Evaluation of Food Security Based on Remote Sensing Data—Taking Egypt as an Example
Egypt, a country with a harsh natural environment and rapid population growth, is facing difficulty in ensuring its national food security. A novel model developed for assessing food security in Egypt, which applies remote sensing techniques, is presented. By extracting the gray-level co-occurrence...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/12/2876 |
| _version_ | 1827656954340704256 |
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| author | Shuzhu Shi Yu Ye Rui Xiao |
| author_facet | Shuzhu Shi Yu Ye Rui Xiao |
| author_sort | Shuzhu Shi |
| collection | DOAJ |
| description | Egypt, a country with a harsh natural environment and rapid population growth, is facing difficulty in ensuring its national food security. A novel model developed for assessing food security in Egypt, which applies remote sensing techniques, is presented. By extracting the gray-level co-occurrence matrix (GLCM) mean texture features from Sentinel-1 and Landsat-7 images, the arable land used to grow grain crops was first classified and extracted using a support vector machine. In terms of the classified results, meteorological data, and normalized difference vegetation index (NDVI) data, the Carnegie–Ames–Stanford approach (CASA) model was adopted to compute the annual net primary production (NPP). Then, the NPP yield conversion formula was used to forecast the annual grain yield. Finally, a method for evaluating food security, which involves four dimensions, i.e., quantity security, economic security, quality security, and resource security, was established to evaluate food security in Egypt in 2010, 2015, and 2020. Based on the proposed model, a classification accuracy of the crop distribution map, which is above 82%, can be achieved. Moreover, the reliability of yield estimation is verified compared to the result estimated using statistics data provided by Food and Agriculture Organization (FAO). Our evaluation results show that food security in Egypt is declining, the quantity and quality security show large fluctuations, and economic and resource security are relatively stable. This model can satisfy the requirements for estimating grain yield at a wide scale and evaluating food security on a national level. It can be used to provide useful suggestions for governments regarding improving food security. |
| first_indexed | 2024-03-09T22:35:34Z |
| format | Article |
| id | doaj.art-330457ba6f874c2c861ba0eda58b549c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T22:35:34Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-330457ba6f874c2c861ba0eda58b549c2023-11-23T18:48:15ZengMDPI AGRemote Sensing2072-42922022-06-011412287610.3390/rs14122876Evaluation of Food Security Based on Remote Sensing Data—Taking Egypt as an ExampleShuzhu Shi0Yu Ye1Rui Xiao2School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaEgypt, a country with a harsh natural environment and rapid population growth, is facing difficulty in ensuring its national food security. A novel model developed for assessing food security in Egypt, which applies remote sensing techniques, is presented. By extracting the gray-level co-occurrence matrix (GLCM) mean texture features from Sentinel-1 and Landsat-7 images, the arable land used to grow grain crops was first classified and extracted using a support vector machine. In terms of the classified results, meteorological data, and normalized difference vegetation index (NDVI) data, the Carnegie–Ames–Stanford approach (CASA) model was adopted to compute the annual net primary production (NPP). Then, the NPP yield conversion formula was used to forecast the annual grain yield. Finally, a method for evaluating food security, which involves four dimensions, i.e., quantity security, economic security, quality security, and resource security, was established to evaluate food security in Egypt in 2010, 2015, and 2020. Based on the proposed model, a classification accuracy of the crop distribution map, which is above 82%, can be achieved. Moreover, the reliability of yield estimation is verified compared to the result estimated using statistics data provided by Food and Agriculture Organization (FAO). Our evaluation results show that food security in Egypt is declining, the quantity and quality security show large fluctuations, and economic and resource security are relatively stable. This model can satisfy the requirements for estimating grain yield at a wide scale and evaluating food security on a national level. It can be used to provide useful suggestions for governments regarding improving food security.https://www.mdpi.com/2072-4292/14/12/2876crop classificationgrain yieldevaluation modelSentinel-1Landsat 7 |
| spellingShingle | Shuzhu Shi Yu Ye Rui Xiao Evaluation of Food Security Based on Remote Sensing Data—Taking Egypt as an Example Remote Sensing crop classification grain yield evaluation model Sentinel-1 Landsat 7 |
| title | Evaluation of Food Security Based on Remote Sensing Data—Taking Egypt as an Example |
| title_full | Evaluation of Food Security Based on Remote Sensing Data—Taking Egypt as an Example |
| title_fullStr | Evaluation of Food Security Based on Remote Sensing Data—Taking Egypt as an Example |
| title_full_unstemmed | Evaluation of Food Security Based on Remote Sensing Data—Taking Egypt as an Example |
| title_short | Evaluation of Food Security Based on Remote Sensing Data—Taking Egypt as an Example |
| title_sort | evaluation of food security based on remote sensing data taking egypt as an example |
| topic | crop classification grain yield evaluation model Sentinel-1 Landsat 7 |
| url | https://www.mdpi.com/2072-4292/14/12/2876 |
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