An introduction to spatial and mobile object detection, spatial analysis and visualization using geographical information system and remote sensing

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

Remote Sensing, Geospatial Technologies and Coastal Ecosystems.- Remote Sensing, Geospatial Technologies and Coastal Ecosystems.- Conceptual and Technical Issues.- Sensors and Techniques for Observing Coastal Ecosystems.- Geographic Information Systems and Spatial Analysis for Coastal Ecosystem Research and Management.- Fuzzy Approach for Integrated Coastal Zone Management.- Spatial Data Infrastructures for Coastal Environments.- Remote Sensing of Coastal Waters.- Airborne Remote Sensing of Chlorophyll in Chesapeake Bay, USA.- Bio-Optical Characteristics and Remote Sensing in the Mid Chesapeake Bay Through Integration of Observations and Radiative Transfer Closure.- Mapping Submerged Aquatic Vegetation and Benthic Habitats.- High-Resolution Ocean Color Remote Sensing of Coral Reefs and Associated Benthic Habitats.- An Integrated Approach to Benthic Habitat Mapping Using Remote Sensing and GIS: An Example from the Hawaiian Islands.- Assessment of the Abundance of Submersed Aquatic Vegetation (SAV) Communities in the Chesapeake Bay and its Use in SAV Management.- Distribution and Spatial Change of Hudson River Estuary Submerged Aquatic Vegetation: Implications for Coastal Management and Natural Resource Protection.- Mapping Marine Macrophytes along the Atlantic Coast of Tierra Del Fuego (Argentina) by Remote Sensing.- Shoreline Change, Coastal Wetland and Watershed Characterization.- Shoreline Mapping and Coastal Change Studies Using Remote Sensing Imagery and LIDAR Data.- Remote Sensing of Coastal Mangrove Forest.- Remote Sensing Support for Tidal Wetland Vegetation Research and Management.- Assessment of Coastal-Vegetation Habitats Using Airborne Laser Remote Sensing.- Measuring Habitat Changes in Barrier Island Marshes: An Example from Southeastern North Carolina, USA.- Mapping Fire Scars and Marsh Recovery with Remote Sensing Data.- Response of Reed Mudflats in the Caspian Coastal Zone to Sea Level Fluctuations.- Integrating Satellite Imagery and Geospatial Technologies for Coastal Landscape Pattern Characterization.- Integrated Coastal Ecosystem Assessment.- Remote Sensing and Spatial Analysis of Watershed and Estuarine Processes for Conservation Planning in Elkhorn Slough, Monterey County, California.- Runoff Water Quality, Landuse and Environmental Impacts on the Bellairs Fringing Reef, Barbados.

The basic properties to be dealt with, when considering initially the research needs related to the integration of remote sensing (RS) information into a geographic information system (GIS), are many-sided. The primary combination of remote sensing and GIS is mainly realized by the transforms of data structure. Because of its own limitations, there is an urgent need to investigate the integration of RS and GIS in higher levels. In this paper, we discuss the different types of combinations of RS with GIS, and propose that GIS data should be directly brought into image processing from the beginning. A tentative idea of how to use the method of granularity to study the common processing unit of RS and GIS is described. The example for the determination of granularity of spatial data processing related to run-length-code line is also given.

Land, sometimes referred to as dry land, is the solid surface of Earth that is not permanently covered by water. The study describes the land use and land cover (LULC) dynamics in Bokaro district at Jharkhand from 1991 to 2011 using Remote Sensing and Geographical Information System (GIS). Whatever, human activities is invented or generated begins from LAND. Therefore, its uses and disintegration by human throughout the period of time is very essential to be known. A city grows not only by population but also by change in its spatial dimensions. The present study investigated the change detection of Bokaro district, Jharkhand, India using LANDSAT satellite data for the year 1991, 2001 and 2011. Topo sheet and high resolution images from Google earth were also used for the study. The process used for the study is known as image processing under which supervised classification method was used using different classes for the study area. The results indicated that there is a drastic increase in agriculture and habitation whereas forest, water and fallow land reduced within the district boundary limit. Migration is still continuing in the district and afforestation/human activities done throughout the period effecting in the reduction of water. Keywords: Change detection, land use and land cover, LANDSAT, Geographical Information System (GIS), remote sensing Cite this Article Modi AK, Das P, Panda D. Land Use/Land Cover Change Dynamic of Bokaro District, East India Using Remote Sensing and GIS Techniques. Journal of Remote Sensing & GIS . 2018; 9(3): 16–22p.

