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برآورد شاخصهای پوشش گیاهی برنج با تصاویر چندزمانۀ راداری و اپتیک | ||
| پژوهش های جغرافیای طبیعی | ||
| مقاله 7، دوره 45، شماره 4، اسفند 1392، صفحه 85-96 اصل مقاله (1.12 M) | ||
| نوع مقاله: مقاله کامل | ||
| شناسه دیجیتال (DOI): 10.22059/jphgr.2014.50074 | ||
| نویسندگان | ||
میر مسعود خیرخواه زرکش1؛ مهدی درویشی  2؛ علی اکبر آبکار3؛ غلام رضا احمدی4
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| 1استادیار دانشکدۀ محیط زیست و انرژی، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات | ||
| 2کارشناس ارشد سنجش از دور، دانشکدۀ محیط زیست و انرژی، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات | ||
| 3استادیار دانشکده مهندسی ژئوماتیک، گروه سنجش از دور، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران | ||
| 4دانشجوی دکترای اقلیم شناسی، دانشکده جغرافیا، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران | ||
| چکیده | ||
| قابلیتها و تواناییهای تصویربرداری راداری در بخش تکنولوژی تصویربرداری مایکروویو چشمگیر است. طبقهبندی، تشخیص و پایش محصولات زراعی بهکمک سنجش از دور، امروزه به یکی از بخشهای مهم در مدیریت کشاورزی تبدیل شده است. بهسبب وجود مشکلات تصاویر اپتیک در مناطق شمالی کشور (بهدلیل وجود ابر) و ناکارآمدی روشهای سنتی استفاده از باند مرئی و مادون قرمز و همچنین با توجه به کوچکبودن اندازۀ قطعات شالیهای برنج، تصاویر راداری SAR (رادار روزنۀ مصنوعی) با قابلیتهای خاص خود (نفوذپذیری در هر شرایط آبوهوایی)، میتوانند جایگزین (یا مکمل) مناسبی برای برآورد شاخص پوششهای گیاهی محصول برنج باشند. در پژوهش پیش رو با استفاده از تصاویر چندزمانۀ اپتیکی و راداری که در سه مرحلۀ نشا، داشت و برداشت در منطقۀ بهشهرِ استان مازندران انجام گرفت، به بررسی و مقایسۀ پنج شاخص پوشش گیاهی محصول برنج در تصاویر اپتیک لندست با ضریب پراکندگی راداری ماهوارۀ رادارست-1، در پلاریزاسیون HH پرداخته شده است. در این پژوهش، یک مدل ریاضی رگرسیون خطی با ضریب همبستگی ارائه شد و این نتیجه بهدست آمد که شاخص پوشش گیاهی NDVI با ضریب همبستگی 92/0 و شاخص SR با ضریب همبستگی 86/0، بهترتیب دارای بالاترین ضریب همبستگی با ضریب پراکندگی راداری هستند. | ||
| کلیدواژه‌ها | ||
| اسپکل (نویز لکهای)؛ رادار؛ شاخص پوشش گیاهی؛ ضریب پراکندگی رادار؛ فیلتر | ||
| عنوان مقاله [English] | ||
| Estimation of Rice Vegetation Indices with Multitemporal RADAR and optic Images | ||
| نویسندگان [English] | ||
| Mir Masoud KheirkhahZarkesh1؛ Mehdi Darvishi2؛ Ali Akbar Abkar3؛ Gholam Reza Ahmadi4 | ||
| 1Assistant Prof. Dep. of environment and energy, Science and research branch, Islamic Azad University, Tehran, Iran | ||
| 2MSc Candidate in GIS&RS, Dep. of environment and energy, Science and research branch, Islamic Azad University, Tehran, Iran | ||
| 3Assistant Prof. Geometrics’ Engineering Faculty, K.N. Toosi University of Technology, Iran | ||
| 4PhDCandidate, Geography Department, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| چکیده [English] | ||
| Introduction Due to capabilities of imaging radar, there has been an enormous surge of interest in microwave imaging technology. Unlike optical imaging, understanding the theoretical underpinnings of imaging radar can be challenging, particularly when new to the field. The technology is relatively complicated, and understanding the interaction of the incident microwave energy with the landscape to form an image has a degree of complexity well beyond that normally encountered in optical imaging. The aim of this paper is to assess the use of RADARSAT data for estimation of rice vegetation indices. The radar backscatter coefficient of rice fields appears to have a significant temporal variation. Due to weather conditions in the north of Iran, microwave sensors can be more effective in monitoring rice growth than optical sensors, since a longer wavelength electromagnetic wave is less affected by clouds and precipitation events. The backscattering measurements of rice-growing areas have already been acquired using satellite synthetic aperture radars. Time series RADARSAT fine beam mode data was acquired from May till August 1998 for seashore of Behshahr, Behshahr seashore of Mazandaran province to assess and monitor rice crop from the space. Vegetation indices (VIs) are among the oldest tools in remote sensing studies. Although many variations exist, most of them are