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.

🌍 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.

🌍 Land Use / Land Cover Mapping of Lagos State Using Google Earth EngineUnderstanding how land is being used is essentia...
10/03/2026

🌍 Land Use / Land Cover Mapping of Lagos State Using Google Earth Engine

Understanding how land is being used is essential for sustainable urban planning and environmental management. I recently conducted a Land Use and Land Cover (LULC) classification for (2023) using multispectral imagery from within the cloud-based platform .

🔍 Project Highlights • Generated a cloud-free Landsat composite for 2023
• Applied Random Forest machine learning classification
• Identified five major land cover classes:
– Water bodies
– Vegetation
– Built-up areas
– Bare land
– Agricultural land
• Produced a 30 m resolution LULC map for spatial analysis and decision support

📊 Why this matters

is one of Africa’s fastest-growing megacities. Monitoring land cover patterns is critical for: • Urban planning and infrastructure development
• Environmental sustainability
• Flood risk and coastal management
• Agricultural land protection

This project demonstrates how cloud computing and satellite data can enable rapid, scalable, and reproducible geospatial analysis for urban environments.

Next, I plan to enhance the analysis by integrating spectral indices (NDVI, NDBI, NDWI) and conducting classification accuracy assessment to improve model performance.

🌍 Land Use / Land Cover Mapping of Benue State Using Cloud-Based Geospatial AnalysisI recently completed a Land Use and ...
10/03/2026

🌍 Land Use / Land Cover Mapping of Benue State Using Cloud-Based Geospatial Analysis
I recently completed a Land Use and Land Cover (LULC) classification for 2023 for Benue State using satellite imagery and machine learning in the Google Earth Engine environment.
Using Landsat 8 Surface Reflectance imagery, I developed a workflow that:
▪ Filters satellite data for 2023 with cloud cover

🌍 Mapping Kano State’s Land Use and Land Cover with Google Earth EngineI recently completed a Land Use / Land Cover (LUL...
09/03/2026

🌍 Mapping Kano State’s Land Use and Land Cover with Google Earth Engine
I recently completed a Land Use / Land Cover (LULC) classification for Kano State (2023) using Landsat 8 Surface Reflectance imagery and a Random Forest classifier within Google Earth Engine.
Key highlights of the project:
Generated a cloud-free Landsat 8 composite for 2023
Classified the state into five land cover classes: Water, Vegetation, Built-up, Bare Land, and Agriculture
Implemented Random Forest machine learning for accurate classification
Added map layout elements including title, legend, north arrow, and scale for visualization
Exported results as a GeoTIFF at 30 m resolution for further analysis
This work demonstrates the power of cloud-based geospatial platforms for rapid, scalable, and reproducible LULC mapping. The resulting map provides critical insights for urban planning, agriculture, and environmental management in Kano State.
Next steps include improving classification accuracy using spectral indices, higher-resolution imagery, and generating quantitative land cover statistics.

🌍 Mapping Kaduna State’s Land Use and Land Cover with Google Earth EngineI recently developed a Land Use / Land Cover (L...
09/03/2026

🌍 Mapping Kaduna State’s Land Use and Land Cover with Google Earth Engine
I recently developed a Land Use / Land Cover (LULC) classification map for Kaduna State, Nigeria (2023) using Landsat 8 Surface Reflectance imagery and the Random Forest algorithm in Google Earth Engine.
Key highlights:
Created a cloud-free Landsat 8 composite for 2023
Classified the state into five land cover classes: Water, Vegetation, Built-up, Bare Land, and Agriculture
Implemented Random Forest machine learning for accurate classification
Added map layout elements like title, legend, north arrow, and scale for visualization
Exported results as a GeoTIFF at 30 m resolution for further analysis
This project demonstrates how cloud-based geospatial platforms can efficiently process large datasets for environmental monitoring, urban planning, and sustainable land management.
📊 Next steps: Enhance classification accuracy with spectral indices, higher-resolution imagery, and provide area statistics per land cover class.
If you’re interested in geospatial analytics, remote sensing, or land management, I’d be happy to discuss methods and applications!

