This document summarizes a study that used GIS and remote sensing to estimate soil loss in the Gerdi watershed in Ethiopia. The RUSLE model was applied using data on rainfall (R factor), soils (K factor), vegetation (C factor), topography (LS factor), and conservation practices (P factor). The total estimated annual soil loss for the watershed was 28,732.5 tons/year. About 64% of the land had low soil loss rates under the soil loss tolerance value, while 36% had moderate to high soil loss potential. The study found soil loss rates ranged from very low to extremely high across the watershed and demonstrated RUSLE can provide good estimates of soil loss when used with
Change detection analysis in land use / land cover of Pune city using remotel...Nitin Mundhe
Lecture delivered in the National Conference entitled “Monitoring Degraded Lands” jointly organized by Agasti Arts, Commerce and Dadasaheb Rupwate Science
College, Akole and Maharashtra Bhugolshastra Parishad Pune to be held on 4 to 6 February 2014.
Land Use/Land Cover Mapping Of Allahabad City by Using Remote Sensing & GIS IJMER
The present study was carried out to produce and evaluate the land use/land cover maps by on
screen visual interpretation. The studies of land cover of Allahabad city (study area) consist of 87517.47 ha
out of which 5500.35 ha is build up land (Urban / Rural) Area. In this respect, the Build up land (Urban /
Rural) area scorers 6.28% of the total area. It has also been found that about 17155.001ha (19.60 %) of
area is covered by current fallow land. The double/triple crop land of 30178.44ha (34.84%). The area
covered by gullied / ravines is 1539.20 ha (1.75 %) and that of the kharif crop land is 2828.00 ha (3.23 %).
The area covered by other wasteland is 2551.05ha (2.91%). Table 4.1 shows the area distribution of the
various land use and land cover of Allahabad city.
Role of geo-informatics in land use/land coverRohit Kumar
Geo-informatics, which consists of remote sensing and GIS, plays an important role in land use/land cover studies. Remote sensing provides synoptic and multi-temporal data on land use/cover patterns that can be analyzed using GIS. Together, remote sensing and GIS allow researchers to study land use/cover changes over time in a cost effective and accurate manner. Geo-informatics has been widely used for land use/cover mapping and monitoring due to its time-saving capabilities and ability to store, analyze, and display land use/cover data to support planning, management, and utilization of land resources.
Use of remote sensing for land cover monitoring servir science applicationsKabir Uddin
This document discusses land cover mapping using remote sensing. It provides background on land cover mapping and monitoring in the Himalayan region, where deforestation and forest degradation have been issues. Remote sensing using satellite imagery and tools like GIS allows accurate land cover mapping over large areas. The document discusses different remote sensing platforms and sensors, as well as image classification techniques including unsupervised, supervised and object-based classification. It provides examples of software used for object-based image analysis, and outlines the steps involved in land cover mapping projects using remote sensing.
Application of Remote Sensing Techniques for Change Detection in Land Use/ La...iosrjce
IOSR Journal of Applied Geology and Geophysics (IOSR-JAGG) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of Applied Geology and Geophysics. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Applied Geology and Geophysics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Land use Land Cover Highlight for Jibia Local Government, Nigeriaijtsrd
Land use and land cover is very important for appropriate planning and budget of a community. In Jibia several activities are carried on the land without actually knowing the percentage of the land cover of features. Remote sensing and GIS technology are valuable tools in managing land use and land cover. This research described the land use and land cover coverage of the entire LGA land using Remote Sensing and GIS. Landsat Satellite imagery of 2009 of the study area was processed and classified into 5 groups namely Build up sharp sand, Farm Land trees, Vegetation shrubs, Water body and Barren land. Statistical analysis was employed to show the percentage distribution of the land. The study shows the percentage coverage of land physical feature which in turn describe the land use. The research revealed that agriculture is the major activity in the study area. It also concluded by recommending the need for adequate measures to avoid desert encroachment and government support to enhance agricultural produce. Lugga M. S | Babale Z. T | Yamel A. G "Land use Land Cover Highlight for Jibia Local Government, Nigeria" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31134.pdf Paper Url :https://www.ijtsrd.com/other-scientific-research-area/other/31134/land-use-land-cover-highlight-for-jibia-local-government-nigeria/lugga-m-s
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
IMPACT OF COAL MINING ON LAND USE/LAND COVER USING REMOTE SENSING AND GIS TEC...Abhiram Kanigolla
The document discusses two case studies that analyze the impact of coal mining on land use and land cover over time using remote sensing and GIS techniques. Both studies find that coal mining activities have significantly degraded forests and agricultural lands through the creation of mining pits and dumping of overburden materials. The first case study examines changes between 1993 and 2010 around Singrauli, India, finding increases in mining and settlements and decreases in forests and water bodies. The second assesses changes from 1992 to 2009 in South Karanpura, India, documenting total forest destruction near some mines. Both studies demonstrate the ability of remote sensing and GIS to accurately measure and monitor land use/cover changes over periods of mining.
Change detection analysis in land use / land cover of Pune city using remotel...Nitin Mundhe
Lecture delivered in the National Conference entitled “Monitoring Degraded Lands” jointly organized by Agasti Arts, Commerce and Dadasaheb Rupwate Science
College, Akole and Maharashtra Bhugolshastra Parishad Pune to be held on 4 to 6 February 2014.
Land Use/Land Cover Mapping Of Allahabad City by Using Remote Sensing & GIS IJMER
The present study was carried out to produce and evaluate the land use/land cover maps by on
screen visual interpretation. The studies of land cover of Allahabad city (study area) consist of 87517.47 ha
out of which 5500.35 ha is build up land (Urban / Rural) Area. In this respect, the Build up land (Urban /
Rural) area scorers 6.28% of the total area. It has also been found that about 17155.001ha (19.60 %) of
area is covered by current fallow land. The double/triple crop land of 30178.44ha (34.84%). The area
covered by gullied / ravines is 1539.20 ha (1.75 %) and that of the kharif crop land is 2828.00 ha (3.23 %).
The area covered by other wasteland is 2551.05ha (2.91%). Table 4.1 shows the area distribution of the
various land use and land cover of Allahabad city.
Role of geo-informatics in land use/land coverRohit Kumar
Geo-informatics, which consists of remote sensing and GIS, plays an important role in land use/land cover studies. Remote sensing provides synoptic and multi-temporal data on land use/cover patterns that can be analyzed using GIS. Together, remote sensing and GIS allow researchers to study land use/cover changes over time in a cost effective and accurate manner. Geo-informatics has been widely used for land use/cover mapping and monitoring due to its time-saving capabilities and ability to store, analyze, and display land use/cover data to support planning, management, and utilization of land resources.
Use of remote sensing for land cover monitoring servir science applicationsKabir Uddin
This document discusses land cover mapping using remote sensing. It provides background on land cover mapping and monitoring in the Himalayan region, where deforestation and forest degradation have been issues. Remote sensing using satellite imagery and tools like GIS allows accurate land cover mapping over large areas. The document discusses different remote sensing platforms and sensors, as well as image classification techniques including unsupervised, supervised and object-based classification. It provides examples of software used for object-based image analysis, and outlines the steps involved in land cover mapping projects using remote sensing.
