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
• GeoComp & Modelling 2022 course
• GeoComp & ML 2023 course

GEO DATA

• Geomorpho90m: technical documentation

LINUX VIRTUAL MACHINE

• Prepare Colab for Spatial Ecology courses
• Prepare OSGeoLive for Spatial Ecology courses

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
• Introdction to pyjeo: installation and raster visualization
• Performing raster and vector operations in Python using pyjeo

R

• R Introduction

PYTHON

• Introduction to Python
• Python & GeoComputation

GRASS

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

ASSIGNMENTS

• Assignments Fall 2022

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.
• The real data
• LSTM for Regression Using the Window Method
• LSTM for Regression with Time Steps
• LSTM with Memory Between Batches
• Stacked LSTMs with Memory Between Batches
• Adding Early Stopping
• Multivariate Time-series - Data

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
• 2. 2022 MATERA
  • 2.1. Janusz Godziek: Damaged vs undamaged trees - Random Forest classification
    • 2.1.1. Project description
    • 2.1.2. Geocomputation part
    • 2.1.3. Modelling part
  • 2.2. Alonso Gonzalez: Stream Network Abstraction
  • 2.3. Sebastian Walter: Images shadow removal
    • 2.3.1. IDEA
    • 2.3.2. DATA
  • 2.4. Jaime García: Modelling freshwater biodiversity: setting the scene, from geo-data to text-data
    • 2.4.1. First step: environmental information
    • 2.4.2. Data preparation
    • 2.4.3. Sub-catchments as unit of analysis for freshwater biodiversity analysis
    • 2.4.4. Computational Units as units of data processing
    • 2.4.5. Scripting procedures
      • 2.4.5.1. Observations
      • 2.4.5.2. Snapping observations to stream network
      • 2.4.5.3. Calculating upstream basins from observation points
      • 2.4.5.4. gdallocationinfo: at pixel value extraction
      • 2.4.5.5. Stream order tables from vector .gpkg
      • 2.4.5.6. r.univar for zonal statistics
      • 2.4.5.7. Land cover
      • 2.4.5.8. Deriving new data
      • 2.4.5.9. The final set of tables
  • 2.5. Maria Üblacker: Spectral clustering of freshwater habitats
    • 2.5.1. Project description
    • 2.5.2. Setup R Markdown Python Engine
    • 2.5.3. R and Python packages
    • 2.5.4. Study region
    • 2.5.5. Example code for filtering data from text files by sub-catchment ID
    • 2.5.6. Load the data
    • 2.5.7. Principal Component analysis (PCA)
    • 2.5.8. Evaluation of the best number of clusters
    • 2.5.9. Spectral clustering
    • 2.5.10. Predict clusters for the whole dataset
    • 2.5.11. Exporting sub-catchment IDs and k for reclassification
    • 2.5.12. Example code for reclassifying a raster file
    • 2.5.13. Spatial cluster plots
    • 2.5.14. Conclusion
    • 2.5.15. Reference
  • 2.6. Afroditi Grigoropoulou: Species Distribution Model with Random Forest
    • 2.6.1. Taxon: genus Prebaetodes (Mayfly), occurring in a drainage basin in Colombia
    • 2.6.2. Goal: Predict which subcatchments provide suitable habitats for this genus
    • 2.6.3. Classify the subcatchments of the basin as 1 or 0, based on whether their habitat is suitable or not
    • 2.6.4. Random forest classification
      • 2.6.4.1. 1. Extract predictors for each subcatchment of this drainage basin
      • 2.6.4.2. 2. Extract the predictors for the subcatchment where the genus occurs, plus for 10,000 random subcatchments that will serve as pseudoabsence data
      • 2.6.4.3. 3. Run SDM with random forest
    • 2.6.5. 1. Extract predictors for each subcatchment of this drainage basin
    • 2.6.6. Computational unit
