Normalized Difference Vegetation Index

... Y. Uno a , SO Prasher a , Corresponding Author Contact Information , E-mail The Corresponding Author , R. Lacroix b , PK Goel a , Y ... Due to its great adaptability, stepwise multiple linear regression (SMLR) is one of the most commonly used methods to develop empirical models ...

Reflectance measurements were conducted in field plots of Golden Smoothee 2038 apple cultivar subjected to different levels of mite attack (from 0 to 500 cumulative mite days) over a 2-year period. Chlorophyll concentration decreased and carotenoid/chlorophyll a ratio increased with increasing level of attack. Classical reflectance indices such as red edge wavelength or the Normalized Difference Vegetation Index were unable to distinguish among treatments. On the other hand, spectral indices based on carotenoid/chlorophyll a (SRPI R430/R680) and chlorophyll degradation (Normalized Phaeophytinization Index calculated as (R415-R435)/(R435 +R415)) were correlated with the level of attack. These reflectance indices show potential uses of visible reflectance as non-invasive, non-destructive techniques for detection of mite effects.

Normalized Difference Vegetation Index (NDVI) is a crucial land surface characteristic derived from satellite data. NDVI derived from AVHRR/NOAA is now the most widely used in medium to large scale studies to determine vegetation-climate interactions. Experiments with MSG (METEOSAT 8/9) SEVIRI data have demonstrated a good potential for new generation imaging instruments onboard geostationary satellites to provide routine monitoring of the vegetation state through NDVI. In this study, we present pre-operational daily and weekly NDVI products derived from MSG SEVIRI data. Full disc MSG coverage red and near infrared bands are processed and the products are masked using the derived Cloud Mask products. Validation is performed against Land SAF fractional vegetation cover (FVC) products. Additionally, MSG NDVI products are compared with METOP AVHRR based NDVI products. Temporal stability analysis (diurnal change) is also performed.

Climate change and human activities in particular are important causes of the possible variations in Mediterranean basin forest health conditions. Over the last decades, deciduous oak-forest mortality has been a recurrent problem in central and southern Italy. Despite the perception of increasingly visible damage in oak forests in drought sites, the role of various environmental factors in their decline is not completely clear. Among the modern methods of monitoring terrestrial ecosystems, remote sensing is of prime importance thanks to its ability to provide synoptic information on large areas with a high frequency of acquisition. This paper reports the preliminary results regarding a replicable and low cost monitoring tool planned to quantify forest health conditions based on the application of the Normalized Difference Vegetation Index (NDVI), using the diachronic images provided by the Sentinel-2 satellite. The study area is represented by a peri-urban forest of natural Mediterr...

Satellite-based remote sensing (RS) data, and weather data for Rabi season are used for estimating seasonal peak crop evapotranspiration, (ETc) and crop water requirement in Sabarmati Right Bank Main Canal (SRBMC), command of North Gujarat, India One scene for each month of IRS P6 Awifs sensor of 1:56 Km resolution was used to study the crop growth stages and evaluate the period of full canopy ie. Flowering stage. The IRS P6 Liss-III sensor linear self-scanning and 1:23 km resolution scene, for February month, modeled the NDVI and SAVI maps for the cropped area of the region. The generated NDVI map is used for developing spatial crop coefficient map (Kc) map and crop acreage estimation. Kc (crop coefficient) map for cropped area is combined using ERDAS imagine software, with single average point value of (reference crop evapotranspiration), ETo to evaluate spatial ETc map. Estimation of crop water requirement during entire growing phase is done, using the ETo (reference crop evapotranspiration the month of February 2008.The ETo (reference crop evapotranspiration) is evaluated using modified Pennman Monteith method of FAO24.The local weather data for Mehasana district for the month of February, 2008 were acquired from www.impune.com,were feeded to FAO CROPWAT 8.0 software, ETo calculator.

