scholarly journals Remote Sensing of City Extension and Vegetal Cover Changes along Lagos-Ibadan Access Strip in Nigeria

2021 ◽  
Vol 25 (8) ◽  
pp. 1371-1377
Author(s):  
J.A. Oyedepo ◽  
D.E. Oluyege ◽  
E.I. Babajide
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Negative Correlation ◽  
Supervised Classification ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
The Third ◽  
Urban Land Cover ◽  
Green Area ◽  
Vegetal Cover

The paper employed Remote sensing data in a multi-decadal assessment of vegetal to urban land cover transition along Lagos-Ibadan expressway. The forty-year assessment commenced in 1980 and ended in 2020. Landsat imageries acquired for the respective periods were subjected to supervised classification. Results reveal massive conversion of vegetated areas into built-up areas. The transition became pronounced from the second decade with 30,226 and cumulative of 48,455 Hectares of vegetation transforming into built-up area. During the third decade (2000 to 2010), additional 44,780 and cumulative of 93,235 Hectares of green area was converted into built-up areas. The largest transition was recorded in the last decade (2010 to 2020) during which vegetated area covering 50,827 Hectares was converted to living or industrial areas giving a cumulative transition of 141,065 in year 2020 Pearson moment correlation showed a high negative correlation with a coefficient value of -0.86. Hectares of vegetal areas into built-up or bare surfaces.

scholarly journals Remote Sensing of City Extension and Vegetal Cover Changes along Lagos-Ibadan Access Strip in Nigeria

2021 ◽  
Vol 25 (8) ◽  
pp. 1453-1459
Author(s):  
J.A. Oyedepo ◽  
D.E. Oluyege ◽  
E.I. Babajide ◽  
O.D. Onayemi
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Negative Correlation ◽  
Supervised Classification ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
The Third ◽  
Urban Land Cover ◽  
Green Area ◽  
Vegetal Cover

The paper employed Remote sensing data in a multi-decadal assessment of vegetal to urban land cover transition along Lagos-Ibadan expressway. The forty-year assessment commenced in 1980 and ended in 2020. Landsat imageries acquired for the respective periods were subjected to supervised classification. Results reveal massive conversion of vegetated areas into built-up areas. The transition became pronounced from the second decade with 30,226 and cumulative of 48,455 Hectares of vegetation transforming into built-up area. During the third decade (2000 to 2010), additional 44,780 and cumulative of 93,235 Hectares of green area was converted into built-up areas. The largest transition was recorded in the last decade (2010 to 2020) during which vegetated area covering 50,827 Hectares was converted to living or industrial areas giving a cumulative transition of 141,065. In year 2020 Pearson moment correlation showed a high negative correlation with a coefficient value of -0.86. Hectares of vegetal areas into built-up or bare surfaces.

scholarly journals Improving Urban Land Cover Classification with Combined Use of Sentinel-2 and Sentinel-1 Imagery

2021 ◽  
Vol 10 (8) ◽  
pp. 533
Author(s):  
Bin Hu ◽  
Yongyang Xu ◽  
Xiao Huang ◽  
Qimin Cheng ◽  
Qing Ding ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Urban Land ◽  
Support Vector ◽  
Land Cover Mapping ◽  
Sensing Data ◽  
Urban Land Cover ◽  
Sar Data

Accurate land cover mapping is important for urban planning and management. Remote sensing data have been widely applied for urban land cover mapping. However, obtaining land cover classification via optical remote sensing data alone is difficult due to spectral confusion. To reduce the confusion between dark impervious surface and water, the Sentinel-1A Synthetic Aperture Rader (SAR) data are synergistically combined with the Sentinel-2B Multispectral Instrument (MSI) data. The novel support vector machine with composite kernels (SVM-CK) approach, which can exploit the spatial information, is proposed to process the combination of Sentinel-2B MSI and Sentinel-1A SAR data. The classification based on the fusion of Sentinel-2B and Sentinel-1A data yields an overall accuracy (OA) of 92.12% with a kappa coefficient (KA) of 0.89, superior to the classification results using Sentinel-2B MSI imagery and Sentinel-1A SAR imagery separately. The results indicate that the inclusion of Sentinel-1A SAR data to Sentinel-2B MSI data can improve the classification performance by reducing the confusion between built-up area and water. This study shows that the land cover classification can be improved by fusing Sentinel-2B and Sentinel-1A imagery.

scholarly journals Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

2021 ◽  
Vol 13 (21) ◽  
pp. 4483
Author(s):  
W. Gareth Rees ◽  
Jack Tomaney ◽  
Olga Tutubalina ◽  
Vasily Zharko ◽  
Sergey Bartalev
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Boreal Forest ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Fundamental Parameter ◽  
Sensing Data ◽  
Growing Stock ◽  
Stock Volume ◽  
Sentinel 2

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data.

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