Digital processing of Landsat TM data for wasteland mapping in parts of Aligarh District (Uttar Pradesh), India

Saha, S. K.; Kudrat, M.; Bhan, S. K.

doi:10.1080/01431169008955034

Cited by 35 publications

(22 citation statements)

References 3 publications

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“…The overall accuracy of 80% was achieved by this technique in this study. Saha et al (1990) also reported overall accuracy of 96% in wasteland classification of Aligarh district, U.P. India through digitally classifying Landsat TM data using maximum likelihood algorithm.…”

Section: Fig 3 Distribution Of Land Use and Vegetation Typesmentioning

confidence: 96%

Land-use, biomass and carbon estimation in dry tropical forest of Chhattisgarh region in India using satellite remote sensing and GIS

Bijalwan

Swamy

Sharma

et al. 2010

Journal of Forestry Research

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A study was conducted to characterize the land use, biomass and carbon status of dry tropical forest in Raipur district of Chhattisgarh, India using satellite remote sensing data and GIS techniques in the year of 2001−2002. The main forest types observed in the area are Teak forest, mixed forest, degraded forest and Sal mixed forest. The aspect and slope of the sites influenced the forest vegetation types, biomass and carbon storage in the different forests. The standing volume, above ground biomass and carbon storage varied from 35.59 to 64.31 m 3 ·ha -1 , 45.94 to 78.31 Mg·ha -1 , and 22.97 to 33.27 Mg·ha -1 , respectively among different forest types. The highest volumes, above ground biomass and carbon storage per hectare were found in the mixed forest and lowest in the degraded forest. The total standing carbon present in the entire study area was 78170.72 Mg in mixed forest, 81656.91 Mg in Teak forest, 7833.23 Mg in degraded forest and 7470.45 Mg in Sal mixed forest, respectively. The study shows that dry tropical forests of the studied area in ChhattisResponsible editor: Zhu Hong garh are in growing stage and have strong potential for carbon sequestration.

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Section: Fig 3 Distribution Of Land Use and Vegetation Typesmentioning

confidence: 96%

Land-use, biomass and carbon estimation in dry tropical forest of Chhattisgarh region in India using satellite remote sensing and GIS

Bijalwan

Swamy

Sharma

et al. 2010

Journal of Forestry Research

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“…Menenti et al (1986) found that Landsat 1 Thematic Mapper (TM) bands 1 through 5 and 7 were good for identifying salt minerals, at least when they are a dominant soil constituent. TM bands 3, 4, 5, and 7 were recommended for salt detection by Saha et al (1990) and Naseri (1998). Madrigal et al (2003) and Verma et al (1994) detected soil salinity of cropped areas by correlating soil EC determined at point sites within previously designated fields, to spectral values extracted from bands 2, 3 and 4 but integration of thermal band 6 headed to solve the problem of spectral similarity in the latter case.…”

Section: Soil Salinity Mapping and Monitoringmentioning

confidence: 99%

Relating soil electrical conductivity to remote sensing and other soil properties for assessing soil salinity in northeast Thailand

Shrestha¹

2006

Land Degrad Dev

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Mapping and monitoring of soil salinity is required to establish its areal extent and also to keep track of changes in salinity in order to formulate appropriate and timely management strategies for reclamation and rehabilitation of such soils. Remote sensing data have been increasingly used in soil-salinity studies as they are not only quicker but are also useful for making realistic predictions. A study was conducted in northeast Thailand to understand the relationship of spectral reflectance and physico-chemical soil properties to electrical conductivity (EC) by using remote sensing data (Landsat 1 ETM þ ) and laboratory analysis of soil sample data. Multiple regression analysis was used to examine the relationships between EC and spectral/soil properties and to generate several models. In the case of spectral properties, mid-infrared band (Landsat 1 band 7) and near-infrared (band 4) were found to be most correlated with the observed EC values of the surface layer of the soil. For the soil properties, chloride (Cl), sodium (Na), phosphorus (P), and sodium adsorption ratio (SAR) were found to be most correlated with observed EC values. Similarly, for the subsurface layer, Cl and P were found to be significant predictors of observed EC values.

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“…They indicated that using TM data along with aerial photographs for mapping soil in arid areas give valuable results. Reference [9] used bands 3, 4, 5, and 7 of TM data for mapping salt-affected and waterlogged areas in India. Reference [10] used False Colour Composite (FCC) of TM bands 2, 3 and 4 for visual interpretation of moderately and strongly sodic soils.…”

Section: A Remote Sensing Approach For Monitoringmentioning

confidence: 99%

Evaluation of the spatio-temporal of soil salinity variation using data mining approach

Polous

Farshad

Zarinkafsh

et al. 2011

Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services

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The issue of temporal and spatial variation in soil salinity is considered as a fundamental element in salinity monitoring. The aim of this study is to develop a framework which integrates image mining techniques with Fuzzy logic methodology to improve the evaluation of spatio-temporal variation of soil salinity in areas with lack of available ground observation. Intensity and duration of salinity was characterized in space by the deviation of the current NDVI at each location from its corresponding temporal mean value. Landsat and ASTER images data was used to provide frequent Normalized Difference Vegetation Index (NDVI) in cultivation phase for a period of 22 years. Evolution of salinity condition before planting season was assessed by applying stepwise regression method on image data for two available dataset. The regression equation was obtained between reflectance value of band three and the measured soil Electrical Conductivity (EC) from field. Validation of the developed algorithm was done by comparing the obtained outputs with 50 ground observations, available salinity reports, and previous soil salinity maps. The result revealed that the proposed framework can be considered as a cost and time effective tool for proper assessment of the spatio-temporal variation of soil salinity.

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Digital processing of Landsat TM data for wasteland mapping in parts of Aligarh District (Uttar Pradesh), India

Cited by 35 publications

References 3 publications

Land-use, biomass and carbon estimation in dry tropical forest of Chhattisgarh region in India using satellite remote sensing and GIS

Land-use, biomass and carbon estimation in dry tropical forest of Chhattisgarh region in India using satellite remote sensing and GIS

Relating soil electrical conductivity to remote sensing and other soil properties for assessing soil salinity in northeast Thailand

Evaluation of the spatio-temporal of soil salinity variation using data mining approach

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