Measuring Landscapes Soils and Surface Environments

Landscape soils and surface environments - Week 3 Workshop 1a”

Raphael Viscarra Rossel & Lewis Walden

2026-03-03

Where we are

Week 1: Landscapes are spatially organized systems

  • Vary at multiple scales across space and time

Week 2: CLORPT explains soil formation

  • processess create different soils: mineralogy → properties → ecosystem function


Today: How do we measure and quantify this variability.

Before we start lets recall how soil properties interact: Activity (7 min)

Explore how texture, organic matter, and clay mineralogy affect other impoortant properties like CEC, plant-available water, pH, and bulk density.

Link to activity

Or copy and paste this URL into your browser:

https://ravr19.github.io/lsse_teaching/soil_property_explorer_app.html


⓵ Use sliders to see how interacting physical and biochemical properties interact.

⓶ Answer the questions in your own words. If you dont finish you can complete them later.

What we’ll cover today

First half: Measurement methods

  • Measuring vegetation properties
  • Measuring soil properties
  • Sensing technologies (proximal and remote)
  • Demonstration: soil spectroscopy

Second half: Sampling. Where to measure?

  • Importance of sampling desing
  • Sampling desings
  • Comparing desings for landscape studies

Measurement matters in variable landscapes

"To measure is to know. If you cannot measure it, you cannot improve it." – Lord Kelvin

Two fundamental questions:

  1. What methods can we use to measure soil and vegetation properties?
    • Direct measurements (field and lab)
    • Remote sensing (satellite, aerial)
    • Proximal sensing (ground-based sensors)
  2. Where should we sample to capture variability?

Why this matters: SDG 15 — Life on Land

Protect, restore and promote sustainable use of terrestrial ecosystems

We can’t manage what we don’t measure:

  • Where is land degraded?
  • Is restoration working?
  • Where do we prioritise intervention?

Important

Measurement provides the evidence base for action

Spatial variability drives measurement needs

Take soil organic carbon (SOC) stocks, it is highly variable over short (<10 m) and longer (>50 m) distances

How many samples to capture this?

1-5 ~ likely misrepresents the landscape

5-20 ~ provides a rough estimate of the mean

20-50+ ~ start capturing spatial structure

100+ ~ suitable for spatial modelling and mapping

This is why sampling design matters → Second half of today

Temporal variability must be considered

A single measurement is a snapshot — not representative

Important

When we measure matters as much as where we measure

Temporal variability matters: Monthly vegetation greenness (NDVI)


  • Monthly vegetation change
  • Maximum: peak growing season
  • Minimum: summer drought period


WHEN we sample matters as much as WHERE

Three complementary measurement approaches

Direct measurements

  • Field observations
  • Laboratory analysis
  • High accuracy
  • Limited coverage
  • Expensive, slow

Remote sensing

  • Satellite, airborne, drone
  • Large coverage
  • Indirect measurements
  • Surface only
  • Cost-effective per area

Proximal sensing

  • Ground-based sensors
  • Close/contact measurement
  • High throughput
  • Balance speed & accuracy
  • Multiple properties at once

Three complementary measurement approaches


  • No single approach is “best”
  • Depends on objective
  • Integration provides richest understanding

Used together - complementary

Comparing measurement approaches

Approach Bias Coverage Throughput Cost
Lab analysis Low Points 10s /day 💲💲💲
Field observation Moderate Points 10s /day 💲💲
Proximal sensing Moderate Dense points 100s /day 💲
RS (drone/air) Moderate ha–km² 100s ha /day 💲
RS (satellite) High km²–regional 1000s km² /pass 💲, free

Note

No single approach measures everything - often need to use a combinaton

Field measurements: Landscape context

Landscape description provides spatial framework

  • Position: hilltop, slope, valley, floodplain
  • Landform: dune, plain, ridge, terrace
  • Topography: slope gradient, aspect, drainage class
  • Surface: texture, roughness, stoniness, crusting
  • Erosion status: stable, sheet, rill, gully erosion
  • Vegetation: cover, type, structure

A digital elevation model (DEM) of our study area.

Field measurement methods: Vegetation, what do we measure?


Important

Measurements must match the ecological question and scale of the landscape.

Field measurement methods: Vegetation, how do we measure?


Important

Measurements must be consistent and repeatable for reliable ecological data.

Laboratory measurements: Vegetation

Common methods include:

  • Dry biomass (oven-dried)
  • Tissue nutrient analysis (N, P, K)
  • Organic carbon content
  • Mass spectrometry for compositional analysis
  • Stable isotopes
  • etc…

Field measurements: Soil profile descriptions

  • Horizons (A, B, C) and depths
  • Colour (Munsell chart)
  • Texture, structure, consistence
  • Roots, pores, coatings

Important

Field descriptions provide context that laboratory cannot

Field measurements: Soil properties

  • pH, EC (portable meters)
  • Bulk density (core method)
  • Infiltration rate
  • Soil moisture (TDR), etc…

Important

Field measurements provide information at field conditions, labs cannot.