Application of Remote Sensing Techniques for Change Detection in Land Use/ La...iosrjce
IOSR Journal of Applied Geology and Geophysics (IOSR-JAGG) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of Applied Geology and Geophysics. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Applied Geology and Geophysics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Land use Land Cover Highlight for Jibia Local Government, Nigeriaijtsrd
Land use and land cover is very important for appropriate planning and budget of a community. In Jibia several activities are carried on the land without actually knowing the percentage of the land cover of features. Remote sensing and GIS technology are valuable tools in managing land use and land cover. This research described the land use and land cover coverage of the entire LGA land using Remote Sensing and GIS. Landsat Satellite imagery of 2009 of the study area was processed and classified into 5 groups namely Build up sharp sand, Farm Land trees, Vegetation shrubs, Water body and Barren land. Statistical analysis was employed to show the percentage distribution of the land. The study shows the percentage coverage of land physical feature which in turn describe the land use. The research revealed that agriculture is the major activity in the study area. It also concluded by recommending the need for adequate measures to avoid desert encroachment and government support to enhance agricultural produce. Lugga M. S | Babale Z. T | Yamel A. G "Land use Land Cover Highlight for Jibia Local Government, Nigeria" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31134.pdf Paper Url :https://www.ijtsrd.com/other-scientific-research-area/other/31134/land-use-land-cover-highlight-for-jibia-local-government-nigeria/lugga-m-s
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
IMPACT OF COAL MINING ON LAND USE/LAND COVER USING REMOTE SENSING AND GIS TEC...Abhiram Kanigolla
The document discusses two case studies that analyze the impact of coal mining on land use and land cover over time using remote sensing and GIS techniques. Both studies find that coal mining activities have significantly degraded forests and agricultural lands through the creation of mining pits and dumping of overburden materials. The first case study examines changes between 1993 and 2010 around Singrauli, India, finding increases in mining and settlements and decreases in forests and water bodies. The second assesses changes from 1992 to 2009 in South Karanpura, India, documenting total forest destruction near some mines. Both studies demonstrate the ability of remote sensing and GIS to accurately measure and monitor land use/cover changes over periods of mining.
Land Use and Land Cover change monitoring of Surajpur Wetland, Uttar Pradesh:...Arnab Saha
Abstract:
Wetlands are extremely important areas throughout the world for wildlife protection, recreation, sediment control and flood prevention. Wetlands are important bird’s habitats and birds use them for feeding, roosting, nesting and rearing their young. In Surajpur Wetland are mainly used for agriculture, fisheries, reclamation for harboring and irrigation purposes. In this paper an attempt is made to study the changes in land use and land cover in Surajpur wetland area over 11 years’ period (2003-2014). LULC is an important component in understanding the interactions of the human activities with the environment and thus it is necessary to be able to simulate changes. The land cover mapping of study area was attempted using remotely sensed images of Landsat and Google Earth imagery. The study area was classified into five categories on the basis of field study, geographical conditions, and remote sensing data. LULC changes have been detected by image processing method in EDRAS imagine 2014 and ArcGIS 10.3. The eleven years’ time period of 2003-2014 shows the major type of land use change. Vegetation area that occupied about around 60 per cent of the Surajpur wetland area in 2003 has decreased to 34.25 percent in 2014. Wetland is increased 8.17 percent and Urban area, Fallow land and Water body also have experienced change. Finally, through the work it is recommended that the wetlands need detail mapping through the use of advance remote sensing techniques like microwave and LIDAR for restoration and management of wetland.
Keywords: LULC, ArcGIS, Surajpur, ERDAS, Remote Sensing
This summarizes a document about change detection techniques in remote sensing for analyzing land use and land cover changes. Remote sensing using aerial photographs and satellite imagery allows efficient monitoring of land cover changes compared to traditional field surveys. Change detection involves identifying transformations of land cover types over time and space using multi-temporal remote sensing data. Common techniques include comparing imagery from Landsat, QuickBird and other satellite sensors to detect changes in agriculture, deforestation, urban growth and other human and natural impacts on the earth's surface.
Land use land cover mapping for smart village using gisSumit Yeole
This document summarizes a presentation on land use and land cover mapping for a smart village in India using GIS. The objectives were to understand GIS and remote sensing technologies and their applications in precision agriculture. The presenter described collecting satellite imagery, classifying land use types, and mapping them for the village of Kundewadi to identify agriculture, settlements, vegetation, water bodies and other land types. Pie charts showed the results, which found people primarily used the land for agriculture and suggested ways to improve wastewater, groundwater, solid waste management and increase agriculture land and trees.
The presentation was given by Mr. Bas Kempen and Ms. V.L. Mulder, ISRIC, during the GSOC Mapping Global Training hosted by ISRIC - World Soil Information, 6 - 23 June 2017, Wageningen (The Netherlands).
This document provides a training report on thematic mapping through remote sensing and GIS techniques in Siwani area, Bhiwani, Haryana, India. It acknowledges the support received from Haryana Space Applications Centre (HARSAC) in providing facilities and guidance for the summer training project. The project aimed to prepare base maps, land use/land cover maps, and geomorphology maps of the study area. It also aimed to familiarize the author with GIS techniques for map preparation and with using global positioning systems. The report includes chapters on the study area description, data and methodology used, and results and discussion of the project.
- The document analyzes land use/land cover change and urban heat island effect in Bilaspur City, India between 2002 and 2017 using Landsat satellite imagery.
- Supervised classification identified 8 land use classes and showed built up land increased 172.4 hectares while agriculture land decreased 84.89 hectares, indicating conversion of rural to urban land.
- Urban heat island phenomenon was evident from land surface temperature images, with certain parts of the city becoming extremely hot, highlighting the need for sustainable urban planning.
Remote Sensing and GIS in Land Use / Land Cover MappingVenkatKamal1
This document discusses using remote sensing and GIS for land use/land cover mapping. It describes analyzing agricultural versus urban land to ensure development doesn't degrade farmland. Land cover refers to ground surface characteristics like vegetation or bare soil, while land use refers to how land is used, such as agriculture or recreation. The document outlines classification systems and criteria for remote sensing-based land use/land cover mapping. It also discusses digital classification techniques, global and national land use datasets, and applications of remote sensing for natural resource management and change detection analysis.
This document discusses geostatistics and its applications in geoinformatics. It begins with introducing key concepts like remote sensing, GIS, GPS and their history. It then discusses spatial sampling designs and geostatistics, which involves incorporating both statistical distribution and spatial correlation of sample data. Common applications of geostatistics and geoinformatics discussed include agriculture management, environmental management, infrastructure management and crisis management using remote sensing. It concludes with discussing different spatial sampling techniques like contiguous unit based spatial sampling and stratified contiguous unit based spatial sampling.
The document discusses methods for generating a global soil organic carbon map. It describes using data from the Harmonized World Soil Database to calculate soil organic carbon stocks in the topsoil layer (0-30 cm) and subsoil layers (30-100 cm), and combining these values to estimate stocks to a 1m depth. Where data is missing, values are supplemented from other sources. The document also discusses analytical methods for determining soil organic matter and carbon, and calculating carbon stocks based on parameters like bulk density and stone content. Upscaling procedures are described, with digital soil mapping identified as the preferred method.
The presentation was given by Mr. Bas Kempen & Ms. V.L. Mulder, ISRIC, during the GSOC Mapping Global Training hosted by ISRIC - World Soil Information, 6 - 23 June 2017, Wageningen (The Netherlands).
IRJET- Land Use & Land Cover Change Detection using G.I.S. & Remote SensingIRJET Journal
This document discusses land use and land cover change detection in Vadodara, India between 1998 and 2008 using remote sensing and GIS techniques. Specifically, it analyzed Landsat satellite images from those two decades to map and classify land use, including built up area, vegetation, vacant land, and water bodies. The methodology involved image preprocessing like geometric correction and radiometric normalization. Images were then enhanced and classified using both supervised and unsupervised classification. Comparing the classified maps from 1998 and 2008 allowed analyzing changes in land use over that 10-year period and calculating the rate of land consumption. The study aimed to provide information to urban planners for predicting future growth and avoiding problems associated with rapid urbanization.
