Using AI to download environmental variables from Google Earth Engine

 What are environmental variables? 

Remote sensing environmental variables are measurable properties of the Earth's land, water, and atmosphere captured using satellite, airborne, or ground-based sensors.

Environmental variables are sometimes called 'geophysical parameters' in remote sensing. When classified by international Earth observation frameworks (such as NASA Earthdata or the Global Climate Observing System), they are specifically referred to as Essential Climate Variables (ECVs) or Essential Variables (EVs) when used to monitor Earth's processes, land surface dynamics, and climate change.

The Google Earth Engine Data Catalog hosts a massive, multi-petabyte repository of geospatial data. The environmental variables accessible on the platform are structured across several main scientific categories: 

1. Atmosphere & Climate

Data parameters detailing the composition, dynamics, and short- to long-term conditions of the lower atmosphere: 

• Temperature: Surface air temperature, land surface temperature (LST), maximum/minimum temperature, and ocean/sea surface temperature (SST).
• Precipitation: Total rainfall, snowfall, convective precipitation, and rainfall intensity. 
• Atmospheric Water & Humidity: Specific humidity, relative humidity, precipitable water vapor, and dewpoint temperature. 
• Wind: Wind speed, wind vector components (u and v vectors), and gust speed. 
• Radiation & Solar: Downwelling shortwave/longwave radiation, solar irradiance, and photosynthetically active radiation (PAR). 
• Atmospheric Composition: Ozone levels, methane concentrations (CH₄), carbon dioxide (CO₂), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and aerosol optical depth (AOD). 
• Derived Bioclimatic Indexes: 19 bioclimatic variables (e.g., from WorldClim) including annual mean temperature, seasonality, and precipitation of the driest/wettest quarters. 

2. Land Surface & Topography

Physical parameters of the Earth's crust, elevation, and terrain: 

• Elevation & Topography: Digital Elevation Models (DEMs), slope, aspect, hillshade, terrain roughness, and topographic position index (TPI).
• Soil Properties: Soil moisture (surface and root-zone), soil organic carbon, soil pH, bulk density, and sand/silt/clay fractions.
• Land Use & Land Cover (LULC): Crop classification maps, global forest cover change, urban footprint, and global ecosystem classifications.
• Geophysical Disturbance: Areas annually burned, active thermal anomalies (fires), and time-since-fire indices. 

3. Biosphere & Vegetation Indices

Variables indicating the distribution, health, and productivity of plant life: 

• Vegetation Indices: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Soil Adjusted Vegetation Index (SAVI).
• Plant Productivity: Gross Primary Productivity (GPP), Net Primary Productivity (NPP), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and Leaf Area Index (LAI).
• Forest & Biomass: Tree canopy height, above-ground biomass, and canopy cover fraction. 

4. Hydrology & Cryosphere

Water dynamics on the surface and frozen landscapes: 

• Surface and Ground Water: Surface water extent, river discharge, groundwater storage anomalies, and water quality indicators (chlorophyll-a, turbidity).
• Evapotranspiration: Potential evapotranspiration (PET), actual evapotranspiration (ET), and latent heat flux.
• Cryosphere (Ice & Snow): Snow water equivalent (SWE), snow cover extent, albedo, glacier velocity, and sea ice concentration. 

5. Human Dimensions & Infrastructure

Socio-environmental datasets representing human modification of landscapes: 

• Human Footprint: Nighttime lights (radiance), population density, human modification index, and road network proximity. 

