logistic regression

Climate change has been a serious issue in the last two decades and many studies have focused on its various aspects. Therefore, it is necessary to determine the potential habitat of rangeland index species, currently and in the coming years, under climate warning models. Therefore, in the present study, by preparing a prediction map of the current and future range of Bromus tomentellus species, under two climate warning models (Rcp4.5 and Rcp8.5 scenarios), its movement, in latitudes, at the level of rangeland ecosystems of Ardabil province was investigated.

For this purpose, in the growing season of 2020, from 19 bioclimatic variables and three physiographic variables and a logistic regression model, to determine the quantity of climate change in the next three decades (2050) and to carefully examine its effects on the change in the range of B. tomentellus species now and in the future, used. The output maps are divided into four categories with the probability of occurrence between zero and one; unsuitable habitat (0-0.25), almost suitable habitat (0.25-0.5), habitat with high suitability (0.5-0.75) and habitat with very high suitability (0.75-1), it was grouped and based on the coefficients of the variables in the regression relationships, the effective variables for the current and future range were introduced.

The average annual temperature (BIO1), the annual temperature range (BIO7) and the average temperature of the coldest season (BIO11) are the most important for the suitability of the habitat, and their values increase with the harsher climatic conditions. The average annual temperature of its suitable habitats will increase by 1.6 to 2.1 degrees Celsius in the next three decades. The height of its suitable habitats will increase from 115 to 190 meters. As a result, the level of its suitable habitat decreases in response to climate change. Also, under climate scenarios, 30.2% of suitable climate habitats will be lost in 2050, and current unsuitable habitats will increase by 29.4%.

In general; climate change and increase in temperature will cause B. tomentellus species to move to higher latitudes along the altitude gradient. Therefore, in the next three decades, there is a risk of removing it from the rangeland ecosystems of Ardabil province.

Species distribution modeling is a common method for understanding the relationships between a species and its environment and is used to predict the changes in distribution due to environmental changes. A lot of research has been done around the world based on these models. In this study, the optimal habitat of Juniperus excelsa, an indicator species, and the most important tree species in Irano-Turanian forests in the Alborz Mountains have been investigated using 38 environmental parameters and domain and logistic regression models.

The study area consists of parts of the Alborz Mountains located in the north of Iran with an area of 14656 km2. In the present study, a stratified random sampling method was used to determine the sampling sites. Finally, 390 occurrence sites (240 presence sites and 150 absentee sites) of J. excelsa at a 30-second scale (approximately 1 km×1 km) as the input model was studied. Also, 38 environmental parameters were considered as predictive variables for implementing two models of the domain and logistic regression.

The results showed that the domain model had a high performance for predicting the habitat of J. excelsa with AUC =0.97, Kappa =0.730, and TSS = 0.91. Based on the results, the areas with the lowest potential for the presence of J. excelsa were 5665.95 km2, the moderate potential was 2033.1 km2, the good potential was 3076.38 km2, the very good potential was 3063.42 km2, and the high potential was 817.29 km2. The results obtained from the implementation of the logistic regression model indicated that 5084.37 km2 of the studied area was in the class of least potential, 2539.35 km2 had moderate potential, 1410.21 km2 had good potential, 1104. 84 km2 had very good potential, and 4517.37 km2 had high potential. Also, the results showed that the suitable habitats for J. excelsa were regions with a mean diurnal range of 13 °C and 15.5 °C, annual precipitation of 120-220 mm, precipitation of 64-90 mm, precipitation of coldest quarter of 35-60 mm, the altitude from the sea level of 3100-1800 m, and a slope of 30-10% in the southern, southeast, and east directions. Also, the probability of species occurrence was more in areas near the snow and water line as well as on calcic rocks. Also, in the habitat of J. excelsa the NDVI index varied between 0.38 and 0.20. The results of the performance evaluation of the models showed that the domain model had higher performance in predicting the suitable habitat of J. excelsa than the logistic regression model in the study area.

Species distribution models can provide a suitable management plan for different parts of the habitat. Areas with low potential of suitable habitat are usually partial habitats and populations in these areas are more vulnerable than the others, whereas areas with very good potential are excellent for a protected area. Areas with good to the excellent potential that do not have significant tree cover are suitable for forestry and native ecosystem restoration. The present study showed that the domain model, despite using only presence data, can be a useful method for predicting the suitable habitat of J. excelsa in the Alborz Mountains. Therefore, it can be stated that species distribution models, with acceptable accuracy, have enough performance in the evaluation of species distribution and can be used to execute conservation strategies.

The change in forest cover plays a vital role in ecosystem services, atmospheric carbon balance and thus climate change. The goal of this study is comparison of three procedure of Artificial Neural Network, Logistic regression and Similarity weighted Instance-based Learning (SIM Weight) to predict spatial trend of forest cover change.

