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Day and night airborne thermal infrared image data at 5 m spatial resolution acquired with the 15-channel (0.45 micron - 12.2 micron) Advanced Thermal and Land Applications Sensor (ATLAS) over Alabama, Huntsville on 7 September, 1994 were used to study changes in the thermal signatures of urban land cover types between day and night. Thermal channel number 13 (9.6 micron - 10.2 micron) data with the best noise-equivalent temperature change (NEAT) of 0.25 C after atmospheric corrections and temperature calibration were selected for use in this analysis. This research also examined the relation between land cover irradiance and vegetation amount, using the Normalized Difference Vegetation Index (NDVI), obtained by ratioing the difference and the sum of the red (channel number 3: 0.60-0.63 micron) and reflected infrared (channel number 6: 0.76-0.90 micron) ATLAS data. Based on the mean radiance values, standard deviations, and NDVI extracted from 351 pairs of polygons of day and night channel number 13 images for the city of Huntsville, a spatial model of warming and cooling characteristics of commercial, residential, agricultural, vegetation, and water features was developed using a GIS approach. There is a strong negative correlation between NDVI and irradiance of residential, agricultural, and vacant/transitional land cover types, indicating that the irradiance of a land cover type is greatly influenced by the amount of vegetation present. The predominance of forests, agricultural, and residential uses associated with varying degrees of tree cover showed great contrasts with commercial and services land cover types in the center of the city, and favors the development of urban heat islands. The high-resolution thermal infrared images match the complexity of the urban environment, and are capable of characterizing accurately the urban land cover types for the spatial modeling of the urban heat island effect using a GIS approach.

Land use and land cover change is driven by human actions and also drives changes that limit availability of products and services for human and livestock, and it can undermine environmental health as well. Therefore, this study was aimed at understanding land use and land cover change in Kersa district over the last 30 years. Time-series satellite images that included Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI/TIRS, which covered the time frame between (1990-2020), were used to determine the change in land use and land cover using object based classification. The object based classification result revealed that in 1990 TM Landsat imagery, natural forest (16.07%), agroforestry (9.21%), village (12.03%), urban (1.93%), and agriculture (60.76%) were identified. The change result showed a rapid reduction in natural forest cover of 25.04%, 9.15%, and 23.11% occurred between (1990-2000), (2000-2010) and (2010-2020) study periods, respectively. Similarly agroforestry decreased by 0.88% and 63.9% (2000-2010) and (2010-2020), respectively. The finding indicates the increment of agricultural land, village, and urban, while the natural forest and agroforestry cover shows a declining trend. Overall, proper and integrated approach in implementing policies and strategies related to land use and land cover management should be required in Kersa District. 

Detecting smoke plumes and analyzing smoke impacts using remote sensing and GIS for the McNally Fire incident

Detecting smoke plumes and analyzing smoke impacts using remote sensing and GIS for the McNally Fire incident

Mehmet Ali ÇULLU-Mustafa H. AYDOĞDU-Murat AYDOĞDU-COĞRAFİ BİLGİ SİSTEMİ (CBS) VE UZAKTAN ALGILAMA (UA) İLE TUZLULUĞUN ETKİSİ ALTINDA PAMUKTA VERİM KAYBININ BELİRLENMESİ (GAP BÖLGESİ, AKÇAKALE ÖRNEĞİ) TÜRKİYE* THE DETERMINATION OF THE COTTON PRODUCTIVITY AND LOSSES UNDER THE EFFECT OF SALINITY BY USING GEOGRAPHICAL INFORMATION SYSTEM (GIS) AND REMOTE SENSING (RS) (GAP REGION, AKÇAKALE SAMPLING) TURKEY

Mehmet Ali ÇULLU-Mustafa H. AYDOĞDU-Murat AYDOĞDU-COĞRAFİ BİLGİ SİSTEMİ (CBS) VE UZAKTAN ALGILAMA (UA) İLE TUZLULUĞUN ETKİSİ ALTINDA PAMUKTA VERİM KAYBININ BELİRLENMESİ (GAP BÖLGESİ, AKÇAKALE ÖRNEĞİ) TÜRKİYE* THE DETERMINATION OF THE COTTON PRODUCTIVITY AND LOSSES UNDER THE EFFECT OF SALINITY BY USING GEOGRAPHICAL INFORMATION SYSTEM (GIS) AND REMOTE SENSING (RS) (GAP REGION, AKÇAKALE SAMPLING) TURKEY

STHs can infect all members of a population but school-age children living in poverty are at greater risk. Infection can be controlled with drug treatment, health education and sanitation. Helminth control programs often lack resources and reliable information to identify areas of highest risk to guide interventions and to monitor progress. Objectives: To use RS/GIS to identify the environmental variables that correlate with the ecology of STHs and with the prevalence of STH infections. Methods: Geo-referenced in situ prevalence data will be overlaid over an ecological map derived from the RS environmental data using ESRI s ArcGIS 9.3. Prevalence data and RS environmental data matching at the same geographical location will be analyzed for correlation and those RS environmental variables that better correlate with prevalence data will be included in a multivariate regression model. Temperature, vegetation, and distance to bodies of water will be inferred using data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, and Thematic Mapper (TM) and Enhance Thematic Mapper Plus (ETM+) satellite sensors onboard Landsat 5 and Landsat 7 respectively. Elevation will be estimated with data from The Shuttle Radar Topography Mission (SRTM). Prevalence and intensity of infections will be determined by parasitological survey (Kato Katz) of children enrolled in rural schools in Boaco, Nicaragua, in the communities of El Roblar, Cumaica Norte, Malacatoya 1, and Malacatoya 2). Expected Results: Associations between RS environmental data and prevalence in situ data will be determined and their applications to public health will be discussed. Discussion/Conclusions: The use of RS/GIS data to predict the prevalence of STH infections could be useful for helminth control programs, providing improved geographical guidance of interventions while increasing cost-effectiveness. Learning Objectives: (1) To identify the RS environmental variables that can help predict the prevalence of STH infections. (2) To understand potential applications of RS/GIS to national helminth control programs. (3) To asses the applicability of RS/GIS to control STH infections.

STHs can infect all members of a population but school-age children living in poverty are at greater risk. Infection can be controlled with drug treatment, health education and sanitation. Helminth control programs often lack resources and reliable information to identify areas of highest risk to guide interventions and to monitor progress. Objectives: To use RS/GIS to identify the environmental variables that correlate with the ecology of STHs and with the prevalence of STH infections. Methods: Geo-referenced in situ prevalence data will be overlaid over an ecological map derived from the RS environmental data using ESRI s ArcGIS 9.3. Prevalence data and RS environmental data matching at the same geographical location will be analyzed for correlation and those RS environmental variables that better correlate with prevalence data will be included in a multivariate regression model. Temperature, vegetation, and distance to bodies of water will be inferred using data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, and Thematic Mapper (TM) and Enhance Thematic Mapper Plus (ETM+) satellite sensors onboard Landsat 5 and Landsat 7 respectively. Elevation will be estimated with data from The Shuttle Radar Topography Mission (SRTM). Prevalence and intensity of infections will be determined by parasitological survey (Kato Katz) of children enrolled in rural schools in Boaco, Nicaragua, in the communities of El Roblar, Cumaica Norte, Malacatoya 1, and Malacatoya 2). Expected Results: Associations between RS environmental data and prevalence in situ data will be determined and their applications to public health will be discussed. Discussion/Conclusions: The use of RS/GIS data to predict the prevalence of STH infections could be useful for helminth control programs, providing improved geographical guidance of interventions while increasing cost-effectiveness. Learning Objectives: (1) To identify the RS environmental variables that can help predict the prevalence of STH infections. (2) To understand potential applications of RS/GIS to national helminth control programs. (3) To asses the applicability of RS/GIS to control STH infections.

