Layers and Features

Dynamic Lands Labs’ geospatial and spatiotemporal analysis tools and algorithms, built on a custom codebase, enable in-depth study of LiDAR, multispectral satellite imagery, and other GIS datasets to extract meaningful, site-specific insights.

DLL delivers insights through interactive visualization platforms, allowing clients to upload, explore, and filter their processed data in an intuitive web environment.

For design professionals, DLL also has a suite of custom Grasshopper tools for Rhino, enabling seamless integration of analytical outputs directly into 3D design workflows—ideal for architects, landscape architects, and students seeking to connect environmental data with design intent.

Spectroscopy with Multispectral Satellite Imagery

Multispectral satellite imagery captures reflected energy across a range of wavelengths, and each band highlights different physical properties of the landscape. By combining these bands in specific ways, we can derive powerful spectral indices that reveal surface temperature patterns, vegetation health (NDVI), moisture content (NDMI), and even distinguish between different material types. These composite measurements allow us to translate raw pixel data into meaningful insights about site conditions, environmental performance, and surface characteristics.

A colorful data flow diagram illustrating the transformation and processing of input data through various stages, ending in visualization.

Spatiotemporal Spectral Insights

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Pixelated map showing areas marked in green with some grid lines, city and road outlines, and some terrain features.
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Vegetation (NDVI)

NDVI (Normalized Difference Vegetation Index), measures how much infrared light is reflected by chlorophyll. This measurement allows us to measure spatial and temporal patterns of vegetation health and density.

A pixelated map with blue and red colors, showing terrain features and roads.
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Moisture (NDMI)

NDMI (Normalized Difference Moisture Index) measures the relative moisture content of vegetation by comparing near-infrared and shortwave infrared reflectance. This measurement allows us to assess spatial and temporal patterns of vegetation water stress, canopy moisture, and drought response across a landscape.

A weather radar map displaying storm activity in shades of yellow, orange, and dark red, with some locations outlined by white lines and roads.
Icon of a thermometer with a bulb at the bottom and a temperature indicator.

Land Surface Temperature

Land Surface Temperature (°F), derived from the thermal bands of Landsat 8/9 multispectral imagery, is analyzed to reveal both spatial and temporal temperature patterns. Changes are measured year over year at the pixel level to pinpoint areas where a site has warmed or cooled and the correlation between NDVI, NDMI and terrain signals.

Understand annualized and seasonal shifts in land dynamics

Standard DLL analysis periods observe 10 years of multispectral signals providing durable, longitudinal insights and historical benchmarks

A series of scientific heat maps and images showing data trends over years from 2016 to 2025, categorized by different metrics such as LST, NDMI, and NDVI, arranged in a grid format. The maps display variations in color indicating different data values across the years.

Combine Multispectral Signals and 3D Terrain Signals

Composite image showing a topographic satellite map on the left and multiple smaller satellite and other map sections on the right, displaying terrain features, elevation data, and land cover patterns.

Multispec + LiDAR

Dynamic Lands Lab integrates multispectral satellite imagery with high-resolution LiDAR terrain analytics to reveal the environmental forces shaping the landscape over time. By combining signals such as NDVI, NDMI, and land surface temperature with 3D terrain intelligence, including drainage behavior, slope directionality, flow convergence, erosion patterns, and ephemeral channel formation. The analysis pipeline uncovers why specific environmental conditions emerge and how they evolve seasonally. This fusion of spectral and terrain-based analysis enables deeper insight into hydrologic behavior, vegetation dynamics, moisture accumulation, and landscape change at both local and watershed scales

Terrain Signals

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A digital elevation map with river networks overlaid, displaying terrain in shades of yellow, orange, and purple.

3D Elevation & Slope

Terrain characteristics are analyzed using local directional slope signals derived from USGS 3DEP data. At each location, elevation differences are evaluated across the surrounding neighborhood to capture how terrain changes in multiple directions rather than along a single slope axis.

False color topographic map showing a rugged terrain with elevation changes, ridges, and valleys.

Anisotropy

Directional anisotropy is calculated to describe how organized or diffuse local relief is—distinguishing planar slopes, ridgelines, channels, and isotropic features such as knolls or depressions. This measure reveals whether terrain structure is directionally focused or evenly distributed.

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Signal Direction

The dominant slope change direction and angle are extracted from the directional signal to identify the primary orientation of downhill movement, providing insight into flow paths, aspect-driven processes, and terrain-controlled transport.

A colorful, pixelated map showing a grid of roads, rivers, and terrain features in an abstract style.

Downhill Fraction

Uphill and downhill fractions quantify how many surrounding directions contribute to descent or ascent, helping to differentiate dispersive surfaces such as ridges from focused flow features such as channels and slope breaks.

A stylized mountain logo with a black background and white mountain shapes.
Colorful digital map of a river system with branching tributaries in various colors against a black background.
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Topographic Contours

Map topographical characteristics with topographic contours, segmented from 3D elevation models.

Colorful abstract topographic map with contour lines in yellow, pink, purple, red, and blue on a dark background.

Convergence and Divergence Areas

Downhill-fraction segmentation uncovers water divergence ridges and water convergence areas.

Correlations with Moisture and Vegetation Signals

Understand how watershed characteristics impact moisture retention and vegetation by mapping correlations between multispectral signals with segmented convergence and divergence areas.

Dominant Slope Directions

Map and understand directional signals and the impact on environmental factors such as drainage, wind and erosion.

Topography and Hydrologic

Transform raster data into vector-based segmented layers to uncover topological and hydrologic insights

A digital map showing the tree-like network of rivers and streams in a specific region.
A digital map of Chicago showing city streets in yellow, surrounding highways, and 3D block representations of buildings in pink and blue.

GIS Data Integration

Structures

A computer screen displaying a 3D city model with colorful buildings and data analytics panels on the right side, showing multispectral analysis and spectral correlations for Chicago.
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Dynamic Lands Lab integrates open-source GIS data to map and analyze surrounding context for any site. Our platform compiles information on existing structures, active construction sites, hospitals, and other critical facilities to quantify proximity and relationships within the built environment. We measure distances to nearby assets and generate aggregated summaries that reveal what surrounds your location. Estimated building footprints and massing models are provided in both GeoJSON and OBJ formats, enabling clients to interactively explore surrounding infrastructure through the web app or seamlessly integrate it into 3D design environments.

Extreme Weather Events

Color-coded map of Dallas, Texas area showing traffic congestion, with red, yellow, and green areas and labels for nearby cities.
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High Winds

Tornados

Hail

Dynamic Lands Lab analyzes historical patterns of extreme weather events including tornadoes, high winds, and hail to help clients understand environmental risk over time. Using spatial and temporal aggregation, we quantify the frequency, intensity, and proximity of severe weather occurrences affecting your site and its surrounding region. These insights reveal long-term exposure trends, enabling more informed decisions related to resilience planning, design mitigation, and operational risk management.