کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8867746 | 1621785 | 2018 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Combined use of agro-climatic and very high-resolution remote sensing information for crop monitoring
ترجمه فارسی عنوان
استفاده ترکیبی از اطلاعات مربوط به سنجش از دور در محدوده زیست محیطی و با وضوح بسیار بالا برای نظارت بر محصول
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کلمات کلیدی
RMSEsum of squared errorSoil Adjusted Vegetation IndexVariSAVITminTDBSWLGliBCCHGCPtmaxEVISSESfMDTMExGEToGDDRgb - RGBcrop evapotranspiration - تبخیر تعرق محصولreference evapotranspiration - تبخیر تعرق مرجعgrowing degree days - روزهای درجه رشدStepwise Linear regression - رگرسیون خطی گام به گامroot mean squared error - ریشه متوسط خطای مربعStructure from motion - ساختار از حرکتGPS - سامانه موقعیتیاب جهانیGlobal position system - سیستم موقعیت جهانیnormalized difference vegetation index - شاخص تنوع گیاه شناسی نرمال شدهLeaf area index - شاخص سطح برگNDVI - شاخص نرمالشده تفاوت پوشش گیاهی Aridity index - شاخص نسبیتLAI - شبیهadjusted coefficient of determination - ضریب اصلاح تنظیم شدهPearson’s correlation coefficient - ضریب همبستگی پیرسونDigital terrain model - مدل زمین دیجیتالETc - و غیرهUnmanned Aerial Vehicle - وسیلهی نقلیهی هوایی بدون سرنشینUAV - پهپاد
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
کامپیوتر در علوم زمین
چکیده انگلیسی
Accurate and real-time yield forecasting is one of the main pillars for decision making in farming and thus for farmers' profitability. Biomass has been traditionally predicted by multi- and hyperspectral vegetation indices from low- and medium-resolution platforms. This research work aimed to assess the accuracy of the combined use of agro-climatic information and very high-resolution products obtained with RGB cameras mounted on unmanned aerial vehicles (UAVs) for biomass predictions in maize (Zea mays L.). Two agro-climatic predictors, reference evapotranspiration (ETo) and growing degree days (GDDs), and twelve vegetation indices (VIs) derived from RGB bands were calculated for the entire growing cycle. The root mean squared error (RMSE) of the model that considers only GDD to estimate total dry biomass (TDB) was 692.7â¯gâ¯mâ2, which was reduced to 509.3â¯gâ¯mâ2 when introducing as predictor variables the VARI and GLI vegetation indices. Difficulties in the radiometric calibration of consumer grade RGB cameras together with sources of error such as the bidirectional reflectance distribution function and the blending algorithms in the photogrammetry processing could decrease the applicability of the obtained relationship and should be further evaluated. This study illustrated the advantage of the combined use of agro-climatic predictors (GDD) and green-based VIs derived from RGB consumer grade cameras for biomass predictions.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Applied Earth Observation and Geoinformation - Volume 72, October 2018, Pages 66-75
Journal: International Journal of Applied Earth Observation and Geoinformation - Volume 72, October 2018, Pages 66-75
نویسندگان
R. Ballesteros, J.F. Ortega, D. Hernandez, A. del Campo, M.A. Moreno,