Background and Objectives: Leaf Area Index (LAI) is one of the most important structural parameters in agricultural, range and forest ecosystems and variation of energy, water, and gases are tightly coupled to LAI. According to the novelty of LAI research in Iran, this study aimed to evaluate different direct ground-based methods for determining LAI. Material and Methodology: This research has been done by literature reviews by using internet databases. There are two main categories of procedures to measure LAI: ground-based and remote sensing methods, and ground-based methods divided into direct and indirect (estimation) methods. Findings: Although, in the past two decades, the tendency to use indirect ground-based as well as remote sensing methods for estimating g LAI has increased, but for reasons such as needing semi-sophisticated and sophisticated instruments and lack of free access to satellite images in the greater part of the world, caused the direct ground-based methods for calculating LAI has been widely used. Moreover, the high precision and accuracy of direct ground-based methods compared to direct ground-based and remote sensing methods is another reason for preferring direct ground-based methods. Discussion and Conclusion: The results of this study showed that the Litter Trap method could be the most efficient method for measuring LAI, and we proposed this method in future research, concerning very less destruction to nature as well as high accuracy and precision.

The leaf area index (LAI) as an ecological index has a great importance in the study of the health of the trees as well as vegetation stress in the forest. In this case study, we investigate the effects of industrial dust on the health of forest plants in Hyrcanian forests of the north of the country. For this purpose, a hemispherical photography method has been used to estimate leaf area index. A total of 8 sample lines were collected in two directions around the industrial dust area with an attitude up to 600 meters For data analysis, variance1 analysis method was used. There was a significant difference between the mean leaf area index at three levels of distance from the contamination center (p <0. 05). The results of the Post Hoc Test, to pair-wise comparison of leaf area index in different distances from dust center using the Tukey ­ index, showed that the average of leaf area index was less than 150 m and a significant difference in a distance of more than 300 meters was revealed. Regression analysis was used to eliminate the effect of varieties of plant species and dominant wind direction on leaf area index. The results of the hierarchical regression of leaf area index showed that the variables of the type of plant species and the dominant wind direction affect the prediction of the leaf area index value. In the following, the correlation between distance classes of industrial dust and the average of leaf area index at Land acquisition points was investigated. The results showed that the best correlation between the two above variables in the east direction of this center and along the dominant winds was observed. The amount of R2 was 0. 847 with positive power function.

In order to study the effect of increasing density of red root pigweed on leaf area index (LAI) of corn, and also on pigweed in different levels of nitrogen application, two field experiments were conducted during 2010 and 2011 crop years in Research Field of Azad Islamic University of Astara (north west of Gilan, Iran). The experimental design in each year was split plot based on randomized complete block design with 3 replications. The main factor was nitrogen amount in four different levels including zero (control), 100 (recommended nitrogen amount in the region), 160 and 220 kg nitrogen per hectare. The secondary factor was intensity of red root pigweed in four levels including zero (corn pure culture), 5, 10 and 20 plants per square meter. Results demonstrated that 25-35 days after growth of two plants, leaf area index of corn crop was more than red root pigweed in all levels of nitrogen and all intensities of pigweed. After this period, leaf area index increased in pigweed more than corn. The highest leaf area index of corn was observed in nitrogen application level of 160 kg per hectare and increasing nitrogen level and pigweed intensity resulted its decrease and increase in the corn and pigweed, respectively. The most grain yield of corn was acquired using 160 and 220 kg nitrogen per hectare, respectively with zero weed intensity and their values were 15.9 and 12.3 tons per hectare, respectively that their values decreased severely by increasing weed intensity in the farm.

The aim of present study is investigating the effect of physiographic factors on leaf area index (LAI), leaf mass per area (LMA) and canopy water content (CWC). 125 square plots with dimensions of 30 meters by 30 meters in cluster method model were sampled. Plot elevation was determined by using digital elevation model (DEM) map. Also 9 aspects were determined by using a compass device. Photographing the canopy and collecting leaves from the canopy were done to estimate the indices. The results showed that the elevation has a significant effect on all indices. The aspect has a significant effect on LAI and LMI indices. LAI and CWC indices decrease from low to high elevations and increase on the highest elevation. The LMA attribute also increases from low to high elevations and decreases in the highest elevation class again. The high LAI value observed in the north and northeast aspect and the lowest LAI observed in the south and southwest aspects. The CWC is higher in northern aspects and it is lower in the southern aspect. LMA is lower in the northern and northeastern aspects while it was higher in the western and south western aspects. Knowing the conditions of vegetation indicators status in different aspects and elevations, provides the possibility of prioritizing support and maintenance measures in different topographical conditions of the region.

