20 years MODIS-NDVI monitoring suggests that vegetation has increased significantly around Tehri Dam reservoir, Uttarakhand, India

2021 ◽  
Vol 24 ◽  
pp. 100610
Author(s):  
Pulak Das
Keyword(s):  
Dam Reservoir ◽  
Modis Ndvi ◽  
Tehri Dam

scholarly journals Application of CCME WQI to evaluate feasibility of potable water availability: A case study of Tehri dam reservoir

2014 ◽  
Vol 15 (1&2) ◽  
pp. 13-19
Author(s):  
R. Bhutiani ◽  
D.R. Khanna ◽  
Prashant Kumar Tyagi ◽  
Bharti Tyagi
Keyword(s):  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Research Work ◽  
Oxygen Demand ◽  
Total Coliform ◽  
Dam Reservoir ◽  
Canadian Council ◽  
Ccme Wqi ◽  
Tehri Dam

In the present research work, the Canadian Council of Ministers of the Environment water quality index 1.0 (CCME WQI 1.0) was applied to assess water quality of Tehri dam reservoir by using the drinking water standard prescribed by the WHO (1999) and BIS (IS:10500, 1991). The physico-chemical parameters, ions concentration and heavy metals concentration used in the index calculation were total dissolved solids, pH, alkalinity, dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, total hardness, calcium, chloride, phosphate, sulphate, nitrate, total coliform (MPN/100 ml), turbidity, zinc, manganese, lead, nickel, iron and chromium. It was observed during the course of study that at all the four sites BOD, phosphate and total coliform showed greater deviation from the objective values. Total coliform was found to be more deviated from the normal values. Few important parameters were observed beyond the permissible limit for many times. The values of water quality index have shown that most of the sites are not fit for drinking purpose. Finally it was concluded that reservoir water should not be consumed for drinking purposes frequently without proper treatment.

Water quality of Tehri dam reservoir and contributing rivers in the Himalayan region, India

2019 ◽  
Vol 5 (4) ◽  
pp. 1951-1961 ◽  
Author(s):  
Gajanan K. Khadse ◽  
Dilip B. Meshram ◽  
Prashant Deshmukh ◽  
Pawan K. Labhasetwar
Keyword(s):  
Water Quality ◽  
Himalayan Region ◽  
Dam Reservoir ◽  
Tehri Dam

Monitoring multiple cropping index using MODIS NDVI data-A case study of Bohai Rim

2013 ◽  
Vol 20 (12) ◽  
pp. 1657-1663 ◽  
Author(s):  
Shou-Zhen LIANG ◽  
Wan-Dong MA ◽  
Ping SHI ◽  
Jin-Song CHEN
Keyword(s):  
Multiple Cropping ◽  
Modis Ndvi ◽  
Bohai Rim

HYDRODYNAMIC STRUCTURE IN A LARGE DAM RESERVOIR DURING EARLY AUTUMN

2018 ◽  
Vol 74 (5) ◽  
pp. I_481-I_486
Author(s):  
Makoto UMEDA ◽  
Yuta NAITO ◽  
Bunyu KOBORI ◽  
Tetsuya SHINTANI ◽  
Kazushi OMOE ◽  
...  
Keyword(s):  
Dam Reservoir ◽  
Early Autumn ◽  
Hydrodynamic Structure ◽  
Large Dam

scholarly journals Start of the Green Season and Normalized Difference Vegetation Index in Alaska’s Arctic National Parks

2021 ◽  
Vol 13 (13) ◽  
pp. 2554
Author(s):  
David K. Swanson
Keyword(s):  
National Parks ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Free Surfaces ◽  
Modis Ndvi ◽  
Curve Fit ◽  
Winter Snow ◽  
Systematic Sample ◽  
Low Levels ◽  
Northern Alaska

Daily Normalized Difference Vegetation Index (NDVI) values from the MODIS Aqua and Terra satellites were compared with on-the-ground camera observations at five locations in northern Alaska. Over half of the spring rise in NDVI was due to the transition from the snow-covered landscape to the snow-free surface prior to the deciduous leaf-out. In the fall after the green season, NDVI fluctuated between an intermediate level representing senesced vegetation and lower values representing clouds and intermittent snow, and then dropped to constant low levels after establishment of the permanent winter snow cover. The NDVI value of snow-free surfaces after fall leaf senescence was estimated from multi-year data using a 90th percentile smoothing spline curve fit to a plot of daily NDVI values vs. ordinal date. This curve typically showed a flat region of intermediate NDVI values in the fall that represent cloud- and snow-free days with senesced vegetation. This “fall plateau” was readily identified in a large systematic sample of MODIS NDVI values across the study area, in typical tundra, shrub, and boreal forest environments. The NDVI level of the fall plateau can be extrapolated to the spring rising leg of the annual NDVI curve to approximate the true start of green season.

