Quantifying Spatio Temporal Changes in Coastal Buit-up area of South Goa based on Landsat Imageries using Google Earth Engine

Urban flooding has become a significant concern across many towns and cities in the Asia Pacific. Vulnerabilityand its components must be understood in order to minimize flood risks. Rapid urban growth occurs in developing countries,resulting from unplanned settlements growing along the rivers, and coastlines are at greater risk. On average, a total of 40%of the world’s population lives in narrow coastal belts that take up 7% of the total world land area. Coastal areas areurbanizing at an unprecedented rate that is posing a common threat to humans and ecosystems. Low-lying coastal areas areespecially susceptible to climate change related coastal hazards such as; sea level rise, storm surge, coastal flooding, landsubsidence etc.This study has been carriedoutacross four talukas of South Goa district, India's smallest state, locatedalong the Arabian sea. The low-lying coastal belt of South Goa district is dotted with world famous sandy beaches ofPalolem, Agonda, Colva etc. which attract millions of tourists every year. The present study has assessed the spatio-temporalgrowth of built-up land in low-lying coastal areas (Marmugao, Salcette, Quepem and Canacona) of South goa district.GoogleEarthEngineplatformwasusedtoestimateNormalizedDifferenceBuildIndex(NDBI)basedonLandsatETM+/OLI imageries for 2009, 2015 and 2020 to determine and map spatio-temporal changes in the total built-up area. Theresult revealed that there had been a rapid built-up area increment in South goa coastal belt by 24.94 Sq. Km between 2009(88.46 Sq. Km) and 2015 (113.40 Sq. Km) and by 15.14 Sq. Km between 2015 (113.40 Sq. Km) and 2020 (128.54 Sq. Km).The main driving force behind this phenomenon is the extensive land use changes for haphazard tourism development (inSalcetteandCanacona)andimmigration(inMarmugao).However,theconversionoftraditionalpaddyfieldsandmodification of natural drainage systemtoincrease built-up areas cansignificantly increase the physical andsocialvulnerability in low lying areas of Salcette and Canacona against the coastal hazards. This study may help urban planners/authoritiestolettheregiondevelopin sustainablemanne

PERCEPTION OF SPATIAL VARIATION IN ACTIVITIES PREPARED BY GIS IN GEOGRAPHY EDUCATION

In this study, it is aimed to give students the ability to perceive spatial variation and to reveal the impact of the activities on students' course success by using Geographical Information Systems (GIS) in geography education. The study group consisted of a total of 60 students attending to the 10th grade of secondary school in 2018-2019 academic year, which was determined by appropriate sampling method in Berat Hayriye Cömertoğlu Anatolian High School in Alanya district of Turkey. In this quantitative study, pretest-posttest quasi-experimental research model was used. The courses were taught with traditional methods and GIS based activity techniques for the control and experimental groups, respectively. The data were collected with the help of the subject achievement test prepared by the researchers in accordance with the expert opinions in the field. At the end of the posttest, data were analyzed by performing t-test in SPSS 22.0. As a result, it was determined that the courses taught with GIS based activities gave students a higher level of perception of spatial variation skills compared to the courses taught with traditional methods. Also, it was clarified that the students in the course which were taught with GIS based activities were more successful. &nbsp

ANALYSIS OF URBAN LAND USE CHANGE USING REMOTE SENSING AND DIFFERENT CHANGE DETECTION TECHNIQUES: THE CASE OF ANKARA PROVINCE

This study aims to use remote sensing techniques to map the urban region of Ankara from the past to the present, assessing the nature, magnitude and direction of changes within the area, including the transformation of LULC classes and explaining the driving forces behind these transformations. The study encompasses three stages. Firstly, Landsat 7 ETM+ images from 2000 and Sentinel-2 satellite images from 2020 were obtained for Ankara city and surroundings through the Google Earth Engine (GEE) platform. Image classification was conducted for both 2000 and 2020 using 'Blue', 'Green', 'Red', 'Vegetation Red Edge1', 'Vegetation Red Edge2', 'Vegetation Red Edge3', 'NIR', 'Vegetation Red Edge4', 'Water vapour', ' SWIR1', 'SWIR2' bands, as well as 'NDWI', 'NDVI', 'NDBI' indices on the GEE platform. LULC was classified using the Random Forest (RF) classifier, which included six classes: urban area, forest, water surfaces, open areas, agricultural areas and roads. Secondly, the LULC maps of the 2000 and 2020 images were classified using RF. The study employed the 'Categorical Change, Pixel Value Change and Time Series Change' methods to determine the transformations between LULC categories. Specifically, the urban change within the study area increased by 70% between 2000 and 2020. Over the past 20 years, from 2000 to 2020, the urban areas in Ankara expanded by 170%. Consequently, accurately determining the nature, magnitude and direction of urban development using remote sensing data offers valuable baseline information for various disciplines related to spatial planning at local and national scales

