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BLM Climate Engine - Riparian & Wetland

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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 using Climate Engine to monitor riparian and wetland resources with satellite-derived indicators and supporting climate/hydrology datasets.

After following this training you will have:

  • Gained an understanding of the core satellite and contextual climate and hydrology datasets that are most applicable for R&W decision support.
  • Learned*workflows in the Climate Engine web application to analyze status and trends in R&W resources using satellite indicators.
  • Gained confidence applying satellite indicators to produce maps and figures for decision documents, resource monitoring activities, and other reporting processes.
  • Become able to access recordings and supporting materials for the session for asynchronous study and integration of materials.

Background and Overview of Climate Engine

This section emphasizes the role of this riparian/wetland training within the broader Climate Engine and BLM collaboration and demonstrates opportunities to use Climate Engine for decision-support:

  • Why Climate Engine exists: reduce technical barriers to accessing and analyzing remote sensing and climate data for resource management and applied research uses.
  • Why this matters for BLM activities: agency policies emphasize measurable outcomes, use of quantitative thresholds, and standardized monitoring practices, and recognize the value of integrating remote sensing into monitoring strategies.
  • How remote sensing supports riparian/wetland decisions: 1) historical context (multi-decadal archives), 2) consistent data across space, 3) multi-scale analysis (project, allotment, field office, etc.), 4) frequent observations (sub-annual dynamics), 5) paired climate/hydrology context for interpretation.

New Functionality in Climate Engine

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

1) High-resolution basemaps

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 high-resolution images from the USGS archive
  • USGS 3DEP: 1 m topographic variables (e.g., hillshade, slope)

2) Categorical raster overlays

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

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

3) Categorical masking

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. VBET NWI, or BLM land only).

This makes “riparian-only” and “network-scale” analysis much more tractable and reduces the risk of diluting a signal by averaging across upland pixels.

Dataset Overview for Riparian & Wetland Monitoring

This section provides a practical overview of the core dataset types (extents, climate, vegetation/water resources indicators) most useful in riparian and wetland applications. The emphasis is on understanding the role each dataset plays within a defensible workflow rather than treating layers as interchangeable.

Extent and mapping layers (where is the riparian/wetland system?)

Extent layers are used to define analysis footprints and create masks.

National Wetlands Inventory (NWI) often serves as the starting point because it provides detailed wetland mapping where updates are recent and complete. However, update age and completeness vary by location, and Climate Engine incorporates only the wetland type attributes.

VBET (Valley Bottom Extraction Tool) delineates geomorphic valley bottoms and is useful for network-scale corridor analysis and connected valley-bottom environments. Performance depends on DEM quality and hydrologic inputs and is generally stronger in larger valleys than in small headwater or spring-head systems.

NLCD (National Land Cover Database) provides broad land cover context, including wetland-related classes. Its 30 m resolution can limit uses in narrow riparian corridors, and class confusion (e.g., wetlands versus pasture/hay) can occur in some western landscapes.

Climate and drought context (what are the drivers?)

Climate datasets provide necessary driver context for interpreting vegetation change. For riparian and wetland analyses, variables are often aggregated over meaningful windows (e.g., season or water year). Standardized indices such as the standardized precipitation index (SPI) can or drought indicators like the long-term and short-term drought blends support comparison across years and help interpret longer-term hydrologic drought (often 9–12 months in water-limited systems).

Vegetation and water indicators (what is changing?)

Vegetation status and trend can be assessed using spectral indices collected by satellites (e.g., NDVI, NDWI) rather than upland-optimized derived products (e.g., RAP, RCMAP), which may not transfer well to wetlands.

Greenness indices (NDVI) act as proxies for photosynthetic activity and vigor. Wetness or water indices (NDWI variants) are critical for distinguishing vegetation change from shifts in surface water extent.

Common pitfalls and how to avoid them

Surface water can reduce NDVI and mimic vegetation decline; interpret NDVI alongside wetness indices and NAIP imagery can help to avoid drawing incorrect conclusions. Phenology varies year to year, so use consistent seasonal windows (often summer/late summer) and confirm patterns with time series.

Be mindful of spatial and temporal trade-offs: Landsat offers a long record (mid-1980s–present) at 30 m resolution, Sentinel-2 provides 10 m resolution with a shorter record (~2017–present), and NAIP offers very fine detail but infrequent acquisition and is not appropriate for quantitative analysis.

Finally, upland-derived products may perform poorly in wetland pixels and should be applied cautiously and validated before drawing conclusions.

BLM Practitioner Highlight (Wyatt Fereday)

This practitioner segment shows how Climate Engine is used in real BLM workflows to support monitoring, interpretation, and communication—often by pairing NDVI trends/time series with precipitation or groundwater context.

