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.
Naser Shafiei Sabet; Alireza Shakiba; Ashkan Mohammadi
Abstract
Extended Abstract Introduction Nowadays,satellite imagery is used as a suitable toolforproduction of land use maps. It is also considered to be an important resource used for urban and rural land use planning. Due to the general coverage of different phenomena and natural resources, satellite imageriesplay ...
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Extended Abstract Introduction Nowadays,satellite imagery is used as a suitable toolforproduction of land use maps. It is also considered to be an important resource used for urban and rural land use planning. Due to the general coverage of different phenomena and natural resources, satellite imageriesplay a major role in spatial and temporal analysis. Using these images in various fields can show us their capabilities and limitations. The important point is to consider increasing advances in their spectral and spatial capabilities. Systematicexploitation of natural resources requires patterns and models of the region, so that related regulations are observedand sustainable utilization is also considered.Obviously,exact, accurate, fast and economic estimate of these changes is impossible without modern technologiesused for regional and environmental studies.Land use change modelingis an indispensable tool for environmental analysis, planning and management. Eastern parts of Tehran metropolis are among regions facing unstructuredand unscheduled constructions in Iran. Urban development and population growth have led to rapid changes in spatial patterns and have severely affected land use and natural resources. Materials and methods In order to investigate land use changes, the present study takes advantage of satellite imageries, remote sensing techniques and spatial information systems.The trend of land use changeswas separately extracted from satellite imageries received in1986, 2002, and 2018.After visual interpretation and error correction,four categories were selected (residential and non-residential construction, vegetation, mountain and grassland) based on which changes were investigated. After data collection (including imageries received from Landsat satellite and TM, ETM and OLI sensors) classification and detection commenced.Then, suitable band was selected for classification, spectral reflectance curves of each land use class were evaluated and bands correlation histograms were compared.since changing bandsgives a comprehensive understanding of the classes, their relations and resolution, two-band diagram of pixels’ distribution in two different bands was used.Properties of the texture were extracted using GLCM matrix and principal component analysis was performed. Support Vector Machine was selected as an optimal classification method. Feature vectors and the training rangeweregiven to this algorithm as its input.Markov chain works well in predicting probability of change, and especiallyland use changes. Cellular automaton is also a powerful method used for detecting changes in spatial component. Thus,Markov chain and automated cells model were both used in order to predict changes in quantity and space, and land use map was predicted and simulated for 2050.Results indicate that Markov models provide useful information which can be beneficial for future land use planning. Results and discussion Calculations indicate thatdue to creeping discrete growth and in some areas continuous growth, most changes in Damavand (in Tehran)have happened in the category of residential construction (9.06%) and road (1%).This increasing trend has reduced two classes of mountain/grassland and vegetation cover by 9.07% and 0.1%, respectively. After field operations and sampling with dual-frequency GPS receivers, data was introduced to software and classification was performed using support vector machines with an average overall accuracy of 96.62% and a mean kappa coefficient of 85.33%. Change detection studiesindicate that in time period of 1986 to 2002,most changes have occurred in residential and non-residential construction category. In fact, residential and non-residential construction has reached from 3.1% in 1986 to 6.1% in 2002 year, while mountain and grassland category has faced 2.96% decrease. Also, vegetation cover has decreased by 0.76%.Likewise, we also saw a 6.15% increase in residential and non-residential construction, a 6.11% decrease in mountain and grassland and a 0.22% decrease in vegetation cover of the study area in the time period of 2002 to 2018.Road category had an 81% increase in the first time period and an 18% increase in the second time period. Overall, residential/non-residential construction and roads have increased, while mountains/grassland and vegetation cover have decreasedin the time period of 1986 to 2018. Due to population overflow in recent decades, and unplanned construction, land uses like vegetation cover and grassland have changed into residential construction, and especially industrial land use in the area under study (Jajrood, Kamard, KhorramDasht, Shamsabad, Mehrabad, Pardis and Siasang). Conclusion While investigating spatial evolution and agricultural land use changes, it is important to distinguish betweenrapidly changing phenomenon, and slowly changing one.Results of the present study indicate that compared to other land uses,vegetation cove has changed more severely. Therefore, without necessary policies and actions to prevent this process,pressure on naturalresources, land use changes, and consequently destruction of valuable resourceswill result in harmful environmental impacts. This will also change the economic performance of the villages, and have many negative spatial, socio-economic consequences.
Mohammad Rahim Rahnama; Mohammad Ajza Shokouhi; behnam ata
Abstract
Extended abstract Introduction Cities are always influenced by various forces and factors. They are transformed by social changes, demographic displacements, economic changes, and technological innovations. As the population grows, activities and investments are greatly expanded and the ...
