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دورسنجی بقایای گیاهی سوزانده شده در مزارع با بهره‌گیری از تصاویر سنجندۀ لندست | ||
| پژوهش های جغرافیای طبیعی | ||
| مقاله 11، دوره 49، شماره 2، تیر 1396، صفحه 341-353 اصل مقاله (603.89 K) | ||
| نوع مقاله: مقاله کامل | ||
| شناسه دیجیتال (DOI): 10.22059/jphgr.2017.62849 | ||
| نویسندگان | ||
| فاطمه علینقی زاده1؛ مجید دولتی* 2؛ محمد علی رستمی3؛ ناصر برومند4 | ||
| 1کارشناس ارشد گروه مهندسی مکانیک بیوسیستم، دانشگاه جیرفت | ||
| 2استادیار گروه مهندسی مکانیک بیوسیستم، دانشگاه جیرفت | ||
| 3استادیار پژوهش، بخش تحقیقات فنی و مهندسی کشاورزی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان کرمان، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرمان، ایران | ||
| 4دانشیار گروه مهندسی علوم خاک، دانشگاه شهید باهنر کرمان | ||
| چکیده | ||
| این تحقیق در مزارع ذرت و گندم منطقة ارزوئیة استان کرمان انجام شده است. در این مطالعه، توانایی تصاویر سنجندة لندست برای پایش مزارعی که در آنها بقایای گیاهی آتش زده شده، با استفاده از شاخصهای طیفی و آنالیز جداسازی طیفی خطی، ارزیابی شد. بدین منظور، چهار وضعیت سطح خاکـ شامل زمینِ خاکورزینشده، زمین با بقایای گیاهی، زمین با پوشش گیاهی سبز، و زمین با بقایای گیاهی سوزاندهشدهـ درنظر گرفته شد و چهار شاخص طیفی NDVI، BAI، NBR، و NBRT برای قطعات آزمایشی ایجاد شد. نتایج نشان داد شاخص BAI قادر است، بهتر از سایر شاخصهای مورد بررسی، مزارعی را که در آنها بقایای گیاهی آتش زده شده از سایر عوارض موجود در سطح زمین جدا کند. مساحت مزارع آزمایشیِ آتشزدهشده در تصاویر ماهوارهای، که به وسیلة شاخص BAI محاسبه شده بود، با مساحت واقعی مزارع آزمایشی همبستگی بسیاری (95/0R2 =) داشت. بنابراین، برای تمایز قائلشدن بین مزارع سوخته و سایر عوارض، میتوان از شاخص BAI استفاده کرد. همچنین، سطح مزارع سوخته، که با آنالیز جداسازی طیفی خطی تخمین زده شده بود، با دادههای بهدستآمده از روش زمینی همبستگی مناسبی (89/0R2 =) داشت. | ||
| کلیدواژه‌ها | ||
| آتشسوزی؛ بقایای گیاهی؛ خاکورزی حفاظتی؛ سنجش از دور؛ لندست 8 | ||
| عنوان مقاله [English] | ||
| Remote sensing of burned plant residue in fields using Landsat sensor imagery | ||
| نویسندگان [English] | ||
| Fatemeh Alinaghizadeh1؛ Majid Dowlati2؛ Mohammad Ali Rostami3؛ Naser Boroomand4 | ||
| 1MA in Mechanical Engineering, University of Jiroft, Jiroft, Iran | ||
| 2Assistant Professor of Mechanical Engineering, University of Jiroft, Jiroft, Iran | ||
| 3Assistant Professor of Agricultural Engineering, Kerman Agricultural and Resource Research and Education Center, AREEO, Kerman, Iran | ||
| 4Associate Professor of Soil Science, University of Jiroft, Jiroft, Iran | ||
| چکیده [English] | ||
| Introduction In the recent years, due to the benefits of conservation tillage and also disadvantages of crop residue burning, extension and education of conservation tillage has been highlighted on the agenda of agriculture policymakers. In this regard, subsidies or crimes are, respectively, considered for the farmers who use conservation tillage or crop residue burning on their farms. Lack of information, time consuming and high cost efforts of information gathering from the farm using conventional methods led to the poor performance of law enforcement. Therefore, the present research was carried out to find an accurate and fast method for monitoring the residue management. In this research, the ability of Landsat-8 satellite imagery for monitoring of burned fields was evaluated using spectral indices and linear spectral unmixing analysis. Materials and methods The present research was carried out in the Orzooiyeh region of Kerman province. For conducting the experiment, an area with approximate size of 25 square kilometers is considered and 10 farms (with burnt residues of wheat or corn) were selected randomly in this area as 10 replications of experimental plots. The images were downloaded from the landsat-8 website and all features were extracted from the images using ENVI software. On the other hand, the data of real burned areas on the farm were collected using handheld GPS device and the exact date of residue burning was also recorded directly on the field. The maps of experimental farms were prepared using ArcGIS software. The correlation between data of real burned area on the farms and ENVI extracted data of the burned areas were studied and real burned areas were expressed as a function of burned area that extracted from satellite images by a linear regression curve. Finally, the accuracy of regression functions and correlation between real data and satellite data were calculated. For this purpose, spectral indices including Normalized Difference of Vegetation Index (NDVI), Burned Area Index (BAI), Normalized Burn Ratio (NBR) and Normalized Burn Ratio Thermal (NBRT) were created for experimental lands and four soil surface conditions as experimental plots were considered including no planted field, residue covered field, green vegetation field and burned residue field. Results and discussion In the present study, because of extracting the