Land Cover Classification of Remotely Sensed Satellite Data using Bayesian and Hybrid classifier

Pradhan, Ratika; Ghose, M. K.; Jeyaram, A.

doi:10.5120/1295-1783

Cited by 18 publications

(12 citation statements)

References 8 publications

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“…The land-cover map was classified to estimate the biomass and carbon stock for each class using Erdas software. The classifications including DEF, MDF, DDF, DF, and PFi with trees were analyzed using a hybrid classification technique that uses both supervised and unsupervised classifications with GIS [34,51]. The hybrid classification involved developing training patterns via the use of an unsupervised classification followed by a supervised classification [51].…”

Section: Land-cover Classification Methodsmentioning

confidence: 99%

“…The classifications including DEF, MDF, DDF, DF, and PFi with trees were analyzed using a hybrid classification technique that uses both supervised and unsupervised classifications with GIS [34,51]. The hybrid classification involved developing training patterns via the use of an unsupervised classification followed by a supervised classification [51]. For the unsupervised classification, a K-means clustering algorithm was used to search for natural groups of pixels called clusters, which were located in the data by assessing the relative locations of the pixels in the feature space for separations between vegetation and non-vegetation classes.…”

Section: Land-cover Classification Methodsmentioning

confidence: 99%

“…Clouds and shadows were filled using nearby pixels, Google Maps, land-use Shapefile data, and land-cover classifications from older and newer images. As the resolution of Landsat images is moderate (30 × 30 m), the use of a combined hybrid classification technique improved the accuracy of the land-cover classification [34,51]. An accuracy assessment was applied to evaluate the quality of the land cover map [34].…”

Section: Land-cover Classification Methodsmentioning

confidence: 99%

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Carbon Stock Assessment Using Remote Sensing and Forest Inventory Data in Savannakhet, Lao PDR

et al. 2014

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Savannakhet Province, Lao People's Democratic Republic (PDR), is a small area that is connected to Thailand, other areas of Lao PDR, and Vietnam via road No. 9. This province has been increasingly affected by carbon dioxide (CO 2 ) emitted from the transport corridors that have been developed across the region. To determine the effect of the CO 2 increases caused by deforestation and emissions, the total above-ground biomass (AGB) and carbon stocks for different land-cover types were assessed. This study estimated the AGB and carbon stocks (t/ha) of vegetation and soil using standard sampling techniques and allometric equations. Overall, 81 plots, each measuring 1600 m 2 , were established to represent samples from dry evergreen forest (DEF), mixed deciduous forest (MDF), dry dipterocarp forest (DDF), disturbed forest (DF), and paddy fields (PFi). In each plot, the diameter at breast height (DBH) and height (H) of the overstory trees were measured. Soil samples (composite n = 2) were collected at depths of 0-30 cm. Soil carbon was assessed using the soil depth, soil bulk density, and carbon content. Remote sensing (RS; Landsat Thematic Mapper (TM) image) was used for land-cover classification and OPEN ACCESS Remote Sens. 2014, 6 5453 development of the AGB estimation model. The relationships between the AGB and RS data (e.g., single TM band, various vegetation indices (VIs), and elevation) were investigated using a multiple linear regression analysis. The results of the total carbon stock assessments from the ground data showed that the MDF site had the highest value, followed by the DEF, DDF, DF, and PFi sites. The RS data showed that the MDF site had the highest area coverage, followed by the DDF, PFi, DF, and DEF sites. The results indicated significant relationships between the AGB and RS data. The strongest correlation was found for the PFi site, followed by the MDF, DDF, DEF, and DF sites.

