كليدواژه :
زيست توده , ضريب بازده انرژي , NDVI , تصاوير ديجيتال , تصاوير ماهواره اي , لندست
چكيده فارسي :
مطالعه و برآورد مستمر زيستتوده از طريق تصاوير ماهوارهاي يكي از مهمترين موضوعاتي ميباشد كه در زمينه مطالعات اكوسيستم مورد توجه هستند. روشهاي اندازه گيري مستقيم زيست توده عليرغم دقيق بودن، معمولا با تخريب بخشي از پوشش گياهي همراه ميباشد كه اين عمل با توجه تعداد مورد نياز براي مدلسازي ماهوارهاي، علاوه بر ضرر رساندن به محيط زيست و كشاورزي، نيازمند تجهيزات خاص مي باشد. در اين پژوهش روشي ارائه شده است كه بوسيله آن ميتوان با استفاده از تصاوير دوربين ديجيتال و بدون نياز به تخريب فيزيكي، مقدار زيست توده را محاسبه كرد. در روش پيشنهادي با بررسي رابطه مقدار پوشش گياهي (گندم زمستانه) كه از تصوير دوربين ديجيتال از مزارع بدست ميآيد و NDVI كه از دادههاي ماهوارهاي همان مزارع محاسبه ميشود، يك مدل توسعه داده شده است. با استفاده از اين مدل، از تصاوير دوربين ديجيتال مقدار NDVI محاسبه ميشود. با محاسبه NDVI از عكس ديجيتال، مقدار انرژي جذب شده فتوسنتزي با روابط معتبر موجود، محاسبه شد. سپس با استفاده از مدلهاي LUE (مونتيس) مقدار زيست توده برآورد گرديد. ضريب تعيين بدست آمده در اين پژوهش براي برآورد NDVI از تصاوير دوربين ديجيتال برابر با 71/0 ((RMSE= 0.087 ميباشد. همچنين ضريب تعيين بدست آمده در برآورد زيستتوده با استفاده از داده هاي عكس ديجيتال برابر با 63/0 با RMSE= 238 g/m2 محاسبه شده است. از مزاياي اين روش نسبت به روشهاي زميني سرعت، كمي هزينه و سهولت انجام آن ميباشد. همچنين از اين روش ميتوان در غياب دسترسي به داده هاي ماهوارهاي نيز استفاده نمود.
چكيده لاتين :
Plant growth and biomass assessments are required in production and research. Such assessments are followed by major decisions (e.g., harvest timing) that channel resources and influence outcomes. In research, resources required to assess crop status affect other aspects of experimentation and, therefore, discovery. Destructive harvests are important because they influence treatment selection, replicate number and size, and the opportunity for true repeated measures. For indirect biomass estimation, remote sensing data are used to determine agriculture species biomass using multiple regression analysis or Radiation Use Efficiency (RUE) models. In agriculture, RUE or Light Use Efficiency (LUE) is defined as dry biomass produced per unit of solar absorbed radiation or Photosynthetic Active Radiation.
The LUE model needs a time series of NDVI index. Here, the lack of a few satellite images may make this time series incomplete. To overcome this deficiency, the farmer provided digital images that can be replaced for the missing satellite pixels/images that were deployed. Digital cameras can provide a consistent view of vegetation phenology at fine spatial and temporal scales that are impractical to collect manually and are currently unobtainable by satellite and most aerial-based sensors.
This study demonstrated a reliable, fast, and cost-effective approach for estimating NDVI using digital camera images. High-resolution digital images were acquired in the wheat field, and automated image processing methods were developed to segment the wheat canopy from the soil background. Exponential models for aboveground total NDVI showed acceptable precision and accuracy. Canopy cover estimated with images from digital cameras was sufficiently well correlated with satellite NDVI. Here, using a regression model, the NDVI index was estimated from the digital photographs. This method is named Digital NDVI (DNDVI). To develop this method, the relationship between the vegetation fractions (VF) obtained from the digital photos and the NDVI calculated from the satellite image of the same location were examined. For calculation of DNDVI to be used in cloudy days, the farmer is asked to supply a few photos from different parts of the farm (the number of photos depends on the size of the farm). These photos will be sent to the server where the VF values and then the averaged DNDVI will be calculated. The uncertainty of the DNDVI model in estimating biomass was 0.071 with relative RMSE of about 0.14. Next, wheat biomass was calculated using DNDVI and LUE model. The results of LUE model (and in estimating biomass show a coefficient of determination (R2) 0.62 with an RMSE of 238 (gm-2).
In conclusion, as a near-ground remote assessment tool, digital cameras have good potential for monitoring wheat NDVI and growth status.