In this study, land use maps for the periods 1984 and 2012 derived from Landsat TM satellite imagery, was used. Transition potential modeling using artificial neural network, Logistic regression and Similarity weighted Instance-based Learning and prediction based on the best model using Markov chain model was performed. In order to assess the accuracy of modeling, statistics of relative performance characteristic (ROC), ratio Hits/False Alarms and figure of merit was used.

The results show the accuracy of artificial neural network with the ROC equal to 0.975, the ratio Hits/False Alarms equal to 63 percent and the figure of merit is equal to 12 percent.

 Discussion and Conclusions

Artificial Neural Networks in comparison with Logistic Regression and Similarity weighted Instance-based Learning has higher accuracy and less error in modeling and predicting of forest changes.

Natural habitats such as rangelands and forests as one of the most sensitive elements of the environment and an important role in living organisms, including humans. In the decades before the present era of urban development has been such that an imbalance in the use of urban land led and pristine converted to urban land in others. In the present study to understand the changes in urban land, pasture and forest in Behbahan, images Landsat ETM + sensor OLI 2000 and 2014 were analyzed. CROSSTAB was used to assess the changes occurred. As well as to predict the trends of the year 2028 from the transmission potential maps logistic regression and Markov chain method was used. Results of the survey area in the first period (2000-2014) shows that urban land area of 1605 hectares in 2000 to 3157 ha in 2014 has increased. The most degradation of rangelands (6233 ha) and forests has occurred. The second period (2014-2028) compared to the 2014forest area remains unchanged, but increased urban development and reducing the area of pastures in Outlook 2028 will continue. In the period studied, the destruction of agricultural land for conversion into residential areas has increased significantly so that during 2000 to 2014, 291 hectares and in 2028, 626 hectares of agricultural land for construction purposes have been demolished.

