Results 251 to 260 of about 196,280 (287)

Relationships between percent vegetation cover and vegetation indices

International Journal of Remote Sensing, 1998
In this paper, percent vegetation cover is estimated from vegetation indices using simulated Advanced Very High Resolution Radiometer (AVHRR) data derived from in situ spectral reflectance data. Spectral reflectance measurements were conducted on grasslands in Mongolia and Japan.
TS. Purevdorj, R. Tateishi, T. Ishiyama, Y. Honda   +3 more
openaire   +1 more source

Monitoring Vegetation Water Content Using Microwave Vegetation Indices

IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008
Effectively monitoring vegetation water is essential to improve our understanding of agriculture and hydrology. Vegetation water content is often estimated using vegetation indices derived from optical satellite sensors. In this study, we introduced the new microwave vegetation indices (MVIs) and derived the new MVIs using observation from the Advanced
Jing Tao, Jiancheng Shi, Tom Jackson, Rajat Bindlish, Jinyang Du, Lixin Zhang   +5 more
openaire   +1 more source

Fruit and Vegetable Pigments as Indicators

Science, 1930
(1) Solutions of many fruit pigments act as indicators. (2) These solutions are easily prepared and stable, and the pH range of their color changes is in most cases conveniently near the neutral point. (3) As liquid indicators they can be used in titrating acids, but not bases.
O B, Pratt, H O, Swartout
openaire   +2 more sources

ANALYSIS OF VEGETATION INDICES OF URBAN VEGETATION IN ZAGREB (CROATIA)

SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings, 2018
Copernicus is a European Union programme designed to provide a service in delivering free, openly accessible data about the Earth’s surface through satellite and in-situ observations. On the other hand, Landsat represents the world's longest standing continuously acquired collection of space-based, moderate-resolution, land remote sensing data for ...
Krtalić, Andrija, Racetin, Ivan, Ivelja, Tamara   +2 more
openaire   +2 more sources

Refinement of microwave vegetation indices

SPIE Proceedings, 2010
Previous investigations have established the basis for a new type of vegetation index, Microwave Vegetation Indices (MVIs), based on passive microwave satellite observations. In this technique, the quantitative basis of the MVIs can be derived from the zeroth-order radiative transfer solution.
L. Chai, J. Shi, L. Zhang, T. Jackson
openaire   +1 more source

Linearized Vegetation Indices

2011
Most remote sensing satellite sensors offer multispectral images besides panchromatic images. This multispectral information is extremely valuable to detect various objects in the image. In this book, we summarize the multispectral information via vegetation and shadow–water indices.
Cem Ünsalan, Kim L. Boyer
openaire   +1 more source

A review of vegetation indices

Remote Sensing Reviews, 1995
Abstract In the field of remote sensing applications, scientists have developed vegetation indices (VI) for qualitatively and quantitatively evaluating vegetative covers using spectral measurements. The spectral response of vegetated areas presents a complex mixture of vegetation, soil brightness, environmental effects, shadow, soil color and moisture.
A. Bannari, D. Morin, F. Bonn, A. R. Huete   +3 more
openaire   +1 more source

Vegetation indices in crop assessments

Remote Sensing of Environment, 1991
Abstract Vegetation indices (VI), such as greenness (GVI), perpendicular (PVI), transformed soil adjusted (TSAVI), and normalized difference (NDVI), measure the photosynthetic size of plant canopies and portend yields. A set of equations, called spectral components analysis (SCA), that interrelates VI or cumulative seasonal VI (∑VI), leaf area index (
C.L. Wiegand, A.J. Richardson, D.E. Escobar, A.H. Gerbermann   +3 more
openaire   +1 more source

Vegetation detection using vegetation indices algorithm supported by statistical machine learning

Environmental Monitoring and Assessment, 2022
In precision agriculture (PA), the usage of image processing, artificial intelligence, data analysis, and internet of things provides an increase in efficiency, energy, and time saving. In image processing-based applications, vegetation detection, in other words, segmentation that allows monitoring of plant growth and health as well as identification ...
openaire   +2 more sources

Vegetation spatial variability and its effect on vegetation indices

International Journal of Remote Sensing, 1987
Landsat MSS data were used to simulate low resolution satellite data, such as NOAA AVHRR, to quantify the fractional vegetation cover within a pixel and relate the fractional cover to the normalized difference vegetation index (NDVI) and the simple ratio (SR).
J. P. ORMSBY, B. J. CHOUDHURY, M. OWE
openaire   +1 more source