Remote Sensing for Land Use and Land Cover Change Training Course

Introduction

Land is a finite and crucial resource, undergoing constant transformation due to both natural processes and human activities. Monitoring these changes in Land Use (LU) and Land Cover (LC) is paramount for sustainable development, environmental management, resource allocation, and policy formulation. Land Cover refers to the physical material at the surface of the Earth, such as forests, water bodies, bare ground, or impervious surfaces. Land Use, on the other hand, describes how humans utilize the land for various purposes, such as agriculture, urban development, recreation, or conservation. While distinct, these two concepts are intrinsically linked, and changes in one often influence the other.

Remote Sensing provides an unparalleled capability to systematically and efficiently map, monitor, and analyze Land Use and Land Cover (LULC) changes over large areas and across different time periods. Satellite imagery, with its repetitive coverage and multi-spectral capabilities, offers a cost-effective and objective means to detect and quantify landscape transformations, from deforestation and urban sprawl to agricultural expansion and wetland degradation. Understanding LULC change is vital for addressing global challenges like climate change, biodiversity loss, food security, and natural disaster impacts. However, effectively detecting, mapping, and analyzing these changes from remote sensing data requires specialized knowledge of image processing, classification techniques, change detection algorithms, and GIS integration. Many environmental managers, urban planners, and researchers understand the importance of LULC change but lack the practical skills to leverage remote sensing tools for robust analysis.

Conversely, mastering remote sensing for LULC change detection empowers professionals to identify critical trends, quantify environmental impacts, inform land management decisions, and contribute to evidence-based policy. This specialized skill set is crucial for transforming raw satellite data into actionable insights, leading to more resilient landscapes and sustainable practices. Our intensive 5-day "Remote Sensing for Land Use and Land Cover Change" training course is meticulously designed to equip GIS professionals, environmental scientists, urban planners, natural resource managers, researchers, and development practitioners with the essential theoretical knowledge and practical skills required to confidently apply remote sensing techniques for comprehensive LULC change analysis.

Duration

5 Days

Target Audience

The "Remote Sensing for Land Use and Land Cover Change" training course is ideal for a broad range of professionals and researchers who need to understand and analyze landscape transformations using satellite imagery. This includes:

  • Environmental Scientists and Conservationists: For monitoring deforestation, habitat loss, and ecosystem changes.
  • Urban Planners and Developers: For tracking urban growth, sprawl, and land conversion.
  • Natural Resource Managers: For assessing changes in forest cover, water bodies, and agricultural lands.
  • GIS Analysts and Specialists: Seeking to specialize in remote sensing applications for LULC.
  • Agriculturalists and Agronomists: For monitoring agricultural expansion and land degradation.
  • Researchers and Academics: In geography, environmental studies, urban planning, and related fields.
  • Policy Makers and Government Officials: Involved in land use planning and environmental regulations.
  • Climate Change Researchers: Analyzing LULC impacts on carbon cycles and climate patterns.
  • Disaster Management Professionals: Assessing land cover changes before and after natural events.
  • Anyone interested in using satellite imagery to understand how landscapes are changing.

Course Objectives

Upon successful completion of the "Remote Sensing for Land Use and Land Cover Change" training course, participants will be able to:

  • Understand the fundamental concepts of land use, land cover, and the dynamics of their change.
  • Identify appropriate remote sensing data sources and platforms for LULC change detection studies.
  • Perform essential pre-processing steps for multi-temporal satellite imagery.
  • Apply various image classification techniques to generate LULC maps.
  • Execute different change detection algorithms to quantify and map LULC transformations.
  • Assess the accuracy of LULC classification and change detection results.
  • Integrate remote sensing findings with GIS for further analysis and visualization of change.
  • Formulate a comprehensive workflow for conducting a LULC change detection project from start to finish.

 Course Modules

Module 1: Introduction to LULC Concepts and Remote Sensing for Change

  • Definitions of Land Use (LU) and Land Cover (LC) and their interrelationship.
  • Drivers and impacts of LULC change at local, regional, and global scales.
  • Why remote sensing is indispensable for LULC change detection: Advantages and capabilities.
  • Overview of remote sensing platforms and sensors suitable for LULC change (e.g., Landsat, Sentinel, MODIS, high-resolution imagery).
  • Understanding the different types of LULC classification schemes (e.g., Anderson, CORINE).

Module 2: Multi-temporal Data Acquisition and Pre-processing for Change Detection

  • Identifying and acquiring multi-temporal satellite imagery from various data archives.
  • Importance of image selection criteria (e.g., seasonality, cloud cover, sensor type).
  • Radiometric Normalization: Adjusting images to account for variations in illumination and atmospheric conditions.
  • Geometric Registration/Correction: Aligning images from different dates precisely for accurate comparison.
  • Masking out clouds, shadows, and water bodies for LULC analysis.

