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 3D 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 study 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 (LPI) achieved the most accurate prediction result (R²=0.61, nRMSE=19%). The MSI-derived models, which contained both structural features and spectral signatures, provided similar predicting effectiveness, with predicted R²0.5 and nRMSE22%. 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 3D model only required a single predictor variable, while an MSI-derived model relied on multiple independent variables in our case.