coefficients of the reflection of light in the red and NIR ranges of the electromagnetic spectrum to separate the landscape into water, soil, and vegetation. To determine the density of green on a patch of land, researchers must observe the distinct colors (wavelengths) of visible and near-infrared sunlight reflected by the plants. As can be seen through a prism, many different wavelengths make up the spectrum of sunlight. When sunlight strikes objects, certain wavelengths of this spectrum are absorbed and other wavelengths are reflected. The pigment in plant leaves, chlorophyll, strongly absorbs visible light (from 0.4 to 0.7 µm) for use in photosynthesis. The cell structure of the leaves, on the other hand, strongly reflects near-infrared light (from 0.7 to 1.1 µm). The more leaves a plant has, the more these wavelengths of light are affected. Theoretical analyses and field studies have shown that VIs are near-linearly related to photo synthetically active radiation absorbed by a plant canopy, and therefore to light-dependent physiological processes, such as photosynthesis, occurring in the upper canopy. Practical studies have used time-series VIs to measure primary production and evapotranspiration. In this paper 5 VIs was investigated and compared with radar backscatter coefficient of rice and made a mathematical linear regression model with the correlation coefficients for estimation VIs from RADAR images. Methodology This research is a descriptive-analytical study based on acquired data and statistical methods. The following stages and procedures are to be considered: Pre-processing stage; included: 1. Co registration 2. Calibration (speckle reduction) 3. and extraction from time series of RADAR images. Atmospheric correction of optical Landsat images with FLAASH (MODTRAN4) module. processing stage; included: 1. Converting DN into radiance and reflectance coefficients in optical bands of Landsat images 2. Generating NDVI, DVI, IPVI, SR, RDVI, vegetation indices from three Landsat images (red and near infrared bands). 3. Extraction of statistical parameters in 10 test site same as RADAR images. Calculation of obtained statistical data with MATLAB software and creation of linear regression equations and correlation coefficients. Results and Discussion To further explore the relationship between paddy growth stage and radar backscatter, mean backscatter values were calculated for all the test fields for three different dates. The plants showed very low backscattering in early stage of plantation –12dB to –10dB. It started increasing up to –6 dB during vegetative phase of the plants, which is due to increase in height as well canopy cover. There was an increase up to –5 dB further in reproductive stage of the plants. During ripening phase, backscatter remained almost same until the field was being harvested. This is due to not much change in plant growth during the ripening period. All considered VIs in this research shows increasing in reflectance proportional to paddy growth stage. Conclusion Because of high correlation between red and near infrared bands in optical images with chlorophylls and fresh biomass of plant (VIs) and again, high correlation between radar backscatter coefficients and biophysical parameters (content of water, canopy, height plant, plant structure), we can make a connection between those statistical parameters and create a mathematical model (simple linear regression equations) with different correlation coefficients. The results showed that the NDVI with R=0.92 has the best performance among the other four VIs. NDVI is calculated from the visible and near-infrared light reflected by vegetation. Healthy vegetation (left) absorbs most of the visible light that hits it, and reflects a large portion of the near-infrared light. Unhealthy or sparse vegetation (right) reflects more visible light and less near-infrared light. The numbers on the figure above are representative of actual values, but real vegetation is much more varied. | ||
| کلیدواژه‌ها [English] | ||
| filter, radar backscattering coefficient, Radar, speckle, Vegetation index | ||
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