scholarly journals Pemanfaatan Data Satelit Landsat 8 Untuk Menduga Produktivitas Tanaman Padi (Studi Kasus Kabupaten Karawang)

2020 ◽  
Vol 16 (2) ◽  
pp. 178
Author(s):  
Bustomi Bustomi ◽  
Dede Dirgahayu Domiri ◽  
Slamet Abadi ◽  
Kasdi Pringadi
Keyword(s):  
Vegetation Index ◽  
Crop Productivity ◽  
Equation Model ◽  
Landsat 8 ◽  
Linear Regression Models ◽  
Department Of Agriculture ◽  
Rice Productivity ◽  
Average Value ◽  
Landsat Satellite ◽  
The Relationship

This study aims to know prediction the productivity of rice by using Landsat Satellite data 8 in Karawang District. The research method used was descriptive, infrensial and modeling. For a presumes the productivity of the rice being estimated by using the Enhanced Vegetation Index (EVI). Prediction of productivity based on linear regression models between EVI from satellite imagery analysis results with the highest productivity of the rice plant of the Department of Agriculture Karawang District. The results showed that the analysis of Landsat 8 Satellite images obtained the average EVI value from 2017 and 2018, in 2017 the average EVI value was 0.36. while in the year 2018 average value of EVI was 0.48. Estimates of rice productivity in Karawang District 2017 and 2018 were obtained by using the regression equation model the relationship between EVI value and rice productivity yielding Anova obtained Sig = 0.000 <0.05, so that a significant model means the model can be used to estimate rice crop productivity. The z-Test Two Sample for Means statistical test for productivity on the EVI model and in the field shows that in 2017 Zhit = -0.0015 and 2018 Zhit = -0.0002 with areas of rejection and acceptance H0 then Zhit is located in the reception area which produces both results not real difference. This shows that the equation model can be said to be close to the yield of rice productivity in Karawang District and the prediction of rice productivity in the Karawang District in 2019 which is equal to 7.447 tons / ha.

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 ASSESSMENT OF RELATIONSHIP BETWEEN LAND SURFACE TEMPERATURE AND NORMALIZED DIFFERENT VEGETATION INDEX USING LANDSAT IMAGES IN SOME REGIONS OF DIYALA GOVERNORATE

2021 ◽  
Vol 52 (4) ◽  
pp. 793-801
Author(s):  
Al-Jbouri & Al-Timimi
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Conditions ◽  
Landsat 8 ◽  
Landsat Images ◽  
Remote Sensing Techniques ◽  
The Relationship

Agriculture is the most important and most dependent economic activity and influenced by climatic conditions as the climate elements represented by solar radiation, temperature, wind and relative humidity. Therefore, is necessary that analyze and understand the relationship between climate and agriculture. The aim of this study to assessment the relationship between land surface temperature (LST) and normalized difference vegetation index (NDVI) for three regions of Diyala Governorate in Iraq (Al Muqdadya, Baladrooz, and Baquba) by through using of remote sensing techniques and geographic information system (GIS).The Normalized difference vegetation index NDVI and land surface temperature (LST) were used in two of the Landsat-5 ETM + and Landsat-8 OLI satellite imagery during the years 1999 and 2019.  The results showed that increased in NDVI and decreased in LST for 2019, while for 1999 increased in LST and decreased in NDVI for the three regions. Finally, the regression was used to obtain that correlation between LST and NDVI. It was concluded that the correlation coefficient between NDVI and LST is negative, where the strongest correlation was 0.76 for Baquba and weakest correlation was 0.55 for Muqdadyia.

scholarly journals The relationship between adiposity and cognitive function in a large community-dwelling population: data from the Trinity Ulster Department of Agriculture (TUDA) ageing cohort study

