Yamai Geospatial Consults Nigeria Limited

Yamai Geospatial Consults Nigeria Limited Contact information, map and directions, contact form, opening hours, services, ratings, photos, videos and announcements from Yamai Geospatial Consults Nigeria Limited, Surveyor, 96 Sokoto Street Kafanchan, Kaduna.

We offer a range of services such as High - Level Drone and Land Surveying, GIS Application, Full Drone data Processing, and Analysis Providing Geodatabase for several Agencies, Satellite Data Acquisition, and Modelling of Drone and Bathymetry Data.

🗺️ UNDERSTANDING THE GEOLOGY OF NIGERIA 🇳🇬Have you ever wondered why groundwater, minerals, soils, agriculture, and even...
28/08/2026

🗺️ UNDERSTANDING THE GEOLOGY OF NIGERIA 🇳🇬

Have you ever wondered why groundwater, minerals, soils, agriculture, and even environmental conditions vary from one part of Nigeria to another?

One major reason is geology.
This geological map of Nigeria shows the distribution of different geological formations across the country. These formations represent different rock types and geological histories, and they play an important role in understanding Nigeria's natural resources.

🔹 Groundwater: Geological formations influence how water infiltrates, is stored, and moves underground. Weathered and fractured basement rocks can provide groundwater, while some sedimentary formations can contain extensive aquifer systems.

🔹 Mineral resources: The geological history of an area is closely related to its mineral potential. Understanding rock types and geological structures is therefore essential for mineral exploration.

🔹 Agriculture and soils: Rocks serve as parent materials for soil formation. Differences in geology can contribute to variations in soil properties, drainage and agricultural potential.

🔹 Flood and erosion assessment: Geology can influence infiltration, runoff and landscape characteristics. When combined with rainfall, elevation, soil, drainage and land-cover data, geological information can improve environmental-risk modelling.

🔹 GIS & Remote Sensing: Geological datasets can be integrated with DEM, satellite imagery, rainfall, NDVI, soil moisture, land-use/land-cover and hydrological datasets to develop advanced spatial models.

🌍 The bigger picture
Geology should not be viewed simply as a map of rocks. It is one of the fundamental layers for understanding Nigeria's environment and natural resources.
With modern GIS, Remote Sensing and Machine Learning, geological information can be transformed into practical decision-support systems for:
📌 Groundwater potential mapping
📌 Mineral prospectivity mapping
📌 Flood susceptibility assessment
📌 Soil erosion modelling
📌 Agricultural land suitability
📌 Environmental management
📌 Natural-resource planning
Geology + GIS + Remote Sensing + Data Science = Better understanding of Nigeria's natural resources. 🇳🇬🌍

Yamai geospatial
28/08/2026

Yamai geospatial

Monitoring Nigeria’s Vegetation in 2026 with Satellite Data 🛰️🌿We used MODIS NDVI data + Google Earth Engine to map ve...
20/08/2026

Monitoring Nigeria’s Vegetation in 2026 with Satellite Data 🛰️🌿

We used MODIS NDVI data + Google Earth Engine to map vegetation conditions across Nigeria from 1 January to 20 August 2026.

What did we do?
1. Used MODIS MOD13Q1 data at 250m resolution
2. Applied quality control to remove clouds/bad pixels
3. Created a mean NDVI composite for the whole country
4. Visualized and exported the results for analysis

What does NDVI tell us?
NDVI measures how green and healthy vegetation is.
High values = dense forests, farmlands, moist areas
Low values = bare soil, dry areas, urban zones

Early results show the expected north-to-south gradient : Southern Nigeria shows denser vegetation, while northern areas show more seasonal/sparse cover due to climate differences.

This approach shows how satellite remote sensing + cloud computing can help monitor agriculture, drought, land degradation, and food security across Nigeria in near real-time.

Next steps: Compare 2026 with historical data, add rainfall + soil moisture, and generate state-level stats.

Full methodology on request.

