scholarly journals Mapping Spatiotemporal Dynamics of Akure Industrial Layout for Sustainable Development

2021 ◽  
Vol 04 (09) ◽  
Author(s):  
Ogunlade, Simeon. O. Ph.D. ◽  
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Land Cover Classification ◽  
Spatiotemporal Dynamics ◽  
Thematic Mapper ◽  
Landsat Images ◽  
Continuous Increase ◽  
The Future ◽  
Ondo State ◽  
Industrial Layout

This research aimed at mapping the spatiotemporal dynamics of the Industrial Layout located in Akure Ondo State Nigeria. The dataset used are the administrative map of Ondo State, Akure Industrial Layout Boundary,various Landsat imageries of 32m resolution which are Thematic Mapper (TM) of 1986 & 1991, Enhanced Thematic Mapper Plus (ETM)+ of 2002, Operational Land Imager / Thermal Infrared Sensor (OLI/TIRS) of 2014, 2017, 2020; and Worldview 3 image 2020 of 1.24m resolution. The Landsat data were used to extract the different Land use/Land cover (LULC) within the study area. GPS receiver and Worldview 3 image were used to obtain the coordinates of the different LULC classes, which aided in the classification of image, and also for accuracy assessment of the classified image. All the Landsat standard data products were processed, to ascertain that they are free of radiometric and geometric errors using the Level 1 Product Generation System (LPGS) and extracted to obtain the landsat image bands. The extracted Landsat images (bands) were used in the processing and calculating the Normalized Difference Vegetation Index (NDVI) and calculation of LULC changes. Evaluation the accuracy of the results produced from the land cover classification was carried out by comparing the results of ground coordinates with the coordinates obtained from a higher resolution image (Worldview 3 image) in order to determine the accuracy of the land cover classification in the study area. The trend of changes of land cover in these areas was assessed and also, the prediction for the future condition both in terms of development was determined based on the results obtained from the initial results. Results from various maps produced and numerical data generated showed that Akure Industrial Layout was mainly dominated by shrub and grass land in 1986 and has in 34 years experienced transformation of 604% in the built environment (18% /year), 119% of Bareland (3.5%/year), and -29% of Grassland (0.9%/year), -66% of Shrub (2%/year). The forecast of the probable spatial extent for the years 2025 and 2030 were estimated to be 175.3Ha and 214.8Ha respectively, which shows there will be a continuous increase in the future development in Akure Industrial layout. The research recommended a proactive action from the government and end-users that will ensure a sustained manageability of the layout.

scholarly journals Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine

2019 ◽  
Vol 11 (24) ◽  
pp. 3023 ◽  
Author(s):  
Shuai Xie ◽  
Liangyun Liu ◽  
Xiao Zhang ◽  
Jiangning Yang ◽  
Xidong Chen ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Vegetation Index ◽  
Time Series Data ◽  
Land Cover Classification ◽  
Google Earth ◽  
Thematic Mapper ◽  
Series Data ◽  
Land Cover Mapping ◽  
Google Earth Engine

The Google Earth Engine (GEE) has emerged as an essential cloud-based platform for land-cover classification as it provides massive amounts of multi-source satellite data and high-performance computation service. This paper proposed an automatic land-cover classification method using time-series Landsat data on the GEE cloud-based platform. The Moderate Resolution Imaging Spectroradiometer (MODIS) land-cover products (MCD12Q1.006) with the International Geosphere–Biosphere Program (IGBP) classification scheme were used to provide accurate training samples using the rules of pixel filtering and spectral filtering, which resulted in an overall accuracy (OA) of 99.2%. Two types of spectral–temporal features (percentile composited features and median composited monthly features) generated from all available Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data from the year 2010 ± 1 were used as input features to a Random Forest (RF) classifier for land-cover classification. The results showed that the monthly features outperformed the percentile features, giving an average OA of 80% against 77%. In addition, the monthly features composited using the median outperformed those composited using the maximum Normalized Difference Vegetation Index (NDVI) with an average OA of 80% against 78%. Therefore, the proposed method is able to generate accurate land-cover mapping automatically based on the GEE cloud-based platform, which is promising for regional and global land-cover mapping.