2018 ◽  
Vol 120 (5) ◽  
pp. 517-527 ◽  
Author(s):  
Ontefetse Ntlholang ◽  
Kevin McCarroll ◽  
Eamon Laird ◽  
Anne M. Molloy ◽  
Mary Ward ◽  
...  
Keyword(s):  
Older Adults ◽  
Cohort Study ◽  
Cognitive Function ◽  
Community Dwelling ◽  
Cognitive Domain ◽  
Linear Regression Models ◽  
Department Of Agriculture ◽  
Negatively Associated ◽  
The Relationship ◽  
The Trinity

AbstractPrevious reports investigating adiposity and cognitive function in the population allude to a negative association, although the relationship in older adults is unclear. The aim of this study was to investigate the association of adiposity (BMI and waist:hip ratio (WHR)) with cognitive function in community-dwelling older adults (≥60 years). Participants included 5186 adults from the Trinity Ulster Department of Agriculture ageing cohort study. Neuropsychological assessment measures included the Mini-Mental State Examination (MMSE), Frontal Assessment Battery (FAB) and Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Multi-variable linear regression models were used to assess the association between adiposity and cognitive function adjusting for insulin resistance, inflammation and cerebrovascular disease. The mean ages were 80·3 (sd6·7), 71·0 (sd7·3) and 70·2 (sd6·3) years on the cognitive, bone and hypertensive cohorts, respectively. In the cognitive cohort, BMI was positively associated with immediate and delay memory, visuospatial/constructional ability, language and MMSE, and negatively with FAB (log-transformed), whereas WHR was negatively associated with attention. In the bone cohort, BMI was not associated with any cognitive domain, whereas WHR was negatively associated with visuospatial/constructional ability, attention and MMSE. In the hypertensive cohort, BMI was not associated with any cognitive domain, whereas WHR was negatively associated with immediate and delayed memory, visuospatial/constructional ability, language and MMSE and positively with FAB (log-transformed). In the cognitive and bone cohorts, the association of WHR and attention disappeared by further controlling for C-reactive protein and HbA1C. In this study of older adults, central adiposity was a stronger predictor of poor cognitive performance than BMI. Older adults could benefit from targeted public health strategies aimed at reducing obesity and obeseogenic risk factors to avoid/prevent/slow cognitive dysfunction.

scholarly journals Estimation of Surface and Near-Surface Air Temperatures in Arid Northwest China Using Landsat Satellite Images

2021 ◽  
Vol 9 ◽  
Author(s):  
Yi Liu ◽  
Samuel Ortega-Farías ◽  
Fei Tian ◽  
Sufen Wang ◽  
Sien Li
Keyword(s):  
Spatial Distribution ◽  
Land Surface ◽  
Vegetation Index ◽  
Meteorological Data ◽  
Northwest China ◽  
Landsat 8 ◽  
Linear Regression Models ◽  
Temporal And Spatial Distribution ◽  
Near Surface ◽  
Temporal And Spatial

Near-surface air (Ta) and land surface (Ts) temperatures are essential parameters for research in the fields of agriculture, hydrology, and ecological changes, which require accurate datasets with different temporal and spatial resolutions. However, the sparse spatial distribution of meteorological stations in Northwest China may not effectively provide high-precision Ta data. And it is not clear whether it is necessary to improve the accuracy of Ts which has the most influence on Ta. In response to this situation, the main objective of this study is to estimate Ta for Northwest China using multiple linear regression models (MLR) and random forest (RF) algorithms, based on Landsat 8 images and auxiliary data collected from 2014 to 2019. Ts, NDVI (Normalized Difference Vegetation Index), surface albedo, elevation, wind speed, and Julian day were variables to be selected, then used to estimate the daily average Ta after analysis and adjustment. Also, the Radiative Transfer Equation (RTE) method for calculating Ts would be corrected by NDVI (RTE-NDVI). The results show that: 1) The accuracy of the surface temperature (Ts) was improved by using RTE-NDVI; 2) Both MLR and RF models are suitable for estimating Ta in areas with few meteorological stations; 3) Analyzing the temporal and spatial distribution of errors, it is found that the MLR model performs well in spring and summer, and is lower in autumn, and the accuracy is higher in plain areas away from mountains than in mountainous areas and nearby areas. This study shows that through appropriate selection and combination of variables, the accuracy of estimating the pixel-scale Ta from satellite remote sensing data can be improved in the area that has less meteorological data.