Dear Naomi and the Open Dialogues Team,I hope you are well.Thank you so much for the kind email and for the certificate ...
19/08/2026

Dear Naomi and the Open Dialogues Team,

I hope you are well.

Thank you so much for the kind email and for the certificate of participation for the recent ‘DialogueON: Water Sustainability’. I truly appreciate the opportunity to contribute to such an important conversation.

It was a privilege to share perspectives alongside other passionate participants. The dialogue was insightful, and I left feeling inspired by the collective commitment to water sustainability.

Thank you also for considering me for future speaking opportunities in September. I would be glad to share ideas and support the upcoming event. I will also complete the feedback form and share the recording on my networks.

Please keep me updated on future DialogueON sessions. I look forward to joining again.

Once again, thank you for your great work and for including me.

Yours in open dialogue,
Dennis Luka

🏥 NEW MAP: Healthcare Accessibility in Kaduna State, Nigeria Made with Malaria Atlas Project Data + Google Earth Engine ...
14/08/2026

🏥 NEW MAP: Healthcare Accessibility in Kaduna State, Nigeria
Made with Malaria Atlas Project Data + Google Earth Engine

How long does it take you to reach the nearest hospital or clinic?

I used 2019 Malaria Atlas Project data and Google Earth Engine to map travel time to healthcare facilities across all 23 LGAs in Kaduna State.

Why travel time matters:
Having a clinic nearby on paper isn’t enough. Roads, terrain, and distance decide if someone can get care in an emergency, for maternal health, or for malaria treatment.

The 2025 Accessibility Map shows 5 categories:
🟢 Very High Access : ≤15 minutes to nearest hospital/clinic
🟡 High Access : 15–30 minutes
🟠 Moderate Access : 30–60 minutes
🔴 Low Access : 60–120 minutes
⚫ Very Low Access : >120 minutes

What this tells us:
1. Health Planning : Pinpoint communities with >2 hours travel time that may need new facilities
2. Emergency Response : Identify areas furthest from care during crises
3. Rural Health : Highlight LGAs where transport + facility gaps are biggest
4. SDGs : Supports SDG 3 Good Health and SDG 10 Reduced Inequalities

Quick facts:
- Data : Malaria Atlas Project 2019 Healthcare Accessibility
- Measure : Travel time in minutes to nearest hospital/clinic
- Tool : Google Earth Engine
- Output : 2 GeoTIFF maps — Continuous travel time + Classified accessibility map
- CRS : WGS 84 / UTM Zone 32N

⚠️ Note: This map shows geographical accessibility only. It doesn’t measure hospital quality, staff, drugs, or cost. But it’s a critical first step to find "access gaps" and prioritize where to build or improve services.

Next steps I recommend:
Combine this with population data to see how many people live in low-access areas, and add roads + LGA stats to guide decisions.

As a GIS Professional, I believe data like this can help government, NGOs, and communities make better decisions for health in Kaduna 🇳🇬

Would you like me to share the map or GEE script?
Which LGA do you think needs more health facilities? Drop it in the comments ??

🌧️ NEW STUDY: Spatial Assessment of Surface Runoff in Kaduna State, Nigeria Using ERA5-Land Data + Google Earth EngineWa...
12/08/2026

🌧️ NEW STUDY: Spatial Assessment of Surface Runoff in Kaduna State, Nigeria
Using ERA5-Land Data + Google Earth Engine

Water that flows over the land surface during heavy rain = Surface Runoff .
It’s a major driver of floods, soil erosion, and watershed degradation — especially in places like Kaduna State with large farms, growing towns, and changing rainfall.

What I did:
I used Google Earth Engine and ERA5-Land Daily data to map total accumulated surface runoff across Kaduna State for 2024 .

