Showing 200 of total 217 results (show query)

simonmoulds

lulcc:Land Use Change Modelling in R

Classes and methods for spatially explicit land use change modelling in R.

Maintained by Simon Moulds. Last updated 5 years ago.

19.4 match 41 stars 5.37 score 38 scripts

e-sensing

sits:Satellite Image Time Series Analysis for Earth Observation Data Cubes

An end-to-end toolkit for land use and land cover classification using big Earth observation data, based on machine learning methods applied to satellite image data cubes, as described in Simoes et al (2021) <doi:10.3390/rs13132428>. Builds regular data cubes from collections in AWS, Microsoft Planetary Computer, Brazil Data Cube, Copernicus Data Space Environment (CDSE), Digital Earth Africa, Digital Earth Australia, NASA HLS using the Spatio-temporal Asset Catalog (STAC) protocol (<https://stacspec.org/>) and the 'gdalcubes' R package developed by Appel and Pebesma (2019) <doi:10.3390/data4030092>. Supports visualization methods for images and time series and smoothing filters for dealing with noisy time series. Includes functions for quality assessment of training samples using self-organized maps as presented by Santos et al (2021) <doi:10.1016/j.isprsjprs.2021.04.014>. Includes methods to reduce training samples imbalance proposed by Chawla et al (2002) <doi:10.1613/jair.953>. Provides machine learning methods including support vector machines, random forests, extreme gradient boosting, multi-layer perceptrons, temporal convolutional neural networks proposed by Pelletier et al (2019) <doi:10.3390/rs11050523>, and temporal attention encoders by Garnot and Landrieu (2020) <doi:10.48550/arXiv.2007.00586>. Supports GPU processing of deep learning models using torch <https://torch.mlverse.org/>. Performs efficient classification of big Earth observation data cubes and includes functions for post-classification smoothing based on Bayesian inference as described by Camara et al (2024) <doi:10.3390/rs16234572>, and methods for active learning and uncertainty assessment. Supports region-based time series analysis using package supercells <https://jakubnowosad.com/supercells/>. Enables best practices for estimating area and assessing accuracy of land change as recommended by Olofsson et al (2014) <doi:10.1016/j.rse.2014.02.015>. Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.

Maintained by Gilberto Camara. Last updated 1 months ago.

big-earth-datacbersearth-observationeo-datacubesgeospatialimage-time-seriesland-cover-classificationlandsatplanetary-computerr-spatialremote-sensingrspatialsatellite-image-time-seriessatellite-imagerysentinel-2stac-apistac-catalogcpp

7.6 match 494 stars 9.50 score 384 scripts

pik-piam

magpie4:MAgPIE outputs R package for MAgPIE version 4.x

Common output routines for extracting results from the MAgPIE framework (versions 4.x).

Maintained by Benjamin Leon Bodirsky. Last updated 2 days ago.

8.4 match 2 stars 7.87 score 254 scripts 9 dependents

dnychka

fields:Tools for Spatial Data

For curve, surface and function fitting with an emphasis on splines, spatial data, geostatistics, and spatial statistics. The major methods include cubic, and thin plate splines, Kriging, and compactly supported covariance functions for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance function parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter). A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview. The fields source code is deliberately commented and provides useful explanations of numerical details as a companion to the manual pages. The commented source code can be viewed by expanding the source code version and looking in the R subdirectory. The reference for fields can be generated by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development of this package was supported in part by the National Science Foundation Grant 1417857, the National Center for Atmospheric Research, and Colorado School of Mines. See the Fields URL for a vignette on using this package and some background on spatial statistics.

Maintained by Douglas Nychka. Last updated 9 months ago.

fortran

4.5 match 15 stars 12.60 score 7.7k scripts 295 dependents

bappa10085

LST:Land Surface Temperature Retrieval for Landsat 8

Calculates Land Surface Temperature from Landsat band 10 and 11. Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data. Jimenez-Munoz JC, Cristobal J, Sobrino JA, et al (2009). <doi: 10.1109/TGRS.2008.2007125>. Land surface temperature retrieval from LANDSAT TM 5. Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004). <doi:10.1016/j.rse.2004.02.003>. Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data. Srivastava PK, Majumdar TJ, Bhattacharya AK (2009). <doi: 10.1016/j.asr.2009.01.023>. Mapping land surface emissivity from NDVI: Application to European, African, and South American areas. Valor E (1996). <doi:10.1016/0034-4257(96)00039-9>. On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. Van de Griend AA, Owe M (1993). <doi:10.1080/01431169308904400>. Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method. Yu X, Guo X, Wu Z (2014). <doi:10.3390/rs6109829>. Calibration and Validation of land surface temperature for Landsat8-TIRS sensor. Land product validation and evolution. Skoković D, Sobrino JA, Jimenez-Munoz JC, Soria G, Julien Y, Mattar C, Cristóbal J. (2014).