STHs can infect all members of a population but school-age children living in poverty are at greater risk. Infection can be controlled with drug treatment, health education and sanitation. Helminth control programs often lack resources and reliable information to identify areas of highest risk to guide interventions and to monitor progress. Objectives: To use RS/GIS to identify the environmental variables that correlate with the ecology of STHs and with the prevalence of STH infections. Methods: Geo-referenced in situ prevalence data will be overlaid over an ecological map derived from the RS environmental data using ESRI s ArcGIS 9.3. Prevalence data and RS environmental data matching at the same geographical location will be analyzed for correlation and those RS environmental variables that better correlate with prevalence data will be included in a multivariate regression model. Temperature, vegetation, and distance to bodies of water will be inferred using data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, and Thematic Mapper (TM) and Enhance Thematic Mapper Plus (ETM+) satellite sensors onboard Landsat 5 and Landsat 7 respectively. Elevation will be estimated with data from The Shuttle Radar Topography Mission (SRTM). Prevalence and intensity of infections will be determined by parasitological survey (Kato Katz) of children enrolled in rural schools in Boaco, Nicaragua, in the communities of El Roblar, Cumaica Norte, Malacatoya 1, and Malacatoya 2). Expected Results: Associations between RS environmental data and prevalence in situ data will be determined and their applications to public health will be discussed. Discussion/Conclusions: The use of RS/GIS data to predict the prevalence of STH infections could be useful for helminth control programs, providing improved geographical guidance of interventions while increasing cost-effectiveness. Learning Objectives: (1) To identify the RS environmental variables that can help predict the prevalence of STH infections. (2) To understand potential applications of RS/GIS to national helminth control programs. (3) To asses the applicability of RS/GIS to control STH infections.

STHs can infect all members of a population but school-age children living in poverty are at greater risk. Infection can be controlled with drug treatment, health education and sanitation. Helminth control programs often lack resources and reliable information to identify areas of highest risk to guide interventions and to monitor progress. Objectives: To use RS/GIS to identify the environmental variables that correlate with the ecology of STHs and with the prevalence of STH infections. Methods: Geo-referenced in situ prevalence data will be overlaid over an ecological map derived from the RS environmental data using ESRI s ArcGIS 9.3. Prevalence data and RS environmental data matching at the same geographical location will be analyzed for correlation and those RS environmental variables that better correlate with prevalence data will be included in a multivariate regression model. Temperature, vegetation, and distance to bodies of water will be inferred using data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, and Thematic Mapper (TM) and Enhance Thematic Mapper Plus (ETM+) satellite sensors onboard Landsat 5 and Landsat 7 respectively. Elevation will be estimated with data from The Shuttle Radar Topography Mission (SRTM). Prevalence and intensity of infections will be determined by parasitological survey (Kato Katz) of children enrolled in rural schools in Boaco, Nicaragua, in the communities of El Roblar, Cumaica Norte, Malacatoya 1, and Malacatoya 2). Expected Results: Associations between RS environmental data and prevalence in situ data will be determined and their applications to public health will be discussed. Discussion/Conclusions: The use of RS/GIS data to predict the prevalence of STH infections could be useful for helminth control programs, providing improved geographical guidance of interventions while increasing cost-effectiveness. Learning Objectives: (1) To identify the RS environmental variables that can help predict the prevalence of STH infections. (2) To understand potential applications of RS/GIS to national helminth control programs. (3) To asses the applicability of RS/GIS to control STH infections.

An integrated approach of Remote sensing GIS and geoelectrical resistivity methods was employed to produce groundwater potential map for Jos and environs through theGISraster based Multi-Criteria Evaluation (MCE) analysis of five thematic layers. These layers; drainage density, lineament density, Landuse/cover, slope steepness and overburden thickness wereextractedfrom Nigeria Sat-X, ASTERGDEM and geoelectrical resistivity methods. The result revealed four categories of groundwater potential zones as, very high, high, moderate, and low. About 105km2 (18.4%) was classified as very high groundwater potential areas, while155km2 (27.4%) of the study area was classified as high groundwater potential areas. Similarly, 225km2(39.8%) was classified as moderate potential areas and about 82km2 (14.5%)of the study area was classified as low potential areas. The model developed was in agreement with the borehole yield obtained in the area. Gujahar Rengje Danlami Rogers | Abdullahi Ayegba"Application of Satellite Remote Sensing, GIS and Geoelectrical Resistivity Methods for Groundwater Potential Mapping for Jos and Environs, Plateau State, North Central Nigeria" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-5 , August 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2452.pdf Article URL: http://www.ijtsrd.com/other-scientific-research-area/other/2452/application-of-satellite-remote-sensing-gis-and-geoelectrical-resistivity-methods-for-groundwater-potential-mapping-for-jos-and-environs-plateau-state-north-central-nigeria/gujahar-rengje-danlami-rogers

An integrated approach of Remote sensing GIS and geoelectrical resistivity methods was employed to produce groundwater potential map for Jos and environs through theGISraster based Multi-Criteria Evaluation (MCE) analysis of five thematic layers. These layers; drainage density, lineament density, Landuse/cover, slope steepness and overburden thickness wereextractedfrom Nigeria Sat-X, ASTERGDEM and geoelectrical resistivity methods. The result revealed four categories of groundwater potential zones as, very high, high, moderate, and low. About 105km2 (18.4%) was classified as very high groundwater potential areas, while155km2 (27.4%) of the study area was classified as high groundwater potential areas. Similarly, 225km2(39.8%) was classified as moderate potential areas and about 82km2 (14.5%)of the study area was classified as low potential areas. The model developed was in agreement with the borehole yield obtained in the area. Gujahar Rengje Danlami Rogers | Abdullahi Ayegba"Application of Satellite Remote Sensing, GIS and Geoelectrical Resistivity Methods for Groundwater Potential Mapping for Jos and Environs, Plateau State, North Central Nigeria" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-5 , August 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2452.pdf Article URL: http://www.ijtsrd.com/other-scientific-research-area/other/2452/application-of-satellite-remote-sensing-gis-and-geoelectrical-resistivity-methods-for-groundwater-potential-mapping-for-jos-and-environs-plateau-state-north-central-nigeria/gujahar-rengje-danlami-rogers

Selection of suitable sites for construction of dam is the most important phase because a number of factors are required to consider that include topography, geology, tectonic settlements and the slope. We selected Diamer Basha dam site to analyze it feasibility considering real-time field data. Geologically the study site is a part of Chilas Mafic Igneous Complex which is not ophiolite. Matic complex is a block which is 40km in depth and 300km in length. These rocks are comparatively hard in nature and are considered good for construction of dam. Tectonically, we observed that the area under investigation was highly active tectonically. Surface deformation rates of the study site are highest throughout the world because this area is comprised of multiple fault lines that include Main Mantle Thurst (MMT), Main Karakoram Thrurst (MKT), Main Boundary Thurst (MBT) and many others. This area has become a hot cake for the geologist worldwide due to it’s very high surface deformation rates.Tectonically active regions are considered worst for construction sites, e.g., for dam sites. The dam site is actcually laying on the MKT which is not favorable for construction of Diamir Basha dam. A low-level earthquake may generate small cracks in concrete structure and any leakage of water may produce big holes with passage of time which are not remidable. A big level earthquake may vanish the dam site completely. Therefore, the current site is not favorable for construction of dam.

