I am an advocate of sharing data and code for advancing research. Following are some programs we wrote as part of our daily research and which we would like to share with the research community. If you have any questions or feedback, need data we produced, or similar, don’t hesitate to contact me.

sgdm: an R package for performing sparse generalized dissimilarity modelling

The sgdm package bundles a set of functions to run sparse generalized dissimilarity models using high-dimensional data sets as predictor, such as hyper-spectral or hyper-temporal remote sensing data. The model has been used successfully to map beta-biodiversity from air-borne hyperspectral imagery (Leitão 2015). You can install the package from GitHub.

Leitão, J. P., Schwieder, M. and Senf, C. (2017) sgdm: an R package for performing sparse generalized dissimilarity modelling, including tools for gdm. ISPRS International Journal of Geo-Information, 6(1), 23-35. https://doi.org/10.3390/ijgi6010023

phenoBayes: Bayesian hierarchical models for estimating spatial and temporal patterns in vegetation phenology from Landsat time series

The phenoBayes package (well, it’s not a package yet) includes a set of models for estimating the spatial and temporal patterns of vegetation phenology from Landsat time series. It makes use of a Bayesian hierachical modeling apprach implemented in the free software Stan. The package includes simple models as well as processed-oriented models for testing hypothesis on environmental controls on vegetation phenology. You can download the code and data for building phenological models yourself from GitHub.

Senf, C., Pflugmacher, D., Heurich, M. and Krueger T. (2017) A Bayesian hierarchical model for estimating spatial and temporal variation in vegetation phenology from Landsat time series. Remote Sensing of Environment, 194, 155-160. https://doi.org/10.1016/j.rse.2017.03.020