Maintained by Bappa Das. Last updated 4 months ago.

12.4 match 3.70 score 9 scripts

globalecologylab

poems:Pattern-Oriented Ensemble Modeling System

A framework of interoperable R6 classes (Chang, 2020, <https://CRAN.R-project.org/package=R6>) for building ensembles of viable models via the pattern-oriented modeling (POM) approach (Grimm et al.,2005, <doi:10.1126/science.1116681>). The package includes classes for encapsulating and generating model parameters, and managing the POM workflow. The workflow includes: model setup; generating model parameters via Latin hyper-cube sampling (Iman & Conover, 1980, <doi:10.1080/03610928008827996>); running multiple sampled model simulations; collating summary results; and validating and selecting an ensemble of models that best match known patterns. By default, model validation and selection utilizes an approximate Bayesian computation (ABC) approach (Beaumont et al., 2002, <doi:10.1093/genetics/162.4.2025>), although alternative user-defined functionality could be employed. The package includes a spatially explicit demographic population model simulation engine, which incorporates default functionality for density dependence, correlated environmental stochasticity, stage-based transitions, and distance-based dispersal. The user may customize the simulator by defining functionality for translocations, harvesting, mortality, and other processes, as well as defining the sequence order for the simulator processes. The framework could also be adapted for use with other model simulators by utilizing its extendable (inheritable) base classes.

Maintained by July Pilowsky. Last updated 20 days ago.

biogeographypopulation-modelprocess-based

4.9 match 10 stars 8.05 score 59 scripts 2 dependents

ropensci

FedData:Download Geospatial Data Available from Several Federated Data Sources

Download geospatial data available from several federated data sources (mainly sources maintained by the US Federal government). Currently, the package enables extraction from nine datasets: The National Elevation Dataset digital elevation models (<https://www.usgs.gov/3d-elevation-program> 1 and 1/3 arc-second; USGS); The National Hydrography Dataset (<https://www.usgs.gov/national-hydrography/national-hydrography-dataset>; USGS); The Soil Survey Geographic (SSURGO) database from the National Cooperative Soil Survey (<https://websoilsurvey.sc.egov.usda.gov/>; NCSS), which is led by the Natural Resources Conservation Service (NRCS) under the USDA; the Global Historical Climatology Network (<https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily>; GHCN), coordinated by National Climatic Data Center at NOAA; the Daymet gridded estimates of daily weather parameters for North America, version 4, available from the Oak Ridge National Laboratory's Distributed Active Archive Center (<https://daymet.ornl.gov/>; DAAC); the International Tree Ring Data Bank; the National Land Cover Database (<https://www.mrlc.gov/>; NLCD); the Cropland Data Layer from the National Agricultural Statistics Service (<https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>; NASS); and the PAD-US dataset of protected area boundaries (<https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-data-overview>; USGS).

Maintained by R. Kyle Bocinsky. Last updated 3 months ago.

peer-reviewed

4.2 match 100 stars 8.20 score 364 scripts

framverse

framrsquared:FRAM Database Interface

A convenient tool for interfacing with FRAM access databases in R environments.

Maintained by Ty Garber. Last updated 2 months ago.

6.6 match 6 stars 5.06 score 9 scripts

mikejohnson51

climateR:climateR

Find, subset, and retrive geospatial data by AOI.

Maintained by Mike Johnson. Last updated 3 months ago.

aoiclimatedatasetgeospatialgridded-climate-dataweather

3.6 match 187 stars 8.74 score 156 scripts 1 dependents

pik-piam

mrland:MadRaT land data package

The package provides land related data via the madrat framework.

Maintained by Jan Philipp Dietrich. Last updated 9 days ago.

5.6 match 5.61 score 3 scripts 4 dependents

isciences

exactextractr:Fast Extraction from Raster Datasets using Polygons

Quickly and accurately summarizes raster values over polygonal areas ("zonal statistics").

