نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه بوعلی سینا، همدان، ایران
2 دانشآموخته کارشناسی ارشد، گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه بوعلی سینا، همدان، ایران
چکیده
استفاده از تکنیک سنجش از دور امروزه در کشاورزی کاربردهای فراوانی دارد ازجمله تعیین سطح زیرکشت و پیشبینی عملکرد محصول. در این پژوهش از تصاویر ماهوارهای جهت تفکیک گندم آبی و دیم در استان همدان استفاده شد. شاخصهای NDVI ،EVI و NDWI از تصاویر 16 روزه سنجندههای لندست، مادیس و سنتینل 3 در بازه پنج ساله مورد مطالعه (2015-2019) استخراج گردید. نتایج شاخصها نشان داد کاهش شدید NDVI/EVI بعد از نقطه اوج بهدلیل آن است که زمان زرد شدن و یا برداشت محصول فرا رسیده است. بهعلاوه NDWI بهترتیب در بیشینه سبزینگی گندم در کشت آبی و دیم 0.767 و 0.736 دیده شد. سامانه Google Earth Engine محیط انجام محاسبات پردازش تصاویر و استخراج شاخصها و نقشهها بود و نرمافزار R نیز برای آنالیزهای طبقهبندی و تفکیک کشت دیم و آبی بهکار رفت. نتایج نشان داد نقشه استان بر اساس سطح زیر کشت دیم و آبی ماهواره سنتینل 3 جزییات بیشتری را نشان داد. همچنین استفاده همزمان از چند شاخص NDVI ،EVI و NDWI توانست قدرت تفکیک را افزایش دهد. علیرغم شباهتهای موجود، الگوریتمهای SVM و MD نیز با دقت قابلقبولی تفکیک کشت دیم و آبی استان را ارائه دادند. نتایج نشان داد کشت دیم و آبی گندم استان با دقت 0.737 تفکیک شد و تفکیک گندم از سایر کشتها با دقت 0.945 انجام گردید.
کلیدواژهها
موضوعات
Open Access
©2021 The author(s). This article is licensed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.
- Ajadi O., Barr, J., Liang, S. Z., Ferreira, R., Kumpatla, S. P., Patel, R., & Swatantran, A. (2021). Large-scale crop type and crop area mapping across Brazil using synthetic aperture radar and optical imagery. International Journal of Applied Earth Observations and Geoinformation, 97, 1-16. https://doi.org/10.1016/j.jag.2020.102294
- Akbari, M., Mamanpoush, A. R., Gieske, A., Miranzadeh, M., Torabi, M., & Salemi, H. R. (2006). Crop and land cover classification in Iran using Landsat 7 imagery. International Journal of Remote Sensing, 27(19), 4117-4135. https://doi.org/10.1080/01431160600784192
- Alexandridis, T. K., Zalidis, G. C., & Silleos, N. G. (2008). Mapping irrigated area in Mediterranean basins using low cost satellite Earth Observation. Computers and Electronics in Agriculture, 64(2), 93-103. https://doi.org/10.1016/j.compag.2008.04.001
- Alipour, F., Agh-Khani, M. H., Abbaspour-Fard, M. H., & Sepehr, A. (2014). Limiting and estimating the area under cultivation of agricultural products to help satellite images (Case study: Astan Quds Razavi sample farm). Journal of Agricultural Machinery, 4(2), 244-254. (in Persian). https://doi.org/10.22067/jam.v4i2.34827
- Arekhi, S., & Adib-nejad, M. (2011). Evaluating the efficiency of support vector machine algorithms for land use classification using Landsat + ETM satellite data (Case study: Ilam area). Iranian Range and Desert Research, 3(44), 420-440. (in Persian).
- Bazzi, H., Baghdadi, N., Ienco, D., El Hajj, M., Zribi, M., Belhouchette, H., Escorihuela, M. J., & Demarez, V. (2019). Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain. Remote Sensing, 11, 1836. https://doi.org/10.3390/rs11151836
- Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, 121-167.
