مدلسازی زیستگاه ملخ کوهاندار تاغ با استفاده از شاخص های بیوفیزیکی استخراج شده از تصاویر لندست 8
محورهای موضوعی :
توسعه سیستم های مکانی
سیروس هاشمی دره بادامی
1
,
بهرام جمعه زاده
2
,
علی درویشی بلورانی
3
,
عبدالحسین خاکیان
4
1 - دانشجوی کارشناسی ارشد سنجش از دور و سیستم اطلاعات جغرافیایی، دانشگاه تهران
2 - دانشجوی کارشناسی ارشد سنجش از دور و سیستم اطلاعات جغرافیایی، دانشگاه تهران
3 - استادیار دانشکده جغرافیا، دانشگاه تهران
4 - دانشجوی کارشناسی ارشد محیط زیست، دانشگاه تهران
تاریخ دریافت : 1394/01/29
تاریخ پذیرش : 1394/06/09
تاریخ انتشار : 1395/01/01
کلید واژه:
لندست 8,
آنالیز مؤلفه های اصلی,
شاخص های بیوفیزیکی,
ملخ مهاجر,
چکیده مقاله :
استفاده از تصاویر ماهواره ای یک راه ساده و ارزان در شناسایی زیستگاه و پایش آفت ها از جمله ملخ مهاجر است. استفاده از فناوری سنجش از دور به گونه ای رشد نموده است که سیاست های کنترل ملخ از روش های درمانی به روش پیشگیرانه تغییر رویه داده اند. از آنجاییکه مدیریت کارآمد هجوم آفت های حشره ای بر پایه دانش کامل از زیست شناسی و بوم شناسی آن استوار است، این تحقیق با هدف ارزیابی استفاده از شاخص های بیوفیزیکی استخراج شده از تصاویر ماهواره ای، به منظور شناسایی و نظارت بر زیستگاه ملخ انجام شده است. بدین منظور از شاخص های بیوفیزیکی (شاخص های پوشش گیاهی، شاخص های رطوبت موجود در گیاه، شاخص خشکی زمین و دمای سطح زمین) استخراج شده از تصاویر ماهواره ای لندست 8 (OLI/TIRS)، همزمان با داده های دیده بانی زمینی استفاده شد. سپس اطلاعات شاخص ها با استفاده از آنالیز مؤلفه های اصلی، در یک تصویر خلاصه شد. در نهایت با استفاده از داده های میدانی بدست آمده از دیده بانی و روش آستانه گذاری، نقشه پهنه بندی زیستگاه اولیه ملخ با ریسک بالا، ریسک متوسط و ریسک پایین تهیه شد. صحت مکانی نتایج بدست آمده با استفاده از داده های ملخ مشاهده شده به عنوان داده های مرجع، ارزیابی شد و صحت کلی 74% و ضریب کاپای 62% برای زیستگاه اولیه با ریسک بالا، صحت کلی 87% و ضریب کاپای 71% برای زیستگاه اولیه با ریسک بالا و متوسط و صحت کلی 94% و ضریب کاپای 88% برای هر سه زیستگاه بدست آمد.
چکیده انگلیسی:
Using satellite images is a simple and inexpensive way to identify the habitats and monitor the migratory pests such as locusts. Using remote sensing technology for locust control policies has shifted from treatment methods to preventive ones. Considering the effective management of insect pest infestations based on thorough knowledge of biology and ecology, this study aimed to evaluate the use of biophysical indices derived from satellite images in order to identify and monitor the locust habitats. For this purpose, we used biophysical indicators (vegetation indices, vegetation, water content indices, drought index and land surface temperature) derived from Landsat 8 (OLI/TIRS) images coinciding with in-situ data monitoring. Then, the information of indices was summarized in one image using principal component analysis. Finally, the primary locust habitat zoning map with high risk, medium risk and low risk was developed using in-situ data obtained from the monitoring and thresholding methods. The spatial accuracy of results was evaluated by locust observed data as reference data; on the other hand, the overall accuracy and Kappa coefficient for high-risk habitat were given as 62% and 74%, respectively. For moderate-risk habitat, they were also obtained as 87% and 71%, respectively. For all of three habitats, they were estimated as 94% and 88%.