Geographic information system(GIS) and its applications in agricultureKiranmai nalla
This document presents a seminar on geographic information systems (GIS) given by Nalla Anthony Kiranmai. The seminar discusses the principles, components, functions, applications and advantages of GIS. It covers topics such as the linkage between remote sensing and GIS, vector vs raster data representation, spatial data analysis functions including overlays and buffers, and applications of GIS in fields like agriculture, land suitability analysis, and groundwater assessment. The seminar aims to provide an introduction to GIS concepts and demonstrate how GIS can be used as an integrated technology for spatial analysis and decision support.
Application of GIS and Remote Sensing in the analysis of Landuse/Landcover ch...ADITYA SHRESHTKAR
This document summarizes a study that analyzed land use and land cover changes on Jharkhali Island in the Sundarbans region of West Bengal, India between 1990 and 2010 using remote sensing and GIS techniques. The objectives were to classify land cover, determine changes in vegetation cover, and evaluate the socioeconomic implications. Supervised classification and change detection methods revealed accretion of new land, a shift to more anthropogenic land uses, and ongoing pressure on the local ecology from population growth. Similar changes were found to be occurring elsewhere in the Sundarbans region.
The document provides an overview of land use and land cover (LULC) analysis using remote sensing and GIS techniques. It discusses key terminologies like land cover and land use. LULC studies are important for planning, management and monitoring programs. The methodology involves data collection, preprocessing like geometric and radiometric corrections, image classification using supervised or unsupervised methods to produce LULC maps. A case study on LULC change detection in Sikkim Himalaya, India from 1988-2017 is presented which found increases in dense forest and agriculture land areas over the study period. RS and GIS techniques are concluded to be very useful for LULC monitoring and assessment.
Application of GIS in Mine Contamination and Associated Environmental ImpactsArsalan Syed, PMP
This document discusses the application of GIS and remote sensing methods to measure environmental impacts from mining contamination. It outlines two case studies where GIS was used: 1) A study in Turkey that generated DEM and flow accumulation maps from ASTER satellite imagery to identify trace element contamination patterns from an abandoned coal mine. Higher concentrations were found along flow pathways downstream from contamination sources. 2) A study with the Navajo tribe that created water hauling and soil restriction maps using GIS to develop an effective risk communication strategy about uranium exposures from abandoned mines. The maps aided risk understanding but language barriers remained a limitation. In conclusion, remote sensing and GIS provide low-cost alternatives for mapping contamination to inform remediation efforts.
Mapping of degraded lands using remote sensing andsethupathi siva
Remote sensing and GIS techniques can be used to efficiently map soil resources and degraded lands over large areas. High-resolution satellite imagery allows identification of soil types and boundaries with greater precision than conventional surveying. Multiple dates of imagery also facilitate monitoring of land use/land cover changes and degradation over time. GIS is a powerful tool for analyzing and displaying spatial relationships between soils, land use, degradation patterns and other geographic data. The document provides examples of studies mapping soils at different scales, identifying wastelands, and characterizing degraded landforms using these remote sensing and GIS methods.
3. Technical introduction to the Digital Soil MappingFAO
Digital soil mapping involves creating digital maps of soil types and properties by using numerical models. It utilizes legacy soil data such as soil samples, profiles, and maps along with spatial data on soil forming factors like climate, organisms, relief, parent material, and lithology. Common soil inference models used in digital soil mapping include data mining techniques like regression, classification trees, and neural networks as well as geostatistical methods. The process produces quantified estimates of prediction uncertainty since soil variation cannot be perfectly modeled.
Morphometric Analysis of Markandeya River Sub Basin (MRSB), Belgaum District,...IJERD Editor
This document discusses a study that used remote sensing and GIS techniques to analyze the morphometric parameters of the Markandeya River Sub-Basin located in Belgaum district, Karnataka, India. The study categorized the basin into 4 mini-watersheds and analyzed various linear and shape parameters including stream order, bifurcation ratio, drainage density, circularity ratio, and form factor. Key findings were that the watershed has a sub-dendritic to dendritic drainage pattern with weak structural control. The form factor values indicate moderately high peak flows for shorter durations. Based on the compound parameter values calculated, Watershed 1 was found to have the highest priority for soil conservation measures due to its high erosion potential.
A study on geographical characteristics of the krishna western delta using gi...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Morphometric analysis of a vrishabhavathi sub watershed upstream side of gali...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Land Use and Land Cover change monitoring of Surajpur Wetland, Uttar Pradesh:...Arnab Saha
Abstract:
Wetlands are extremely important areas throughout the world for wildlife protection, recreation, sediment control and flood prevention. Wetlands are important bird’s habitats and birds use them for feeding, roosting, nesting and rearing their young. In Surajpur Wetland are mainly used for agriculture, fisheries, reclamation for harboring and irrigation purposes. In this paper an attempt is made to study the changes in land use and land cover in Surajpur wetland area over 11 years’ period (2003-2014). LULC is an important component in understanding the interactions of the human activities with the environment and thus it is necessary to be able to simulate changes. The land cover mapping of study area was attempted using remotely sensed images of Landsat and Google Earth imagery. The study area was classified into five categories on the basis of field study, geographical conditions, and remote sensing data. LULC changes have been detected by image processing method in EDRAS imagine 2014 and ArcGIS 10.3. The eleven years’ time period of 2003-2014 shows the major type of land use change. Vegetation area that occupied about around 60 per cent of the Surajpur wetland area in 2003 has decreased to 34.25 percent in 2014. Wetland is increased 8.17 percent and Urban area, Fallow land and Water body also have experienced change. Finally, through the work it is recommended that the wetlands need detail mapping through the use of advance remote sensing techniques like microwave and LIDAR for restoration and management of wetland.
Keywords: LULC, ArcGIS, Surajpur, ERDAS, Remote Sensing
This summarizes a document about change detection techniques in remote sensing for analyzing land use and land cover changes. Remote sensing using aerial photographs and satellite imagery allows efficient monitoring of land cover changes compared to traditional field surveys. Change detection involves identifying transformations of land cover types over time and space using multi-temporal remote sensing data. Common techniques include comparing imagery from Landsat, QuickBird and other satellite sensors to detect changes in agriculture, deforestation, urban growth and other human and natural impacts on the earth's surface.
Land use land cover mapping for smart village using gisSumit Yeole
This document summarizes a presentation on land use and land cover mapping for a smart village in India using GIS. The objectives were to understand GIS and remote sensing technologies and their applications in precision agriculture. The presenter described collecting satellite imagery, classifying land use types, and mapping them for the village of Kundewadi to identify agriculture, settlements, vegetation, water bodies and other land types. Pie charts showed the results, which found people primarily used the land for agriculture and suggested ways to improve wastewater, groundwater, solid waste management and increase agriculture land and trees.
The presentation was given by Mr. Bas Kempen and Ms. V.L. Mulder, ISRIC, during the GSOC Mapping Global Training hosted by ISRIC - World Soil Information, 6 - 23 June 2017, Wageningen (The Netherlands).
This document provides a training report on thematic mapping through remote sensing and GIS techniques in Siwani area, Bhiwani, Haryana, India. It acknowledges the support received from Haryana Space Applications Centre (HARSAC) in providing facilities and guidance for the summer training project. The project aimed to prepare base maps, land use/land cover maps, and geomorphology maps of the study area. It also aimed to familiarize the author with GIS techniques for map preparation and with using global positioning systems. The report includes chapters on the study area description, data and methodology used, and results and discussion of the project.
- The document analyzes land use/land cover change and urban heat island effect in Bilaspur City, India between 2002 and 2017 using Landsat satellite imagery.
- Supervised classification identified 8 land use classes and showed built up land increased 172.4 hectares while agriculture land decreased 84.89 hectares, indicating conversion of rural to urban land.
- Urban heat island phenomenon was evident from land surface temperature images, with certain parts of the city becoming extremely hot, highlighting the need for sustainable urban planning.
