Potential of Landsat-8 and Sentinel-2A composite for land use land cover analysis

2018 ◽  
Vol 34 (14) ◽  
pp. 1552-1567
Author(s):  
Divyesh Varade ◽  
Anudeep Sure ◽  
Onkar Dikshit
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Sentinel 2A

scholarly journals LAND USE/LAND COVER CHANGE DETECTION USING MULTI–TEMPORAL SATELLITE DATASET: A CASE STUDY IN ISTANBUL NEW AIRPORT

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 17-22 ◽  
Author(s):  
D. Akyürek ◽  
Ö. Koç ◽  
E. M. Akbaba ◽  
F. Sunar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Environmental Changes ◽  
Satellite Image ◽  
The Black Sea ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Environmental Consequences ◽  
Transportation Capacity ◽  
Sentinel 2A

<p><strong>Abstract.</strong> In recent years, especially in metropolitan cities such as Istanbul, the emerging needs of the increasing population and demand for better air transportation capacity have led to big environmental changes. One of them is originated due to the construction of the new airport (Istanbul Grand Airport &amp;ndash; IGA), located on the Black Sea coast on the European side of Turkey and expected as “The biggest hub in Europe” by the early 2020s. The construction has five phases and first construction phase is scheduled to finish up by the end of 2018. With an advanced space technologies including remote sensing, environmental consequences due to Land Use/Land Cover changes (LULC) can be monitored and determined efficiently. The aim of this paper is to analyse LULC changes especially in the forest areas and water bodies by using two different satellite image dataset. In this context, supervised classification method and different spectral indices are applied to both Landsat-8 (2013&amp;ndash;2017) and Sentinel 2A (2015&amp;ndash;2017) image datasets to demonstrate the total and annual changes during the construction of the first phase. The efficiency of two datasets is outlined by comparison of the output thematic map accuracies.</p>

scholarly journals Mapping Land use/land cover using a combination of Radar Sentinel-1A and Sentinel-2A optical images

2021 ◽  
Vol 652 (1) ◽  
pp. 012021
Author(s):  
T T H Nguyen ◽  
T N Q Chau ◽  
T A Pham ◽  
T X P Tran ◽  
T H Phan ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Use Land Cover ◽  
Optical Images ◽  
Sentinel 2A

scholarly journals Monitoring the Spatial Variation of Aerosol Optical Depth and Its Correlation with Land Use/Land Cover in Wuhan, China: A Perspective of Urban Planning

2021 ◽  
Vol 18 (3) ◽  
pp. 1132
Author(s):  
Qijiao Xie ◽  
Qi Sun
Keyword(s):  
Land Use ◽  
Air Quality ◽  
Urban Planning ◽  
Land Cover ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Water Bodies ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
The Impact

Aerosols significantly affect environmental conditions, air quality, and public health locally, regionally, and globally. Examining the impact of land use/land cover (LULC) on aerosol optical depth (AOD) helps to understand how human activities influence air quality and develop suitable solutions. The Landsat 8 image and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products in summer in 2018 were used in LULC classification and AOD retrieval in this study. Spatial statistics and correlation analysis about the relationship between LULC and AOD were performed to examine the impact of LULC on AOD in summer in Wuhan, China. Results indicate that the AOD distribution expressed an obvious “basin effect” in urban development areas: higher AOD values concentrated in water bodies with lower terrain, which were surrounded by the high buildings or mountains with lower AOD values. The AOD values were negatively correlated with the vegetated areas while positively correlated to water bodies and construction lands. The impact of LULC on AOD varied with different contexts in all cases, showing a “context effect”. The regression correlations among the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI), and AOD in given landscape contexts were much stronger than those throughout the whole study area. These findings provide sound evidence for urban planning, land use management and air quality improvement.