The concept of remote object detection is categorized by science and innovation domain, in which the qualities of objects are recognized, estimated or examined based on attributes without closed interaction, i.e., on remote object detection an electro attractive radiation, which is get replicated from an item is a typical concept behind the remote sensing technology. A sensor or far off sensors are used to recognize the electro attractive radiation, that is reflected from objects are known as a far off sensor or sensor , digital camera or scanner be the best instance of far off sensor. Generally, a vehicle is to convey the sensor is known as stage , Airplane or Satellite is utilized for stage. Usually, remote detection technology is used to incorporate geography and mineral investigation, oceanography, horticulture, ranger service, land corruption, natural observing, etc. Dr. Meena Y R | Dr. Vijaya Kumar Y M "Innovative Applications of Remote Sensing Technology and GIS" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49404.pdf Paper URL: https://www.ijtsrd.com/engineering/civil-engineering/49404/innovative-applications-of-remote-sensing-technology-and-gis/dr-meena-y-r

A GIS based hydrological analysis has been carried out to explore the possibility of diverting storm runoff generated from the upper catchment safely through a canal system constructed at the foothill to avoid flooding at downstream. The study area consisted of Kalapahar-Udyachal hills (5.38 km sq) in the Kahilipara- Odalbakra area, situated in the city of Guwahati, Assam. The Digital Elevation Model (DEM) of the study area was developed from the Survey of India(SOI) toposheet (1972) using Arcgis software. Watershed delineation and derivation of required topographic parameters for for calculating the peak discharge from different watersheds were done with the help of the generated DEM. Based on the hydrological analysis, means of safe diversion of runoff water from hillocks was found out and canal design of varying geometry capable of handling the peak discharge suggested.

A GIS based hydrological analysis has been carried out to explore the possibility of diverting storm runoff generated from the upper catchment safely through a canal system constructed at the foothill to avoid flooding at downstream. The study area consisted of Kalapahar-Udyachal hills (5.38 km sq) in the Kahilipara- Odalbakra area, situated in the city of Guwahati, Assam. The Digital Elevation Model (DEM) of the study area was developed from the Survey of India(SOI) toposheet (1972) using Arcgis software. Watershed delineation and derivation of required topographic parameters for for calculating the peak discharge from different watersheds were done with the help of the generated DEM. Based on the hydrological analysis, means of safe diversion of runoff water from hillocks was found out and canal design of varying geometry capable of handling the peak discharge suggested.

 Assam is known for its extensive forest areas and availability of rich floras and faunas besides other valuable forest products. As per information available from the State Forest Department, the total area under forests in Assam was 26,781.91 Sq. km. at the end of March 2003, out of which 15,492.329 sq. km. was under Reserved Forests and 2,860.942 Sq. Km. under Protected Forest Area. Thus, the Forests area constitutes about 34.14 percent of the total geographical area of Assam. The forests products of the State mainly comprised of industrial wood, fuel wood, bamboo, stone, thatch, cane, sand etc. The State Government from its various Forest products earns a good amount of revenue. The importance of Forestry lies not only with its productivity but also in its capacity to prevent soil erosion. The State Government has therefore continued to impose ban on indiscriminate felling of trees in the State. The comparative study from Survey of India Topographical Map, 1974 and LISS-III 1D, 2005 of two districts of Assam namely Jorhat and Golaghat, it is very clear that there is substantial decrease in the Reserve Forest Area. Dayang Reserve forest is totally vanished by encroachment, same is the condition of Nambar, Holongapar and Disai Valley Reserve Forest where the crown coverage now is less than 40 percent of the total Reserve Forest Area. The total reserve forest area of Jorhat and Golaghat district during 1974 and 2005 is 458.8 sq km and 176.68 sq km respectively.

 Assam is known for its extensive forest areas and availability of rich floras and faunas besides other valuable forest products. As per information available from the State Forest Department, the total area under forests in Assam was 26,781.91 Sq. km. at the end of March 2003, out of which 15,492.329 sq. km. was under Reserved Forests and 2,860.942 Sq. Km. under Protected Forest Area. Thus, the Forests area constitutes about 34.14 percent of the total geographical area of Assam. The forests products of the State mainly comprised of industrial wood, fuel wood, bamboo, stone, thatch, cane, sand etc. The State Government from its various Forest products earns a good amount of revenue. The importance of Forestry lies not only with its productivity but also in its capacity to prevent soil erosion. The State Government has therefore continued to impose ban on indiscriminate felling of trees in the State. The comparative study from Survey of India Topographical Map, 1974 and LISS-III 1D, 2005 of two districts of Assam namely Jorhat and Golaghat, it is very clear that there is substantial decrease in the Reserve Forest Area. Dayang Reserve forest is totally vanished by encroachment, same is the condition of Nambar, Holongapar and Disai Valley Reserve Forest where the crown coverage now is less than 40 percent of the total Reserve Forest Area. The total reserve forest area of Jorhat and Golaghat district during 1974 and 2005 is 458.8 sq km and 176.68 sq km respectively.

GIS and Remote sensing as new technologies can be used in such fields; environment, health care, civil engineering, agriculture…etc. Some author used these techniques in tracking pollutants, in biodiversity management and highlighting areas most vulnerable to diseases. In marine environment, some scientists make tracking to different organisms as dolphins using environmental DNA (eDNA) and use GIS in the geographic distribution of these organisms. These techniques consider addition to science and help governorates economically.