Maintained by Daniel Baston. Last updated 7 months ago.

gisrasterrcppgeoscpp

2.5 match 286 stars 12.13 score 1.4k scripts 14 dependents

alanarnholt

BSDA:Basic Statistics and Data Analysis

Data sets for book "Basic Statistics and Data Analysis" by Larry J. Kitchens.

Maintained by Alan T. Arnholt. Last updated 2 years ago.

3.3 match 7 stars 9.11 score 1.3k scripts 6 dependents

flr

AAP:Aarts and Poos Stock Assessment Model that Estimates Bycatch

FLR version of Aarts and Poos stock assessment model.

Maintained by Iago Mosqueira. Last updated 1 years ago.

9.8 match 2.70 score 5 scripts

ices-tools-prod

icesFO:Functions to support the creation of ICES Fisheries Overviews

Functions to support the creation of ICES Fisheries Overviews.

Maintained by Adriana Villamor. Last updated 9 months ago.

7.2 match 2 stars 3.41 score 260 scripts

doserjef

rFIA:Estimation of Forest Variables using the FIA Database

The goal of 'rFIA' is to increase the accessibility and use of the United States Forest Services (USFS) Forest Inventory and Analysis (FIA) Database by providing a user-friendly, open source toolkit to easily query and analyze FIA Data. Designed to accommodate a wide range of potential user objectives, 'rFIA' simplifies the estimation of forest variables from the FIA Database and allows all R users (experts and newcomers alike) to unlock the flexibility inherent to the Enhanced FIA design. Specifically, 'rFIA' improves accessibility to the spatial-temporal estimation capacity of the FIA Database by producing space-time indexed summaries of forest variables within user-defined population boundaries. Direct integration with other popular R packages (e.g., 'dplyr', 'tidyr', and 'sf') facilitates efficient space-time query and data summary, and supports common data representations and API design. The package implements design-based estimation procedures outlined by Bechtold & Patterson (2005) <doi:10.2737/SRS-GTR-80>, and has been validated against estimates and sampling errors produced by FIA 'EVALIDator'. Current development is focused on the implementation of spatially-enabled model-assisted and model-based estimators to improve population, change, and ratio estimates.

Maintained by Jeffrey Doser. Last updated 9 days ago.

compute-estimatesfiafia-databasefia-datamartforest-inventoryforest-variablesinventoriesspace-timespatial

3.6 match 49 stars 5.93 score

pik-piam

mrremind:MadRat REMIND Input Data Package

The mrremind packages contains data preprocessing for the REMIND model.

Maintained by Lavinia Baumstark. Last updated 3 days ago.

3.4 match 4 stars 6.25 score 15 scripts 1 dependents

dankelley

ocedata:Oceanographic Data Sets for 'oce' Package

Several Oceanographic data sets are provided for use by the 'oce' package, and for other purposes.

Maintained by Dan Kelley. Last updated 2 years ago.

3.4 match 8 stars 5.07 score 146 scripts

pieterprovoost

landr:Add land polygons to ggplot2 maps

Add land polygons to ggplot2 maps.

Maintained by Pieter Provoost. Last updated 2 years ago.

9.0 match 1 stars 1.78 score 12 scripts

idem-lab

sdmtools:Utility tools for Species Distribution Modelling

What the package does (one paragraph).

Maintained by Gerry Ryan. Last updated 3 months ago.

2.0 match 9 stars 6.13 score 674 scripts

pedrocoutinhosilva

shiny.pwa:Progressive Web App Support for Shiny

Adds Progressive Web App support for Shiny apps, including desktop and mobile installations.

Maintained by Pedro Silva. Last updated 4 years ago.

pwashiny

1.9 match 57 stars 5.76 score 20 scripts

drj001

hett:Heteroscedastic t-Regression

Functions for the fitting and summarizing of heteroscedastic t-regression.

Maintained by Julian Taylor. Last updated 4 years ago.

3.8 match 2.19 score 26 scripts 2 dependents

pik-piam

mrwater:madrat based MAgPIE water Input Data Library

Provides functions for MAgPIE cellular input data generation and stand-alone water calculations.

Maintained by Felicitas Beier. Last updated 5 months ago.

1.3 match 6.45 score 4 scripts 3 dependents

predictiveecology

fireSenseUtils:Utilities for Working With the 'fireSense' Group of 'SpaDES' Modules

Utilities for working with the 'fireSense' group of 'SpaDES' modules.

Maintained by Eliot J B McIntire. Last updated 30 days ago.