scholarly journals Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

2018 ◽  
Vol 10 (10) ◽  
pp. 3421 ◽  
Author(s):  
Rahel Hamad ◽  
Heiko Balzter ◽  
Kamal Kolo
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Land Cover ◽  
Land Use Changes ◽  
Landsat 8 ◽  
Chain Model ◽  
Land Use Land Cover ◽  
Kurdistan Region ◽  
The Past ◽  
Past Trend

Multi-temporal Landsat images from Landsat 5 Thematic Mapper (TM) acquired in 1993, 1998, 2003 and 2008 and Landsat 8 Operational Land Imager (OLI) from 2017, are used for analysing and predicting the spatio-temporal distributions of land use/land cover (LULC) categories in the Halgurd-Sakran Core Zone (HSCZ) of the National Park in the Kurdistan region of Iraq. The aim of this article was to explore the LULC dynamics in the HSCZ to assess where LULC changes are expected to occur under two different business-as-usual (BAU) assumptions. Two scenarios have been assumed in the present study. The first scenario, addresses the BAU assumption to show what would happen if the past trend in 1993–1998–2003 has continued until 2023 under continuing the United Nations (UN) sanctions against Iraq and particularly Kurdistan region, which extended from 1990 to 2003. Whereas, the second scenario represents the BAU assumption to show what would happen if the past trend in 2003–2008–2017 has to continue until 2023, viz. after the end of UN sanctions. Future land use changes are simulated to the year 2023 using a Cellular Automata (CA)-Markov chain model under two different scenarios (Iraq under siege and Iraq after siege). Four LULC classes were classified from Landsat using Random Forest (RF). Their accuracy was evaluated using κ and overall accuracy. The CA-Markov chain method in TerrSet is applied based on the past trends of the land use changes from 1993 to 1998 for the first scenario and from 2003 to 2008 for the second scenario. Based on this model, predicted land use maps for the 2023 are generated. Changes between two BAU scenarios under two different conditions have been quantitatively as well as spatially analysed. Overall, the results suggest a trend towards stable and homogeneous areas in the next 6 years as shown in the second scenario. This situation will have positive implication on the park.

scholarly journals Hydrological Responses to Land Use Land Cover Changes in the Fincha’a Watershed, Ethiopia

Land ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 916
Author(s):  
Urgessa Kenea ◽  
Dereje Adeba ◽  
Motuma Shiferaw Regasa ◽  
Michael Nones
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Local Governments ◽  
Swat Model ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Hydrological Response ◽  
African Countries ◽  
Hydrological Responses ◽  
Basin Scale

Land use land cover (LULC) changes are highly pronounced in African countries, as they are characterized by an agriculture-based economy and a rapidly growing population. Understanding how land use/cover changes (LULCC) influence watershed hydrology will enable local governments and policymakers to formulate and implement effective and appropriate response strategies to minimize the undesirable effects of future land use/cover change or modification and sustain the local socio-economic situation. The hydrological response of the Ethiopia Fincha’a watershed to LULCC that happened during 25 years was investigated, comparing the situation in three reference years: 1994, 2004, and 2018. The information was derived from Landsat sensors, respectively Landsat 5 TM, Landsat 7 ETM, and Landsat 8 OLI/TIRS. The various LULC classes were derived via ArcGIS using a supervised classification system, and the accuracy assessment was done using confusion matrixes. For all the years investigated, the overall accuracies and the kappa coefficients were higher than 80%, with 2018 as the more accurate year. The analysis of LULCC revealed that forest decreased by 20.0% between the years 1994–2004, and it decreased by 11.8% in the following period 2004–2018. Such decline in areas covered by forest is correlated to an expansion of cultivated land by 16.4% and 10.81%, respectively. After having evaluated the LULCC at the basin scale, the watershed was divided into 18 sub-watersheds, which contained 176 hydrologic response units (HRUs), having a specific LULC. Accounting for such a detailed subdivision of the Fincha’a watershed, the SWAT model was firstly calibrated and validated on past data, and then applied to infer information on the hydrological response of each HRU on LULCC. The modelling results pointed out a general increase of average water flow, both during dry and wet periods, as a consequence of a shift of land coverage from forest and grass towards settlements and build-up areas. The present analysis pointed out the need of accounting for past and future LULCC in modelling the hydrological responses of rivers at the watershed scale.