Present research paper is an attempt to examine the dynamics of vegetation line by using Normalized Difference Vegetation Index NDVI method in Gori Ganga watershed, Kumaun Himalaya, Uttarakhand India . For the study of detect vegetation line shifting used of Landsat 5,8 and Cartosat 1 satellite imageries of three different time periods, i.e., Landsat 5 Thematic Mapper TM of 1990, Landsat 5 TM of 1999 and Landsat 8 OLI and TIRS of 2016 and Cartosat 1 of 2008. Geographical distribution of vegetation line average height reveals that in 1990 about 4294.97 m, 1999 about 4394.51 m and in 2016 about 4758.55 m which is change from non vegetation to vegetation area. During 1990 1999 about 150.67 km2 areas and during 1999 2016 about 434.03 km2 area of the Gori Ganga watershed was converted from non vegetation cover to vegetation cover area. These data suggest that due to global warming about 463.58 m vegetation line average height and 584.70 km2 vegetation area of Gori Ganga watershed has been shifted into non vegetation cover to vegetation cover area at an average rate of 17.83 m year and 22.49 km2 year during 1990 to 2016. D. S. Parihar | J. S. Rawat | Mahendra Singh | N. C. Pant "Spatio-Temporal Change of Vegetation Line in the Gori Ganga Watershed, Kumaun Himalaya by Using Remote Sensing and GIS Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-1 , December 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47806.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/enviormental-science/47806/spatiotemporal-change-of-vegetation-line-in-the-gori-ganga-watershed-kumaun-himalaya-by-using-remote-sensing-and-gis-techniques/d-s-parihar

This article is from Sensors (Basel, Switzerland) , volume 8 . Abstract Jingjinji area (namely Beijing, Tianjin and He Bei Province) is one of the three largest regional economic communities in China. Urban expansion has sped up in the past 20 years in this area due to the rapid economic and population growth. Evaluating the land-use suitability for urban growth on a regional scale is an urgent need, because the most suitable areas and the most suitable scale of urban growth can thus be determined accordingly. In order to meet this requirement, remote sensing and geographic information system (GIS) techniques were adopted, and an integrated evaluating model was developed supported by AHP method. The integrated urban development suitability index (UDSI) was calculated using this model. According to the UDSI result, the spatial distribution of urban development suitability and its driving forces were analyzed. Urban boundaries in 1995, 2000 and 2005, which were derived from Landsat TM/ETM+ satellite data, were overlaid on the UDSI map, and the suitable urban develop tendency in this area were discussed. The result of this study indicated that integrated evaluation of urban development could be conducted in an operational way using remote sensing data, GIS spatial analysis technique and AHP modeling method.

Sand dunes is one of desertification phenomenon that hinder land resources and human activities. It threaten to bury human settlement, roads, farms, water and other resources. Due to many environmental and climate conditions, there are many places around the world suffering from sand movement and mobile dune creep onto cultivated land and human settlements. Sand dunes have a fragile environment where, instability with a series of changes lead to a system not in equilibrium with its surroundings within an arid and hyper arid climates changes. These changes usually characterized by increase of evaporation and long periods of dryness, very low rainfall and vegetation. The aim of this research work is to apply remote sensing and GIS techniques to monitor and analyze sand dunes accumulation in the northern part of Sudan. Three successive satellite images acquired in different dates were used as the main source of data in this research work. A digital elevation model was also needed for topographic analysis. GIS was used to analyze output remote sensing data. Results, reflected that, sand dines accumulated during the last years and its accumulation in progress by 0.4 every year. Moreover, 50 of the study area is expected to be covered by sand dunes after less than 20 years. From topographic point of view, sand dune heights reached be 20m. These results present clear indicators of desertification that faces the study area. Dr. Abdelrahim Elhag | Dr. Nagi Zomrawi | Sahar Khidir "Analysis of Sand Dunes Accumulation using Remote Sensing and GIS" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29507.pdfPaper URL: https://www.ijtsrd.com/computer-science/computer-network/29507/analysis-of-sand-dunes-accumulation-using-remote-sensing-and-gis/dr-abdelrahim-elhag

In both developed and developing countries, the coastal zone is likely to undergo the most profound change in the near future. More than 60 percent of the worldʹs population lives within 60 km of the coast. By the turn of the century two‐thirds of the population (3.7 billion) in developing countries have occupied the coast. Consequently, unless careful environmental management and planning are instituted, severe conflicts over coastal space and resource utilization are likely, and the degradation of natural resources will close development options. In addition to the population pressure, the world’s coastal areas and small islands are highly vulnerable to climate change. Low‐lying delta, barrier coasts, low‐elevation reef islands, and coral atolls are especially sensitive to the rising sea level, as well as to changes in rainfall, storm frequency, and intensity. Inundation, flooding, erosion, and saltwater intrusion are only a few of the potential impacts of climate change. Iran, connected to Caspian Sea in its north and to Persian Gulf and Gulf of Oman in its south, has totally about 5700 kilometers(scale 1:25000)coastlines and this country has the largest coastline in the Persian Gulf. A part of this coastline is located in Bushehr Province. For coastal zone monitoring, coastline extraction in various times is a fundamental work. Coastline, defined as the line of contact between land and a body of water , as one of the most important linear features on the earthʹs surface, holds a dynamic nature; therefore, coastal zone management requires the information about coastline changes. The main objective of this research was to estimate the coastline changes for a period of 1990 to 2005 using RS and GIS. In this research, TM satellite data, dated 1990, were compared with ETM+ satellite data of 2000 and 2005 in order to deduce changes. Different image processing techniques have been carried out to enhance the changes from 1990 to 2005. Band math, band ratio, supervised and unsupervised classification, post classification, band selection and masking were applied using GIS software. In this research, coastlines of the study area were extracted using satellite imagery. These changes were perpetual. However, the coastline has been changed significantly from 2001 to 2005. These great changes have happened as a consequence of development of the south Pars exclusive zone of energy (asaloyeh). A new approach was employed for coastline extraction, for which a histogram threshold together with band ratio techniques was utilized. In order to assess the accuracy of the results, they have been compared with ground truth observations. The accuracy of the extracted coastline has been estimated as 1.2 pixels (pixel size=30 m).

Agricultural drought is one of the hydrometeorological disasters that cause significant losses because it affects food stocks. In addition, agricultural droughts, impact the physical and socio-economic development of the community. Remote sensing technology is used to monitor agricultural droughts spatially and temporally for minimizing losses. This study reviewed the literature related to remote sensing and GIS for monitoring drought vulnerability in Indonesia. The study was conducted on an island-scale on Java Island, a provincial-scale in East Java and Bali, and a district-scale in Indramayu and Kebumen. The dominant method was the drought index, which involves variable land surface temperature (LST), vegetation index, land cover, wetness index, and rainfall. Each study has a strong point and a weak point. Low-resolution satellite imagery has been used to assess drought vulnerability. At the island-scale, it provides an overview of drought conditioAgricultural drought is one of the hydrometeorological disasters that cause significant losses because it affects food stocks. In addition, agricultural droughts, impact the physical and socio-economic development of the community. Remote sensing technology is used to monitor agricultural droughts spatially and temporally for minimizing losses. This study reviewed the literature related to remote sensing and GIS for monitoring drought vulnerability in Indonesia. The study was conducted on an island-scale on Java Island, a provincial-scale in East Java and Bali, and a district-scale in Indramayu and Kebumen. The dominant method was the drought index, which involves variable land surface temperature (LST), vegetation index, land cover, wetness index, and rainfall. Each study has a strong point and a weak point. Low-resolution satellite imagery has been used to assess drought vulnerability. At the island-scale, it provides an overview of drought conditions, while at the provincial-scale, it focuses on paddy fields and has little detailed information. In-situ measurements at the district-scale detect meteorological drought accurately, but there were limitations in the mapping unit's detailed information. Drought mapping using GIS and remote sensing at the district-scale has detailed spatial information on climate and physiographic aspects, but it needs temporal data monitoring.ns, while at the provincial-scale, it focuses on paddy fields and has little detailed information. In-situ measurements at the district-scale detect meteorological drought accurately, but there were limitations in the mapping unit's detailed information. Drought mapping using GIS and remote sensing at the district-scale has detailed spatial information on climate and physiographic aspects, but it needs temporal data monitoring.

