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🔒 Pro Feature

This feature is available in LandPulse Pro.

✅ Core

  • Change detection
  • Disturbance alerts
  • Wetland monitoring
  • Carbon reports
  • Annual PDF reports
  • Fire alerts
Click to select Core

⭐ Pro

  • Everything in Core
  • Forest health diagnostics
  • TreeMap stand inventory
  • LandTrendr history
  • Fuel phase reports
  • Invasive species
Click to select Pro
Pro features — request access below.

🔑 Create a free account

Sign up with your email to run LandPulse analyses.
🔍 Find Parcels by Owner
Last name first. Add first initial for prefix match.
Required. Use "County, ST" format.
Enter an owner name and region to search.
+
Tap map to place photopoint
Photopoint #1
📍 Loading GPS...
Captured photo

📖 Photopoint Guide

Quick Start

  1. 🎯 Find your location — Tap to center map on GPS
  2. 📍 Add Photopoint — Tap button, then tap map to place
  3. 📷 Take photo — Wait for green GPS (±5m), then capture
  4. 🔄 Return visits — Previous photo shows as overlay for alignment

5 Rules for Good Photopoints

  1. Permanent anchor — Include a large tree, rock, or fence post that won't move
  2. Clear sightline — Avoid spots where vegetation will block the view
  3. Reproducible position — Stand somewhere you can find again
  4. Consistent direction — Face the same way each visit
  5. Good light — Morning or afternoon, avoid midday glare

What to Photograph

🏠StructuresBuildings, fences, roads
🌲Forest edgesTreeline changes, clearing
🌊Water featuresStreams, wetland edges
🌿Vegetation plotsMeadows, restoration areas
⚠️ConcernsErosion, invasives, damage

Using the Overlay (Return Visits)

  1. Tap a saved photopoint marker on map
  2. Use "🎯 To photopoint: Xm" to walk to the spot
  3. Previous photo appears semi-transparent
  4. Move until the overlay aligns with live view
  5. Match horizon first, then anchor objects
  6. 80% aligned is still very useful!

What AI Will See Over Time

2 yearsNew structures, clearing, obvious changes
5 yearsVegetation trends, woody encroachment/forest health rates
10 yearsEcological trajectories, succession patterns

Key Reminders

  • 📡 Wait for good GPS — Green ✓ means ±5m or better
  • 📅 Same time each year — Reduces seasonal confusion
  • 💾 Export regularly — Back up before changing phones
  • 🛰️ Combine with satellite — Ground photos verify what Sentinel detects

LandPulseForest

Satellite stewardship intelligence for working forests
Selected Area
Draw a polygon to begin
Start by defining your property — draw on the map, upload a boundary, or look it up by owner.
Upload boundary
GeoJSON · Shapefile · KML
Tip: enable the 🔍 Owner Search button on the map to find parcels by owner name.
📂 My Saved Properties
Analysis Period
Season Preset
Baseline Period
Current Period
Vegetation & Land Cover
Water & Fire
Riparian Buffer Analysis
🌾 Invasive Species Detection

Detect invasive species using phenological signatures from Sentinel-2 satellite imagery. Species recommendations based on your property location.

ℹ️ Select a species
Draw a property boundary to see species recommendations for your area, or select a species from the dropdown to see detection details.
🎯 Smart Scan — Analyzes only species likely in your area (fastest)
🔬 Scan ALL — Checks every species regardless of location
⚠️ Note: Detection accuracy varies by species density and land cover. Field verification recommended.
💧 Wetland Analysis

Comprehensive wetland analysis: classification, change detection, and habitat quality assessment.