scholarly journals Land Use/Land Cover Changes of Ado-Ekiti LGA, Ekiti State, Nigeria Using Remote Sensing Techniques

2021 ◽  
pp. 32-42
Author(s):  
Ajagbe, Abeeb Babajide ◽  
Oguntade, Sodiq Solagbade ◽  
Abiade, Idunnu Temitope
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Local Government ◽  
Land Cover ◽  
Water Body ◽  
Local Government Area ◽  
Land Cover Changes ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Bare Surface

Land use assessment and land cover transition need remote sensing (RS) and geographic information systems (GIS). Land use/land cover changes of Ado-Ekiti Local Government Area, Ekiti State, Nigeria, were examined in this research. Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI were acquired for 1985, 2000, and 2015 respectively. Image scene with path 190 and row 055 was used for the three Landsat Images. A supervised digital image classification approach was used in the study, which was carried out using the ArcMap 10.4 Software. Five land use/land cover categories were recognised and recorded as polygons, including Built-up Areas, Bare surface, water body, Dense Vegetation and Sparse Vegetation. The variations in the area covered by the various polygons were measured in hectares. This study revealed that between 1985 and 2015, there was a significant change in Built-up areas from 1694 hectares to 5656 hectares. However, there was a reduction in water body from 25 hectares in 1985 to 19 hectares in 2015; there was a severe reduction in the bare surface from 4641 hectares in 1985 to 2237 hectares in 2015. Generally, the findings show that the number of people building houses in the study area has grown over time, as many people reside in the outskirts of the Local Government Area, resulting in a decrease in the vegetation and bare surfaces. The maps created in this research will be useful to the Ekiti State Ministry of Land, Housing, Physical Planning, and Urban Development to develop strategies and government policies to benefit people living in the Ado-Ekiti Local Government Area of the State.

scholarly journals MODELING THE DEVELOPMENTS IN URBANISATION AND RELATIONSHIP WITH VEGETATION COVER IN ALANYA

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 291-297
Author(s):  
B. İşler ◽  
Z. Aslan
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Landsat 8 ◽  
World Population ◽  
Land Use Land Cover ◽  
Temperature And Precipitation ◽  
Atmospheric Variables

Abstract. The increase in the world population and the migration of people from rural to urban areas causes an increase in artificial surfaces and causes many negative effects on the ecosystem, regional climate variations and global diversity. Nowadays, as the effects of climate change are felt more and more, it has gained importance in researches on this subject. Therefore, the estimation of the change in the vegetation density for the coming years and the determination of the land use / land cover (LULC) change in cities are very essential for urban planning. In this study, the effects of regional urbanization on vegetation are examined by using satellite data and atmospheric variables. In the vegetation analysis, multi-time index values obtained from TERRA-MODIS satellite, EVI (Enhanced Vegetation Index) and LST (Land Surface Temperature) were taken into account between the years of 2005 and 2018 in Alanya, Turkey. Temperature and precipitation were selected as the atmospheric variables and expected variations in EVI value until 2030 were estimated. In the study employed a wavelet-transformed artificial neural network (WANN) model to generate long-term (12-year) EVI forecasts using LST, temperature and precipitation. The relationship between land use / land cover and urbanization is investigated with NDBI (Normalized Difference Built-up Index) data obtained from the Landsat 8 OLI / TIRS satellite sensor. The simulation results show that The EVI value, which was 0.30 in 2018, will decrease to 0.25 in 2030.

scholarly journals Satellite-Based Assessment of Land Use and Land Cover (LULC) Changes around Lake Fitri, Republic of Chad

