Editor Papa Research October 9, 2019

Measuring phenological variability from satellite imagery

Vegetation phenological phenomena area unit closely associated with seasonal dynamics of the lower atmosphere and area unit thus necessary components in world models and vegetation observation. Normalized distinction vegetation index (NDVI) knowledge derived from the National Oceanic and part Administration’s Advanced terribly High Resolution meter (AVHRR) satellite sensing element supply a way of expeditiously and objectively evaluating phenological characteristics over massive areas. Twelve metrics connected to key phenological events were computed supported time‐series NDVI knowledge collected from 1989 to 1992 over the conterminous u.  s.. These measures embrace the onset of greenness, time of peak NDVI, most NDVI, rate of greenup, rate of senescence, and integrated NDVI. Measures of central tendency and variability of the measures were computed and analyzed for numerous land cowl sorts. [1]

Tropical Cyclone Intensity Analysis and Forecasting from Satellite Imagery

A technique for victimisation satellite footage to analyse and forecast tropical cyclone intensifies is delineate. The cloud options accustomed estimate the cyclone’s intensity and its future modification of intensity are delineate. Procedures for deciphering cloud characteristics associated their day-by-day changes at intervals the steering and constraints of an empirical model of tropical cyclone changes are printed. [2]

Removal of atmospheric effects from satellite imagery of the oceans

In trying to watch the colour of the ocean from satellites, it’s necessary to get rid of the consequences of region and ocean surface scattering from the upward radiance at high altitude so as to watch solely those photons that were backscattered out of the ocean and thence contain info concerning submarine conditions. The observations that (1) the upward radiance from the unwanted photons is divided into those ensuing from Rayleigh scattering alone and people resulting from aerosol scattering alone, (2) the aerosol scattering part operate ought to be nearly freelance of wavelength, associate degreed (3) the Rayleigh element is computed while not a information of the ocean surface roughness are combined to yield an formula for removing an outsized portion of this unwanted radiance from satellite imagination of the ocean. [3]

The Spatial and Temporal Influence of Cloud Cover on Satellite-Based Emergency Mapping of Earthquake Disasters

The ability to speedily access optical satellite imagination is currently Associate in Nursing intrinsic element of managing the disaster response that follows a significant earthquake. These pictures offer synoptic information on the impacts, extent, and intensity of injury, that is important for mitigating any losses by feeding into the response coordination. However, while the potency of the response is hampered once bad weather limits image availableness, spatio-temporal variations in bad weather have not been thought-about as a part of the look of effective disaster mapping. Here we have a tendency to show however annual variations in bad weather could have an effect on our capability to reply speedily throughout the year and consequently contribute to overall earthquake risk. [4]

Application of Satellite Imagery in the Differentiation of the Invasive Species of Nipa from Mangrove Vegetation

Mangrove forests kind one amongst the first coastal ecosystems within the tropical and climatic zone regions of the globe with a high diverseness price. angiospermous tree species are unambiguously tailored to the Nigerian coasts, providing varied diverseness and scheme services and supporting coastal livelihoods at intervals the Niger Delta. The gradual decline within the size of the angiospermous tree scheme, thanks to nipa palm infestation, has spanned a amount of over forty years. So far, no quantitative estimate of loss of those angiospermous tree habitats has been meted out. this can be as a results of the closeness in spectral characteristics between Nipa and completely different species of angiospermous tree and also the issue of differentiating Nipa mistreatment earlier remote sensing merchandise like Landsat, JERS, Radarsat, SPOT and ERS. [5]


[1] Reed, B.C., Brown, J.F., VanderZee, D., Loveland, T.R., Merchant, J.W. and Ohlen, D.O., 1994. Measuring phenological variability from satellite imagery. Journal of vegetation science, 5(5), (Web Link)

[2] Dvorak, V.F., 1975. Tropical cyclone intensity analysis and forecasting from satellite imagery. Monthly Weather Review, 103(5), (Web Link)

[3] Gordon, H.R., 1978. Removal of atmospheric effects from satellite imagery of the oceans. Applied Optics, 17(10), (Web Link)

[4] The Spatial and Temporal Influence of Cloud Cover on Satellite-Based Emergency Mapping of Earthquake Disasters
Tom R. Robinson, Nick Rosser & Richard J. Walters
Scientific Reports volume 9, Article number: 12455 (2019) (Web Link)

[5] Onwuteaka, J. and Uwagbae, M. (2018) “Application of Satellite Imagery in the Differentiation of the Invasive Species of Nipa from Mangrove Vegetation”, Journal of Geography, Environment and Earth Science International, 13(3), (Web Link)

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