GIS for Forestry Course

Lesson 1 • Introduction to Geographic Information Systems

Covers GIS components, data models, and spatial thinking fundamentals. Establishes the vocabulary and conceptual framework used throughout the course.

Lesson 2 • Forest Surveying Objectives and Workflows

Defines the goals of forest inventory, mapping, and monitoring surveys. Links each survey objective to specific GIS tasks introduced later in the course.

Lesson 3 • Coordinate Systems and Map Projections

Explains geographic and projected coordinate systems and their distortion properties. Correct projection selection is critical for accurate forest area measurements.

Lesson 1 • Remote Sensing Data Sources

Introduces satellite and aerial imagery platforms used in forest mapping. Students identify appropriate sensors for canopy cover, species, and disturbance detection.

Lesson 2 • Existing Forest Spatial Datasets

Surveys authoritative national and global forest inventory, land cover, and boundary datasets. Students evaluate dataset currency, resolution, and fitness for specific survey tasks.

Lesson 3 • Data Management and File Organization

Establishes best practices for naming, storing, and versioning spatial datasets. Proper data management prevents errors in multi-dataset forest survey projects.

Lesson 4 • LiDAR Data for Forest Structure

Explains LiDAR point cloud acquisition and its unique value for measuring forest vertical structure. Students distinguish between discrete-return and full-waveform LiDAR products.

Lesson 1 • Attribute Table Management

Teaches field calculation, joins, and relates for enriching spatial features with tabular data. Attribute accuracy directly affects forest classification and reporting outputs.

Lesson 2 • Vector Data Editing and Cleaning

Covers topology rules, snapping, and error correction for polygon and line features. Clean vector data prevents misclassification errors in stand delineation.

Lesson 3 • Coordinate System Alignment

Resolves projection mismatches across datasets from multiple sources. Aligned coordinate systems ensure spatial overlay operations produce accurate results.

Lesson 4 • Raster Data Processing Techniques

Addresses reprojection, resampling, mosaicking, and clipping of raster datasets. These operations standardize multi-source imagery for consistent forest analysis.

Lesson 1 • Visual Image Interpretation for Stands

Develops skills to interpret tone, texture, pattern, and shadow in aerial and satellite imagery. Visual interpretation is the baseline method for manual stand boundary delineation.

Lesson 2 • Spectral Indices for Forest Mapping

Introduces vegetation indices derived from multispectral bands to characterize forest condition. Indices such as NDVI and EVI quantify canopy density and health efficiently.

Lesson 3 • Stand Attribute Assignment and Validation

Links classified polygons to field-measured attributes including species, basal area, and age class. Field validation confirms classification accuracy before final map delivery.

Lesson 4 • Supervised Image Classification

Covers training sample collection, classifier selection, and accuracy assessment for land cover mapping. Supervised classification automates stand-type mapping across large forest areas.

Lesson 1 • Terrain-Based Site Index Mapping

Combines terrain variables with forest inventory data to model site productivity across the landscape. Site index maps guide species selection and yield forecasting decisions.

Lesson 2 • Digital Elevation Model Fundamentals

Explains DEM types, sources, and quality metrics relevant to forested terrain. Understanding DEM limitations prevents errors in slope and drainage analysis.

Lesson 3 • Road and Harvest Access Planning

Uses slope and terrain analysis to identify feasible routes for forest roads and harvesting equipment. Terrain-constrained access planning reduces environmental impact and cost.

Lesson 4 • Slope, Aspect, and Hillshade Derivation

Derives primary terrain attributes from DEMs to characterize site conditions for forest surveys. Slope and aspect drive species distribution, harvesting access, and erosion risk.

Lesson 1 • Volume and Biomass Estimation by Stand

Applies allometric equations and stand-level summaries to estimate timber volume and above-ground biomass. Results are stored as stand polygon attributes for spatial reporting.

Lesson 2 • Spatial Interpolation of Forest Variables

Uses kriging, IDW, and spline methods to create continuous surfaces from discrete plot measurements. Interpolated surfaces reveal spatial patterns in basal area and site quality.

Lesson 3 • Plot Data Integration and Spatial Joins

Links field plot measurements to spatial layers through coordinate matching and spatial joins. Accurate plot-to-polygon linkage is essential for stand-level volume estimation.

Lesson 4 • Spatial Sampling Design for Inventory

Covers systematic, stratified, and cluster sampling designs and their spatial implementation in GIS. Efficient sampling design reduces field cost while maintaining statistical validity.

Lesson 1 • Monitoring Dashboard and Reporting

Designs automated workflows and map outputs for recurring forest monitoring programs. Repeatable monitoring workflows support compliance reporting and adaptive management.

Lesson 2 • Image Differencing and Index Change

Applies band differencing and vegetation index change to identify areas of forest gain and loss. Threshold selection determines the sensitivity and accuracy of detected changes.

Lesson 3 • Disturbance Detection with LiDAR

Uses multi-temporal LiDAR canopy height models to detect windthrow, harvesting, and fire effects. Height change metrics provide precise structural disturbance measurements.

Lesson 4 • Multi-Temporal Image Preparation

Addresses radiometric normalization, atmospheric correction, and co-registration of image time series. Consistent image preparation is prerequisite to reliable change detection.

Lesson 1 • Carbon Stock Mapping and Reporting

Combines biomass estimates, land cover, and disturbance data to produce spatially explicit carbon stock maps. Carbon maps support forest carbon project verification and national reporting.

Lesson 2 • Wildlife Habitat and Connectivity Modeling

Applies habitat suitability modeling and least-cost corridor analysis to support biodiversity conservation. Connectivity maps identify critical forest linkages for wildlife movement.

Lesson 3 • Spatial Modeling with Geoprocessing Tools

Builds automated geoprocessing models using model builder and scripting environments. Automated models increase repeatability and efficiency in complex forest survey workflows.

Lesson 4 • Multi-Criteria Spatial Decision Analysis

Combines weighted raster layers to evaluate forest management alternatives spatially. Multi-criteria analysis supports transparent, evidence-based land use decisions.