scholarly journals Application of geo-informatics engineering to estimate above-ground carbon sequestration

2018 ◽  
Vol 38 (3) ◽  
pp. 303-308
Author(s):  
Teerawong Laosuwan ◽  
Yannawut Uttaruk ◽  
Tanutdech Rotjanakusol ◽  
Kusuma Arsasana
Keyword(s):  
Carbon Sequestration ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Regression Equations ◽  
Landsat 8 Oli ◽  
Green Vegetation ◽  
Simple Ratio ◽  
The Relationship

This research aims to estimate above-ground carbon sequestration of orchards by using the data collected from Landsat 8 OLI. Regression equations are applied to study the relationship between the amount of above-ground carbon sequestration and vegetation indices from Landsat 8 OLI, in which the data was collected in 2015 in 3 methods: 1) Difference Vegetation Index (DVI), 2) Green Vegetation Index (GVI), and 3) Simple Ratio (SR). The results are as follows: 1) By DVI method, it results in the equation y = 0.3184e0.0482x and the coefficient of determination R² = 0.8457. The amount of the above-ground sequestration calcula-tion's result is 213.176 tons per rai. 2) Using the GVI method, it results in the equation y = 0.2619e0.0489x and the coefficient of determination R²=0.8763. The amount of the above-ground sequestration calculation's result is 220.510 tons per rai. 3) Using the SR method, it results in the equation y = 0.8900e0.0469x and the coefficient of determination R² = 0.7748. The amount of the above-ground sequestration calculation's result is 234.229 tons per rai.

scholarly journals Monitoring LST-NDVI Relationship Using Premonsoon Landsat Datasets

2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Subhanil Guha ◽  
Himanshu Govil ◽  
Prabhat Diwan
Keyword(s):  
Correlation Coefficient ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Landscape ◽  
Landsat 8 ◽  
Landsat 7 ◽  
Systematic Planning ◽  
Landsat Satellite ◽  
The City

The present study monitors the interrelationship of land surface temperature (LST) with normalized difference vegetation index (NDVI) in Raipur City of India using premonsoon Landsat satellite sensor for the season of 2002, 2006, 2010, 2014, and 2018. The results describe that the mean LST of Raipur City is gradually increased with time. The value of mean NDVI is higher in the area below mean LST compared to the area above mean LST. The value of mean NDVI is also higher in Landsat 8 data than Landsat 5 and Landsat 7 data. A strong negative LST-NDVI correlation is observed throughout the period. The correlation coefficient is higher in the area above mean LST and lower in the area below mean LST. The value of the correlation coefficient is decreased with time. The mixed urban landscape of the city is closely related to the changes of LST-NDVI relationship. These results provide systematic planning of the urban environment.

scholarly journals DYNAMICS OF GREEN SPACES IN ST. PETERSBURG ACCORDING TO REMOTE SENSING DATA

2021 ◽  
Vol 2 (6) ◽  
pp. 38-41
Author(s):  
D.K. Alexeev ◽  
◽  
A.V. Babin ◽  
V.Yu. Sargaeva
Keyword(s):  
Spatial Distribution ◽  
Quantitative Assessment ◽  
Satellite Images ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Green Spaces ◽  
Landsat 8 ◽  
Landsat 7 ◽  
Landsat Satellite ◽  
The City

