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BLM Climate Engine - Upland Ecosystems

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Training Structure and Slides

The training was recorded and is available on the Climate Engine YouTube Channel. This training recording is embedded throughout this article with timestamped clips for each section. The slides from the training are available below.

Access the slides at this link.

Self-paced Study

This page mirrors the workshop agenda and demos. Each section starts with a timestamped, embedded clip from the full recording.

Sections in this training

Full Recording

Introduction & Session Overview

This thematic training focuses on landscape-scale monitoring of upland ecosystems using satellite data products available in Climate Engine. The emphasis is practical: learning a small set of repeatable workflows that produce maps and figures suitable for decision documents, resource monitoring, and reporting.

You should come away able to:

  • recognize which satellite and climate datasets are most applicable for upland monitoring questions;
  • use the Climate Engine app to analyze vegetation status and trend with satellite-derived indicators;
  • gain confidence producing maps/figures that can be carried into BLM workflows.

Background: Why Satellite Monitoring for Uplands

Remote sensing complements field monitoring by providing a consistent, repeatable view of landscape change over large areas. This is especially useful for upland decisions because many satellite-derived products provide a multi-decadal record (often back to the mid-1980s), are collected frequently, and have spatial resolution (10–30 m) that is relevant for many upland management questions. Climate Engine also co-locates climate and drought context so you can interpret whether observed vegetation patterns are plausibly related to climate variability versus other drivers.

The training also notes that remote sensing is increasingly referenced alongside existing data streams in BLM guidance and processes (e.g., NEPA and land health frameworks), and that the utility of remote sensing products has increased alongside improvements in the products themselves.

New Capabilities: Adding Context with Basemaps, Overlays, and Masking

This section introduces three new features used heavily in the demos:

1) High-resolution basemaps (interpretation + QA/QC)

Used to visually interpret coarser satellite products and understand on-the-ground context.

  • NAIP: true color, false color, NDVI (high-resolution aerial imagery)
  • Historical imagery (where available)
  • USGS 3DEP: 1 m topographic derivatives (e.g., hillshade, slope)

2) Categorical raster overlays (context layers)

New “map overlay” layers provide categorical context such as:

  • National Landcover Database (land cover),
  • National Wetland Inventory (wetland inventory),
  • Surface Management Agency (ownership/management),
  • Valley Bottom Extraction Tool (topographic valley bottoms),
  • other thematic layers (e.g., GDE indicators in relevant regions).

3) Categorical masking (focus the analysis where it matters)

This new feature leverages the new categorical raster overlays as data masks, allowing users to subset their maps and time series to focus on:

  • valley bottoms (VBET),
  • wetland classes (NWI),
  • land ownership/management classes,
  • or combinations (e.g., NLCD shrublands on BLM land only).

This allows for landscape-scale reporting of status and trends of BLM lands at the scale of grazing allotments and HUC10 watersheds tractable.

BLM Practitioner Highlight (Ken Holsinger)

This practitioner segment shows how Climate Engine is used in real BLM workflows to support monitoring, interpretation, and communication—often by using fractional vegetation cover products in analysis, mapping, and modeling to support a range of activities

Three example patterns are highlighted:

1) Rangeland Health Assessment
This section focused on four assessments including 1) trend analysis, 2) invasive annual grass and forb mapping, 3) mapping of Sen's slope trends in vegetation types, and 4) using fractional vegetation cover to predict land health outcomes.

2) Gunnison Sage-grouse Habitat Assessment
This example highlighted the use of fractional vegetation cover trend maps to characterize multi-decade changes in shrub cover in core sage-grouse habitat and relate that to estimates of sage-grouse populations.

3) Threatened and Endangered Species Post Delisting Monitoring
In this case, fractional vegetation cover was used to monitor vegetation changes related to the Colorado Hookless Cactus, with a particular focus on annual forbs and grasses across different habitat zones.

Demo 1: Site Characterization Reports for Land Health Assessment

This demo is about starting from decision-ready reporting outputs. Using the Climate Engine Site Characterization reports workflow, the session walks through selecting a pre-run report for a Stillwater Field Office grazing allotmentallotment and orienting to the report layout, including current fractional cover maps, summary tables, long-term trend summaries, time series context, and climate/

In practice, this report format is designed to be easy to apply in documentation and reporting because you can view results in-browser and download underlying data (CSV) and figures (PNG/PDF)

In particular, this demo highlights three ways of accessing reports:

  • Accessing reports for BLM land units (state offices, district offices, field offices, grazing allotments) through reports.climateengine.org
  • Producing reports for custom land units (e.g. shapefile upload)
  • Accessing reports for BLM land units using Feature Summaries in the Climate Engine app.

Demo 2: Invasive Annual Grass Controls and Restoration Outcomes

This demo uses the Southeast Oregon Wildfire Resiliency Project case study to show how to frame a treatment/outcomes question in the Climate Engine app: examine imagery through time for context, map pre-restoration conditions, and then evaluate post-treatment outcomes using satellite-derived indicators (including RAP 10 m invasive annual grasses and longer-history products for comparison).

A key analytic move in the transcript is using categorical masking (NLCD) to focus summaries on the most invaded grassland pixels, which makes both the “peak invasion” and the post-treatment decline more visible in the timeseries analysis.

Example links shown in the slides (subset):

Demo 3: Energy Development Disturbance and Reclamation Monitoring

This demo focuses on monitoring disturbance and reclamation dynamics in the Pinedale Anticline Project Area (PAPA). The training highlights ways that remote sensing datasets such as RAP and RCMAP can be used and some of the strengths of these datasets in this context, in particular, that remote sensing provides consistent, repeatable quantification of where development expanded, how the footprint changed, and whether disturbed or reclaimed areas show evidence of recovery through time.

The workflow centers on mapping bare ground status and trends (including long-term trends and shorter-window/decadal trends), and then using site-specific time series for comparison among well pads, reclaimed sites, and undisturbed reference areas.

Example links shown in the slides (subset):

Closing, Contacts & Resources

This session emphasizes that you do not need to code to reproduce the core workflows shown: the focus is on using Reports and the Web App for exploratory analysis, figure generation, and sharing outputs with colleagues.

Resources

Instructor contacts

  • Eric Jensen — eric.jensen@dri.edu
  • Kristen O’Shea — kristen.oshea@dri.edu