Wetland classification automatically combines spring + summer + growing-season imagery — your choice here only changes which composite is rendered on the headline map.
📤 Export
💡 Wetland Types Detected:
🔵 Open water | 💧 Seasonal water
🌿 Emergent wetland | 🌾 Wet meadow / shrub
🌳 Forested wetland | 🌲 Riparian corridor
🟫 Drained wetland (restoration candidate) | 🏜️ Upland
🏺 Archaeological Site Prediction
Ancestral Puebloan site probability · Colorado Plateau
⚠ ARPA Restricted — Site probability data is sensitive under the Archaeological Resources Protection Act. Do not share map tiles or hotspot coordinates with unauthorized parties.
Camps, villages, room blocks — water-focused predictors
DISCOVERY GAP
Combines site probability × post-1985 conifer encroachment to find areas that were open during the survey era but are now under canopy.
LIDAR DETAIL SCAN
1m LiDAR micro-topography. Use after running the probability model — draw a tight polygon (≤500 ac) over a specific high-probability zone. Detects mounds, check dams, walls, and depressions.
SITE MONITORING
Predictor weights (All Sites model)
💧 Water proximity — 22% ☀️ South aspect — 16% 🏔 Height above drain — 14% 📐 Gentle slope — 12% 🗻 Elevation band — 12% 🌽 Agricultural potential — 8% 🪨 Shelter / alcove — 8% 🔀 Canyon confluence — 5%
Dark red = highest probability · Ref: Yaworsky et al. 2020
🪲 Forest Health — Comprehensive Detection

Sentinel-2 multi-temporal analysis for PNW bark beetles & defoliators, Midwest white oak decline with drought index regression, and Oregon white oak encroachment + MOB.

Select Threat
Bark Beetle Mortality — Year-over-year NDMI/NBR decline detects Douglas-fir beetle, mountain pine beetle, and fir engraver. Red/grey attack = strong signal (NDMI drops 0.15-0.30). TreeMap host matching identifies likely beetle species.
📡 Jul-Aug imagery (YoY) • NDMI + NBR change • Host species filter
📊 Forest Data Layers

Analyze canopy height, carbon stocks, and forest disturbance history using satellite-derived datasets.

Select Analysis
Canopy Height Analysis — Measures tree height across the property using ETH Global Canopy Height (2020). Provides mean/max height, canopy cover %, and height distribution.
📡 10m resolution • 2020 baseline • Height histogram included
🌲 Use for: Fuel structure, stand maturity assessment, habitat classification

Currently only 2020 data available (ETH baseline)

🌿 Forage & Habitat Analysis

Analyze rangeland forage quality and elk habitat using USDA Rangeland Analysis Platform data.

⚠️ Western US Only: RAP data covers AZ, CA, CO, ID, KS, MT, NE, NV, NM, ND, OK, OR, SD, TX, UT, WA, WY
📊 Data Source: USDA Rangeland Analysis Platform (RAP)
Resolution: 30m | History: 1986-present
🐄 Cattle: AUM capacity, forage production
🦌 Elk: Forage, cover, edge habitat scoring
Ground Photo Monitoring

Document ground conditions anywhere. Photos are GPS-tagged and show overlay for repeat visits.

📖 See Photopoint Guide
Tap anywhere on the map to place a photopoint
Photopoints (0)
No photopoints yet. Tap 🎯 to go to your location, then add a photopoint.
🤖 AI Photo Analysis

Take a photo in the field. Claude compares what it sees against LandPulse's satellite predictions for your exact GPS location.

📡 Powered by Claude vision + LP satellite context. Best for species verification, log quality, and crown health. Not a substitute for a forester.
1. Photo
Next photo type:
3. GPS Location
📑 Plan Builder — Joint Management Plan

Conversational goals interview with Claude, then auto-generate a CAP-106 / ATFS / FSP-compliant forest stewardship plan PDF for the property.

📡 How it works: Draw a polygon → start a 10-14 turn chat with Claude → answer goals questions → click Generate Plan PDF. ~$0.30/session + ~3 min PDF render.
⚠️ Draw a polygon on the map first.
🛡️ Sentinel — Certification Monitoring

Generate audit-ready compliance packets for FSC, SFI-FM, SFI-FS, SBP, and Investments & Decisions (I&D) standards. Pulls satellite-based indicators from LandPulse and maps them to each standard's principles and criteria.

