دورية أكاديمية
Winter wheat mapping using a random forest classifier combined with multi-temporal and multi-sensor data
| العنوان: | Winter wheat mapping using a random forest classifier combined with multi-temporal and multi-sensor data |
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| المؤلفون: | Jiantao Liu, Quanlong Feng, Jianhua Gong, Jieping Zhou, Jianming Liang, Yi Li |
| المصدر: | International Journal of Digital Earth, Vol 11, Iss 8, Pp 783-802 (2018) |
| بيانات النشر: | Taylor & Francis Group, 2018. |
| سنة النشر: | 2018 |
| المجموعة: | LCC:Mathematical geography. Cartography |
| مصطلحات موضوعية: | winter wheat, random forest, phenology, landsat-8, gf-1, Mathematical geography. Cartography, GA1-1776 |
| الوصف: | Wheat is a major staple food crop in China. Accurate and cost-effective wheat mapping is exceedingly critical for food production management, food security warnings, and food trade policy-making in China. To reduce confusion between wheat and non-wheat crops for accurate growth stage wheat mapping, we present a novel approach that combines a random forest (RF) classifier with multi-sensor and multi-temporal image data. This study aims to (1) determine whether an RF combined with multi-sensor and multi-temporal imagery can achieve accurate winter wheat mapping, (2) to find out whether the proposed approach can provide improved performance over the traditional classifiers, and (3) examine the feasibility of deriving reliable estimates of winter wheat-growing areas from medium-resolution remotely sensed data. Winter wheat mapping experiments were conducted in Boxing County. The experimental results suggest that the proposed method can achieve good performance, with an overall accuracy of 92.9% and a kappa coefficient (κ) of 0.858. The winter wheat acreage was estimated at 33,895.71 ha with a relative error of only 9.3%. The effectiveness and feasibility of the proposed approach has been evaluated through comparison with other image classification methods. We conclude that the proposed approach can provide accurate delineation of winter wheat areas. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1753-8947 1753-8955 17538947 |
| Relation: | https://doaj.org/toc/1753-8947; https://doaj.org/toc/1753-8955 |
| DOI: | 10.1080/17538947.2017.1356388 |
| URL الوصول: | https://doaj.org/article/3fb1387c73094cb9b59a57e4fae2896f |
| رقم الأكسشن: | edsdoj.3fb1387c73094cb9b59a57e4fae2896f |
| قاعدة البيانات: | Directory of Open Access Journals |
PDF full text from Publisher | تدمد: | 17538947 17538955 |
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| DOI: | 10.1080/17538947.2017.1356388 |