Integration Of Remote Sensing Research Articles

Integration of Remote Sensing and Aerogeophysical Data Applied in the Geological Characterization of the Ipueira-Medrado Segment, Andorinhas/BA

The segment of the Ipueira-Medrado mines constitutes two of the main chromium mines operated by Companhia Ferro-ligas da Bahia (Ferbasa). The research area is located in the municipality of Andorinhas, in the north center of the state of Bahia, in the Piemonte Norte Do Itapicuru region. Therefore, the main objective of the present work is the use of Remote Sensing and Aerogeophysics data to elaborate the geological map of the study area and highlight new target areas for mineral research. The Digital Image Processing (PDI) stage was carried out in an environment in the ENVI® 5.3 software for the scenes of the optical Alos and radar images, Palsar. The treatment of magnetic and radiometric data was carried out in the environment of the software Oasis Montaj 9.10 by Geosoft®. In the Geographic Information System (GIS) platform of ArcGis 10.7.1®, multisource data was integrated. This integration supported the screening of information from local lithologies and the identification of four lithological units: Sienito Itiúba, Santa Luz Unit, São Bento Unit and Jacurici Valley Complex. In this last unit, a combined analysis between Map Algebra and Fuzzy logic was applied to all input raster data, in order to mark out a zoning of potential areas for the occurrence of mafic-ultramafic rocks. In the end, a good relationship was noted between the results obtained with models that already exist in the bibliography. In view of this, the application of the methodology developed in this research requires more robustness for the local geological knowledge, as it will also help in the development of new mineral research endeavors.

Regional estimates of gross primary production applying the Process-Based Model 3D-CMCC-FEM vs. Remote-Sensing multiple datasets

ABSTRACT Process-based Forest Models (PBFMs) offer the possibility to capture important spatial and temporal patterns of carbon fluxes and stocks in forests. Yet, their predictive capacity should be demonstrated not only at the stand-level but also in the context of broad spatial and temporal heterogeneity. We apply a stand scale PBFM (3D-CMCC-FEM) in a spatially explicit manner at 1 km resolution in southern Italy. We developed a methodology to initialize the model that includes information derived from the integration of Remote Sensing (RS) and the National Forest Inventory (NFI) data and regional forest maps to characterize structural features of the main forest species. Gross primary production (GPP) is simulated over 2005–2019 period and the model predictive capability of the model in simulating GPP is evaluated both aggregated as at species-level through multiple independent data sources based on different nature RS-based products. We show that the model is able to reproduce most of the spatial (~2800 km2) and temporal (32 years in total) patterns of the observed GPP at both seasonal, annual and interannual time scales, even at the species-level. These promising results open the possibility of confindently applying the 3D-CMCC-FEM to investigate the forests’ behaviour under climate and environmental variability over large areas across highly variable ecological and bio-geographical heterogeneity of the Mediterranean region.

Integration of Remote Sensing and GIS Technologies for Blue Carbon Ecosystem Monitoring in Lakkang Island, Makassar City

Blue carbon ecosystem has a very important role in maintaining the quality of environments. Form of monitoring processes is needed to be able to provide direction for decision making, related to its management planning. Remote sensing technologies that is integrated with Geographic Information Systems (GIS) is a form of monitoring methods that can be carried out more quickly, easily and accurately. Lakkang Island in Makassar City has a blue carbon ecosystem in form of mangroves, that have very important role in absorbing carbon emissions. This research was conducted with the aim of integrating remote sensing and GIS technologies in monitoring blue carbon ecosystems. Monitoring processes focused on distribution and extentions of the blue carbon ecosystem which is carried out through a random forest algorithm. Monitoring of the physical condition like density levels of the blue carbon ecosystem is carried trough the NDVI analysis. The results showed that the distribution of the blue carbon ecosystem on Lakkang Island tends to follow the main flow of the Tallo River, with an area of 58.29 hectare. Physical conditions of the blue carbon ecosystem is dominated by dense mangroves that reaching 46% from total area of the ecosystem.Keywords: Blue Carbon, Ecosystem Monitoring, Lakkang

Assessment of groundwater potential zones using an integration of Remote Sensing, GIS and 2D Electrical Resistivity imaging in the Echway watershed, Baro River Basin, Southwest Ethiopia

