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spatial-ecology

Spatial Ecology

spatial-sem

Script for article "Including spatial correlation in structural equation modelling of soil properties". Submitted to Spatial Statistic Journal on 17/Nov/2017

spatial_correction_lavaan

A method to alter standard errors from spatial autocorrelation using lavaan and ape

spatial_factor_analysis

Spatial factor analysis: a tool for decomposing species density into a small number of latent maps

spatial_warnings

code for paper Kefi et al on spatial indicators

spatialanova

ANOVA accounting for spatial autocorrelation

spatialdiversity

Spatial patterns in biodiversity change

spatialstress

Investigation of EWS behavior in ecosystems with spatially explicit stress

spatialwarningsgis

GIS extensions for spatialwarnings

spatio_temporal_causality

Towards causal inference for spatio-temporal data: conflict and forest loss in Colombia

speciespool

R Package to calculate plot-specific species pools using large vegetation databases

speciesraster

indexing of species by cell and calculation of community metrics

speciesrichness-glmvsrf-lidar

R-codes for: Lopatin, J., Dolos, K., Hernández, J., Galleguillos, M., Fassnacht, F. E. (2016): Comparing Generalized Linear Models and random forest to model vascular plant species richness using LiDAR data in a natural forest in central Chile. Remote Sensing of Environment 173, pp. 200–210. 10.1016/j.rse.2015.11.029

spectralrao

Applying Rao's index to remote sensing data

spedinstabr

:exclamation: This is a read-only mirror of the CRAN R package repository. SPEDInstabR — Estimation of the Relative Importance of Factors Affecting Species Distribution Based on Stability Concept

spiga

:exclamation: This is a read-only mirror of the CRAN R package repository. SPIGA — Compute SPI Index using the Methods Genetic Algorithm and Maximum Likelihood

spocc

Species occurrence data toolkit for R

spp-down

Satellite Precipitation Products Download

spra_results

This repository has results of the Vegetation Optical Depth (VOD), surface roughness parameter, and soil moisture obtained from the Simultaneous Parameter Retrieval Algorithm (SPRA), which is applied to the X-band AMSR-E brightness temperature observations. The data corresponds to the manuscript submitted for possible publication at Water Resources Research (WRR)

sptemexp

The approach of ensemble spatiotemporal mixed models is to make reliable estimation of air pollutant concentrations at high resolutions. (1) Extraction of covariates from the satellite images such as GeoTiff and NC4 raster (e.g NDVI, AOD, and meteorological parameters); (2) Generation of temporal basis functions to simulate the seasonal trends in the study regions; (3) Generation of the regional monthly or yearly means of air pollutant concentration; (4) Generation of Thiessen polygons and spatial effect modeling; (5) Ensemble modeling for spatiotemporal mixed models, supporting multi-core parallel computing; (6) Integrated predictions with or without weights of the model's performance, supporting multi-core parallel computing; (7) Constrained optimization to interpolate the missing values; (8) Generation of the grid surfaces of air pollutant concentrations at high resolution; (9) Block kriging for regional mean estimation at multiple scales.

sr_remotesensing

Super-Resolution deep learning models for remote sensing data

srtm_country

Download SRTM for an entire country with R {raster}

ssdm-jsdm

Codes for Zurell et al. (2019) Testing species assemblage predictions from stacked and joint species distribution models. Journal of Biogeography. DOI: 10.1111/jbi.13608.

ssdms_saturation_richness

Companion repository to Grenié et al. 2019 Accepted in Ecological Indicators

sst-mannkendalltrendanalysis

Perform a Mann-Kendall Trend Analysis on Sea Surface Temperature (SST) time series data

stabeco

Stability of Ecological Systems

stability-metrics

code to analyse the database of stability metrics

stabilityii

stabilitymetrics

Calculation of multiple stability metrics of EVI from satellite data

stand-ls

Spatio-Temporal ANalysis of Large-Scale Drought (STAND-LS) encompasses scripts to analyse, since a geospatial and temporal perspective, the variation of drought by using drought indicators and tools for spatiotemporal analysis