    • 2.6.7. Basin
    • 2.6.8. Subcatchments
    • 2.6.9. Extract BIOCLIM data
    • 2.6.10. Extract land cover mean proportion per subcatchment
    • 2.6.11. Extract mean and sd of elevation, flow accumulation and slope gradient per subcatchment
    • 2.6.12. Join all predictors in R
    • 2.6.13. 2. Extract the predictors for the subcatchments where the genus occurs, plus for 10,000 random subcatchments that will serve as pseudoabsence data
    • 2.6.14. For the prediction, we need the subcatchment raster to be cropped to the extent of the basin
    • 2.6.15. 3. Run SDM with random forest
    • 2.6.16. Predict with fitted model
    • 2.6.17. Reclassify subcatchment raster based on predicted values
    • 2.6.18. Predicted habitat suitability
  • 2.7. Gidske L. Andersen: Topographic and hydrological influence on vegetation in an arid environment
    • 2.7.1. Background
    • 2.7.2. 1 GLAD ARD Landsat images
      • 2.7.2.1. Data download
      • 2.7.2.2. Calculate vegetation index
      • 2.7.2.3. Masking and statistics
    • 2.7.3. Generate stacks and extract statistics in Pyjeo
    • 2.7.4. 2 Creating topographical and hydrological layers in GRASS
      • 2.7.4.1. Download SRTM
      • 2.7.4.2. Calculate variables
    • 2.7.5. 3 Extract statistics for random points
      • 2.7.5.1. Extract values for response and predictor variables
    • 2.7.6. 4 Modelling vegetation
      • 2.7.6.1. Prepare table
      • 2.7.6.2. Random forest
      • 2.7.6.3. Random Forest tuning
      • 2.7.6.4. Predict on the raster using pyspatialml
    • 2.7.7. 5 Follow up
  • 2.8. Yusdiel Torres-Cambas: Distribution of freshwater biodiversity across Cuba
    • 2.8.1. Introduction
    • 2.8.2. 1. Predictor variables
      • 2.8.2.1. 1.1. Download and mosaic tiles of a digital elevation model
      • 2.8.2.2. 1.2. Download bioclimatic variables
      • 2.8.2.3. 1.3. Download and mosaic tiles of tree cover
      • 2.8.2.4. 1.4. Calculate slope
      • 2.8.2.5. 1.5. Crop and reproject
    • 2.8.3. 2. Stream network and sub-basins
      • 2.8.3.1. 2.1. Make a GRASS GIS database
      • 2.8.3.2. 2.2. Extract flow direction, flow accumulation, stream network, basins and sub-basins
      • 2.8.3.3. 2.3. Aggregate predictors by sub-basin
      • 2.8.3.4. 3.2. Make maps with presence and pseudoabsences
      • 2.8.3.5. 3.3. Make inputs required to create an SSN object, a kind of R object necessary to fit a Spatial Linear Models for Stream Networks (Hoef et al. 2014, Peterson et al. 2020).
    • 2.8.4. 5. Species distribution modelling
      • 2.8.4.1. 5.1. Model calibration and evaluation
      • 2.8.4.2. 5.2. Model ensamble
  • 2.9. Txomin Bornaetxea: Modeling debris flow source areas
    • 2.9.1. 1.0 Objectives of the project
    • 2.9.2. 2.0 Data Pre-Processing
      • 2.9.2.1. 2.1- DEM EXPLORATION AND COMPOSITION
      • 2.9.2.2. 2.2- LANDSLIDE INVENTORY EXPLORATION AND EXTRACTION OF SOURCE ZONES
      • 2.9.2.3. 2.3- GENERARION OF THE SPATIAL VARIABLES
      • 2.9.2.4. 2.4- EXTRACT THE DATA TABLE FROM RASTER LAYERS
    • 2.9.3. 3.0 Data Exploration
    • 2.9.4. 4.0 Machine Learning
      • 2.9.4.1. 4.1 Data set splitting
      • 2.9.4.2. 4.2 Random Forest
      • 2.9.4.3. 4.3 Support Vector Machine for Regression (SVR)
    • 2.9.5. Multilayer Perceptron (MP)
      • 2.9.5.1. ML Perceptron CONCLUSIONS
    • 2.9.6. GENERAL CONCLUSIONS
  • 2.10. Ritwika Mukhopadhyay: Comparative Analysis of the prediction of AGB using Random Forest Regression, Support Vector Machine for Regression & FeedForward Neural Network
    • 2.10.1. Project Description
    • 2.10.2. Data Description
    • 2.10.3. Random Forest Regression (RFR)
