scholarly journals Stratifying land use/land cover for spatial analysis of disease ecology and risk: an example using object-based classification techniques

2007 ◽  
Vol 2 (1) ◽  
pp. 15 ◽  
Author(s):  
David E. Koch ◽  
Rhett L. Mohler ◽  
Douglas G. Goodin
Keyword(s):  
Land Use ◽  
Spatial Analysis ◽  
Land Cover ◽  
Disease Ecology ◽  
Land Use Land Cover ◽  
Classification Techniques ◽  
Object Based

scholarly journals Auxiliary datasets improve accuracy of object-based land use/land cover classification in heterogeneous savanna landscapes

2019 ◽  
Vol 233 ◽  
pp. 111354 ◽  
Author(s):  
P. Hurskainen ◽  
H. Adhikari ◽  
M. Siljander ◽  
P.K.E. Pellikka ◽  
A. Hemp
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Classification ◽  
Land Use Land Cover ◽  
Object Based ◽  
Improve Accuracy

Hybrid object-based approach for land use/land cover mapping using high spatial resolution imagery

2011 ◽  
Vol 25 (6) ◽  
pp. 1025-1043 ◽  
Author(s):  
Eva Savina Malinverni ◽  
Anna Nora Tassetti ◽  
Adriano Mancini ◽  
Primo Zingaretti ◽  
Emanuele Frontoni ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Land Cover Mapping ◽  
Land Use Land Cover ◽  
Object Based ◽  
High Spatial Resolution Imagery

scholarly journals Land Cover Change Detection Using Object-Based Classification Technique: A Case Study Along The Kosi River, Bihar

2014 ◽  
Vol XL-8 ◽  
pp. 839-843 ◽  
Author(s):  
M. Modi ◽  
R. Kumar ◽  
G. Ravi Shankar ◽  
T.R. Martha
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Time Series Data ◽  
Feature Space ◽  
Series Data ◽  
Land Use Land Cover ◽  
Shape Context ◽  
Object Property ◽  
Object Based ◽  
Remote Sensing Techniques

Land use/land cover (LULC) is dynamic in nature and can affect the ability of land to sustain human activities. The Indo-Gangetic plains of north Bihar in eastern India are prone to floods, which have a significant impact on land use / land cover, particularly agricultural lands and settlement areas. Satellite remote sensing techniques allow generating reliable and near-realtime information of LULC and have the potential to monitor these changes due to periodic flood. Automated methods such as object-based techniques have better potential to highlight changes through time series data analysis in comparison to pixel-based methods, since the former provides an opportunity to apply shape, context criteria in addition to spectral criteria to accurately characterise the changes. In this study, part of Kosi river flood plains in Supaul district, Bihar has been analysed to identify changes due to a flooding event in 2008. Object samples were collected from the post-flood image for a nearest neighbourhood (NN) classification in an object-based environment. Collection of sample were partially supported by the existing 2004–05 database. The feature space optimisation procedure was adopted to calculate an optimum feature combination (i.e. object property) that can provide highest classification accuracy. In the study, for classification of post-flood image, best class separation was obtained by using distance of 0.533 for 28 parameters out of 34. Results show that the Kosi flood has resulted in formation of sandy riverine areas.

scholarly journals A COMPARISON OF OBJECT-BASED AND PIXEL-BASED APPROACHES FOR LAND USE/LAND COVER CLASSIFICATION USING LAPAN-A2 MICROSATELLITE DATA

2017 ◽  
Vol 14 (1) ◽  
pp. 27 ◽  
Author(s):  
Jalu Tejo Nugroho ◽  
. Zylshal ◽  
Nurwita Mustika Sari ◽  
Dony Kushardono
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Confusion Matrix ◽  
Digital Camera ◽  
Land Use Land Cover ◽  
Central Java ◽  
Object Based ◽  
Accuracy Result ◽  
Blurring Effect

In recent years, small satellite industry has been a rapid trend and become important especially when associated with operational cost, technology adaptation and the missions. One mission of LAPAN-A2, the 2nd generation of microsatellite that developed by Indonesian National Institute of Aeronautics and Space (LAPAN), is Earth observation using digital camera that provides imagery with 3.5 m spatial resolution. The aim of this research is to compare between object-based and pixel-based classification of land use/land cover (LU/LC) in order to determine the appropriate classification method in LAPAN-A2 dataprocessing (case study Semarang, Central Java).The LU/LC were classified into eleven classes, as follows: sea, river, fish pond, tree, grass, road, building 1, building 2, building 3, building 4 and rice field. The accuracy of classification outputs were assessed using confusion matrix. The object-based and pixel-based classification methods result for overall accuracy are 31.63% and 61.61%, respectively. According to accuracy result, it was thought that blurring effect on LAPAN-A2 data may be the main cause ofaccuracy decrease. Furthermore, the result is suggested to use pixel-based classification to be applied inLAPAN-A2 data processing.

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