Persistent Homology for Land Cover Change Detection

2021 ◽  
Author(s):  
Djamel Bouchaffra ◽  
Faycal Ykhlef
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Vegetation Index ◽  
Homology Theory ◽  
Urban Morphology ◽  
Detection Methods ◽  
Landsat Images ◽  
Data Repositories ◽  
Land Cover Change Detection

The need for environmental protection, monitoring, and security is increasing, and land cover change detection (LCCD) can aid in the valuation of burned areas, the study of shifting cultivation, the monitoring of pollution, the assessment of deforestation, and the analysis of desertification, urban growth, and climate change. Because of the imminent need and the availability of data repositories, numerous mathematical models have been devised for change detection. Given a sample of remotely sensed images from the same region acquired at different dates, the models investigate if a region has undergone change. Even if there is no substantial advantage to using pixel-based classification over object-based classification, a pixel-based change detection approach is often adopted. A pixel can encompass a large region, and it is imperative to determine whether this pixel (input) has changed or not. A changed image is compared to the available ground truth image for pixel-based performance evaluation. Some existing change detection systems do not take into account reversible changes due to seasonal weather effects. In other words, when snow falls in a region, the land cover is not considered as a change because it is seasonal (reversible). Some approaches exploit time series of Landsat images, which are based on the Normalized Difference Vegetation Index technique. Others evaluate built-up expansion to assess urban morphology changes using an unsupervised approach that relies on labels clustering. Change detection methods have also been applied to the field of disaster management using object-oriented image classification. Some methodologies are based on spectral mixture analysis. Other techniques invoke a similarity measure based on the evolution of the local statistics of the image between two dates for vegetation LCCD. Probabilistic approaches based on maximum entropy have been applied to vegetation and forest areas, such as Hustai National Park in Mongolia. Researchers in this field have proposed an LCCD scheme based on a feed-forward neural network using backpropagation for training. This paper invokes the new concept of homology theory, a subfield of algebraic topology. Homology theory is incorporated within a Structural Hidden Markov Model.

scholarly journals Comparison between Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) Assessment of Vegetation Indices

2017 ◽  
Vol 1 (2) ◽  
pp. 355-366
Author(s):  
E. O. Makinde ◽  
A. D. Obigha
Keyword(s):  
Statistical Analysis ◽  
Land Cover ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Thematic Mapper ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Water Index ◽  
Landsat 7 ◽  
Index 2

The Landsat system has contributed significantly to the understanding of the Earth observation for over forty years. Since May 2013, data from Landsat 8 has been available online for download, with substantial differences from its predecessors, having an extended number of spectral bands and narrower bandwidths. The objectives of this research were majorly to carry out a cross comparison analysis between vegetation indices derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) and also performed statistical analysis on the results derived from the vegetation indices. Also, this research carried out a change detection on four land cover classes present within the study area, as well as projected the land cover for year 2030. The methods applied in this research include, carrying out image classification on the Landsat imageries acquired between 1984 – 2016 to ascertain the changes in the land cover types, calculated the mean values of differenced vegetation indices derived from the four land covers between Landsat 7 ETM+ and Landsat 8 OLI. Statistical analysis involving regression and correlation analysis were also carried out on the vegetation indices derived between the two sensors, as well as scatter plot diagrams with linear regression equation and coefficients of determination (R2). The results showed no noticeable differences between Landsat 7 and Landsat 8 sensors, which demonstrates high similarities. This was observed because Global Environmental Monitoring Index (GEMI), Improved Modified Triangular Vegetation Index 2 (MTVI2), Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Leaf Area Index (LAI) and Land Surface Water Index (LSWI) had smaller standard deviations. However, Renormalized Difference Vegetation Index (RDVI), Anthocyanin Reflectance Index 1 (ARI1) and Anthocyanin Reflectance Index 2 (ARI2) performed relatively poorly because their standard deviations were high. the correlation analysis of the vegetation indices that both sensors had a very high linear correlation coefficient with R2 greater than 0.99. It was concluded from this research that Landsat 7 ETM+ and Landsat 8 OLI can be used as complimentary data.

scholarly journals Monitoring Land Cover Change Using Remote Sensing and GIS Techniques: a Case Study of Al-Dalmaj Marsh, Iraq

