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Yamai Geospatial Consulting Limited Contact information, map and directions, contact form, opening hours, services, ratings, photos, videos and announcements from Yamai Geospatial Consulting 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.

🌍 LAND USE/LAND COVER MAPPING OF ZARIA, KADUNA STATE, USING GOOGLE EARTH ENGINEUnderstanding how land is being used and ...
03/10/2026

🌍 LAND USE/LAND COVER MAPPING OF ZARIA, KADUNA STATE, USING GOOGLE EARTH ENGINE

Understanding how land is being used and how the landscape is changing is essential for sustainable urban planning, agricultural development, environmental management, and natural resource conservation.

As part of my geospatial analysis work, I developed a 2025 Land Use/Land Cover (LULC) map of Zaria, Kaduna State, Nigeria, using Google Earth Engine (GEE) and the Dynamic World dataset.

🛰️ DATASET & METHODOLOGY
The analysis utilized the Dynamic World V1 global land-cover dataset, which provides land-cover information at approximately 10 m spatial resolution.

For the 2025 assessment, Dynamic World observations covering the study area between 1 January and 31 December 2025 were processed. The classification band was extracted and an annual modal (mode) classification was generated to represent the dominant land-cover class for each pixel throughout the year.

The study area was defined around Zaria using a 15 km buffer around the approximate Zaria coordinates and subsequently converted to a rectangular study extent.

🗺️ LAND COVER CLASSES
The resulting LULC map identifies nine Dynamic World classes:

🔹 Water
🌳 Trees
🌱 Grass
💧 Flooded Vegetation
🌾 Crops
🌿 Shrub & Scrub
🏘️ Built Area
🟤 Bare Ground
❄️ Snow & Ice

The inclusion of these classes provides a spatial overview of the landscape characteristics surrounding Zaria.

📊 SPATIAL ANALYSIS & AREA ESTIMATION
Beyond visualization, the workflow calculates the area occupied by each LULC class.

Using the Google Earth Engine pixelArea() function, the area of each class was estimated in hectares (ha). The results were then organized into an area statistics table and visualized using a column chart.

This provides an additional quantitative perspective on the composition of the study area rather than relying solely on visual interpretation of the map.

💻 WHY GOOGLE EARTH ENGINE?
Google Earth Engine makes it possible to process large volumes of satellite and geospatial datasets without requiring all the computational resources locally.
For this project, the workflow demonstrates
how GEE can be used to:

✅ Access satellite-derived land-cover datasets
✅ Process annual observations
✅ Generate LULC maps
✅ Calculate land-cover areas
✅ Create visualization charts
✅ Export GeoTIFF raster data
✅ Export statistical results as CSV
✅ Export the study-area boundary as a shapefile

📁 OUTPUTS
The workflow generates three major outputs:

1️⃣ Zaria 2025 LULC GeoTIFF – suitable for
further GIS analysis.

2️⃣ LULC Area Statistics CSV – containing the area of each land-cover class in hectares.

3️⃣ Study Area Shapefile – containing the boundary used for the analysis.

🌍 APPLICATIONS
This type of LULC analysis can support several applications, including:

• Urban and regional planning
• Agricultural land management
• Environmental monitoring
• Vegetation assessment
• Watershed management
• Flood-risk assessment
• Natural resource management
• Infrastructure planning
• Climate and environmental studies
• Monitoring land-use dynamics over time
One of the major advantages of this workflow is that it can be adapted to other locations and years, making it useful for multi-temporal land-use/land-cover change analysis.

📌 PROJECT SUMMARY
Location: Zaria, Kaduna State, Nigeria
Year: 2025

Dataset: Dynamic World V1
Platform: Google Earth Engine
Spatial Resolution: 10 m

Analysis: Annual modal LULC classification
Coordinate Reference System: UTM Zone 32N (EPSG:32632)

This project demonstrates the potential of combining remote sensing, GIS, cloud computing, and spatial analysis to generate actionable information for sustainable land and environmental management.
🔎 From satellite data to spatial intelligence — geospatial technology continues to provide powerful tools for understanding our changing landscape.

