Evaluation of Leaf Area Index (LAI) of Broadacre Crops Using UAS-Based LiDAR Point Clouds and Multispectral Imagery

Abstract

Leaf area index (LAI) is an established structural variable that reflects the three-dimensional (3-D) leaf layering of vegetation in response to environmental inputs. In this context, unmanned aerial system (UAS) based methods present a new approach to such plant-to field-scale LAI assessment for precision agriculture applications. This article used UAS-based light detection and ranging (LiDAR) data and multispectral imagery (MSI) as two modalities to evaluate the LAI of a snap bean field, toward eventual yield modeling and disease risk assessment. LiDAR-derived and MSI-derived metrics were fed to multiple biophysical-based and regression models. The correlation between the derived LAI and field-measured LAI was significant. Six LiDAR-derived metrics were fit in eight models to predict LAI, among which the square root of the laser penetration index achieved the most accurate prediction result ( ${R^2}$= 0.61, nRMSE = 19%). The MSI-derived models, which contained both structural features and spectral signatures, provided similar predicting effectiveness, with predicted ${R^2}$≈0.5 and nRMSE≈22%. We furthermore observed variation in model effectiveness for different cultivars, different cultivar groups, and different UAS flight altitudes, for both the LiDAR and MSI approaches. For data collected at a consistent flight altitude, MSI-derived models could even exceed LiDAR-derived models, in terms of accuracy. This finding could support the possibility of replacing LiDAR with more cost-effective MSI-based approaches. However, LiDAR remains a viable modality, since a LiDAR-derived 3-D model only required a single predictor variable, while an MSI-derived model relied on multiple independent variables in our case.