Laboratory measurements: Soil properties

Samples must be dried, crushed, sieved before they are analysed

Remote sensing

Measuring from a distance (>2m above surface)

Platforms:

  • Satellites: 10-30m resolution (Sentinel, Landsat)
  • Aircraft: 0.5-5m resolution
  • Drones/UAVs: cm resolution

Advantages:

  • Large spatial coverage
  • Repeatable (monitoring)
  • Historical archives
  • Cost-effective
  • Non-destructive

Limitations:

  • Indirect measurements
  • Coarse resolution (10-30m)
  • Surface only (unless active sensors)
  • Clouds, weather sensitivity
  • Vegetation obscures soil
  • Requires calibration

Remote sensing: The electromagnetic spectrum

Wavelength Measures
Visible Chlorophyll, soil colour
Near-IR Vegetation structure, biomass
Shortwave IR Clay minerals, water, OM
Thermal IR Temperature, water stress
Microwave Soil moisture, roughness

NDVI = (NIR − Red) / (NIR + Red) - (greenness)

Healthy veg:: ⬆ NIR reflectance, ⬇ red ➡️ ⬆ NDVI

Remote sensing: What can we measure?

Vegetation:

  • Cover, biomass, productivity
  • Stress indicators (drought, disease)
  • Phenology (seasonal changes)

Soil (bare soil only):

  • Colour (OM, iron oxides, moisture)
  • Mineralogy (clay types, Fe oxides)
  • Salinity, erosion features

Warning

Vegetation, rocks, etc. obscure soil | Measures only top few mm | Require calibration.

Proximal sensing: Overview

Measuring in close proximity (<2m from soil)

Sensors:

  • Vis-NIR / MIR spectroscopy
  • XRF (X-ray fluorescence)
  • EMI (electromag. induction)
  • GPR (ground-penet. radar)
  • \(\gamma\)-ray spectrometry, … etc.

Advantages:

  • 100–500 samples/day
  • May be non-destructive
  • Multiple properties
  • Field deployable

Limitations:

  • Requires calibration
  • Lower accuracy than lab
  • Sensor-specific training
  • Some affected by moisture

Proximal sensing and the EM spectrum

Advantages over lab:

  • 100-500 samples/day
  • $1-5 per sample
  • Non-destructive
  • Real-time results
  • Field portable

Advantages over remote:

  • Direct contact/proximity
  • Not affected by weather
  • Higher resolution
  • No issue with vegetation cover

Remote and proximal sensing and the EM spectrum

Indigenous landscape reading: “Reading Country” principles

  • Holistic integration of multiple indicators
  • Multi-scale observation: plant → patch → landscape
  • Multi-sensory: visual, tactile, olfactory cues
  • Temporal knowledge: seasonal patterns, long-term change
  • Relational: how components interact, not just isolated properties


Indigenous landscape reading

Vegetation as indicators:

  • Certain plants signal soil fertility
  • Moisture indicators (Melaleuca - waterlogged)
  • Vegetation structure - landscape position
  • Post-fire succession - time since burning

Integrated soil assessment:

  • Color, texture, structure (field-based)
  • Landscape position context
  • Vegetation associations
  • Water behavior, animal activity
  • Functional evaluation: Does it grow healthy plants? Hold water? Resist erosion?

Integration: Remote + proximal + lab + Indigenous

Activity (10-15 min): Near infrared soil spectroscopy

We’ll measure 3 soil samples:

  1. Sand: Quartz-dominated, low clay, low OM (bright, minimal absorption)
  2. Red-brown soil: Kaolinite, iron oxides (Fe absorption features)
  3. Black vertosol: Smectite clay, high OM (strong absorption)

For each soil sample:

  • Collect spectrum (30 seconds)
  • Show spectral signature on screen
  • Describe key features

See how:

  • Different soils have distinct “signatures”
  • Mineralogy shows up in spectra
  • Speed and ease of measurement

Summary: Measurement methods

1. Spatial and temporal variability drive measurement requirements

2. Three complementary approaches — no single method is sufficient

  • Direct (field/lab) → accuracy and calibration
  • Remote sensing → spatial coverage and context
  • Proximal sensing → bridges the gap (speed + accuracy + coverage)

3. Indigenous landscape reading provides context that instruments alone cannot

4. Measurement underpins adaptive management and sustainability

Next: Where should we sample?

We’ve learned how to measure — after the break we’ll tackle where

The challenge:

  • Landscapes and their components are spatially and temporally variable
  • We can’t measure everywhere (cost, time constraints) thus we need to sample
  • Sampling design affects what we can learn about landscape patterns

Bring what you’ve learned: landscape position, vegetation, and measurement trade-offs all inform sampling design

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