منابع و مأخذ:
Amiri R, Weng Q, Alimohammadi A, Alavipanah SK. 2009. Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. Remote Sensing of Environment, 113(12): 2606-2617.
Chavez PS. 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment, 24(3): 459-479.
Cressman K. 1996. Current methods of desert locust forecasting at FAO 1. EPPO Bulletin, 26(3‐4): 577-585.
Despland E, Rosenberg J, Simpson SJ. 2004. Landscape structure and locust swarming: a satellite's eye view. Ecography, 27(3): 381-391.
Dinku T, Ceccato P, Cressman K, Connor SJ. 2010. Evaluating detection skills of satellite rainfall estimates over desert locust recession regions. Journal of Applied Meteorology and Climatology, 49(6): 1322-1332.
Gangwere SK, Muralirangan M, Muralirangan M. 1997. The bionomics of grasshoppers, katydids and their kin. CAB international, 529 pp.
Gao BC. 1996. NDWI: A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3): 257-266.
Ghulam A, Qin Q, Zhan Z. 2007. Designing of the perpendicular drought index. Environmental Geology, 52(6): 1045-1052.
Gitelson AA, Kaufman YJ, Merzlyak MN. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3): 289-298.
Hielkema J, Roffey J, Tucker C. 1986. Assessment of ecological conditions associated with the 1980/81 desert locust plague upsurge in West Africa using environmental satellite data. International Journal of Remote Sensing, 7(11): 1609-1622.
Hielkema J. 1981. Desert locust habitat monitoring with satellite remote sensing. ITC Journal (Netherlands), 4: 387-417.
Hielkema JU, Snijders F. 1994. Operational use of environmental satellite remote sensing and satellite communications technology for global food security and locust control by FAO: The ARTEMIS and DIANA systems. Acta Astronautica, 32(9): 603-616.
Huete A, Justice C, Liu H. 1994. Development of vegetation and soil indices for MODIS-EOS. Remote Sensing of Environment, 49(3): 224-234.
Huete A, Justice C, Van Leeuwen W. 1999. MODIS vegetation index (MOD13). Algorithm theoretical basis document, 3:213- 228.
Huete, A., Didan, K., van Leeuwen, W., Miura, T.,& Glenn, E. (2011). MODIS vegetation indices. In Land remote sensing and global environmental change (pp. 579-602). Springer New York.
Hunt ER, Rock BN. 1989. Detection of changes in leaf water content using near-and middle-infrared reflectances. Remote sensing of environment 30 (1):43-54.
Jimenez-Munoz JC, Sobrino JA, Skokovic D, Mattar C, Cristobal J. 2014. Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. Geoscience and Remote Sensing Letters (IEEE), 11(10): 1840-1843.
Jiménez-Muñoz J-C, Sobrino JA. 2008. Split-window coefficients for land surface temperature retrieval from low-resolution thermal infrared sensors. Geoscience and Remote Sensing Letters (IEEE), 5(4): 806-809.
Karnieli A, Bayasgalan M, Bayarjargal Y, Agam N, Khudulmur S, Tucker C. 2006. Comments on the use of the vegetation health index over Mongolia. International Journal of Remote Sensing, 27(10): 2017-2024.
Latchininsky A, Sword G, Sergeev M, Cigliano MM, Lecoq M. 2011. Locusts and grasshoppers: behavior, ecology, and biogeography. Psyche: A Journal of Entomology, Volume 2011, Article ID 578327: 1-4.
