R-universe search: avhrr

Showing 5 of total 5 results (show query)

hvillalo

satin:Visualisation and Analysis of Ocean Data Derived from Satellites

With 'satin' functions, visualisation, data extraction and further analysis like producing climatologies from several images, and anomalies of satellite derived ocean data can be easily done. Reading functions can import a user defined geographical extent of data stored in netCDF files. Currently supported ocean data sources include NASA's Oceancolor web page <https://oceancolor.gsfc.nasa.gov/>, sensors VIIRS-SNPP; MODIS-Terra; MODIS-Aqua; and SeaWiFS. Available variables from this source includes chlorophyll concentration, sea surface temperature (SST), and several others. Data sources specific for SST that can be imported too includes Pathfinder AVHRR <https://www.ncei.noaa.gov/products/avhrr-pathfinder-sst> and GHRSST <https://www.ghrsst.org/>. In addition, ocean productivity data produced by Oregon State University <http://sites.science.oregonstate.edu/ocean.productivity/> can also be handled previous conversion from HDF4 to HDF5 format. Many other ocean variables can be processed by importing netCDF data files from two European Union's Copernicus Marine Service databases <https://marine.copernicus.eu/>, namely Global Ocean Physical Reanalysis and Global Ocean Biogeochemistry Hindcast.

Maintained by Héctor Villalobos. Last updated 10 months ago.

avhrr copernicus ghrsst modis

11.8 match 4 stars 3.30 score 9 scripts

zhaokg

Rbeast:Bayesian Change-Point Detection and Time Series Decomposition

Interpretation of time series data is affected by model choices. Different models can give different or even contradicting estimates of patterns, trends, and mechanisms for the same data--a limitation alleviated by the Bayesian estimator of abrupt change,seasonality, and trend (BEAST) of this package. BEAST seeks to improve time series decomposition by forgoing the "single-best-model" concept and embracing all competing models into the inference via a Bayesian model averaging scheme. It is a flexible tool to uncover abrupt changes (i.e., change-points), cyclic variations (e.g., seasonality), and nonlinear trends in time-series observations. BEAST not just tells when changes occur but also quantifies how likely the detected changes are true. It detects not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is applicable to real-valued time series data of all kinds, be it for remote sensing, economics, climate sciences, ecology, and hydrology. Example applications include its use to identify regime shifts in ecological data, map forest disturbance and land degradation from satellite imagery, detect market trends in economic data, pinpoint anomaly and extreme events in climate data, and unravel system dynamics in biological data. Details on BEAST are reported in Zhao et al. (2019) <doi:10.1016/j.rse.2019.04.034>.

Maintained by Kaiguang Zhao. Last updated 6 months ago.

anomoly-detection bayesian-time-series breakpoint-detection changepoint-detection interrupted-time-series seasonality-analysis structural-breakpoint technical-analysis time-series time-series-decomposition trend trend-analysis

3.4 match 302 stars 7.63 score 89 scripts

daniel-dok

phenex:Auxiliary Functions for Phenological Data Analysis

Provides some easy-to-use functions for spatial analyses of (plant-) phenological data sets and satellite observations of vegetation.

Maintained by Daniel Doktor. Last updated 8 years ago.

7.8 match 2 stars 2.28 score 32 scripts 1 dependents

babaknaimi

rts:Raster Time Series Analysis

This framework aims to provide classes and methods for manipulating and processing of raster time series data (e.g. a time series of satellite images).

Maintained by Babak Naimi. Last updated 9 months ago.

1.7 match 6 stars 6.59 score 107 scripts 1 dependents

rspatial

luna:Tools for Satellite Remote Sensing (Earth Observation) Data Processing

Tools for acquiring and (pre-) processing satellite remote sensing data. Including for downloading data from NASA such as LANDSAT and MODIS.

Maintained by Robert J. Hijmans. Last updated 3 months ago.

openblas cpp

1.7 match 34 stars 3.95 score 52 scripts