🌍 Understanding Nigeria’s Soil Landscape Through GISHave you ever wondered why soils differ so much from one part of Nig...
10/09/2026

🌍 Understanding Nigeria’s Soil Landscape Through GIS

Have you ever wondered why soils differ so much from one part of Nigeria to another?
This Digitized Soil Map of Nigeria provides a fascinating spatial view of the country’s diverse soil resources. Using GIS, different soil units are mapped across Nigeria, revealing significant variations from the northern Sahelian environment to the humid southern region.

🗺️ What does the map tell us?
The distribution of soil types is influenced by several factors, including:
🌧️ Climate and rainfall – differences in rainfall and temperature affect soil formation and moisture conditions.
🪨 Geology and parent material – the underlying rocks and sediments contribute significantly to soil characteristics.
⛰️ Topography – elevation, slope and landscape position influence erosion, drainage and soil development.
🌿 Vegetation and land cover – vegetation affects organic matter, nutrient cycling and protection against erosion.
💧 Drainage and hydrology – areas with different drainage conditions develop distinct soil characteristics.
🌱 Why is this important?
Soil information is not just useful for identifying soil types. It is a critical dataset for:

✅ Agricultural suitability and crop planning
✅ Soil erosion and land degradation assessment
✅ Flood and watershed modelling
✅ Groundwater potential studies
✅ Land-use and land-cover planning
✅ Environmental management
✅ Climate-smart agriculture
✅ Sustainable natural-resource management

For example, soil data can be integrated with rainfall, elevation, NDVI, land cover, soil moisture and drainage data in GIS and machine-learning models to better understand environmental risks and agricultural potential.

However, an important caution: a national-scale soil map should not be used alone to make farm-level decisions. Detailed soil sampling and laboratory analysis are still necessary where high spatial accuracy is required.

📍 GIS is not just about making maps. It is about transforming spatial data into information that can support better decisions.

This is why digitized soil datasets remain valuable for researchers, agricultural planners, environmental scientists, hydrologists and GIS professionals working toward more sustainable land management in Nigeria.

What other environmental datasets would you combine with this soil map to build a national agricultural or environmental suitability model?

🗺️ Land Use/Land Cover (LULC) Mapping of Ogbai Government Council Secretariat — 2023I recently completed a Land Use/Land...
07/09/2026

🗺️ Land Use/Land Cover (LULC) Mapping of Ogbai Government Council Secretariat — 2023

I recently completed a Land Use/Land Cover (LULC) classification of the Ogbai Government Council Secretariat and its surrounding environment for 2023, using Remote Sensing and GIS techniques.

The classification identifies seven major land-cover classes:

🌳 Vegetation – the dominant land-cover type across the area
🏘️ Built-up Area – concentrated mainly around the central/developed portion
🌊 River – forming an important drainage network across the landscape
💧 Pond – smaller surface-water features
🌾 Farmland – areas utilized for agricultural activities
🟤 Bare Land – areas with limited or no vegetation
🟡 Soil – exposed soil surfaces
The map provides a useful picture of how natural resources, agricultural activities, surface-water systems, and human development interact spatially.

One important observation is the close relationship between the built-up areas and drainage network, highlighting the need for proper land-use planning, protection of waterways, and consideration of flood risks during future development.

LULC mapping is more than just producing a colourful map—it provides evidence for sustainable planning, environmental monitoring, agricultural management, flood-risk assessment, and natural-resource conservation.

📍 Study Area: Ogbai Government Council Secretariat
📅 Year: 2023
🛰️ Approach: Remote Sensing & GIS
🗺️ Application: Land-use planning and environmental management

🌍 MAPPING AFRICA WITH GOOGLE EARTH ENGINE: DEVELOPING A POLITICAL MAP USING THE EQUAL EARTH PROJECTIONI recently develop...
06/09/2026

🌍 MAPPING AFRICA WITH GOOGLE EARTH ENGINE: DEVELOPING A POLITICAL MAP USING THE EQUAL EARTH PROJECTION

I recently developed a political map of Africa using Google Earth Engine (GEE), combining administrative boundary data, raster processing, cartographic visualization, and the WGS 84 / Equal Earth Greenwich projection (EPSG:8857).

Although Google Earth Engine is widely known for satellite image processing, climate analysis, vegetation monitoring, land-use/land-cover classification, and environmental modelling, this project demonstrates another important application: reproducible geospatial cartography at continental scale.

🗺️ What did I map?

The objective was to create a visually clear political map of Africa where individual countries are represented using different colours, while maintaining clear national boundaries and geographic reference information.