🌱 Mapping Soil Organic Carbon in Kano State, Nigeria with Google Earth Engine 🌍Soil organic carbon (SOC) is a key indica...
25/02/2026

🌱 Mapping Soil Organic Carbon in Kano State, Nigeria with Google Earth Engine 🌍
Soil organic carbon (SOC) is a key indicator of soil health, fertility, and carbon sequestration potential. Understanding its spatial distribution is crucial for sustainable land management and climate action.
In a recent study, I leveraged Google Earth Engine and Random Forest regression to map SOC density (g/kg) across Kano State using publicly available datasets:
SOC density: OpenLandMap
Elevation & slope: SRTM
Vegetation: MODIS NDVI
Rainfall: CHIRPS
Key highlights:
✅ Predicted SOC density with 5,000 training points
✅ Visualized SOC patterns with legend, north arrow, and scale bar
✅ Identified high SOC areas in wetter, vegetated regions, and low SOC in semi-arid zones
✅ Fully reproducible workflow using cloud-based processing
This approach provides actionable insights for soil management, carbon monitoring, and sustainable agriculture planning.
Next steps: integrating soil depth and bulk density to convert SOC density into stock, and expanding to multi-year analyses for change detection.

🌍 Assessing Soil Erosion Risk in Nigeria Using Google Earth Engine 🌍I've completed a nationwide assessment of soil erosi...
06/01/2026

🌍 Assessing Soil Erosion Risk in Nigeria Using Google Earth Engine 🌍

I've completed a nationwide assessment of soil erosion risk in Nigeria using the Revised Universal Soil Loss Equation (RUSLE) framework in Google Earth Engine (GEE).

Key findings:

- High-risk zones identified in northern and central highlands with steep slopes, high rainfall, and intensive land use
- Coastal and forested southern regions show lower erosion risk
- Extreme erosion risk areas are predominantly found in agricultural regions with poor vegetation cover and erodible soils

Methodology:

- Integrated multi-source geospatial datasets (CHIRPS, ISRIC SoilGrids, SRTM DEM, ESA WorldCover)
- Computed RUSLE factors: Rainfall Erosivity (R), Soil Erodibility (K), Topographic Factor (LS), Cover Management Factor (C)
- Produced a high-resolution soil erosion risk map of Nigeria

Implications:

- Guides soil conservation planning, land use management, and policy interventions
- Demonstrates the power of cloud-based geospatial tools for large-scale environmental monitoring

Let's connect and collaborate on soil conservation efforts! 😊 "

Exciting Research Opportunity!I'm working on a project monitoring vegetation dynamics in Oyo State, Nigeria, using Senti...
04/01/2026

Exciting Research Opportunity!

I'm working on a project monitoring vegetation dynamics in Oyo State, Nigeria, using Sentinel-2 NDVI data for 2025. The goal? To assess vegetation health, identify areas under stress, and inform land management decisions.

Key Highlights:

- Utilized Sentinel-2 imagery and cloud masking to generate a median NDVI composite
- Analyzed spatial patterns of vegetation health across Oyo State
- Implications for agriculture, climate policy, and sustainable development

Would love to connect with experts in remote sensing, GIS, and sustainable development! Any insights or collaborations welcome 😊.

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"🌍 Exciting Project Update! 🌍I'm thrilled to share that I've generated a 2024 Land Use and Land Cover (LULC) map of Kano...
04/01/2026

"🌍 Exciting Project Update! 🌍

I'm thrilled to share that I've generated a 2024 Land Use and Land Cover (LULC) map of Kano State, Nigeria using Google Earth Engine and the Dynamic World dataset! 🚀

This map provides valuable insights into land use patterns, including:
- 🌾 Agricultural expansion in rural areas
- 🏙️ Urbanization in Kano metropolis
- 🌳 Sparse vegetation around river valleys
- 💧 Water bodies scattered across the state

The Dynamic World dataset offers 10m resolution, near-real-time LULC mapping, enabling effective monitoring and decision-making for urban planning, agriculture, and environmental management.

Want to learn more about the project or collaborate? Let's connect! 😊 "

"Excited to share my 2026 strategy! 🌟As I embark on another year of growth, my vision is to establish myself as a leadin...
01/01/2026

"Excited to share my 2026 strategy! 🌟

As I embark on another year of growth, my vision is to establish myself as a leading geospatial and data professional in Africa, leveraging GIS, climate, and health data to inform policy and drive sustainable development.

My strategic pillars for 2026:
- Elevate my profile as a GIS-Data-Policy expert through publications, fellowships, and global collaborations
- Develop impactful data tools and research on climate, health, and environmental challenges
- Influence policy with data-driven insights and engage with government agencies, development partners, and global data communities
- Build my personal brand and leadership through speaking, mentoring, and online presence
- Achieve financial stability through consultancy, training, and data analysis services

Let's connect and collaborate! 😊 "

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Kaduna

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+2347086126817

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