Multi-sensor satellite data integrated with Global Positioning System enabled geophysical and geochemical information has the potential to infer the availability and quality of groundwater in metamorphic Aravali formation of National Capital Region. Surface manifestation of satellite data has the potential to infer subsurface geology as well as soil moisture in weathered aquifer material. Normalized Difference Vegetation Index computed from the vegetation above the inferred lineament has the potential to infer groundwater quality. Electrical resistivity, X-Ray diffraction (XRD) and Induced Couple Plasma Emission Spectrophotometer (ICPES) analysis further support the spectral reflectance anomaly in litho units. Deep seated fractures with interconnected morphological manifestation have been inferred by using Proton Precession Magnetometer anomaly data. Although it was not possible to infer very deep seated potential fractures by using satellite data but the accurate ground truthing by...

Temperature is an important magnitude for many environmental models: (1) energy and matter exchange between atmosphere and surface, (2) weather prediction, (3) global ocean circulation, (4) climatic change, etc. Several methods have been developed to obtain ...

Drought is a serious climatic condition that affects nearly all climatic zones worldwide, with semi-arid regions being especially susceptible to drought conditions because of their low annual precipitation and sensitivity to climate changes. Drought indices such as the standardized precipitation index (SPI) using meteorological data and vegetation indices from satellite data were developed for quantifying drought conditions. Remote sensing of semi-arid vegetation can provide vegetation indices which can be used to link drought conditions when correlated with various meteorological data based drought indices. The present study was carried out for drought monitoring for three districts namely Bhilwara, Kota and Udaipur of Rajasthan state in India using SPI, normalized difference vegetation index (NDVI), water supply vegetation index (WSVI) and vegetation condition index (VCI) derived from the Advanced Very High resolution Radiometer (AVHRR). The SPI was computed at different time scales of 1, 2, 3, 6, 9 and 12 months using monthly rainfall data. The NDVI and WSVI were correlated to the SPI and it was observed that for the three stations, the correlation coefficient was high for different time scales. Bhilwara district having the best correlation for the 9-month time scale shows late response while Kota district having the best correlation for 1-month shows fast response. On the basis of the SPI analysis, it was found that the area was worst affected by drought in the year 2002. This was validated on the basis of NDVI, WSVI and VCI. The study clearly shows that integrated analysis of ground measured data and satellite data has a great potential in drought monitoring.

Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status required for estimating evapotranspiration (ET) and detecting the onset and severity of drought. While empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI) have demonstrated utility in monitoring ET and drought conditions over large areas, they may provide ambiguous results when other factors (soil moisture, advection, air temperature) are affecting plant stress. A more physically based interpretation of LST and NDVI and their relationship to sub-surface moisture conditions can be obtained with a surface energy balance model driven by TIR remote sensing. The Atmosphere-Land Exchange Inverse (ALEXI) model is a multi-sensor TIR approach to ET mapping, coupling a two-source (soil+canopy) land-surface model with an atmospheric boundary layer model in time-differencing mode to routinely and robustly map daily fluxes at continental scales and 5-10 km resolution using thermal band imagery and insolation estimates from geostationary satellites. A related algorithm (DisALEXI), spatially disaggregates ALEXI fluxes down to finer spatial scales using moderate resolution TIR imagery from polar orbiting satellites. An overview of this modeling approach is presented, along with strategies for fusing information from multiple satellite platforms and wavebands to map daily ET down to resolutions of 30 m. The ALEXI/DisALEXI model has potential for global applications by integrating data from multiple geostationary meteorological satellite systems, such as the US Geostationary Operational Environmental Satellites, the European Meteosat satellites, the Chinese Fen-yung 2B series, and the Japanese Geostationary Meteorological Satellites. Work is underway to further evaluate multi-scale ALEXI implementations over the US, Europe and, Africa and other continents with geostationary satellite coverage.

Ordinary kriging and cokriging are two geostatistical techniques used to create continuous maps of spatially autocorrelated attributes. Both procedures use the same primary variable, but cokriging, like multivariate statistics, incorporates additional independent variables. In this study, the primary variable is cubic foot volume and the secondary variable, mean normalized difference vegetation index, is calculated from an August 1996 Landsat Thematic