How it works:
1. Pulled daily runoff data from ERA5-Land for the whole year
2. Aggregated it, converted to mm, and clipped to Kaduna’s boundary
3. Ran stats: mean, 2nd & 98th percentile to understand the range
4. Created a runoff map and exported as GeoTIFF for GIS analysis

Why this matters for Kaduna:
- Flood Risk : Helps identify areas that contribute most water during rains
- Watershed Management : Supports erosion control and drainage planning
- Agriculture : Informs water conservation and land-use decisions
- Climate Resilience : Tracks how runoff patterns change year to year

Quick facts:
- Data : ERA5-Land `surface_runoff_sum`, ∼11km resolution
- Period : Jan 1 – Dec 31, 2024
- Tool : Google Earth Engine
- Output : Annual runoff map in mm + stats for the state

⚠️ Important: This is _modelled surface runoff_, not direct river measurements. It shows regional patterns best and should be combined with rainfall, soil, terrain, and land cover for detailed flood-risk mapping.

This workflow can be repeated every year to monitor changes in Kaduna’s hydrology and support better planning for floods, farming, and water resources.

Tools used: Google Earth Engine, ERA5-Land, QGIS/ArcGIS Pro for mapping

As a GIS Professional, I’m passionate about using satellite + climate data to support sustainable water and land management in Nigeria 🇳🇬

Would you like me to share the runoff map or the GEE script once the analysis is done?
Comment below 👇

🌍 NEW MAP: Land Surface Temperature Heatmap of Nigeria - 2025 Made with MODIS + Google Earth Engine Have you ever wond...
12/08/2026

🌍 NEW MAP: Land Surface Temperature Heatmap of Nigeria - 2025
Made with MODIS + Google Earth Engine

Have you ever wondered which parts of Nigeria are the hottest on the surface?

I used Google Earth Engine and NASA MODIS MOD11A2 data to map Nigeria’s average daytime Land Surface Temperature (LST) for 2025.

What is LST?
It’s the temperature of the Earth’s surface itself — not air temperature. It’s affected by vegetation, soil moisture, urban areas, and climate.

Key findings from the 2025 heatmap:
🔥 Hotter areas : Northern Nigeria, bare soil, built-up areas, and places with low vegetation & moisture
🌿 Cooler areas : Forested regions, wetlands, areas with dense vegetation & higher soil moisture in the south

Why this matters:
This kind of map helps with:
- Agriculture : Detecting crop stress and drought risk
- Urban Planning : Understanding urban heat islands
- Climate Monitoring : Tracking surface temperature changes over time
- Land Degradation : Spotting areas losing vegetation cover

Quick facts about the data:
- Source : NASA MODIS MOD11A2, 8-day composite, ∼1km resolution
- Period : Jan 1 – Dec 31, 2025
- Tool : Google Earth Engine
- Output : Annual mean daytime LST in °C, exported as GeoTIFF

⚠️ Note: This is _Land Surface Temperature_, not the temperature you feel outside. It shows how hot the ground/roof/soil is, which is critical for environment and agriculture work.

This workflow can be repeated for monthly, seasonal, or multi-year analysis to track how Nigeria’s surface temperature is changing.

Tools I used: Google Earth Engine, MODIS, ArcGIS/QGIS for visualization

As a GIS Professional, I’m passionate about using satellite data to support climate resilience, agriculture, and sustainable development in Africa.

🦟 Mapping Environmental Malaria Risk in Nigeria Using Satellites & Google Earth EngineMalaria is still one of Nigeria’s...
07/08/2026

🦟 Mapping Environmental Malaria Risk in Nigeria Using Satellites & Google Earth Engine

Malaria is still one of Nigeria’s biggest public health challenges. But transmission isn’t random — it’s driven by environment: temperature, rain, water, vegetation, and terrain.

To understand , where conditions may favor mosquitos ,I built an Environmental Malaria Suitability Map for Nigeria using Google Earth Engine .

Important:
This map does NOT show how many people have malaria.
It shows where environmental conditions are more favorable for mosquito breeding and survival.

