linear regression

Oak is a common and the most important species in Zagros forests of Iran. Zagros forests play a crucial and effective role in water supply, soil conservation and climate modification. Unfortunately, a significant part of those forests suffer from oak decline. Zagros forest covers almost 40% of Iran’s woodland which especially during the last two decades the population of oak trees (Quercus spp.) decreased mainly due to drought, diseases and insect pests. One of the most important pests in western oak forests is green oak leaf roller, Tortrix viridana L., (Lep.: Tortricidae) feed on oak leaves as the main host.  

This research aimed to assess and predict the degree of damage to the oak forests using remote sensing and GIS methods through regression analysis of the oak foliage remaining ratios of field plots with a vegetation indices of Sentinel-2 data during a year. Field monitoring carried on from April- 27-2020 to May-14-2020. For this purpose, in a two-kilometer-wide vector along the route from Sarovabad city to Bashmaq border, 100 sampling locations were randomly determined using GIS software. In each sampling location, four trees were selected in the four main geographical directions (in order to reduce the effect of directions on distribution of T. viridana population). Then, four branches (length about 100 cm) were randomly cut as a sampling unit, and the number of fifth instar larvae of T. viridana, were counted. The correlation between the number of T. viridana and elevation, slope, distance from the road, distance from the river, distance from residential areas, NDVI and solar radiation index were analyzed using Pearson's correlation test and multiple linear regression. Data normality was checked using the Kolmogorov-Smirnov statistical test. Data were analyzed using SPSS software (Version 26).

The results of Pearson's correlation analysis between the those variables and T. viridana population show the highest and lowest correlation of the T. viridana population with elevation (r= 0.651) and slope (r= -0.015), respectively. According to multiple linear regression, elevation had the highest correlation coefficient with T. viridana population density. In addition, solar radiation index, NDVI and distance from the river were ranked with strong to weak correlation, respectively. Results showed that, the relationship between slope and vegetation density is also significant (P= 0.032; r= 0.214). According to the distribution intensity map of the T. viridana, which was obtained using the multiple linear regression equation, it has been shown that the most distribution of the T. viridana was observed in the southwest of Sarovabad city. Clearly NDVI was convenient for separating different levels of damage.

It can be concluded that the population of T. viridana is obviously high at high altitude and in high tree densities. The results showed that the population density of T. viridana is higher wherever a dense vegetation was occurred. This research provided a feasible and quantitative method in the spatiotemporal prediction of green leaf roller occurrence by remote sensing and GIS. In conclusion, with the availability of population models, it is possible to limit the spread of T. viridana through earlier detection of pest incidence.

Fire is one of the destructive phenomena that have devastated a significant portion of forests and grasslands in Gilan Province in recent years. This study aimed to investigate the temporal and spatial relationship between climatic variables and wildfires in Gilan Province. The wildfire variables included the number and extent of wildfires, and the climatic variables consisted of seven parameters over the past 26 years (2001-2026). Pearson correlation and regression analysis were utilized to examine the temporal relationship. The relative importance of climatic variables in wildfire occurrence was determined using Mean Decrease Gini (MDG) and Mean Decrease Accuracy (MDA) statistics. For modeling and generating probability maps of wildfire occurrence, 70% of wildfire locations and various machine learning models (Logistic Regression, Random Forest, Support Vector Machine, and SVM-RF Hybrid) were employed using the R programming language. Model validation was conducted using 30% of wildfire locations and the Area Under the Curve (AUC) metric. The temporal results showed that during the 26-year period (2001-2026), a significant negative correlation was observed between the number of wildfires and the average seasonal precipitation, while positive correlations were found between the number of wildfires and the average seasonal wind speed and maximum wind speed at a 95% confidence level. Furthermore, a significant negative correlation was observed between the extent of wildfires and the average seasonal precipitation at a 95% confidence level. The spatial relationship analysis indicated that the average maximum temperature, average seasonal precipitation, and average relative humidity had the highest importance in wildfire occurrence within the geographical extent of Gilan Province. Model validation results revealed that the Random Forest model (AUC: 0.82) and the SVM-RF Hybrid model (AUC: 0.79) outperformed others in predicting the occurrence of wildfires. Therefore, predicting wildfires resulting from climatic factors in the forests and grasslands of Gilan Province using the aforementioned maps is feasible and can significantly aid natural resource managers in implementing protective measures in high-risk wildfire areas. Hence, it is imperative that proactive measures be taken by the Natural Resources and Watershed Management Organization of the province to prevent future wildfires with greater sensitivity.