The objective of this study is to delineate and identify the groundwater potential zone of degraded land in the Echway watershed, southwest Ethiopia, employing a combined approach that includes 2D electrical resistivity, the analytical hierarchy process (AHP), geographic information systems (GIS) and remote sensing (RS). Using a geographic information system (GIS) and remote sensing data, groundwater potential zones were identified by taking into account the soil types, drainage density, geology, lineament density, Land use and land cover, rainfall and slope. Using the AHP method, calculate the weight of thematic layers and rank of subclasses based on the effects of various thematic layers on groundwater potential. A significant weight was provided on the geology of the research region, total annual rainfall, and lineament density. Due to significant effects on groundwater potential zones, only three of the seven theme levels were assigned significant weight in this analysis. The weighted overlay analysis was used to construct the diagram of the zones with groundwater potential. 2D electrical resistivity was utilized to find the groundwater aquifer, and four major electrical layers were discovered: clayey silt, sand, moderately weathered/fractured rock, and basement parent rock. The groundwater potential zones in the study area have been classified into five categories: very low (22.97%), low (13.43%), moderate (32.50%), high (25.12%), and very high (5.98%) covered, respectively. The groundwater potential zone image was created by combining GIS, remote sensing, AHP, and an electrical survey

Slums Evolution and Sustainable Urban Growth: A Comparative Study of Makoko and Badia-East Areas in Lagos City

This research addresses the evolution of slums in two specific areas of Lagos City, a consequence of rapid urbanization in developing economies. The study aims to analyze the key characteristics of slums’ evolution while analyzing mathematical spatial changes within the Badia-East and Makoko slum areas in two decades, focusing on promoting sustainable urban growth. The integration of Remote Sensing (RS) technology and Geographic Information System (GIS) tools has dramatically facilitated the monitoring of Land Use Land Cover (LULC) changes over two decades. This research used Landsat imagery acquired in 2000, 2010, and 2020 to meet our research aims. The research applied supervised classification and the Normalized Difference Built-up Index (NDBI) for image mapping and mathematical-based analysis. Research created a spatial framework using grid-cell maps, performed change detection, and generated detailed maps to support our analysis. A comparative analysis was also performed in the selected areas with a different history in slum management systems. The findings reveal that both Makoko and Badia-East exhibit slum-like characteristics. Makoko witnessed a significant increase in informal built-up expansion of 27.6% and 7.7% between 2000 and 2010 and between 2010 and 2020, respectively. These changes converted 34.65 hectares of non-built-up land in Makoko into built-up areas. Conversely, Badia-East reported an increase in built-up areas only between 2000 and 2010, which amounted to 8.5%. However, the government’s intervention caused a decrease of 12.7% in built-up areas of Badia-East between 2010 and 2020. The study underscores the urgent need for slum clearance and upgrading initiatives in Makoko, like those implemented in Badia-East between 2013 and 2017. The conclusion drawn from the study of Makoko’s informal built-up areas is that it is causing adverse effects on human life and the environment. The expansion has resulted in an increase in air and water pollution, which is having adverse effects. Therefore, the study recommends upgrading and clearing out Makoko and suggests taking lessons from the successful experience in Badia-East. The study also highlights the importance of prioritizing community needs and voices to ensure efficient resource utilization while safeguarding the well-being of future generations.

Integration of Remote Sensing and Field Observations in Evaluating DSSAT Model for Estimating Maize and Soybean Growth and Yield in Maryland, USA