/**
 * 1. DEFINE YOUR STUDY AREA & TIME PERIOD
 * Replace 'geometry' below with your hand-drawn polygon or asset link if needed.
 */
// Default study area: A 20km square box around a sample point
var studyArea = ee.Geometry.Point([-122.25, 37.87]).buffer(20000); 
Map.centerObject(studyArea, 10);
Map.addLayer(studyArea, {color: 'red'}, 'Study Area Boundary', false);

var startDate = '2025-01-01';
var endDate = '2025-12-31';

/**
 * 2. LOAD & PROCESS ENVIRONMENTAL VARIABLES
 */

// --- Variable A: Temperature (ERA5-Land Mean Air Temperature) ---
var temp = ee.ImageCollection("ECMWF/ERA5_LAND/MONTHLY_AGGR")
  .filterBounds(studyArea)
  .filterDate(startDate, endDate)
  .select('temperature_2m')
  .mean() // Average over the year
  .subtract(273.15) // Convert Kelvin to Celsius
  .clip(studyArea)
  .rename('mean_temperature_celsius');

// --- Variable B: Precipitation (CHIRPS Daily Rainfall) ---
var precip = ee.ImageCollection("UCSB-CHG/CHIRPS/DAILY")
  .filterBounds(studyArea)
  .filterDate(startDate, endDate)
  .select('precipitation')
  .sum() // Total annual rainfall
  .clip(studyArea)
  .rename('total_precipitation_mm');

// --- Variable C: Soil Moisture (NASA USDA Global Soil Moisture) ---
var soilMoisture = ee.ImageCollection("NASA_USDA/HSL/SMAP10KM_soil_moisture")
  .filterBounds(studyArea)
  .filterDate(startDate, endDate)
  .select('ssm') // Surface soil moisture
  .mean()
  .clip(studyArea)
  .rename('surface_soil_moisture');

// --- Variable D: Topography (NASADEM Elevation) ---
var elevation = ee.Image("NASA/NASADEM_HGT/001")
  .select('elevation')
  .clip(studyArea)
  .rename('elevation_meters');

// --- Variable E: Vegetation Health (Sentinel-2 NDVI) ---
// Function to mask clouds in Sentinel-2
function maskS2clouds(image) {
  var qa = image.select('QA60');
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(qa.bitwiseAnd(cirrusBitMask).eq(0));
  return image.updateMask(mask).divide(10000);
}

var s2 = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED")
  .filterBounds(studyArea)
  .filterDate(startDate, endDate)
  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))
  .map(maskS2clouds);

var ndvi = s2.median().normalizedDifference(['B8', 'B4']) // NIR and Red bands
  .clip(studyArea)
  .rename('ndvi_median');

/**
 * 3. VISUALIZE DATA LAYERS ON THE MAP
 */
Map.addLayer(temp, {min: 0, max: 35, palette: ['blue', 'yellow', 'red']}, 'Mean Temp (°C)');
Map.addLayer(precip, {min: 200, max: 2000, palette: ['brown', 'yellow', 'blue']}, 'Total Precip (mm)');
Map.addLayer(soilMoisture, {min: 0, max: 25, palette: ['red', 'yellow', 'green']}, 'Surface Soil Moisture');
Map.addLayer(elevation, {min: 0, max: 2000, palette: ['green', 'yellow', 'brown']}, 'Elevation (m)');
Map.addLayer(ndvi, {min: 0, max: 0.8, palette: ['white', 'yellow', 'green']}, 'NDVI Median');

/**
 * 4. COMBINE AND EXPORT TO GOOGLE DRIVE
 * This combines all bands into a single multiband GeoTIFF file.
 */
var completeDataset = ee.Image.cat([temp, precip, soilMoisture, elevation, ndvi]);

Export.image.toDrive({
  image: completeDataset,
  description: 'Environmental_Variables_Export',
  folder: 'GEE_Exports',
  scale: 30, // Resolution in meters (Adjust higher if exporting massive areas to avoid errors)
  region: studyArea,
  maxPixels: 1e13,
  fileFormat: 'GeoTIFF'
});

print("Layers compiled! Head to the 'Tasks' tab on the right and click 'Run' to begin the download.");

Thank you for reading.

Designing Graphical Abstract for a research paper titled: Economic Valuation of Regional Agricultural Outputs: Grains, Fruits, and Fisheries in Nigeria

 This post is about designing a graphical abstract for an assumed paper titled "Economic Valuation of Regional Agricultural Outputs: Grains, Fruits, and Fisheries in Nigeria". The graphical assests focus on the core takeways of the paper which are: Nigeria, Grains, Fruits, and Fisheries.

