scholarly journals PERFORMANCE ASSESSMENT OF CLASSIFICATION ALGORITHMS FOR LANDUSE / LANDCOVER CHANGE USING SENTINEL 2 DATA – A CASE STUDY OF TIRUPPUR

2021 ◽  
Vol XLIV-M-3-2021 ◽  
pp. 139-142
Author(s):  
B. Prawin ◽  
P. Masilamani ◽  
S. Abdul Rahaman
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Kappa Coefficient ◽  
Urban Land Use ◽  
Support Vector ◽  
Classification Algorithms ◽  
Rapid Urbanization ◽  
Important Indicator ◽  
Natural Land

Abstract. In the history of mankind, one of the vibrant geographical phenomena is urbanization. The urbanization process is characterized by the expansion of the city from the core to peripheral areas which includes economic development, social, political forces and population density. Very rapid urbanization in the highly populated country like India, which changes natural land cover into urban land use, which is unavoidable. However, the study region Tiruppur is known as the knitwear capital of India that induces urban development in the region which results in the modification of the natural land cover. For understating the interaction between the natural landscape and human activities, land use and land cover (LULC) is considered as the important indicator. Research on land-use and land cover changes using remote sensing technology has a long history to evident. The advancement in the Remote Sensing and GIS techniques provide the fine resolution of data sets to proceed. Sentinel-2B imagery was chosen for this study for two main reasons one is that compare to Landsat imagery it has a high spatial resolution of 10 m and its radiometry includes three vegetation red edge bands. These two characteristics make the Sentinel-2B data appealing for LULC mapping. Different types of classification algorithms have been used to perform land use and land cover mapping. The study aims to create land use and land cover classification by making a comparison between different algorithms in Tiruppur by using Sentinel-2B satellite imagery. The commonly known classification algorithms, K-means, IsoData, support vector machines (SVMs), and maximum likelihood (ML) classification are adopted for investigation. This is followed by the selection of training pixels from the remaining classes to perform and compare different supervised learning algorithms for the first- and second-level classification in terms of accuracy rates. Accuracy was assessed through metrics derived from an error matrix, but primarily overall accuracy and kappa coefficient was used in allocating algorithm hierarchy. Finally, after the comparison, the highly accurate algorithm was suggested for the mapping of urban areas. The highest overall accuracy and kappa coefficient was produced by support vector machine (SVM) is due to the algorithm’s relatively small number of complex decision boundaries. The results are helpful to understand the performance of the classification algorithm for the future studies.

scholarly journals Fusion Methods and Multi-classifiers to Improve Land Cover Estimation Using Remote Sensing Analysis

2021 ◽  
Author(s):  
Hayder Dibs ◽  
Hashim Ali Hasab ◽  
Ammar Shaker Mahmoud ◽  
Nadhir Al-Ansari
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Spatial Resolution ◽  
Kappa Coefficient ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Multispectral Imagery ◽  
Spectral Angle Mapper ◽  
Spectral Angle

AbstractAdopting a low spatial resolution remote sensing imagery to get an accurate estimation of Land Use Land Cover is a difficult task to perform. Image fusion plays a big role to map the Land Use Land Cover. Therefore, This study aims to find out a refining method for the Land Use Land Cover estimating using these steps; (1) applying a three pan-sharpening fusion approaches to combine panchromatic imagery that has high spatial resolution with multispectral imagery that has low spatial resolution, (2) employing five pixel-based classifier approaches on multispectral imagery and fused images; artificial neural net, support vector machine, parallelepiped, Mahalanobis distance and spectral angle mapper, (3) make a statistical comparison between image classification results. The Landsat-8 image was adopted for this research. There are twenty Land Use Land Cover thematic maps were generated in this study. A suitable and reliable Land Use Land Cover method was presented based on the most accurate results. The results validation was performed by adopting a confusion matrix method. A comparison made between the images classification results of multispectral imagery and all fused images levels. It proved the Land Use Land Cover map produced by Gram–Schmidt Pan-sharpening and classified by support vector machine method has the most accurate result among all other multispectral imagery and fused images that classified by the other classifiers, it has an overall accuracy about (99.85%) and a kappa coefficient of about (0.98). However, the spectral angle mapper algorithm has the lowest accuracy compared to all other adopted methods, with overall accuracy of 53.41% and the kappa coefficient of about 0.48. The proposed procedure is useful in the industry and academic side for estimating purposes. In addition, it is also a good tool for analysts and researchers, who could interest to extend the technique to employ different datasets and regions.

scholarly journals An Analysis of Urban Land Use/Land Cover Changes in Blantyre City, Southern Malawi (1994–2018)

2020 ◽  
Vol 12 (6) ◽  
pp. 2377 ◽  
Author(s):  
John Mawenda ◽  
Teiji Watanabe ◽  
Ram Avtar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Land ◽  
Urban Land Use ◽  
Sustainable Growth ◽  
Sub Saharan Africa ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Lulc Changes ◽  
Bare Land

