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Abstract Grassy ecosystems cover >25% of the world's land surface area. The abundance of herbaceous vegetation in these systems directly impacts a variety of ecological processes, including carbon sequestration, regulation of water and nutrient cycling, and support of grazing wildlife and livestock. Efforts to quantify herbaceous biomass, however, are often limited by a trade‐off between accuracy and spatial scale. Here, we describe a method for using Light Detection and Ranging (LiDAR) to estimate continuous aboveground biomass (AGB) at sub‐meter resolutions over large (10–10 000 ha) spatial scales. Across two African savanna ecosystems, we compared field‐ and LiDAR‐derived structural metrics—including measures of vegetation height and volume—with destructively harvested AGB by aligning our geospatial data with the location of harvested quadrats. Using this combination of approaches, we develop scaling equations to estimate spatially continuous herbaceous AGB over large areas. We demonstrate the utility of this method using a long‐term, large herbivore exclosure experiment as a case study and comprehensively compare common field‐ and LiDAR‐derived metrics for estimating herbaceous AGB. Our results indicate that UAV‐borne LiDAR provides comparable accuracy to standard field methods but over considerably larger areas. Nearly every measure of vegetation structure we quantified using LiDAR provided estimates of AGB that were comparable in accuracy (R2 > 0.6) to the suite of common field methods we evaluated. However, marked differences between our two sites indicate that, for applications where accurate estimation of absolute biomass is a priority, site‐specific parameterization with destructive harvesting is necessary regardless of methodology. With the increasing availability of high‐resolution remote sensing data globally, our results indicate that many measures of herbaceous vegetation structure can be used to accurately compare AGB, even in the absence of complementary field data.
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Handheld lidar sensors can accurately measure aboveground biomass
Abstract Many recent studies have explored remote sensing approaches to facilitate non‐destructive sampling of aboveground biomass (AGB). Lidar platforms (e.g., iPhone and iPad PRO models) have recently made remote sensing technologies widely available and present an alternative to traditional approaches for estimating AGB. Lidar approaches can be completed within a fraction of the time required by many analog methods. However, it is unknown if handheld sensors are capable of accurately predicting AGB or how different modeling techniques affect prediction accuracy. Here, we collected AGB from 0.25‐m2plots (N = 45) from three sites along an elevational gradient within rangelands surrounding Flagstaff, Arizona, USA. Each plot was scanned with a mobile laser scanner (MLS) and iPad before plants were clipped, dried, and weighed. We compared the capability of iPad and MLS sensors to estimate AGB via minimization of model normalized root mean square error (NRMSE). This process was performed on predictor subsets describing structural, spectral, and field‐based characteristics across a suite of modeling approaches including simple linear, stepwise, lasso, and random forest regression. We found that models developed from MLS and iPad data were equally capable of predicting AGB (NRMSE 26.6% and 29.3%, respectively) regardless of the variable subsets considered. We also found that stepwise regression regularly resulted in the lowest NRMSE. Structural variables were consistently selected during each modeling approach, while spectral variables were rarely included. Field‐based variables were important in linear regression models but were not included after variable selection within random forest models. These findings support the notion that remote sensing techniques offer a valid alternative to analog field‐based data collection methods. Together, our results demonstrate that data collected using a more widely available platform will perform similarly to a more costly option and outline a workflow for modeling AGB using remote sensing systems alone.
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- Award ID(s):
- 1906243
- PAR ID:
- 10610366
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Ecosphere
- Volume:
- 16
- Issue:
- 6
- ISSN:
- 2150-8925
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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