This article is from Sensors (Basel, Switzerland) , volume 8 . Abstract Accurate and timely information about land use and land cover (LULC) and its changes in urban areas are crucial for urban land management decision-making, ecosystem monitoring and urban planning. Also, monitoring and representation of urban sprawl and its effects on the LULC patterns and hydrological processes of an urbanized watershed is an essential part of water resource planning and management. This paper presents an image analysis study using multi temporal digital satellite imagery of LULC and changes in the Kucukcekmece Watershed (Metropolitan Istanbul, Turkey) from 1992 to 2006. The Kucukcekmece Basin includes portions of the Kucukcekmece District within the municipality of Istanbul so it faces a dramatic urbanization. An urban monitoring analysis approach was first used to implement a land cover classification. A change detection method controlled with ground truth information was then used to determine changes in land cover. During the study period, the variability and magnitude of hydrological components based on land-use patterns were cumulatively influenced by urban sprawl in the watershed. The proposed approach, which uses a combination of Remote Sensing (RS) and Geographical Information System (GIS) techniques, is an effective tool that enhances land-use monitoring, planning, and management of urbanized watersheds.

Protected areas are defined as a pattern of land use in the environmental planning process. However, they are excluded from any physical exploitation or their use is conditional. They are no less important than other lands. This study aimed to evaluate the ecological degradation of Kosalan protected area using remote sensing and GIS techniques. Images of two time periods 1989 and 2020 were prepared. For this purpose, after radiometric and atmospheric corrections of NDVI index, changes of vegetation in the region in the two time periods were evaluated. Then, to model the ecological degradation, eight criteria including distance from the village, distance from the road, landslide points, erosion intensity, slope, direction, altitude and vegetation were used. The criteria were weighted by hierarchical analysis process method and standardized by fuzzy model. Vegetation changes at different thresholds were analyzed. According to the vegetation situation in the region, 3 thresholds of 0.1-3, 0.1-1 and 0.1-1 were used. The results of this evaluation showed that the quality and density of vegetation has decreased a lot during 31 years. The results of ecological degradation modeling showed that the most effective criterion in causing degradation is distance from the village. In general, 50% of the area has a high potential for ecological degradation. The results showed that the greatest potential for ecological degradation was in the central areas of the southeast and center of the region, where the slope and altitude are high. The impact of slope is so high in these places.

As a result of the importance of remote sensing and GIS data in various sectors of the country today, and taking into cognizance the sensitive nature of some of these data as well as the rising cases of cyber threats in the recent times, the role of cybersecurity in Remote sensing and GIS cannot be overemphasized. The aim of this research work was to study the role of Cybersecurity in Remote Sensing and GIS. The research made use of secondary data which includes but not limited to published research works, videos and library materials, while it adopted explanatory and descriptive research methods. The results showed that cybersecurity when integrated with Remote Sensing and GIS will help in the protection of the satellite or spatial images against cyberattacks, ensures the data reliability and availability, as well as maintain data integrity and confidentiality. In addition, it was observed from the results that cybersecurity can be integrated with GIS and Remote sensing in order to achieve the above goals through data encryption, implementation of user authentication and authorization protocols, Incident Response Planning application, access control method, and others. From the results, it was concluded that Cybersecurity plays an important role in Remote sensing and GIS. It was recommended that the scope of the research should be increased to the general Space Science and Technology instead of a branch of it which is Remote Sensing and GIS studied in this work. In addition, it was also recommended that the role of Remote sensing and GIS in cybersecurity should also be carried out in future work. Felix Ale | Abdullahi Ayegba | Emmanuel Omomoh | Gujahar Rengje Danlami Rogers | Urukwe Ando | Desmond Wysenyuy | Oyibo Muazu | Iruemi Olohimai Juliet | Ndahi Aisha "The Role of Cybersecurity in Remote Sensing and Geographical Information System (GIS)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-4 , August 2024, URL: https://www.ijtsrd.com/papers/ijtsrd67150.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-security/67150/the-role-of-cybersecurity-in-remote-sensing-and-geographical-information-system-gis/felix-ale

Land use and land cover, a dynamic process, has become a key component of current natural resource management and environmental monitoring systems. Drivers and Dynamics of LULC in the Churia region were assessed with an integrated approach using remote sensing and GIS techniques

Land use and land cover, a dynamic process, has become a key component of current natural resource management and environmental monitoring systems. Drivers and Dynamics of LULC in the Churia region were assessed with an integrated approach using remote sensing and GIS techniques

This article is from Sensors (Basel, Switzerland) , volume 11 . Abstract Koa (Acacia koa) forests are found across broad environmental gradients in the Hawai‘ian Islands. Previous studies have identified koa forest health problems and dieback at the plot level, but landscape level patterns remain unstudied. The availability of high-resolution satellite images from the new GeoEye1 satellite offers the opportunity to conduct landscape-level assessments of forest health. The goal of this study was to develop integrated remote sensing and geographic information systems (GIS) methodologies to characterize the health of koa forests and model the spatial distribution and variability of koa forest dieback patterns across an elevation range of 600–1,000 m asl in the island of Kaua‘i, which correspond to gradients of temperature and rainfall ranging from 17–20 °C mean annual temperature and 750–1,500 mm mean annual precipitation. GeoEye1 satellite imagery of koa stands was analyzed using supervised classification techniques based on the analysis of 0.5-m pixel multispectral bands. There was clear differentiation of native koa forest from areas dominated by introduced tree species and differentiation of healthy koa stands from those exhibiting dieback symptoms. The area ratio of healthy koa to koa dieback corresponded linearly to changes in temperature across the environmental gradient, with koa dieback at higher relative abundance in warmer areas. A landscape-scale map of healthy koa forest and dieback distribution demonstrated both the general trend with elevation and the small-scale heterogeneity that exists within particular elevations. The application of these classification techniques with fine spatial resolution imagery can improve the accuracy of koa forest inventory and mapping across the islands of Hawai‘i. Such findings should also improve ecological restoration, conservation and silviculture of this important native tree species.

This article provides a thorough overview of a wide range of advanced statistical methods that have found extensive and resilient applications in the intricate field of spatial modeling for variables in a geographical information system (GIS) platform. The noteworthy triumph of these approaches can be due to a convergence of speed, dependability, precision, and an inherent eco-consciousness that coexist to reshape the scenario of environmental data analysis. The utilization of these models has outshined conventional methods in the present terrain of scientific investigation and environmental analysis, becoming an authentication of innovative research and decision-making procedures. These approaches demonstrate commendable data utilization efficiency by effectively accepting reduced sample sizes. This not only saves resources but also aligns with the ethical imperative of minimizing environmental effects wherever possible. Furthermore, the combination of these statistical techniques with GIS has paved the way that greatly expands their utility. This tool helps to discover deep spatial linkages, extrapolate trends, and findings into actionable insights that are relatable across all disciplines. These approaches encompass not only predictive modeling but also the realms of error assessment and efficiency evaluation. In conclusion, the adoption of these statistical methods is quite useful in facilitating sound decision-making environmental studies. Some of the domains include soil properties, air quality parameters, vegetation distribution, land cover and land use, water quality parameters, temperature and climate variables, natural hazards, urban infrastructure planning, ecological habitats, noise pollution levels, and radiation and exposure assessment. As the trajectory of scientific growth unfolds, these techniques will serve in directing researchers, practitioners, and policymakers to a future where empirical accuracy and environmental consciousness meet synergistically.