Rapid and unplanned urban growth has adverse environmental and social consequences. This is prominent in sub-Saharan Africa where the urbanisation rate is high and characterised by the proliferation of informal settlements. It is, therefore, crucial that urban land use/land cover (LULC) changes be investigated in order to enhance effective planning and sustainable growth. In this paper, the spatial and temporal LULC changes in Blantyre city were studied using the integration of remotely sensed Landsat imageries of 1994, 2007 and 2018, and a geographic information system (GIS). The supervised classification method using the support vector machine algorithm was applied to generate the LULC maps. The study also analysed the transition matrices derived from the classified map to identify prominent processes of changes for planning prioritisation. The results showed that the built-up class, which included urban structures such as residential, industrial, commercial and public installations, increased in the 24-year study period. On the contrary, bare land, which included vacant lands, open spaces with little or no vegetation, hilly clear-cut areas and other fallow land, declined over the study period. This was also the case with the vegetation class (i.e., forests, parks, permanent tree-covered areas and shrubs). The post-classification results revealed that the LULC changes during the second period (2007–2018) were faster compared to the first period (1994–2007). Furthermore, the results revealed that the increase in built-up areas systematically targeted the bare land and avoided the vegetated areas, and that the vegetated areas were systematically cleared to bare land during the study period (1994–2018). The findings of this study have revealed the pressure of human activities on the land and natural environment in Blantyre and provided the basis for sustainable urban planning and development in Blantyre city.

scholarly journals LAND USE AND LAND COVER MAPPING – A CASE STUDY OF AHMEDABAD DISTRICT

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 189-193
Author(s):  
D. Rawal ◽  
A. Chhabra ◽  
M. Pandya ◽  
A. Vyas
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Spectral Reflectance ◽  
Land Cover Classification ◽  
Land Cover Mapping ◽  
Classification Algorithms ◽  
Classification Technique ◽  
Object Based

Abstract. Land cover mapping using remote-sensing imagery has attracted significant attention in recent years. Classification of land use and land cover is an advantage of remote sensing technology which provides all information about land surface. Numerous studies have investigated land cover classification using different broad array of sensors, resolution, feature selection, classifiers, Classification Techniques and other features of interest from over the past decade. One, Pixel based image classification technique is widely used in the world which works on their per pixel spectral reflectance. Classification algorithms such as parallelepiped, minimum distance, maximum likelihood, Mahalanobis distance are some of the classification algorithms used in this technique. Other, Object based image classification is one of the most adapted land cover classification technique in recent time which also considers other parameters such as shape, colour, smoothness, compactness etc. apart from the spectral reflectance of single pixel.At present, there is a possibility of getting the more accurate information about the land cover classification by using latest technology, recent and relevant algorithms according to our study. In this study a combination of pixel-by-pixel image classification and object based image classification is done using different platforms like ArcGIS and e-cognition, respectively. The aim of the study is to analyze LULC pattern using satellite imagery and GIS for the Ahmedabad district in the state of Gujarat, India using a LISS-IV imagery acquired from January to April, 2017. The over-all accuracy of the classified map is 84.48% with Producer’s and User’s accuracy as 89.26% and 84.47% respectively. Kappa statistics for the classified map are calculated as 0.84. This classified map at 1:10,000 scale generated using recent available high resolution space borne data is a valuable input for various research studies over the study area and also provide useful information to town planners and civic authorities. The developed technique can be replicated for generating such LULC maps for other study areas as well.

scholarly journals Prediction of Land Cover Change in Bodetabek Area using Remote Sensing Technique

2020 ◽  
Vol 8 (5) ◽  
pp. 3053-3060
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Least Squares Method ◽  
Urban Land Use ◽  
Sustainable Utilization ◽  
University Of Maryland ◽  
Remote Sensing Technique ◽  
Monitoring And Forecasting ◽  
Land Use Plan

There are many parts of the world that have been affected and changed because of humans activities. Land use/cover change (LULC) is one them. Monitoring and forecasting changes of LULC is very important which aims at development of conservation and sustainable utilization, one them is urban land use plan. In this study the main objective was to analyzed and forecasted land cover changes that will happen in the future based on historical changes which happened 11 years before. To achieve the goals, there are 11 years (2007 to 2017) of remote sensing images (MODIS MCD12QI V6) from Bodetabek area which has been classified into 16 classes based on University of Maryland classification scheme (UMD) and has been processed in several stages to be use as the dataset. There are view of method has been use to finished the experiment, change detection method has been used to computed the data to be analyzed and forecasted used least squares method.

scholarly journals Perbandingan Quadratic Discriminant Analysis dan Support Vector Machine untuk Klasifikasi Tutupan Lahan di DKI Jakarta

