Forecasting NDVI in multiple complex areas using neural network techniques combined feature engineering
NDVI (Normalized difference vegetation index) is a critical variable for monitoring climate change, studying ecological balance, and exploring the pattern of regional phenology. Traditional neural network models only consider image features in time series prediction, while historical data and its ch...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2020-12-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2020.1808718 |
| _version_ | 1797678555029569536 |
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| author | Changlu Cui Wen Zhang ZhiMing Hong LingKui Meng |
| author_facet | Changlu Cui Wen Zhang ZhiMing Hong LingKui Meng |
| author_sort | Changlu Cui |
| collection | DOAJ |
| description | NDVI (Normalized difference vegetation index) is a critical variable for monitoring climate change, studying ecological balance, and exploring the pattern of regional phenology. Traditional neural network models only consider image features in time series prediction, while historical data and its changes play an important role in time series forecasting. For this study, we proposed convolutional neural networks (CNN) combined feature engineering forecasting model (SF-CNN), which integrated both the advantages of image characteristics learned from CNN and statistic characteristics calculated by historical data related to the forecast period to improve the accuracy of NDVI predictions in the next 3 months with 30-day interval at multiple complex areas. To intuitively show the performance of SF-CNN, it was compared with CNN using the same parameters. Results mainly showed that (1) in terms of visual analysis, the texture, pattern, and structure of predicted NDVI using SF-CNN are similar to the observed NDVI, and SF-CNN exhibits strong generalization ability; (2) in terms of quantitative assessment, SF-CNN generally outperforms CNN, and it can improve the reliability and robustness for predicting NDVI through simple statistical characteristics while reducing the uncertainties; (3) SF-CNN can learn seasonal and sudden changes in four different and complex study areas with considerable accuracy and without extra data. |
| first_indexed | 2024-03-11T23:01:33Z |
| format | Article |
| id | doaj.art-5e6a77ee7a16499fb8537d3ef1aa1524 |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T23:01:33Z |
| publishDate | 2020-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-5e6a77ee7a16499fb8537d3ef1aa15242023-09-21T14:57:09ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552020-12-0113121733174910.1080/17538947.2020.18087181808718Forecasting NDVI in multiple complex areas using neural network techniques combined feature engineeringChanglu Cui0Wen Zhang1ZhiMing Hong2LingKui Meng3School of Remote Sensing and Information Engineering, Wuhan UniversitySchool of Remote Sensing and Information Engineering, Wuhan UniversitySchool of Remote Sensing and Information Engineering, Wuhan UniversitySchool of Remote Sensing and Information Engineering, Wuhan UniversityNDVI (Normalized difference vegetation index) is a critical variable for monitoring climate change, studying ecological balance, and exploring the pattern of regional phenology. Traditional neural network models only consider image features in time series prediction, while historical data and its changes play an important role in time series forecasting. For this study, we proposed convolutional neural networks (CNN) combined feature engineering forecasting model (SF-CNN), which integrated both the advantages of image characteristics learned from CNN and statistic characteristics calculated by historical data related to the forecast period to improve the accuracy of NDVI predictions in the next 3 months with 30-day interval at multiple complex areas. To intuitively show the performance of SF-CNN, it was compared with CNN using the same parameters. Results mainly showed that (1) in terms of visual analysis, the texture, pattern, and structure of predicted NDVI using SF-CNN are similar to the observed NDVI, and SF-CNN exhibits strong generalization ability; (2) in terms of quantitative assessment, SF-CNN generally outperforms CNN, and it can improve the reliability and robustness for predicting NDVI through simple statistical characteristics while reducing the uncertainties; (3) SF-CNN can learn seasonal and sudden changes in four different and complex study areas with considerable accuracy and without extra data.http://dx.doi.org/10.1080/17538947.2020.1808718sf-cnnfeature engineeringcnnndvitime series prediction |
| spellingShingle | Changlu Cui Wen Zhang ZhiMing Hong LingKui Meng Forecasting NDVI in multiple complex areas using neural network techniques combined feature engineering International Journal of Digital Earth sf-cnn feature engineering cnn ndvi time series prediction |
| title | Forecasting NDVI in multiple complex areas using neural network techniques combined feature engineering |
| title_full | Forecasting NDVI in multiple complex areas using neural network techniques combined feature engineering |
| title_fullStr | Forecasting NDVI in multiple complex areas using neural network techniques combined feature engineering |
| title_full_unstemmed | Forecasting NDVI in multiple complex areas using neural network techniques combined feature engineering |
| title_short | Forecasting NDVI in multiple complex areas using neural network techniques combined feature engineering |
| title_sort | forecasting ndvi in multiple complex areas using neural network techniques combined feature engineering |
| topic | sf-cnn feature engineering cnn ndvi time series prediction |
| url | http://dx.doi.org/10.1080/17538947.2020.1808718 |
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