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

• Spatial Ecology course trainers

COURSES AROUND THE WORLD

• Western Connecticut State University 2021
• Stockholm University 2021
• GeoComp & ML 2022 course

GEO DATA

• Geomorpho90m: technical documentation

LINUX VIRTUAL MACHINE

• Prepare Colab for Spatial Ecology courses
• Prepare OSGeoLive for Spatial Ecology courses
• Prepare OSGeoLive para el curso de ecología espacial

WEB SEMINARS

• Raster/Vector Processing using GDAL/OGR
• Image Processing using Pktools
• Introduction to GRASS GIS
• GeoComputation with High Performance Computing

BASH

• Linux Operation System as a base for Spatial Ecology Computing
• Manipulate text files in bash
• Multi-core bash

AWK

• AWK Tutorial

GDAL

• Use GDAL/OGR for raster/vector operations - osgeo
• Use GDAL/OGR for raster/vector operations - colab

PKTOOLS

• Use PKTOOLS for raster/vector operations - osgeo
• Use PKTOOLS for raster/vector operations - colab

R

• R Introduction

PYTHON

• Introduction to Python
• Python & GeoComputation

GRASS

• GRASS Introduction
• Using GRASS for stream-network extraction and basins delineation

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
    • 1.1.1. Calculating landcover distribution
    • 1.1.2. Open water and flooded vegetation extraction
      • 1.1.2.1. Open water extraction
      • 1.1.2.2. Flooded vegetation extraction by using unsupervised Kmean classification
  • 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
    • 1.4.1. Background & Introduction
    • 1.4.2. Methods
      • 1.4.2.1. Data aquisition
      • 1.4.2.2. Data exploration
      • 1.4.2.3. Data processing
      • 1.4.2.4. CRU NCEP ———————————————————
        • 1.4.2.4.1. Time series analysis (DTW)
        • 1.4.2.4.2. Basic model evaluation
    • 1.4.3. Results & Discussion
    • 1.4.4. Impact on model outputs
    • 1.4.5. Summary
    • 1.4.6. References
  • 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
      • 1.5.3.1. Abstract
    • 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
    • 1.6.1. Felicity Holmes
      • 1.6.1.1. General Project Description
      • 1.6.1.2. Part 1: Point data exploration
      • 1.6.1.3. Part 2: Time series of Velocity and Surface Mass Balance
  • 1.7. pan-Arctic classified slope and aspect maps (Geo computation only)
    • 1.7.1. Alexandra Hamm
    • 1.7.2. Background
    • 1.7.3. Aim
    • 1.7.4. Challenges
    • 1.7.5. Workflow
    • 1.7.6. Displaying data
    • 1.7.7. Final map for reviewer response and manuscript
  • 1.8. Seasonal Analisis of discharges in the Mälaren catchement.
    • 1.8.1. Find ID’s for all the Mälaren’s Catchement sub-catchements.
      • 1.8.1.1. Conclusion
  • 1.9. Mapping of soil organic carbon stocks with Random Forest
    • 1.9.1. Preprocessing (Bash)
      • 1.9.1.1. Calculating DEM Derivates from Arctic DEM
    • 1.9.2. Training and prediction of Random Forest Model (R)
      • 1.9.2.1. Preparing training and prediction Datasets
      • 1.9.2.2. Training of the Model
      • 1.9.2.3. Prediction of the Model
      • 1.9.2.4. Clip prediction result to extent of the Catchment shapefile (Bash)
  • 1.10. NDVI Computation
    • 1.10.1. Spring term 2021 Stockolm University
      • 1.10.1.1. Proceed to calculate NDVI for each semi-natural grasslands,extract the for grazed and non-grazed polygons
  • 1.11. Phase Change Analysis
    • 1.11.1. Project description
      • 1.11.1.1. Geocomputain datasets
      • 1.11.1.2. Data processing and computation procedure:
    • 1.11.2. Here, we see 10 pixels with highest correletation with field data
  • 1.12. Relationship between continental-scale patterns of fire activity and modes of climate variability
    • 1.12.1. Project discription
    • 1.12.2. Section 1. Data
    • 1.12.3. Section 2. Climate indices
      • 1.12.3.1. 2.1 ENSO 3.4
      • 1.12.3.2. 2.2 NAO
      • 1.12.3.3. 2.3 SNAO
      • 1.12.3.4. 2.4 AMO
    • 1.12.4. Section 3. PCA & different technics of interpolation.
      • 1.12.4.1. 3.1 Check variances explained by leading EOFs
    • 1.12.5. Section 4. Correlation analysis

OUTDOOR

• Do not get lost in the wilderness

TALKS

• Intelligent modelling in time and space: combine GeoComputation and Machine Learning for environmental application.

ADMIN

• Install pktools on the gdrive and be able to use from any Colab
• Video tips
• Compiling OTB from source

Indices and tables

• Index

• Module Index

• Search Page