This article provides a thorough overview of a wide range of advanced statistical methods that have found extensive and resilient applications in the intricate field of spatial modeling for variables in a geographical information system (GIS) platform. The noteworthy triumph of these approaches can be due to a convergence of speed, dependability, precision, and an inherent eco-consciousness that coexist to reshape the scenario of environmental data analysis. The utilization of these models has outshined conventional methods in the present terrain of scientific investigation and environmental analysis, becoming an authentication of innovative research and decision-making procedures. These approaches demonstrate commendable data utilization efficiency by effectively accepting reduced sample sizes. This not only saves resources but also aligns with the ethical imperative of minimizing environmental effects wherever possible. Furthermore, the combination of these statistical techniques with GIS has paved the way that greatly expands their utility. This tool helps to discover deep spatial linkages, extrapolate trends, and findings into actionable insights that are relatable across all disciplines. These approaches encompass not only predictive modeling but also the realms of error assessment and efficiency evaluation. In conclusion, the adoption of these statistical methods is quite useful in facilitating sound decision-making environmental studies. Some of the domains include soil properties, air quality parameters, vegetation distribution, land cover and land use, water quality parameters, temperature and climate variables, natural hazards, urban infrastructure planning, ecological habitats, noise pollution levels, and radiation and exposure assessment. As the trajectory of scientific growth unfolds, these techniques will serve in directing researchers, practitioners, and policymakers to a future where empirical accuracy and environmental consciousness meet synergistically.

This article provides a thorough overview of a wide range of advanced statistical methods that have found extensive and resilient applications in the intricate field of spatial modeling for variables in a geographical information system (GIS) platform. The noteworthy triumph of these approaches can be due to a convergence of speed, dependability, precision, and an inherent eco-consciousness that coexist to reshape the scenario of environmental data analysis. The utilization of these models has outshined conventional methods in the present terrain of scientific investigation and environmental analysis, becoming an authentication of innovative research and decision-making procedures. These approaches demonstrate commendable data utilization efficiency by effectively accepting reduced sample sizes. This not only saves resources but also aligns with the ethical imperative of minimizing environmental effects wherever possible. Furthermore, the combination of these statistical techniques with GIS has paved the way that greatly expands their utility. This tool helps to discover deep spatial linkages, extrapolate trends, and findings into actionable insights that are relatable across all disciplines. These approaches encompass not only predictive modeling but also the realms of error assessment and efficiency evaluation. In conclusion, the adoption of these statistical methods is quite useful in facilitating sound decision-making environmental studies. Some of the domains include soil properties, air quality parameters, vegetation distribution, land cover and land use, water quality parameters, temperature and climate variables, natural hazards, urban infrastructure planning, ecological habitats, noise pollution levels, and radiation and exposure assessment. As the trajectory of scientific growth unfolds, these techniques will serve in directing researchers, practitioners, and policymakers to a future where empirical accuracy and environmental consciousness meet synergistically.

This article provides a thorough overview of a wide range of advanced statistical methods that have found extensive and resilient applications in the intricate field of spatial modeling for variables in a geographical information system (GIS) platform. The noteworthy triumph of these approaches can be due to a convergence of speed, dependability, precision, and an inherent eco-consciousness that coexist to reshape the scenario of environmental data analysis. The utilization of these models has outshined conventional methods in the present terrain of scientific investigation and environmental analysis, becoming an authentication of innovative research and decision-making procedures. These approaches demonstrate commendable data utilization efficiency by effectively accepting reduced sample sizes. This not only saves resources but also aligns with the ethical imperative of minimizing environmental effects wherever possible. Furthermore, the combination of these statistical techniques with GIS has paved the way that greatly expands their utility. This tool helps to discover deep spatial linkages, extrapolate trends, and findings into actionable insights that are relatable across all disciplines. These approaches encompass not only predictive modeling but also the realms of error assessment and efficiency evaluation. In conclusion, the adoption of these statistical methods is quite useful in facilitating sound decision-making environmental studies. Some of the domains include soil properties, air quality parameters, vegetation distribution, land cover and land use, water quality parameters, temperature and climate variables, natural hazards, urban infrastructure planning, ecological habitats, noise pollution levels, and radiation and exposure assessment. As the trajectory of scientific growth unfolds, these techniques will serve in directing researchers, practitioners, and policymakers to a future where empirical accuracy and environmental consciousness meet synergistically.

This article provides a thorough overview of a wide range of advanced statistical methods that have found extensive and resilient applications in the intricate field of spatial modeling for variables in a geographical information system (GIS) platform. The noteworthy triumph of these approaches can be due to a convergence of speed, dependability, precision, and an inherent eco-consciousness that coexist to reshape the scenario of environmental data analysis. The utilization of these models has outshined conventional methods in the present terrain of scientific investigation and environmental analysis, becoming an authentication of innovative research and decision-making procedures. These approaches demonstrate commendable data utilization efficiency by effectively accepting reduced sample sizes. This not only saves resources but also aligns with the ethical imperative of minimizing environmental effects wherever possible. Furthermore, the combination of these statistical techniques with GIS has paved the way that greatly expands their utility. This tool helps to discover deep spatial linkages, extrapolate trends, and findings into actionable insights that are relatable across all disciplines. These approaches encompass not only predictive modeling but also the realms of error assessment and efficiency evaluation. In conclusion, the adoption of these statistical methods is quite useful in facilitating sound decision-making environmental studies. Some of the domains include soil properties, air quality parameters, vegetation distribution, land cover and land use, water quality parameters, temperature and climate variables, natural hazards, urban infrastructure planning, ecological habitats, noise pollution levels, and radiation and exposure assessment. As the trajectory of scientific growth unfolds, these techniques will serve in directing researchers, practitioners, and policymakers to a future where empirical accuracy and environmental consciousness meet synergistically.

Soil erosion is a gradual process that occurs when the impact of water or wind detaches and removes soil particles, causing the soil to deteriorate and lead to significant impacts such as significant decrease in the productive capacity of the land and sedimentation. Mada town in zamfara state has experienced loss of farm lands due to erosion in the mada stream area. This research studies the quantification of soil erosion by measuring the increment on the size of the stream for a twenty years period from year 2000 to 2020. Remote Sensing RS and Geographic Information Systems GIS techniques have been implemented for the assessment of the rate of erosion in the study area. Landsat satellite imageries for 2nd April, 2000, 7th April, 2010 and 25th May, 2020 were processed and digitized to map the stream boundary for each image to obtain the area enclosed by each. The results shows that from 2000 to 2010 and 2010 to 2020 there is an increase in size by 31849.8497 m² and 33767.7233 m² respectively. It also shows that area eroded from 2000 to 2020 is 65617.5730 m² which is the farm land area lost by erosion in the study area which if not addressed may lead to more farm lands in the future. The research ends by recommending policies such as providing vegetation cover which will reduce the impact of erosion in the study area. M. S. Lugga | Emakoji M. A "Evaluating Erosion using Remote Sensing and GIS Technique: A Case Study of Mada Town, Zamafara State" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-4 , June 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50043.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/50043/evaluating-erosion-using-remote-sensing-and-gis-technique-a-case-study-of-mada-town-zamafara-state/m-s-lugga

