نوع مقاله : مقاله پژوهشی

نویسندگان

• سیده کوثر حمیدی ¹
• اصغر فلاح ²
• نسترن نظریانی ³

¹ دکتری گروه جنگلداری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری

² استاد گروه جنگلداری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری ، ساری، ایران

³ پژوهشگر پسادکترای گروه جنگلداری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری

چکیده

تغییر اقلیم تأثیر قابل ملاحظه‌ای بر محیط‌زیست دارد و منجر به حساسیت متفاوت پوشش گیاهی به‌عوامل آب و هوایی در مقیاس های مکانی- زمانی مختلف می شود. آگاهی از وضعیت پوشش گیاهی به‌دلیل کاربرد در برنامه‌ریزی‌های خرد و کلان در حال حاضر یکی از ارکان مهم در تولید اطلاعات است .با توجه به پرهزینه و زمان‌بر بودن استفاده از روش‌های مبتنی بر مشاهدات، امروزه فناوری سنجش از دور به‌عنوان راهکار جدید در بهبود این روش‌ها مطرح شده است. در پژوهش پیش‌رو هدف بررسی اثر عوامل اقلیمی بر روند پوشش گیاهی جنگل فریم در استان مازندران با استفاده از تصاویر سنتینل 2 و تعیین مناسب‌ترین شاخص برای این منطقه است. به‌منظور مدل ‌سازی از فاکتورهای اقلیمی (درجه حرارت و بارندگی) مربوط به منطقه به‌‌دست آمده از نزدیک‌ترین ایستگاه هواشناسی مربوط، استفاده شد. بعد از پیش‌پردازش و پردازش‌ تصاویر سنتینل 2 ارزش‌های رقومی متناظر از باندهای طیفی استخراج و به‌عنوان متغیرهای مستقل در نظر گرفته شد. رابطه درجه حرارت و بارندگی با شاخص‌های پوشش گیاهی با ضریب همبستگی 0.43 و 0.56 و میزان AIC و BIC به‌ترتیب (565 و 3209) و (739  و 3383) به‌دست آمد. همچنین نتایج نشان داد بیش‌ترین اثرگذاری در رابطه با هر دو فاکتور درجه حرارت و بارندگی مربوط به شاخص پوشش گیاهی تفاضلی (DVI) است، که نشان‌دهنده کارائی بالای این شاخص در منطقه است. با توجه به نتایج فوق، می‌توان بیان کرد که شاخص مذکور به‌منظور بررسی تأثیر متغیرهای اقلیمی بر جنگل مورد مطالعه، انطباق و همبستگی مناسبی دارد.

کلیدواژه‌ها

• ایستگاه هواشناسی
• بارندگی
• درجه حرارت
• تغییر اقلیم
• سنتینل 2
• فناوری سنجش از دور

موضوعات

• دورسنجی

عنوان مقاله [English]

Modeling the Most Appropriate Vegetation Indicators under the Influence of Climatic Factors using Sentinel 2 Images - Case study: Farim Forest

نویسندگان [English]

• Seyedeh Kosar Hamidi ¹
• Asghar Fallah ²
• Nastaran Nazaryani ³

¹ 1. PhD, Department of Forestry, Faculty of Natural Resources, Sari Agriculture Sciences and Natural Resource University, Sari, Mazandaran, Iran

² Professor, Department of Forestry, Faculty of Natural Resources, Sari University of Agricultural Sciences and Natural Resources, Sari, Iran

³ 3. Postdoctoral researcher, Department of Forestry, Faculty of Natural Resources, Sari Agriculture Sciences and Natural Resource University, Sari, Mazandaran, Iran

چکیده [English]

Extended Abstract
Introduction:
Climate change has a consider impact on the environment and has led to different sensitivity of vegetation to weather factors at different spatial-temporal scales. Knowledge of the state of vegetation due to its use in micro and macro planning is currently one of the important pillars in the production of information, considering the high cost and time-consuming use of methods based on ground station observations for Estimating the relative heat of cities using air temperature measurement on the one hand. and providing data with relatively high spatial resolution and capable of measuring ground surface parameters on the other hand, nowadays remote sensing technology as a new solution It has been proposed to improve these methods. Quantitative relationship between vegetation pattern and climatic elements is one of the most important applications of remote sensing in the global and regional scale. Forecasting the amount of vegetation is necessary and essential for planning its exploitation and protection.
Materials & Methods:
In the present research, the aim is to investigate the effect of climatic factors on the vegetation trend of Frame forest using Sentinel 2 images and to determine the most suitable index for this area in Mazandaran province. In order to model the climatic factors (temperature and precipitation) related to the region obtained from the nearest weather station related to the city of Farim together with the climatic data of the city of Farim have been used in such a way that the changes in height from the surface The sea was used. After the pre-processing and processing of the Sentinel 2 images, the corresponding digital values were extracted from the spectral bands and consider as independent variables. ENVI software was used for image processing and STATISTICA and R software were used for modeling. The obtained data are divided randomly into training and testing data, so that 70% of the data was used for training and the rest was used for testing or evaluating the model. Mean square error, correlation, size of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to evaluate the presented models. The models with the highest correlation value and the lowest error value of the criterion, the mean square error, the Akaike information evaluation criterion and the Bayesian evaluation criterion were selected as the best models for evaluating the studied variables.
Results & Discussion:
The relationship between temperature and precipitation with vegetation indices was obtained with a correlation coefficient of 0.43 and 0.56 and AIC and BIC values (565 and 3209) and (739 and 3383) respectively. Also, the results showed that the most effective in relation to both temperature and precipitation factors is related to the Differential Vegetation Index (DVI), which indicates the high efficiency of this index in the region. The analysis of the effects of temperature on the vegetation index in the region indicated that with the increase in temperature, only the differential vegetation indices, the normalized green differential vegetation index and the green differential vegetation index increase, and there is a negative relationship with the vegetation index. It has been normalized. Precipitation is considered one of the most important factors affecting vegetation, the fluctuation and change of precipitation from year to year always affects vegetation. The results of the effects of precipitation on vegetation indices show that differential vegetation index, differential green vegetation index, normalized differential green vegetation index, non-linear vegetation index and normalized difference vegetation index have a greater impact on precipitation in have an area in forest ecosystems, changes in climatic factors may have different effects on forest trees.
 Conclusion:
One of the solutions in this field is to investigate the relationship between climatic variables and tree characteristics. Obtaining information about the state of forest vegetation is very important, and this study tried to investigate its relationship with climatic variables in addition to investigating vegetation indicators. On the other hand, satellite data is a suitable tool for investigating forest ecosystems, because it has a good ability to investigate vegetation at a relatively low cost and provides the possibility of continuous monitoring of land surface coverage. According to the above results, it can be stated that climatic factors are among the influencing factors on vegetation indicators in the study area in this research. Vegetation, through the balance of environmental factors, causes the protection and stability of the environment. Due to the importance of vegetation, many researchers have taken steps to understand the growth and spatial patterns of vegetation in different regions; In line with the current research, it is suggested to investigate the effect of climatic factors on the vegetation of the studied areas in different geographical directions. In addition, if information is available, other climatic factors such as relative humidity, wind speed, evaporation, transpiration, and images with higher accuracy should be used in order to achieve results that are more accurate.

کلیدواژه‌ها [English]

• Precipitation
• Remote sensing
• Sentinel 2
• Temperature
• Vegetation indicators