Assessing the desertification trend using neural network classification and object-oriented techniques (Case study: Changouleh watershed - Ilam Province of Iran)
Desertification consists of decline in production and ecological activities, which may happen due to either natural or unnatural (human) factors.This phenomenon is more evident in arid and semi-arid areas. The aim of this study is to assess the desertification trend using neural network classificati...
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| Format: | Article |
| Language: | English |
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İstanbul University
2016-07-01
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| Series: | İstanbul Üniversitesi Orman Fakültesi Dergisi |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.17099/jffiu.75819 |
| _version_ | 1797915600186507264 |
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| author | Abdolreza Mohamadi Zahedeh Heidarizadi Hadi Nourollahi |
| author_facet | Abdolreza Mohamadi Zahedeh Heidarizadi Hadi Nourollahi |
| author_sort | Abdolreza Mohamadi |
| collection | DOAJ |
| description | Desertification consists of decline in production and ecological activities, which may happen due to either natural or unnatural (human) factors.This phenomenon is more evident in arid and semi-arid areas. The aim of this study is to assess the desertification trend using neural network classification and object-oriented techniques in Changouleh watershed which covers an area of 9949 hectare and is located in the south of Ilam province. For this study, TM and ETM+ satellite images of 1984 and 2013 were used. After conducting geometric and atmospheric corrections, images were classified using two neural network and object-orientedalgorithms. Moreover, to evaluate the accuracy and control the correctness of the obtained maps, typical parameters such as Kappa coefficient, the Confusion matrix, and stability of the classification were extracted for assessing the accuracy. The results show that most changes are related to increase in bare lands and decrease in poor and fair rangelands; therefore, approximately 18% of these areas has turned into desert. The results of evaluation of maps correctness show that these two methods are of high accuracy, but the object-oriented approach with Kappa coefficient (94%) and overall accuracy (96.26 %); in addition to being able to detect and categorize more classes, has a high accuracy compared to neural network method. |
| first_indexed | 2024-04-10T12:45:22Z |
| format | Article |
| id | doaj.art-745ec506611a42e58a04576b77de7a1c |
| institution | Directory Open Access Journal |
| issn | 0535-8418 0535-8418 |
| language | English |
| last_indexed | 2024-04-10T12:45:22Z |
| publishDate | 2016-07-01 |
| publisher | İstanbul University |
| record_format | Article |
| series | İstanbul Üniversitesi Orman Fakültesi Dergisi |
| spelling | doaj.art-745ec506611a42e58a04576b77de7a1c2023-02-15T16:14:08Zengİstanbul Universityİstanbul Üniversitesi Orman Fakültesi Dergisi0535-84180535-84182016-07-0166268369010.17099/jffiu.75819Assessing the desertification trend using neural network classification and object-oriented techniques (Case study: Changouleh watershed - Ilam Province of Iran)Abdolreza Mohamadi0Zahedeh Heidarizadi1Hadi Nourollahi2Ilam University, Graduated in Combating Desertification, IranIlam University, Faculty of Agriculture, Master student of Combating Desertification, IranIlam University, Faculty of Agriculture, Graduated in Combating Desertification, IranDesertification consists of decline in production and ecological activities, which may happen due to either natural or unnatural (human) factors.This phenomenon is more evident in arid and semi-arid areas. The aim of this study is to assess the desertification trend using neural network classification and object-oriented techniques in Changouleh watershed which covers an area of 9949 hectare and is located in the south of Ilam province. For this study, TM and ETM+ satellite images of 1984 and 2013 were used. After conducting geometric and atmospheric corrections, images were classified using two neural network and object-orientedalgorithms. Moreover, to evaluate the accuracy and control the correctness of the obtained maps, typical parameters such as Kappa coefficient, the Confusion matrix, and stability of the classification were extracted for assessing the accuracy. The results show that most changes are related to increase in bare lands and decrease in poor and fair rangelands; therefore, approximately 18% of these areas has turned into desert. The results of evaluation of maps correctness show that these two methods are of high accuracy, but the object-oriented approach with Kappa coefficient (94%) and overall accuracy (96.26 %); in addition to being able to detect and categorize more classes, has a high accuracy compared to neural network method.http://dx.doi.org/10.17099/jffiu.75819Neural network classificationobject-oriented classificationland use changesChangouleh watershed |
| spellingShingle | Abdolreza Mohamadi Zahedeh Heidarizadi Hadi Nourollahi Assessing the desertification trend using neural network classification and object-oriented techniques (Case study: Changouleh watershed - Ilam Province of Iran) İstanbul Üniversitesi Orman Fakültesi Dergisi Neural network classification object-oriented classification land use changes Changouleh watershed |
| title | Assessing the desertification trend using neural network classification and object-oriented techniques (Case study: Changouleh watershed - Ilam Province of Iran) |
| title_full | Assessing the desertification trend using neural network classification and object-oriented techniques (Case study: Changouleh watershed - Ilam Province of Iran) |
| title_fullStr | Assessing the desertification trend using neural network classification and object-oriented techniques (Case study: Changouleh watershed - Ilam Province of Iran) |
| title_full_unstemmed | Assessing the desertification trend using neural network classification and object-oriented techniques (Case study: Changouleh watershed - Ilam Province of Iran) |
| title_short | Assessing the desertification trend using neural network classification and object-oriented techniques (Case study: Changouleh watershed - Ilam Province of Iran) |
| title_sort | assessing the desertification trend using neural network classification and object oriented techniques case study changouleh watershed ilam province of iran |
| topic | Neural network classification object-oriented classification land use changes Changouleh watershed |
| url | http://dx.doi.org/10.17099/jffiu.75819 |
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