pure spectral data of soil and residue, directly from Landsat-8 images, spectral unmixing analysis was not sensitive to the spectral changes that caused by conditions such as moisture content of soil and plant residue. The average value of BAI index obtained the values of 88.39, 9.29, 4.20 and 6.87 for burned residue field, no planted field, residue covered field and green vegetation field, respectively. As it can be seen, the average value of BAI index for burned residue field is significantly higher than the values for other soil surface conditions. This difference is because of the very low percentage of spectral reflectance of ash in the red and near-infrared bands. Therefore, BAI index was selected as an indicator to distinguish between burnt residue and other three surface conditions in the farm. The result showed that there is a significant difference between means in four soil surface conditions of the studied indices. Also, the results showed that the BAI index can be used as a good indicator for separation of burned fields. By the BAI index, location and area of trial burned farms were determined with higher accuracy than other indices. The average of burned fields that had been separated from other fields using BAI index had high correlation (R2=0.95) with ground-truth data. Also, the area of burned fields estimated by linear spectral unmixing analysis had a good correlation (R2=0.89) with the obtained data from the ground-based method. Conclusion According to the results, BAI index had the most accuracy for estimating burned area of farms and BAI index is proposed for separation of the areas of burnt fields. However, there is a slight error in estimation of the burned areas using spectral indicators and linear spectral unmixing analysis due to pixel nature of satellite images. Since there is only one spectral data for each pixel of satellite images, spectral data of the pixels that are more than the threshold value are considered as the burned pixels while it is possible that actual amount of burned area have been overestimated. For spectral data of the pixels that are less than the threshold value, we considered the unburned pixels while that may be estimating the burned area less than the actual amount. | ||
| کلیدواژه‌ها [English] | ||
| burned residue, Imagery, Landsat-8, remote sensing | ||
| مراجع | ||
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فاطمی، س.ب. و رضایی، ی. (1393). مبانیسنجشازدور، انتشارات آزاده. Alonso, F.G.; Miguel, S.M. and Cuevas, J.M. (2007). Using AWiFS and MODIS data to estimate burned areas: the case of Galicia forest fires during the 2006 summer season, Remote Sensing Laboratory, CIFOR-INIA, Madrid, PP.1-4.
Bannari, A.; Pacheco, A.; Staenz, K.; McNairn, H. and Omari, K. (2006). Estimating and mapping crop residues cover on agricultural lands using hyperspectral and IKONOS data, Remote Sensing of Environment, 104(4): 447-459.
Fatemi, S.B. and Rezaie, Y. (2014). Principals of Remote Sensing, Azadeh, Tehran.
Holden, Z.A.; Smith, A.M.S.; Morgan, P.; Rollins, M.G. and Gessler, P.E. (2005). Evaluation of novel thermally enhanced spectral indices for mapping fire perimeters and comparisons with fire atlas data, International Journal of Remote Sensing, 26(21): 4801-4808.
Key, C. and N. Benson (2005). Landscape Assessment: Remote sensing of severity, the Normalized Burn Ratio; and ground measure of severity, the Composite Burn Index, In FIREMON: Fire Effects Monitoring and Inventory System, RMRS-GTR, Ogden, UT: USDA Forest Service, Rocky Mountain Research Station.
Lillesand, T.M. and Keifer, R.W. (2000). Remote sensing and Image Interpretation, John Wiley & Sons, New York.
Lopez Garcia, M.J. and Caselles, V. (1991). Mapping Burns and Natural Reforestation using Thematic Mapper Data, Geocarto International, 6: 31-37.
Martín, M. P. (1998). Cartografía e inventario de incendios forestales en la Península Ibérica a partir de imágenes NOAA-AVHRR. Doctoral thesis, Departamento de Geografía. Alcalá de Henares, Universidad de Alcalá, Spain.
Pacheco, A. and McNairn, H. (2010). Evaluating multispectral remote sensing and spectral unmixing analysis for crop residue mapping, Remote Sensing of Environment, 114(10): 2219-2228.
Pacheco, A. and McNairn, H. (2012). Mapping Crop Residue Cover Over Regional Agricultural Landscapes in Canada, Agriculture and Agri-Food Canada, 960 Carling Avenue, Ottawa, Ontario, Canada K1A, 0C6: 1-4.
Sullivan, D.G.; Fulmer, J.L.; Strickland, T.C.; Masters, M. and Yao, H. (2007). Field scale evaluation of crop residue cover distribution using airborne and satellite remote sensing, Proceedings of the 2007 Georgia Water Resources Conference, at the University of Georgia, PP. 127-150.
Tucker, C.J. (1979). Red and Photographic Infrared Linear Combinations for Monitoring Vegetation, Remote Sensing of Environment, 8(2): 127-150.
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