Geographic Information System (GIS)
Ahmad Mazidi; Foroogh Mohammadi Ravari
Abstract
Extended Abstract
Introduction:
Time series analysis is a suitable tool that is used in mathematical modeling, predicting future events, revealing trends, investigating diffraction in climate data, as well as reconstructing incomplete data, and expanding information. Climatic changes are mainly caused ...
Read More
Extended Abstract
Introduction:
Time series analysis is a suitable tool that is used in mathematical modeling, predicting future events, revealing trends, investigating diffraction in climate data, as well as reconstructing incomplete data, and expanding information. Climatic changes are mainly caused by fluctuations, fluctuations, or changes in climatic elements, especially temperature and precipitation. These developments leave undeniable effects on local phenomena, hence the evidence of the past climate can be traced in all wet and dry, hot and cold environments, and biological areas (Ghayour, 2006:85). The temperature of the earth's surface is an important parameter for evaluating the energy budget of the earth's surface (Trigo et al, 2008:1). With the change in climate (temperature and rainfall), many changes are made on the surface of the earth, including vegetation. In fact, with the increase in temperature and decrease in rainfall, vegetation in the region decreases. Considering the importance of the issue and the relationship between climatic indicators and vegetation, by determining the relationship between them, one can predict the changes based on the other, which leads to an increase in the speed and accuracy of the work. Therefore, it seems important to use satellite images and extract and investigate the relationship between temperature and rainfall factors as well as vegetation in different areas, especially watersheds (Zhu et al, 2016:792). With the expansion of satellite technology, satellite images have widely provided access to information on land resources, and remote sensing tools have taken an important role in obtaining information about climate phenomena, because multi-spectral satellite images have important advantages, including They have the availability and ability of digital interpretation (Lillesand and Kiefer, 1994:750).
Materials & Methods:
In this research, using monthly rainfall data from a CHIRPS sensor with a spatial resolution of five kilometers, NDVI vegetation index from a MODIS sensor for 16 days, with a resolution of 250 meters, and day and night surface temperature of 8 days from a MODIS sensor with a resolution of one kilometer, to analyze the changes in surface temperature and its relationship with climatic factors in Kerman province during a statistical period of 22 years (2001-2022) were studied. In the investigation of the annual precipitation fluctuations of Kerman province, standardized values of Z have been used, and these values have varied between -1.5 and +1.5. After receiving the data, first the CHIRPS images, then the NDVI and LST images were processed in the ArcGIS software environment and the values were extracted for Kerman province and then analyzed in the Excel software environment.
Results & Discussion:
According to SPI results, drought is observed in 2010, 2016, 2018, and 2021, and drought in 2004, 2009, 2017, 2019 and 2020. In the rest of the years, the SPI index has been normal. Also, the seasonal rainfall showed that the highest rainfall was in the winters of 2005, 2017, and 2019 with an amount of 90 mm and more and the lowest rainfall was in the summer of 2019 with an amount of less than 1.04 mm. The value of the vegetation cover index (NDVI) is also in the spring season with a value of 1.05, which has an increasing trend, and the lowest value of the vegetation cover index (NDVI) in the autumn and winter seasons, whose lowest value is 0.35 and 0.42 on December 19 and November 17 with a trend A decrease is shown. The seasonal vegetation also shows that as we move from the west of the region to the east, the amount of vegetation decreases. The seasonal changes in the temperature of the surface of the earth during the day in Kerman province show that the hottest seasons are summer and spring and the coldest season is winter. The seasonal changes in the earth's surface temperature at night also show that the highest surface temperature is related to summer and spring, and the lowest is in autumn and winter.
Conclusion:
In general, the results show that according to temperature fluctuations, there is a positive and significant relationship between the temperature of the earth's surface and vegetation (P-value at the 0.01 level). And there is a negative and significant relationship between the temperature of the earth's surface and precipitation. So precipitation has the greatest effect on the variability of the earth's surface temperature and vegetation has the least effect on the surface temperature changes. The increase in day and night temperatures in the spring and summer seasons causes an increase in evaporation and a subsequent decrease in water resources throughout the province and pressure on underground water. On the other hand, with the increase in temperature, the amount of evaporation and transpiration (plants' water needs) will also increase and will lead to a potential decrease in water resources, especially in the eastern regions of the province, but the presence of vegetation can almost reduce the temperature of the earth's surface. In the autumn and winter seasons, during the last decades, with the increase in temperature, the amount of precipitation and vegetation has decreased. Also, an increase in temperature can increase the water demand, which in turn leads to more extraction of surface and underground water resources. This means that the surface temperature has increased significantly in the mentioned statistical period. Also, the different conditions of each region are important factors in determining the type of relationship between temperature, vegetation, and precipitation. The results of this research on the relationship between the earth's surface temperature and climatic factors with the research of Mianabadi et al (2023), and Mazidi et al (2023) based on the method of the experimental relationship between surface temperature and other factors are consistent. According to the findings, the temperature trend in Kerman province is significant and the possibility of heat stress will increase in the future.
