ارزیابی ریسک اجرای طرح جنگلداری بر تغییرات تراکم مکانی پوشش گیاهی زیرآشکوب جنگل با رهیافت مدل‌سازی شبکه عصبی مصنوعی

هدف از این پژوهش مدل سازی تغییرات مکانی تراکم پوشش گیاهی زیرآشکوب جنگل با توجه به ساختار اکوسیستم جنگل و فعالیت های طرح جنگلداری به عنوان یک مخاطره زیست محیطی می باشد. واحدهای همگن محیط زیستی با استفاده از منابع اکولوژیکی و سیستم اطلاعات جغرافیایی، تهیه گردید. در این تحقیق به کمک شبکه های عصبی مصنوعی، تراکم پوشش گیاهی زیرآشکوب جنگل بر اساس متغیرهای اکولوژیکی و انسانی شبیه سازی شد. شبکه پرسپترون چندلایه با یک لایه پنهان و 4 نرون در هر یک از لایه های مخفی و آرایش نهایی 1423، با توجه به بیشترین مقدار ضریب تبیین آزمون شبکه (معادل 8576/0) و کمترین مقدار میانگین مربعات خطا و میانگین خطای مطلق (به ترتیب معادل 866/0 و 736/0)، بهترین عملکرد بهینه سازی توپولوژی را نشان می دهد. بر اساس نتایج آنالیز حساسیت، عوامل اکولوژیکی و انسانی شامل تراکم تاج پوشش توده، تراکم دام در جنگل، شدت فرسایش خاک و شدت کوبیدگی آن به ترتیب بیشترین تاثیر را در تغییرات پوشش گیاهی زیرآشکوب جنگل از خود نشان دادند. نتایج حاصله در این تحقیق در چهارچوب روش تجزیه و تحلیل سیستمی منجر به مدل سازی پوشش گیاهی زیرآشکوب جنگل در ارزیابی مخاطرات محیط زیستی طرح های جنگلداری و طراحی سامانه پشتیبان تصمیم گیری شده است که می‌تواند راه گشای تصمیم‌گیری در مورد ساختار طرح و اجرای پروژه‌های مشابه در مکان‌های مشابه باشد.

Risks assessment of forest project implementation in spatial density changes of forest under canopy vegetation using artificial neural network modeling approach

Risks assessment of forest project implementation in spatial density changes of forest under canopy vegetation using artificial neural network modeling approach Nowadays, environmental risk assessment has been defined as one of the effective in environmental planning and policy making. Considering the position and structure of vegetation on the forest floor, the main role of forest under canopy vegetation cover can be noted in attracting and preventing runoff in the forest floor and reducing subsequent environmental risks. The purpose of this article is forest under canopy vegetation density changes modeling considering forest ecosystem structure and forest management activities as an environmental risk. The main objectives of this study were to: (1) model forest under canopy vegetation density in forest ecosystem to elucidate the ecological and management factors affecting on under canopy vegetation density; (2) prioritize the impacts of model inputs (ecological and management factors) on under canopy vegetation density using model sensitivity analysis and (3) determining the trend model output changes in respond to model variables changes.In this study, Land Management Units (LMUs) were formed in the region considering ecological characteristics of land. LMUs were mapped out based on Ian McHarg rsquo;s overlay technique by ARC GIS 9.3 software. Ecological factor classes of an LMU differ from ecological factor classes of adjacent LMUs (at least in one ecological factor class). The following types of data were solicited for each LMU:(1) Ecological variables: Altitude or elevation (El), Slope (Sl), Aspect (As), soil depth (SD), Soil Drainage (SDr),Soil Erosion (SE), Precitipation (Pr), Temprature (Te), trees Diameter at Breast Height (DBH), Canopy Cover (CC), and forest Regeneration Cover (RC).(2) Management variables: Cattle Density (CD), Animal husbandry Dsitance (AD), Road Dsitance (RD), Trail Dsitance (TD), logs Depot Dsitance (DD), Soil Compaction (SC), Torist impacts (To), Skidding impacts (Sk), Logging impacts (Lo), Harvested trees volume (Ha), artificial Regeneration (Re) and Seed Planting (SP).(3) Forest under canopy vegetation density: The percentage of under canopy vegetation density in each LMU was estimated by systematic random sampling method. In each LMU, a one square meter sample was taken. The average percentage of under canopy vegetation density in sample units of each LMU was calculated and used in the modeling process.ANN learns by examples and it can combine a large number of variables. In this study, an ANN is considered as a computer program capable of learning from samples, without requiring a prior knowledge of the relationships between parameters. To objectively evaluate the performance of the network, four different statistical indicators were used. These indicators are MeanSquared Error (MSE), Root MeanSquared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2).Various MLFNs were designed and trained as one and two layers to find an optimal model prediction for the under canopy vegetation density and variables. Training procedure of the networks was as follows: different hidden layer neurons and arrangements were adapted to select the best production results. Altogether, many configurations with different number of hidden layers (varied between one and two), different number of neurons for each of the hidden layers, and different interunit connection mechanisms were designed and tested.In this research, 129 LMUs were totally selected, then ecological and management variables were recorded in them. In the structure of artificial neural network, ecological and management variables were tagged as inputs of artificial neural network and the percentage of under canopy vegetation density was tagged as output layer. Considering trained networks (the structure of optimum artificial neural network has been summarized in Table1), Multilayer Perceptron network with one hidden layer and 4 neurons in each hidden layer created the best function of topology optimization with higher coefficient of determination of test data (which equals 0.857) and the lowest MSE and MAE (which are 0.866 and 0.736 respectively). Considering the results of sensitivity analysis, ecological and management variables like the forest canopy density, cattle density in forest, soil erosion and soil compaction respectively show the highest impact on forest under canopy vegetation density changes (Fig1). Table1. The structure of optimum artificial neural network in forest under canopy vegetation densityOutput LayerFirst Hidden LayerNetwork featuresLinearHyperbolic tangentTransmission LayerGradient descentGradient descentOptimization Algorithm0.70.7Momentum14Number of Neurons0.9 up to 0.90.9 up to 0.9Normalization Table2. The structure of optimum artificial neural network in test dataMSEMAERMSER2DataThe structure of network( the number of neurons)epoch0.7160.6780.8460.931TrainningTanh(4)1600.7930.7030.8910.894Validation0.8660.7360.9310.857Test Fig1. The results of sensitivity analysis of artificial neural network model Nowadays, artificial neural network modeling in natural environments has been applied successfully in many researches such as water resources management, forest sciences and environment assessment. The results of research declared that designed neural network shows high capability in forest under canopy vegetation density modeling which is applicable in forest management of studied area. Sensitivity analysis identified the most effective variables which are influencing under canopy vegetation density.So, to identify hazardous LMUs in study area, we should pay attention to the canopy density of LMUs as the variable with high priority in determination of under canopy vegetation density. We believe that, in hazardous LMUs in forests, we should pay attention to some modifiable factors of LMU, which is cattle density in forest, by timely plan for livestock elimination. The forest under canopy vegetation density assessment model, in forest projects impact assessment, could be a solution in decision making about forest plan structure and implementation of similar projects in similar locations. Keywords: Forest plan, Environmental impact assessment, Multilayer perceptron, under canopy vegetation, artificial neural network