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Extended abstract Introduction Cities are always influenced by various forces and factors. They are transformed by social changes, demographic displacements, economic changes, and technological innovations. As the population grows, activities and investments are greatly expanded and the physical system of the cities undergoes fundamental changes.Along with the rapid urbanization process, a large amount of natural lands, such as forests and wetlands, has turned into agricultural land and residential areas. Quick land use changes have had profound effects on natural and human environments. For example, agricultural developments and structures lead to deforestation, soil erosion, water basin degradation, and biodiversity loss and pollution. In addition, changes in the use of agricultural land and the acceleration of urbanization have led to an increase in impenetrable levels, which has led to the development of a transport network and the accumulation of non-disturbing contaminations associated with surface runoff. Due to these great effects, the detection and anticipation of land use changes has become an important topic in environmental management and land use planning. At their initial stagesof formation, most of the cities in Iran were established near or in the middle of thehigh-quality agricultural lands with the purpose of using high-quality soil foragriculture and then, these lands were gradually buried under the cities throughdevelopment of villages and changing into cities and then development of thecities. Accordingly agricultural activities were inevitably receded to the poorlands. Materials & Methods To access fundamental maps for analysis of data and use of different methods to achieve the goal of this study, satellite images related to the years 1987,2000 and 2010 are used. Topographic maps of 1:50000 scales obtained from the army geographical organization are used for geometric correction. At this stage, geometric correction was performed on the images using image sensor TM of the year 2010 image-vector, which were geo-referenced. To perform this task, 42ground control points with appropriate distribution in road junctions, water channels, etc. were used. In this research, to process data, make models, and analyze the output, land cover maps produced in the years 1987 and 2014 as inputs of the LCM model, were selected to analyze the changes in the region and predict land use changes in the year 1404. The LCM model requires two maps covering lands belonging to different times as inputs. In this study, Gains and losses, net changes, unchanged regions, transitions from each user to another in different classes of land cover, were mapped to the model analysis section of the model.ENVI, IDRISI Selva and ARCGIS10 were used to categorize the uses of most-probability-models and methods and finally Ca_Markov model was used to predict and calculate changes in 2025, 2035 and 2045. Results & Discussion Multi-temporal images used in this study were used in mapping land coverafter geometric corrections. With regard to existing images and maps and the condition of the area under investigation and field visit for mapping land cover, five types of applications are discovered for land namely, residential lands,irrigated lands, rain-fed lands, sterile lands, parks and gardens. Altogether, during this time (27years), agricultural and residential land cover has increased and sterile land and rain-fed land cover has decreased. Agricultural lands consume a huge amount of water due to exploiting water from deep holes and land overuse that has turned rain-fed lands and sterile lands into water-fed and residential lands. As the table of predicting areas indicates, the greatest increase of about 1744/74 hectares belongs to agricultural lands and 1741/79 hectares belong to the urban lands which includes: residential lands, trade centers, military areas, hospitals, higher education institutes, etc. The least change which is 274/18 hectares, belong to parks and gardens in and around the cities. The most decline of 2261/59 hectares, is observed in sterile lands. Of the total net changes, one can conclude that urban use has increased and all land cover has become largely urbanized, as well as water lands with the rise and development of deep wells. The need to preserve these lands from the physical development of the city in this direction is essential in order to develop the sustainable development of the city. There are many undeveloped lands in the old days due to the lack of water and the lack of facilities. The advancement of agriculture, turned these lands into agricultural lands. Today, landless areas are mostly on the suburbs or around the cities. This is mainly because of the farmers who leave their lands in a state of desert in hope of urban development to gain huge profits.This is the case where the city of Gonbad-e-Kavoos is not an exception to this rule. Parks and Gardens also have a rational increase in the city, therefore, in urban development projects, parks have been created but the size of the gardens is very low in the city of Gonbad-e-Kavoos. By predicting the changes in usages, it is concluded that the most changes will take place in urban usages and rain-fed and sterile lands with dramatic increase and decrease respectively.As the population of the city of Gonbad- e-Kavoosgrows, some steps should be taken to develop the spatial area of the city so as to prevent the destruction of fertile lands for the sake of human construction. Conclusion In this study, the effect of physical expansion of Gonbad-e-Kavoos city on agricultural lands is investigated. Findings indicate that during 45 years, around 1880 hectares of fertile farmlands surrounding the city are destroyed. The main reason behind this destruction is the horizontal expansion of the city. Hence, one of the fundamental bases of sustainable urban development is the increase of city density. It is concluded that horizontal expansion of the city is totally in contradiction to sustainable development and it leads to more instability of the city.
Vahed Kiani; Jahangir Feghhi; Aliakbar Nazari Samani; Afshin Alizadeh Shabani
Volume 22, Issue 87 , November 2013, , Pages 29-31
Abstract
Multispectral remote sensing data is an important informational resource used for recognizing surface changes. To the extent that today, remote sensing images can provide the latest information on vegetation and land use. The present study seeks to detect changes in vegetation and land use across Taleqan ...
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Multispectral remote sensing data is an important informational resource used for recognizing surface changes. To the extent that today, remote sensing images can provide the latest information on vegetation and land use. The present study seeks to detect changes in vegetation and land use across Taleqan area in time period between 1988 and 2007 using remote sensing. Taleqan is located in Alborz province (Karaj) and Taleqan basin. Results indicate that area dedicated to gardening has increased to 2.28 percent, while agricultural lands have faced a 15.05 decrease. On the other hand, rangelands have decreased to 16.25 percent and bare lands have increased to 28.08 percent. The most important change happened with the construction of Taleqan storage dam in 1999 which submerged more than 1100 hectares of the most desirable lands in the area. Since bare lands have increased and rangelands have decreased, thus from an ecological viewpoint it is possible to say that vegetation is degrading. Therefore, in order to restore bare lands, performing rangeland plans and avoiding unplanned changes can be suggested.
