بررسی تغییرات پوشش گیاهی ایران با استفاده از سری های زمانی ndvi سنجنده noaa-avhrr و تجزیه وتحلیل هارمونیک سری های زمانی (hants)

بررسی تغییرات پوشش‌های گیاهی می‌تواند اطلاعات ارزشمندی را در مورد گرمایش جهانی،چرخه کربن، چرخه آب و تبادل انرژی به همراه داشته باشد. استفاده از سری‌های زمانی تصاویر ماهواره‌ای و روش‌های سنجش از دور اطلاعات زیادی را در مورد تغییرات و پویایی‌های پوشش‌های گیاهی به ما عرضه می‌دارند. هدف از پژوهش حاضر، تعیین تغییرات هر کدام از مولفه‌های سری‌های فوریه پوشش‌های گیاهی ایران در طول سه دهه گذشته می‌باشد. بدین منظور در این مطالعه ازمحصول ndvi روزانه سنجنده avhrrبا قدرت تفکیک مکانی 0.05 در 0.05 درجه با نام avh13c1 استفاده شد. سپس با استفاده از الگوریتم hants اجزای هارمونیک چهار سری زمانی یک‌ساله در زمان گذشته (1982، 1983، 1984 و 1985) و چهار سری زمانی یک‌ساله در سال‌های اخیر (2015، 2016، 2017 و 2018) تولید شد. در نهایت تغییرات اجزای هارمونیک یا همان تصاویر دامنه و فاز در سال‌های اخیر نسبت به سال‌های گذشته تعیین شد و اختلاف میانگین ارزش‌های اجزای هارمونیک بین چهار سری زمانی یک‌ساله در گذشته وحال با تجزیه واریانس یک طرفه بررسی شد و نقشه‌های معنی‌داری اختلاف بین میانگین‌ها بدست آمد. با توجه به نتایج،در مناطق مرکزی، شرق و شمال شرق ایران دامنه صفر (میانگین پوشش گیاهی) در سطح احتمال 95 درصد (f-value<0/05 ) کاهش یافته و در مناطق شمال و شمال غرب و غرب به ویژه ارتفاعات البرز و زاگرس دامنه صفر به طور معنی‌دار (f-value<0/05) افزایش یافته است. اختلاف میانگین ارزش فازها در چهار سری زمانی در گذشته و سال‌های اخیر در مناطق غرب و شمال غرب و همچنین شرق و شمال شرق ایران در سطح احتمال 95 درصد (f-value<0/05) معنی‌دار می‌باشد. فازهای سالانه این مناطق به میزان 14 درجه کاهش یافته است که این موضوع نشان دهنده شروع زودتر فرآیندهای رشد و فنولوژی گیاهان این مناطق نسبت به سه دهه گذشته می‌باشد.

Investigating vegetation changes in Iran using NDVI time series of NOAAAVHRR sensor and Harmonic ANalysis of Time Series (HANTS)

Introduction  Investigation of vegetation changes can provide valuable information on global warming, the carbon cycle,water cycle and energy exchange. Satellite imagery timeseriesandremote sensing techniques offers a great deal of information on variations and dynamics of vegetation. Harmonic ANalysis of Time Series (HANTS) has been effectively used to eliminate missing and outliers in time series of vegetation indices and land surface temperature (LST). However, the algorithm has been less frequently used to detect changes in vegetation and phenology. HANTSalgorithm decomposes periodic phenomena into their components(different sines and cosineswith different amplitudes and phases). The value of phases and amplitudes contains valuable information that can be used to investigate variations and identify different characteristics of vegetation such as growth and phenology. The present study aims to determine changes in each componentof vegetation time series in Iranin the past (1982, 1983, 1984 and 1985) and in recent years (2015, 2016, 2017 and 2018).   Materials & Methods  A daily NDVI product of AVHRR sensor, with a resolution of 0.05 at 0.05 ° (i.e. AVH13C1) was used in the present study. To obtain reliable harmonic components (amplitude and phase images), a reliable curve has to be fitted on the primary time series data. To do so, first,parameters of HANTS algorithm were determined and then Root Mean Square Error (RMSE) of the curves fitted on data related to four oneyear time series in the past year’s category (1982, 1983, 1984 and 1985) and four oneyear time series in recent year’s category (2015, 2016, 2017 and 2018) was estimated. This classification (i.e. four oneyear time series in the past and recent years) was used for two reasons. First, extraction and comparison of harmonic components in a single time series in the past and recentyears’ categories cannot reflect real changes, as these changes may occur under the influence ofimpermanent dynamics of vegetation, such as dryor wet periods. Second, with four oneyear time series in the past category (1982, 1983, 1984 and 1985), and four oneyear time series (2015, 2016, 2017 and 2018) in recent years, statistical comparison of the harmonic components through oneway analysis of variance becomes possible. Following the production of reliable harmonic components, variations of the harmonic components in recent years were compared with their variations in the past using difference method, and mean difference ​​of the harmonic components’value in four oneyear time seriesin the past and present categories wasdetermined using oneway analysis of variance. Finally, some maps were produced to exhibitthe significance of differenceinmeans.   Results & Discussion According to the findings of the present study, mean RMSE of the fitted curves in the four oneyear periods ofpresent and past time series were always less than 0.1 unit of NDVI. Moreover, mean RMSEof total area of Iranin the past and present time series were 0.037 and 0.039, respectively. This demonstrates high efficiency of the HANTS algorithm in elimination of missing and outlier data in the dailyNDVI time series ofNOAAAVHRR. Results indicate thatrange of zero amplitude (the mean value of NDVI or the average vegetation coverage) decreasesin the central, eastern and northeastern regions of Iran atthe 95% probability level (Fvalue <0.05), whileit increases significantly (Fvalue <0.05)in the north, northwestern and western regions (especially, the Alborz and Zagros mountains). The meandifferenceof phases value in the fourtime series of the past and recent years’categories wassignificant at the 95% probability level (Fvalue <0.05). Compared to the past time series, first harmonic phase average of total area of Iran in the new time series has decreased by almost 14 degrees. This decrease in the value of the annual and 6month phases indicates a quicker growth phase and phenological processes of plants compared to past times.   Conclusion  Results indicated that HANTS algorithm can effectively eliminateand reconstruct outliers in the NDVI time series. Zero harmonic (mean value) represents the overall level of vegetation cover and the firstharmonic phase in a oneyear time series determines the starting time of growth in seasonal plants or thosewith agrowth period of6month or less. Annual Phase indicates the angular starting position of the annual cycles and the 6month phase inherently indicates the fluctuation and angular position of a halfyear or 6month curve. However, interpreting 6month amplitude and phases are difficult. As most changes are controlled by the first harmonic phase, the first harmonic phase in a oneyear time series contains important information about the beginning of growth and the phenological processes of plants. Therefore, harmonic components of a periodic time series canbeusedto identify and determine changes in vegetation coverage and phenological processes.