Crop models are useful for evaluating crop growth and yield at the field and regional scales, but their applications and accuracies are restricted by input data availability and quality. To overcome difficulties inherent to crop modeling, input data can be enhanced by the incorporation of remotely sensed and field observations into crop growth models. This approach has been recognized to be an important way to monitor crop growth conditions and to predict yield at the field and regional scale. In recent years, satellite remote sensing has provided high-temporal and high-spatial-resolution data that allow for generating continuous time series of biophysical parameters such as vegetation indices, leaf area index, and phenology. The objectives of this study were to use remote sensing along with field observations as inputs to the Decision Support System for Agro-Technology (DSSAT) model to estimate soybean and maize growth and yield. The study used phenology and leaf area index (LAI) data derived from Planet Fusion (daily, 3 m) satellite imagery along with field observation data on crop growth stage, LAI and yield collected at the United State Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center (BARC), Beltsville, Maryland. For maize, a total of 17 treatments (site years) were used (ten treatments for model calibration and seven treatments for validation), while for soybean (maturity groups three and four), a total of 18 treatments were used (nine for calibration and nine for validation). The calibrated model was tested against an independent, multi-location and multi-year set of phenology and yield data (2017–2020) from BARC fields. The model accurately simulated maize and soybean days to flowering and maturity and produced reasonable yield estimates for most fields and years. Model run for independent locations and years produced good results for phenology and yields for both maize and soybean, as indicated by index of agreement (d) values ranging from 0.65 to 0.93 and normalized root-mean-squared error values ranging from 1 to 20%, except for soybean maturity group four. Overall, model performances with respect to phenology and grain yield for maize and soybean were good and consistent with other DSSAT evaluation studies. The inclusion of remote sensing along with field observations in crop-growth model inputs can provide an effective approach for assessing crop conditions, even in regions lacking ground data.

Groundwater Potential Assessment of Penang Island, Malaysia, Through Integration of Remote Sensing and GIS with Validation by 2D ERT

Groundwater Potential Assessment of Penang Island, Malaysia, Through Integration of Remote Sensing and GIS with Validation by 2D ERT

Integration of Remote Sensing and Field Data in Ophiolite Investigations: A Case Study of Logar Ophiolite Complex, SE Afghanistan

Mafic–ultramafics complexes are crucial for their tectonic implication, upper mantle condition, and for hosting industrial minerals in a region. This study aims to highlight and characterize the mafic–ultramafic rocks of the Logar Ophiolite Complex using the integration of geospatial technology and field data. The spatial distribution of the ophiolitic complex was examined in this study using the mineralogical indices (MI), band ratio (BR), and spectral angle mapper (SAM) methods within the framework of geospatial technology using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Additionally, several samples were collected from the identified complexes for validation, petrographic, and mineralogical analyses. Combining geospatial technology and conventional approaches, e.g., field sampling and geological data analysis yields efficient discrimination of mafic–ultramafic rocks with their associated hydrothermal altered minerals. The serpentinization and carbonate processes are predominantly seen along the eastern side of the active fault zone following the detection of ophiolites. Detailed mapping of the ophiolitic complex and associated rocks was achieved using refined mafic index (MI), band ratio 12/14 and 4/8 for rocks and SAM for highlighting the mafic–ultramafic altered minerals, and petrographic analysis of the collected samples. The field works verified the results of the ASTER data. The findings of this study can significantly contribute to detailed tectonic and geologic studies of the detected ophiolites in terms of their emplacement mechanism and ages.

Integration of Remote Sensing and Big Data to Study Spatial Distribution of Urban Heat Island for Cities with Different Terrain

Urban microclimate has posed a detrimental effect on the life of the urban population. This research drives with an aim of identifying environmentally conscious factor vis-a-vis urban planning which leads to the vicious cycle of urban climate change. The vicious cycle is inclusive of many urban dynamics’ parameters, which are complicated to understand. This research emphasizes on using Remote Sensing Big Data on Google Earth Engine as an advancement to study Climate Vulnerability leading to Urban Climate Gentrification. Temporal data of Landsat for the past 30 years has been taken into consideration for the study. Three cities with diverse geographical and terrain characteristics have been selected for the study, to understand the modern decisive planning is in coherence with the Sustainable Development Goals. Understanding spatial and temporal information of Urban hotspots using High-Resolution Satellite data is just not enough to suffice the need to decrease the temperature by 2- 3°C. The present study is a toll on how the reasons for microclimate change vary along with the terrain, spatial location, and urban growth pattern of the city.