2018 ◽  
Vol 11 (5) ◽  
pp. 71
Author(s):  
Kim-Ndor Djimadoumngar ◽  
Jimmy Adegoke
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Water Volume ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Alternative Source ◽  
Lake Chad ◽  
Erdas Imagine ◽  
The Republic ◽  
Total Study Area

Lake Fitri, located northeast of the Republic of Chad&rsquo;s Capital, N&rsquo;Djamena, and southeast of Lake Chad, is especially important because it serves as an alternative source to Lake Chad in supporting the livelihood of pastoralists and subsidence farmers displaced from the Lake Chad region. It therefore serves to relieve population pressures on Lake Chad, which has undergone drastic reduction in total water volume in the last few decades. The area has also been the epicenter of recent violent campaigns and devastating insurgency mounted by the so-called Boko Haram. This study investigated the land use land cover around Lake Fitri from 1986 to 2003, and from 2003 to 2013 using Landsat 5 (TM), Landsat 7 (ETM+), and Landsat 8 (OLI_TIRS). The satellite imageries were retrieved from the Global Visualization (GloVis) web-based platform and analyzed using ERDAS Imagine 2014. Supervised classification of areas around the lake was performed into five land use land cover classes. The results revealed significant changes in three land use types, namely Farmland and Grassland combined, Forest, Savanna, and Steppe combined, and Wetland. Farmland and Grassland combined increased from a mere 0.38% of the total study area in 1986 to 41.05% in 2013. At the same time, Forest, Savanna, and Steppe combined decreased from about 23% in 1986 to about 7.40% in 2013. This increase in farmlands and grasslands coverage and the concomitant decrease in trees and shrubs can be explained by the persistent pressures on land from increasing population and livestock in the area. The findings also show a major decline in Wetland, which decreased from about 14% of the total study area to 3% in the same time period. This loss in wetland coverage is regrettable because of the important environmental and ecological functions of wetlands.

scholarly journals Land Use/Land Cover (LULC) Using Landsat Data Series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco

Environments ◽  
2018 ◽  
Vol 5 (12) ◽  
pp. 131 ◽  
Author(s):  
Meriame Mohajane ◽  
Ali Essahlaoui ◽  
Fatiha Oudija ◽  
Mohammed El Hafyani ◽  
Abdellah El Hmaidi ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Development ◽  
High Density ◽  
Thematic Mapper ◽  
Data Series ◽  
Landsat 8 ◽  
Low Density ◽  
Land Use Land Cover ◽  
Middle Atlas

The study of land use/land cover (LULC) has become an increasingly important stage in the development of forest ecosystems strategies. Hence, the main goal of this study was to describe the vegetation change of Azrou Forest in the Middle Atlas, Morocco, between 1987 and 2017. To achieve this, a set of Landsat images, including one Multispectral Scanner (MSS) scene from 1987; one Enhanced Thematic Mapper Plus (ETM+) scene from 2000; two Thematic Mapper (TM) scenes from 1995 and 2011; and one Landsat 8 Operational Land Imager (OLI) scene from 2017; were acquired and processed. Ground-based survey data and the normalized difference vegetation index (NDVI) were used to identify and to improve the discrimination between LULC categories. Then, the maximum likelihood (ML) classification method was applied was applied, in order to produce land cover maps for each year. Three classes were considered by the classification of NDVI value: low-density vegetation; moderate-density vegetation, and high-density vegetation. Our study achieved classification accuracies of 66.8% (1987), 99.9% (1995), 99.8% (2000), 99.9% (2011), and 99.9% (2017). The results from the Landsat-based image analysis show that the area of low-density vegetation was decreased from 27.4% to 2.1% over the past 30 years. While, in 2017, the class of high-density vegetation was increased to 64.6% of the total area of study area. The results of this study show that the total forest cover remained stable. The present study highlights the importance of the image classification algorithms combined with NDVI index for better understanding the changes that have occurred in this forest. Therefore, the findings of this study could assist planners and decision-makers to guide, in a good manner, the sustainable land development of areas with similar backgrounds.

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