The workflow started with the FAO Global Administrative Unit Layers (GAUL) 2015 Level 0 dataset, which provides country-level administrative boundaries.

Using Google Earth Engine, I filtered the dataset to extract the countries belonging to the African continent.

From there, I created a raster representation of the countries and assigned each country a unique numerical identifier based on its administrative code.

🎨 Country-level visualization

The country ID raster was then converted into a coloured visualization using a multi-colour palette.

This means that neighbouring countries can be visually distinguished from one another, making the final product useful for:

• Geographic visualization
• Regional analysis
• GIS education
• Environmental reporting
• Development studies
• Agricultural mapping
• Climate and environmental communication

It is important to note that the colours are categorical, meaning they are used to distinguish countries rather than represent a quantitative variable.

🧭 Adding country boundaries

I also generated a separate raster layer containing the country boundaries.

The boundaries were displayed in black and overlaid on the coloured country map.

A thicker external boundary was additionally created to emphasize the outline of the African continent.

This creates a visual hierarchy between:

Country → National boundary → Continental boundary

which improves the readability of the final map.

🌐 Why Equal Earth?

One of the most interesting parts of this project was the use of the WGS 84 / Equal Earth Greenwich projection — EPSG:8857.

The Equal Earth projection is an equal-area projection, meaning that it preserves relative areas.

For continental and global thematic mapping, projection choice is important because representing the curved surface of the Earth on a flat map inevitably introduces some form of distortion.

Equal Earth is particularly useful when working with datasets involving:

🌱 Land cover
🌾 Agriculture
🌍 Environmental resources
👥 Population
🌧️ Climate
💧 Water resources
🌳 Forests
📊 Regional development indicators

Using an equal-area projection can be advantageous when the spatial analysis involves comparing areas across large geographic regions.

📐 Adding a graticule

To improve geographic orientation, I generated a latitude and longitude graticule at 10-degree intervals.

The longitude lines were generated from approximately 30°W to 60°E, while latitude lines covered approximately 40°S to 40°N.

The graticule was then converted to a raster layer and incorporated into the mapping workflow.

This provides users with a geographic reference framework for interpreting the position of countries across the continent.

🧩 Cartographic elements

The map was designed with several standard cartographic components:

📌 Map title
📌 Subtitle
📌 North arrow
📌 Legend
📌 Country boundaries
📌 Continental outline
📌 Geographic graticule
📌 Data source information
📌 Coordinate reference system information

The objective was not simply to generate a map, but to produce something that communicates geographic information clearly.

💻 Why Google Earth Engine?

One of the major advantages of using Google Earth Engine for this type of workflow is reproducibility.

Instead of manually creating every component in desktop GIS software, the map is generated through code.

This means that the workflow can easily be modified to:

🔹 Change country colours
🔹 Change boundary thickness
🔹 Change graticule intervals
🔹 Change the export resolution
🔹 Modify the geographic extent
🔹 Add additional thematic datasets
🔹 Integrate satellite imagery
🔹 Integrate climate datasets
🔹 Perform country-level spatial analysis
🔹 Automate map production

This becomes particularly powerful when political boundaries need to be combined with environmental or socioeconomic datasets.

📤 GeoTIFF outputs

Another important component of the workflow was the export of the map products as GeoTIFF files.

I configured four major outputs:

1️⃣ Coloured Africa Political Map

🗺️ GIS-Based Drainage Network Analysis of the River Benue BasinI recently worked on a GIS-based analysis of the River Be...
02/09/2026

🗺️ GIS-Based Drainage Network Analysis of the River Benue Basin

I recently worked on a GIS-based analysis of the River Benue Basin drainage network, focusing on the spatial distribution and hierarchy of streams within the basin.

The map shows the Strahler stream-order classification, ranging from 1st-order streams to 7th-order channels.

The numerous first-order streams represent headwater drainage channels, which progressively merge to form larger tributaries and eventually the major drainage system of the River Benue.

🔹 Stream Order 1: Small headwater channels
🔹 Stream Order 2–3: Intermediate tributaries
🔹 Stream Order 4–5: Major tributary systems
🔹 Stream Order 6–7: Higher-order drainage channels and major river pathways

This type of analysis is important for understanding surface-water movement, runoff concentration, watershed connectivity, erosion processes, and flood dynamics.