Remote Sensing and GIS in Land Use / Land Cover MappingVenkatKamal1
This document discusses using remote sensing and GIS for land use/land cover mapping. It describes analyzing agricultural versus urban land to ensure development doesn't degrade farmland. Land cover refers to ground surface characteristics like vegetation or bare soil, while land use refers to how land is used, such as agriculture or recreation. The document outlines classification systems and criteria for remote sensing-based land use/land cover mapping. It also discusses digital classification techniques, global and national land use datasets, and applications of remote sensing for natural resource management and change detection analysis.
This document discusses geostatistics and its applications in geoinformatics. It begins with introducing key concepts like remote sensing, GIS, GPS and their history. It then discusses spatial sampling designs and geostatistics, which involves incorporating both statistical distribution and spatial correlation of sample data. Common applications of geostatistics and geoinformatics discussed include agriculture management, environmental management, infrastructure management and crisis management using remote sensing. It concludes with discussing different spatial sampling techniques like contiguous unit based spatial sampling and stratified contiguous unit based spatial sampling.
The document discusses methods for generating a global soil organic carbon map. It describes using data from the Harmonized World Soil Database to calculate soil organic carbon stocks in the topsoil layer (0-30 cm) and subsoil layers (30-100 cm), and combining these values to estimate stocks to a 1m depth. Where data is missing, values are supplemented from other sources. The document also discusses analytical methods for determining soil organic matter and carbon, and calculating carbon stocks based on parameters like bulk density and stone content. Upscaling procedures are described, with digital soil mapping identified as the preferred method.
The presentation was given by Mr. Bas Kempen & Ms. V.L. Mulder, ISRIC, during the GSOC Mapping Global Training hosted by ISRIC - World Soil Information, 6 - 23 June 2017, Wageningen (The Netherlands).
IRJET- Land Use & Land Cover Change Detection using G.I.S. & Remote SensingIRJET Journal
This document discusses land use and land cover change detection in Vadodara, India between 1998 and 2008 using remote sensing and GIS techniques. Specifically, it analyzed Landsat satellite images from those two decades to map and classify land use, including built up area, vegetation, vacant land, and water bodies. The methodology involved image preprocessing like geometric correction and radiometric normalization. Images were then enhanced and classified using both supervised and unsupervised classification. Comparing the classified maps from 1998 and 2008 allowed analyzing changes in land use over that 10-year period and calculating the rate of land consumption. The study aimed to provide information to urban planners for predicting future growth and avoiding problems associated with rapid urbanization.
Geographic information system(GIS) and its applications in agricultureKiranmai nalla
This document presents a seminar on geographic information systems (GIS) given by Nalla Anthony Kiranmai. The seminar discusses the principles, components, functions, applications and advantages of GIS. It covers topics such as the linkage between remote sensing and GIS, vector vs raster data representation, spatial data analysis functions including overlays and buffers, and applications of GIS in fields like agriculture, land suitability analysis, and groundwater assessment. The seminar aims to provide an introduction to GIS concepts and demonstrate how GIS can be used as an integrated technology for spatial analysis and decision support.
Application of GIS and Remote Sensing in the analysis of Landuse/Landcover ch...ADITYA SHRESHTKAR
This document summarizes a study that analyzed land use and land cover changes on Jharkhali Island in the Sundarbans region of West Bengal, India between 1990 and 2010 using remote sensing and GIS techniques. The objectives were to classify land cover, determine changes in vegetation cover, and evaluate the socioeconomic implications. Supervised classification and change detection methods revealed accretion of new land, a shift to more anthropogenic land uses, and ongoing pressure on the local ecology from population growth. Similar changes were found to be occurring elsewhere in the Sundarbans region.
The document provides an overview of land use and land cover (LULC) analysis using remote sensing and GIS techniques. It discusses key terminologies like land cover and land use. LULC studies are important for planning, management and monitoring programs. The methodology involves data collection, preprocessing like geometric and radiometric corrections, image classification using supervised or unsupervised methods to produce LULC maps. A case study on LULC change detection in Sikkim Himalaya, India from 1988-2017 is presented which found increases in dense forest and agriculture land areas over the study period. RS and GIS techniques are concluded to be very useful for LULC monitoring and assessment.
Application of GIS in Mine Contamination and Associated Environmental ImpactsArsalan Syed, PMP
This document discusses the application of GIS and remote sensing methods to measure environmental impacts from mining contamination. It outlines two case studies where GIS was used: 1) A study in Turkey that generated DEM and flow accumulation maps from ASTER satellite imagery to identify trace element contamination patterns from an abandoned coal mine. Higher concentrations were found along flow pathways downstream from contamination sources. 2) A study with the Navajo tribe that created water hauling and soil restriction maps using GIS to develop an effective risk communication strategy about uranium exposures from abandoned mines. The maps aided risk understanding but language barriers remained a limitation. In conclusion, remote sensing and GIS provide low-cost alternatives for mapping contamination to inform remediation efforts.
Mapping of degraded lands using remote sensing andsethupathi siva
Remote sensing and GIS techniques can be used to efficiently map soil resources and degraded lands over large areas. High-resolution satellite imagery allows identification of soil types and boundaries with greater precision than conventional surveying. Multiple dates of imagery also facilitate monitoring of land use/land cover changes and degradation over time. GIS is a powerful tool for analyzing and displaying spatial relationships between soils, land use, degradation patterns and other geographic data. The document provides examples of studies mapping soils at different scales, identifying wastelands, and characterizing degraded landforms using these remote sensing and GIS methods.
3. Technical introduction to the Digital Soil MappingFAO
Digital soil mapping involves creating digital maps of soil types and properties by using numerical models. It utilizes legacy soil data such as soil samples, profiles, and maps along with spatial data on soil forming factors like climate, organisms, relief, parent material, and lithology. Common soil inference models used in digital soil mapping include data mining techniques like regression, classification trees, and neural networks as well as geostatistical methods. The process produces quantified estimates of prediction uncertainty since soil variation cannot be perfectly modeled.
Morphometric Analysis of Markandeya River Sub Basin (MRSB), Belgaum District,...IJERD Editor
This document discusses a study that used remote sensing and GIS techniques to analyze the morphometric parameters of the Markandeya River Sub-Basin located in Belgaum district, Karnataka, India. The study categorized the basin into 4 mini-watersheds and analyzed various linear and shape parameters including stream order, bifurcation ratio, drainage density, circularity ratio, and form factor. Key findings were that the watershed has a sub-dendritic to dendritic drainage pattern with weak structural control. The form factor values indicate moderately high peak flows for shorter durations. Based on the compound parameter values calculated, Watershed 1 was found to have the highest priority for soil conservation measures due to its high erosion potential.
A study on geographical characteristics of the krishna western delta using gi...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Morphometric analysis of a vrishabhavathi sub watershed upstream side of gali...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
This research is about an integrated impact analysis of socioeconomic and biophysical processes at the watershed level on the current status of Dal Lake using multi-sensor and
multi-temporal satellite data, simulation modelling together with field data verification. Thirteen watersheds (designated as ‘W1–W13’) were identified and investigated
for land use/land cover change detection, quantification of erosion and sediment loads and socioeconomic analysis (total population, total households, literacy rate and economic development status).
The document provides an overview of a presentation on remote sensing and GIS and their applications. It discusses what remote sensing is, the steps involved which include the source, sensors, and processing units. It describes different types of remote sensing based on the energy source, including passive sensors like Landsat and active sensors like LIDAR and RADAR. It outlines applications of remote sensing in areas like agriculture, natural resource management, and national security. It also provides an introduction to GIS, describing it as a computer-based information system for capturing and displaying spatially referenced data, and listing some of its functions and advantages.