      • 2.10.3.1. Support Vector Machine for Regression (SVR)
    • 2.10.4. Feedfoward neural network (FF-NN)
      • 2.10.4.1. Predict the raster using RFR
  • 2.11. Hyeyoung Sim: Clustering Electric Vehicle Charging Pattern with DTW and EV Charging energy demand Prediction with LSTM
  • 2.12. Hemalatha Velappan: Classification of different tree species plantations using deep learning
    • 2.12.1. The goal of this work is to develop a model to identify planted forests and the tree species growing there. The model is developed using the
      • 2.12.1.1. (1) known locations of planted forests based on literature and personal communications,
      • 2.12.1.2. (2) image analysis and feature extraction of planted trees
      • 2.12.1.3. (3) spectral signatures unique to each species
        • 2.12.1.3.1. Performing zonal statistics on the polygon shapefile wrt the sentinel satellite image
        • 2.12.1.3.2. The following are the tree species and the corresponding label numbers
          • 2.12.1.3.2.1. Acrocarpus fraxinifolius 1
          • 2.12.1.3.2.2. Calycophyllum spruceanum 2
          • 2.12.1.3.2.3. Cedrela Mixed 3
          • 2.12.1.3.2.4. Guazuma crinita 4
          • 2.12.1.3.2.5. Miconia barbeyana 5
          • 2.12.1.3.2.6. Ochroma pyramidale 6
          • 2.12.1.3.2.7. Other Mixed 7
          • 2.12.1.3.2.8. Swietenia Cedrela Mixed 8
          • 2.12.1.3.2.9. Swietenia macrophylla 9
          • 2.12.1.3.2.10. Swietenia Mixed 10
        • 2.12.1.3.3. The input and output variables are split between training and testing by 70:30
      • 2.12.1.4. All the 14 columns are X variables. The final column that has categorical numbers is the target or Y variable
        • 2.12.1.4.1. The feedforward module with 3 hidden layers are built with different activation functions applied to the layers
        • 2.12.1.4.2. Creating a single-layer perceptron model
    • 2.12.2. Results:
      • 2.12.2.1. Using single-layer perceptron and multi-layer feedforward deep learning models did not yield high accuracy in distinguishing different tree plantations using Sentinel-2 dataset. There could be multiple reasons behind this. Since spectral band information from Sentinel 2 satellite image constitute most of the X variables and because of the high spectral similarity between tree species, the model couldn’t find a way to distinguish the species. It can also because of the poor modeling parameters such as poor selection of activation functions, learning rate, epoch numbers etc. which could have affected the model as well. In the future, more input parameters like texture metrics, band information from sentinel-1 etc. can be added into the model for better variance. Further different modelling parameters can be explored to improve the accuracy.
  • 2.13. Myriam Marending: Ships and economic activity: a starter
    • 2.13.1. 1. Data sources
      • 2.13.1.1. 1.1 Port satellite images
      • 2.13.1.2. 1.2 Ship sample data
    • 2.13.2. 2 Training and learning from the ship sample
      • 2.13.2.1. 2.1 Simple CNN
      • 2.13.2.2. 2.2 Predict ships in Lagos with simple CNN
      • 2.13.2.3. 2.3 Update the simple CNN using Adam
      • 2.13.2.4. 2.4 Predict ships in Lagos with updated CNN
  • 2.14. Florian Ellsäßer: Using a LSTM network and SHAP to determine the impact of drought and season on winter wheat
    • 2.14.1. load the data
    • 2.14.2. align the data properties
    • 2.14.3. apply ufunc to go through the raster cells
    • 2.14.4. ML part
      • 2.14.4.1. do with all data
    • 2.14.5. now implement shap for variable importance

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