Sampling procedure of land use and soil fertility map under uncertainty
Description
The dataset is composed of input data, the code to process the data and the output data from a sampling procedure for spatial data. The sampling procedure is used to derive samples, that result in extreme lower and upper bound single-objective values. The extreme lower and upper bound samples are used to approximate the extreme Pareto fronts when performing the multi-objective optimization with the samples as input data. To the input data belong a land use map, a patch ID map, a soil fertility map and an uncertainty map, which all have 10*10 cells. In addition, two matrices are included. One matrix defines transition constraints for the land uses of the land use map, the second matrix defines related classification accuracies per land use. The land use map, patch ID map, transition matrix and soil fertility map were reused from the CoMOLA project, produced for research from Strauch et al. 2019 (https://doi.org/10.1016/j.envsoft.2019.05.003). The outputs are divided into three categories. One category is about creating samples from the uncertain land use map with the original soil fertility map. The second category is about creating samples from the uncertain soil fertility map with the original soil fertility map. The third category is about creating samples from both uncertain maps combined. For the first category, the land use maps and the corresponding patch ID maps were stored that resulted in extremely low or high objective values of habitat heterogeneity, forest species richness, crop yield and water yield from 1000 samples (computation time: <1 minute). For category 2, the soil fertility maps are stored that resulted in extremely low and high crop yield objective values with the original land use map were stored from 1000 samples, because the soil fertility map only influences the objective crop yield (computation time: <10 seconds). For category 3, two different soil fertility maps and two different land use map with the corresponding patch ID maps that lead to the extreme lower and upper bound crop yield values are stored. Here, the 1000 samples of category 1 and 2 are reproduced and each sampled land use map is evaluated against every sampled soil fertility map (computation time: ~2 hours). For convenience, additional pickle files were saved containing extreme solutions, extreme objective values and extreme objective values per iteration of the sampling procedure to plot the progression. The execution of the Python script SpatialSampling.py (Python 3.8) needs to be executed to reproduce the sampling procedure. After the simulations, plots are generated in case that the boolean parameter "show_visualizations" (beginning of script) is set to True. Pseudo-random states are used to assure reproducibility despite the stochastic processes.
Files
Steps to reproduce
1. Download directory Sampling Procedure 2. Set up Python environment: Python 3.8, install numpy, pandas, seaborn, scipy, matplotlib 3. Execute SpatialDataSampling.py