This study aimed to identify suitable areas for surface irrigation along the Erer Watershed in Eastern Hararghe Zone, Ethiopia using GIS-based multi-criteria analysis. Fifteen factors were considered in the analysis including soil properties, land use/cover, slope, and distance to river outlets. Soil data were obtained from the Harmonized World Soil Database and land cover data were extracted from a Landsat 8 image. The factors were standardized and weighted based on their importance for irrigation suitability. A weighted overlay analysis was performed to determine areas highly, moderately, marginally, and not suitable for surface irrigation. The results identified 386,731ha as highly suitable, 151,120ha as marginally suitable, 50
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This document provides an overview of assessing soil erosion using the RUSLE (Revised Universal Soil Loss Equation) model with remote sensing and GIS. It defines soil erosion and describes the types and causes of erosion. It also discusses the global and Indian scenarios of soil erosion and different erosion modeling approaches. The document explains the need for using remote sensing and GIS with RUSLE modeling. It describes the RUSLE equation and factors in detail and provides the framework for implementing the RUSLE model in a GIS.
The document discusses the Universal Soil Loss Equation (USLE) model for predicting soil erosion. The USLE model uses five factors - rainfall erosivity, soil erodibility, topographic factor, cropping management factor, and support practice factor - to calculate the average annual soil loss. Values for each factor can be obtained from global datasets, equations, and lookup tables. The USLE provides an estimate of long-term average soil loss from sheet and rill erosion on agricultural land and can help identify effective soil conservation measures.
GIS Mapping of Large Soil Groups, Current Land Use, Soil Depths and Slopes, E...Premier Publishers
This study was conducted within the scope of the spatial evaluation of the current land uses, large soil groups, soil depth and slope distributions and soil erosion data in Kırşehir province of Turkey. In the study, digital soil maps produced by the Abolished General Directorate of Rural Services in Turkey were used. Geographical Information Systems (GIS) (Arc GIS 10.3.1) software was used for the spatial evaluation of soil data in the study. According to the spatial analysis results; Dry marginal agricultural areas with 2857.7 km2 constitute a large part of the current land use of Kırşehir Province. This area covers 44% of the total. When the large soil groups of Kırşehir province are examined, brown soils constitute a large part of the region. It has been determined that brown soils correspond to 2710.4 km2 area and 41% of the existing area. The soil depth structure has been observed to be generally medium depth soils. Medium deep soils have an area of 2052.1 km2 and constitute 31% of the total area. As for the soil slope class, it was seen that a large part of the region was between the 2nd degree slope group (7-12%) and the 3rd degree slope group (13-20%). When the soil erosion degree was examined, it was seen that a large part of the region had 2nd degree erosion. Soils with 2nd degree erosion group constitute an area of 2294.3 km2 (35%). Sharing the digital land use data obtained in the study will provide significant contributions to the investor organizations that will invest in the region and contribute to agricultural production.
Assessment of wheat crop coefficient using remote sensing techniquesPremier Publishers
Irrigation water consumption under physical and climatic conditions for large scale will be easier with remote sensing techniques. Crop evapotranspiration (ETc) uses crop coefficient (Kc) and reference evapotranspiration (ETo). Kc plays an essential role in agricultural practices and it has been widely used to estimate ETc. In this paper Normalized Deference Vegetation Index (NDVI) used to estimate crop coefficient according to satellite data (KcSat) through simple model (KcSat = 2NDVI - 0.2). Landsat8; bands 4 and 5 provide Red (R) and Near Infra-Red (NIR) measurements and it used to calculate NDVI. Single KcFAO estimated under Egyptian conditions according to FAO 56 paper. The KcFAO used to validate KcSat. Linear relationship between KcFAO and KcSat was established and R2 was 0.96. The main objective of this paper is estimation of wheat crop coefficient using remote sensing techniques.
Integrated Approach of GIS and USLE for Erosion Risk Analysis in the Sapanca ...theijes
The primary objectives of this study is to establish a Geographical Information System (GIS) for soil loss based upon the Universal Soil Loss Equation (USLE) method, and to determine erosion risk zones in the Sapanca lake watershed. In this study, rainfall erosivity (R) factor was computed from monthly and annual precipitation data of six methodological stations. Soil erodibility (K) factor were extracted from soil map by the Ministry of Food, Agriculture and Livestock. Land cover and management (C) factor were derived from Landsat TM imagery and from Statip 2009 map. Topographic (LS) factor was interpolated from a digital elevation model (DEM). Support practice (P) factor was assigned a value of 1 due to lack of support practices in the watershed. The study indicated that the method can be reasonably used for soil erosion risk analysis in the Sapanca Lake Watershed, and also moderate and highly eroded areas associated with new settlements and bare lands since new settlers either cleared of native forests or used intensively for agriculture. Such analysis is essential for water management practices, specifically identification of critical risk zones for investigating watershed management strategies to achieve management goals.
Protection of soil from the loss of organic carbon by taking into account ero...ExternalEvents
This presentation was presented during the 1 Parallel session on Theme 3.3, Managing SOC in: Dryland soils, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Sergio Saia, from CREA – Italy, in FAO Hq, Rome
A knowledge-based model for identifying and mapping tropical wetlands and pea...ExternalEvents
This presentation was presented during the 2 Parallel session on Theme 3.1, Managing SOC in: Soils with high SOC – peatlands, permafrost, and black soils, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Thomas Gumbricht, from Center for International Forestry Research – Indonesia, in FAO Hq, Rome
This document discusses the use of geoinformatics techniques to map and identify wastelands in Chitradurga District, Karnataka, India. The study utilized geospatial tools including topographic maps, satellite imagery from IRS-1D PAN+LISS III, and Google Earth. Vegetation, forest cover, lithological formations, soil types, and land use/land cover maps were generated to identify and delineate different categories of wastelands. The mapping found that the major causes of wasteland formation were unscientific agricultural practices and deforestation. The wasteland database provides spatial information to aid in planning sustainable reclamation and development strategies.
Estimation Of Soil Erosion In Andhale Watershed Using USLE And GISIRJET Journal
This document describes a study that used the Universal Soil Loss Equation (USLE) and GIS techniques to estimate soil erosion in the Andhale watershed region of India. The USLE model requires factor maps for rainfall erosivity, slope length and steepness, vegetation cover, soil erodibility, and erosion control. These factor maps were created using rainfall data, a digital elevation model, land use/land cover maps, and soil sample analysis. The factors were combined in the USLE model to produce a map of estimated average annual soil loss across the watershed, which ranged from 11.16 to 60.11 tons/ha/year. The study found areas of high erosion risk and concluded that maintaining vegetation cover
The document discusses irrigation scheduling and estimating crop water requirements in dry climates. It summarizes that irrigation scheduling can help use water efficiently without negatively impacting crop yields. It then describes the methodology used, which includes identifying crop types and areas using satellite images, estimating crop water needs using the Penman-Monteith method in CROPWAT software, and determining total water requirements by crop for the study area in Karnataka, India. The results show the classified land use map identifying different crop areas and the decade-wise irrigation water requirements in mm for various crops in the Rabi and Kharif seasons.
The document summarizes research on using RADARSAT-2 satellite data to monitor soil moisture for agricultural risk reduction in Canada. It finds that a calibrated Integral Equation Model can estimate regional soil moisture with an average error of 3.23% and detect changes in soil moisture over time. However, site-specific estimates have higher errors of 7.71% due to spatial variability not captured. Further analysis is needed to reduce errors and better quantify relative changes in soil moisture.
