Mega port project - Environmental study (imagery and data analysis)

TL;DR:

This serves as the main repo for the satellite imagery analysis and cartography project for the Algerian mega port project in El-Hamdania, Cherchell, Tipaza.

Open for contribution! (I see you, hacktoberfest enthousiasts 👀)

Project website: https://indigowizard.github.io/mega-port-environment/webmap.html

IMPORTANT NOTE: ⚠️

The Earth Engine token expires every 48H, so after that the layers won't show up and the webapp may seem like not working, it's not, it's just that I don't update the token from my personal google earth engine account unless you need a live demo, otherwise check the preview section.

Table of Content

• Introduction
• Features
• Preview
• User and installation guide
• Contribution
• Credit

Introduction:

This project is meant to create a script(s) that help analyse different satellite imagery layers to calculate various environmental indices and generate land-surface data that can help study the environmental impact in the mega port project's area.

Features:

A breif list of features supported in this webapp (see release note for further details):

• Various vector-based thematic basemaps (OSM, topography, terrain... etc.).
• Various raster-based high-resolution basemaps from different services for better comparision between app results and real life on-site data (ESRI, Google).
• Vector based layers in GeoJSON format representing the various spatial features of the port project, both natural and artificial. E.g:
  • Artificial: infrastructure, current construction zones, future and planned logistical and industrial zones... etc.
  • Natural: Forests affected by the project plans, forests preserved in the project plan, farms and agricultural lands affected, waterways and water bodies... etc.
• Interactive vector layers: Various data on every single entity of the different vector layers as pop-ups on hover (including highlight hover effect for better visibility), e.g: (surface area, administrative jurisdiction, project status, species availble whithin the area... etc).
• Computed raster-based geographical data from satellite imagery (Sentinel-2, Landsat... etc).
• Various calculated topography data (elevation, slopes, DEM... etc).
• Imagery analysis of computed environmental indices: NDVI, NDWI, classified computed raster data... (more indicies can be added).
• A collapsible layer panel for optimal view.
• A draggable legend window.
• Adraggable project stats and links.
• A mini map

More features will be added in the future (contribution is welcome!).

Preview

A layered static preview of the multiple availble layers within the map currently.

User and installation guide

For end users:

You can simply access the website of the project as it is here: Project website

For devs:

The project uses various technologies to help generate and analyse geographical data, using GIS and related tech. Main tech used in the project:

• Folium (data visualization)
• Earth Engine (geospatial processing service)

If you want to develop this project or or contribute to it, you can fork it and clone it then follow configuration steps:

Google Earth Engine:

1. To use this script, you must first sign up for a Google Earth Engine account.
2. Install gcloud CLI for the Google Earth Engine account authentication.
3. Install the rest of project dependency from this file: requirement.txt

Using Anaconda (recomanded):

Method 1: The fastest and cleanest method:

The project folder contains two following files, use one of the two:

• environment.yml Use this file to recreate the the very same developement environement (with it's dependencies) I used for this project called "geospatial":

From console: conda env create -f environment.yml

Or use Anaconda Navigator (UI) and select path to the environment.yml file, anaconda does the rest.

That's it, you're good to go!

Method 2: If you prefer to start a new anaconda environement:

1. Make a conda virtual environment, call it "geospatial":

conda create --name geospatial

2. Activate geospatial environement:

conda activate geospatial

3. Install folium:

conda install -c conda-forge folium

4. Install earthengine-api from conda forge within geospatial env:

conda install -c conda-forge earthengine-api

That's it, you're good to go!

Using pip:

• Install folium: pip install folium
• Install earthengine-api: pip install earthengine-api

Contribution:

The project is open for contribution where you can help improve, fix or add features. Please refer to CONTRIBUTING.md for more details and guidelines.

It's important that you check the issues section before:

• Reporting a bug
• Fixing a bug
• Requesting features
• Implementing features
• Writing documentation

If no issue alrady exists regarding your contribution subject you may open an issue and get to work.

Credit

Project by Ahmed I. Mokhtari.