Three examples are highlighted:

1) Pre/post exclosure response at a spring. NDVI time series show a clear change after fencing, with precipitation plotted alongside to demonstrate that the observed improvement is not simply an effect of variable weather conditions. 2) Riparian decline plausibly consistent with groundwater drawdown. A riparian area showing strong NDVI decline is compared with nearby groundwater-level trends. The framing is intentionally cautious: the analysis is presented as correlation/screening evidence, not a definitive attribution study. 3) Evaluating potential effects of groundwater/geothermal development. NDVI is related to available discharge/monitoring information to extend context back in time and strengthen interpretation where field records are limited.

Demo 1: Maggie Creek (network-scale status and trend mapping)

This demo focuses on map-based analysis (rather than time series) and demonstrates how to create riparian network-scale products using the new masking tools.

Workflow highlights include:

  • Build a late-season NDVI map (often Jul 15–Sep 30) to emphasize water-limited conditions and to isolate riparian signal from the surrounding uplands where vegetation has already senesced.
  • Use the valley bottom extraction tool (VBET) to restrict analysis to low-lying and elevated valley bottoms, turning a broad NDVI map into a network-scale corridor-focused map product.
  • Compute long-term change using the Sen's slope slope of trend (example windows span multiple decades) and examine negative and positive trends in NDVI alongside high-resolution imagery products from NAIP.
  • Apply a p-value/confidence mask to keep only statistically supported trends.
  • Use NAIP and 3DEP hillshade/slope to interpret where changes are occurring (channel position, corridor geomorphology, woody expansion, etc.).

Demo 2: Diamond Valley (groundwater impacts + climate context)

This demo shifts from mapping toward time series analysis and climate-adjusted analysis in a groundwater-dependent wetland complex in Diamond Valley, Nevada. Here, we perform an analysis to determine whether vegetation change exceeds what would be expected from climate variability alone.

Workflow highlights include:

  • Establish spatial context by interpreting imagery and contextual layers. Historical imagery shows landscapes prior to agricultural expansion, helping frame legacy land use. The Surface Management Agency layer clarifies the management mosaic (private vs. public/BLM), which is often important for interpretation. The National Wetland Inventory (NWI) is then used to define the wetland footprints that will anchor all subsequent analysis.
  • Identify where change is occurring. Create a long-term Landsat NDVI trend map over a consistent summer window. Focus on wetland features showing coherent decline signals rather than isolated pixel noise. This narrows attention to areas where change appears systematic.
  • Verify remote sensing trends with high-resolution imagery. Use multi-year NAIP imagery to visually confirm whether mapped wetlands show contraction or compositional change. Recognize that NAIP acquisition timing differs among years, so interpretation should account for phenological differences.
  • Quantify spring degradation with masked time series. Use Summary Timeseries to extract annual late-season NDVI summaries through time. Draw a broad AOI polygon, then mask by the relevant NWI wetland class (e.g., freshwater emergent wetlands). This step isolates our analysis of NDVI to only vegetated wetlands.
  • Add climate context (avoid over-attribution). Use a two-variable analysis with Variable 1: gridMET precipitation (water year) or an appropriate drought index and Variable 2: late-season NDVI. Use a two-variable scatterplot (baseline vs. impacted) to assess whether the vegetation signal shifts downward even when precipitation is comparable. This distinguishes groundwater or management effects from purely meteorological drivers.

Demo 3: Rowland Spring (restoration effectiveness + AIM integration)

This capstone demo shows how Climate Engine complements AIM field data within a multiple-lines-of-evidence workflow. The goal is not to replace field measurements, but to situate them within longer-term satellite and climate context to strengthen interpretation and conclusions.

Workflow highlights include:

  • Frame the site within disturbance and management history. Rowland Spring includes wildfire history, an earlier spring-head exclosure, more recent fencing expansion and restoration actions, and targeted AIM sampling across the site (inside, near, and below exclosures). This layered context makes the site well suited for pre/post and spatially explicit analysis.
  • Clarify what AIM contributes. AIM indicators provide detailed, on-the-ground condition information (e.g., cover metrics, wetland-associated species signals), but are often limited to a small number of sampling years. This constrains historical context when used alone.
  • Evaluate resolution trade-offs for long-term context. Compare Sentinel-2 (10 m) and Landsat (30 m) over the same plot footprints to determine whether Landsat resolution is sufficient for historical analysis at a small site.
  • Conduct pre/post inference with climate adjustment. Use a climate-adjusted scatterplot approach with Variable 1: gridMET precipitation (water year) or an appropriate drought index and Variable 2: late-season NDVI. Assess whether post-intervention observations shift outside the baseline vegetation–climate relationship, while maintaining appropriate caution about causality.
  • Ensure sampling-date comparability. Use dense harmonized Landsat/Sentinel time series to contextualize AIM sampling dates across years and account for phenological differences. Download CSV outputs for custom plotting or statistical analysis in R or Python when needed

Closing, Contacts & Resources

The closing reinforces that the core workflows demonstrated are reproducible without code and are designed to support rapid exploration and decision-ready outputs.

Resources

Contacts

  • Eric Jensen eric.jensen@dri.edu
  • Alex Brooks alex.brooks@dri.edu
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