Supervised Classification of Spectral Signatures from Agricultural Land-Cover in Panama Using the Spectral Angle Mapper Algorithm

2019 ◽  
Author(s):  
Javier E. Sanchez-Galan ◽  
Jorge Serrano Reyes ◽  
Jose Ulises Jimenez ◽  
Evelyn Itzel Quiros-McIntire ◽  
Jose R. Fabrega
Keyword(s):  
Land Cover ◽  
Supervised Classification ◽  
Agricultural Land ◽  
Spectral Angle Mapper ◽  
Spectral Signatures ◽  
Spectral Angle

scholarly journals Selection of Suitable Supervised Classification Techniques for the Geographic Analysis of Land Using GIS Techniques

2018 ◽  
Vol 7 (3.27) ◽  
pp. 82
Author(s):  
S L. Senthil Lekha ◽  
S S.Kumar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Kappa Coefficient ◽  
Landsat Image ◽  
Training Data ◽  
Spectral Angle Mapper ◽  
Gis Techniques ◽  
Spectral Angle

Nation has realised the changes in the land surface and the influence of this in the whole ecosystem. The activities of human on land is directly deteriorating the environment quality. This paper mainly focuses on the analysis of the destruction of land cover with the development of land use. The performance of five different Supervised Classification algorithms, which are Parallelepiped, Mahalanobis, Neurel Net, Adaptive Coherence and Spectral Angle Mapper  have been analysed in classifying the Landsat Image of kanyakumari district. Automatic classification of five classes using training data have been performed and the best suitable algorithm for the classification of each class have been analysed. Being a tourism centre with coastal areas on all three sides, the development and the deterioration of kanyakumari district have to be monitored constantly. The proposed system is an automatic approach which helps in the analysis of the patterns of land use and land cover which constantly changes and to map each class clearly and distinct from each other using GIS techniques. The system was evaluated using the performance measures like accuracy and  kappa coefficient using the tools Envi, ArcGIS and QGIS. From the performance analysis, the Spectral Angle Mapper with an overall accuracy  of 97% and kappa coefficient of 0.54 has been selected as the best suitable algorithm for the classification of landsat image of kanyakumari district. 

scholarly journals Forest vegetation assessment using geoinformation tools: a case of the Burla pine forest, Novosibirsk Region, Russia

2018 ◽  
Vol 11 ◽  
pp. 00008
Author(s):  
Tatiyana S. Chernikova ◽  
Yury S. Otmakhov ◽  
Daria D. Daibova
Keyword(s):  
Remote Sensing ◽  
Supervised Classification ◽  
Remote Sensing Data ◽  
Pine Forest ◽  
Spectral Angle Mapper ◽  
Sensing Data ◽  
Classification Technique ◽  
Spectral Angle ◽  
Birch Forests

The paper presents the vegetation thematic classification of the Burla banded pine forest carried on using "Canopus-V" remote sensing data and the supervised classification technique by a spectral angle mapper. Areas of selected elements have been assessed: 1. Pine forests, 2. Birch forests; 3. Meadows; 4. Anthropogenic objects (roads, etc.); 5. Agricultural lands; 6. Water objects. Sites of anthropogenic disturbed forests are identified according to remote sensing data. The results show that the data obtained in the classification by a spectral angle can be used to compile geobotanical maps, but due to low spectral resolution of Canopus-V satellite data, it is not always possible to classify individual objects validlys.

scholarly journals Watershed Health Changes based on Vegetated Land Cover in the Upper Citarum Watershed, West Java, Province, Indonesia

2021 ◽  
Vol 940 (1) ◽  
pp. 012045
Author(s):  
K Marko ◽  
D Sutjiningsih ◽  
E Kusratmoko
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Supervised Classification ◽  
Agricultural Land ◽  
Landsat Imagery ◽  
West Java ◽  
Health Changes ◽  
The North ◽  
Health Level ◽  
Watershed Health

Abstract The increase in built-up land and the decrease in vegetated land due to human activities have worsened watershed health from time to time. This study aims to assess the watershed’s health and changes every ten years based on the percentage of vegetated land cover except agricultural land in the Upper Citarum watershed, West Java. Land cover information was obtained from the processing of Landsat imagery in 1990, 2000, 2010, and 2020 based on remote sensing using the supervised classification method. The watershed health level is determined by calculating the percentage of vegetated land cover of 173 catchments. The results show that the area of the vegetated land cover decreased from 1990 to 2000, then increased from 2000 to 2010, and decreased again from 2010 to 2020. Changes in the area of vegetated land in each period of the year affect the health level of the watershed in a spatiotemporal manner. Although these changes occur in a fluctuating manner, the number of unhealthy catchments in the Upper Citarum watershed is increasing, especially in the Ci Kapundung sub-watershed in the north and Ci Sangkuy in the south.

