Crop Pattern Mapping of Tumkur Taluk Using NDVI Technique: A Remote Sensing and GIS Approach

Bharathkumar, L.; Mohammed‐Aslam, M. A.

doi:10.1016/j.aqpro.2015.02.181

Cited by 29 publications

(12 citation statements)

References 8 publications

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“…The Pleiades imagery were categorized into specified classes according to the NDVI threshold value in the supervised classification. The implementation value of NDVI range from -1 to 1 was used to reclassify NDVI maps as refer to vegetation rates based on spectral reflections (Bharathkumar and Mohammed-Aslam, 2015).…”

Section: Image Processingmentioning

confidence: 99%

Urban Vegetation Classification With Ndvi Threshold Value Method With Very High Resolution (Vhr) Pleiades Imagery

Hashim

Latif

Adnan

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

124

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Abstract. Recently the sensing data for urban mapping used is in high demand together with the accessible of very high resolution (VHR) satellite data such as Worldview and Pleiades. This article presents the use of very high resolution (VHR) remote sensing data for urban vegetation mapping. The research objectives were to assess the use of Pleiades imagery to extricate the data of urban vegetation in urban area of Kuala Lumpur. Normalized Difference Vegetation Index (NDVI) were employs with VHR data to find Vegetation Index for classification process of vegetation and non-vegetation classes. Land use classes are easily determined by computing their Normalized Difference Vegetation Index for Land use land cover classification. Maximum likelihood was conducted for the classification phase. NDVI were extracted from the imagery to assist the process of classification. NDVI method is use by referring to its features such as vegetation at different NDVI threshold values. The result showed three classes of land cover that consist of low vegetation, high vegetation and non-vegetation area. The accuracy assessment gained was then being implemented using the visual interpretation and overall accuracy achieved was 70.740% with kappa coefficient of 0.5. This study gained the proposed threshold method using NDVI value able to identify and classify urban vegetation with the use of VHR Pleiades imagery and need further improvement when apply to different area of interest and different land use land cover characteristics. The information achieved from the result able to help planners for future planning for conservation of vegetation in urban area.

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Section: Image Processingmentioning

confidence: 99%

Urban Vegetation Classification With Ndvi Threshold Value Method With Very High Resolution (Vhr) Pleiades Imagery

Hashim

Latif

Adnan

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

124

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“…Using ENVI 5.0., two indexes are created for each one of the satellite images to allow detecting vegetation and infrastructures zones with great ease. The first index is the normalized difference vegetation index (NDVI) [22]. This index is used to determine the amount of vegetation in an area.…”

Section: Land Cover Creationmentioning

confidence: 99%

Detection of Geothermal Potential Zones Using Remote Sensing Techniques

González¹,

Rodríguez‐Gonzálvez²

2019

Remote Sensing

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The transition towards a new sustainable energy model-replacing fossil fuels with renewable sources-presents a multidisciplinary challenge. One of the major decarbonization issues is the question of to optimize energy transport networks for renewable energy sources. Within the range of renewable energies, the location and evaluation of geothermal energy is associated with costly processes, such as drilling, which limit its use. Therefore, the present research is aimed at applying different geomatic techniques for the detection of geothermal resources. The workflow is based on free/open access geospatial data. More specifically, remote sensing information (Sentinel-2A and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)), geological information, distribution of gravimetric anomalies, and geographic information systems have been used to detect areas of shallow geothermal potential in the northwest of the province of Orense, Spain. Due to the variety of parameters involved, and the complexity of the classification, a random forest classifier was employed, since this algorithm works well with large sets of data and can be used with categorical and numerical data. The results obtained allowed identifying a susceptible area to be operated on with a geothermal potential of 80 W·m −1 or higher.2 of 20 drilling operations. For this reason, different researchers have tried to develop methodologies to reduce costs in the search for these areas, taking into great consideration the use of remote sensing as a means to prioritize zones of potential geothermal energy [8,9], due to the availability of free satellite imagery and its cost-effective ratio for the geothermal exploration of large areas. One approach that involves remote sensing is the search for areas of rocks altered by hydrological action using images from one or more sensors, since they are indicative of thermal fluids being discharged along faults [10]. Surface thermal properties are an indicator of geothermal resources, which can be measured by the use of images from thermal sensors [11,12]. Additionally, temperatures can also be obtained using multispectral imagery [13] acquiring land surface temperature (LST) values.Among the geomatic techniques, geographic information systems (GIS) offer a whole new dimensionality by combining the remote sensing data with other geographic information sources as in [14], in which gravimetric anomalies, thermal images, and the location of geological faults were employed. Among the available GIS techniques, the use of random forest classifiers stands out, since they allow the ability to extract information from categorical and numerical variables. As a result, they support the decision-taking process without significant computational requirements. These algorithms are popular among the scientific community due to its ease of use and being able to be applied in multiple fields as in the case of [15], in which they were used to obtain a model of the demand of electricity. Another transversal example is the app...

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“…GIS is used to capture, edit and analyse multi-layered environmental and ancillary data layers along with its geographic location and temporal variation (Lillesand et al, 2004) and enables the user to visualise, question, analyse and interpret data to understand relationships, patterns and trends (ESRI, 2014). GIS and RS has been used successfully in agricultural applications such as the mapping of GHGs and energy requirements for agricultural fertiliser and pesticide production in Australia (Navarro et al, 2013), the integration of GIS and RS to map crop suitability and crop production in India (Bharathkumar & Mohammed-Aslam;2015) and the mapping of hydrology and groundwater potentiality (Oikonomidis et al, 2015).…”

Section: Remote Sensing Gis and Lcamentioning

confidence: 99%

Integrated spatial technology to mitigate greenhouse gas emissions in grain production

Engelbrecht

Biswas

Pritchard

et al. 2015

Remote Sensing Applications: Society and Environment

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The causes and implications of climate change are currently at the forefront of many researching agendas. Countries that have ratified the Kyoto Protocol are bound by agreements to focus on and reduce greenhouse gas emissions which impact on the natural and anthropogenic environment. Internationally agriculture contributes to environmental impacts such as land use change, loss of biodiversity, greenhouse gas emissions, increased soil salinity, soil acidity and soil erosion. To combat and control the greenhouse gas emissions generated during agricultural production, methodologies are being developed and investigated worldwide. Agriculture is the second largest emitter of greenhouse gases in Australia and consequently the integrated spatial technology was developed using data from a crop rotation project conducted by the Department of Agriculture and Food, Western Australia. The aim of the integrated spatial technology was to combine remote sensing, geographical information systems and life cycle assessment, to ascertain the component or system within the agricultural production cycle, generating the most greenhouse gases. Cleaner production strategies were then used to develop mitigation measures for the reduction of greenhouse gases within the integrated spatial technology.

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Crop Pattern Mapping of Tumkur Taluk Using NDVI Technique: A Remote Sensing and GIS Approach

Cited by 29 publications

References 8 publications

Urban Vegetation Classification With Ndvi Threshold Value Method With Very High Resolution (Vhr) Pleiades Imagery

Urban Vegetation Classification With Ndvi Threshold Value Method With Very High Resolution (Vhr) Pleiades Imagery

Detection of Geothermal Potential Zones Using Remote Sensing Techniques

Integrated spatial technology to mitigate greenhouse gas emissions in grain production

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