance,landform evolution and progression as archived by the Quaternary deposits of the KRB.The previous studies demonstrated the evolutions of various landforms,such as the uplifted fluvial terraces,formation of the gorges,uplifted alluvial fan sequences,which can be ideally used to reconstruct the neotectonic history along active faults of KRB.Considering this,the analysis of the accessible and supportive data,including geochronology provided by earlier studies along with some new dataset for a superior knowledge on the Quaternary tectonic forces prevailing in the KRB,have been carried out.Furthermore,we also emphasized the differences and directions for future potential research issues.The observations of variability in uplift rates across the various active faults in the KRB suggest a complex geological history during the Quaternary period.The results show that the vertical uplift rate along the significant active faults range from 0.8 to 2.8 mm/yr,demonstrating the variable tectonic stress regime prevailing in the KRB.The uplift rates constricted from geomorphic and chronological aspects suggest that the tectonic movements within the Kachchh intraplate region is regulated by the fault segments and the present tectonic stress field is in accordance with the encompassing tectonic stress field associated with the northward movement of the Indian plate corresponding to the Eurasian plate.

Classification of features selected through Optimum Index Factor (OIF) for improving classification accuracy

The present investigation was performed to determine if the features selected through Optimum Index Factor （OIF） could provide improved classification accuracy of the various categories on the satellite images of the individual years as well as stacked images of two different years as compared to all the features considered together. Further, in order to determine if there occurs increase in the classification accuracy of the different categories with corresponding increase in the OIF values of the features extracted from both the individual years＇ and stacked images, we performed linear regression between the producer＇s accuracy （PA） of the various categories with the OIF values of the different combinations of the features. The investigations demonstrated that there occurs significant improvement in the PA of two impervious categories viz. moderate built-up and low density built-up determined from the classification of the bands and principal components associated with the highest OIF value as compared to all the bands and principal components for both the individual years＇ and stacked images respectively. Regression analyses exhibited positive trends between the regression coeffi- cients and OIF values for the various categories determined for the individual years＇ and stacked images respectively signifying the prevalence of direct relationship between the increase in the information content with corresponding increase in the OIF values. The research proved that features extracted through OIF from both the individual years＇ and stacked images are capable of providing significantly improved PA as compared to all the features pooled together.

Cloth simulation-based construction of pitfree canopy height models from airborne LiDAR data

Background:The universal occurrence of randomly distributed dark holes(i.e.,data pits appearing within the tree crown)in LiDAR-derived canopy height models(CHMs)negatively affects the accuracy of extracted forest inventory parameters.Methods:We develop an algorithm based on cloth simulation for constructing a pit-free CHM.Results:The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details.Our pitfree CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms,as evidenced by the lowest average root mean square error(0.4981 m)between the reference CHMs and the constructed pit-free CHMs.Moreover,our pit-free CHMs show the best performance overall in terms of maximum tree height estimation(average bias=0.9674 m).Conclusion:The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

Response of drainage to tectonics and PS-InSAR derived deformation study in Bilaspur,northwestern Himalaya,India

The study of drainage patterns in tectonically active regions is conducive to the prediction of regional geomorphology.Subtle subsurface changes can be detected by drainage conditions and manifested in the form of drainage anomalies.The Satluj valley of Bilaspur,which is traversed by numerous faults in northwest Himalayan region,was selected to analyze the effect of active tectonics on drainage evolution.With the Persistent Scatterer Interferometric Synthetic Aperture Radar(PS-InSAR)technique,SENTINEL-1A data were used to estimate the active surface deformation between September 2015 and December 2020.The results show that the region between Barasar Thrust(BrT)and Main Central Thrust(MCT)is undergoing deformation of±12 mm/yr.The Stream Power Incision Model(SPIM)was used to predict deformation patterns.To validate the tectonic activity generated by the drainage network,seismic bvalues were calculated,indicating the accumulating stresses.This study shows the importance of drainage anomalies in tectonically active areas.When used in close combination with other seismotectonic parameters,drainage anomalies can be effective in delineating tectonically active regions.

Seismic vulnerability assessment of urban buildings and traffic networks using fuzzy ordered weighted average

Urban buildings and urban traffic network are considered as the vital arteries of cities which have particular effects especially after the crisis in the search and rescue operations. The aim of this study is to determine the vulnerability of urban areas especially, buildings and traffic networks using multicriteria geographic information systems and decisionmaking methods. As there are many effective criteria on the seismic vulnerability that they have uncertain and vague properties, the method of this paper is applying fuzzy ordered weighted average(OWA) to model the seismic vulnerability of urban buildings and traffic networks in the most optimistic and pessimistic states. The study area is district 6 of Tehran that is affected by the four major faults, and thus will be threatened by the earthquakes. The achieved results illustrated the vulnerability with different degrees of risk levels including very high, high, medium, low and very low. The results show that in the most optimistic case 14% and in the pessimistic case 1% of buildings tolerate in very low vulnerability. The vulnerability of urban street network also indicates that in the optimistic case 12% and in the pessimistic case at most 9% of the area are in appropriate condition and the North and NorthEast of the study area are more vulnerable than South of it.

Seismic vulnerability assessment of urban buildings using the rough set theory and weighted linear combination

Seismic vulnerability assessment of urban buildings is among the most crucial procedures to post-disaster response and recovery of infrastructure systems.The present study proceeds to estimate the seismic vulnerability of urban buildings and proposes a new framework training on the two objectives.First,a comprehensive interpretation of the effective parameters of this phenomenon including physical and human factors is done.Second,the Rough Set theory is used to reduce the integration uncertainties,as there are numerous quantitative and qualitative data.Both objectives were conducted on seven distinct earthquake scenarios with different intensities based on distance from the fault line and the epicenter.The proposed method was implemented by measuring seismic vulnerability for the seven specified seismic scenarios.The final results indicated that among the entire studied buildings,71.5%were highly vulnerable as concerning the highest earthquake scenario(intensity=7 MM and acceleration calculated based on the epicenter),while in the lowest earthquake scenario(intensity=5 MM),the percentage of vulnerable buildings decreased to approximately 57%.Also,the findings proved that the distance from the fault line rather than the earthquake center(epicenter)has a significant effect on the seismic vulnerability of urban buildings.The model was evaluated by comparing the results with the weighted linear combination(WLC)method.The accuracy of the proposed model was substantiated according to evaluation reports.Vulnerability assessment based on the distance from the epicenter and its comparison with the distance from the fault shows significant reliable results.