Forest roads are constructed to facilitate forest protection, reforestation, logging operations and maximizing the value of forest products. Therefore forest roads are key infrastructures in the development of the region. This study aims to plan forest road network using artificial neural network and GIS regarding forest road technical principles. First the criteria were chosen using Delphi method and then they were weighted regarding their importance in road planning. After that the criteria were combined with corresponding weighs to achieve suitability map based on the degree of suitability for road allocation. Value and coordinates of each pixel were extracted by ENVI software and were normalized in the range of 0-1 for modeling by MATLAB software. In this study two neural networks were used for modeling, including multilayer perceptron (MLP) and radial-bases functions (RBF). The neural networks estimated suitability of different pixels in other districts based on the Patom district results. Using an ArcView GIS extension, PEGGER, two forest road networks were planned. The results showed that MLP provides better ability for estimating suitability of pixels for road passage in comparison with RBF with the R2 of 0.994. A linear regression was also used to compare the results of the proposed neural networks. The results revealed that neural network improves the results in comparison with the linear regression and results showed that the second road alternative was optimum network with regard to the unit cost

The aim of this study was to investigate the relationships between vegetation changes and climate variables including rainfall and drought using MODIS satellite images during the years 2000-2014 in Chaharmahal and Bakhtiari Province. In order to evaluate the impact of rainfall and drought on vegetation changes in 32 synoptic, climatology and rain gauge stations in and around the province, SPI index was calculated in short time series (3, 6, 9 months) and long time series (12, 24 months). All the time scales were leading to early June in which the maximum growth of vegetation of the province is occurred. To this end, SPI drought maps and NDVI images were prepared at different time scales by kriging method and linear regression analysis was performed to define their relationships. Comparisons of NDVI and SPI over the study period showed that the vulnerability of vegetation to drought mostly depends on the life forms and plant species. In this study, depending on the type of vegetation, different correlation coefficients were observed between vegetation and SPI indices. Among different rangeland and forest vegetation types, higher correlations were found between NDVI and SPI in rangeland vegetation types compared to the forest ones and the highest correlations belonged to the annual grass and annual forb vegetation cover. This confirms the higher impact of reducing rainfall on the decline of rangeland cover compared to the forest vegetation. Overall, the findings of this study indicate that the MODIS NDVI can be used to study SPI in the assessment and monitoring of drought effects on vegetation cover. By studying and recognition of vegetation cover and applying NDVI index to assess changes in vegetation cover, vulnerable types of vegetation to drought could be identified.

Height - diameter equations are often used to estimate tree height، when tree diameter is the only measured variable. Considering that tree diameter measuring is often easier and inexpensive than tree height، height-diameter functions are generally used to estimate tree height. In this study، nineteen non-linear height-diameter equations were fitted using data of Fagus orientalis Lipsky in the Experimental and Educational Forest of Tarbiat Modares University. The data were split into two groups: 315 individual trees for model calibration and 290 for validation. Root Mean Square Error، adjusted R2، Akaike Information Criterion and Mean Difference criteria، as well as biological evaluation were applied to select the most appropriate model. Moreover، validation datasets were divided into 5-cm DBH classes and Mean Difference was computed for each model in each class. The results showed that the used models could explain a variation amount of 0. 70 – 0. 76. Weibull، Schnute، and Richards appeared to be suitable models with regard to mathematical and biological features. We recommend the Richards model as the best model to predict beech height trees for its better performance than the others in the Experimental and Educational Forest of Tarbiat Modares University.

The main objective of this study was estimation of three forest biophysical properties including volume, basal area and number per hectare using SPOT-HRG data in district 1 Darabkola’s forest. Images were orthorectified to reduce relief displacement errors. Different synthetic bands were generated trough principal component analysis and suitable vegetation indices were created by rationing. After take territorial information, accomplished calculation volume, basal area and number per hectare values in sample plat. Since analyze normalized data, evaluating correlation between territorial and spectral by linear regression. Cross validation getting best correlation model investigating by some sample plat and by relevant evaluation criterions. For volume estimation, results of this study showed that linear combination of NIR and SWIR bands by adjusted determination coefficient 74.5% and root mean square relative error 77.1 m /ha were better predictors than other combination. For basal area estimation, linear combination

Climate change is defined as long term and irreversible changes in the climatic behavior of a region. Many studies have been conducted in different regions of the world on climate change. The results of these studies show considerable changes in climatic factors especially in precipitation and temperature. In this research، the trend of changes in annual and seasonal rainfall and temperature in 24 synoptic stations over a 50-year data period (1956-2005) was investigated in Iran. The Mann- Kendall test and linear regression technique were used to analyze the trend of changes in climatic factors. The results showed both increasing and decreasing trends in annual rainfall at various regions of Iran. Annual rainfall in northern slopes of Alborz and western slopes of the Zagros Mountain as well as in eastern and southeast parts of Iran had a decreasing trend while in the central of Iran the trend of changes was increasing. In southern region of Iran the rainfall had an increasing trend. The results also showed that temperature in most of the studied stations over the considered period was increasing. The highest and lowest changes in temperature were seen in the mean temperature of summer and winter، respectively. Ahwaz and Khorramabad stations had a decreasing trend and over all seasons would go toward more cooling. The temperature trend in Oromiye was decreasing in autumn and summer while Shahrekord and Bandar abbas had the same trend in summer and winter.