We are modifying our environment at unprecedented rates and scales. We can, however, debate the specific spatial dimensions, rates and significance of these changes. Throughout history, technology has always been a key factor facilitating change. Technology in use today can create environmental change on spatial and temporal scales never before possible. Yet, it also offers us the ability to facilitate our investigations and leads to a more complete understanding of human impact on our environment. Through appropriate use of technologies we can move a significant way toward an environmentally sound management of the Earth's natural resources. Planning and development can no longer take these natural resources for granted. We have to consider not only the needs of the current generations but also those of future generations. The concept of sustainable development, originally set forth in the World Conservation Strategy (1980) and popularized by the Brundtland Commission in 1987 has been defined as “development that meets the needs of the present without compromising the ability of future generations to meet them” (Estes et al. 1992). This statement, however, is still a declaration of intent and has to be specified and tested as to the degree to which it can be made operational within given regions of the globe for specific resources of concerns. Further compounding the issue is the growing necessity of examining such local, regional or national problems in the context of global climatic change scenarios. To carry out this concept of resource development with due consideration of the mid-term and the long-term effects of such development on the environment will require that researcher, planners and decision-makers have access to unprecedented quantities and types of resource and environmental information. Today, a whole new suite of technologies that can facilitate the acquisition of data and the integration of these data into resources management decision processes are emerging. Use of these technologies for data collection, manipulation, analysis and information extraction can improve our understanding of the global environment and through site characterization of specific study areas, measure, map, monitor and model changes in that environment, and provide decision-makers with the information they require for environmentally sound planning and development. Significant among these technologies are remote sensing and geographic information systems (GIS). Remote sensing devices on aircraft and satellites are capable of recording environmental information at staggering rates with significant economies of scale for many purposes. Geographic information systems (GIS) can integrate these data with other spatial data (for example, maps) and non-spatial date (for example, tables or text) to facilitate new forms of analyses. It has to be noted, however, that these technologies are crossing the boundaries of many traditional disciplines, and the development of academic programs in remote sensing and GIS for environmental monitoring and management poses a challenge to established programs. This paper addresses the potential of remote sensing and GIS technologies for environmental monitoring and management. It reports on the efforts at the University of Vechta to establish a new University program in environmental monitoring and management. This program is placed in an interdisciplinary environment and integrates advanced spatial technologies such as remote sensing and GIS with ecological science and environmental planning. First results of a new graduate level course in Environmental Monitoring will be presented.

Groundwater is one of the most valuable natural resources, which supports human health, economic development and ecological diversity. Overexploitation and unabated pollution of this vital resource is threatening our ecosystems and even the life of future generations. With the advent of powerful personal computers and the advances in space technology, efficient techniques for land and water management have evolved of which RS (remote sensing) and GIS (geographic information system) are of great significance. These techniques have fundamentally changed our thoughts and ways to manage natural resources in general and water resources in particular. The main intent of the present paper is to highlight RS and GIS technologies and to present a comprehensive review on their applications to groundwater hydrology. A detailed survey of literature revealed six major areas of RS and GIS applications in groundwater hydrology: (i) exploration and assessment of groundwater resources, (ii) selection of artificial recharge sites, (iii) GIS-based subsurface flow and pollution modeling, (iv) groundwater-pollution hazard assessment and protection planning, (v) estimation of natural recharge distribution, and (vi) hydrogeologic data analysis and process monitoring. Although the use of these techniques in groundwater studies has rapidly increased since early nineties, the success rate is very limited and most applications are still in their infancy. Based on this review, salient areas in need of further research and development are discussed, together with the constraints for RS and GIS applications in developing nations. More and more RS- and GIS-based groundwater studies are recommended to be carried out in conjunction with field investigations to effectively exploit the expanding potential of RS and GIS technologies, which will perfect and standardize current applications as well as evolve new approaches and applications. It is concluded that both the RS and GIS technologies have great potential to revolutionize the monitoring and management of vital groundwater resources in the future, though some challenges are daunting before hydrogeologists/hydrologists.

