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 Developing Land Use Land Cover Maps for the Lower Mekong Basin to Aid Hydrologic Modeling and Basin Planning

2018 ◽  
Vol 10 (12) ◽  
pp. 1910 ◽  
Author(s):  
Joseph Spruce ◽  
John Bolten ◽  
Raghavan Srinivasan ◽  
Venkat Lakshmi
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Data ◽  
Reference Data ◽  
Assessment Tool ◽  
Deciduous Forest ◽  
Land Use Land Cover ◽  
Modis Ndvi ◽  
Mekong Basin ◽  
Lower Mekong Basin

This paper discusses research methodology to develop Land Use Land Cover (LULC) maps for the Lower Mekong Basin (LMB) for basin planning, using both MODIS and Landsat satellite data. The 2010 MODIS MOD09 and MYD09 8-day reflectance data was processed into monthly NDVI maps with the Time Series Product Tool software package and then used to classify regionally common forest and agricultural LULC types. Dry season circa 2010 Landsat top of atmosphere reflectance mosaics were classified to map locally common LULC types. Unsupervised ISODATA clustering was used to derive most LULC classifications. MODIS and Landsat classifications were combined with GIS methods to derive final 250-m LULC maps for Sub-basins (SBs) 1–8 of the LMB. The SB 7 LULC map with 14 classes was assessed for accuracy. This assessment compared random locations for sampled types on the SB 7 LULC map to geospatial reference data such as Landsat RGBs, MODIS NDVI phenologic profiles, high resolution satellite data, and Mekong River Commission data (e.g., crop calendars). The SB 7 LULC map showed an overall agreement to reference data of ~81%. By grouping three deciduous forest classes into one, the overall agreement improved to ~87%. The project enabled updated regional LULC maps that included more detailed agriculture LULC types. LULC maps were supplied to project partners to improve use of Soil and Water Assessment Tool for modeling hydrology and water use, plus enhance LMB water and disaster management in a region vulnerable to flooding, droughts, and anthropogenic change as part of basin planning and assessment.

scholarly journals Multi-Temporal Landsat8 Satellite Data Based Crops and Other Land Use Spatial Estimation in Okara District of Pakistan

2018 ◽  
Author(s):  
Ibrar ul Hassan Akhtar ◽  
Athar Hussain ◽  
Kashif Javed ◽  
Hammad Ghazanfar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Data ◽  
Urban Areas ◽  
Integrated Approach ◽  
Kappa Statistics ◽  
Digital Information ◽  
Land Use Land Cover ◽  
Land Utilization ◽  
Cropping Patterns

Developing countries like Pakistan is among those where lack of adoption to science and technology advancement is a major constraint for Satellite Remote Sensing use in crops and land use land cover digital information generation. Exponential rise in country population, increased food demand, limiting natural resources coupled with migration of rural community to urban areas had further led to skewed official statistics. This study is an attempt to demonstrate the possible use of freely available satellite data like Landsat8 under complex cropping system of Okara district of Punjab, Pakistan. An Integrated approach has been developed for the satellite data based crops and land use/cover spatial area estimation. The resultant quality was found above 96% with Kappa statistics of 0.95. Land utilization statistics provided detail information about cropping patterns as well as land use land cover status. Rice was recorded as most dominating crop in term of cultivation area of around 0.165 million ha followed by autumn maize 0.074 million ha, Fallow crop fields 0.067 million ha and Sorghum 0.047 million ha. Other minor crops observed were potato, fodder and cotton being cultivated on less than 0.010 million ha. Population settlements were observed over an area of around 0.081 million ha of land. 

scholarly journals Classification of various land features using RISAT-1 dual polarimetric data

2014 ◽  
Vol XL-8 ◽  
pp. 833-837 ◽  
Author(s):  
V. N. Mishra ◽  
P. Kumar ◽  
D. K. Gupta ◽  
R. Prasad
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
Uttar Pradesh ◽  
Land Cover Classification ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Remotely Sensed Data ◽  
Sar Data ◽  
Land Cover Maps

