Delineation and classification of rural–urban fringe using geospatial technique and onboard DMSP–Operational Linescan System

Mustak, Sk.; Baghmar, Naresh Kumar; Srivastava, Prashant K.; Singh, Sudhir Kumar; Binolakar, Ramesh

doi:10.1080/10106049.2016.1265594

Cited by 25 publications

(10 citation statements)

References 38 publications

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“…According to the positive relation of NTL intensity with the urban center [37], [38], a higher NTL intensity is clustered, and a more significant center is likely to be. Based on the aforementioned theory, first, this article determined the multicenters of the urban agglomeration, thereby dividing the subregions with the help of the distance of the multicenters.…”

Section: A Multicenters and Subregions Exaction Approach Using Ntl Imentioning

confidence: 99%

A Novel SUHI Referenced Estimation Method for Multicenters Urban Agglomeration using DMSP/OLS Nighttime Light Data

Wang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The surface urban heat island (SUHI) of urban agglomeration has always been an important topic in the studies of urban heat island, especially with the development of satellitebased land surface temperature (LST) products. However, most studies are limited to the perspective of a single city, ignoring the impact of urban agglomeration and the changes of LST at day and night on the reference LST (RLST) (e.g., rural areas). Consequently, this article proposed a novel method about SUHI intensity estimation for the multicenters (mcSUHII) of urban agglomeration in Guangdong-Hong Kong-Macao Greater Bay Area (GHMBay) using nighttime light (NTL) data (i.e., DMSP/OLS) obtained in October, 2010. The mcSUHII method considered the RLST of SUHII estimation based on multicenter structure, and was more flexible to adapt the impact of human activity intensity. The study showed that compared with other RLSTs, such as suburban and forest, mcSUHII mitigates the underestimation bias caused by ignoring the multicenter structure. Importantly, the change in SUHII for urban agglomerations is greater than for a single city. Moreover, it was illustrated that the variation of SUHII presented an obvious inverted U-shape along the gradient from the inland to the coastal cities. The highest SUHIIs in the delta cities at day and night are ∼7.27 ± 1.71°C and ∼4.46 ± 1.42°C, respectively. Additionally, NTL served as the dominator together with other factors that were capable of explaining more than 90% of the spatial variation in SUHII in GHMBay. Therefore, considering multicenters more in estimation of SUHII of urban agglomeration for the sustainable development.

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Section: A Multicenters and Subregions Exaction Approach Using Ntl Imentioning

confidence: 99%

A Novel SUHI Referenced Estimation Method for Multicenters Urban Agglomeration using DMSP/OLS Nighttime Light Data

Wang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…All of the Landsat images are free of cloud or haze whereby the haze removal could improve the quality of image and helps in achieving a better classification of image. After Mustak et al (2016), the NOAA National Geophysical Data Center, USA supplied freely available cloud-free annual composite of low resolution (GSD 2. …”

Section: Data Acquisition and Pre-processingmentioning

confidence: 99%

Dynamics of Land Cover Changes and Delineation of Groundwater Recharge Potential Sites in the Aguanaval Aquifer, Zacatecas, Mexico

González-Trinidad¹

2017

Appl Ecol Env Res

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“…which provides night-time light imagery to sense light illumination coming from the built-up urban area. The nighttime light very helpful for the discrimination of built-up urban area from the non-built-up area (Mustak et al, 2018). This study was carried out using google earth engine, a cloud computing API.…”

Section: Datasets and Software Usedmentioning

confidence: 99%

“…Because, locational variation of land use/cover ideally related to the variation of elevation although having similar spectral and textural response. • Night-time light: NOAA launched the Defence Meteorological Satellite Program with Operational Linescan System (DMSP-OLS) which provided night-time light satellite imagery (Mustak et al, 2018). The nighttime light satellite imagery capable to distinguish from built-up urban from the rural areas based on light illumination (Mustak et al, 2018).…”

mentioning

confidence: 99%

“…• Night-time light: NOAA launched the Defence Meteorological Satellite Program with Operational Linescan System (DMSP-OLS) which provided night-time light satellite imagery (Mustak et al, 2018). The nighttime light satellite imagery capable to distinguish from built-up urban from the rural areas based on light illumination (Mustak et al, 2018). All the image features were normalized using max-min method which value is varied from 0 to 1.…”

mentioning

confidence: 99%

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Assessing the Transferability of Machine Learning Algorithms Using Cloud Computing and Earth Observation Datasets for Agricultural Land Use/Cover Mapping

Praveen

Mustak

Sharma

2019

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

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<p><strong>Abstract.</strong> Mapping of agricultural land use/cover was initiated since the past several decades for land use planning, change detection analysis, crop yield monitoring etc. using earth observation datasets and traditional parametric classifiers. Recently, machine learning, cloud computing, Google Earth Engine (GEE) and open source earth observation datasets widely used for fast, cost-efficient and precise agricultural land use/cover mapping and change detection analysis. Main objective of this study was to assess the transferability of the machine learning algorithms for land use/cover mapping using cloud computing and open source earth observation datasets. In this study, the Landsat TM (L5, L8) of 2018, 2009 and 1998 were selected and median reflectance of spectral bands in Kharif and Rabi season were used for the classification. In addition, three important machine learning algorithms such as Support Vector Machine with Radial Basis Function (SVM-RBF), Random forest (RF) and Classification and Regression Tree (CART) were selected to evaluate the performance in transferability for agricultural land use classification using GEE. Seven land use/cover classes such as built-up, cropland, fallow land, vegetation etc. were selected based on literature review and local land use classification scheme. In this classification, several strategies were employed such as feature extraction, feature selection, parameter tuning, sensitivity analysis on size of training samples, transferability analysis to assess the performance of the selected machine learning algorithms for land use/cover classification. The result shows that SVM-RBF outperforms the RF and CART for both spatial and temporal transferability analysis. This result is very helpful for agriculture and remote sensing scientist to suggest promising guideline to land use planner and policy-makers for efficient land use mapping, change detection analysis, land use planning and natural resource management.</p>

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Delineation and classification of rural–urban fringe using geospatial technique and onboard DMSP–Operational Linescan System

Cited by 25 publications

References 38 publications

A Novel SUHI Referenced Estimation Method for Multicenters Urban Agglomeration using DMSP/OLS Nighttime Light Data

A Novel SUHI Referenced Estimation Method for Multicenters Urban Agglomeration using DMSP/OLS Nighttime Light Data

Dynamics of Land Cover Changes and Delineation of Groundwater Recharge Potential Sites in the Aguanaval Aquifer, Zacatecas, Mexico

Assessing the Transferability of Machine Learning Algorithms Using Cloud Computing and Earth Observation Datasets for Agricultural Land Use/Cover Mapping

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