Utilizing Nighttime Photos to Locate Attraction Zones at the Metropolitan Scale: An Analysis of Istanbul

Up-to-date information about different forms of land-use (residential areas, industrial areas, central business districts, recreational areas, etc.) is essential for city planning processes to obtain better urban and regional planning decisions. Traditional methods (e.g., field surveying or WEB/GPS based data collection) used to gather up-to-date information can often contain some errors and can also be time-consuming and expensive, especially for large metropolitan urban areas. With the integration of Remote Sensing (RS) and Geographic Information Systems (GIS) related technologies, the difficulty of providing up-to-date information about different types of land-use can be greatly reduced. On the other hand, in terms of urban and regional planning, the level of utilization of these technologies is still considered to be insufficient. In this respect, authors wanted to draw attention to another possible usage of night-time data in urban and regional planning discipline for the purpose of determination of the location, size, and hierarchy of the attraction zones in urban scale which are mostly composed of central business districts (CBD), commercial zones, touristic corridors and/or concentration areas etc., as these regions are more illuminated areas compared to other zones of a city. Thus, a methodology based on GIS and RS integration and spatial and statistical analysis capabilities of GIS is presented in this study to determine the boundary and size of the attraction zones and their hierarchical levels by using the night-time imageries. To show how effective the suggested model is, the proposed methodology has been used in the city of Istanbul. The assessment of the location, size, and hierarchy of the attraction zones could give an essential decision support for the decision makers, especially those working in the urban planning discipline, as the attraction zones of cities need to be developed in a more specific and detailed manner. Thus, the model's outputs' reliability and potential applications in the field of urban planning are also examined.

Remote Sensing and GIS-aided Land and Water Resources Management Plan for a Prioritized Sub-watershed of the Kotni Watershed, Chhattisgarh, India

Land and water are the prime resources that support all forms of life through various processes. Integration of Remote Sensing and GIS is incredibly helpful in planning and management of this resources, it is superior to the limitation of typical land planning, causes it embraces the metaverse of planning and development of resources through modern technology. In the study area, these resources are in critical condition; approximately 6.5 % of land is agricultural fallow land, 3 % is barren land, and 50 % is current fallow in Rabi season caused by the majority of agricultural land being unproductive due to poor land and water resource management. This research makes prudent and effective use of the watershed resources to increase productivity and production in the study area. The runoff computation of the watershed was done using the SCS curve number approach for 11 years (2009-2019), it reveals average annual rainfall of 1329 mm, with 41% of runoff generation capacity, which is estimated as 483.95 Mm3 of generated runoff. Methodology also includes Multi-Criteria Decision Analysis (MCDA) techniques and weighted overlay analysis (WOA) which effectively uses various thematic maps to delineate suitable zones for water storage and harvesting structures. Finally, suitable sites for 7 check dams, 10 percolation tanks for groundwater recharging and 20 check dams, 61 farm ponds for water harvesting were identified and proposed in the study area. The research findings will help to conserve water and soil resources and may increase the agricultural area as well as the production and productivity of the respected field.

Integration of Remote Sensing and Offshore Geophysical Data for Monitoring the Short-Term Morphological Evolution of an Active Volcanic Flank: A Case Study from Stromboli Island

The Sciara del Fuoco (SdF) collapse scar at Stromboli is an active volcanic area affected by rapid morphological changes due to explosive/effusive eruptions and mass-wasting processes. The aim of this paper is to demonstrate the importance of an integrated analysis of multi-temporal remote sensing (photogrammetry, COSMO-SkyMed Synthetic Aperture Radar amplitude image) and marine geophysical data (multibeam and side scan sonar data) to characterize the main morphological, textural, and volumetric changes that occurred along the SdF slope in the 2020–2021 period. The analysis showed the marked erosive potential of the 19 May 2021 pyroclastic density current generated by a crater rim collapse, which mobilized a minimum volume of 44,000 m3 in the upper Sciara del Fuoco slope and eroded 350,000–400,000 m3 of material just considering the shallow-water setting. The analysis allowed us also to constrain the main factors controlling the emplacement of different lava flows and overflows during the monitored period. Despite the morphological continuity between the subaerial and submarine slope, textural variations in the SdF primarily depend on different processes and characteristics of the subaerial slope, the coastal area, the nearshore, and “deeper” marine areas.

Volcanic Processes Monitoring and Hazard Assessment Using Integration of Remote Sensing and Ground-Based Techniques

The monitoring of active volcanoes is a complex task based on multidisciplinary and integrated analyses that use ground, drones, and satellite monitoring devices [...]