Excessive runoff and high soil erosion rate are the critical problems in the Himalayan terrain, mainly due to rugged topography and high intensity rains. Accurate quantification of runoff and erosion is thus of paramount importance for taking appropriate measures to sustain the soil productivity in the Himalayan watersheds. Distributed, process-based hydrological and erosion models are ideal for this purpose. However, model parameterization in the rugged, inaccessible and thus generally a data scarce Himalayan watershed is a major challenge. The present study primarily investigates the applicability of kinematic runoff and erosion model (KINEROS2) model in a Himalayan watershed besides exploring the potential of satellite remote sensing and GIS in spatially distributed runoff modeling. The KINEROS2 model, is an event-based, distributed, water and erosion process model. It discretizes the watershed into a mosaic of planes and channels based on topography. The runoff is estimated for each plane which eventually flows to adjacent channel and is then routed to estimate the total runoff at the watershed outlet. Remote sensing is primarily used for model parameterization, i.e., characterizing the individual planes and channels. Optimized digital elevation model and fine-scale land-use/land-cover information are generated using high-resolution panchromatic and multi-spectral optical and microwave satellite imagery. The resulting data on near-surface soil moisture from radar imagery (ENVISAT ASAR) calibrated the initial soil moisture in the model, whose performance is evaluated using root mean square error and Nash–Sutcliffe that reveals that KINEROS2 model works quite well in a small Himalayan watershed. The sensitivity analysis indicates that saturated soil hydraulic conductivity is the most sensitive parameter influencing the runoff compared to Manning’s coefficient and initial soil moisture. The model output is also used for validating the remote sensing and geographical information system (GIS) based hydrologic response units delineated in a previous research study. The study highlights that the coupling of remote sensing and GIS with process models, such as KINEROS2, can provide valuable information in planning sustainable watershed management practices in the Himalayan watersheds.

This study uses GIS spatial analysis and open-source data based on flood hazards formulas to provide a thorough analysis and forecast of floods using Geographic Information Systems (GIS) and Remote Sensing (RS). The Wadi Baysh Dam overflowed with water in 2016 as a result of the torrential downpour that hit the dam, its streams, and the surrounding surroundings results the increasing the storage capacity. In terms of water reserves, it is the dam that is thought to be the biggest in the Kingdom. Some settlements in Baysh, which are under siege since their highways are blocked off, suffered greatly as a result of flooding. However the storage capacity of Wadi Baysh Dam around 200 million cubic meters of water. In order to identify the actual Wadi Baysh flood-vulnerable area, this study combined data from remote sensing, the Geographic Information System (GIS) technique, and an Analytical Hierarchy Process (AHP) computation. The investigation was carried out to determine the viability of using remote sensing open-source data,basemap of the study area andArcGIS10.4 software in Wadi Baysh risk management. Slope (10%), height (15%), drainage density (20%), distance to stream (20%), rainfall intensity (25%), and land cover (10%) all have a proportion of relative weight that determines how flooding is analyzed. As a result of the most recent GIS analysis (weighted overlay approach), five zones are identified within the extracted flood region based on the categories of hazard. There are ten blocks in all—five in a high-risk area and five in a moderately dangerous one. The paper looks at the flood risk in Wadi Baysh using a combination and integration betweenremote sensing opensources data and GIS models. High precipitation and runoff in the dam’s downstream sections will be the main cause of difficulties, causing a great deal of death and destruction to agricultural and urban areas. Eventually, a control room connected to the GIS model and remote sensing devices will be needed in this valley to implement an efficient warning system and flood hazard management system.

