Exploring Ndvi and Ndbi Relationship Using Landsat 8 Oli/Tirs in Khangarh Taluka, Ghotki (original) (raw)
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Indian Journal of Geo-Marine Sciences, 2019
Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) have been computed and their relationships with Land surface temperature (LST) in each season were examined. LST retrieved by thermal data analysis represents the spatial and temporal distribution of surface temperature. NDBI is describing the built-up index and NDVI the proportion of vegetation in the watershed. Relationships of LST with NDBI & NDVI were developed in each season. Correlation results of LST & NDBI has shown strong positive relationship i.e. R 2 = 0.991 in Jan.2016, 0.981 in May 2016 & 0.965 in Oct.2016, where as strong negative correlation were found in between LST & NDVI i.e. R 2 = 0.993, 0.992, & 0.911 in each season. Relationship between NDVI & NDBI was also developed and is showing strong negative correlation i.e. R 2 = 0.979, 0.988, & 0.913.
A Study on Relationship between NDVI and Precipitation over Kolong River Basin , Assam , India
2016
Vegetative productivity of any region is mainly dependent on meteorological parameters like precipitation and temperature of the region. The northeastern region of India has diverse vegetation, mainly controlled by its unique climatic as well as physiographic conditions. An index used for determining vegetative condition of an area is NDVI (Normalized Difference Vegetation Index). This study focuses on the relationship between precipitation and NDVI in regard to the Kolong Basin of Assam and attempts to evaluate the changing trend of NDVI values which in turn depicts the vegetative productivity of the region. The NDVI values were computed from multispectral images of LANDSAT(TM and ETM+) and IRS (LISS III) satellites and rainfall data were collected from the website of IMD for the years 1987, 1999 and 2008.Temporal changes of NDVI were related to rainfall patterns and the trend of NDVI was calculated. The critical changes of NDVI and the correlation-coefficients between NDVI and rai...
Keti Bunder is a small coastal community situated at about 200 km south east of Karachi. It has four major creeks namely Chann, Hajamro, Kangri (Turchhan) and Khober with an arid subtropical climate and temperature remaining moderate throughout the year. This paper reports the application of an integration of Remote Sensing (RS) and Geographic Information Systems (GIS) for analysis and monitoring of the relationship of land surface temperature (LST) with Normalized Difference Vegetation Index (NDVI) in the area. LST is one of the critical elements in the natural phenomena of surface energy and water balance at local and global extent. [1-5]. Remote sensing in accord with tradition utilizes the NDVI to provide specific information on vegetation abundance to the LST–vegetation relationship. For mapping purposes, satellite images of Landsat-5 ETM+, Landsat-7 TM and Landsat-8 OLI / TIRS images, acquired on March 08, 2000, April 29, 2010 and April 08, 2014 respectively, were used. The results indicate that the maximum land surface temperature increased gradually from 39°C in 2000, to 42°C in 2010 and 45°C in 2014. Due to global warming and climatic changes. Keti Bunder of the Indus delta has experienced a serious condition over the past few years; the local communities have suffered badly from climate change impacts as heavy rainfalls, floods and cyclones have forced people to migrate to other places for their livelihood and shelter. However, mean NDVI value increased to 0.009 in 2014 as compared to 2010 (-0.165), due to several plantations of mangroves being established by the government. In the past, the mangrove forest was degraded due to lack of freshwater and seawater intrusion. The rate of degradation of mangrove forest in the delta was approximately 6 percent per year between 1980 and 1995 and only a small percentage of mangroves are now considered to be healthy [6-7].
Spatio - temporal changes in NDVI and rainfall over Western Rajasthan and Gujarat region of India
Journal of Agrometeorology, 2018
This study examines the MODIS time series NDVI datasets to detect greenness regeneration over Western Rajasthan and the Gujarat region of India. Time series analysis was applied to 17-years (2000-2016). MODIS NDVI satellite data product. Rainfall data for the same period were also analyzed to understand its impact over vegetation. NDVI time series datasets of MODIS 16-day composite provedsufficient for deriving statistically significant trend values for identifying areas of change in vegetation cover. Areas showing positive changes in NDVI trend was clearly correlated with areas which were brought under irrigational network over these areas, indicating an increase in vegetation, due to availability of water supply. Trends in NDVI were also compared with the trends in rainfall over the selected locations from Gujarat and Western Rajasthan. NDVI was positively correlated with the rainfall in both the regions. The NDVI time series trend analysis successfully detected the changes in gre...
GMPI Conference Series
Liquefaction and landslide can occur during earthquakes caused by changes in soil saturation levels so that the soil loses strength due to loss of tension between grains. One of the determinations of soil moisture data using satellite imagery analysis is Landsat. Landsat has provided moderate, global, synoptic spatial resolution and repeated earth's soil surface coverage. This paper discusses the multi-hazard susceptibility using Landsat-8 satellite imagery with a combination of NDMI (Normalized Difference Moisture Index) ratio bands in Sunurraya Village and Simpang Saga Village, South OKU Regency, South Sumatra. The combination of NDMI bands determines the spread of soil saturation levels and differences in moisture in vegetation conditions. Other supporting data are soil's physical properties, including water content, density, hydrometer analysis, and Atterberg limits analysis. Overlay of NDMI data analysis and soil test analysis shows the level of liquidation insecurity i...
