RESUMO
Rapidly and accurately predicting leaf area (LA) and leaf dry mass (LDM) are essential for exploring the response of plant traits to climate change. Empirical models suitable for predicting LA and LDM of a single leaf for various broadleaved tree species at the regional scale have not been proposed. We selected six broadleaved tree species in four mixed broadleaved-Korean pine (Pinus koraiensis) forests in northeastern China, including Betula platyphylla, Tilia amurensis, Populus davidiana, Betula costata, Fraxinus mandshurica and Ulmus laciniata, and measured leaf length, leaf width, leaf thickness, LA and LDM at different canopy layers (top, middle, and low). Using the median of leaf length and width ratio as the classification criterion, the six species were sorted into two groups. We tested whether different canopy layers for each group of broadleaved tree species had significant impacts on the empirical model for predicting LA and LDM. We constructed empirical models suitable for predicting LA and LDM of a single leaf at different canopy layers at the regional scale, and verified their forecast accuracy, and further evaluated their applicability for predicting LA and LDM of same broadleaved tree species in other regions. These results showed that the LA of a single leaf increased significantly with the decreases of canopy height for the six tree species, while the LDM of some broadleaved tree species showed a downward trend. The canopy height had significant impacts on constructing the empirical model for LA and LDM. The average forecast accuracy of empirical model was 95% and 83% for LA and LDM of a single leaf across canopy layers for two groups of broadleaved tree species, respectively. The average forecast accuracy was 94% and 80% for predicting LA and LDM of corresponding broadleaved tree species in other regions, respectively, indicating that the empirical models constructed in this study had a universal applicability in Northeast China.
Assuntos
Monitoramento Ambiental/métodos , Florestas , Pinus , Árvores , China , Folhas de PlantaRESUMO
The ever-growing world population brings the challenge for food security in the current world. The gene modification tools have opened a new era for fast-paced research on new crop identification and development. However, the bottleneck in the plant phenotyping technology restricts the alignment in geno-pheno development as phenotyping is the key for the identification of potential crop for improved yield and resistance to the changing environment. Various attempts to making the plant phenotyping a "high-throughput" have been made while utilizing the existing sensors and technology. However, the demand for 'good' phenotypic information for linkage to the genome in understanding the gene-environment interactions is still a bottleneck in the plant phenotyping technologies. Moreover, the available technologies and instruments are inaccessible, expensive, and sometimes bulky. This work attempts to address some of the critical problems, such as exploration and development of a low-cost LiDAR-based platform for phenotyping the plants in-lab and in-field. A low-cost LiDAR-based system design, LiDARPheno, is introduced in this work to assess the feasibility of the inexpensive LiDAR sensor in the leaf trait (length, width, and area) extraction. A detailed design of the LiDARPheno, based on low-cost and off-the-shelf components and modules, is presented. Moreover, the design of the firmware to control the hardware setup of the system and the user-level python-based script for data acquisition is proposed. The software part of the system utilizes the publicly available libraries and Application Programming Interfaces (APIs), making it easy to implement the system by a non-technical user. The LiDAR data analysis methods are presented, and algorithms for processing the data and extracting the leaf traits are developed. The processing includes conversion, cleaning/filtering, segmentation and trait extraction from the LiDAR data. Experiments on indoor plants and canola plants were performed for the development and validation of the methods for estimation of the leaf traits. The results of the LiDARPheno based trait extraction are compared with the SICK LMS400 (a commercial 2D LiDAR) to assess the performance of the developed system.