scholarly journals Experiment of Multispectral Images using Spectral Angle Mapper Algorithm for Land Cover Classification

2019 ◽  
Vol 8 (6S4) ◽  
pp. 96-99
Keyword(s):  
Land Cover ◽  
Satellite Images ◽  
Water Bodies ◽  
Classification Algorithms ◽  
Multispectral Images ◽  
Rapid Urbanization ◽  
Spectral Angle Mapper ◽  
Time Period ◽  
Steep Decline ◽  
Spectral Angle

Urbanization plays a key role in the health of the water bodies in any region. In a rapidly growing country like India, especially Bangalore district, rapid urbanization has seen a steep decline in the number of water bodies the region is famous for. In this paper, Land Use and Land Cover change is analysed for the remotely sensed images of Bangalore District using Spectral Angle Mapper Algorithm. Data for the purpose of analysis was obtained from BHUVAN (NRSC, ISRO). The study area is Bangalore District and data was collected from the time period 2008-2016. The major classes used in the classification are Land(Built-up), water bodies (Lakes), Vegetation (Gardens), Soil (Barren and fertile). The satellite images and the accompanying classification algorithms indicate that the percentage of water bodies have drastically shrunk (from 2.9% in 2008to1.8% in 2016) in the area of study. The results of this study can be used by the civic authorities to implement decisions to conserve the water bodies in the area.

scholarly journals Methods of performance evaluation for the supervised classification of satellite imagery in determining land cover classes

2013 ◽  
Vol 40 (2) ◽  
pp. 419-428 ◽  
Author(s):  
Carlos H. Wachholz de Souza ◽  
Erivelto Mercante ◽  
Victor H. R. Prudente ◽  
Diego D.D. Justina
Keyword(s):  
Performance Evaluation ◽  
Land Cover ◽  
Satellite Imagery ◽  
Supervised Classification

scholarly journals Remote sensing appraisal of Lake Chad shrinkage connotes severe impacts on green economics and socio-economics of the catchment area

2017 ◽  
Vol 4 (11) ◽  
pp. 171120 ◽  
Author(s):  
Olapeju Y. Onamuti ◽  
Emmanuel C. Okogbue ◽  
Israel R. Orimoloye
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Catchment Area ◽  
Supervised Classification ◽  
Economic Status ◽  
Land Use Land Cover ◽  
Economic Activities ◽  
Lake Chad ◽  
Border Communities

Lake Chad commonly serves as a major hub of fertile economic activities for the border communities and contributes immensely to the national growth of all the countries that form its boundaries. However, incessant and multi-decadal drying via climate change pose greater threats to this transnational water resource, and adverse effects on ecological sustainability and socio-economic status of the catchment area. Therefore, this study assessed the extent of shrinkage of Lake Chad using remote sensing. Landsat imageries of the lake and its surroundings between 1987 and 2005 were retrieved from Global Land Cover Facility website and analysed using Integrated Land and Water Information System version 3.3 (ILWIS 3.3). Supervised classification of area around the lake was performed into various land use/land cover classes, and the shrunk part of its environs was assessed based on the land cover changes. The shrinkage trend within the study period was also analysed. The lake water size reduced from 1339.018 to 130.686 km 2 (4.08–3.39%) in 1987–2005. The supervised classification of the Landsat imageries revealed an increase in portion of the lake covered by bare ground and sandy soil within the reference years (13 490.8–17 503.10 km 2 ) with 4.98% total range of increase. The lake portion intersected with vegetated ground and soil also reduced within the period (11 046.44–10 078.82 km 2 ) with 5.40% (967.62 km 2 ) total decrease. The shrunk part of the lake covered singly with vegetation increased by 2.74% from 1987 to 2005. The shrunk part of the lake reduced to sand and turbid water showed 5.62% total decrease from 1987 to 2005 and a total decrease of 1805.942 km 2 in area. The study disclosed an appalling rate of shrinkage and damaging influences on the hydrologic potential, eco-sustainability and socio-economics of the drainage area as revealed using ILWIS 3.3.

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