Main trend of ecotourism is nature and its beauties; so finding the beautiful landscapes and evaluating aesthetic values of the area would be considered as one of the principles of recreational planning. Scenic beauty is a major component of every encounter with the natural environment in tourism and recreation activities. Visual elements of landscape not only present aesthetical values but also verify the mutual relationships of these values in cultural, economic and biological dimensions. The perceived aesthetic value of landscape is beyond identifying processes of physical and biological signs on the landscape and virtually is a perceptional process originated from visual aesthetic exchanges between the observer and geographic space. This perception is a process through which sensory information can be detected and classified into meaningful structures. The aesthetic appreciation of a landscape as it is perceived by humans has been a subject of theory development in various disciplines. With the development of land use planning, and its requirement for environmental data on which to base land use decisions, came an increased desire to elaborate valid means to quantify the scenic characteristics of landscapes. Due to the fact that visual sense has the highest amount of influence on the quality of individuals’ recreational experience, visual quality assessment seems to be essential. Integration of GIS beside field surveys has provided more sophisticated decision support tools for solving complex management problems such as evaluating the scenic value considered as a non-quantifiable source in the recent past. Most studies have been conducted to assess the landscape visual quality in Iran is based on subjective approach and there is no references about objective assessment. Accordingly, the purpose of this research is to evaluate the visual quality of landscapes to single out more valuable landscapes.
Since the main trend of ecotourism is nature and its beauties so finding beautiful landscapes and evaluating aesthetic values of the area would be considered as one of the principles of recreational planning. Special geographic position, climate diversity, special topographic and geomorphologic statuses are considered as unique potentials of ecotourism. Given that, Ziarat watershed which is one of the tourism poles of the Golestan province in Iran and comprises the above mentioned characteristics was selected as the study area to assess and model the aesthetic value of its walking tracks using an objective approach as it named logistic regression method.
Regression model is a statistical model which explained the relationship between a phenomenon (the dependent variable) and some of its elements (independent variables) based on a defined set of observed data. Logistic regression model is a special type of regression model which independent variable in it is Boolean and attaches only zero or one. The main assumption of regression logistic is that the possibility of which the dependent variable attaches the one score (a positive response) is followed by a logistic curve and this amount would be calculated using the equation (1):Equation (1): p(Y=1/X)=(exp∑▒BX)/(1笚∑▒BX)
According to the above equation:P: Is the probability in which the dependent variable attaches One score.
X: Is the independent variables
B: Is the coefficients of the independent variables
This logarithmic change caused that the predicted possibility was in the range of 0 to 1.
Accuracy assessment of regression model:Accuracy assessment of regression model will be calculated using Pseudo-R2 and ROC indices. Pseudo-R2 will be examined the fitness of the model based up on the rate of the possibility as it followed (equation 2):Equation (2): Pseudo- R2 =1-( log(likelihood)/ log(Lo))
If Pseudo- R2 were higher than 0.2 , it would be regarded as a good fitness of model in spatial studies.
In summary, the main steps of this research are as it follows:Identifying effective criteria on scenic values of the study area (independent variables)
Marking the most beautiful points of the study area (dependent variable)
Mapping independent variables
Standardization of independent variables
Running the regression model
Accuracy assessment of regression model
Discussion of After data collection and mapping the factors affecting the aesthetic value of the study area, these criteria were standardized between 0-255. Finally eight criteria including tree type, vegetation density, diversity of vegetation density, ecotone of tree type, viewshed layer for waterfalls, peaks and rivers along the walking tracks and visibility of points with higher diversity were inserted in the model as independent variables. As mentioned in previous part, dependent variable is a Boolean layer including beautiful and non-beautiful point of the study area. Finally, after the implementation of the model, each variable with respect to their impact on the aesthetic value, has allocated separated regression weight as it shown in table 1.
Table 1. Regression coefficient assigned to each criterion
Independent variable Regression coefficient
Tree type 0.00113912
Vegetation density 0.01807187
Diversity of vegetation density 0.01763468
Ecotone of tree type
Waterfall viewshed -0.00324110
0.08655779
Peak’s viewshed 0.00562768
River’s viewshed 0.00970483
High diversity point viewshed 0.01256638
Intercept -7.2442
The results of regression model (coefficients) shows that the ecoton of tree type has an inverse relationship with aesthetic value and by increasing the ecotone value, the aesthetic value will be decreased While other parameters have a direct relationship with aesthetic value.
The model was validated using Pseudo-R2 and ROC indices. The estimated value of Pseudo- R2 for this model was equal to 0.4129, and this amount was higher than 0.2 which represented the good fitness of model. Validating by ROC confirmed the results of the model too, ROC index was equal to 0.875.
Fig (1) shows the prediction map of the model. This map has predicted aesthetic value of the study area using the independent variables.
Fig (1): Prediction map of the aesthetic value using logistic regression model
In order to determine the importance of each independent variable, we tried to remove each of variables from the regression equation and examined its impact of each criterion (fig 2). The indices of river and the visibility of high diverse point were the most effective criteria.
Fig (2): The elimination effect of each independent variable on model’s validity
The development of measuring aesthetic/environmental quality has made progress over the last years. In addition, it is possible to create statistically reliable maps to predict visual quality of environment. The process is relatively efficient and effective. Planners, designers, and citizens can measure the perceived effects of spatial treatments and can assess the perceived impact of various proposals and plans. The represented approach in this research is one more tool in a toolbox of expert and statistical measures to understand the impacts proposals and plans may have upon the environment. The proposed approach (not with the same criteria as each region can differ in terms of their biophysical characteristics) can be conducted in other similar geographic regions to evaluate and rank the scenic beauty of landscapes.
The results showed that the zones which have had more aesthetic value often has been located in central region, eastern and western ridge and south part of the study area. Validating the regression model by Pseudo-R2 and ROC indexes showed the high capability of model to determine the areas which have the high aesthetic quality. The results of this research can

Striped hyena is a member of carnivorous species that eats dead animals and plays an important role in clearing nature and preventing disease. There is little information about the biology and distribution of this species in Iran. In this study habitat and distribution of striped hyena have evaluated in lashgardar protected area. At first study area divided in two parts and determined traversal paths in each part. Striped hyenas present point was investigated with the direct observation and using siegne like dung, hyaena carcasses and the track. Field observation of the hyena presence point recorded on the Map by using Gps data in Arc GIS 9.2 software. Field Survey carried from October 2010 to octobr 2011 and Lasted 30 days. Environmental variables by the presence/absence data in 80 locations recorded and data analyzed by using binary logistic regression. The results showed that hyaena suitable habitat depends on altitude and habitat type, distance from residential and the intensity of land use change. The most frequency of the sings observed in part 2.

Aim and scope: The area, shape and configuration of habitat patches are crucial features in landscape ecology studies, and they are also important to habitat evaluation and habitat suitability.Wild sheep is from Bovidae Family found in Kavir national park. The most important feature of the habitat of wild sheep is proximity to escape terrain (as cover for avoiding of predators). Understanding habitat requirements in landscape level is essential for successful management and conservation of wild sheep.
Materials: We used logistic regression approach to develop habitat suitability model for wild sheep in Kavir national park with Using landscape ecology metrics such as shape, size and proximity of habitat patches and based on field data gathered from autumn 2009 to summer 2010 .

Due to the Hosmer-Lemeshow goodness-of-fit test statistic is greater than .05, So the model has adequate fit. Habitat suitability model indicated good discrimination based on the receiver-operation characteristic (ROC) criteria (ROC> 0.97), indicating habitat suitability model can explain distribution of wild sheep reasonably well.