Turkey’s National Geospatial Soil Organic Carbon Information SystemExternalEvents
This presentation was presented during the 3 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Sevinç Madenoglu, from Ministry of Food, Agriculture, and Livestock - Turkey, in FAO Hq, Rome
Flooding is one of the most devastating natural
disasters in Nigeria. The impact of flooding on human activities
cannot be overemphasized. It can threaten human lives, their
property, environment and the economy. Different techniques
exist to manage and analyze the impact of flooding. Some of these
techniques have not been effective in management of flood
disaster. Remote sensing technique presents itself as an effective
and efficient means of managing flood disaster. In this study,
SPOT-10 image was used to perform land cover/ land use
classification of the study area. Advanced Space borne Thermal
Emission and Reflection Radiometer (ASTER) image of 2010 was
used to generate the Digital Elevation Model (DEM). The image
focal statistics were generated using the Spatial Analyst/
Neighborhood/Focal Statistics Tool in ArcMap. The contour map
was produced using the Spatial Analyst/ Surface/ Contour Tools.
The DEM generated from the focal statistics was reclassified into
different risk levels based on variation of elevation values. The
depression in the DEM was filled and used to create the flow
direction map. The flow accumulation map was produced using
the flow direction data as input image. The stream network and
watershed were equally generated and the stream vectorized. The
reclassified DEM, stream network and vectorized land cover
classes were integrated and used to analyze the impact of flood on
the classes. The result shows that 27.86% of the area studied will
be affected at very high risk flood level, 35.63% at high risk,
17.90% at moderate risk, 10.72% at low risk, and 7.89% at no
risk flood level. Built up area class will be mostly affected at very
high risk flood level while farmland will be affected at high risk
flood level. Oshoro, Imhekpeme, and Weppa communities will be
affected at very high risk flood inundation while Ivighe, Uneme,
Igoide and Iviari communities will be at risk at high risk flood
inundation level. It is recommended among others that buildings
that fall within the “Very High Risk” area should be identified
and occupants possibly relocated to other areas such as the “No
Risk” area.
Analysis of Sand Dunes Accumulation using Remote Sensing and GISijtsrd
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
Hazard Mapping of Landslide Vulnerable Zones in a Rainfed Region of Southern ...IRJET Journal
1) The document describes a study that uses remote sensing and GIS techniques to map landslide vulnerable zones in Wayanad, a rainfed region of southern India.
2) Factors like slope, elevation, rainfall, soil type, land use, geology, drainage density, road density, and lineament density were analyzed as layers in a GIS. Weights were assigned to each factor based on their influence on landslides.
3) The weighted factors were overlaid to produce a landslide vulnerability map categorizing the study area into stable, moderately stable, moderately unstable, highly unstable, and critical zones. The predicted vulnerable zones agreed with past landslide occurrences.
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A geographic information system based soil loss and sediment estimation in gerdi watershed, highlands of ethiopia
1. Journal of Environment and Earth Science www.iiste.org
ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)
Vol.4, No.19, 2014
62
A Geographic Information System Based Soil Loss and Sediment
Estimation in Gerdi Watershed, Highlands of Ethiopia
Gizachew Ayalew
Amhara Design and Supervision Works Enterprise (ADSWE), Bahir Dar, Ethiopia
E-mail:gizachewayalew75@yahoo.com
Abstract
This study was carried out to spatially predict the soil loss rate of Gerdi watershed with a Geographic
Information System (GIS) and Remote Sensing (RS). RUSLE adapted to Ethiopian conditions was used to
estimate potential soil losses by utilizing information on rainfall erosivity (R) using interpolation of rainfall data,
soil erodibility (K) using soil map, vegetation cover (C) using satellite images, topography (LS) using Digital
Elevation Model (DEM) and conservation practices (P ) using satellite images. Based on the analysis, the total
annual soil loss potential of the study watershed was 28,732.5 tons/yr. Out 147.9 ha (64%) of the land’s
watershed was categorized none to slight class which under soil loss tolerance (SLT) values ranging from 5 to 11
tons ha-1
yr-1
. The study results indicated that the rate of potential soil loss in the watershed ranged from very low
to extremely high. The area covered by none to slight potential soil loss was about 147.9 ha (64%) whereas
moderate to high soil loss potential covered about 202.1 ha (36%) of the study watershed. The study
demonstrates that the RUSLE together with GIS provide a good estimate soil loss rate over areas.
Keywords: soil erosion; RUSLE; GIS; Gerdi watershed; Ethiopia
1. INTRODUCTION
Agriculture is the mainstay of the Ethiopia’s economy where its production is highly dependent on natural
resources (Akililu and Graaff, 2007). It accounts for the employment of 90% of its population, over 50% of the
country’s gross domestic product (GDP) and over 90% of foreign exchange earnings (ECACC, 2002). However,
low productivity characterizes the country’s agriculture.
Soil erosion has accelerated on most of the world, especially in developing countries including Ethiopia,
due to different socio-economic, demographic factors and limited resources (Bayramin et.al, 2003). To
effectively estimate soil erosion the Revised Universal Soil Loss Equation (RUSLE) has been used in many
countries including Ethiopia. The rate of soil erosion is severe in the highlands of Ethiopia. Accelerated soil
erosion by water has been a major threat to crop production in Ethiopia (Hurni, 1993; Sutcliffe, 1993 and
Tamene, 2005). In the Ethiopian highlands only, an annual soil loss reaches 200-300 tons ha-1
yr-1
(FAO, 1984
and Hurni, 1993). The impact of soil erosion can be most problematical in the developing countries and unable
to improve soil fertility through application of purchased inputs (Lulseged and Vlek, 2008). In the Ethiopian
highlands only, an annual soil loss reaches 200-300 tons ha-1
yr-1
, and can be as much as 23.4x109 metric tons per
year (FAO, 1984 and Hurni, 1993). Hurni (1988), and Hurni, Herweg, Portner and Liniger (2008) estimates that
soil loss due to erosion of cultivated fields in Ethiopia amounts to about 42 metric tons ha-1
yr-1
.Therefore, it
becomes a destructive process when it is exacerbated by a number of anthropogenic factors such as deforestation,
overgrazing, incorrect methods of tillage and unscientific agricultural practices (Lal, 2003; Zhou and Wu, 2008).
Despite the severity of soil erosion and its consequences in the study watershed, there have been few studies at
watershed level to quantify erosion rates at watershed scale. In addition, study watershed, Gerdi is one of the
most erosion-prone watersheds in the highlands of Ethiopia which received little attention. It was, therefore,
essential to assess rates of soil loss and develop a soil loss intensity map of the study watershed using RUSLE
within a GIS environment and identify severity areas for specific soil conservation plans.
2. MATERIALS AND METHODS
2.1 Description of the study watershed
Gerdi watershed is located in Awi Zone at about 450 km northwestern of Addis Ababa. The watershed lies
within 1213313 to 1217144 m north and 245870 to 251285 m East in UTM coordinates with altitude ranges of
1920 up to 2291 m.a.s.l. (figure 1) with the total area of 1225.56 ha. Agro-ecologically, 51% and 49% of the
watershed is found to be warm and hot zone, respectively. Rainfall is ranging from 720 mm to 1253.2 mm.
Temperature extends from 12.80
C to 30.150
C. The elevation ranges from 1920 up to 2291 m.a.s.l. The mean
annual precipitation ranges from 1800-2000 mm.
2. Journal of Environment and Earth Science www.iiste.org
ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)
Vol.4, No.19, 2014
63
Figure 4: Location Map of Gerdi Watershed
2.2 Methods
The input thematic data included rainfall, soil units, slopes and land use/cover and determined as follow.
2.2.1 Determination of Soil Loss factors
Rainfall Erosivity Factor
The monthly amounts of precipitation for the watershed were collected over 15 years by the Amhara Regional
Meteorological Agency. Monthly rainfall records from these meteorological stations covering the period 1998-
2012 were used to calculate the rainfall erosivity Factor (R-value). The mean annual rainfall was first
interpolated to generate continuous rainfall data for each grid cell by “3D Analyst Tools Raster Kriging
Interpolation” in ArcGIS environment. Then, the R-value corresponds to the mean annual rainfall of the
watershed was found using the R-correlation established in Hurni (1985) to Ethiopia condition.