Delineation of Groundwater Potential Zones (GWPZs) in a Semi-Arid Basin through Remote Sensing, GIS, and AHP Approaches

Groundwater occurrence in semi-arid regions is variable in space and time due to climate patterns, terrain features, and aquifer properties. Thus, accurate delineation of Groundwater Potential Zones (GWPZs) is essential for sustainable water resources management in these environments. The present research aims to delineate and assess GWPZs in a semi-arid basin of San Luis Potosi (SLP), Mexico, through the integration of Remote Sensing (RS), Geographic Information System (GIS), and Analytic Hierarchy Process (AHP). Seven thematic layers (geology, lineament density, land use and land cover, topographic wetness index (TWI), rainfall, drainage density, and slope) were generated in raster format. After the AHP procedure and rank assignment, the thematic layers were integrated using the raster calculator to obtain the GWPZs map. The results indicated that 68.21% of the area is classified as low groundwater potential, whereas 26.30% is classified as moderate. Validation was done by assessing the water residence time data from 15 wells distributed in the study area. Furthermore, the Receiver Operating Characteristics (ROC) curve was obtained, indicating a satisfactory accuracy prediction (AUC = 0.677). This study provides valuable information for decision-makers regarding the conservation and sustainable management of groundwater resources.

A Systematic Review on the Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Health Attributes, Indicators, and Measures

It is important to protect forest and grassland ecosystems because they are ecologically rich and provide numerous ecosystem services. Upscaling monitoring from local to global scale is imperative in reaching this goal. The SDG Agenda does not include indicators that directly quantify ecosystem health. Remote sensing and Geographic Information Systems (GIS) can bridge the gap for large-scale ecosystem health assessment. We systematically reviewed field-based and remote-based measures of ecosystem health for forests and grasslands, identified the most important ones and provided an overview on remote sensing and GIS-based measures. We included 163 English language studies within terrestrial non-tropical biomes and used a pre-defined classification system to extract ecological stressors and attributes, collected corresponding indicators, measures, and proxy values. We found that the main ecological attributes of each ecosystem contribute differently in the literature, and that almost half of the examined studies used remote sensing to estimate indicators. The major stressor for forests was “climate change”, followed by “insect infestation”; for grasslands it was “grazing”, followed by “climate change”. “Biotic interactions, composition, and structure” was the most important ecological attribute for both ecosystems. “Fire disturbance” was the second most important for forests, while for grasslands it was “soil chemistry and structure”. Less than a fifth of studies used vegetation indices; NDVI was the most common. There are monitoring inconsistencies from the broad range of indicators and measures. Therefore, we recommend a standardized field, GIS, and remote sensing-based approach to monitor ecosystem health and integrity and facilitate land managers and policy-makers.

Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine.

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2 = 0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

Groundwater Potential Zonation using Integration of Remote Sensing and AHP/ANP Approach in North Kashmir, Western Himalaya, India

The demand for groundwater resource estimation has increased radically attributed to the growing population and rapid urbanization. To analyze the groundwater scenario the present study aims to delineate the groundwater potential zones (GWPZ’s) using the integrated remote sensing (RS) and Geographic Information System (GIS) based approach and AHP/ANP model in Western Himalaya. For the groundwater potentiality analysis, the thematic layers that were selected for the preparation of GWPZ map include lithology, Topographic Wetness Index (TWI), Topographic Position Index (TPI), slope, drainage density, lineament density, land use land cover and rainfall. The final output GWPZ map can be categorized as high (39.57%), moderate (16.23%), low (31.91%), and very low (12.29%). In light of the growing demand for groundwater, the estimated area under good groundwater capacity appears to be inadequate. As a result, the state government in general, as well as the water resources and planning departments, must develop appropriate strategies to combat the impending water scarcity. To relieve pressure on groundwater resources, the study recommends increasing the use of surface water from the sub-regional basin. With the applicability of remote sensing and GIS technology coupled the integration of AHP/ANP model the comprehensive groundwater prospecting is achieved and groundwater as quintessential resource can be harnessed and utilized as anthropogenic stresses and changing climate has accentuated the demands for water consumption.