The understanding of the species habitat requirements and parameters affecting the habitat selection would be essential for sustainable coservation. The black-beilled sandgrous (Pterocles orientalis) occurs in some southern plains of Sabzevar including Shir-Ahmad Wildlife Refuge. Although it suffers intensive habitat destruction and hunting, still there is no enough information about its population ecology in this distribution area. The study area was surveyed for recording presence and absence sites of bird. Using logistic regression analysis the association between species presence/absence and habitat parameters was examined. It was found that vegetation height, height of tallest shrub, canopy cover percentage, stony coverage, distance to roads, distance to water resource, altitude and slope, negatively and ant nest occurring, gravel percentage and stone number ,positively, in assessment plots are significantly associated with the black-beilled sandgrous presence (p<0.05).

Land cover changes and residential developments result in destruction of natural habitats and biodiversity. Land cover models are one of the most important methods to evaluate this trend. The objective of this study was comparison of Logistic Regression (LR) and Multi-Layer Perceptron Artificial Neural Networks (MLP ANNs) algorithms for transition potential modeling of coastal areas of Mazindaran province. Landsat satellite imageries, specifically 1988, 2001, 2006, and 2010 were used for change analysis. In addition, transition potential modeling was conducted using a logistic regression and multi layer perceptron artificial neural network. Each calibration period 2001–2006 was examined using Markov chain and hard prediction for extrapolating the year 2010. The accuracy assessment model was determined by kappa index. The results showed that logistic regression (0.8456) was more accurate than the multi layer perceptron artificial neural network (0.8276) in this study area.

Despite their high diversity and importance as top predators in many ecosystems, the ecology of many snakes remains poorly understood. Here, we describe macro- and microhabitat use by the Ladle snake (Naja naja oxiana) in Golestan National Park. Presence and absence of species and 13 micro- and macroenvironmental variables were detected in each of 101 sampling plots distributed throughout the study area. The 12×12 meter sampling plots were established two times in each presence plot. Therefore 53 presence and 53 absence plots in spring and also 48 presence and 48 absence plots in summer were sampled. Data analysis was conducted using binary logistic regression. The results showed that habitat parameters including slope, area covered by stone, and elevation were the most significant predictors affecting Ladle snake presence in spring. In summer, the slope, density of woody stems and elevation were the most significant predictors affecting Ladle snake presence. Due to the characteristics including low slope, low elevation, high value of area covered by stone and higher density of woody stems, steppe habitats in Golestan National Park have the capability of supporting Ladle snake.

The distribution of plant communities is based on the tolerance range of plant species to various environmental factors. Thus, recognition of the environmental factors affecting the establishment and distribution of vegetation is efficient for the introduction of native species and using them in the process of restoration of rangeland. Such studies are done using several statistical methods. This research had the goal of investigation using logistic regression in the extraction of relationships between vegetation and environmental factors and was performed in a semiarid region (in Plour rangeland). Environmental factors including 15 edaphic factors, 3 topographic factors and 6 climatic factors were measured. The sampling method was randomized-systematic and 3 vegetation types were recognized in the study area. Logistic regression technique was used to determine effective factors on each plant type and model of plant types. The results showed that the vegetation distribution is mainly related to soil characteristics such as Caco3, WC and climate factors such as mrhs, mmps and and topographic factors such as elevation.

Natural aquifer vulnerability can be defined as reaching pollution to groundwater and the possibility to propogate in it after land contamination. This is a relative specification, with no dimension and cannot be measured; which it is not only depended on aquifer specification but also on geological and hydrological characteristics of the aquifer of the region. Varieties of methods have been developed for evaluating groundwater vulnerability and because of the ease of implementation index method and particular DRASTIC method are among of the widely used methods. Each parameter in the DRASTIC method that potentially affects the likelihood of contamination is categorized on the same scale and after applying coefficients of the parameters, vulnerability assessment is provided with a score. One important point in this method is that ranking and weighting of the parameters is highly depended on the personal decision and judgment which can reduce the quality of the results. Many suggestions for improving DRASTIC model have been developed by researchers. Most of the researchers suggested excluding less important parameters, adding affective parameters, modifying the model coefficients, and ranking parameters.In this research, in order to overcome the above mentioned drawbacks and selecting the appropriate model for assessing the vulnerability of aquifers; three

logistic regression, modified DRASTIC and AHP-DRASTIC are examined and compared, and after collecting input parameters, vulnerability is assessed based on the models. In order to select the most appropriate model and evaluating performance of the models, Spearman correlation coefficient for nitrate concentration and vulnerability class is used. The results indicate high performance of AHP-DRASTIC in comparison to the combined method.