Bidirectional reflectance distribution function (BRDF) effects are a persistent issue for the analysis of vegetation in airborne imaging spectroscopy data, especially when mosaicking results from adjacent flightlines. With the advent of large airborne imaging efforts from NASA and the U.S. National Ecological Observatory Network (NEON), there is increasing need for methods that are flexible and automatable across images with diverse land cover. Flexible bidirectional reflectance distribution function (FlexBRDF) is built upon the widely used kernel method, with additional features including stratified random sampling across flightline groups, dynamic land cover stratification by normalized difference vegetation index (NDVI), interpolation of correction coefficients across NDVI bins, and the use of a reference solar zenith angle. We demonstrate FlexBRDF using nine long (150–400 km) airborne visible/infrared imaging spectrometer (AVIRIS)‐Classic flightlines collected on 22 May 2013 over Southern California, where diverse land cover and a wide range of solar illumination yield significant BRDF effects. We further test the approach on additional AVIRIS‐Classic data from California, AVIRIS‐Next Generation data from the Arctic and India, and NEON imagery from Wisconsin. Comparison of overlapping areas of flightlines show that models built from multiple flightlines performed better than those built for single images (root mean square error improved up to 2.3% and mean absolute deviation 2.5%). Standardization to a common solar zenith angle among a flightline group improved performance, and interpolation across bins minimized between‐bin boundaries. While BRDF corrections for individual sites suffice for local studies, FlexBRDF is an open source option that is compatible with bulk processing of large airborne data sets covering diverse land cover needed for calibration/validation of forthcoming spaceborne imaging spectroscopy missions.

Remote monitoring of plants using hyperspectral imaging has become an important tool for the study of plant growth, development, and physiology. Many applications are oriented towards use in field environments to enable non-destructive analysis of crop responses due to factors such as drought, nutrient deficiency, and disease, e.g., using tram, drone, or airplane mounted instruments. The field setting introduces a wide range of uncontrolled environmental variables that make validation and interpretation of spectral responses challenging, and as such lab- and greenhouse-deployed systems for plant studies and phenotyping are of increasing interest. In this study, we have designed and developed an open-source, hyperspectral reflectance-based imaging system for lab-based plant experiments: the HyperScanner. The reliability and accuracy of HyperScanner were validated using drought and salt stress experiments with Arabidopsis thaliana. A robust, scalable, and reliable system was created. The system was built using open-sourced parts, and all custom parts, operational methods, and data have been made publicly available in order to maintain the open-source aim of HyperScanner. The gathered reflectance images showed changes in narrowband red and infrared reflectance spectra for each of the stress tests that was evident prior to other visual physiological responses and exhibited congruence with measurements using full-range contact spectrometers. HyperScanner offers the potential for reliable and inexpensive laboratory hyperspectral imaging systems. HyperScanner was able to quickly collect accurate reflectance curves on a variety of plant stress experiments. The resulting images showed spectral differences in plants shortly after application of a treatment but before visual manifestation. HyperScanner increases the capacity for spectroscopic and imaging-based analytical tools by providing more access to hyperspectral analyses in the laboratory setting.

The aboveground biomass (AGB) of individual trees is a critical indicator for assessing urban forest productivity and carbon storage. In the context of global warming, it plays a pivotal role in understanding urban forest carbon sequestration and regulating the global carbon cycle. Recent advances in light detection and ranging (LiDAR) have enabled the detailed characterization of three-dimensional (3D) structures, significantly enhancing the accuracy of individual tree AGB estimation. This review examines studies that use LiDAR-derived 3D structural metrics to model and estimate individual tree AGB, identifying key metrics that influence estimation accuracy. A bibliometric analysis of 795 relevant articles from the Web of Science Core Collection was conducted using R Studio (version 4.4.1) and VOSviewer 1.6.20 software, followed by an in-depth review of 80 papers focused on urban forests, published after 2010 and selected from the first and second quartiles of the Chinese Academy of Sciences journal ranking. The results show the following: (1) Dalponte2016 and watershed are more widely used among 2D raster-based algorithms, and 3D point cloud-based segmentation algorithms offer greater potential for innovation; (2) tree height and crown volume are important 3D structural metrics for individual tree AGB estimation, and biomass indices that integrate these parameters can further improve accuracy and applicability; (3) machine learning algorithms such as Random Forest and deep learning consistently outperform parametric methods, delivering stable AGB estimates; (4) LiDAR data sources, point cloud density, and forest types are important factors that significantly affect the accuracy of individual tree AGB estimation. Future research should emphasize deep learning applications for improving point cloud segmentation and 3D structure extraction accuracy in complex forest environments. Additionally, optimizing multi-sensor data fusion strategies to address data matching and resolution differences will be crucial for developing more accurate and widely applicable AGB estimation models.