These takeaways will serve as inputs, methodologically linked by directional arrows to illustrate how the study processes them into a final output. This is what 'graphical abstract' is all about.

The video guide is available on this youtube link. At the end, you should be able to transform this hand sketch idea into the publication-ready graphical abstract below using Inkscape software.

Thank you.

AI Agent (by TerraLab) in QGIS to fetch security installations

Mapping Abuja’s Security Infrastructure: How to Fetch Point Layers using TerraLab's AI Agent for QGIS

Geospatial data extraction has historically required jumping between specialized data web portals, building complex Overpass API queries, or manually digitizing locations. However, with the emergence of AI agents built directly into GIS environments, extracting specific spatial data is now as simple as writing a chat message.

In this guide, we will walk through using the AI Agent by TerraLab inside QGIS to automatically pull a comprehensive point layer of security installations within the Abuja Municipal Area Council (AMAC), Nigeria. We will cover police stations, military cantonments/barracks, and the Nigeria Security and Civil Defence Corps (NSCDC) offices.

I decided to try the AI Agent in QGIS, I installed the AI agent by TerraLab via the plugin manager, it was a great expereice to say the least. It literally completed a task for me in way less minutes than I would have done it manually. Here are the details of what I tasked it to do;-

Task/Prompt: I need point layer for all the security agents offices in Abuja Municipal Area Council (AMAC) including Police stations, Millitary cantonments/baracks and Nigeria Security & Civil Defence Corps.

Result: As seen below, it successfully retrieved 53 points for the result. Although, not all were security infrastructures I expected, so I did some manual verification and cleaning.

How to Install and Initialize the AI Agent

Before prompting the agent, ensure you have the plugin installed and ready in your workspace.

1. In QGIS, navigate to the top menu and select Plugins > Manage and Install Plugins.
2. Search for AI Agent by TerraLab and click Install Plugin.
3. Once installed, open the agent interface panel using the keyboard shortcut Ctrl + Alt + A (or click the TerraLab icon in your toolbar).
4. Sign in to your account to activate your monthly free message allowance.

After you are signed in, enter the prompt and once you hit submit, the AI Agent creates an execution plan which you can monitor live in the side panel:

1. Boundary Retrieval: The agent queries global administrative databases to find the official polygon layer for AMAC.
2. Data Extraction: It simultaneously queries regional open datasets using tags like amenity=police, military=barracks, and generic security/government office infrastructure filters tailored to Nigeria's public safety sectors.
3. Reprojection & Layer Creation:

The data is transformed into your project’s Coordinate Reference System (CRS) and compiled into a dedicated point feature layer.

4. Symbology Application: Instead of generic circles, the agent can apply categorized styling, assigning separate colors or icons for Police, Military, and NSCDC sites if that was stated in the initial prompt.

One of the most powerful aspects of the TerraLab AI Agent is its built-in safety net. If the agent misinterprets a layout or grabs points outside your target area, you don't have to manually delete elements. You can use the one-click Undo checkpoint feature in the panel to revert the changes and refine your prompt.

Once you are satisfied with the layer, you can right-click the newly generated layer in your QGIS Layers Panel and select Export > Save Features As... to save the clean dataset permanently as a GeoPackage or Shapefile for your local security intelligence audits.

By leveraging the AI Agent by TerraLab, what used to be a tedious multi-step data mining workflow is reduced to a single conversational task. Whether you are mapping critical infrastructure for urban planning or conducting risk assessments in Nigeria's capital, AI-driven GIS tools ensure your time is spent analyzing data rather than hunting for it.

TerraLab provides three major AI-powered plugins for QGIS: AI Agent (for automating GIS workflows via text chat), AI Segmentation (for extracting vector polygons from imagery), and AI Edit (for visualizing spatial planning and changing pixels with text prompts).

