ارزیابی تغییرات شاخص شدت بیابان‌زایی در استان کرمان با استفاده از سنجش‌ازدور

با توجه به اهمیت پدیده بیابان‌زایی و گسترش آن در سطح جهان، در پژوهش حاضر با استفاده از فناوری سنجش‌ازدور به ارزیابی روند تغییرات بیابان‌زایی استان کرمان پرداخته شد. بدین منظور ابتدا بر اساس میانگین بلندمدت ndvi ماه‌های مارس (اسفند-فروردین)، آوریل (فروردین-اردیبهشت)، می (اردیبهشت-خرداد) و ژوئن (خرداد-تیر) انتخاب شدند. با بهره‌ گیری از داده‌های سنجنده مودیس، تصاویر ndvi و سپیدایی در طول سال‌های 2022-2001 (1401-1380) تهیه و شاخص شدت بیابان‌زایی (ddi‌) برای 22 سال محاسبه شد. سپس با آزمون من‌کندال، روند تغییرات شاخص مذکور بررسی شد. همچنین با استفاده از تحلیل رگرسیون خطی، شدت تغییرات بیابان‌زایی در قالب ماه‌ های زمانی مورد‌نظر در طول 22 سال با استفاده از شاخص ddi‌ برآورد شد. نتایج پژوهش حاضر نشان داد که طبقه بدون روند تغییرات در تمام ماه‌های مورد بررسی بیشترین مساحت را به خود اختصاص داده است که برای ماه‌های مارس، آوریل، می و ژوئن به‌ ترتیب 82.45، 59.83، 49.96 و 51.79% می‌شود. بیشترین درصد مساحت شدت بیابان‌زایی ماه مارس با مقدار 23.93% به سال 2012 اختصاص داشت. همچنین بیشترین شدت بیابان‌زایی ماه آوریل، می و ژوئن به‌ ترتیب با مقدار 22.23، 20.14 و 22.42% به سال‌های 2012، 2001 و 2001 مربوط بود. نتایج شدت تغییرات بیابان‌زایی در واحد زمان نیز نشان داد که طبقه زیاد شدت تغییرات بیابان‌زایی در ماه مارس 14.81%، در ماه آوریل 17.83%، در ماه می 17.48% و در ماه ژوئن 17.75% مساحت استان را شامل می‌شود.

assessment of desertification trends in kerman province using remote sensing

extended abstract introductiondesertification is land degradation that occurs in arid, semi-arid, and dry sub-humid areas, where water is the most important limiting factor for land use in the ecosystem (19). desertification (31) is a term used to describe the decrease in biological productivity in ecosystems of arid, semi-arid, and dry sub-humid regions caused by climate change and human activities. to provide management solutions and prevent desertification spread globally, it is necessary to evaluate the phenomenon and its changes over time. at the global level, numerous studies have been conducted to evaluate and monitor desertification using various methods and evaluate its changes over time. in recent years, there has been a lot of attention given to remote sensing in this regard. lamchin et al. (2016) examined land cover changes and desertification in mongolia using remote sensing indices such as ndvi, albedo, and tgsi. it was discovered that the study area has experienced an increase in desertification each year (16). ontel et al. (2023) investigated the trend of land degradation and desertification in romania using remote sensing indices. the research findings revealed that 60% of the limited study area (22) had been improved and restored. in iran, the majority of the land is located in arid, semi-arid, and hyper-arid climates (4), and the duration of the warm season is increasing in 80 percent of the regions (10). these conditions will lead to the continued growth and expansion of desertification in iran. therefore, finding methods to assess this phenomenon, its causes, and predict its trends will become increasingly important. the management of vegetation cover and natural resources requires an assessment of the desertification process due to the semi-arid and desert climate of kerman province. this study aims to examine the trend of desertification over time using remote sensing due to the issue’s importance and the climatic conditions of kerman province. material and methodskerman province, with an area of 183,000 square kilometers, accounts for approximately 11% of the total area of the country. satellite images were used to calculate the intensity of desertification to achieve the set objectives. to calculate the intensity of desertification, the ddi index introduced by pan and li (2013) was utilized in this study. to evaluate desertification in kerman province, the first step was to establish the target months by utilizing the monthly ndvi average. estimates of ndvi were made on the google earth engine platform from 2001 to 2022 using the mod09a1 product layers of the modis sensor, and the monthly average for the study period was then determined. an average albedo coefficient map was calculated for the period 2001 to 2022 after determining the target months and obtaining the average ndvi map from 2001 to 2022 in these months. the intensity of desertification for the target months was calculated by performing a linear regression between ndvi and albedo coefficient in the next step. the highest intensity of desertification for every target month was determined by determining the year with the lowest ddi value at pixel level. after calculating the ddi index for the 22-year study period, to examine the trend of desertification changes, the trend of ddi index changes for the target months was calculated from 2001 to 2022 using the non-parametric kendall test in the terrset software. the slope of changes in the ddi index for the target months in time units was calculated using terrset software for 22 years. to simulate the trend of changes, linear regression analysis can be utilized. results and discussionthe highest ndvi values over the 22 years are in march, april, may, and june, which indicate the highest growth and greenness of vegetation during these months, as per the results obtained. the four months with the highest ndvi values were chosen to investigate the trend of desertification changes based on the results of this section. the most severe desertification in march was 23.93% in 2012, while the lowest was 23.0% in 2016. the highest and lowest severity of desertification in april were 22.23% and 35.0% in 2012 and 2006, respectively. the most severe desertification was recorded at 20.14% and 22.42% during may and june in 2001, while the least was 32.0% and 11.0% in 2017, respectively. the results indicate that the trendless class has allotted the largest area throughout the 22-year period, with values of 82.45%, 59.83%, 49.96%, and 51.79% in march, april, may, and june, respectively. the results indicate that desertification changes with high and very high intensity mostly occur in the southwest, south, and southeast regions of kerman province. the northwest and northeast regions of the province are also a part of this class. the vegetation cover values in march, april, may, and june were the highest in the year, as shown by the average monthly ndvi results. behrangmanesh et al. (2019) and alamdarloo et al. (2018) reported that the vegetation cover in most regions of iran is at its peak during these months. the results indicated that the intensity of desertification is increasing considerably in kerman province in all four selected months, particularly in the southern regions. the assessment of desertification changes showed that the southern regions of kerman province are classified as high and very high in all four selected months. the effectiveness of the desertification intensity assessment method proposed by pan and li (2013) can be demonstrated by analyzing the results of this study in kerman province and comparing it to previous research in this area.