@article { author = {Erfanian, Mahdi and Vafaei, Nasrin and Rezaeianzadeh, Mehdi}, title = {A New Method for Drought Risk Assessment by Integrating the TRMM Monthly Rainfall Data and the Terra/MODIS NDVI Data in Fars Province, Iran}, journal = {Physical Geography Research}, volume = {46}, number = {1}, pages = {93-108}, year = {2014}, publisher = {University of Tehran}, issn = {2008-630X}, eissn = {2423-7760}, doi = {10.22059/jphgr.2014.50621}, abstract = {IntroductionDrought monitoring and assessment is usually done through either ground observation orremote sensing. Due to having some limitations, gathering and analyzing ground observationsare a time-consuming and expensive way to approach a precise drought monitoring andassessment. In contrast, remote sensing represents a fast and economic way of monitoring, butan applicable approach needs to be developed. To this end, using satellite sensor data which arecontinuously available provides cost-effective data for a better understanding of the region.They can be used to detect the drought commencement, duration and magnitude. TropicalRainfall Measuring Mission monthly data (TRMM-3B43) and Monthly Normalized DifferenceVegetation Index (NDVI) data of the MODIS on Terra satellite are freely available for thisobjective. The main objectives of the present study, which was carried out in the Fars Province,Iran, were: 1. integrating the satellite data for mapping drought severity classes using theStandardized Precipitation Index (SPI) and the NDVI anomaly maps, 2. creating drought riskmaps, 3. calculating the percentage of drought affected area by drought risk level, 4. showingthe effectiveness of satellite derived drought indices as an indicator for drought assessment, and5. identifying the most drought vulnerable areas of the surveyed region.􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯􀀯∗E-mail: Erfanian.ma@gmail.com Tel: +98 9123328494Physical Geography Research Quarterly, 46 (1), Spring 2014 17MethodologyThis research was carried out in Fars Province, Iran. It is located between 50􀃛30’ and 55􀃛36 Elongitude and from 27􀃛03’ to 31􀃛42 N latitude and cover an approximate area of 122661 km2.This study aimed to map drought risk area in the Fars Province, by integrating the StandardPrecipitation Index (SPI) and the Normalized Difference Vegetation Index (NDVI) Anomalymethods. As the first step, the growing season-based SPI (April- September) at 44 stations werecalculated for 2000-2008 period using the standard normal distribution. The SPI raster layer (foreach year), was created using the ordinary Kriging method. Then, all SPI maps were reclassifiedinto five drought severity classes. As the second step, NDVI anomaly maps were created for thegrowing season based-NDVI anomaly of MODIS during the same period (9-year period). TheNDVI anomaly map in each year was reclassified into five classes in a similar way. At the nextpart, for both methods, Boolean drought frequency map (presence or absence of drought)derived for each year. The derivation of final drought risk map was done by a simple weightedlinear combination of the drought frequency maps. In this research, another drought risk mapwas created by integrating the NDVI anomaly and the TRMM-based SPI maps to introduce anew remote sensing method.Results and DiscussionThe ground-based SPI method applied for the growing seasons showed that in 2000, 2001, 2005and 2008, some severe droughts occurred whereas the NDVI anomaly resulted in 2000, 2001and 2008. The drought severity maps of TRMM based on SPI method indicated some noticeabledrought occurrences in the Fars Province in 2000, 2005, and 2008 as well. The comparison ofdrought risk maps created by the TRMM-based SPI and the ground-based SPI methods showedthat the majority of the surveyed regions are highly prone to drought occurrence. The TRMMcould predict the monthly rainfall at most of 44 rain-gauge stations. Comparing drought riskmaps, the high and moderate risk classes in the first method contain % 59.58 and % 39.84,while in the TRMM based method, they cover %61.1 and %37.12 of the area, respectively.Before drought risk assessment, it is highly recommended to evaluate the TRMM data for futureevents. The risk maps can be compared with the actual decrease in agricultural products for abetter understanding of the events and their verifications.ConclusionThe method applied in this study showed that almost whole the province is prone to droughtoccurrences. The northern and southern areas of the province were more susceptible to droughtwith different severities during the growing seasons in 2000-2008. It is notable to express thatthere are still some limitations to apply the satellite data for a long period. These might be dataavailability problem with moderate spatial resolution. The TRMM and the MODIS data havebeen available since 2000 and 1998, respectively. Furthermore, the TRMM data calibration andvalidation is required before creating the TRMM-based SPI maps. Despite their shortages, theapplication of remote sensing data for drought risk assessment can still be done as an acceptablemethod in ungauged regions.}, keywords = {Drought,Fars,MODIS,NDVI Anomaly,TRMM}, title_fa = {ارائۀ یک روش نوین برای ارزیابی ریسک خشکسالی استان فارس با تلفیق داده های ماهانۀ بارندگی ماهوارۀ TRMM و داده های شاخص پوشش گیاهی NDVI سنجندۀ Terra/MODIS}, abstract_fa = {این پژوهش با هدف تهیۀ نقشۀ خطر خشکسالی استان فارس با ترکیب روش شاخص خشکسالی هواشناسی SPI و روش آنومالی NDVI انجام گرفته است. ابتدا به‎کمک داده‌های ماهانۀ بارندگی از 44 ایستگاه استان فارس طی دورۀ آماری 2008-2000، شاخص خشکسالی SPI فصل رشد گیاهان محاسبه شد. سپس نقشه­های SPI را با استفاده از روش کریجینگ معمولی تهیه شده و در پنج کلاس از نظر شدت خشکسالی (در هر سال) قرار گرفتند. پس از اعتبارسنجی داده­های ماهواره TRMM، نقشۀ SPI فصل رشد گیاهان در هر سال به‎دست آمد. نتایج پژوهش بیانگر انطباق قابل قبول نقشه­های SPI داده­های زمینی و SPI مبتنی بر داده­های TRMM است. در مرحلۀ بعد، نقشه‌های آنومالی شاخص NDVI فصل رشد گیاهان با استفاده از لایه‌های NDVI سنجندۀ MODIS در دورۀ نُه‎ساله تهیه شد و در پنج کلاس شدت خشکسالی (در هرسال) طبقه­بندی شدند. نقشۀ فراوانی خشکسالی از روی نقشه‌های باینری سالانه (بودن یا نبودن خشکسالی) استخراج شده است. از ترکیب وزنی خطی نقشه­های احتمال وقوع خشکسالی دو روش شاخص SPI و آنومالی NDVI، نقشۀ ریسک خشکسالی به‎دست آمد. براساس این نقشه، تقریباً بیشتر استان فارس مستعد خشکسالی بوده و خشکسالی با شدت­های مختلف را در دورۀ آماری مذکور تجربه کرده است.}, keywords_fa = {Drought,Fars,MODIS,NDVI Anomaly,TRMM}, url = {https://jphgr.ut.ac.ir/article_50621.html}, eprint = {https://jphgr.ut.ac.ir/article_50621_bf69485e9bdea41df3bda56ad6b623ff.pdf} }