Extraction, processing, production and display of geographic data
Maryam Kouhani; Abbas Kiani; Yasser Ebrahimian Ghajari
Abstract
Extended AbstractIntroductionVegetation has always been affected by various environmental and human factors that have directly or indirectly affected the conditions and performance of the environment over time. Consequently, monitoring and investigating the vegetation cover in the northern regions of ...
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Extended AbstractIntroductionVegetation has always been affected by various environmental and human factors that have directly or indirectly affected the conditions and performance of the environment over time. Consequently, monitoring and investigating the vegetation cover in the northern regions of Iran is also highly considered important. Research suggests that the destruction and change of vegetation cover and forests are among the most important factors influencing natural hazards such as floods, erosion, and earthquakes. In addition to processing and presenting well-known spatial data, remote sensing can also be used to improve human understanding of annual changes in vegetation cover, from a local to a global scale. In this regard, the anomaly evaluation criterion with high differentiation can separate and display anomalous areas in order to recognize the change process and reveal the areas with anomalies over time. Thus, medium-resolution images, vegetation indices, and anomaly criteria can be used to evaluate long-term vegetation changes. Therefore, a positive step in reducing the environmental effects of a region can be made by locating the urban areas that have experienced changes over time and making decisions related to future planning.Material and methodsThis study utilized a time series of Landsat 5, 7, and 8 images downloaded from the Google Earth engine. To get the best representation of the vegetation in this study, spring and summer were chosen because vegetation at this time is at its greenest. The main focus of this study was on the evaluation of vegetation changes over time quantitatively and qualitatively, using remote sensing data from Google Earth Engine to prepare a map of vegetation changes over time. The general process of implementing this research can be summarized in 7 phases. The first phase involves taking Landsat images and preparing statistical meteorological data. In the second phase, the time series images were collected according to the specific period and in the third phase, the obtained images were corrected and pre-processed. As a next step, the EVI index is extracted from all Landsat images, and then to determine the anomaly of changes, a series of statistical analyses, including the mean and standard deviation, are applied. The next step involves generating the map of anomalous time series changes and extracting the map of vegetation changes to improve understanding. The end of the process also includes evaluating the results obtained from this research. Results and DiscussionSince vegetation and drought changes are non-uniform depending on location and distance from the sea and humid areas, and vegetation is destroyed to build villas, residential areas, commercial areas, and towns, several study areas were divided into smaller pieces. Then each area was analyzed and evaluated separately for its changes. It has been observed in the first and third study areas that vegetation has generally been on the rise in the past 36 years, although sometimes there have been anomalies and fluctuations in EVI value. It was significant to see the reduced vegetation in 2008 in both regions. For example, 262.5 mm of precipitation in the first region fell this year, indicating a rain shortage. The results obtained from the second region, considered one of the coastal regions, indicate that the anomaly graph in the region during the period had a downward slope in the direction of decreasing vegetation, and EVI values reached 0.14 in 2005 and 0.09 in 2013. The 4th and 5th regions have shown a lot of fluctuations in anomalous changes and EVI values, although the trend has generally been downward. Results obtained in the 4th region show that vegetation cover peaked in 2004 and 2011. Rainfall in the 5th region, a highland region, in 2008 was deficient, with 259.8 mm reported by the meteorological station. The anomaly value in this year was -1.96. According to the Department of Meteorology in Mazandaran province, most droughts that have affected the underground water in the province have taken place in coastal and plain areas in the province's east and center, and in western cities, they have mostly affected mountainous areas.ConclusionThirty-six years of EVI time series images obtained from Landsat images were utilized in this study to investigate the changes and identify anomalies. In order to conduct a more detailed investigation, the study area was divided into several different regions, and each region was evaluated separately. The results obtained with existing meteorological statistical data were analyzed because vegetation can be affected by climatic and environmental conditions such as weather conditions. According to the results from study area )4(, vegetation cover has consistently decreased over the last three decades due to various factors like tree cutting, landslides, or land use changes. As shown in the map showing the obtained changes, there appears to be an increase in the value of the vegetation index in some northern areas of Chalus city until around 2002, indicating an improvement in greenness. While In some areas close to the Caspian Sea and the coastline, because of the construction of villas and commercial areas, there has been a loss of vegetation, such as in area (2) based on the changed map, a major part of the vegetation in that area has been destroyed because of the establishment of a settlement and construction of a road. As a result of comparing the evaluation of two anomaly approaches, it has also been concluded that both modes show almost the same trend of changes, but the graphs in "Anomaly compared to the overall average" mode compared to "Anomaly compared to the average of each set" display the change process better.
