scholarly journals Object-oriented classification for land cover of North Thang Long Industrial area using Worldview-2 data

2021 ◽  
Vol 62 (1) ◽  
pp. 10-18
Author(s):  
Ha Thu Thi Le ◽  
Long Van Hoang ◽  
Trung Van Nguyen ◽  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Resolution ◽  
Spectral Characteristics ◽  
Object Oriented ◽  
Remote Sensing Data ◽  
Industrial Area ◽  
Classification Method ◽  
Land Use Classification ◽  
Kappa Index

Land cover/land use classification using high spatial resolution remote sensing data has the biggest challenge is how to distinguish object classes from different spectral values based on structures, shapes, and spatial elements. This paper focuses on the object-oriented classification method to extract artificial surface at industrial area by Worldview-2 data with a spatial resolution of 1.8 m. Extraction of 05 types of land cover/land use using object-oriented classification method based on reflectance spectral characteristics, shape index, location of objects, brightness, NDVI index, and density objects are archive efficiency to the quality of classification results. The overall accuracy of classification result for land cover/land use of Thang Long industrial area is about 0.85 and Kappa index is about 0.81.

Establishment of land cover map using object-oriented classification method for VNREDSat-1 data

2020 ◽  
Vol 61 (2) ◽  
pp. 134-144 ◽  
Author(s):  
Lan Thi Pham ◽  
Son Phi Nguyen ◽  
Nghia Viet Nguyen ◽  
Huong Van Dao ◽  
Long Duc Doan ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spectral Characteristics ◽  
Object Oriented ◽  
Remote Sensing Data ◽  
Shape Index ◽  
Classification Method ◽  
Land Use Classification ◽  
Land Cover Map ◽  
Level 3

Land cover/land use classification using high resolution remote sensing data has the biggest challenge is how to distinguish object classes from different spectral values, structures, shapes, and spatial elements. This paper reveals the object-oriented classification method to establish the land cover map using VNREDSat-1 data, with a spatial resolution of 10 m. Land cover/land use system is classified according to CORINE with level 3 with 14 types of land cover/land use. Extraction of 14 types of land cover/land use using object-oriented classification method based on reflectance spectral characteristics, shape index, location of objects, brightness, NDVI plant index, and density objects. The overall accuracy of classification result is about 0.71%.

scholarly journals ESTIMATION OF GROSS PRIMARY PRODUCTION USING SATELLITE DATA AND GIS IN URBAN AREA, DENPASAR

2011 ◽  
Vol 7 (1) ◽  
Author(s):  
A.R. As-syakur ◽  
T. Osawa ◽  
IW.S. Adnyana
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Primary Production ◽  
Spatial Resolution ◽  
Satellite Data ◽  
High Spatial Resolution ◽  
Gross Primary Production ◽  
Remote Sensing Data ◽  
Sensing Data

Remote sensing data with high spatial resolution is very useful to provideinformation about Gross Primary Production (GPP) especially over spatial coverage in theurban area. Most models of ecosystem carbon exchange based on remote sensing data usedlight use efficiency (LUE) model. The aim of this research was to analyze the distributionof annual GPP urban area of Denpasar. Two main satellite data used in this study wereALOS/AVNIR-2 and Aster satellite data. Result showed that annual value of GPP usingALOS/AVNIR-2 varied from 0.130 gC m-2 yr-1 to 2586.181 gC m-2 yr-1. Meanwhile, usingAster the value varied from 0.144 gC m-2 yr-1 to 2595.264 gC m-2 yr-1. The annual value ofGPP ALOS was lower than the value of Aster, because ALOS have high spatial resolutionand smaller interval of spectral resolution compared to Aster. Different land use couldeffect the value of GPP, because the different land use has different vegetation type,distribution, and different photosynthetic pathway type. The high spatial resolution of theremote sensing data is crucial to discriminate different land cover types in urban region.With heterogeneous land cover surface, maximum value of GPP using ALOS/AVNIR-2was smaller than that of Aster, however, the annual mean of GPP value usingALOS/AVNIR-2 was higher than that of Aster.

scholarly journals A Comparative Analysis of Spatial Data and Land Use/Land Cover Classification in Urbanized Areas and Areas Subjected to Anthropogenic Pressure for the Example of Poland

2021 ◽  
Vol 13 (6) ◽  
pp. 3070
Author(s):  
Patrycja Szarek-Iwaniuk
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Classification ◽  
Classification Systems ◽  
Anthropogenic Pressure ◽  
Land Use Land Cover ◽  
Land Use Classification ◽  
Land Cover Data ◽  
Spatial Changes ◽  
Land Use Types

Urbanization processes are some of the key drivers of spatial changes which shape and influence land use and land cover. The aim of sustainable land use policies is to preserve and manage existing resources for present and future generations. Increasing access to information about land use and land cover has led to the emergence of new sources of data and various classification systems for evaluating land use and spatial changes. A single globally recognized land use classification system has not been developed to date, and various sources of land-use/land-cover data exist around the world. As a result, data from different systems may be difficult to interpret and evaluate in comparative analyses. The aims of this study were to compare land-use/land-cover data and selected land use classification systems, and to determine the influence of selected classification systems and spatial datasets on analyses of land-use structure in the examined area. The results of the study provide information about the existing land-use/land-cover databases, revealing that spatial databases and land use and land cover classification systems contain many equivalent land-use types, but also differ in various respects, such as the level of detail, data validity, availability, number of land-use types, and the applied nomenclature.

scholarly journals Comparison of supervised classifiers in the discrimin ation of preservation areas in a hydroelectric reservoir

2019 ◽  
Vol 11 (3) ◽  
Author(s):  
Jefferson Francisco Soares ◽  
Gláucia Miranda Ramirez ◽  
Mirléia Aparecida de Carvalho ◽  
Marcelo de Carvalho Alves ◽  
Christiany Mattioli Sarmiento ◽  
...  
Keyword(s):  
Land Use ◽  
Spatial Resolution ◽  
Nearest Neighbor ◽  
High Spatial Resolution ◽  
Object Oriented ◽  
Visual Object ◽  
Hydroelectric Power ◽  
K Nearest Neighbor ◽  
High Spatial Resolution Image ◽  
Resolution Image

The maintenance of riparian forests is considered one of the main vegetative practices for mitigating the degradation of water resources and is mandatory by law. However, in Brazil there is still a progressive and constant decharacterization of these areas. Facing this reality, it is necessary to broaden researches that identify the occurring changes and provide efficient solutions at a fast pace and low cost. Remote sensing techniques show great application potential in characterizing natural resources. The objective of this work was to map, to characterize the land use and occupation and to verify the best method of high spatial resolution image classification of the Permanent Preservation Areas of the Funil Hydroelectric Power Plant reservoir, located between the municipalities of Lavras, Perdões, Bom Sucesso, Ibituruna, Ijací and Itumirim, in the state of Minas Gerais. The methods used to classify the high spatial resolution image from the Quickbird satellite were visual, object-oriented and pixel-by-pixel. Results showed the best method for mapping land use and occupation of the study area was object-oriented classification using the K-nearest neighbor algorithm, with kappa coefficient of 0.88 and global accuracy of 91.40%.

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