scholarly journals A comparative analysis of Resourcesat-2 LISS-3 and Landast-8 OLI imagery

2014 ◽  
Vol XL-8 ◽  
pp. 987-989 ◽  
Author(s):  
E. Venkateswarlu ◽  
T. Sivannarayana ◽  
K. V. Ratna Kumar
Keyword(s):  
Land Use ◽  
Comparative Analysis ◽  
Land Cover ◽  
Andhra Pradesh ◽  
Vegetation Indices ◽  
Land Cover Classification ◽  
Landsat 8 ◽  
Electromagnetic Spectrum ◽  
Data Gaps ◽  
Spectral Vegetation Indices

Linear Imaging Self Scanning Sensor (LISS-3) onboard Resoucesat-1 and 2 Satellites have been used extensively for various land cover-land use applications. In this study, we examined the potential of using Resourcesat-2 LISS-3 images in the absence of LANDSAT-8 Operational Land Imager (OLI) images. This paper compares the capabilities of LISS-3 sensor with OLI sensor. LISS-3 images were selected for comparison because of their close resemblance in electromagnetic spectrum range with LS-8 OLI images. Images of LS-8 OLI and RS-2 LISS-3 of the same area in Andhra Pradesh were used to evaluate the comparative performances based on the intra-inter band correlation, spectral vegetation indices and land cover classification. The results showed that in most cases the LS-8 OLI and the RS-2 LISS-3 images are comparable. This study also indicated that LISS-3 images could fill the data gaps in OLI images for land-cover studies, vice versa.

scholarly journals Land use/land cover classification using machine learning models

2022 ◽  
Vol 12 (2) ◽  
pp. 2040
Author(s):  
Subhra Swetanisha ◽  
Amiya Ranjan Panda ◽  
Dayal Kumar Behera
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Land Cover ◽  
Land Cover Classification ◽  
Support Vector ◽  
Landsat 8 ◽  
Electromagnetic Spectrum ◽  
Learning Models ◽  
Extreme Gradient Boosting ◽  
Machine Learning Models

<p>An ensemble model has been proposed in this work by combining the extreme gradient boosting classification (XGBoost) model with support vector machine (SVM) for land use and land cover classification (LULCC). We have used the multispectral Landsat-8 operational land imager sensor (OLI) data with six spectral bands in the electromagnetic spectrum (EM). The area of study is the administrative boundary of the twin cities of Odisha. Data collected in 2020 is classified into seven land use classes/labels: river, canal, pond, forest, urban, agricultural land, and sand. Comparative assessments of the results of ten machine learning models are accomplished by computing the overall accuracy, kappa coefficient, producer accuracy and user accuracy. An ensemble classifier model makes the classification more precise than the other state-of-the-art machine learning classifiers.</p>

scholarly journals Land cover classification using landsat-8 optical data and supervised classifiers

2018 ◽  
Vol 7 (2.17) ◽  
pp. 101
Author(s):  
K V. Ramana Rao ◽  
Prof P. Rajesh Kumar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Classification ◽  
Optical Data ◽  
Landsat 8 ◽  
Barren Land ◽  
Detection Techniques ◽  
Time Periods ◽  
Classification Information ◽  
Landsat Satellite

Land use and land cover information of an area has got importance in various aspects mainly because of various development activities that are taking place in every part of the world. Various satellite sensors are providing the required data collected by remote sensing techniques in the form of images using which the land use land cover information can be analyzed.  Constistency of Landsat satellite is illustrated with two time periods such as Operational Land Imager (OLI) of 2013 and consecutive 2014 procured by earth explorer with quantified changes for the same period in visakhapatnam of hudhud cyclone. Since this city is consisting of mainly urban, vegetation, few water bodies, some area of agriculture and barren,five classes have been chosen from the study area. The results indicate that due to the hudhud event some changes took place.  vegetation and built-up land have been increased by An increase of 19.1% (6.3 km2) and 11% (5.36 km2) has been observed in the case of vegetation and built up area  where as a decrease of 1.2% (4.06 km2), 6.1% (1.70 km2) and 1.2% (0.72 km2) has been observed in the case of  agriculture, barren land, and water body respectively. With the help of available satellite imagery belonging to the same area and of different time periods along with the  change detection techniques landscape dynamics have been analyzed. Using various classification algorithms along with the data available from the satellite sensor the land use and land cover classification information of the study area has been obtained. The maximum likelihood algorithm provided better results compared to other classification techniques and the accuracy achieved with this algorithm is 99.930% (overall accuracy) and 0.999 (Kappa coefficient).  

scholarly journals Comparative analysis of RISAT-1 and simulated RADARSAT-2 hybrid polarimetric SAR data for different land features

2014 ◽  
Vol XL-8 ◽  
pp. 799-802 ◽  
Author(s):  
V. Kumar ◽  
Y. S Rao
Keyword(s):  
Land Use ◽  
Comparative Analysis ◽  
Land Cover ◽  
Satellite Data ◽  
Andhra Pradesh ◽  
Land Use Land Cover ◽  
Polarimetric Sar ◽  
Sar Data ◽  
Simulated Hybrid

The purpose of this study is to compare the performance of first hybrid polarimetric spaceborne satellite RISAT-1 data and simulated hybrid polarimetric data from quad-pol RADARSAT-2 data for different land use land cover (LULC) classes. The present study compares Stokes (g0, g1, g2 and g3) and its decomposed parameters (m, chi, delta and CPR) for satellite data acquired from RISAT- 1 and RADARSAT-2 over Vijayawada, Andhra Pradesh, India. Further, backscattering coefficients are also compared for different LULC types. The results indicate that both the satellites are following approximately the same trend for different classes except for settlements in RISAT-1.

scholarly journals Land Cover Classification in an Ecuadorian Mountain Geosystem Using a Random Forest Classifier, Spectral Vegetation Indices, and Ancillary Geographic Data

Geosciences ◽  
2017 ◽  
Vol 7 (2) ◽  
pp. 34 ◽  
Author(s):  
Johanna Ayala-Izurieta ◽  
Carmen Márquez ◽  
Víctor García ◽  
Celso Recalde-Moreno ◽  
Marcos Rodríguez-Llerena ◽  
...  
Keyword(s):  
Random Forest ◽  
Land Cover ◽  
Vegetation Indices ◽  
Land Cover Classification ◽  
Random Forest Classifier ◽  
Geographic Data ◽  
Spectral Vegetation Indices
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