2020 ◽  
Vol 9 (1) ◽  
pp. 12-20
Author(s):  
Kamaluddin Junianto Dimas ◽  
Rahma Anisa ◽  
Itasia Dina Sulvianti
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Land Cover ◽  
Training Data ◽  
Support Vector ◽  
Quadratic Discriminant Analysis ◽  
Remote Sensing Technology ◽  
Sensing Technology

DKI Jakarta is a center of government as well as economy and business of Indonesia, thus development projects in Jakarta continue every year. Therefore, monitoring for land use has to be improved in accordance to DKI Jakarta Spatial Planning. The attempt needs to be supported by continuous data availability regarding land cover condition in Jakarta. The aforementioned data collecting process become easier due to remote sensing technology development. Remote sensing technology can be utilized for analyzing the size of land use area by using classification analysis. It has been found that the level of accuracy depends on the type of classification method and number of training data. This research evaluated the level of overall accuracy, sensitivity, and specificity of Quadratic Discriminant Analysis (QDA) and Support Vector Machine (SVM) along with number of data training used in classifying Jakarta land cover in 2017. The results showed that in both methods, the variance of all the aforementioned criteria were getting smaller along with the increasing number of training data. QDA and SVM had similar performance based on overall accuracy and specificity. However, SVM was better than QDA on sensitivity.

scholarly journals Geospatial assessment of land use and land cover dynamics in the mid-zone of Ghana

2020 ◽  
Vol 62 (4) ◽  
pp. 288-305
Author(s):  
Addo Koranteng ◽  
Isaac Adu-Poku ◽  
Emmanuel Donkor ◽  
Tomasz Zawiła-Niedźwiecki
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Forest Cover ◽  
Anthropogenic Activities ◽  
Arable Land ◽  
Rapid Urbanization ◽  
The Past ◽  
Open Forest

AbstractLand use and land cover (LULC) terrain in Ghana has undergone profound changes over the past years emanating mainly from anthropogenic activities, which have impacted countrywide and sub-regional environment. This study is a comprehensive analysis via integrated approach of geospatial procedures such as Remote Sensing (RS) and Geographic Information System (GIS) of past, present and future LULC from satellite imagery covering Ghana’s Ashanti regional capital (Kumasi) and surrounding districts. Multi-temporal satellite imagery data sets of four different years, 1990 (Landsat TM), 2000 (Landsat ETM+), 2010 (Alos and Disaster Monitoring Constellation-DMC) and 2020 (SENTINEL), spanning over a 30-year period were mapped. Five major LULC categories – Closed Forest, Open Forest, Agriculture, Built-up and Water – were delineated premised on the prevailing geographical settings, field study and remote sensing data. Markov Cellular Automata modelling was applied to predict the probable LULC change consequence for the next 20 years (2040). The study revealed that both Open Forest and Agriculture class categories decreased 51.98 to 38.82 and 27.48 to 20.11, respectively. Meanwhile, Built-up class increased from 4.8% to 24.8% (over 500% increment from 1990 to 2020). Rapid urbanization caused the depletion of forest cover and conversion of farmlands into human settlements. The 2040 forecast map showed an upward increment in the Built-up area up to 35.2% at the expense of other LULC class categories. This trend from the past to the forecasted future would demand that judicious LULC resolutions have to be made to keep Ghana’s forest cover, provide arable land for farming activities and alleviate the effects of climate change.

scholarly journals Land Use and Land Cover Mapping of Davangere using Google Earth Engine

2019 ◽  
Vol 8 (3) ◽  
pp. 474-477
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Environmental Changes ◽  
Satellite Image ◽  
Google Earth ◽  
Kappa Coefficient ◽  
Error Matrix ◽  
Sensing Applications ◽  
Google Earth Engine

Ever since the advent of modern geo information systems, tracking environmental changes due to natural and/or manmade causes with the aid of remote sensing applications has been an indispensable tool in numerous fields of geography, most of the earth science disciplines, defence, intelligence, commerce, economics and administrative planning. One among these applications is the construction of land use and land cover maps through image classification process. Land Use / Land Cover (LULC) information is a crucial input in designing efficient strategies for managing natural resources and monitoring environmental changes from time to time. The present study aims to know the extent of land cover and its usage in Davangere region of Karnataka, India. In this study, satellite image of Davangere during October-November 2018 was used for LULC supervised classification with the help of remote sensing tools like QGIS and Google Earth Engine. Six LULC classes were decided to locate on the map and the accuracy assessment was done using theoretical error matrix and Kappa coefficient. The key findings include LULC under Water bodies (8%), Built up Area (15.1%), Vegetation (9%), Horticulture (20.8%), Agriculture (39.3%) and Others (7%) with overall accuracy of 94.8% and Kappa coefficient of 0.866 indicating almost accurate goodness of classification

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