2018 ◽  
Vol 24 (9) ◽  
pp. 96 ◽  
Author(s):  
Marwah Moojid Kadhim
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Accurate Information ◽  
Past Century ◽  
Landsat Images ◽  
Gis And Remote Sensing ◽  
Vegetation Water ◽  
Set Up

Al-Dalmaj marsh and the near surrounding area is a very promising area for energy resources, tourism, agricultural and industrial activities. Over the past century, the Al-Dalmaje marsh and near surroundings area endrous from a number of changes. The current study highlights the spatial and temporal changes detection in land cover for Al-Dalmaj marsh and near surroundings area using different analyses methods the supervised maximum likelihood classification method, the Normalized  Difference Vegetation Index (NDVI), Geographic Information Systems(GIS),  and Remote Sensing (RS). Techniques spectral indices were used in this study to determine the change of wetlands and drylands area and of other land classes, through analyses Landsat images for different three years (1990, 2003, 2016). The results indicated that there was an annual increase in vegetation was from 1990 with 980.68 km2, and 1420.35km2 in 2003 to 2072.98km2 in 2016. Whereas, the annual water coverage was about 185.95km2 in 1990 then dropped to 68.27km2 in 2003, and rose to 180.23 km2 in 2016. The water coverage increasing was on the account of barren lands areas, which were significantly decreased. These collected data can be used to deliver accurate information of the values of vegetation,water, wetlands and drylands sustainability of resources which can be used to make plans to increase tourism and protected areas by using barren lands which cannot be reclaimed for agriculture, and cultivate a new renewable energy can be set up  as solar power stations.  

scholarly journals Cambios en la cobertura del suelo en el municipio de Nechí: Una aproximación al impacto ambiental de la minería, 1986-2010

2017 ◽  
Vol 26 (45) ◽  
Author(s):  
Michael Ezequiel Gómez-Rodríguez ◽  
Francisco José Molina-Pérez ◽  
Diana María Agudelo-Echavarría ◽  
Julio Eduardo Cañón-Barriga ◽  
Fabio De Jesús Vélez-Macías
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Bare Soil ◽  
Water Bodies ◽  
Actual State ◽  
Landsat Images ◽  
Mining History ◽  
The Difference

The municipality of Nechí (Antioquia, Colombia) has a long mining history associated with the extraction of gold. This paper evaluates the evolution of land cover changes caused by this mining activity over 24 years. The spatial analysis was based on the Normalized Difference Vegetation Index (NDVI) of three LANDSAT images (1986, 1996 and 2010). The difference in NDVI values between 1986 and 2010 were used to determine the actual state of vegetation, the direction of change (improvement, stability or deterioration), and the area associated with each soil cover. Polygons for different types of coverage (forest, pasture, bare soil, and water bodies) were extracted from each satellite image to quantify the changes and develop land cover maps for each year. Results show that almost 124.8 km² of forest have been lost during the analyzed period. By contrast, water bodies gained an area of 66.3 km². Both results may be related to the type of gold exploitation in the region.

Using Combination Technique for Land Cover Classification of Optical Multispectral Images

2021 ◽  
Vol 12 (4) ◽  
pp. 22-39
Author(s):  
Keerti Kulkarni ◽  
Vijaya P. A.
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Vegetation Index ◽  
Temporal Dynamics ◽  
Geographical Area ◽  
Land Cover Classification ◽  
Combination Method ◽  
Support Vector ◽  
Combination Technique ◽  
Land Cover Map

The need for efficient planning of the land is exponentially increasing because of the unplanned human activities, especially in the urban areas. A land cover map gives a detailed report on temporal dynamics of a given geographical area. The land cover map can be obtained by using machine learning classifiers on the raw satellite images. In this work, the authors propose a combination method for the land cover classification. This method combines the outputs of two classifiers, namely, random forests (RF) and support vector machines (SVM), using Dempster-Shafer combination theory (DSCT), also called the theory of evidence. This combination is possible because of the inherent uncertainties associated with the output of each classifier. The experimental results indicate an improved accuracy (89.6%, kappa = 0.86 as versus accuracy of RF [87.31%, kappa = 0.83] and SVM [82.144%, kappa = 0.76]). The results are validated using the normalized difference vegetation index (NDVI), and the overall accuracy (OA) has been used as a comparison basis.

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