Free & OpenSource Software Documentation for BigGeoData Processing

Welcome to the Spatial Ecology’s documentation! The content of this documentation is free and open source, (CC-BY-SA license) it can be used, but WITHOUT ANY WARRANTY. You can remix, tweak, and build upon our work as long as you credit us and license your new creations under the identical terms. Software we use have a GNU General Public License GPL or GPL / MIT compatible licenses.
Table of Contents
COURSE TRAINERS
COURSES AROUND THE WORLD
LINUX VIRTUAL MACHINE
WEB SEMINARS
BASH
AWK
GDAL
PKTOOLS
CASE STUDY
- SDM1 : Montane woodcreper - Gecomputation
- SDM1 : Montane woodcreper - Model
- SDM2 : Varied Thrush - Model
- Manipulate GSIM files
- Data type in GTiff
- Temporal interpolation of landsat images
- Dynamic Time Warping
- Estimating nitrogen and phosphorus concentrations in streams and rivers
- Estimating nitrogen concentrations in streams and rivers using NN
- Autoencoder (AE), Variational Autoencoder (VAE) and Generative Adversarial Network (GAN)
- LSTM Network
- Estimation of tree height using GEDI dataset - Data explore
- Estimation of tree height using GEDI dataset - Support Vector Machine for Regression (SVR)
- Estimation of tree height using GEDI dataset - Random Forest prediction
- Estimation of tree height using GEDI dataset - Perceptron 1
- Estimation of tree height using GEDI dataset - Clean Data - Perceptron 2
- Estimation of tree height using GEDI dataset - Neural Network 1
- Neural Nets (pt.3), Interpretability and Convolutional Neural Networks
- Using Multi-layer Perceptron and Convolutional Neural Networks for Satellite image classification.
Students Projects
- 1. 2021 SWEDEN
- 1.1. Calculating landcover distribution & vegetation extraction
- 1.2. Compiling OTB from source
- 1.3. Observed and simulated internal variability climate feedbacks comparison.
- 1.3.1. 1. Project description
- 1.3.2. 2. Data set and Methods
- 1.3.3. 2.1 Data
- 1.3.4. 2.1.1 Observations
- 1.3.5. 2.1.2 Simulations
- 1.3.6. 2.2 Methods
- 1.3.7. 2.2.1 Preprocessing
- 1.3.8. Bash script to preprocess observations (detrend and deseasonalize)
- 1.3.9. Bash script to preprocess simulations CMIP6 historical (detrend and deseasonalize)
- 1.3.10. Bash script to preprocess simulations AMIP (detrend and deseasonalize)
- 1.3.11. Bash script to preprocess simulations CMIP6 piControl and Abrupt
- 1.3.12. 2.2.2 Feedbacks
- 1.3.13. 3. Results
- 1.3.14. 3.1 Observed feedbacks
- 1.3.15. 3.2 Simulated feeedbacks
- 1.4. Statistical comparison global gridded climate datasets and their influence on LPJ-GUESS model outputs
- 1.5. Emulating FLEXPART with a Multi-Layer Perceptron
- 1.5.1. Carlos Gómez-Ortiz
- 1.5.2. Department of Physical Geography and Ecosystem Science
- 1.5.3. Lund University
- 1.5.4. Inverse modeling is a commonly used method and a formal approach to estimate the variables driving the evolution of a system, e.g. greenhouse gases (GHG) sources and sinks, based on the observable manifestations of that system, e.g. GHG concentrations in the atmosphere. This has been developed and applied for decades and it covers a wide range of techniques and mathematical approaches as well as topics in the field of the biogeochemistry. This implies the use of multiple models such as a CTM for generating background concentrations, a Lagrangian transport model to generate regional concentrations, and multiple flux models to generate prior emissions. All these models take several computational time besides the proper computational time of the inverse modeling. Replacing one of these steps with a tool that emulates its functioning but at a lower computational cost could facilitate testing and benchmarking tasks. LUMIA (Lund University Modular Inversion Algorithm) (Monteil & Scholze, 2019) is a variational atmospheric inverse modeling system developed within the regional European atmospheric transport inversion comparison (EUROCOM) project (Monteil et al., 2020) for optimizing terrestrial surface CO2 fluxes over Europe using ICOS in-situ observations. In this Jupyter Notebook, I will apply a AI tool to emulate the Lagrangian model FLEXPART to simulate the regional concentrations at one of the stations whitin the EUROCOM project.
- 1.5.4.1. Import packages
- 1.5.4.2. Download data
- 1.5.4.3. Retrieve data from swestore
- 1.5.4.4. Plot fluxes and observations
- 1.5.4.4.1. Read the fluxes
- 1.5.4.4.2. Read the observations
- 1.5.4.4.3. Determine the spatial and temporal coordinates
- 1.5.4.4.4. Plot the observation sites
- 1.5.4.4.5. Compute monthly and daily fluxes in PgC
- 1.5.4.4.6. Plot daily fluxes aggregated over the domain
- 1.5.4.4.7. Plot the fit to observations
- 1.5.4.5. Modeling observations
- 1.5.4.5.1. Time-series
- 1.5.4.5.2. Preparing input data
- 1.5.4.5.3. Importing additional packages
- 1.5.4.5.4. MLP model
- 1.5.4.5.5. Function to calculate results
- 1.5.4.5.6. Generating training and validation datasets
- 1.5.4.5.7. Training the MLP
- 1.5.4.5.8. Plotting results
- 1.5.4.5.9. Training MLPs for all sites
- 1.5.4.5.10. Plotting results for all sites
- 1.5.4.5.11. Conclusions
- 1.5.4.5.12. References
- 1.5.4.5.13. LSTM model
- 1.6. Processing Elmer/Ice output
- 1.7. pan-Arctic classified slope and aspect maps (Geo computation only)
- 1.8. Seasonal Analisis of discharges in the Mälaren catchement.
- 1.9. Mapping of soil organic carbon stocks with Random Forest
- 1.10. NDVI Computation
- 1.11. Phase Change Analysis
- 1.12. Relationship between continental-scale patterns of fire activity and modes of climate variability