Land Consumption Monitoring with SAR Data and Multispectral Indices

Land consumption is the increase in artificial land cover, which is a major issue for environmental sustainability. In Italy, the Italian Institute for Environmental Protection and Research (ISPRA) and National System for Environmental Protection (SNPA) have the institutional duty to monitor land consumption yearly, through the photointerpretation of high-resolution images. This study intends to develop a methodology in order to produce maps of land consumption, by the use of the semi-automatic classification of multitemporal images, to reduce the effort of photointerpretation in detecting real changes. The developed methodology uses vegetation indices calculated over time series of images and decision rules. Three variants of the methodology were applied to detect the changes that occurred in Italy between the years 2018 and 2019, and the results were validated using ISPRA official data. The results show that the produced maps include large commission errors, but thanks to the developed methodology, the area to be photointerpreted was reduced to 7300 km2 (2.4% of Italian surface). The third variant of the methodology provided the highest detection of changes: 70.4% of the changes larger than 100 m2 (the pixel size) and over 84.0% of changes above 500 m2. Omissions are mainly related to single pixel changes, while larger changes are detected by at least one pixel in most of the cases. In conclusion, the developed methodology can improve the detection of land consumption, focusing photointerpretation work over selected areas detected automatically

City-level comparison of urban land-cover configurations from 2000-2015 across 65 countries within the global Belt and Road

The configuration of urban land-covers is essential for improving dwellers' environments and ecosystem services. A city-level comparison of land-cover changes along the Belt and Road is still unavailable due to the lack of intra-urban land products. A synergistic classification methodology of sub-pixel un-mixing, multiple indices, decision tree classifier, unsupervised (SMDU) classification was established in the study to examine urban land covers across 65 capital cities along the Belt and Road during 2000-2015. The overall accuracies of the 15 m resolution urban products (i.e., the impervious surface area, vegetation, bare soil, and water bodies) derived from Landsat Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) images were 92.88% and 93.19%, with kappa coefficients of 0.84 and 0.85 in 2000 and 2015, respectively. The built-up areas of 65 capital cities increased from 23,696.25 km(2) to 29,257.51 km(2), with an average growth rate of 370.75 km(2)/y during 2000-2015. Moreover, urban impervious surface area (ISA) expanded with an average rate of 401.92 km(2)/y, while the total area of urban green space (UGS) decreased with an average rate of 17.59 km(2)/y. In different regions, UGS changes declined by 7.37% in humid cities but increased by 14.61% in arid cities. According to the landscape ecology indicators, urban land-cover configurations became more integrated (oShannon's Diversity Index (SHDI) = -0.063; oPatch Density (PD) = 0.054) and presented better connectivity (oConnectance Index (CON) = +0.594). The proposed method in this study improved the separation between ISA and bare soil in mixed pixels, and the 15 m intra-urban land-cover product provided essential details of complex urban landscapes and urban ecological needs compared with contemporary global products. These findings provide valuable information for urban planners dealing with human comfort and ecosystem service needs in urban areas

Assessment of human‑induced effects in the Sultan marshes (Ramsar Protection), Kayseri (Turkey)

This study examines the drying in the Sultan Marshes and the spatio-temporal change of different land cover classes. Corine land cover change outputs were examined for four periods (1990–2000; 2000–2006; 2006–2012; and 2012–2018). During these analyses, the period when the water area changes in the lakes occur the most was determined. Moreover, other land cover changes occurring in the region were defined. The LCC results were compared and discussed in terms of some human factors (i.e., human development index and terrestrial human footprint). According to the results of this study, it was observed that there was a severe decline in the lake surface water located in the Sultan Marshes National Park Area. The water’s surface in the lakes decreased by 50% in the 2000s compared to previous years and decreased until 2006. This withdrawal was prominent especially in Lake Yay and Lake Çöl. Considering the human factors (Human Development Index) and variables (terrestrial Human Footprint) in terms of the spatio-temporal land cover change, it is seen that the human development in the region increased from 0.54 to 0.81 from 1990 to 2018, and the human footprint increased the most in 1993. Water area changes occurred at a high rate between 1990–2000 and 2000–2006. It results from the growing demand for basic needs (such as water consumption and food diversity) with increasing human development and expanded agricultural practices in the region and overuse of the ground and aboveground waters that are the source of the lakes. Especially between 1990 and 2000, the high number of human interventions in the region caused the human footprint to be higher in 1993 than in 2009. Unless the Sultan Marshes have the proper planning and policies, it faces the danger of complete drying up with the effects of climate change in the future