Surface Runoff Responses to Suburban Growth: An Integration of Remote Sensing, GIS, and Curve Number

Suburban growth and its impacts on surface runoff were investigated using the soil conservation service curve number (SCS-CN) model, compared with the integrated advanced remote sensing and geographic information system (GIS)-based integrated approach, over South Kingston, Rhode Island, USA. This study analyzed and employed the supervised classification method on four Landsat images from 1994, 2004, 2014, and 2020 to detect land-use pattern changes through remote sensing applications. Results showed that 68.6% urban land expansion was reported from 1994 to 2020 in this suburban area. After land-use change detection, a GIS-based SCS-CN model was developed to examine suburban growth and surface runoff estimation. The developed model demonstrated the spatial distribution of runoff for each of the studied years. The results showed an increasing spatial pattern of 2% to 10% of runoff from 1994 to 2020. The correlation between runoff co-efficient and rainfall indicated the significant impact of suburban growth in surface runoff over the last 36 years in South Kingstown, RI, USA, showing a slight change of forest (8.2% area of the total area) and agricultural land (4.8% area of the total area). Suburban growth began after 2000, and within 16 years this land-use change started to show its substantial impact on surface runoff. We concluded that the proposed integrated approach could classify land-use and land cover information to understand suburban growth and its potential impact on the area.

AUTOMATIC LINEAMENTS MAPPING AND EXTRACTION IN RELATIONSHIP TO NATURAL HYDROCARBON SEEPAGE IN UGWUEME, SOUTH-EASTERN NIGERIA

The study focus on the integration of Remote Sensing and Geographic Information System for identification and delineation of lineaments in relation to natural hydrocarbon seepage, which occur in Ugwueme, South-Eastern Nigeria. To achieve this objective, remotely sensed data (ASTER Digital Elevation Model and Landsat 8 OLI/TIRS) were used to depict the surface expression of faults, folds and fractures which are expressed in the form of lineaments. The global positioning system (GPS) was also used for ground verification. The geology map of the study area, which is elucidated in the geology of Nigeria was used to show the distribution of rocks and other geologic structures. The delineation of lineament features was done automatically with the PCI Geomatica while the Rock ware was used to generate the Rose diagram for demonstration of the direction of the extracted lineaments. The classification of the lineaments density and the lineaments intersection analysis were categorized as very low, low, moderate, high and very high classes respectively. Areas classified as very high to high lineaments density are potential zone, which act as conduits for hydrocarbon seepage. The result shows that a total lineament frequency of 947 km and a total lineament length of 946 km were delineated from the satellite data. The result further shows that areas with high lineaments density are concentrated in the southwest, south, central and northern part of the study area while areas with low lineament density were found within the eastern part of Ugwueme. The Rose diagram highlight the major trend in the (NE-SW), (N-S) and (NW-SE) directions, and the minor trend in the (W-E) direction. These directional trends depict the directions of lineaments which act as conduits zones for hydrocarbon seepage in the region. The overall findings of the study shows that lineament density, lineament intersection and rose diagrams are concepts applicable in hydrocarbon oil and gas seepages.

GEOMORPHOLOGY, CHARACTERISTICES AND CLASSIFICATION OF SOILS IN SOME AREAS IN ISMAILIA GOVERNORATE, EGYPT

The current work aims to identifying the geomorphological, characteristics and classification of soils in some areas in Ismailia governorate, Egypt. The study area is located between longitudes 32o 06′ 45 and 32o 22′ 30 E and latitudes 30o 22' 30 and 30o 57' 00 N. The integration of Remote Sensing (RS) and Geographic Information System (GIS) techniques was used to achieve this work. The geomorphic map produced by processing and identifying the Landsat 8 image indicated that,the studied area has six main geomorphic units with different landforms.These units are: 1) Depressions, 2) Terraces (including Low, and High Terraces), 3) Basins (over flow basins and Decantation basins), 4) peneplains (Low and High), 5) Sandy plains (High, Moderate and Low) and 6) Mountain (Foot slope and Crest). Twenty soil profiles were selected representing these units.The land and site features are observed and registered. The soil profiles were dug, morphologically described, and then samples were collected representing the subsequent layers in each profile for integrated physical and chemical analyses. The studied area has almost flat with deep to very deep and well drained soils. Most of the studied soils have loamy sand texture and some parts have clay loam texture. The analytical data revealed that, the studied soils are slightly alkaline, mostly non-saline and haven’t sodicity effect. The soils are moderately calcareous having Low gypsum and organic matter contents. All studies soils haven't any diagnostic horizons, therefore they affiliated to Entisols and classified as Typic Torripsaments for 89.4% and as Typic Torriorthents for 10.6%from the studied area.