2016
Remote sensing technology in combination with geographic information system (GIS) can render reliable information on vegetation cover. The analysis of the spatial extent and temporal change of vegetation cover using remotely sensed data is of critical importance to agricultural sciences.This paper investigates the Spatio-Temporal change of vegetation cover of Kheragarh taluka of Agra district. For this study, Landsat images (TM and ETM+) of 12 February, 2002 and 27February, 2015 were used. For recognition of vegetation reflectance, layer stacking of band 4, 3 and 2 (false color composite) for TM and ETM+ was performed. To cross check the vegetation cover information obtained through images, ground truth verification of certain sample locations through GPS device was done. The images were then classified into water body, forest/vegetation cover, built-up area, non-forested and agriculture. Supervised classification was done and maximum likelihood operation was performed to generate vegetation cover maps. Afterwards, vegetation cover map of 2002 and 2015 were crossed to generate the map of change of vegetation cover for the respective dates and to find out the changing pattern of vegetation cover. In addition, the use of spectral vegetation index, namely the Normalized Difference Vegetation Index (NDVI) was applied to detect areas of vegetation cover decrease. The study reveals that vegetation cover of the area has changed significantly during the study period.
Assessing the Relation Between NDVI and Rainfall over India
The Indian subcontinent has diverse vegetation with the climate varying from monsoonal in south to temperate in the North. The biological productivity of the vegetation cover therefore largely controlled by water and temperature stresses. The Normalized Deferential Vegetation Index (NDVI) was shown to be sensitive to changes in vegetation conditions. Since it is directly influenced by the chlorophylls absorption of the suns radiation. In this study rainfall data (from IMD) and MODIS-NDVI data (GLAM project) for 28 states of India was used. NDVI data from MODIS (with a resolution of 250 km) images was correlated with state wise annual precipitation for the year 2004-2008. The correlation value for NDVI and rainfall was observed to be 0.64 and R value was 0.40. From this study we can conclude that the NDVI is majorly dependent on the rainfall. Other 2 factors like temperature, humidity, radiation etc., also influence the vegetation growth and productivity but in lesser proportion compared to precipitation.
Specific features of NDVI, NDWI and MNDWI as reflected in land cover categories
Landscape & Environment, 2016
The remote sensing techniques provide a great possibility to analyze the environmental processes inlocal or global scale. Landsat images with their 30 m resolution are suitable among others for landcover mapping and change monitoring. In this study three spectral indices (NDVI, NDWI, MNDWI) wereinvestigated from the aspect of land cover types: water body (W); plough land (PL); forest (F); vineyard(V); grassland (GL) and built-up areas (BU) using Landsat-7 ETM+ data. The range, the dissimilaritiesand the correlation of spectral indices were examined. In BU – GL – F categories similar NDVI valueswere calculated, but the other land cover types differed significantly. The water related indices (NDWI,MNDWI) were more effective (especially the MNDWI) to enhance water features, but the values of othercategories ranged from narrower interval. Weak correlation were found among the indices due to thedifferences caused by the water land cover class. Statistically, most land cover types differe...
Relationship Between NDVI and Rainfall Relationship over India by S.K. Dubey, S.K Tripathi
The Indian subcontinent has diverse vegetation with the climate varying from monsoonal in south to temperate in the North. The biological productivity of the vegetation cover therefore largely controlled by water and temperature stresses. The Normalized Deferential Vegetation Index (NDVI) was shown to be sensitive to changes in vegetation conditions. Since it is directly influenced by the chlorophylls absorption of the suns radiation. In this study rainfall data (from IMD) and MODIS-NDVI data (GLAM project) for 28 states of India was used. NDVI data from MODIS (with a resolution of 250 km) images was correlated with state wise annual precipitation for the period 2004-2008. The critical changes of NDVI and the correlation coefficients between NDVI and rainfall were examined for each pixel. The average correlation value for NDVI and rainfall was observed to be 0.64 and R value was 0.40. Spatially very strong relationship is observed in north east and 2 southern part of country. From this study we can conclude that the NDVI is majorly dependent on the rainfall. Other factors like temperature, humidity, radiation etc., also influence the vegetation growth and productivity but in lesser proportion compared to precipitation.
Journal of the Indian Society of Remote Sensing, 2011
Sustainability of irrigated agriculture in arid and semi arid lands depends, mainly on the level of soil salinity and the quality of irrigation water. Remotely sensed data can provide information about the extent of vegetated irrigated areas. Al-Hassa oasis, Saudi Arabia is probably the largest oasis in the world depends mostly on tapped ground water to irrigate mainly date palm groves for its economic survival. This study tried to investigate the extent of soil salinity and the quality of irrigation water and the relationship with vegetation growth, employing NDVI derived from Landsat satellite imagery.