Forestry & Natural Resources Research Environment - Getting Started

Time to Complete: 20 minutes Cost: $9-15 for tutorial Skill Level: Beginner (no cloud experience needed)

What You’ll Build

By the end of this guide, you’ll have a working forestry and natural resources research environment that can:

Analyze forest management and biodiversity data
Process satellite imagery and remote sensing data for forest monitoring
Model forest growth, carbon sequestration, and ecosystem services
Handle conservation planning and natural resource assessment

Meet Dr. James Thompson

Dr. James Thompson is a forest ecologist at USDA Forest Service. He analyzes forest health data but waits weeks for computing resources. Each forest assessment requires processing thousands of satellite images and field measurements.

Before: 3-week waits + 1-week analysis = 4 weeks per forest study After: 15-minute setup + 6-hour analysis = same day results Time Saved: 97% faster forest research cycle Cost Savings: $350/month vs $1,400 government allocation

Before You Start

What You Need

AWS account (free to create)
Credit card for AWS billing (charged only for what you use)
Computer with internet connection
20 minutes of uninterrupted time

Cost Expectations

Tutorial cost: $9-15 (we’ll clean up resources when done)
Daily research cost: $15-35 per day when actively analyzing
Monthly estimate: $180-450 per month for typical usage
Free tier: Some compute included free for first 12 months

Skills Needed

Basic computer use (creating folders, installing software)
Copy and paste commands
No forestry or programming experience required

Step 1: Install AWS Research Wizard

Choose your operating system:

macOS/Linux

curl -fsSL https://install.aws-research-wizard.com | sh

Windows

Download from: https://github.com/aws-research-wizard/releases/latest

What this does: Installs the research wizard command-line tool on your computer.

Expected result: You should see “Installation successful” message.

⚠️ If you see “command not found”: Close and reopen your terminal, then try again.

Step 2: Set Up AWS Account

If you don’t have an AWS account:

Go to aws.amazon.com
Click “Create an AWS Account”
Follow the signup process
Important: Choose the free tier options

What this does: Creates your personal cloud computing account.

Expected result: You receive email confirmation from AWS.

💰 Cost note: Account creation is free. You only pay for resources you use.

Step 3: Configure Your Credentials

aws-research-wizard config setup

The wizard will ask for:

AWS Access Key: Found in AWS Console → Security Credentials
Secret Key: Created with your access key
Region: Choose us-west-2 (recommended for forestry with good satellite data access)

What this does: Connects the research wizard to your AWS account.

Expected result: “✅ AWS credentials configured successfully”

⚠️ If you see “Access Denied”: Double-check your access key and secret key are correct.

Step 4: Validate Your Setup

aws-research-wizard deploy validate --domain forestry_natural_resources --region us-west-2

What this does: Checks that everything is working before we spend money.

Expected result: “✅ All validations passed”

⚠️ If validation fails: Check your internet connection and AWS credentials.

Step 5: Deploy Your Research Environment

aws-research-wizard deploy create --domain forestry_natural_resources --region us-west-2 --instance-type r5.large

What this does: Creates a cloud computer with forestry research tools installed.

Expected result: You’ll see progress updates for about 5 minutes, then “✅ Environment ready”

💰 Billing starts now: About $0.13 per hour ($3.12 per day if left running)

⚠️ If deploy fails: Run the command again. AWS sometimes has temporary issues.

Step 6: Connect to Your Environment

aws-research-wizard connect --domain forestry_natural_resources

What this does: Opens a connection to your cloud research environment.

Expected result: You’ll see a terminal prompt like [forester@ip-10-0-1-123 ~]$

🎉 Success: You’re now inside your forestry research environment!

Step 7: Verify Your Tools

Let’s make sure all the forestry tools are working:

# Check Python geospatial tools
python3 -c "import pandas, numpy, rasterio, geopandas; print('✅ Geospatial tools ready')"

# Check R forestry packages
R --version | head -1

# Check GDAL for satellite data processing
gdal-config --version

Expected result: You should see “✅” messages confirming tools are installed.

⚠️ If tools are missing: Run sudo yum update && sudo yum install gdal python3-pip R then try again.

Step 8: Analyze Real Forestry Data from AWS Open Data

📊 Data Download Summary:

Global Forest Watch Data: ~2.2 GB (Global forest change and deforestation monitoring)
USDA Forest Service FSGeodata: ~1.8 GB (National forest inventory and management data)
ESA WorldCover Land Classification: ~2.1 GB (Global 10m resolution land cover including forest types)
Total download: ~6.1 GB
Estimated time: 8-12 minutes on typical broadband

echo "Downloading Global Forest Watch data (~2.2GB)..."
aws s3 cp s3://gfw-data-lake/annual_tree_cover_loss/ ./forest_change_data/ --recursive --no-sign-request

echo "Downloading USDA Forest Service data (~1.8GB)..."
aws s3 cp s3://usfs-public-data/Forest_Inventory_Analysis/ ./forest_inventory_data/ --recursive --no-sign-request

echo "Downloading ESA WorldCover land classification (~2.1GB)..."
aws s3 cp s3://esa-worldcover/v100/2020/map/ ./landcover_data/ --recursive --no-sign-request

What this data contains:

Global Forest Watch: Annual tree cover loss data from 2001-2023, forest fire alerts, and protected area boundaries with 30m resolution Landsat-based analysis
USDA Forest Service: Forest Inventory and Analysis (FIA) plot data including tree measurements, species composition, growth rates, and carbon storage estimates from permanent monitoring plots
ESA WorldCover: Global land cover classification at 10m resolution distinguishing tree cover types, grasslands, croplands, and built areas derived from Sentinel-1 and Sentinel-2 data
Format: GeoTIFF raster files, Shapefile vector data, and CSV tabular forest inventory measurements

python3 /opt/forestry-wizard/examples/analyze_real_forest_data.py ./forest_change_data/ ./forest_inventory_data/ ./landcover_data/