1.6 match 1 stars 4.51 score 2 scripts

evolecolgroup

geoGraph:Walking through the geographic space using graphs

Classes and methods for spatial graphs interfaced with support for GIS shapefiles.

Maintained by Andrea Manica. Last updated 9 days ago.

2.0 match 4 stars 3.30 score 2 scripts

ycroissant

pder:Panel Data Econometrics with R

Data sets for the Panel Data Econometrics with R <doi:10.1002/9781119504641> book.

Maintained by Yves Croissant. Last updated 3 years ago.

3.8 match 1.36 score 15 scripts

ropensci

weatherOz:An API Client for Australian Weather and Climate Data Resources

Provides automated downloading, parsing and formatting of weather data for Australia through API endpoints provided by the Department of Primary Industries and Regional Development ('DPIRD') of Western Australia and by the Science and Technology Division of the Queensland Government's Department of Environment and Science ('DES'). As well as the Bureau of Meteorology ('BOM') of the Australian government precis and coastal forecasts, and downloading and importing radar and satellite imagery files. 'DPIRD' weather data are accessed through public 'APIs' provided by 'DPIRD', <https://www.agric.wa.gov.au/weather-api-20>, providing access to weather station data from the 'DPIRD' weather station network. Australia-wide weather data are based on data from the Australian Bureau of Meteorology ('BOM') data and accessed through 'SILO' (Scientific Information for Land Owners) Jeffrey et al. (2001) <doi:10.1016/S1364-8152(01)00008-1>. 'DPIRD' data are made available under a Creative Commons Attribution 3.0 Licence (CC BY 3.0 AU) license <https://creativecommons.org/licenses/by/3.0/au/deed.en>. SILO data are released under a Creative Commons Attribution 4.0 International licence (CC BY 4.0) <https://creativecommons.org/licenses/by/4.0/>. 'BOM' data are (c) Australian Government Bureau of Meteorology and released under a Creative Commons (CC) Attribution 3.0 licence or Public Access Licence ('PAL') as appropriate, see <http://www.bom.gov.au/other/copyright.shtml> for further details.

Maintained by Rodrigo Pires. Last updated 20 days ago.

dpirdbommeteorological-dataweather-forecastaustraliaweatherweather-datameteorologywestern-australiaaustralia-bureau-of-meteorologywestern-australia-agricultureaustralia-agricultureaustralia-climateaustralia-weatherapi-clientclimatedatarainfallweather-api

0.5 match 32 stars 8.54 score 40 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-detectionbayesian-time-seriesbreakpoint-detectionchangepoint-detectioninterrupted-time-seriesseasonality-analysisstructural-breakpointtechnical-analysistime-seriestime-series-decompositiontrendtrend-analysis

0.5 match 302 stars 7.63 score 89 scripts

ifpri

COLUMNIST:Calculator fOr Land Use harMoNIzation dataSeT

Calculator fOr Land Use harMoNIzation dataSeT.

Maintained by Abhijeet Mishra. Last updated 2 years ago.

3.6 match 1.00 score

manuelabrunner

PRSim:Stochastic Simulation of Streamflow Time Series using Phase Randomization

Provides a simulation framework to simulate streamflow time series with similar main characteristics as observed data. These characteristics include the distribution of daily streamflow values and their temporal correlation as expressed by short- and long-range dependence. The approach is based on the randomization of the phases of the Fourier transform or the phases of the wavelet transform. The function prsim() is applicable to single site simulation and uses the Fourier transform. The function prsim.wave() extends the approach to multiple sites and is based on the complex wavelet transform. The function prsim.weather() extends the approach to multiple variables for weather generation. We further use the flexible four-parameter Kappa distribution, which allows for the extrapolation to yet unobserved low and high flows. Alternatively, the empirical or any other distribution can be used. A detailed description of the simulation approach for single sites and an application example can be found in Brunner et al. (2019) <doi:10.5194/hess-23-3175-2019>. A detailed description and evaluation of the wavelet-based multi-site approach can be found in Brunner and Gilleland (2020) <doi:10.5194/hess-24-3967-2020>. A detailed description and evaluation of the multi-variable and multi-site weather generator can be found in Brunner et al. (2021) <doi:10.5194/esd-12-621-2021>. A detailed description and evaluation of the non-stationary streamflow generator can be found in Brunner and Gilleland (2024) <doi:10.1029/2023EF004238>.

Maintained by Manuela Brunner. Last updated 11 months ago.

3.3 match 1.00 score 8 scripts