sptrends
Statistical Inference for Spatiotemporal Trends in Gridded Data
Provides a unified and reproducible framework for statistical
inference of spatiotemporal trends in gridded environmental data. The
framework addresses the interconnected challenges of serial correlation,
spatial dependence and multiple testing that commonly arise when analysing
gridded environmental time series. Its core methods support
serial-correlation treatment through trend-preserving prewhitening,
pixel-wise and spatially explicit trend inference, slope estimation and
multiple-testing correction. These methods may be applied independently or
integrated within configurable analytical workflows. Dedicated workflows
are also provided to reproduce methodologies published in the scientific
literature: Gutiérrez-Hernández and García (2025)
sptrends 
Statistical Inference for Spatiotemporal Trends in Gridded Data
sptrends provides a reproducible framework for statistical inference of spatiotemporal trends in gridded environmental data. It offers modular methods for serial-correlation treatment, trend testing, spatially explicit inference, global and local spatial-autocorrelation diagnostics, robust slope estimation, multiple-testing correction, simulation, benchmarking, visualisation, mapping, and reporting.
Installation
Install the source package from a local archive or directory:
remotes::install_local("path/to/sptrends")
When starting from a development source directory, regenerate its help
files first with roxygen2::roxygenise("path/to/sptrends"). This step is
not needed for a built source tarball.
Quick start
A bundled annual NDVI dataset is available for reproducible examples:
library(sptrends)
r <- read_ordered_stack(example_data("vhp_ndvi"))
result <- workflow_tst(r, report = FALSE, verbose = FALSE)
plot(result)
With your own data, replace the file path:
# One raster file per time step, ordered from numbers in the file names.
r <- read_ordered_stack("path/to/rasters")
# Published True Significant Trends workflow
# (Gutierrez-Hernandez and Garcia, 2025, https://doi.org/10.1016/j.rsase.2024.101377).
result <- workflow_tst(r)
print(result)
summary(result)
plot(result)
Workflows
For seasonal data, declare the temporal structure and remove the cycle before running a trend workflow. For example, for consecutive monthly layers beginning in August:
# Supply your files in chronological order; files may contain multiple layers.
monthly <- read_ordered_stack(
files = ordered_files, cycle_type = "monthly",
start = as.Date("2001-08-01"), report = FALSE
)
anomalies <- compute_anomalies(monthly, cycle_type = "monthly",
start_position = 8)
result <- workflow_tst(anomalies$anomalies, report = FALSE)
start is the first period's beginning; the default assigned date for a
monthly layer is its centre. Annual layers use 1 January. Anomalies group
layers by position, not by their date metadata; the user supplies the
correct cycle and starting position. files plus time also supports an
explicit date for every layer. See the
loading guide.
workflow_tst()implements True Significant Trends: selective prewhitening, Contextual Mann-Kendall, Theil-Sen and adaptive FDR (Gutiérrez-Hernández and García, 2025).workflow_rta()implements Robust Trend Analysis: Contextual Mann-Kendall, Theil-Sen and FDR-BH without prewhitening (Gutiérrez-Hernández and García, 2024).workflow_trends()combines user-selected preprocessing, trend, slope and FDR modules through the same validated interfaces.
Each analytical stage is also available independently:
pw <- prewhiten(r, method = "TFPW_WS")
trend <- trend_test(pw$series, method = "CMK")
slope <- slope_estimator(pw$series, method = "TS")
fdr <- fdr_correction(trend$stats$p, method = "BKY")
spatial_autocorrelation() provides independent global and local permutation
inference for environmental variables, residuals, coefficients or inferential
fields. Local p-value rasters can be passed to fdr_correction() for BH, BKY
or BY control.
Main modules
read_ordered_stack()andread_netcdf_stack()— raster import with temporal-order checks.compute_anomalies()— deseasonalising, when the series has a known cycle.prewhiten()— serial-correlation treatment, trend-preserving.trend_test()— CMK with configurable odd neighbourhoods (3 by 3 by default), MK, OLS and modified MK.slope_estimator()— Theil-Sen, OLS and Siegel repeated median.fdr_correction()— BH, adaptive BKY and BY.spatial_autocorrelation()— permutation-based spatial diagnostics.sim_trend_stack(),simulation_design(),compare_detections(),benchmark_methods()andbenchmark_summary()— known-truth simulation and reproducible method benchmarking, including S3 performance plots and Monte Carlo summaries (empirical FDR and FWER where applicable) across scenario factors.
The current calculations are vectorised and selected stages can use parallel processing, but analytical rasters are materialised in memory. Test representative dimensions before processing very large datasets.
Documentation
The function help pages describe assumptions, alternatives, computational trade-offs, references and method-specific quality assurance. The vignettes provide concise guides to preprocessing, trend tests, slope estimation, FDR correction and complete workflows:
browseVignettes("sptrends")
Quality assurance
The recorded 1.5.9 local check completed in under 10 minutes with 0 errors,
0 warnings and 0 notes. Coverage was 100% across every R source file with
SPTRENDS_TEST_PARALLEL=true. The recorded win-builder checks for 1.5.8
had 0 errors and 0 warnings, with submission and terminology notes.
These are historical results; see cran-comments.md for the validation
status of the current changes. The 19 parallel-execution tests remain
optional to keep routine checks within CRAN's time limit. Website building
is a separate step. Style, spelling and link integrity are checked
periodically with lintr, goodpractice, spelling and urlchecker.
Beyond the automated tests/testthat/ suite, the package has been validated
through an external battery covering the then-current 18 exported functions with
correctness checks against known-truth simulations, and a comprehensive
integral audit of code, citations and documentation.
trend_test(method = "CMK") has additionally been cross-checked against an
installed copy of ConMK (Antiphon,
GitHub, not on CRAN -- also available as a fork at
geoporttishare/ConMK), the
closest available external reference implementation of the contextual
Mann-Kendall test. The base statistic matched to floating-point precision,
and the optional continuity = TRUE argument reproduces ConMK's own
p-values exactly at the specific edge case where the two implementations
would otherwise be expected to diverge. Full details in ?trend_test's
"External validation" section.
Every change is documented transparently in NEWS.md.
Citation
To cite the package itself:
Gutiérrez-Hernández, O., & García, L. V. (2026). sptrends: Statistical Inference for Spatiotemporal Trends in Gridded Data [R package]. Zenodo. https://doi.org/10.5281/zenodo.21822842
Or, from R:
citation("sptrends")
Published workflows:
- Gutiérrez-Hernández, O. and García, L.V. (2025). Uncovering true significant trends in global greening. Remote Sensing Applications: Society and Environment, 37, 101377. https://doi.org/10.1016/j.rsase.2024.101377
- Gutiérrez-Hernández, O. and García, L.V. (2024). Robust Trend Analysis in Environmental Remote Sensing: A Case Study of Cork Oak Forest Decline. Remote Sensing, 16(20), 3886. https://doi.org/10.3390/rs16203886
Authors
- Oliver Gutiérrez-Hernández (ORCID), Associate Professor, Department of Geography, University of Málaga.
- Luis V. García (ORCID), Tenured Scientist, Institute of Natural Resources and Agrobiology of Seville (IRNAS), Spanish National Research Council (CSIC).
License
GPL (>= 3)