R= -8.12 + 0.562P……………………………………..…………………………… Equation (1)
Where R is the rainfall erosivity factor and P is the mean annual rainfall (mm).
Soil Erodibility Factor
“Spatial Analyst Tool Extract by Mask” in GIS environment was used to obtain soil units map of the study
watershed from Amhara Regional digital soil map at 1:50,000 scale developed by DSA and SCI (2006).The soil
erodibility (K) factor for the watershed was estimated based on soil unit types referred from FAO (1989) soil
database adapted to Ethiopia by Hurni (1985) and Hellden (1987). Finally, the resulting shape file was changed
to raster with a cell size of 30x30 m. The raster map was then reclassified based on their erodibility value as
shown in table 1.
3. Journal of Environment and Earth Science www.iiste.org
ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online)
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Table 2: Soil Types and their Areas
Soil types
Area
Hectare (ha) Percent (%)
Dystric Fluvisols 729.9 59.6
Dystric Gleysols 65.0 5.3
Dystric Nitosols 145.5 11.9
Orthic Acrisols 285.2 23.3
Total 1225.6 100
Figure 5: Soil Map
Slope Length and Slope Steepness
The 30 m spatial resolution DEM (Digital Elevation Model) was used to generate slope as shown figure 6 by
using “Spatial Analyst Tool Surface Slope” in ArcGIS 10.1 environment. The flow accumulation and slope
steepness were computed from the DEM using ArcGIS.
Flow accumulation and slope maps were multiplied by using “Spatial Analyst Tool Map Algebra Raster
Calculator” in Arc GIS 10.1 environment to calculate and map the slope length (LS factor) as shown in equation
(2) and defined by (Wischmeier and Smith 1978).
LS = (Flow Accumulation*Cell size/22.13)0.4
*(Sin slope/0.896)1.3
……………….....Equation (2)
Where: -Cell size- represents the field slope length
-22.13 is the length of the research field plot
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Figure 6: Slope Map of Gerdi Watershed
Land Use/Cover Data and Crop Management Factor
A land-use and land-cover map of the study area was prepared from LANDSAT satellite image acquired on 2013
and supervised digital image classification technique was employed using ENVI 5.0 software. A field checking
effort also was made in order to collect ground truth information. The LAND SAT satellite image was used to
classify the current land use and land cover map. Digital image processing operations were carried out using
ENVI 5.0 software. In addition, ground truth data were used as a vital reference for supervised classification,
accuracy assessment and validation of the result. In supervised image classifications technique, land use and land
cover types were classified so as to use the classified images as inputs for generating crop management (C)
factor and support practice (P) factor. Based on the land cover classification map, a corresponding C value
obtained from Hurni (1985) was assigned in a GIS environment (Table 3).
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Table 3: Land cover types and their areas
Area
Major land cover Slope (%) ha %
Cultivated Land 0-3 126.38 10.31
3-8 99.28 8.1
8-15 176.62 14.41
15-30 70.76 5.77
30-50 7.4 0.6
Sub-total 480.44 39.2
Forest Land 0-3 2.5 0.2
3-8 13.14 1.07
8-15 80.62 6.58
15-30 294.94 24.07
30-50 62.9 5.13
>50 2.43 0.2
Sub-total 456.53 37.25
Grass Land 0-3 35.15 2.87
3-8 22.04 1.8
8-15 87.19 7.11
15-30 47.03 3.84
30-50 1.05 0.09
Sub-total 192.46 15.7
Shrub and Bush Land 0-3 5.15 0.42
3-8 17.95 1.46
8-15 34.67 2.83
15-30 32.41 2.64
30-50 5.96 0.49
Sub-total 96.13 7.84
Grand total 1225.56 100
Figure 7: Land Use/Cover Map of the Watershed
Erosion Management Practice Factor
The P-factor was assessed using major land cover and slope interaction adopted by Wischmeier and Smith (1978)
for Ethiopia condition. The data related to management or support practices of the watershed were collected
during the field work. Therefore, values for erosion management practice factor (P- factor) were assigned
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considering local management practices and it was taken the weighed value for similar land use types. The
corresponding P values were assigned to each land use/land cover classes and slope classes and the P factor map
was produced.
2.2.3 Soil Loss Analysis
The overall methodology involved the use of the RUSLE in a GIS environment with factors obtained from
meteorological stations, soil map, topographic map, Satellite Images and DEM as shown in equation 4 and figure
5. Annual soil loss rate was determined by a cell-by-cell analysis of the soil loss surface by superimposing and
multiplying the respective RUSLE factor values (R, K, LS, C and P) interactively by using “Spatial Analyst Tool
Map Algebra Raster Calculator” in ArcGIS 10.1 environment as shown equation 3 adopted from the
recommendations of Hurni (1985) and Gebreselassie (1996). For the purpose of identifying priority areas for
conservation planning, soil loss potential of the watershed was then categorized into different severity classes
following FAO & UEP (1984) guide line.
A= LS* R* K* C* P…..…………. ………………………………………….……… Equation (3)
Where A is the annual soil loss (metric tons ha-1
yr-1
); R is the rainfall erosivity factor [MJ mm h-1
ha-1
yr-1
]; K is
soil erodibility factor [metric tons ha-1
MJ –1
mm-1
]; LS = slope length factor (dimensionless); C is land cover
and management factor (dimensionless); and P is conservation practice factor (dimensionless). Ground truth data
collected by GPS were used for checking and validation of results.
Figure 8: Flow Chart showing the GIS based Soil Loss Estimation
2.2.4 Sediment Yield
The sediment delivery ratio (SDR) denotes the ratio of the sediment yield at a given stream cross section to the
gross erosion from the watershed upstream from the measuring point (Julien, 1998). To generate the sediment
yield at the outlet, empirical equations were carried out.
SDR = A-0.2
……………………………………………………………………….…...Equation (4)
Where, SDR denotes the sediment delivery ratio and area of the watershed. The SDR physically means the ratio
of the sediment routed to the outlet over the watershed, both overland and channel.
Sediment yield is commonly estimated by the following empirical formula:
Sy=E*(1/A0.2
) .............................................................................................................Equation (5)
Where, Sy= Sediment yield (ton) at the watershed out let; E = total erosion (ton); A = watershed area (ha)
3. RESULTS AND DISCUSSION
3.1 Rainfall Erosivity Factor
Soil loss is closely related to rainfall partly through the detaching power of raindrops striking the soil surface and
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partly through the contribution of rain to runoff (Morgan, 1994). The soil loss is closely related to rainfall partly
through the detaching power of raindrop striking the soil surface and partly through the contribution of rain to
runoff. The average annual rainfall of the watershed is approximately 1900 mm. The result showed that rainfall
erosivity factor (R-factor) value in the watershed ranged between 1059.68 MJmm ha−1
yr−1
.
3.2 Soil Erodibility Factor
The erodibility of a soil is an expression of its inherent resistance to particle detachment and transport by rainfall.
It is determined by the cohesive force between the soil particles, and may vary depending on the presence or
absence of plant cover, the soil’s water content and the development of its structure (Wischmeier and Smith,
1978). The soil erodibility factor (K) represents the effect of soil properties and soil profile characteristics on soil
loss (Renard et al., 1997). Erodibility depends essentially on the amount of organic matter in the soil, the texture
of the soil, the structure of the surface horizon and permeability (Robert & Hilborn, 2000). The results indicated
that soil erodibility value in the study watershed ranged from 0.10 Mgh MJ−1
mm−1
to 0.15 Mgh MJ−1
mm−1
(table 3 and figure 6).
