Growth Rate and Leaf Functional Traits of Four Broad-Leaved Species Underplanted in Chinese Fir Plantations with Different Tree Density Levels

Combo chloro-photosynthetic device and applications for greenhouse gas reduction campaign and smart agriculture

The increasing carbon dioxide (CO2) levels in the air pose a direct threat to all living organisms and the environment. Leveraging the ability of plants to absorb CO2 is one of the most effective methods for countering these rising CO2 levels. The present study aimes to develop a combo photosynthetic and chlorophyll-a sensor based on Non-Dispersive Infrared (NDIR) spectroscopy and an optical method. This sensor enables simultaneous, intensive measurement of net photosynthesis and chlorophyll-a content and yields accurate information. Comparative analysis of the efficacy of the sensors to that of a commercial instrument demonstrated that the measurement values obtained from the developed photosynthetic and chlorophyll-a sensors were not significantly different from those acquired with the commercial instrument (portable photosynthesis system LI-6400) and chlorophyll metre (SPAD-502), with a 95 % confidence level. Furthermore, the developed photosynthetic sensor could be used as a new correlation unit for chlorophyll-a content and net photosynthesis. Therefore, the sensor can be used to propose effective plantation processes to reduce atmospheric CO2 levels and in smart farming systems to control the quality of yields.

Extraction of Broad-Leaved Tree Crown Based on UAV Visible Images and OBIA-RF Model: A Case Study for Chinese Olive Trees

Chinese olive trees (Canarium album L.) are broad-leaved species that are widely planted in China. Accurately obtaining tree crown information provides important data for evaluating Chinese olive tree growth status, water and fertilizer management, and yield estimation. To this end, this study first used unmanned aerial vehicle (UAV) images in the visible band as the source of remote sensing (RS) data. Second, based on spectral features of the image object, the vegetation index, shape, texture, and terrain features were introduced. Finally, the extraction effect of different feature dimensions was analyzed based on the random forest (RF) algorithm, and the performance of different classifiers was compared based on the features after dimensionality reduction. The results showed that the difference in feature dimensionality and importance was the main factor that led to a change in extraction accuracy. RF has the best extraction effect among the current mainstream machine learning (ML) algorithms. In comparison with the pixel-based (PB) classification method, the object-based image analysis (OBIA) method can extract features of each element of RS images, which has certain advantages. Therefore, the combination of OBIA and RF algorithms is a good solution for Chinese olive tree crown (COTC) extraction based on UAV visible band images.