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Section: Land-cover Classification Methodsmentioning

confidence: 99%

Section: Land-cover Classification Methodsmentioning

confidence: 99%

Section: Land-cover Classification Methodsmentioning

confidence: 99%

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Carbon Stock Assessment Using Remote Sensing and Forest Inventory Data in Savannakhet, Lao PDR

et al. 2014

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“…All the imagery processing was performed by Idrisi software (Taiga version). We took advantage of both the supervised (Maximum likelihood algorithm) and unsupervised (Kmeans clustering algorithm) classification methods for producing the land use map (Pradhan et al 2010). The land use map in our study included 6 classes: forest, rangeland, A c c e p t e d M a n u s c r i p t 5 cropland, rocks, urban area and roads.…”

Section: Remote Sensing Datamentioning

confidence: 99%

Predicting soil organic carbon density using auxiliary environmental variables in northern Iran

Falahatkar

Hosseini

Ayoubi

et al. 2015

Archives of Agronomy and Soil Science

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In the present study, artificial neural networks (ANNs) were employed to develop models to predict soil organic carbon density (SOCD) at different depths of soil layers. Selected environmental variables such as vegetation indices, soil particle size distribution, land use type, besides primary and secondary terrain attributes were considered as the input variables. According to the results, the ANN models explained 77% and 72% of the variability in SOCD at soil layer depths of 0-20 cm and 20-40 cm, respectively, at the site studied. Sensitivity analyses showed that the most considerable positive contribution of variables for predicting SOCD included by land use type, normalized difference vegetation index (NDVI)> normalized difference water index (NDWI)> Silt> Clay> Elevation in 0-20 cm soil layer. On the other hand, for 20-40 cm soil layer, land use type following by NDVI> NDWI> Clay> Silt were identified as the most powerful predictive factors. In Deylaman region in both soil layers, Sand had a considerable negative effect on SOCD and most of the terrain attributes had no significant impact on the SOCD prediction. Therefore, these results provide valuable information for sustainable management and decision making on a landscape scale for governors and other users.

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“…(Perumal and Bhaskaran 2010) compared hard classifiers (parallelepipe, minimum distance, maximum likelihood, and Mahalanobis) with some advanced classifiers like an artificial neural network, and spectral angle mapper and concluded that Mahalanobis classifier performed optimally. Similarly, (Pradhan et al 2010) classified land use/ land cover using Bayesian and Hybrid classifiers and observed 91.57% using hybrid and 90.53% using the Bayesian classifier. It was clear that none of these classifiers of remotely sensed data has been applied in any part or the whole of Andoni L.G.A.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Accuracy of Different Supervised Classification Methods of Satellite Image

Abah¹,

Uriah²

2020

ETR

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Assessing the accuracy of the classification map is an essential area in remote sensing digital image process. This is because a poorly classified map will result in inestimable errors of spatial analysis and modeling arising from the use of such data. This study was designed to evaluate different supervised classification algorithms in terms of accuracy assessment with a view of recommending an appropriate algorithm for image processing. The analysis was carried out using Andoni L.G.A. Rivers State, Nigeria as the study area. Supervised classification of ETM+ 2014 Landsat image of the study area was carried out using ENVI 5.0 software. Seven land use/land cover categories were identified on the image data and appropriate information classes were also assigned using region of interest. The classifiers adopted for the study include SAM, SVM, and MDC and each classifier was set using appropriate thresholds and parameters. The output error matrix of the classified map produced overall accuracy and kappa coefficient for MDC as 94.00% and 0.91, SAM as 64.45% and 0.53, and SVM as 98.92% and 0.98 respectively. The overall accuracy obtained from SVM indicates that a perfect classification map will be produced from the algorithm. The advanced supervised classification should be utilized for classification of land use/ land cover for both high and medium resolution images for improved classification accuracy.

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Land Cover Classification of Remotely Sensed Satellite Data using Bayesian and Hybrid classifier

Cited by 18 publications

References 8 publications

Carbon Stock Assessment Using Remote Sensing and Forest Inventory Data in Savannakhet, Lao PDR

Carbon Stock Assessment Using Remote Sensing and Forest Inventory Data in Savannakhet, Lao PDR

Predicting soil organic carbon density using auxiliary environmental variables in northern Iran

Assessing the Accuracy of Different Supervised Classification Methods of Satellite Image

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