Table 4: Soil Erodibility Factor
Soil type K-value
Area
ha %
Dystric Fluvisols 0.1 729.9 59.6
Dystric Gleysols 0.15 65.0 5.3
Dystric Nitosols 0.15 145.5 11.9
Orthic Acrisols 0.15 285.2 23.3
Total 1225.6 100
Figure 9: Soil Erodibility Factor Map
3.3 Slope Length and Slope Steepness Factor
The influence of topography on erosion is complex. The local slope gradient (S sub-factor) influences flow
velocity and thus the rate of erosion. Slope length (L sub-factor) describes the distance between the origin and
termination of inter-rill processes. In RUSLE, the LS factor represents a ratio of soil loss under given conditions
to that at a site with the "standard" slope steepness of 9% and slope length of 22 m plot (Robert & Hilborn, 2000).
The steeper and longer the slope, the higher is the erosion. Some researchers have argued that upslope drainage
area is a better parameter when describing the influence of slope length on erosion, not slope length (Desmet &
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Govers, 1996). The upslope drainage area for each cell in a DEM was calculated with multiple flow algorithms.
The steepness factor value in the study watershed varies from 0.5 to 4.8 (Figure 7). As slope length increases,
total soil erosion and soil erosion per unit area increase due to the progressive accumulation of runoff in the
down slope direction. The slope length and slope steepness can be used in a single index, which expresses the
ratio of soil loss as defined by (Wischmeier and Smith 1978).
Figure 10: Steepness Factor Map
3.4 Land Use and Land Cover and Crop Factor
The attribute and spatial information on the present status of land use/land cover is a pre-requisite to identify and
prioritize areas for soil conservation measures and minimizing further land degradation. The C- value is a ratio
comparing the soil loss from land under a specific crop and management system to the corresponding loss from
continuously fallow and tilled land. It represents the ratio of soil loss under a given crop to that of the base soil
(Morgan, 1994). It measures the combined effect of cropping and management practices in agricultural system
and the effect of ground cover, tree canopy and grass covers in reducing soil loss in non-agricultural condition
(Wischmeier and Smith, 1978). It also reflects the effect of cropping and management practices on the soil
erosion rate (Renard, Foster, Weesies, McCool, and Yoder, 1997). As shown in Table 4 and Figure 8, four land-
use and land-cover classes were recognized in the watershed, dominantly by crop cultivation (39.2%). Crop
management C factor values of the study watershed were ranging from 0.01 to 0.20 similar with the work of
Morgan (2005).
Table 5 : Cover Management (C) Factor values of the study area
Land cover type C-value
Area
ha %
Cultivated Land 0.15 480.4 39.2
Grass Land 0.05 192.5 15.7
Forest 0.01 456.5 37.3
Shrub Land 0.20 96.1 7.8
Total 1225.6 100.0
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Figure 11: Derivation of Cover Factor from Cover Type
3.5 Management Practice Factor
The conservation practices factor (p-values) reflects the effects of practices that will reduce the amount and rate
of the water runoff and thus reduce the amount of erosion. It depends on the type of conservation measures
implemented and requires mapping of conserved areas for it to be quantified. In the study area, there is only a
small area that has been treated with terracing through the agricultural extension programme of the government.
As data were lacking on permanent management factors and there were no management practices, I used the P-
values suggested by Bewket and Teferi (2009), Wang and Sun (2002). Thus, the agricultural lands are classified
into six slope categories and assigned P-values while all non-agricultural lands are assigned a P-value of 1.00
(Table 5 and Figure 9).
Table 6: Land Management Factor (P) values
Land use type Slope (%) P-factor
Area
ha %
Cultivated Land 0-5 0.1 167.8 13.7
5-10 0.12 114.7 9.4
10-20 0.14 165.2 13.5
20-30 0.19 25.5 2.1
30-50 0.25 7.2 0.6
50-100 0.33 0.1 0.0
Other land use All 1 745.1 60.8
Total 1225.6 100
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Figure 12: Derivative of Management Factor from Land Cover and Slope
3.6 Soil Loss Estimation and Prioritization for Soil Conservation Planning
The Revised Universal Soil Loss Equation (RUSLE) has been used widely all over the world (Mellerowicz, Ress,
Chow and Ghanem, 1994) including Ethiopia (Kaltenrieder, 2007; Bewket and Teferi, 2009) because of its
simplicity and limited data requirement. The advent of geographical information system (GIS) technology has
allowed the equation to be used in a spatially distributed manner because each cell in a raster image comes to
represent a field-level unit. Even though the equation was originally meant for predicting soil erosion at the field
scale, its use for large areas in a GIS platform has produced satisfactory results (Mellerowicz, Ress, Chow and
Ghanem, 1994; Renard, Foster,Wessies and Porter, 1994). By delineation of watersheds as erosion prone areas
according to the severity level of soil loss, priority is given for a targeted and cost-effective conservation
planning (Kaltenrieder, 2007; Bewket & Teferi, 2009).
Based on the analysis, the soil loss potential of the study watershed was about 41,424.07 ton per year.
Large portion of the watershed (38.5%, 471.6 ha) was categorized none to slight class which under SLT values
ranging from 5 to 11 tons ha-1
yr-1
(Renard, Foster, Weesies, McCool and Yoder, 1996). The remaining 56.2%
(689.4 ha) of land was classified under moderate to high class about several times the maximum tolerable soil
loss (11 tons ha-1
y-1
) (Table 6 and Figure 10). Mati, Morgan, Gichuki, Quinton, Brewer and Liniger (2000)
estimated average soil loss from croplands in the highlands of Ethiopia as a whole at 100 metric tons ha-1
yr-1
. In
the highlands of Ethiopia and Eritrea soil losses are extremely high with an estimated average of 20 metric tons
ha-1
yr-1
(Hurni, 1985) and measured amounts of more than 300 metric tons ha-1
yr-1
on specific plots. Hurni (1993)
estimated mean soil loss from cultivated fields as 42 metric tons ha-1
yr-1
. The average annual soil loss estimated
by USLE from the entire Gerdi watershed, northwestern Ethiopia was 33.80 ton/ha/yr. Thus, the estimated soil
loss rate was generally realistic, compared to results from previous studies.
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Table 7: Soil Loss Summary of the Watershed
Soil loss rating and class Area
ton/ha/yr mm/yr* class ha %
0-5 0-0.5 Non to slight 65.1 5.3
5-15 0.5-1 Non to slight 471.6 38.5
16-30 1-2.5 Moderate 409.7 33.4
31-50 2.5-4 Moderate 118.6 9.7
51-100 4-6.5 High 84.4 6.9
101-200 6.5-16.5 High 38.5 3.1
>200 >16.5 Very High 38.2 3.1
Total 1225.56 100
Figure 13 : Soil Loss Map of the Watershed
3.7 Sediment Yield
Similar to the soil losses, sediment yields were also very high at the out let of the watershed. The transporting
ability of the runoff to move all the eroded sediments was insufficient. As a result deposition occurs in
reservoirs, depressions, at the toe of the hills where changes slope. Thus, the amount of erosion in the
watershed was generally more than the amount of sediment leaving the watershed at the outlet point. The most
common method for estimating sediment yield is sediment delivery ratio (1/A0.2
), which is developed from
reservoir survey, or measurement of suspended and bed loads at the gauging station and compared with that of
erosion in the watershed.
Sy = 9990.46 tons per year
4. CONCLUSIONS AND RECOMMENDATIONS
The predicted amount of soil loss and sediment yield could facilitate comprehensive and sustainable land
management through conservation planning for the watershed. Areas characterized by high to very high soil loss
should be given special priority to reduce or control the rate of soil erosion by means of conservation
planning. The study demonstrates that the RUSLE together with GIS and RS provides great advantage to
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estimate soil loss rate over areas though the input parameter values need to be calibrated to the specific area.
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