Latchininsky AV, Sivanpillai R, Driese KL, Wilps H. 2007. Can early season Landsat images improve locust habitat monitoring in the Amudarya River Delta of Uzbekistan. Journal of Orthoptera Research, 16(2): 167-173.
Latchininsky AV, Sivanpillai R. 2010. Locust habitat monitoring and risk assessment using remote sensing and GIS technologies. In: Integrated Management of Arthropod Pests and Insect Borne Diseases. Springer, pp 163-188.
Latchininsky AV. 2010. Locusts. In: Breed M.D. and Moore J., (eds.) Encyclopedia of Animal Behavior, volume 2, pp. 288 297 Oxford: Academic Press.
Lenney MP, Woodcock CE, Collins JB, Hamdi H. 1996. The status of agricultural lands in Egypt: the use of multitemporal NDVI features derived from Landsat TM. Remote Sensing of Environment, 56(1): 8-20.
Lockwood JA, Showler AT, Latchininsky AV. 2001. Can we make locust and grasshopper management sustainable? Journal of Orthoptera Research, 10(2): 315-329.
Magor J, Lecoq M, Hunter D. 2008. Preventive control and Desert Locust plagues. Crop Protection, 27(12): 1527-1533.
McCulloch L, Hunter D. 1983. Identification and monitoring of Australian plague locust habitats from Landsat. Remote Sensing of Environment, 13(2): 95-102.
McFeeters S. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7): 1425-1432.
Mohammed L, Diongue A, Yang J-T, Bahia D-M, Michel L. 2015. Location and characterization of breeding Sites of solitary desert locust using satellite images Landsat 7 ETM+ and Terra MODIS. Advances in Entomology, 3(01): 6-15.
Mullié W. 2006. Monitoring Desert Locust control in Africa: the QUEST approach. In: Proc. ANCAP/SETAC International Conference on Pesticide use in Developing Countries: Environmental Fate, Effects and Public Health Implications, Arusha, Tanzania. October 16-20, pp 83-84.
Pedgley DE. 1974. ERTS surveys a 500 km2 locust breeding site in Saudi Arabia. NASA Special Publication, 351 pp.
Pekel JF, Ceccato P, Vancutsem C, Cressman K, Vanbogaert E, Defourny P. 2011. Development and application of multi-temporal colorimetric transformation to monitor vegetation in the desert locust habitat. Selected Topics in Applied Earth Observations and Remote Sensing (IEEE Journal), 4(2): 318-326.
Renier C, Waldner F, Jacques DC, Babah Ebbe MA, Cressman K, Defourny P. 2015. A Dynamic Vegetation Senescence Indicator for Near-Real-Time Desert Locust Habitat Monitoring with MODIS. Remote Sensing, 7(6): 7545-7570.
Rondeaux G, Steven M, Baret F. 1996. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2): 95-107.
Sandholt I, Rasmussen K, Andersen J. 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment, 79(2): 213-224.
Simpson SJ, Sword GA. 2008. Locusts. Current Biology, 18(9): R364-R366.
Sivanpillai R, Latchininsky AV, Driese KL, Kambulin VE. 2006. Mapping locust habitats in River Ili Delta, Kazakhstan, using LANDSAT imagery. Agriculture, Ecosystems & Environment, 117(2): 128-134.
Steedman A. 1988. Locust handbook 2nd ed. Overseas Development. Natural Resource Institute, London. 180 pp.
Symmons P. 2009. A critique of “Preventive control and desert locust plagues”. Crop Protection, 28(10): 905-907.
Tian H, Ji R, Xie B, Li X, Li D. 2008. Using multi–temporal Landsat ETM+ data to monitor the plague of oriental migratory locust. International Journal of Remote Sensing, 29(6): 1685-1692.
Tratalos JA, Cheke RA. 2006. Can NDVI GAC imagery be used to monitor desert locust breeding areas? Journal of Arid Environments, 64(2): 342-356.
Xu H. 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14): 3025-3033.