Module 3: LULC Classification: Supervised Methods

  • Review of image classification principles: Training data, features, classifiers.
  • Supervised Classification: Overview of common algorithms (e.g., Maximum Likelihood, Support Vector Machine, Random Forest).
  • Collecting and preparing accurate training data for LULC classes.
  • Running supervised classification and generating LULC maps for different time periods.
  • Understanding the role of spectral signatures in LULC differentiation.

Module 4: LULC Classification: Unsupervised Methods and Advanced Concepts

  • Unsupervised Classification: K-Means, ISODATA algorithms and their application.
  • Advantages and disadvantages of supervised vs. unsupervised classification for LULC.
  • Hybrid classification approaches combining both methods.
  • Post-classification refinement: Majority filtering, clump and eliminate.
  • Introduction to object-based image analysis (OBIA) for LULC mapping.

Module 5: Fundamental Change Detection Techniques

  • Image Differencing/Ratioing: Simple pixel-by-pixel comparisons.
  • Change Vector Analysis (CVA): Magnitude and direction of change in multi-band space.
  • Principal Component Analysis (PCA) for Change Detection: Highlighting areas of significant change.
  • Visual interpretation of multi-temporal composites for identifying change.
  • Thresholding techniques to delineate areas of change.

Module 6: Post-Classification Change Detection and Transition Matrices

  • Post-Classification Comparison: Comparing independently classified LULC maps from different dates.
  • Generating LULC change matrices/transition matrices: From-to changes.
  • Quantifying area converted from one class to another.
  • Analyzing land use dynamics: Gains and losses, persistence, net change.
  • Interpreting and reporting on LULC change statistics.

Module 7: Accuracy Assessment of LULC Maps and Change Detection

  • Importance of accuracy assessment for LULC maps.
  • Reference data collection for accuracy assessment (e.g., field points, high-resolution imagery).
  • Confusion matrices (error matrices): Producer's accuracy, User's accuracy, Overall accuracy.
  • Kappa coefficient and its interpretation.
  • Assessing the accuracy of change detection results.

Module 8: Advanced LULC Change Applications, Modeling, and Reporting

  • Integration of LULC change results with GIS for further spatial analysis.
  • Modeling LULC change: Markov chains, Cellular Automata (CA), CLUE-S model (conceptual overview).
  • Predicting future LULC scenarios based on past trends and driving forces.
  • Creating compelling maps, charts, and reports to communicate LULC change findings.
  • Case studies and practical applications of LULC change analysis (e.g., deforestation, urban sprawl, agricultural intensification, environmental impact assessment).

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

 

Remote Sensing For Land Use And Land Cover Change Training Course in Bolivia (Plurinational State of)
Dates Fees Location Action
01/09/2025 - 05/09/2025 $1,750 Mombasa
Physical Class Online Class
22/09/2025 - 26/09/2025 $1,500 Nairobi
Physical Class Online Class
06/10/2025 - 10/10/2025 $1,750 Mombasa
Physical Class Online Class
27/10/2025 - 31/10/2025 $1,500 Nairobi
Physical Class Online Class
03/11/2025 - 07/11/2025 $1,750 Mombasa
Physical Class Online Class
24/11/2025 - 28/11/2025 $1,500 Nairobi
Physical Class Online Class
01/12/2025 - 05/12/2025 $1,750 Mombasa
Physical Class Online Class
08/12/2025 - 12/12/2025 $1,500 Nairobi
Physical Class Online Class
05/01/2026 - 09/01/2026 $1,750 Mombasa
Physical Class Online Class
19/01/2026 - 23/01/2026 $1,500 Nairobi
Physical Class Online Class
02/02/2026 - 06/02/2026 $1,500 Nairobi
Physical Class Online Class
16/02/2026 - 20/02/2026 $1,500 Mombasa
Physical Class Online Class
02/03/2026 - 06/03/2026 $1,750 Mombasa
Physical Class Online Class
23/03/2026 - 27/03/2026 $1,500 Nairobi
Physical Class Online Class
06/04/2026 - 10/04/2026 $1,750 Mombasa
Physical Class Online Class
20/04/2026 - 24/04/2026 $1,500 Nairobi
Physical Class Online Class
04/05/2026 - 08/05/2026 $1,750 Mombasa
Physical Class Online Class
25/05/2026 - 29/05/2026 $1,500 Nairobi
Physical Class Online Class
01/06/2026 - 05/06/2026 $1,750 Mombasa
Physical Class Online Class
22/06/2026 - 26/06/2026 $1,500 Nairobi
Physical Class Online Class