Soil erosion is a gradual process that occurs when the impact of water or wind detaches and removes soil particles, causing the soil to deteriorate and lead to significant impacts such as significant decrease in the productive capacity of the land and sedimentation. Mada town in zamfara state has experienced loss of farm lands due to erosion in the mada stream area. This research studies the quantification of soil erosion by measuring the increment on the size of the stream for a twenty years period from year 2000 to 2020. Remote Sensing RS and Geographic Information Systems GIS techniques have been implemented for the assessment of the rate of erosion in the study area. Landsat satellite imageries for 2nd April, 2000, 7th April, 2010 and 25th May, 2020 were processed and digitized to map the stream boundary for each image to obtain the area enclosed by each. The results shows that from 2000 to 2010 and 2010 to 2020 there is an increase in size by 31849.8497 m² and 33767.7233 m² respectively. It also shows that area eroded from 2000 to 2020 is 65617.5730 m² which is the farm land area lost by erosion in the study area which if not addressed may lead to more farm lands in the future. The research ends by recommending policies such as providing vegetation cover which will reduce the impact of erosion in the study area. M. S. Lugga | Emakoji M. A "Evaluating Erosion using Remote Sensing and GIS Technique: A Case Study of Mada Town, Zamafara State" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-4 , June 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50043.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/50043/evaluating-erosion-using-remote-sensing-and-gis-technique-a-case-study-of-mada-town-zamafara-state/m-s-lugga

Over the past decades, significant adverse effects, including the resiliency of critical centers, have emerged. The negative impact has manifested in the vulnerability of critical urban centers during natural disasters and emergencies, leading to their inefficiency, heightened public dissatisfaction, and a breakdown in service delivery during crises. In order to enhance the resilience of key centers, it is essential to first identify and assess the vulnerability of these crucial urban hubs to various risks and threats. In this research, the classification was graded and assessed following the formulation of a questionnaire and the collection of results. Utilizing the arithmetic mean of sample opinions, the Analytic Hierarchy Process (AHP) was applied through the Expert Choice software to assign weights to these criteria and sub-criteria. Subsequently, the key urban centers were identified. The hydrology within the city and its surroundings, along with the modeling of rivers during various return periods, were studied using the HEC_RAS. The results were then integrated into GIS to delineate flood risk zones in the city of Hamedan. Following the input of the arithmetic mean of sample opinions into the Expert Choice, the value of each indicator was meticulously determined. This delineation indicates that the quantitative level indicator benefits from the maximum weight, while the economics of assets hold the least weight in the assessment. Ultimately, by aligning key urban centers with flood-prone zones in the GIS framework, vulnerable centers were enumerated. The method used in this study can be extended to all cities based on their river flow modeling and urban zoning.

Disasters, whether natural or man induced have increased during the last decades in frequency all over the globe threatening a huge population within varied backgrounds. Over the years, remote sensing technologies have been functional in various disasters such as droughts, earthquakes, tsunamis, cyclones, etc. Its large area coverage capacity and observation repeatability makes its application cost effective. This paper tries to give the fundamental contributions and role of geographic information systems and remote sensing in disaster management applications. As an overview, it examines some recent practical application in disaster events. It also tries to look at some measurement characteristics and systems that have been applied to some disaster events within the disaster management framework and technologies. These various techniques and roles of remote sensing and geographic information systems in urban disaster monitoring and controlling, extends to disaster risk management using some sensors and satellites of emerging technologies. This discussion summarises in a single paper some of the many current techniques remote sensing and GIS employ in urban disaster management. Lessons from events can be drawn and implemented in similar scenarios to save lives and property. Nana Yaw Danquah Twumasi | Zhenfeng Shao | Altan Orhan "Remote Sensing and GIS Methods in Urban Disaster Monitoring and Management – An Overview" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23976.pdf Paper URL: https://www.ijtsrd.com/engineering/civil-engineering/23976/remote-sensing-and-gis-methods-in-urban-disaster-monitoring-and-management-%E2%80%93-an-overview/nana-yaw-danquah-twumasi

Wastelands and Land use change is one of the important aspects of the regional ecological restoration research. With remote sensing (RS) image in 2003, 2007 and 2012, using geographic information system (GIS) technologies, the land use pattern changes in Periya Kalrayan hill mountain ecological restoration area in Salem and Villupuram district and its driving force factors were studied.

A land cover-vegetation map with a base classification system for remote sensing use in a tropical island environment was produced of the island of Hawaii for the State of Hawaii to evaluate whether or not useful land cover information can be derived from Landsat TM data. In addition, an island-wide change detection mosaic combining a previously created 1977 MSS land classification with the TM-based classification was produced. In order to reach the goal of transferring remote sensing technology to State of Hawaii personnel, a pilot project was conducted while training State of Hawaii personnel in remote sensing technology and classification systems. Spectral characteristics of young island land cover types were compared to determine if there are differences in vegetation types on lava, vegetation types on soils, and barren lava from soils, and if they can be detected remotely, based on differences in pigments detecting plant physiognomic type, health, stress at senescence, heat, moisture level, and biomass. Geographic information systems (GIS) and global positioning systems (GPS) were used to assist in image rectification and classification. GIS was also used to produce large-format color output maps. An interactive GIS program was written to provide on-line access to scanned photos taken at field sites. The pilot project found Landsat TM to be a credible source of land cover information for geologically young islands, and TM data bands are effective in detecting spectral characteristics of different land cover types through remote sensing. Large agriculture field patterns were resolved and mapped successfully from wildland vegetation, but small agriculture field patterns were not. Additional processing was required to work with the four TM scenes from two separate orbits which span three years, including El Nino and drought dates. Results of the project emphasized the need for further land cover and land use processing and research. Change in vegetation composition was noted in the change detection image.

It is assumed that researchers can go to the journals of the expedition to find the precise locations of specific campsites. Of the core group of 33 people on the expedition, scholars know that at least 9 individuals kept journals. The location of three of those journals is unknown, and they may have been destroyed. However, a close examination for the six available journals demonstrates that not everyone described campside locations the same way, which adds to the confusion.