Till now, Three Gorges Project has been built for about 15 years. The land use/land cover changes (LUCC)and environment changes in Three Gorges Reservoir Region has always been the focus issues, which is the reason of the paper. Mastering the rule of LUCC in Three Gorges Reservoir Region, firstly, is of advantage to wake up to the potential problems in Three Gorges construction, secondly, can provide reference to the sustainable land planning and administration of reservoir region. The Remote Sensing (RS) method provides abundant and real-time information, and Geographic Information System (GIS) has powerful functions in data processing and spatial analysis. The combination of RS and GIS has inestimable advantages compared with the traditional methods, and is highly effective in monitoring dynamic changes of the land use/land cover. In the paper, the realizing steps and methods of how to monitor the land use dynamic changes by RS and GIS are provided.

Data conversion From raster to vector (R2V) is a key function in Geographic Information Systems (GIS) and remote sensing (RS) image processing for integrating GIS and RS data. The R2V module is available in commercial RS software packages, but there is still room to improve the computation efficiency. This paper presents an efficient R2V algorithm that processes large images and automatically builds GIS topology while scanning image lines one by one. The new algorithm, termed Two-Arm Chains Edge Tracing (TACET), has several significant advantages. First, it converts all types of area objects of RS classification in only one processing cycle. Secondly, it constructs complete area topological relationship by recording the shared edge between two polygons only once. Finally, it is scalable when processing large images. The program based on the algorithm is faster in processing large RS images with comparison to commercial software such as ENVI.

Geographic Information Systems (GIS) are central to spatial evidence in Environmental Impact Assessment (EIA). In this review, GIS is used in a broad, integrative sense to refer to an ecosystem of geospatial technologies—such as remote sensing (RS) and GPS—where GIS serves as the core platform for managing, analyzing, and communicating spatial data throughout the EIA process. GIS plays a crucial role at each stage of EIA, from baseline data collection to spatial analysis, ecological sensitivity mapping, impact prediction, scenario simulation, and landscape connectivity assessment. These capabilities support alternatives analysis, risk communication, and decision-making in EIA. This paper synthesizes thematic evidence and presents case studies to illustrate the synergies between GIS, remote sensing, GeoAI, and multisource data fusion. It highlights operational workflows and key deliverables for EIA applications, including urban expansion, transport corridors, and protected-area management. We identify persistent challenges in data quality and standardization, interoperability, model uncertainty, and policy gaps. To address them, we propose a minimum geospatial dataset with clear metadata standards, interpretable GeoAI paired with formal sensitivity analysis, IoT–GIS pipelines for real-time monitoring and adaptive management, and the systematic inclusion of cumulative effects and climate scenarios. By linking GIS methods to typical decision points and reporting standards in EIA, this review clarifies where GIS adds value, how to quantify and communicate uncertainty, and how to align analytical outputs with regulatory requirements and stakeholder expectations. The study offers a practical framework and implementation checklist for standardized, transparent, and reproducible EIA processes, contributing to evidence-based ecological governance.

From green herbage yield, environment, and remote sensing (RS) data recorded in different grassland types in Fukang County, Xinjiang from 1991 to 1996, correlation analyses and grassland yield estimates were obtained using remote sensing and geographic information system (GIS) technologies. Methods of processing images, analysing information, and linking of remote sensing data with ground grassland data were explored. Results showed correlation between fresh herbage yields and ratio vegetation index (RVI) and normalised difference vegetation index (NDVI) (P < 0.01) in four grassland types with correlation coefficient (r) >0.679. Fresh herbage yields correlated better with RVI than with NDVI for lowland meadow, hill desert steppe, and mountain meadow, but not for plains desert steppe. Optimum non‐linear models for estimating yield were selected from six curves, and estimated total yields were verified by ground truth large‐plot investigations and statistical analyses. The effects of estimating green herbage yields using non‐linear models were better than those using linear models in all four grassland regions. The total accuracy of estimating yields by remote sensing was >75% over large areas in the four grassland types using a combination of remote sensing and GIS. Remote sensing, along with GIS, is a new approach to the use, development, and management of grasslands.

