We are building the worlds most advanced general sampling simulator. It is based on the GeoJSON standard for geographical data to be applicable worldwide, and it runs simulations locally in the browser. The simulator is packed with features and even more features will be added in the future. Your data stays local and is not sent to any server. This project is partly funded by the Forest Damage Center at the Swedish University of Agricultural Sciences. Read about data format requirements here.

A GeoJSON editor for creating and editing GeoJSON files that conform to the GeoJSON standard and our requirements. Can also be used to add derived properties and generating properties with random values. More than 30 probability distributions are available for random number generation. Read about data format requirements here.

A small but helpful tool for creating model geometries for the sampling simulators. Model geometries are used as the sampling units when sampling on area frames. This tool allows to create point or line or area geometries to be used as sampling units.

Create forest stands for the sampling simulators. The application allows to create forest stands with different characteristics, e.g. species composition and spatial distribution.

Our cutting-edge forest inventory simulator makes it possible for forestry students at the Swedish University of Agricultural Sciences to gain experience from evaluating different inventory designs. The simulator will be a pedagogical tool that helps increase the understanding of how the inventory methods work and compare. At the same time, visual feedback will enhance learning for our students. Ljungbergsfonden is our main investor for this project. Read about data format requirements here.

We provide a core set of TypeScript packages focused on geospatial operations and statistics. These packages provides the foundation for several envisim applications and they are available on GitHub. Contact us if you are interested in using them for commercial applications.

BalancedSampling is a popular R-package that makes it possible to select balanced and spatially balanced probability samples in multi-dimensional spaces with any prescribed inclusion probabilities. Using such sampling methods often improves the estimation of natural resources by spreading the observations geographically and in other available auxiliary variables. The package is currently being maintained and further developed by us.