scholarly journals Variation in Vegetation and Its Driving Force in the Middle Reaches of the Yangtze River in China

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2036
Author(s):  
Yang Yi ◽  
Bin Wang ◽  
Mingchang Shi ◽  
Zekun Meng ◽  
Chen Zhang
Keyword(s):  
Yangtze River ◽  
Remote Sensing Data ◽  
Slope Aspect ◽  
The Yangtze River ◽  
Vegetation Growth ◽  
Modis Ndvi ◽  
Sunshine Hours ◽  
Ndvi Time Series ◽  
Temporal And Spatial Characteristics ◽  
Afforestation Policy

The temporal and spatial characteristics of vegetation in the middle reaches of the Yangtze River (MRYR) were analyzed from 1999 to 2015 by trend analysis, co-integration analysis, partial correlation analysis, and spatial analysis using MODIS-NDVI time series remote sensing data. The average NDVI of the MRYR increased from 0.72 to 0.80, and nearly two-thirds of the vegetation showed a significant trend of improvement. At the inter-annual scale, the relationship between NDVI and meteorological factors was not significant in most areas. At the inter-monthly scale, NDVI was almost significantly correlated with precipitation, relative humidity, and sunshine hours, and the effect of precipitation and sunshine hours on NDVI showed a pronounced lag. When the altitude was less than 2500 m, NDVI increased with elevation. NDVI increased gradually as the slope increased and decreased gradually as the slope aspect changed from north to south. NDVI decreased as the population density and per capita GDP increased and was significantly positively correlated with afforestation policy. These findings provide new insights into the effects of climate change and human activities on vegetation growth.

scholarly journals Estimating Forest Canopy Cover by Multiscale Remote Sensing in Northeast Jiangxi, China

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 433
Author(s):  
Xiaolan Huang ◽  
Weicheng Wu ◽  
Tingting Shen ◽  
Lifeng Xie ◽  
Yaozu Qin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Forest Canopy ◽  
Canopy Cover ◽  
Google Earth ◽  
Tree Canopy ◽  
Landsat Data ◽  
High Resolution Data ◽  
Modis Ndvi ◽  
Tree Canopy Cover

This research was focused on estimation of tree canopy cover (CC) by multiscale remote sensing in south China. The key aim is to establish the relationship between CC and woody NDVI (NDVIW) or to build a CC-NDVIW model taking northeast Jiangxi as an example. Based on field CC measurements, this research used Google Earth as a complementary source to measure CC. In total, 63 sample plots of CC were created, among which 45 were applied for modeling and the remaining 18 were employed for verification. In order to ascertain the ratio R of NDVIW to the satellite observed NDVI, a 20-year time-series MODIS NDVI dataset was utilized for decomposition to obtain the NDVIW component, and then the ratio R was calculated with the equation R = (NDVIW/NDVI) *100%, respectively, for forest (CC >60%), medium woodland (CC = 25–60%) and sparse woodland (CC 1–25%). Landsat TM and OLI images that had been orthorectified by the provider USGS were atmospherically corrected using the COST model and used to derive NDVIL. R was multiplied for the NDVIL image to extract the woody NDVI (NDVIWL) from Landsat data for each of these plots. The 45 plots of CC data were linearly fitted to the NDVIWL, and a model with CC = 103.843 NDVIW + 6.157 (R2 = 0.881) was obtained. This equation was applied to predict CC at the 18 verification plots and a good agreement was found (R2 = 0.897). This validated CC-NDVIW model was further applied to the woody NDVI of forest, medium woodland and sparse woodland derived from Landsat data for regional CC estimation. An independent group of 24 measured plots was utilized for validation of the results, and an accuracy of 83.0% was obtained. Thence, the developed model has high predictivity and is suitable for large-scale estimation of CC using high-resolution data.

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