Land use/land cover change detection analysis using remote sensing and GIS of Dhanbad distritct, India

Singh, Rajvikram; Singha, Manmohan; Singh, Shailendra Kumar; Pal, Debjeet; Tripathi, Nimisha; Singh, Raj Shekar

doi:10.31195/ejejfs.428381

Cited by 22 publications

(20 citation statements)

References 17 publications

(17 reference statements)

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“…Hegazy and Kaloop [18] argued how Mansoura and Talkha cities in Daqahlia governorate in Egypt are expanding rapidly with varying growth rates and patterns, whereas Belal and Moghanm [7] showed the case of urban expansion that causes loss of productive agricultural lands in Tanta and Quttour districts in Egypt. Finally, measuring the LULC change is necessary for future urban planning at local and global level reported Singh and Singh [34].…”

Section: Study Areamentioning

confidence: 99%

A remote sensing and GIS-based analysis of urban sprawl in Soran District, Iraqi Kurdistan

Hamad

2019

SN Appl. Sci.

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Determination of spatial and temporal patterns of urban growth has become one of the most significant challenges in monitoring and assessing current and future trends of the urban growth issue. Soran district has witnessed very rapid growth in the last two decades, mostly because of its economic, commercial and social attractions. The aim of this work is to study the growth and sprawl dynamics through the land use and land cover (LULC) maps for the area at three different periods (1998, 2008, and 2018) particularly in the urban areas employing GIS and RS techniques. Three Landsat images, Enhanced Thematic Mapper plus in the 1998, Thematic Mapper in the 2008 and Landsat Operational Land (LOL) in the 2018 were used to assess the changes of urban encroachment. A supervised classification technique by maximum likelihood classifier has been employed to create a classified image and has been assessed based on Kappa index. The results obtained showed that the urbanized area increased from 4.51 to 14.93 km 2 from 1998 to 2018. This study demonstrated that the substantial changes in LULC in Soran district since the end of 1990s are directly related to and influenced by the main and secondary roads development on spatial expansion and land use change.

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Section: Study Areamentioning

confidence: 99%

A remote sensing and GIS-based analysis of urban sprawl in Soran District, Iraqi Kurdistan

Hamad

2019

SN Appl. Sci.

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“…Unsupervised classification is a two-step operation of grouping pixels into clusters based on the statistical properties of the measurements, and then labelling the clusters with the appropriate classes [21]. Supervised classification starts from a known set of classes, learns the statistical properties of each class and then assigns the pixels based on these properties [22]. In this study supervised classification was chosen due to the following criteria:…”

Section: Land Cover Classificationmentioning

confidence: 99%

“…This study attempts to perform land cover classification using maximum likelihood (ML), neural network (NN) and support vector machines (SVM) to assess land cover changes in Klang District, located in Selangor, Malaysia. The performance of each of these methods is to be evaluated based on classification accuracy and land cover change analysis www.ijacsa.thesai.org [19], [20], [22], [23]. Comparative analysis is eventually performed among these methods by making use of these performance measures.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Support Vector Machine, Maximum Likelihood and Neural Network Classification on Multispectral Remote Sensing Data

Ahmad¹,

Hashim²,

Othman³

et al. 2018

ijacsa

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Land cover classification is an essential process in many remote sensing applications. Classification based on supervised methods have been preferred by many due to its practicality, accuracy and objectivity compared to unsupervised methods. Nevertheless, the performance of different supervised methods particularly for classifying land covers in Tropical regions such as Malaysia has not been evaluated thoroughly. The study reported in this paper aims to detect land cover changes using multispectral remote sensing data. The data come from Landsat satellite covering part of Klang District, located in Selangor, Malaysia. Landsat bands 1, 2, 3, 4, 5 and 7 are used as the input for three supervised classification methods namely support vector machines (SVM), maximum likelihood (ML) and neural network (NN). The accuracy of the generated classifications is then assessed by means of classification accuracy. Land cover change analysis is also carried out to identify the most reliable method to detect land changes in which showing SVM gives a more stable and realistic outcomes compared to ML and NN.

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“…However, the land use/cover pattern of a given region is an outcome of natural and socio-economic factors [2][3][4]. In developing countries land use land cover (LULC) change is an extensively spreading and accelerating process [5] which is mainly driven via anthropogenic activities and the change brings significant impact on the terrestrial ecosystem [6][7][8]. Globally, there has been an increasing trend of biodiversity crisis over the last four decades; this is due to the conversion of LULC leading to fragmentation of the natural habitats [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Seasonal land use land cover change and the drivers in Kafta-Sheraro National Park, Tigray, Ethiopia

Temesgen

Warkineh

Hailemicael

2021

Preprint

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Kafta-sheraro national park (KSNP) is one of the homes of the African elephant has experienced extensive destruction of woodland following regular land use & land cover change in the past three decades, however, up to date, data and documentation detailing for these changes are not addressed. This study aims to evaluate the land use land cover change and drivers of change that occurred between 1988 and 2018. Landsat 5(TM), Landsat7 (ETM+), and Landsat 8 (OLI/TIRs) imagery sensors, field observation, and socio-economic survey data were used. The temporal and spatial Normalized difference vegetation index (NDVI) was calculated and tested the correlation between NDVI and precipitation/temperature. The study computed a kappa coefficient of the dry season (0.90) and wet season (0.845). Continuous decline of woodland (29.38%) and riparian vegetation (47.11%) whereas an increasing trend of shrub-bushland (35.28%), grassland (43.47%), bareland (27.52%), and cultivated land (118.36 km2) were showed over thirty years. More results showed bare land was expanded from wet to drier months, while, cultivated land and grazing land increased from dry to wet months. Based on the NDVI result high-moderate vegetation was decreased by 21.47% while sparse & non-vegetation was expanded by 19.8% & 1.7% (36.5 km2) respectively. Settlement & agricultural expansion, human-induced fire, firewood collection, gold mining, and charcoal production were the major proximate drivers that negatively affected the park resources. Around KSNP, the local community livelihood depends on farming, expansion of agricultural land is the main driver for woodland dynamics/depletion and this leads to increase resources competition and challenges for the survival of wildlife. Therefore, urgent sustainable conservation of park biodiversity via encouraging community participation in conservation practices and preparing awareness creation programs should be mandatory.

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Land use/land cover change detection analysis using remote sensing and GIS of Dhanbad distritct, India

Cited by 22 publications

References 17 publications

A remote sensing and GIS-based analysis of urban sprawl in Soran District, Iraqi Kurdistan

A remote sensing and GIS-based analysis of urban sprawl in Soran District, Iraqi Kurdistan

Comparative Analysis of Support Vector Machine, Maximum Likelihood and Neural Network Classification on Multispectral Remote Sensing Data

Seasonal land use land cover change and the drivers in Kafta-Sheraro National Park, Tigray, Ethiopia

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