📡 Output: a PDF compliance packet auditors can use as a starting point or supporting evidence for a property's certification.
⚠️ Draw a polygon on the map first.
Standard
🏔️ Conservation Context Card

Auto-generated ecological profile from satellite data and reference layers.

🌲 Stand Inventory — TreeMap Intelligence
👤 Plain-English view — technical metrics (BA, BF/ac) hidden. Click any 🤖 indicator for vision-check detail.

USFS TreeMap 30m species composition and size class mapping. Satellite-derived — field verification recommended before management decisions.

📡 Data: USFS TreeMap (circa 2022) — FIA-imputed, 30m CONUS. Species, size class, basal area, carbon.
Static snapshot of ~2022 conditions. Recent harvests, planting, or mortality may not be reflected.
Analysis
Size Class Map — Colors the property by FIA stand size class (stocking-based). Fastest analysis — shows where sawtimber, poletimber, and saplings sit on the landscape. No stand delineation overhead.
⏱️ 10–20 sec • Shows tile overlay on map
🌿 Silva — Silvicultural Intelligence
The prevention layer. Silva answers three questions about every property: what IS the forest (Stands V2), what SHOULD it be (Site Classification), and is it getting there (Site Match + Regeneration Monitoring). The most effective forest-health strategy is putting the right species on the right sites.
📸 Silva field photo set
1-6 PHOTOS · OPTIONAL · POWERFUL
Each photo informs a different Silva bucket. More photos = stronger evidence. One photo also works — just attach what you have.
0 photos attached ~$0.00 est
📋 Live components
🌲 Current Composition (Stands V2)
LIVE
What IS the forest. AEF-segmented stands with TreeMap species + size class + BA + QMD. Full stand-level detail and vision validation in the Stands tab.
🕰 Disturbance & Trajectory
LIVE
What WAS the forest. 40-year LandTrendr reading: last disturbance year + severity, stand maturity class, recovery status (on-track / stalled / complete). Sets the silvicultural window for prescriptions.
🗺 Ecological Site Classification
LIVE
Synthesizes SSURGO-proxy soils, SRTM topo, PRISM 10-year climate normals, and LANDFIRE BpS into a property-level site description and rule-based species recommendation.
⚖ Site Match Assessment
LIVE
Compares current TreeMap composition vs site-recommended species. Flags mismatches with management implications (encroachment, regen failure, pest susceptibility).
📚 Silva Recommendations
PARTIAL
Citation-backed silvicultural findings mined from USFS Treesearch (60,000+ pubs). 22 findings live; expanding to 100-150 across six batches.
🔬 In development
Silva v0.1 · prevention layer alongside detection algorithms · grant work for US Endowment for Forestry & Communities
🍂 Aspen Detection & Trend Analysis

Map aspen using phenological amplitude — the seasonal NDVI swing between peak green and leaf-off. Aspen shows massive swing; conifers are flat.

Sentinel-2 at 10m — maps aspen probability from seasonal NDVI amplitude
Sentinel-2 + Landsat 5/7/8/9 • Phenological amplitude • 10m/30m
🌱 Harvest Detection & Regen Tracker

Detect harvest openings and regen blocks using AlphaEarth 64D satellite embeddings. Adaptive scale for larger parcels.

Group openings: 0.25–5 ac, threshold 0.15 (partial cuts, group selection)
More Less
Lower values detect lighter harvests but more false positives. 0.15 default, raise to 0.20+ for stand-replacing only.
AlphaEarth Foundations (Google/DeepMind) • Sentinel-2 + S1 + GEDI LiDAR fusion • 10m
📋 Comprehensive Annual Report

Generate a full monitoring report covering vegetation, invasives, forest health, wetlands, and disturbance detection.

Property Information
Analysis Period
For 20-30 year forest health / conifer analysis
Report Modules
LP
LandPulse Forest
Analyzing satellite imagery…
Patent pending