Leyla Karami; Seyed Mohammad Tavakkoli Sabour; Ali Asghar Torahi
Abstract
Extended Abstract
Introduction
Vegetation is considered to be one of the most important elements in all major ecosystems on the Earth. Thus, a proper understanding of vegetation and its growth trends and other environmental factors has always been of particular importance for environmental research. ...
Read More
Extended Abstract
Introduction
Vegetation is considered to be one of the most important elements in all major ecosystems on the Earth. Thus, a proper understanding of vegetation and its growth trends and other environmental factors has always been of particular importance for environmental research. Estimating vegetation phenology parameters (VPPs) requires continuous NDVI data collection over a specific period of time. However, soil moisture, cloud cover, and particulate matter may affect the energy reflected from the vegetation cover and result in noisy images or erroneous data. Vegetation phenology parameters cannot be extracted from raw data due to the presence of random errors. These errors do not follow the phenological process and thus, overestimate or underestimate NDVI and fail to produce accurate results. Smoothing functions and especially the TIMESAT model are used to resolve this issue and eliminate errors in the NDVI time series. There is still no general consensus on which function acts more efficient and accurate in the TIMESAT model especially regarding the highlands. Naturally, each method yields different results in different regions, and thus it is necessary to compare and evaluate different functions used in the TIMESAT model and determine their accuracy in producing a continuous time series. The present study aimed to evaluate the performance of various functions such as asymmetric Gaussian (AG), double-logistic (DL), and Savitzky–Golay (SG) used to extract VPPs especially in mountainous regions.
Materials and Methods
TIMESAT model is a time-series analysis model based on remote sensing (RS) vegetation indices. It includes three functions: Savitzky–Golay, asymmetric Gaussian, and double-logistic, which are used to smooth collected data and identify outliers. Savitzky–Golay is an adaptive-degree polynomial filter (ADPF). The other two functions fit the information using nonlinear functions. These functions use unmodified NDVI data as input to produce modified and smoothed NDVI output. Four wheat farms in cold and warm regions of Khorramabad were used in the present study to investigate plant phenological behaviors and extract VPPs. The northern and eastern parts of Khorramabad have a cold climate, while the southern and western parts have a warm climate. One-year time series (2020) data of MODIS sensor was used in the present study. Using the infrared and near-infrared spectral reflectance values, NDVI was calculated in the Google Earth Engine environment. Errors of the NDVI time series were first corrected and a phenology curve was produced for wheat in both warm and cold farms. Asymmetric Gaussian, double-logistic, and Savitzky–Golay filter functions were also used to adapt the NDVI data. Following the reconstruction of growth curves in the time series of vegetation indices and smoothing the curve, various VPPs such as start of the season (SOS), end of the season (EOS), middle of the season (MOS), length of the growing season (LOS), base limit and value, maximum NDVI, vegetation growth season range, large seasonal integral, and small seasonal integral were extracted.
Results and Discussion
The model indicated that on average, beginning of the wheat growing season (SOS) in the warm regions of Khoramabad coincided with the 31.5th day of the year in the Gregorian calendar, whereas it happened on the 90th day of the year in the cold regions, thus indicating a 1.5-2 month difference between the beginning of the wheat growing season in cold and warm regions. The wheat growing season ended (EOS) on the 163rd day of year in the warm regions and on the 193rd day in the cold regions. In addition, in order to analyze the effect of climate on VPPs such as SOS and EOS, a comparison was made between the parameters obtained from farms in warm and cold regions. On average, the peak of vegetation growth has occurred in late March (Mar. 28, 2020) in farms of warm regions while cold regions experienced the peak of growth on May 20, 2020. In other words, warm regions have experienced peak growth approximately two months earlier than cold regions. Finally, the models were assessed and obtained values were compared with ground-based data collected in field surveys. Validation results showed that with an average RMSE of 2, Savitzky–Golay smoothing model reconstruct data more accurately as compared to asymmetric Gaussian, and double logistic function with an RMSE of 4 and 11, respectively. In other words, Savitzky–Golay estimates SOS and EOS with a higher accuracy and lower errors.
Conclusion
Findings indicate that Savitzky–Golay filter outperformed asymmetric Gaussian and double logistic functions in extracting VPPs in mountainous areas. Accordingly, it is suggested to use Savitzky–Golay in future studies aiming to investigate the phenological behavior of different vegetation covers in other Iranian highlands. The study has also showed that different climatic conditions within the study area affect plant phenological behaviors, which can lead to differences in SOS, peak of growing season, and EOS in different cold and warm regions of the province. Growing season of plants in cold regions of the province has occurred with an approximately two-month delay compared to the warm regions of the province.