- Carlson, T. N., Gillies, R. R., & Perry, E. M. (1994). A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote Sensing Reviews, 9(1-2), 161-173. https://doi.org/10.1080/02757259409532220
- Cheng, Y. B., Zarco-Tejada, P. J., Riaño, D., Rueda, C. A., & Ustin, S. L. (2006). Estimating vegetation water content with hyperspectral data for different canopy scenarios: Relationships between AVIRIS andMODIS indexes. Remote Sensing of Environment, 105(4), 30 2006, 354-366
- Delloye, C., Weiss, M., & Defourny, P. (2018). Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems. Remote Sensing of Environment, 216, 245-261. https://doi.org/10.1016/j.rse.2018.06.037
- Demarez, V., Helen, F., Marais-Sicre, C., & Baup, F. (2019). In-Season Mapping of Irrigated Crops Using Landsat 8 and Sentinel-1 Time Series. Remote Sensing, 11(2), 118. https://doi.org/10.3390/rs11020118
- Dong, J., Kaufmann, R. K., Myneni, R. B., Tucker, C. J., Kauppi, P. E., Liski, J., Buermann, W., Alexeyev, V., & Hughesg, M. K. (2003). Hughes. Remote sensing estimates of boreal and temperate forest woody biomass: Carbon pools, sources, and sinks. Remote Sensing of Environment, 84, 393-410. https://doi.org/10.1016/S0034-4257(02)00130-X
- Droogers, P. 2002. Global irrigated area mapping: overview and recommendations, Working Paper 36, International Water Management Institute. Colombo, Sri Lanka.
- Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Famiglietti, J. S., & Rodell, M. (2013). Water in the balance. Science 340(6138), 1300-1301. https://doi.org/10.1126/science.1236460
- Farajzadeh, M. (2005). Drought from Concept to Solutions. National Geographical Organization Publication.
- Fatemi, S. B., & Rezaee, F. (2005). Fundamental of Remote Sensing. 1st Pub, Azade Publication. Tehran.
- Ferrant, S., Selles, A., Le Page, M., Herrault, P. A., Pelletier, C., Al-Bitar, A., Mermoz, S., Gascoin, S., Bouvet, A., & Saqalli, M. (2017). Detection of irrigated crops from Sentinel-1 and Sentinel-2 data to estimate seasonal groundwater use in south India. Remote Sensing, 9, 11-19. https://doi.org/10.3390/rs9111119
- Gao, B. C. (1996). NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing Environment, 58, 257-266. https://doi.org/10.1016/S0034-4257(96)00067-3
- Gao, F., Schaaf, C. B., Strahler, A. H., Roesch, A., Lucht, W., & Dickinson R. (2005). MODIS biodirectional reflectance distribution function and albedo climate modeling grid products and the variability of albedo for major global vegetation types. Journal of Geophysical Research Atmospheres, 110, 1-13. https://doi.org/10.1029/2004JD005190
- Giannini, A., & Bagnoni, V. (2000). Schede di tecnica irrigua per l’agricoltura toscana. ARSIA– Servizio Telematico Irrigazione. Regione Toscana, EFFEMME Lito, Firenze, pp. 66-97 ISBN 88-8295-015-018.
- Gupta, O., Das, A. J., Hellerstein, J., & Raskar, R. (2018). Machine Learning approaches for large scale classification of produce, Scientific Reports Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. 8:5226. DOI: https://doi.org/10.1038/s41598-018-23394-3
- Guzinski, R., & Nieto, H. (2019). Evaluating the feasibility of using Sentinel-2 and Sentinel-3 satellites for high-resolution evapotranspiration estimations. Remote Sensing of Environment, 221, 157-172. https://doi.org/10.1016/j.rse.2018.11.019
- Hartmann, D. L., Tank, A. M. K., Rusticucci, M., Alexander, L. V., Brönnimann, S., Charabi, Y. A. R., Dentener, F. J., Dlugokencky, E. J., Easterling, D. R., & Kaplan, A. (2013). Observations: atmosphere and surface. Climate Change 2013 the Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK.
- Heenkenda, M. K., Joyce, K. E., Maier, S. W., & De Bruin, S. (2015). Quantifying mangrove chlorophyll from high spatial resolution imagery. ISPRS Photogrammetry of Remote Sensing, 108, 234-244. https://doi.org/10.1016/j.isprsjprs.2015.08.003
- Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2), 195-213.
- Immitzer, M., Vuolo, F., Atzberger, C., Immitzer, M., Vuolo, F., & Atzberger, C. (2016). First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sensing, 8, 166. https://doi.org/10.3390/rs8030166
- Mahmoud, A. M. A., Hasmadi, M., Alias, M. S., & Alias, M. A. (2016). Rangeland degradation assessment in the south slope of the Al-Jabal Al-Akhdar, northeast Libya using remote sensing technology. Rangeland Science, 6(1), 73-81.
- Martimort, P. (2012). Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120, 25-36.
- Matsushita, B., Wei, Y., Jin, C., Yuyichi, O., & Guoyn, Q. (2007). Sensivity of the enhanced vegetation index (EVI) and NDVI to topographic effects: A case study in high-density Cypress forest. Sensors, 7(11), 2636-2651. https://doi.org/10.3390/s7112636
- Morfitt, R., Barsi, J., Levy, R., Markham, B., Micijevic, E., Ong, L., Scaramuzza, P., & Vanderwerff, K. (2015). Landsat-8 operational land imager (OLI) radiometric performance on-orbit. Remote Sensors, 7, 2208-2237.