More importantly, drainage-network analysis can be integrated with DEM, rainfall, soil moisture, land-use/land-cover, slope, flow accumulation, and historical flood data to develop more comprehensive flood-susceptibility and watershed-management models.

🌍 GIS is not just about making maps—it helps us understand the processes behind the patterns we see on the landscape.

This analysis demonstrates how geospatial technology can support sustainable water-resource management, flood-risk reduction, agricultural planning, erosion control, and climate-resilient development in the River Benue Basin.

📍 Location Study Map: Ika Village, Ankpa LGA, Kogi StateI recently worked on a Location Study Map showing Ika Village in...
02/09/2026

📍 Location Study Map: Ika Village, Ankpa LGA, Kogi State

I recently worked on a Location Study Map showing Ika Village in Ankpa Local Government Area of Kogi State, Nigeria.
The map provides a clear spatial perspective of the study area by showing its position from the national level (Nigeria) to Kogi State, then to Ankpa LGA, and finally the specific study location.

One of the key features highlighted is the excavated site, alongside important geographical features such as rivers, streams, roads, settlements, elevation points, and the administrative boundary of Ankpa LGA.

🗺️ Why is this important?
Location mapping is more than simply showing where a place is. Through GIS and spatial analysis, we can understand how a site relates to its surrounding environment, including:

🔹 Drainage and river networks
🔹 Elevation and terrain
🔹 Road accessibility
🔹 Settlement distribution
🔹 Administrative boundaries
🔹 Land use and environmental characteristics

This type of mapping provides a strong foundation for field research, archaeological investigations, environmental assessment, geological studies, land-use analysis, and spatial modelling.

As GIS continues to transform how we understand our environment, accurate location mapping remains an essential first step in turning geographic data into meaningful information.

📍 Study Area: Ika Village, Ankpa LGA, Kogi State, Nigeria
🛰️ Approach: GIS & Spatial Mapping
🗺️ Map Type: Location/Study Area Map

🗺️ Tourist Map of Tunis State, Tunisia 🇹🇳Exploring Tunis through the lens of GIS and cartography! 🌍This tourist map prov...
01/09/2026

🗺️ Tourist Map of Tunis State, Tunisia 🇹🇳
Exploring Tunis through the lens of GIS and cartography! 🌍

This tourist map provides a spatial overview of some of the key tourism and supporting facilities across Tunis State, including Carthage, La Marsa, La Goulette, Tunis, and surrounding areas.

📍 The map highlights:
🏛️ Museums and historical attractions
🕌 Mosques and cultural sites
🏨 Hotels and restaurants
🌳 Parks and recreational facilities
✈️ Airport and transportation infrastructure
🚆 Railway and road networks
🏥 Hospitals and emergency services
💧 Major water bodies
🏦 Banks and other supporting facilities

One of the interesting aspects of this map is how GIS brings tourism resources, transportation, cultural heritage, and urban infrastructure together in one spatial framework.

Maps like this can support tourism planning, route optimization, heritage management, infrastructure development, and sustainable tourism.

🌍 GIS is more than making maps — it is about understanding places, relationships, and opportunities through spatial data.

Professional ProfileDennis Luka is a GIS and Remote Sensing Analyst with expertise in geospatial data analysis, satellit...
31/08/2026

Professional Profile

Dennis Luka is a GIS and Remote Sensing Analyst with expertise in geospatial data analysis, satellite imagery, environmental monitoring, and spatial modelling. He applies GIS, Remote Sensing, Drone Mapping, and Climate Data Science to support better decision-making in Africa.

His areas of interest include climate and health, environmental monitoring, agriculture, water resources, land-use mapping, and climate resilience. He is passionate about using geospatial and data-driven approaches to address environmental and development challenges.

Core Areas:

GIS & Spatial Data Analysis
Remote Sensing & Satellite Image Analysis
Google Earth Engine
Drone Mapping & Photogrammetry
Climate & Environmental Data Analysis
Agriculture and Natural Resource Mapping
Climate & Health Data
Data Visualization & Spatial Modelling

Focus: Better Data • Smarter Decisions • A Healthier & Resilient Africa we are.

Address

96 Sokoto Street Kafanchan
Kaduna

Telephone

+2347086126817

Website

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