عنوان

تخمین شاخص سطح برگ با استفاده از داده های اقلیمی

Number

پ۲۴۶۲۹

.Language of Text, Soundtrack etc

per

Title Proper

تخمین شاخص سطح برگ با استفاده از داده های اقلیمی

First Statement of Responsibility

سپیده کریمی قلعه لر

Name of Publisher, Distributor, etc.

کشاورزی

Date of Publication, Distribution, etc.

۱۳۹۸

Specific Material Designation and Extent of Item

۱۴۵ص

Accompanying Material

سی دی

Dissertation or thesis details and type of degree

دکتری

Discipline of degree

مهندسی آب

Date of degree

۱۳۹۸/۰۶/۲۰

Text of Note

شاخص سطح برگ (LAI)، عبارت است از نصف سطح کل اندام فتوسنتزی گیاه (برگ) در واحد سطح زمین که یک شاخص بدون بعد می¬باشد. این شاخص یک مؤلفه ساختاری مهم در اکوسیستم پیرامون ما به¬شمار می¬رود زیرا تأثیر مستقیم و زیادی بر تبادلات آب، انرژی و کربن بین سطح زمین و اتمسفر دارد. از سوی دیگر، LAI یکی از مؤلفه¬های اساسی در مدل¬سازی فتوسنتز و نیز تعرق گیاه بوده و تا حد بسیار زیادی روابط بین گیاه-اتمسفر را کنترل می¬نماید و بدین¬ترتیب می¬توان از آن به¬عنوان یک عامل کنتر¬ل¬کننده تبادل آب و انرژی بین پوشش گیاهی و اتمسفر یاد نمود. تاکنون روش¬های متعدی نظیر روش¬های مستقیم و غیرمستقیم برای تخمین این پارامتر بسط داده شده¬اند که در این میان، روش¬های مستقیم پرهزینه، وقت¬گیر و مستلزم به¬کارگیری نیروی انسانی زیادی می¬باشند. به¬همین دلیل روش¬های غیرمستقیم که اقدام به کشف و تبیین رابطه بین LAI و میزان تابش دریافتی زیر پوشش گیاهی می-نمایند به¬کار بسته می¬شوند. در میان روش¬های غیر مستقیم نیز روش¬های مبتنی بر مدل¬های رگرسیون¬محور با ایجاد روابط رگرسیونی بین LAI و سایر نمایه¬های پوشش گیاهی (نظیر NDVI) مقدار LAI را تعیین می¬کنند. با این وجود، ایجاد چنین رابطه¬ای در شرایطی که مقادیر LAIبالاتر باشند بسیار دشوار می¬باشد. بنابراین، بسط و توسعه مدل¬هایی که بتوانند با داده¬های زودیافت هواشناسی اقدام به تعیین LAI نمایند برای متخصصین مرتبط با این موضوعات بسیار سودمند خواهد بود.

Text of Note

AbstractLeaf area index (LAI) is a dimensionless variable, which is defined as the total one-sided area of photosynthetic tissues per unit ground surface area. LAI is an important structural characteristic of our ecosystem because it influences the exchanges of water, energy, and carbon between the land surface and atmosphere. It is the main variable for modeling canopy photosynthesis and transpiration, and highly affects the plant–atmosphere interaction. Therefore, it plays a key role in the energy and water exchanges between the canopy and the atmosphere.Direct and indirect methods have been developed to obtain LAI. The direct measurement of LAI is costly, labor intensive and time consuming. Hence, indirect methods have been developed that relate LAI to the radiation intensity below the canopy via a radiative transfer model. Among the indirect LAI estimation models, the regression-based methods usually relate LAI to vegetation indices such as NDVI. However, it is difficult to relate LAI to such indices for higher LAI values (e.g. forests). Therefore, possible relations between easily measured meteorological parameters and LAI would be of interest for the experts of this discipline. The present study aimed at assessing the artificial intelligence-based techniques in simulating LAI values in cropland, grassland and forest land covers through evaluating different data management scenarios. Data from difference sites were obtained from AmeriFlux data bank. At first, the gene expression programming (GEP) and random forest (RF) models were employed by using a k-fold testing data assignment method in both local and spatial (external) scales to estimate LAI values in cropland and grassland sites. Air temperature, relative humidity and NDVI were used as input parameters of the models for these sites. For the local models, one year period was considered as testing patterns at each time of modeling process and the models were trained using the remaining data of that site. The process was repeated until all available years were participated in both the training and testing phases. For the external models, all available patterns of one station were considered as testing patterns each time and the models were trained using the data from the remaining stations. The process was repeated till all stations were considered in training/testing stages. Then, the GEP models were evaluated using a spatial data management scenario for the forest sites. Data from broadleaf and needle leaf sites were obtained and the models were trained/tested using those data through a regional matrix. The obtained results revealed that GEP and RF gave promising results in estimating LAI values in different land covers. Nonetheless, the need for a through data scanning process, e.g. k-fold testing or regional matrix building was confirmed in simulating LAI in both local and spatial scales.

Variant Title

Estimating Leaf Area Index (LAI) Using Climatological Data

Entry Element

‏کریمی قلعه لر،

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‏سپیده

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تهيه کننده

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‏ناظمی،

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اشرف صدرالدینی،

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فاخری فرد،

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Xu

Part of Name Other than Entry Element

‏امیر حسین

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‏علی

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‏ احمد

Part of Name Other than Entry Element

‏ Tongren

Dates

تهيه کننده

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استاد راهنما

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استاد مشاور

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استاد مشاور

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‏ تبریز