. Urban development is formulated as one of seventeen sustainable development goals for the near future. Among the whole range of environmental problems of a modern city, the issues of urban greening occupy a special place. In the course of the work, the analysis of the spatial distribution and assessment of the dynamics of green spaces on the territory of the city of St. Petersburg and its administrative-territorial units (inner-city districts) was carried out according to the data of multispectral satellite images Landsat 7 and Landsat 8 for the period 2002–2018. The normalized vegetation index (NDVI) was used for quantitative assessment. Maps of the spatial distribution of NDVI for the specified period were built. A decrease in the indicators of the provision of green spaces for the specified period for various districts of the city has been established. The obtained maps of the city’s vegetation cover, based on Landsat satellite images, provide a visual representation of the spatial distribution of landscaping indicators with the possibility of their quantitative assessment, and provide planning of landscaping facilities. The data obtained as a result of the work can supplement existing knowledge when carrying out work on process research and monitoring, as well as when making practical decisions

scholarly journals A Novel Viewpoint to the Green City Concept Based on Vegetation Area Changes and Contributions to Healthy Days: A Case Study of Mashhad, Iran

2021 ◽  
Author(s):  
Amir Nejatian ◽  
Masoud Makian ◽  
Mohammad Gheibi ◽  
Amir Mohammad Fathollahi-Fard
Keyword(s):  
Air Pollution ◽  
Vegetation Index ◽  
Green Space ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Large Cities ◽  
First Time ◽  
The Relationship ◽  
Green City

Abstract One of the significant challenges in urbanization is the air pollution. This highlights the need of the green city concept with reconsideration of houses, factories and traffics in a green viewpoint. The literature review confirms that this reconsideration for green space, has a positive effect on the air quality of large cities and to remove the air pollution. The purpose of this study is to evaluate the annual vegetation changes in the green space of Mashhad, Iran as a very populated city in the middle east to study the air pollution. To investigate the relationship between the air pollution and vegetation, the Landsat 8 satellite images for summers of 2013-2019 were used to extract changes in vegetation by calculating the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and the Optimized Soil Adjusted Vegetation Index (OSAVI). The main contribution in comparison with the relevant studies is to study the relationship between clean, healthy and unhealthy days with the green space area for the first time in Mashhad, Iran. The results show that the implementation of green city concept in Mashhad, Iran has been increased by 64%, 81% and 53% by NDVI, EVI and OSAVI, respectively during the study period. The vegetation area of this city is positively correlated to clean and healthy days and has a negative correlation to unhealthy days, in which the greatest values for NDVI, EVI and OSAVI are 0.33, 0.52 and -0.53, respectively.

scholarly journals REMOTE SENSING METHOD FOR LAND COVER CHANGE MONITORING USING LANDSAT MULTI-TEMPORAL DATA (Case study: Tuy Duc and Dak Rap Districts, Dak Nong Province, Central Highlands of Vietnam)

2020 ◽  
Vol 12 (3) ◽  
pp. 357-365
Author(s):  
Hung TRINH ◽  
◽  
Tuyen VU ◽  
Bien TRAN ◽  
Trinh PHAM ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat Tm ◽  
Vegetation Coverage ◽  
Landsat 8 ◽  
Coverage Density ◽  
Central Highland ◽  
Landsat Satellite

In this study, vegetation coverage changes over a 30-year period for the Tuy Duc and Dak R’lap districts,Dak Nong province (central highland of Vietnam) were assessed using remote sensing and Geographic Information Systems (GIS) techniques. 03 Landsat satellite images,including Landsat TM February 13, 1990, Landsat TM February 22, 2005 and Landsat 8 January, 15 2020 were used to calculate the normalized difference vegetation index (NDVI), then assessed the changes in vegetation coverage density. The NDVI differencing method is also used as a change detection method and provides detailed information for monitoring changes in land cover in periods 1990 – 2005, 2005 – 2020 and 1990 – 2020. Analysis of the obtained results showed that the vegetation coverage declined sharply during 1990 – 2005 period,then the vegetation coverage has begun to recover in period 2005 – 2020. From the findings of this study, it can be easily concluded that the Tuy Duc and Dak R’lap areas has lost its valuable vegetation cover both qualitatively and quantitatively.

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