Course Details

GIS in Forest and Wildlife Management Training Course

Introduction

Forests and wildlife are invaluable natural resources, crucial for ecological balance, biodiversity conservation, climate regulation, and human well-being. Effective management of these complex and dynamic ecosystems requires sophisticated tools to monitor, analyze, and make informed decisions across vast and often remote areas. Geographic Information Systems (GIS) stands as a transformative technology in Forest and Wildlife Management, providing the essential framework for integrating, visualizing, and analyzing diverse spatial data – from forest cover types and tree species distribution to wildlife habitats, migration corridors, and human-wildlife conflict zones. GIS empowers foresters, wildlife biologists, conservationists, and land managers to understand spatial patterns, assess habitat suitability, plan sustainable harvesting, monitor illegal activities, model disease spread, and effectively design and implement conservation strategies. Without leveraging the power of GIS, forest and wildlife management initiatives risk being less targeted, less efficient, and potentially unsustainable, leading to habitat degradation, biodiversity loss, and resource depletion. Many professionals in these fields possess strong ecological knowledge but often lack the specialized geospatial skills to translate raw field data and remote sensing imagery into actionable geographic intelligence for effective conservation and management.

Conversely, mastering GIS in forest and wildlife management enables professionals to conduct robust spatial ecological analyses, create detailed habitat maps, monitor forest health, track wildlife populations, assess the impacts of climate change, and develop comprehensive plans for sustainable resource utilization and biodiversity protection. This specialized skill set is crucial for transforming raw environmental data into critical insights that drive informed conservation and management decisions. Our intensive 5-day "GIS in Forest and Wildlife Management" training course is meticulously designed to equip foresters, wildlife biologists, ecologists, conservation practitioners, park rangers, land managers, environmental scientists, and researchers with the essential knowledge and practical skills required to confidently apply GIS and geospatial technologies to enhance their efforts in protecting and sustainably managing forest and wildlife resources.

Duration

5 Days

Target Audience

The "GIS in Forest and Wildlife Management" training course is ideal for a diverse range of professionals, researchers, and students involved in the conservation, management, and study of forests and wildlife resources. This includes:

• Foresters and Forestry Technicians: For timber inventory, sustainable forest management, and silviculture planning.
• Wildlife Biologists and Ecologists: For habitat mapping, species distribution modeling, and population monitoring.
• Conservation Practitioners: Working for NGOs, government agencies, or private organizations focused on biodiversity.
• Park Rangers and Protected Area Managers: For resource protection, visitor management, and anti-poaching efforts.
• Environmental Scientists and Consultants: Assessing ecological impacts and developing conservation strategies.
• Land Use Planners: Incorporating ecological considerations into land development plans.
• GIS Analysts: Specializing in natural resource management.
• Remote Sensing Specialists: Applying imagery analysis to forest and wildlife studies.
• Researchers and Academics in ecology, conservation biology, forestry, or related fields.
• Anyone involved in the sustainable management and conservation of natural ecosystems.

Course Objectives

Upon successful completion of the "GIS in Forest and Wildlife Management" training course, participants will be able to:

• Understand the fundamental role of GIS and remote sensing in forest and wildlife management.
• Acquire, manage, and integrate diverse geospatial data relevant to forest and wildlife ecosystems.
• Perform spatial analysis for forest inventory, habitat mapping, and connectivity analysis.
• Utilize remote sensing for land cover classification, deforestation monitoring, and forest health assessment.
• Apply GIS for wildlife tracking, species distribution modeling, and human-wildlife conflict mitigation.
• Develop and implement GIS-based solutions for forest planning, conservation area design, and ecosystem restoration.
• Interpret and effectively communicate complex ecological insights through compelling maps and visualizations.
• Formulate strategies for applying GIS and remote sensing to support sustainable forest and wildlife conservation efforts.

 Course Modules

Module 1: Introduction to GIS in Natural Resource Management

• Overview of forest and wildlife management principles and challenges.
• The indispensable role of GIS and Remote Sensing in monitoring and managing natural resources.
• Key spatial data types for forest and wildlife (e.g., forest stands, habitat types, animal locations).
• Introduction to geospatial data models (vector, raster) and coordinate systems.
• Case studies of GIS applications in forestry and wildlife conservation.

Module 2: Geospatial Data Acquisition and Preparation

• Sources of forest and wildlife data: Field surveys (GPS), satellite imagery (Landsat, Sentinel, Planet), drone imagery, LiDAR.
• Downloading and managing large-scale remote sensing and field data.
• Data cleaning, quality assessment, and preprocessing techniques for forest inventory and animal tracking data.
• Understanding spatial and temporal resolutions of ecological data.
• Integrating diverse data types for comprehensive ecological analysis.

Module 3: Forest Inventory and Analysis using GIS

• Mapping forest cover types, species distribution, and stand boundaries.
• Estimating forest attributes (e.g., basal area, volume, biomass) using spatial interpolation.
• Analyzing forest disturbance and change (e.g., logging, fire, disease outbreaks).
• GIS for sustainable timber harvest planning and silvicultural treatment zones.
• Modeling forest growth and yield.

Module 4: Remote Sensing for Forest Monitoring and Health

• Image Classification: Supervised and unsupervised classification for land cover and forest type mapping.
• Change Detection Analysis: Quantifying deforestation, afforestation, and degradation over time.
• Vegetation Indices (NDVI, EVI, NBR) for monitoring forest health, stress, and fire severity.
• Utilizing LiDAR for deriving forest structure metrics (e.g., canopy height, density).
• Mapping forest fires, burned area assessment, and post-fire recovery monitoring.

Module 5: Wildlife Habitat Analysis and Species Distribution Modeling

• Mapping wildlife habitats based on vegetation, water sources, and terrain.
• Habitat Suitability Modeling: Combining multiple environmental layers to predict suitable areas for species.
• Connectivity Analysis: Identifying wildlife corridors and barriers to movement.
• Introduction to Species Distribution Models (SDMs) and Ecological Niche Models (ENMs) using environmental covariates.
• Applications: Conservation planning, protected area design, reintroduction programs.

Module 6: Wildlife Tracking and Movement Analysis

• Working with animal telemetry data (GPS collars, satellite tags).
• Creating home ranges and utilization distributions (e.g., Minimum Convex Polygon, Kernel Density Estimation).
• Analyzing wildlife movement patterns, migration routes, and dispersal.
• Identifying human-wildlife conflict hotspots and mitigation strategies.
• GIS for monitoring wildlife populations and assessing conservation effectiveness.

Module 7: Conservation Planning and Protected Area Management

• GIS for designing and evaluating protected area networks.
• Mapping biodiversity hotspots and areas of high conservation value.
• Analyzing threats to conservation (e.g., poaching, illegal logging, encroachment).
• GIS for land acquisition and conservation easement planning.
• Developing management plans for national parks and wildlife reserves.

Module 8: Advanced Concepts, Policy, and Future Trends

• Integrating GIS with population viability analysis (PVA) models.
• Utilizing Big Data and Cloud GIS for large-scale ecological monitoring.
• Introduction to AI/Machine Learning in forest and wildlife management (e.g., automated species identification, deforestation prediction).
• Citizen science and crowdsourcing for ecological data collection.
• Ethical considerations in wildlife tracking and data sharing.
• Communicating conservation science to policymakers and the public using GIS.

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