How we did it:
I combined 5 environmental factors in GEE:
1. Land Surface Temperature - MODIS 2020–2025. Mosquitoes thrive ∼18°C–32°C
2. Rainfall - CHIRPS 2000–2025. Rain creates breeding sites
3. Surface Water - JRC Global Surface Water. Stagnant water = breeding habitats
4. Vegetation - MODIS NDVI 2020–2025. More vegetation = more moisture
5. Elevation - SRTM DEM. Lower areas often warmer and wetter

Each layer was scored 0–1 and combined:
30% Temp + 25% Rain + 20% Water + 15% Vegetation + 10% Elevation

What the map shows:
- Red/Very High : Southern Nigeria, humid, high rainfall, lots of vegetation & water
- Yellow/Moderate : Central Guinea savanna
- Green/Very Low : Northern Sahel, drier and cooler

How this can help:
✅ Target mosquito control and awareness campaigns
✅ Support health agencies to prioritize environmental interventions
✅ Guide research on where to do field surveys
✅ Inform climate adaptation + public health planning
✅ Complement — not replace — hospital and survey data

This was done 100% in the cloud with free satellite data. The approach is scalable to any state, LGA, or community.

Disclaimer : This is an environmental suitability index. Real malaria risk also depends on healthcare access, bed nets, housing, and actual case data.

Done as part of my geospatial work with Yamai Geospatial Consult LTD. Aligns with SDG 3: Good Health & Well-being and SDG 13: Climate Action.

What do you think , should public health programs use more satellite data for planning? 💬

🌍 Mapping Vegetation Health in Kaduna State Using Satellite DataVegetation monitoring is essential for understanding env...
14/03/2026

🌍 Mapping Vegetation Health in Kaduna State Using Satellite Data
Vegetation monitoring is essential for understanding environmental conditions, agricultural productivity, and land management. Using satellite remote sensing and cloud-based geospatial analysis, I recently developed a vegetation health map for Kaduna State, Nigeria based on the Normalized Difference Vegetation Index (NDVI).

The (NDVI) is a widely used indicator for assessing vegetation health by comparing the reflectance of near-infrared and red wavelengths from satellite imagery.

For this analysis, I used imagery from , processed within to generate a vegetation map covering February–March 2023 for
🔎 Methodology
The workflow involved several key steps:
• Acquisition of surface reflectance imagery from Landsat 8
• Filtering images by date and cloud cover
• Generating a median composite to reduce atmospheric noise
• Calculating NDVI using the NIR and red spectral bands
• Clipping the results to the Kaduna State boundary
• Visualizing vegetation density using a standard NDVI color gradient
🌱 Key Insights

The NDVI results highlight spatial variation in vegetation across the state:
Higher NDVI values occur around river corridors and active agricultural areas.
Moderate vegetation is present in rural farming landscapes.
Lower NDVI values appear around urban settlements and bare soil areas.
This type of geospatial analysis helps support:
✔ Agricultural monitoring
✔ Environmental planning
✔ Drought and land degradation assessment
✔ Climate-informed decision making
Cloud platforms like Google Earth Engine are transforming how large satellite datasets are analyzed, enabling faster and more scalable environmental monitoring.
As geospatial technology continues to evolve, integrating remote sensing with data analytics will be increasingly important for addressing environmental and agricultural challenges across Africa.

🌦 Mapping Rainfall in Kaduna State (2020–2024) Using Satellite DataI recently worked on analyzing cumulative rainfall pa...
10/03/2026

🌦 Mapping Rainfall in Kaduna State (2020–2024) Using Satellite Data
I recently worked on analyzing cumulative rainfall patterns across Kaduna State, Nigeria, leveraging the CHIRPS satellite dataset and Google Earth Engine (GEE).
Key highlights:
Generated cumulative rainfall maps for 2020–2024 and annual rainfall layers for each year.
Identified spatial variability: southern and central Kaduna received the highest rainfall, while northern regions were comparatively drier.
Insights from these maps can support agriculture planning, water resource management, and flood risk mitigation.
Using GEE allowed for fast, high-resolution analysis across the entire state, offering a clear advantage over traditional ground-based measurements.
This project demonstrates how geospatial technologies and satellite data can provide actionable insights for sustainable development and climate resilience.

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