The concept of remote sensing and GIS based land capability analysis for agricultural development is incorporated into the study to find out what best use the land can be allocated to. Land capability is done by finding the appropriate-ness of the relative benefit to be derived 

The concept of remote sensing and GIS based land capability analysis for agricultural development is incorporated into the study to find out what best use the land can be allocated to. Land capability is done by finding the appropriate-ness of the relative benefit to be derived 

The concept of remote sensing and GIS based land capability analysis for agricultural development is incorporated into the study to find out what best use the land can be allocated to. Land capability is done by finding the appropriate-ness of the relative benefit to be derived 

1. Introduction UHI (Urban Heat Island) describes the phenomenon of temperature change in urban areas than their surroundings such as bare lands, gardens. Its effects are: increasing energy and water consuming, escalation of air pollution and intrusion on thermal welfare (Hashemi et al., 2019). Different researches about UHI, have assessed the effect of one or several factors, mainly land use, on the escalation of surface and sensible temperature. There are many researches about this issue. In one case in Macedonia, Skopje, heat island was assessed by using of NDVI index. The results indicated NDVI index was effective in weakening heat island and NDBI index had positive correlation with surface temperature which shows manmade areas have effects on heat island intensification (Kaplan et al., 2018). RS (remote sensing) makes it possible to assess all aspects of UHI as a hazard through preparing high quality satellite images. GIS (geographical information system) does too by preparing database, uniform methods, analysis and producing maps. The objective of this paper is temporal-spatial analysis of heat island of this hazard to recognize the UHI places in relation to land uses. This analysis helps urban managers to know more about spatial requirements in city and the importance of proper places (green lands and parks) to moderate urban temperature. 2. Study Area             Urmia city is the central district of Urmia county in the center of west Azerbaijan province in Iran. The city has been located in the distance of 18km from Urmia Lake .  The city is located in 37 ºN latitude and 45 ºE longitude. Its climatehas beenaffected by latitude, winds, Urmia Lake, Mediterranean wet climate, Siberian cold air masses, topography, altitude, and the direction of altitudes (Javan, 2013). 3. Materials and Methods In this study Landsat images for August period of 1989, 1998, 2011 and 2018 were used. Preprocessing, atmospheric and radiometric correction were performed for the images. To prepare heat island maps, land surface temperature (LST) was calculated by the threshold limit of NDVI and Plank principle method for TM images and Split Window algorithm for TIRS image. The algorithms were run in ENVI 5/3. LST formula factors for TM images included Brightness Temperature and Land Surface Emissivity (LSE). To calculate LST for TIRS bands, Split Window algorithm was run by using some important factors including brightness temperature, split window coefficient values, mean LSE, difference in LSE and atmospheric water vapor content. Using of NDVI and land-use images, the relationship of LST, vegetation cover and different land uses was analyzed. For assessment of heat island intensity changes in Urmia, the index of heat island proportion was used for the images. See the following:                        :   temperature of place       Average surface temperature   4. Results and Discussion According to land use maps, during 29 years from 1989 to 2018, Urmia city has had significant growth and expansion. Change detection results indicated  that about 82%, equal to 2475 hectare of bare lands and 72%, equal to 1833 hectare of surrounding farmlands and gardens of the city have been changed to urban land use and related constructions. Using Zonal statistics in Arc GIS the temperature of land uses was assessed .According to the results in 1989 the green cover had the lowest temperature, and the bare lands had the highest average temperature. In August 2018, the maximum temperature is related to urban areas. On August 2018 some new UHIs have been made which are related to producing and industrial workshops, buildings and bare lands in the north east, east and south east of city. The assessment of UHIs changes process showed the escalation of UHI index in Urmia. 5. Conclusion Urban Heat Islands have destructive effects for metropolises and the residents. Urmia as a metropolis has had rapid industrial and population growth in the recent decade. In this research assessments have been done by Landsat images from 1989 to 2018 with four years with temporal distances. The results indicated that in 2018 the area of cold and very cold temperature classes have been decreased. It is because of destroying of large areas of farmlands and gardens and changing to urban land use during studied years. In 1989 and 1998 very high temperature class included bare lands. Passing years and constructing new buildings, industrial and production workshops, new UHIs have been created in east, north east and south east in 2018. The results indicated that during the studied period, according to UHI index results, UHI has been intensified. The UHI index with the amount of 0/2 in 1989, has been reached to 0/37 in 2018. According to these results, in addition to extending of UHI in Urmia, UHI has been intensified. Another research which has been done on UHI in Urmia in July 2015 (Asadi et al., 2019) had the same results of this paper. It proved the effect of industrial and administrative land uses on high temperatures. This research represented the negative relation between LST and green lands and vegetables. Maleki et al. (2018) using synoptic stations information and statistics to assess the UHIs in Urmia and recognized the UHIs in same places like this article. This shows that satellite images have high accuracy and efficiency in analyzing natural or manmade phenomena.

The development of an interactive/batch gridded information system (array of cells georeferenced to USGS quad sheets) and interfacing application programs (e.g., hydrologic models) is discussed. This system allows non-programer users to request any data set(s) stored in the data base by inputing any random polygon's (watershed, political zone) boundary points. The data base information contained within this polygon can be used to produce maps, statistics, and define model parameters for the area. Present/proposed conditions for the area may be compared by inputing future usage (land cover, soils, slope, etc.). This system, known as the Hydrologic Analysis Program (HAP), is especially effective in the real time analysis of proposed land cover changes on runoff hydrographs and graphics/statistics resource inventories of random study area/watersheds.

Dikes are widespread in Egypt and are partly even the prevailing elements in the landscape besides the youngest sedimentary covers, especially in the southern and eastern part of the country. Although dikes are one of the predominant phenomena in Egypt, a systematic, standardized inventory of dikes in the whole country and a dike data base has not been published so far, although several selected areas have been investigated very detailed such as along the Red Sea and in the Sinai. Remote sensing and Geographic Information System (GIS) offer tools for such an inventory. This inventory is of interest for many purposes such as for the mining industry (many dikes are related to mineral occurrence of economic value), and also for hydrogeologic investigations and land use planning. Surface-near dikes, major fault zones, volcanic and structural features were digitized based on Landsat 8 and 9, Sentinel 2, Sentinel 1 and ALOS PALSAR data. High resolution images of World Imagery files/ESRI and Bing Maps Aerial/Microsoft were included into the evaluations. More than 32,000 dikes were digitized and analysed. The evaluations of satellite images allow a geomorphologic differentiation of types of dikes and the description of their characteristics such as dike swarms or ring dikes as well as of their distribution pattern and orientations. Dike density calculations were carried out in ArcGIS to support the detection of areas with dike concentrations. Earthquake data were included into the investigations as dikes might play a role in the development and effects of earthquake swarms in the area of reservoirs.

Water is one of the most vital natural resources used not only for direct consumption purposes but also as an input in the process of production in different sectors of the economy of which agriculture is the most important. Sustainable water management in a river basin requires knowledge of the water availability and water requirements of the basin in the present and future for various purposes. Evapotranspiration is a key component of water resource management influencing the water demands of agriculture. Evapotranspiration is important to the hydrologic cycle because it represents a considerable amount of moisture lost from a watershed. Remote Sensing and GIS is considered as a vital tool for analyzing the Evapotranspiration. The newly developed MODIS satellite is being used in estimating Evapotranspiration. In this project, various hydrological components of the Luni upper sub basin has been calculated with the help of satellite derived information and Evapotranspiration has been analyzed. The availability of multispectral and multi-temporal satellite images provides a clear synoptic view of the study area and can give a good insight in to the topographic and physiographic characteristics of the area. Climate parameters measured using the satellite based sensors are also highly useful in analyzing the climate variability. The FAOPM (Food and Agriculture Organization – Penman Monteith) model is used to derive the evapotranspiration component spatially with the help of remote sensing and GIS techniques. Rainfall pattern of the study area has been analyzed using IMD daily grid data. The study proves that satellite data derived inputs along with other ancillary data can be effectively used to analyses the Evapotranspiration of a given sub basin and its temporal variations.