@article { author = {Markan, َAli Akbar and Darvishzadeh, Roshanak and Hosseiniasl, Amin and Ebrahimi khusfid, Mohsen and Ebrahimi khusfie, Zohre}, title = {Knowledge based drought risk zonation in arid regions using GIS (Case study: Sheitoor, Yazd)}, journal = {Journal of Climate Research}, volume = {1390}, number = {5}, pages = {103-116}, year = {2011}, publisher = {https://www.irimo.ir/}, issn = {2228-5040}, eissn = {2783-395X}, doi = {}, abstract = {Drought is a severe dilemma which influences different aspects of mankind’s life. Drought has a negative impact on economy, environment and agricultural sector and cause heavy damage and losses in many parts of the world. Therefore the quantitative estimation and prediction of drought phenomena has become an important issue for policy makers and the scientific community. In the last three decades, remote sensing has provided a useful tool for drought monitoring and a variety of remotely sensed drought indices based on vegetation indices, land surface temperature (LST) and albedo, have been developed. The main objective of this study was drought riskzonation in Sheitoor basin located in Yazd province by using satellite, climatology and environmental data. The data used in this research consist of ALOS (AVNIR) image collected on 18th July 2009, topographical maps (scale: 1/25000), rainfall, temperature and evaporation data which were obtained from meteorological stations. The Sheitoor basin is located in the central part of Iran. It covers a total area of 416 km2. The altitude varies in the region between 1844 and 2989 meters. Average annual rainfall in the study area is 171 mm and average annual temperature is 14 °C. Based on the Dumarten's climate classification method, the climate of study area is cold arid. At first, the ALOS image was processed to obtain the TOA[1] radiance using gains provided in header file. Next the FLAASH algorithm was used to remove the influence of atmosphere and also for conversion of the TOA spectral radiance into ground reflectance. The image was registered to UTM Zone 40 (WGS 84) coordinates using 1:25000 scale digital maps, 17 control points, a polynomial (degree 2) equation and the nearest neighbor resampling method. In the next step, effective parameters on drought including environmental factors (slope, aspect, height, land cover/use, stream density and vegetation fraction) and also climatic data (temperature, rainfall and evaporation) were mapped in GIS environment. The land cover/use map was extracted from satellite data using supervised classification algorithm. Vegetation fraction was also extracted from image using MSAVI1 index. The other parameters such as height, slope and aspect were produced using topographical maps (scale: 1/25000). Data standardization is a basic task in data analysis when several incomparable criteria are involved. To make comparable various data layers, the data layers which effect on drought were standardized using linear fuzzy. For example,  drought severity decrease with an increase in altitude and areas having more height are less sensitive to drought, so maximum and minimum altitude were converted to 0 and 1. The AHP method was used to identify the weight of each parameter. Results of weighted layers showed maximum weight for land cover/use parameter due to the human intervention in natural ecosystems. Next, Index overlay and various fuzzy logic operators (Fuzzy Sum, Fuzzy product, Fuzzy OR, Fuzzy and) were used to model the drought risk. Drought change land cover, soil moisture and surface roughness, it also influences the exchange of energy and water between the vegetation, soil and the air. Thus, it may affect surface radiation, heat and water balance by changing surface biophysical factors such as the VI, albedo and LST. In general, with the development of a drought, the NDVI decreases, the albedo and surface temperature increase and the soil moisture decrease, provided that other factors are stable. Combinations of these parameters may provide a useful tool for better understanding of the spatio-temporal patterns of drought. Most of the drought indices presented in the last decades are based on the above-mentioned parameters (especially NDVI, LST and Albedo). The retrieval of the surface albedo and the LST contains uncertainties rooted in the atmospheric correction of satellite data, decomposition of mixed pixel information, bidirectional reflectance distribution function (BRDF) modeling and the spectral remedy by a narrowband to broadband conversion. As a consequence, the final error associated with the extraction and quantifying of drought information would be magnified. On the other hand, calculating these indices need time series of satellite data which increase the time and the cost of processing. In Ghulam et al., 2007, the MPDI[2] as a real time index for drought monitoring based on vegetation fraction and soil moisture is presented. This index only needs one image to be calculated. In the present study, results assessed using MPDI. Final results indicated that the index overlay method can signify high-risk areas more accurately (R2=0.81) than the fuzzy operators.       [1] . Top Of Atmosphere [2] . Modified Perpendicular Drought Index}, keywords = {Drought Risk,Remote Sensing,GIS,Fuzzy logic,Index Overlay}, title_fa = {پهنه بندی خطر خشکسالی مناطق خشک با استفاده از روشهای دانش مبنا در محیط GIS (مطالعه موردی: حوضه شیطور، یزد)}, abstract_fa = {خشکسالی تاثیرات منفی بسیاری روی اقتصاد، محیط زیست و کشاورزی می گذارد و خسارات سنگینی را برای قسمت های مختلف جهان به بار می آورد، لذا تخمین و پیش بینی خشکسالی همواره یک مسئله مهم برای تصمیم گیرندگان و برنامه ریزان بوده است. هدف از این تحقیق پهنه بندی خطر خشکسالی در حوضه شیطور واقع در استان یزد با تلفیق داده های ماهواره ای، محیطی و هواشناسی می باشد. بدین منظور از تصاویر ماهواره ای ALOS (تیر 1388)، نقشه های توپوگرافی مقیاس 25000/1 و آمار بارندگی، دما و تبخیر ایستگاههای هواشناسی استفاده شده است. در ابتدا لایه های اطلاعاتی عوامل موثر بر خشکسالی (شیب، جهت، ارتفاع، دما، بارندگی، تبخیر، کاربری اراضی، تراکم شبکه آبراهه ها و درصد پوشش گیاهی) تهیه و سپس با استفاده از منطق فازی و براساس حساسیت به خشکسالی استاندارد گردید. از روش سلسله مراتبی جهت تعیین وزن هر پارامتر استفاده شد. به منظور تلفیق لایه های مذکور از دو روش شاخص وزنی و اپراتورهای مختلف منطق فازی و به منظور ارزیابی نتایج حاصله از شاخص عمودی خشکسالی اصلاح شده[1] (MPDI) استفاده شده است. نتایج نشان داد که از بین روشهای مورد استفاده، روش شاخص وزنی با بالاترین دقت (81/0R2 =) می تواند به منظور پهنه بندی خطر خشکسالی مورد استفاده قرار بگیرد.       [1] Modified Perpendicular Drought Index}, keywords_fa = {خطر خشکسالی,مناطق خشک,سنجش از دور,GIS,روشهای دانش مبنا}, url = {https://clima.irimo.ir/article_14120.html}, eprint = {https://clima.irimo.ir/article_14120_878904947b09cfceea72e0c633fdc904.pdf} }