Happy AIing!

How to create stacked axonometric/isometric map in Inkscape

 To create a stacked axonometric map in Inkscape, you can build the diagram using flat 2D layers and then apply Inkscape’s axonometric grid and transform tools to slant the layers into a uniform 3D perspective.

Sample figure: Axonometric/Isometric Map of Nigeria States + LGA + Wards

Note that the core base maps in the example above were prepared in a GIS software before being imported into inkscape for the layer stacking and transformation into axonometric/isometric map.

Here is the step-by-step workflow to create and stack your map layers perfectly:

Step 1: Set Up an Axonometric Grid

1. Go to File > Document Properties (or press Ctrl + Shift + D).
2. Select the Grids tab.
3. In the dropdown, change Rectangular grid to Axonometric grid and click New.
4. Adjust the Grid units (e.g., px or mm) and set your preferred angles. The default X-angle and Z-angle are usually set to 30.0 degrees, which works perfectly for standard isometric/axonometric drawings.

Step 2: Organize Your Map Layers

Using separate layers will keep your workflow clean and make it easy to move the maps vertically.

1. Open the Layers panel by going to Layer > Layers and Objects (Ctrl + Shift + L).
2. Create a separate layer for each map level you plan to stack (e.g., Layer 1: States, Layer 2: LGAs, Layer 3: Wards etc).

Step 3: Draw or Import Your Flat Assets

It is much easier to design your map elements flat before turning them into 3D.

1. Select your first layer and draw your map elements (roads, site boundaries, shapes) or import an existing 2D vector file.
2. Group the elements of that specific map level together by selecting them and pressing Ctrl + G. Repeat this for every layer.

Step 4: Transform to Axonometric View

If you didn't draw your layers directly on the 3D grid, you can transform flat elements using the Transform Panel:

1. Open the panel via Object > Transform (Ctrl + Shift + M) and click the Rotate tab.
2. Select your flat 2D group and rotate it by 45°. Click Apply.
3. Switch over to the Skew tab in the same panel.
4. Set the Skew X to -26.565° (or 26.565° depending on your preferred orientation) and leave Skew Y at 0. Click Apply.
5. Your flat vector map will now perfectly match a standard 30-degree isometric/axonometric grid line. Repeat this exact sequence for every group on your other layers.

Step 5: Stack and Align Your Layers

1. Turn on Snapping from the top-right corner toolbar. Make sure Snap to grid lines and Snap to nodes/intersections are turned on.
2. Select a layer group and hold Ctrl while dragging it vertically. Holding Ctrl restricts movement to a perfectly straight vertical line.
3. Space out your layers evenly. You can map out a specific distance by counting the grid intersections or using the Align and Distribute panel (Ctrl + Shift + A).

Step 6: Draw Connecting Dash Lines

To show how the layers structurally relate to each other, add vertical guidelines:

1. Select the Bezier Tool (or press B).
2. Click a key anchor point on your bottom map layer (like a site corner or a major intersection point) and draw a straight vertical line upward to the exact same point on the top map layer.
3. Open Object > Fill and Stroke (Ctrl + Shift + F). Go to the Stroke style tab and change the Dashes dropdown to a clean dashed pattern. Lower the opacity or change the color to a light gray so it functions as a subtle guide.

Map of Nigeria Senatorial Districts, Federal Constituencies and State Constituencies

Stacked Map of Nigeria Senatorial Districts on different shades of colors

You can use "SSR technique" (Scale-Shear/Skew-Rotate), to transform any object into top plan, left wall or right wall views. I wrote about SSR technique here.

Transform Menu Settings for Isometric Transformations

That is it!

Axonometric Drawing - SSR (Scale, Shear/Skew, Rotate) technique

 The SSR (Scale, Shear, Rotate) technique is a highly efficient mathematical workflow used in vector graphic programs like Adobe Illustrator and Inkscape to precisely transform flat 2D orthographic drawings into a 3D isometric axonometric projection. Instead of tracing flat blueprints onto an isometric grid by hand, the SSR method automates the deformation using exact numerical inputs.