Reza Sarli; Gholamreza Roshan; Stefan Grab
Abstract
Extended Abstract Introduction change monitoring is generally used to evaluate natural processes such as the long-term effects of climate change, which is affected by the interaction of the climatic system’s constructive components such as the biosphere, lithosphere, or factors that control the ...
Read More
Extended Abstract Introduction change monitoring is generally used to evaluate natural processes such as the long-term effects of climate change, which is affected by the interaction of the climatic system’s constructive components such as the biosphere, lithosphere, or factors that control the climate changes outside the climatic system, over a long period of time, as well as the short-term processes that include vegetation sequence and geomorphological processes. Change monitoring is also used to evaluate the effects derived from human activities such as deforestation, agriculture and urban development. Remote sensing is a very useful technology, which can be used to obtain information layers from the soil and vegetation. Materials and Methods Land Cover Product was used to process the MODIS1 Satellite data which is one of the most frequently used products designed relating to MODIS Satellite, and is used annually. This Sensor with 250-500 meter and also 1-kilometer spatial resolution has 36 spectral bands in the range of visible, reflectional infrared and thermal infrared wavelengths, which can well be used for various applications of the surface, the Earth surface, atmosphere and the oceans. MOD12Q1, which is one of the MODIS products, was used to investigate and analyze the profile of the vegetation changes in Mazandaran province using the NDVI and EVI indicators from 2005 to 2017. The related images have been prepared annually with 500-meter resolution and sine coordinate system in the form of a combination of Terra and Aqua data. Given the standards provided by NASA, the changes were investigated using the “decision tree” classification method, and the map for the prediction of its changes was calculated using the Markov Chain Model. The ArcGis software was then used to analyze these changes in order to determine which use of land with what percentage of changes has been allocated to which area. Results and Discussion In 2005, land-uses associated with dense vegetation dominated an area of 398.77 m2. These land-uses include wasteland, dense vegetation and scattered vegetation. The estimation of the changes occurring in the aforementioned land-uses showed that the maximum changes relating to the low density vegetation with an average of 55.62% are in the northwestern and the eastern parts, and the minimum changes relating to the in dense vegetation with an average of 77.21% are in the central parts of the region, respectively. Furthermore, the observations of the images of the year 2005 show that the use of dense vegetation which has turned into low density vegetation in the image of the year 2017, has had the minimum changes. Finally, considering the prediction of the observed changes, it can be concluded that these changes were more related to the altitude range of 1400 m to 2260 m with the slope coefficients of 15% to 99%. The prediction carried out using the Markov Chain also suggests that the low-density land cover, which was over 864/80 km2 in 2017, will turn into barren lands in proportion to the changes occurringin 2022. Conclusion A major part of the vegetation changes in the area is due tothelack of job opportunities, extra labor attraction and the economic poverty of the inhabitants.In addition,the pressure on the meadow fields hasreached its highest limit by ranchers,which has resulted inthe reduction of grasslands. Eventually, it could be stated that the evaluationmethods and modelsof the vegetation changes have their own featuresand no method on its own is usable andappropriate for all cases, hence,the identification of an appropriate method for evaluating thevegetation changesneeds to be examined quantitatively and qualitativelyin order to provide the best result.
Fatemeh Mohammadyari; Hamidreza Pourkhabaz; Morteza Tavakoli; Hossein Aghdar
Abstract
Knowledge of qualitative and quantitative characteristics of changes are extremely important in environmental planning, land use planning and sustainable development. Currently, using vegetation maps is one of the key factors in data production for macro and micro planning. In this research, information ...
Read More
Knowledge of qualitative and quantitative characteristics of changes are extremely important in environmental planning, land use planning and sustainable development. Currently, using vegetation maps is one of the key factors in data production for macro and micro planning. In this research, information of Landsat ETM + and OLI sensors were used to display the temporal and spatial changes of vegetation in Behbahan city in 1999 and 2013 and the value of NDVI index was calculated for two years. In order to evaluate the quality changes of vegetation, the numerical values of the index were classified into 4 classes of different lush green vegetation including land with excellent, very good, good, and poor coverage. Then, the changes were determined using CROSSTAB. The results showed that the qualitative and quantitative changes in vegetation for the study area have been extensive over 14 years, so that, the area of lands with excellent, very good and poor coverage has increased and the area of landswith good coverage, has decreased. The greatest increase in areashas occurred in lands with excellent coverage, so that, it has increased from 5069.76 hectares (ha) in 1999 to 7735.5 ha in 2013. Also, the highestdecrease in areas has occurred in lands with good coverage thathas reached from 34061.4 ha to 27434.43 ha. Finally, the regression equation was obtained to show better relationship between the two parameters of vegetation and temperature. The results confirmed the point that the areas covered with vegetation have lower temperature and vegetation has cooling effects on the surrounding. Therefore, the degradationof the region’s vegetationwill be followed by the warming of the city and many other environmental consequences.