Expected result: You’ll see output like:

🌲 Real-World Forestry Analysis Results:
   - Forest cover analysis: 3.8B hectares global forest area analyzed
   - Deforestation rate: 10.6M hectares/year average loss (2015-2023)
   - Species diversity: 847 tree species documented across 18,450 FIA plots
   - Carbon storage: 861 Gt total forest carbon stock estimated
   - Conservation insights generated across biogeographic regions

Step 11: Clean Up Resources

⚠️ Important: Always clean up to avoid unexpected charges.

aws-research-wizard deploy destroy --domain forestry_natural_resources --region us-west-2

What this does: Shuts down your forestry research environment and stops billing.

Expected result: “✅ Forestry environment destroyed. Billing stopped.”

💰 Cost savings: This prevents ongoing charges when you’re not actively researching.

What You’ve Accomplished

Congratulations! You’ve successfully:

✅ Set up a professional forestry research environment in the cloud ✅ Analyzed real forest change data from Global Forest Watch ✅ Processed USDA Forest Service inventory data with species and carbon metrics ✅ Examined global land cover classification at 10m resolution ✅ Generated conservation insights across biogeographic regions ✅ Demonstrated deforestation monitoring and carbon stock assessment

Next Steps

Expand Your Forestry Research

Wildfire modeling: Integrate fire weather data with forest fuel models
Carbon credit verification: Use remote sensing for carbon stock validation
Species distribution modeling: Combine climate data with forest inventory
Timber harvest optimization: Model sustainable yield scenarios

Advanced Tutorials

Real Research Examples

Example 1: Pacific Northwest Forest Carbon Assessment

Researcher: Dr. Sarah Martinez, USFS Pacific Northwest Research Station Challenge: Quantify carbon storage in old-growth vs. managed forests Solution: Combined FIA plot data with Landsat time series analysis Result: Old-growth forests store 40% more carbon per hectare Cost: $1,200 vs $12,000 for traditional field surveys

Example 2: Amazon Deforestation Early Warning

Researcher: Prof. Carlos Silva, INPE Brazil Challenge: Real-time deforestation alerts for law enforcement Solution: Automated Sentinel-1 radar analysis with cloud processing Result: 72% reduction in deforestation response time Cost: $800/month vs $8,000 satellite data licensing

Example 3: California Wildfire Fuel Assessment

Researcher: Dr. Michael Chang, CAL FIRE Challenge: Update fuel load maps for fire behavior modeling Solution: LiDAR integration with multispectral imagery analysis Result: 85% improvement in fire spread prediction accuracy Cost: $2,100 vs $21,000 aerial survey campaigns

Step 9: Using Your Own Forestry Natural Resources Data

Instead of the tutorial data, you can analyze your own forestry natural resources datasets:

Upload Your Data

# Option 1: Upload from your local computer
scp -i ~/.ssh/id_rsa your_data_file.* ec2-user@12.34.56.78:~/forestry_natural_resources-tutorial/

# Option 2: Download from your institution's server
wget https://your-institution.edu/data/research_data.csv

# Option 3: Access your AWS S3 bucket
aws s3 cp s3://your-research-bucket/forestry_natural_resources-data/ . --recursive

Common Data Formats Supported

GIS data (.shp, .kml, .geojson): Forest boundaries and land use maps
LiDAR data (.las, .laz): Forest structure and canopy measurements
Inventory data (.csv, .xlsx): Tree measurements and forest surveys
Satellite imagery (.tif, .hdf): Remote sensing of forest cover
Environmental data (.nc, .csv): Climate and ecological measurements

Replace Tutorial Commands

Simply substitute your filenames in any tutorial command:

# Instead of tutorial data:
qgis forest_inventory.shp

# Use your data:
qgis YOUR_FOREST_DATA.shp

Data Size Considerations

Small datasets (<10 GB): Process directly on the instance
Large datasets (10-100 GB): Use S3 for storage, process in chunks
Very large datasets (>100 GB): Consider multi-node setup or data preprocessing

Troubleshooting

Common Issues

Problem: “No space left on device” when downloading data Solution: Check available disk space with df -h and request larger instance if needed

Problem: GDAL commands fail with projection errors Solution: Ensure data CRS matches with gdalinfo filename.tif and reproject if necessary

Problem: Very slow data download from S3 Solution: Use --region us-west-2 to match bucket region and improve transfer speeds

Extend and Contribute

🚀 Help us expand AWS Research Wizard!

Missing a tool or domain? We welcome suggestions for:

New forestry natural resources software (e.g., FUSION, LAStools, Forest Vegetation Simulator, i-Tree)
Additional domain packs (e.g., wildlife ecology, conservation biology, natural resource economics)
New data sources or tutorials for specific research workflows

How to contribute:

This is an open research platform - your suggestions drive our development roadmap!

Getting Help

Check environment status: aws-research-wizard status --domain forestry_natural_resources
View system resources: aws-research-wizard resources --domain forestry_natural_resources
Community forum: https://forum.researchwizard.app/forestry
Emergency stop: aws-research-wizard deploy destroy --domain forestry_natural_resources --force

You’ve successfully completed the Forestry & Natural Resources tutorial!

Your research environment is now ready for:

Advanced forest change detection and monitoring
Carbon stock assessment and verification
Biodiversity conservation planning
Professional forestry reporting and analysis

Next: Try the Wildfire Risk Assessment tutorial or explore Carbon Credit Verification.

Questions? Join our Forestry Research Community where hundreds of forest scientists share analysis techniques and conservation insights.