کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8866304 | 1620996 | 2018 | 29 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Crop type mapping from pansharpened Landsat 8 NDVI data: A case of a highly fragmented and intensive agricultural system
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
کامپیوتر در علوم زمین
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Crop type mapping from pansharpened Landsat 8 NDVI data: A case of a highly fragmented and intensive agricultural system Crop type mapping from pansharpened Landsat 8 NDVI data: A case of a highly fragmented and intensive agricultural system](/preview/png/8866304.png)
چکیده انگلیسی
Retrieving crops and their location, as well as their spatial extent, are useful information for agricultural planning and better management of irrigation water resources as well as for crop health monitoring, towards an increased food production and reduced water use. Multispectral remote sensing images with a spatial resolution of 30â¯m or greater are often used for mapping crops in extensive agricultural systems at global and regional scale. However, that spatial resolution is inadequate for mapping highly fragmented and intensive agricultural landscapes, such as the Tadla Irrigated Perimeter (TIP) in central Morocco. Hence, our study aims to: (1) identify and map major crops in the TIP with improving the spatial resolution of producing maps from 30â¯m to 15â¯m; (2) retrieve the area of major cultivations; (3) compare machine learning classifiers namely, Support Vector Machine (SVM), Random Forest (RF) and Spectral Angle Mapper (SAM) as a distance-based classifier. Our methodology is based on the Landsat-8 OLI (Operational Land Imager) data pan-sharpened to 15â¯m. SAM, RF and SVM classifiers were used and compared for retrieving crops from a multitemporal dataset of the Normalized Difference Vegetation Index (NDVI) for 10 periods during the agricultural season. The RF, SVM and SAM have classified the major crops with overall accuracies of 89.26%, 85.27% and 57.17% respectively, and kappa coefficient of 85%, 80% and 43%, respectively, noting that sugar beet, tree crops and cereals are delineated accurately while alfalfa is not. This study showed a high performance by using time-series pan-sharpened OLI NDVI data coupled with machine learning classifiers for mapping different crops in irrigated, very fragmented and heterogeneous agricultural landscape.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Remote Sensing Applications: Society and Environment - Volume 11, August 2018, Pages 94-103
Journal: Remote Sensing Applications: Society and Environment - Volume 11, August 2018, Pages 94-103
نویسندگان
Jamal-Eddine Ouzemou, Abderrazak El Harti, Rachid Lhissou, Ali El Moujahid, Naima Bouch, Rabii El Ouazzani, El Mostafa Bachaoui, Abderrahmene El Ghmari,