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Context <p>Leaf area index (LAI) strongly influences the carbon and water cycle in drylands, but accurate estimation of LAI relies on field methods that are expensive and time intensive. Very high-resolution imagery from unoccupied aerial systems (UAS) offers a potential solution for monitoring LAI, but estimation methods derived from cost effective red, green, and blue (RGB) sensors are untested in these semi-arid ecosystems.</p> Objectives <p>The objective of our study was to test whether LAI could be estimated with very high resolution UAS collected RGB and canopy height data. Additionally, we sought to validate the model accuracy at the plot (1 m<sup>2</sup>) scale, test the accuracy at the macroplot (1&#xa0;ha) scale, and assess the within plot impact of shadows.</p> Methods <p>We used a Random Forest machine learning model to estimate LAI in a Wyoming big sagebrush community in the Reynolds Creek Experimental Watershed using high resolution (&lt; 1 cm<sup>2</sup>) UAS imagery collected in 2021 as predictors and plot scale point intercept (quadrat design) field data as the LAI reference.</p> Results <p>Random Forest modeled estimates of LAI were accurate at the plot (r<sup>2</sup> = 0.69, MAE = 0.08, RMSE = 0.10), and the macroplot scales (error of 0.065), and mean within plot shadow error was 0.06.</p> Conclusions <p>This research demonstrates high resolution UAS data can rapidly and accurately estimate LAI, with a limited number of field measurements, potentially allowing land managers to survey seasonally and spatially heterogeneous LAI 1 hectare at a time over the vast rangelands in the Great Basin and similar ecosystems worldwide.</p>

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Estimation of leaf area index in sagebrush steppe with low cost unoccupied aerial systems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月22日

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  • Craig D. Woodruff,
  • Patrick E. Clark,
  • Peter J. Olsoy,
  • Josh Enterkine

摘要

Context

Leaf area index (LAI) strongly influences the carbon and water cycle in drylands, but accurate estimation of LAI relies on field methods that are expensive and time intensive. Very high-resolution imagery from unoccupied aerial systems (UAS) offers a potential solution for monitoring LAI, but estimation methods derived from cost effective red, green, and blue (RGB) sensors are untested in these semi-arid ecosystems.

Objectives

The objective of our study was to test whether LAI could be estimated with very high resolution UAS collected RGB and canopy height data. Additionally, we sought to validate the model accuracy at the plot (1 m2) scale, test the accuracy at the macroplot (1 ha) scale, and assess the within plot impact of shadows.

Methods

We used a Random Forest machine learning model to estimate LAI in a Wyoming big sagebrush community in the Reynolds Creek Experimental Watershed using high resolution (< 1 cm2) UAS imagery collected in 2021 as predictors and plot scale point intercept (quadrat design) field data as the LAI reference.

Results

Random Forest modeled estimates of LAI were accurate at the plot (r2 = 0.69, MAE = 0.08, RMSE = 0.10), and the macroplot scales (error of 0.065), and mean within plot shadow error was 0.06.

Conclusions

This research demonstrates high resolution UAS data can rapidly and accurately estimate LAI, with a limited number of field measurements, potentially allowing land managers to survey seasonally and spatially heterogeneous LAI 1 hectare at a time over the vast rangelands in the Great Basin and similar ecosystems worldwide.