The technique relies on mathematically shifting the 2D plane so that a standard square conforms perfectly to the true 30-degree / 120-degree orientation of an isometric drawing.

The Three Pillars of SSR

To project a flat 2D face onto one of the three visible planes of a 3D cube (Top, Left, or Right), you apply three sequential operations to your vector object:

1.  Scale: This accounts for the foreshortening effect of looking at an object from an isometric angle. You scale the vertical height of the object down to 86.602% while leaving the width at 100%.
2.  Shear (or Skew): This slants the vertical edges of the object to mimic a parallel projection angle, tilting them by 30° or -30°.
3.  Rotate: This turns the entire asset into the final orientation required for that specific plane, shifting it by 30° or -30°.

The Standard Transformation Values

Depending on which side of the axonometric drawing you are generating, apply these specific transformations in order:

Isometric Plane	1. Scale (Vertical)	2. Shear (Skew)	3. Rotate
Top Plane 🪵	86.602%	30°	-30°
Left Plane 🧱	86.602%	-30°	-30°
Right Plane 🧱	86.602%	30°	30°

(Note: Some software packages require a 30° Shear and a 30° Rotation for the Left Plane, or flipped symbols depending on how the software measures clockwise vs. counter-clockwise angles).

The 86.602% vertical scale factor in the SSR technique is not an arbitrary number—it is the exact trigonometric value of 

It acts as a mathematical pre-compensation filter. When you skew (shear) an object in a vector program by 30°, the software stretches the geometry. Scaling the vertical axis down to 86.602% beforehand ensures that after it stretches, the final lines return to exactly 100% of their original true length, maintaining a perfect 1:1:1 aspect ratio across all 3D axes.

Here is the exact geometric breakdown of why this happens.

1. The Trigonometry of the Shear Step

When you apply a 30° Shear to a flat square, the horizontal lines of your drawing tilt upward by 30°.

In vector graphics, a shear shifts points along one axis proportional to their distance from the other axis. This distorts the right angles of your flat square into an isometric diamond (rhombus). During this distortion:

• The width (horizontal boundary) of the bounding box remains fixed.
• The horizontal lines tilt up at a 30° angle, turning into the hypotenuse of a right-angled triangle.

According to right-triangle trigonometry:

Rearranging this formula to find the new length:

This means that shearing a flat object by 30° automatically stretches its line segments by roughly 115.47%.

2. Eliminating the Distortion (Why we Scale First)

If you don't scale the graphic before shearing it, your final 3D object will look elongated and structurally warped because the lines on the isometric plane are longer than they should be.

To counteract this 115.47% expansion, you must shrink the asset by the exact reciprocal amount before the shear takes place:

By scaling the vertical size to 86.602%, you intentionally compress the object. When the 30° shear step elongates the lines by 115.47%, the two operations cancel each other out perfectly:

3. Isometric Drawing vs. Isometric Projection

There is a subtle but important distinction in technical drawing that explains why this scale factor is so highly regarded:

• True Isometric Projection: When an actual 3D cube is rotated in physical space (45° horizontally, then 35.264° downward toward the camera), all axes shrink uniformly to 81.65% of their real-world size due to perspective foreshortening.
• Isometric Drawing (The SSR Approach): Graphic designers prefer a 1:1 scale so that a 100px line on a flat blueprint remains exactly 100px long on the 3D isometric axis. The SSR technique achieves this "true measurement" standard. By scaling vertically to 86.602% before shearing, the final 3D drawing retains an un-foreshortened, easily measurable 100% scale on all visible faces.

To calculate alternative scale values for other axonometric projections, you must determine how much the shear (skew) angle stretches the artwork and use trigonometry to cancel out that distortion.

The universal formula to find the required vertical scale factor for any parallel projection using the Shear-Rotate method is:

(Where ϴ is the shear angle you intend to use).

Thank you for reading.