- Myneni, R., & Williams, D. (1994). On the relationship between FAPAR and NDVI. Remote Sensing of Environment, 49, 200-211. https://doi.org/10.1016/0034-4257(94)90016-7
- Nguyen, T. T., Hoang, T. D., Pham, M. T., Vu, T. T., Nguyen, T. H., Huynh, Q. T., & Jo, J. (2020). Monitoring agriculture areas with satellite images and deep learning. Applied Soft Computing, 95, 1-16. https://doi.org/10.1016/j.asoc.2020.106565
- Pageot, Y., Bau, F., Inglada, J., Baghdadi, N., & Demarez, V. (2020). Detection of Irrigated and Rainfed Crops in Temperate Areas Using Sentinel-1 and Sentinel-2 Time Series. Remote Sensing, 12, 1-19. https://doi.org/10.3390/rs12183044
- Pastor-Guzman, J., Brown, L., Morris, H., Bourg, L., Goryl, P., Dransfeld, S., & Dash, J. (2020). The sentinel-3 OLCI terrestrial chlorophyll index (OTCI): algorithm improvements, spatiotemporal consistency and continuity with the MERIS archive. Remote Sensing, 12, 2652-2674. https://doi.org/10.3390/rs12162652
- Pelletier, C., Valero, S., Inglada, J., Champion, N., Marais Sicre, C., & Dedieu, G. (2017). Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series. Remote Sensing, 9, 173. https://doi.org/10.3390/rs9020173
- Peña-Arancibia, J. L., McVicar, T. R., Paydar, Z., Li, L., Guerschman, J. P., Donohue, R. J., Dutta, D., Podger, G. M., van Dijk, A. I. J. M., & Chiew, F. H. S. (2014). Dynamic identification of summer cropping irrigated areas in a large basin experiencing extreme climatic variability. Remote Sensing of Environment, 154, 139-152. https://doi.org/10.1016/j.rse.2014.08.016
- Rahimzadegan, M., & Pourgholam, M. (2016). Determining the area under saffron cultivation using Landsat images (Case study: City Torbat Heydariyeh). Remote Sensing and GIS in Natural Resource, 7(4), 97-115. (in Persian). https://doi.org/10.22048/jsat.2017.48518.1194
- Schucknecht, A., Erasmi, S., Niemeyer, I., & Matschullat, J. (2013). Assessing vegetation variability and trends in north-eastern Brazil using AVHRR and MODIS NDVI time series. Remote Sensing, 46, 40-59. https://doi.org/10.5721/EuJRS20134603
- Sepulcre-Canto, G., Zarco-Tejada, P. J., Sobrino, J. A., Berni, J. A. J., Jimenez-Munoz, J. C., & Gastellu-Etchegorry, J. P. (2008). Discriminating irrigated and rainfed olive orchards with thermal ASTER imagery and DART 3D simulation. Agricultural and Forest Meteorology, 149, 962-975. https://doi.org/10.1016/j.agrformet.2008.12.001
- Shamal, S. A. M., & Weatherhead, K. (2014). Assessing spectral similarities between rainfed and irrigated croplands in a humid environment for irrigated land mapping. IP Publication Ltd, 43(2), 109-114. https://doi.org/10.5367/oa.2014.0168
- Tso, B., & Mather, P. (2009). Support Vector machines, in Classification Methods for Remotely sensed Data. 1st ed: CRC Press: 125-153.
- Vogel, E., Donat, M. G., Alexander, L. V., Meinshausen, M., Ray, D. K., Karoly, D., Meinshausen, N., & Frieler, K. (2019). The effects of climate extremes on global agricultural yields. Environmental Research Letters, 14(5), 1-13. https://doi.org/10.1088/1748-9326/ab154b
- Vuolo, F., Dash, J., Curran, P. J., Lajas, D., & Kwiatkowska, E. (2012). Methodologies and uncertainties in the use of the terrestrial chlorophyll index for the Sentinel-3 mission. Remote Sensing, 4, 1112-1133. https://doi.org/10.3390/rs4051112
- Vuolo, F., Neuwirth, M., Immitzer, M., Atzberger, C., & Ng, W. T. (2018). How much does multi-temporal Sentinel-2 data improve crop type classification? Applied Earth Observation and Geo-information, 72, 122-130. https://doi.org/10.1016/j.jag.2018.06.007
- Wacker, A. G., & Landgrebe, D. A. (1972). Minimum Distance Classification in Remote Sensing. LARS Technical Reports. Paper 25. https://docs.lib.purdue.edu/larstech/25
- Wardlow, B. D., Egbert, S. L., & Kastens, J. H. (2007). Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sensing of Environment, 108(3), 290-310. https://doi.org/10.1016/j.rse.2006.11.021
ارسال نظر در مورد این مقاله