Flood is one of the natural disaster usually take place that in the southern states of Malaysia such as Johor, Pahang and Malacca during rainy season. The number of area affected from this event has increase over the past years. This paper describes a study made to explore the application of GIS and Remote Sensing in flood risk assessment for Bandar Segamat sustainability. The objective is to explore and understand the flood risk assessment characteristics, Remote Sensing (RS) and Geographic Information System (GIS). Open source software such as QuantimGIS and Infoworks RS is used as tools in processing the data. The result show a significant movement of water in Segamat city which causing such loss in terms of property damages and lives. A simulation of flood risk and sustainability at Bandar Segamat was developed which indicate the most, moderate and less affected area in Segamat. The main contribution of the study was to prove that anyone that involved directly or indirectly in GIS or RS field are able to perform the similar study without the need of high cost, great experience and with basic knowledge of the field study.

Groundwater represents a critical natural resource for humanity, particularly in areas with limited surface water availability. The present study was aimed to map the groundwater potential zones in the Teji River catchment using remote sensing (RS) and geographic information systems (GIS). The catchment was selected due to its exposure to significant water stress driven by rapid population growth, agricultural expansion, and the broader impacts of global climate change. The parameters processed were on soil texture, drainage density, land-use and land-cover, rainfall, lithology, slope, topographic wetness index, and lineament density. The Analytical hierarchy process (AHP) method was used. Each criterion was evaluated with AHP and mapped by GIS. The groundwater potential zones were categorized into four distinct classes based on their suitability: very good (12.24%), good (30.88%), moderate (36.05%), and poor (20.8%). The study confirmed that RS and GIS techniques offer enhanced accuracy in identifying groundwater potential zones. Validation results showed that approximately 81.25% of the classified groundwater boreholes corresponded accurately with the zones delineated on the generated groundwater potential map. Further validation utilizing the receiver operating characteristic (ROC) curve gave an AUC of 0.83. The methodology, techniques, and results of the current investigation may be useful in evaluating the groundwater potential zones in similar terrains worldwide. Furthermore, the establishment of purposeful testing wells and field geophysical investigations in potential well-drilling locations is advised to enhance effective groundwater management possibilities. Graphical Abstract This graphical abstract illustrates the key processes and findings of a study on Groundwater Potential Zone Mapping using GIS and Remote Sensing, focusing on the Teji River catchment in Southwest Shewa Zone, Ethiopia. It highlights the methodology used to identify and describe potential groundwater zones within the study area. Moreover, the graphical abstract illustrates the use of AHP for multi-criteria decision-making and integration of thematic layers such as rainfall, LULC, Soil texture, topographic wetness index (TWI), lithology, drainage density, lineament density and slope for spatial analysis and classification. The abstract presents a novel approach for mapping and assessing groundwater potential, providing insights into sustainable water resource management.

This Special Issue collates seven papers regarding the assessment or monitoring of hydrological disasters such as droughts and flood using remote sensing and geography information system (GIS) techniques.. The new published research focused on evaluations and models of various hydrological hazards such as droughts and floods. Furthermore, we include two original scientific articles addressing the subject of water quality. This Special Issue received investigations based on different techniques such as remote sensing, GIS, machine learning and monitoring. All papers present findings characterized as unconventional, provocative, innovative and methodologically new. Scientific findings presented in this Special Issue highlight how a combination of various modern analysis techniques (e.g., remote sensing, GIS) can improve our understanding of complex hydrological hazards such as droughts and floods. We hope that the research contained within this Special Issue is useful to the scientific community, policymakers and stakeholders at large in the field of hydrological hazards.