Estimation of PM10 concentration from Landsat 8 OLI satellite imagery over Delhi, India

2017 ◽  
Vol 8 ◽  
pp. 251-257 ◽  
Author(s):  
Ishan Saraswat ◽  
Rajeev Kumar Mishra ◽  
Amrit Kumar
Keyword(s):  
Satellite Imagery ◽  
Pm10 Concentration ◽  
Landsat 8 ◽  
Landsat 8 Oli

scholarly journals PENGEMBANGAN PENGGUNAAN PENGINDERAAN JAUH UNTUK ESTIMASI PRODUKSI PADI (STUDI KASUS KABUPATEN BEKASI)

2019 ◽  
Vol 19 (1) ◽  
pp. 6-12
Author(s):  
Eka Rudiana ◽  
Ernan Rustiadi ◽  
Muhammad Firdaus ◽  
Dede Dirgahayu
Keyword(s):  
Satellite Imagery ◽  
Vegetation Index ◽  
Rice Yield ◽  
Equation Model ◽  
Rice Production ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Enhanced Vegetation Index ◽  
The One ◽  
The Relationship

The utilization of remote sensing imagery such Landsat-8 (OLI) to estimate harvested area and yield using Enhanced Vegetation Index (EVI) parameter is a new approach to estimate regional rice production. Based on the analysis of the satellite imagery acquisition during May-August 2015, the estimation of rice harvested area in Bekasi District during July-October 2015 is 15.86 thousand ha or 7.74 thousand ha (32.79%) lower than BPS figures in the same period. Based on the relationship between yield (from the crop cutting survei, BPS) and EVI maximum, the equation model for rice yield estimation is: Yield (qu ha-1) = 36.818 + 44.965 EVImax. R2 value is 0.809. Based on the model, the estimation of rice yield in Bekasi District during July-October 2015 is 47.40 qu ha-1. Compared to the data published by BPS, the result is 12.66 qu ha-1 lower than the yield figure in subround I 2015, 6.77 qu ha-1 lower than the one in subround II 2015, 10.15 qu ha-1 lower than the one subround III 2015, and 6.62 qu ha-1 lower than the one in January-December 2015. Meanwhile, based on satellite imagery analysis, the estimation of rice production in the period of July-October 2015 is 75.16 thousand tons of GKG or 55.35 thousand tons of GKG (42.41%) lower than BPS figures during the same period. Keywords: Enhanced Vegetation Index, Landsat-8 (OLI), rice production estimation

scholarly journals MAPPING OF MANGROVE VEGETATION USING LANDSAT SATELLITE IMAGERYIN THE ESTUARY OF GANGSAL RIVER INDRAGIRI HILIR REGENCY RIAU PROVINCE

2020 ◽  
Vol 2 (3) ◽  
pp. 181-189
Author(s):  
Hendri Susilo ◽  
Musrifin Ghalib ◽  
Aras Mulyadi
Keyword(s):  
Community Structure ◽  
Satellite Imagery ◽  
Landsat 8 ◽  
Sampling Point ◽  
Landsat 8 Oli ◽  
Transect Line ◽  
Solid Density ◽  
Mangrove Vegetation ◽  
Landsat 5 Tm ◽  
Landsat Satellite

The research was conducted in January - March 2019. This study aims to map and analyze changes in the area and density of mangrove vegetation based on NDVI values and community structure in the Muara Sungai Gangsal, Indragiri Hilir Regency. To analyze the area and density of NDVI using Landsat 5 TM satellite imagery in 2008 and Landsat 8 OLI/TIRS in 2018. Analysis using ArcGis 10.3 software. The calculation of mangroves based on community structure used the Transect Line Plot method at 6 stations for community structure sampling. The area of mangrove vegetation in 2008 was 2,706 ha and in 2018 it was 2,693 ha. The results of the analysis of mangrove vegetation area from 2008 to 2018 there was a reduction of 13 ha. The NDVI value for 2008 criteria is rarely 133 ha, while 2.009 ha are wide and 564 ha is dense. The NDVI value of the 2018 mangrove vegetation is rarely 16 hectares, while 2,135 hectares are in the area and 542 hectares are dense. Based on the analysis of mangrove density in 2018 at 6 sampling point stations ranging from 866 ind/ha to 1,522 ind/ha. Density criteria are rarely detected at station I with a density of 922 ind/ha and station II with a density of 866 ind/ha. The criterion of moderate density was detected at station V with a density of 1,255 ind/ha and station VI with a density of 1,044 ind/ha. Criteria for solid density were detected at station III with a density of 1,522 ind/ha and station IV with a density of 1,511 ind/ha.

scholarly journals Detection of coffee berry necrosis by digital image processing of landsat 8 oli satellite imagery

2020 ◽  
Vol 85 ◽  
pp. 101983 ◽  
Author(s):  
Jonathan da Rocha Miranda ◽  
Marcelo de Carvalho Alves ◽  
Edson Ampélio Pozza ◽  
Helon Santos Neto
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Satellite Imagery ◽  
Digital Image ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Coffee Berry

scholarly journals A multi-criteria decision analysis for groundwater potential evaluation in parts of Ibadan, southwestern Nigeria

2020 ◽  
Vol 10 (11) ◽  
Author(s):  
Olawale Olakunle Osinowo ◽  
Kolawole Isaac Arowoogun
Keyword(s):  
Decision Analysis ◽  
Satellite Imagery ◽  
Groundwater Potential ◽  
Groundwater Resource ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Southwestern Nigeria ◽  
Resource Potential ◽  
Hydrogeological Parameters ◽  
Groundwater Resource Potential

Abstract Multi-criteria decision analysis based on Saaty’s analytical hierarchy processing technique has been used to establish groundwater potential distribution pattern across some highly populated parts of Ibadan metropolis in southwestern Nigeria. The technique weighted and ranked seven sets of thematic hydrological parameters derived from Landsat 8 OLI satellite imagery, 143 vertical electrical sounding (VES) geophysical data, and geological and topographical data. Filtered and enhanced Landsat 8 OLI satellite imagery, quality-checked and inverted VES data, and categorized geological and other ancillary data were analyzed and used to generate lineaments, subsurface geoelectric parameters and other terrain information employed to extract thematic hydrogeological parameters used to characterize the subsurface in terms of groundwater potential. Weighted, normalized and ranked derived thematic hydrogeological parameters (lineament density, drainage density, coefficient of anisotropy, aquifer thickness, overburden thickness, aquifer resistivity and lithology) were employed to generate groundwater resource potential map. The map delineates the study area into very low (6.5%), low (41.0%), medium (38.1%), high and very high (14.4%) groundwater resource potential zones. Regions underlain by quartzite/quartz schist rocks present medium-to-high groundwater resource potential, while regions underlain by migmatite and granite gneiss rocks mostly have very low–low groundwater resource potential. This study indicates that variation in groundwater resource potential across Ibadan situated within the basement complex terrain is mostly influenced by the heterogeneity of subsurface geology which varies rapidly in terms of rock distribution and associated hydrogeological indices.

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