Land use land cover classification is one of the widely used applications in the field of remote sensing. Accurate land use land cover maps derived from remotely sensed data is a requirement for analyzing many socio-ecological concerns. The present study investigates the capabilities of dual polarimetric C-band SAR data for land use land cover classification. The MRS mode level 1 product of RISAT-1 with dual polarization (HH & HV) covering a part of Varanasi district, Uttar Pradesh, India is analyzed for classifying various land features. In order to increase the amount of information in dual-polarized SAR data, a band HH + HV is introduced to make use of the original two polarizations. Transformed Divergence (TD) procedure for class separability analysis is performed to evaluate the quality of the statistics prior to image classification. For most of the class pairs the TD values are greater than 1.9 which indicates that the classes have good separability. Non-parametric classifier Support Vector Machine (SVM) is used to classify RISAT-1 data with optimized polarization combination into five land use land cover classes like urban land, agricultural land, fallow land, vegetation and water bodies. The overall classification accuracy achieved by SVM is 95.23 % with Kappa coefficient 0.9350.

scholarly journals Landsat8 Based Crops and Other Land Use Mapping in Okara District of Pakistan

2018 ◽  
Author(s):  
Ibrar ul Hassan Akhtar ◽  
Athar Hussain ◽  
Kashif Javed ◽  
Hammad Ghazanfar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Data ◽  
Urban Areas ◽  
Integrated Approach ◽  
Kappa Statistics ◽  
Digital Information ◽  
Land Use Land Cover ◽  
Land Utilization ◽  
Cropping Patterns

Developing countries like Pakistan is among those where lack of adoption to science and technology advancement is major constraint for Satellite Remote Sensing use in crops and land use land cover digital information generation. Exponential rise in country population, increased food demand, limiting natural resources coupled with migration of rural community to urban areas had further led to skewed official statistics. This study is an attempt to demonstrate the possible use of freely available satellite data like Landsat8 under complex cropping system of Okara district of Punjab, Pakistan. An Integrated approach has been developed for the satellite data based crops and land use/cover spatial area estimation. The resultant quality was found above 96% with Kappa statistics of 0.95. Land utilization statistics provided detail information about cropping patterns as well as land use land cover status. Rice was recorded as most dominating crop in term of cultivation area of around 0.165 million ha followed by autumn maize 0.074 million ha, Fallow crop fields 0.067 million ha and Sorghum 0.047 million ha. Other minor crops observed were potato, fodder and cotton being cultivated on less than 0.010 million ha. Population settlements were observed over an area of around 0.081 million ha of land. 

scholarly journals FATE OF AGRICULTURAL AREAS OF KAILALI DISTRICT OF NEPAL: A TEMPORAL LAND USE LAND COVER CHANGE (LUCC) ANALYSIS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1601-1606
Author(s):  
◽  
L. Thapa ◽  
D. P. Shukla
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Data ◽  
Management Practices ◽  
Agricultural Land ◽  
Local Population ◽  
Google Earth ◽  
Series Data ◽  
Land Use Land Cover ◽  
Land Resources

Abstract. Changes of agricultural land into non-agricultural land is the main issue of increasing population and urbanization. The objective of this paper is to identify the various land resources and its changes into other Land Use Land Cover (LULC) type. LANDSAT satellite data for 1990, 2000, 2010 and 2018 years of Kailali district Nepal was acquired for supervised LULC mapping and change analysis using ENVI 5.4 software. Sentinel-2 and Google earth satellite data were used for the accuracy assessment of the LULC map. The time-series data analysis from 1990–2000–2010–2018 shows major changes in vegetation and agriculture. The changes in LULC show that settlement and bare land is continuously increasing throughout these years. The change in land use and land cover during the period of 1990–2018 shows that the settlement area is increased by 204%; and agriculture is decreased by 57%. The fluctuating behavior of vegetation, agriculture and water bodies in which the areas decrease and increase over the selected periods is due to natural calamities and migration of the local population. This shows that human influence on the land resources is accelerating and leading to a deterioration of agricultural land. Thus effective agricultural management practices and policies should be carried out at the government level for minimizing land resources degradation by the human-induced impact.

Sign in / Sign up

Export Citation Format

Share Document