Estimating dry biomass is one of the important parts of production estimation. Among vegetation indices, leaf area index (LAI) is the most common used index to estimate water demand and yield. In this study, attempts have made to estimate LAI without destroying plant and by using the AccuPAR-LP80 crop scanner device. The case study performed in Magsal Agro-Industrial Company Qazvin, Iran with the aim of introducing relations to estimate the amount of dry biomass via LAI for three plant- maize, sugar beet and alfalfa. LAI values of above mentioned plants measured through nondestructive method by calibrated AccuPAR-LP80 crop scanner. Statistical evaluation showed that the highest correlation was for maize with R2=0.96 and the lowest was for alfalfa with R2=0.87. In addition, measured dry biomass was a linear function of fraction of photosynthesis active radation (fPAR). Statistical evaluation showed that correlation coefficient varies from 0.94 to 0.90 and PMSE from 2.85 to 3.3 kg ha-1 for maize and alfalfa, respectively.

Leaf Area Index (LAI), is a key component in estimating crop yield and environmental stresses. Given the importance of accurate determination of these parameters, the present study was aimed to estimate the LAI of rice plant. For this purpose, 20 paddy fields were selected. The data required to perform the operations in the study area were corrected by land impressions (direct method) and measured by AccuPAR (indirect method). Field work to gathering LAI were taken at intervals of 16 days from seedling stage to the flowering stage of rice plants. The results showed that the lowest and highest levels of LAI belongs to seedlings and flowering stages, respectively. The leaf area obtained by both methods, were almost the same for each farm during different stages of plant growth. Consistent with the obtained values by both methods, indicate that the index can be calculated from a derived empirical relationship. Based on this empirical formula for every stage of plant growth, weka 3.7 software was adopted to calculate the mentioned relationship.

This study was performed to evaluate the extent of leaf area in Iran from (2002) to (2016) using Remote sensing. For this purpose, we extracted data collection and leaf area index for the Iranian territory from MODIS website. The database was established with programming in MATLAB software to perform mathematical and Statistical calculations repeated. After the analysis of the data in this software a monthly average long-term map was developed. The maps show that the central, East and South-East are almost empty of leaf area or seen very sparse in some areas. In contrast areas of leaves in the northern and western parts of Iran, are good, which generally includes fields, except forest Arasbaran and Hirkany. Precipitation and the temperature, is the main factors for the growth and development of plants, that these two conditions are enumerated in the west due to being on the way of westerly winds. Lowest leaf area index is for January and February and the highest average of leaf area is for May and June. Next, study of 15 years of leaf area index data by cluster analysis based on the calculation of Euclidean distance and Ward method, showed that all 12 months fit in the two main groups and, in fact, divided for two periods of strong and weak vegetation. In this analysis, , April during the cold period and October in the warm period of the year as the transition months and they are located on a separate cluster.

In order to measuring the leaf area index (LAI) of wheat canopy by AccuPAR at field condition, an experiment was conducted using seven wheat cultivars (Arya, Darya, Kuhdasht, Shiroudi, Tajan, Taro and Zagros) under irrigated and rainfed conditions during 2008-2009 at Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran. This experiment was conducted as a Randomized Complete Block Design with four replications. LAI measuring was done by two methods, direct (destructive sampling) and non-destructive by AccuPAR from tillering stage to the canopy closure stage. The results showed that there was no significant difference between cultivars and the two conditions as aspect of equation coefficients, so one equation is usable for all cultivars under both conditions (LAI=0.54+1.13×LAI AccuPAR, R2=0.70). This equation can be used for estimation of actual LAI from calculated LAI by the AccuPAR.

The objective of this study was to measure and determine the allometric equation of leaf dry mass, specific leaf area and leaf area index of Rhizophora mucronata species located in Sirik city, Hormozgan province, Iran. In this study, in addition to the actual determination of the leaves, the specific leaf area and the leaf area index of Rhizophora mucronata trees were calculated using vegetative characteristics. Then, the correlation between different components, and their allometric equations were calculated. Determining the leaf area index was performed directly by collecting 88 leaves from 22 trees and weighing them and the mean leaf area, the canopy area and leaf dry mass were calculated through collecting one-eighth of the tree canopy and determining the fresh and dry weight. Correlation and model determination were determined using stepwise regression (P≤ 0. 01). According to the results, the mean dry mass, specific leaf area and leaf area index were 3. 33 kg/tree, 39. 74 cm-2/g per tree and 0. 76, respectively. The leaf area index, with two factors of leaf area and tree height, (correlation coefficient=0. 82) explained 65% of the variations in the dependent variable. In determining the specific leaf area, only leaf area factor was introduced as the most effective factor in the equation and explained 56% of the variations. Results showed that tree factors, canopy area, leaf area and height can have key role in tree ecological indices (leaf dry mass, LAI and SLA) estimation and also in evaluation of mangrove stand changes and health.