Atlas of Global Surface Water Dynamics

It is impossible to overstate the importance of freshwater in our daily lives – for proof, try going without it for any length of time. Surface waterbodies (lakes, ponds, rivers, creeks, estuaries… it doesn't matter what name they go under) are particularly important because they come into direct contact with us and our biophysical environment. But our knowledge concerning where and when waterbodies might be found was, until recently, surprisingly sparse. The paucity of information was because trying to map a moving target is actually very difficult – and waterbodies undeniably move, in both geographical space and time. By 2013 the U.S. Geological Survey and NASA were making petabyte scale archives of satellite imagery freely available, archives that covered the entire planet's surface and stretched back decades. Other's such as the European Commission / European Space Agency Copernicus programme were also putting full free and open data access policies into place, and Google's Earth Engine had become a mature, powerful cloud-based platform for processing very large geospatial datasets. Back in 2013 a small team working at the European Commission's Joint Research Centre were looking at ways satellite imagery could be used to capture surface waterbody dynamics, and create new maps that accurately incorporated time dimensions. Concurrently the Google Earth Engine team were focussing their massive computational capabilities on major issues facing humanity, such as deforestation, food security, climate change - and water management. The two teams came together in a partnership based not on financial transactions but on a mutual exchange of complementary capabilities, and devoted thousands of person hours and thousands of CPU years into turning petabytes of Landsat satellite imagery into unique, validated surface water maps, first published in 2016, and made available to everyone through a dedicated web portal, the Global Surface Water Explorer. Since then satellites have continued to image the Earth, surface water has continued to change and the JRC Goole Earth Engine partnership has continued to work on improving our knowledge of surface water dynamics and making sure this knowledge benefits as many people as possible. This Atlas is part of the outreach; it is not a guide to the Global Surface Water Explorer, it is not a Google Earth Engine tutorial (though if it inspires you to visit either of these resources then it has achieved one of its objectives), but it is a stand-alone window into how people and nature affect, and are affected by the 4.46 million km2 of the Earth's landmass that have been under water at some time over the past 35 years.JRC.D.5-Food Securit

Covid-19 kapanma önlemlerinin Türkiye’deki hava kirleticilerine etkisi

Due to the COVID-19 pandemic, the precautions taken in the early period of the pandemic have had a significant impact on the reduction of air pollutants. In this research, the changes in the concentrations of some air pollutants (PM10, NO2, SO2, CO, O3) concentrations have been investigated and evaluated between March 15 - May 31, 2019 and March 15 - May 31, 2020 in Turkey. According to the results, PM10, NO2 and SO2 concentrations decreased by up to 75%, 80% and 77% respectively. However, there has been an increase in CO and O3 concentrations in many cities. Pearson’s correlation analysis showed that there is a strong relevance between NO2 - CO and O3 - CO concentrations in the lockdown period. In addition, with the precautions, the positive correlation between PM10 and NO2 and between SO2 and CO increased, and the negative correlation between PM10 and O3 decreased.COVID-19 nedeniyle pandeminin erken döneminde alınan önlemler, hava kirleticilerinin azaltılmasında önemli bir etki yaratmıştır. Bu araştırmada, Türkiye'de 15 Mart-31 Mayıs 2019 ve 15 Mart31 Mayıs 2020 tarihleri arasında bazı hava kirletici (PM10, NO2, SO2, CO, O3) konsantrasyonlarındaki değişimler araştırılmış ve değerlendirilmiştir. Sonuçlara göre PM10, NO2 ve SO2 konsantrasyonları sırasıyla %75, %80 ve %77'ye kadar düşmüştür. Ancak birçok şehirde CO ve O3 konsantrasyonlarında artış tespit edilmiştir. Pearson korelasyon analizi, kapanma döneminde NO2-CO ve O3-CO konsantrasyonları arasında güçlü bir ilişki olduğunu göstermiştir. Ayrıca alınan önlemlerle PM10 ile NO2 ve SO2 ile CO arasındaki pozitif korelasyon artmış, PM10 ile O3 arasındaki negatif korelasyon azalmıştır

Monitoring land use in cities using satellite imagery and deep learning

Over time, cities expand their physical footprint on land and new cities emerge. The shape of the built environment can affect several domains which are policy relevant, such as carbon emissions, housing affordability, infrastructure costs, and access to services. This study lays a methodological basis for the monitoring and consistent comparison of land use across OECD cities. An advanced form of deep learning, namely the U-Net model, is used to classify land cover and land use in EC-ESA satellite imagery for 2021. This complements conventional statistical data by monitoring large surfaces of land efficiently and in near real-time. In specific, following the availability of detailed data for model training, built-up areas in residential or business-related use are mapped and analysed for 687 European metropolitan areas, as a case application. Recent urban expansion’s speed and shape are explored, as well as the potential for assessing land use in cities beyond Europe