Quantification of methane and carbon dioxide surface emissions from a metropolitan landfill based on quasi-continuous eddy covariance measurement.

The Sudokwon landfill (SL) in the Seoul metropolitan area, South Korea, is among the world's largest landfills, striving to curtail landfill gas (LFG) emissions and achieve carbon neutrality by 2050. Since 2005, the SL Management Corporation (SLC) has measured LFG emissions (i.e., methane (CH4) and carbon dioxide (CO2)) using a dynamic flux chamber proposed by the US EPA. However, uncertainty prevails in validating the reduction of LFG emissions due to the limited spatiotemporal data coverage. In 2020, an eddy covariance (EC) system was installed to enhance measurements, revealing highly fluctuating LFG emissions driven by waste layer LFG production, LFG collection, and atmospheric pressure changes. During the study period, the annual CH4 emission increased slightly from 465.0 ± 4.2 to 485.5 ± 6.4 g C m-2, while that of CO2 decreased by 2/3 (from 408.7 ± 16.5 to 270.6 ± 18.8 g C m-2), primarily due to the doubled CO2 uptake by the vegetated topsoil. Our first long-term (March 2020 to February 2022) quasi-continuous monitoring using EC (with a gap-filling and partitioning technique based on Random Forest) emphasizes the difficulty of temporal upscaling of discontinuously observed surface emissions to quantify the LFG inventory and the need for continuous observations or suitable proxies (e.g., atmospheric CH4 concentration).

Ground-Based NDVI Network: Early Validation Practice with Sentinel-2 in South Korea.

As satellite launching increases worldwide, uncertainty quantification for satellite data becomes essential. Misunderstanding satellite data uncertainties can lead to misinterpretations of natural phenomena, emphasizing the importance of validation. In this study, we established a tower-based network equipped with multispectral sensors, SD-500 and SD-600, to validate the satellite-derived NDVI product. Multispectral sensors were installed at eight long-term ecological monitoring sites managed by NIFoS. High correlations were observed between both multispectral sensors and a hyperspectral sensor, with correlations of 0.76 and 0.92, respectively, indicating that the calibration between SD-500 and SD-600 was unnecessary. High correlations, 0.8 to 0.96, between the tower-based NDVI with Sentinel-2 NDVI, were observed at most sites, while lower correlations at Anmyeon-do, Jeju, and Wando highlighting challenges in evergreen forests, likely due to shadows in complex canopy structures. In future research, we aim to analyze the uncertainties of surface reflectance in evergreen forests and develop a biome-specific validation protocol starting from site selection. Especially, the integration of tower, drone, and satellite data is expected to provide insights into the effect of complex forest structures on different spatial scales. This study could offer insights for CAS500-4 and other satellite validations, thereby enhancing our understanding of diverse ecological conditions.

Construction of deep learning-based disease detection model in plants.

Accurately detecting disease occurrences of crops in early stage is essential for quality and yield of crops through the decision of an appropriate treatments. However, detection of disease needs specialized knowledge and long-term experiences in plant pathology. Thus, an automated system for disease detecting in crops will play an important role in agriculture by constructing early detection system of disease. To develop this system, construction of a stepwise disease detection model using images of diseased-healthy plant pairs and a CNN algorithm consisting of five pre-trained models. The disease detection model consists of three step classification models, crop classification, disease detection, and disease classification. The 'unknown' is added into categories to generalize the model for wide application. In the validation test, the disease detection model classified crops and disease types with high accuracy (97.09%). The low accuracy of non-model crops was improved by adding these crops to the training dataset implicating expendability of the model. Our model has the potential to apply to smart farming of Solanaceae crops and will be widely used by adding more various crops as training dataset.

Deep Learning Algorithms Correctly Classify Brassica rapa Varieties Using Digital Images.

Efficient and accurate methods of analysis are needed for the huge amount of biological data that have accumulated in various research fields, including genomics, phenomics, and genetics. Artificial intelligence (AI)-based analysis is one promising method to manipulate biological data. To this end, various algorithms have been developed and applied in fields such as disease diagnosis, species classification, and object prediction. In the field of phenomics, classification of accessions and variants is important for basic science and industrial applications. To construct AI-based classification models, three types of phenotypic image data were generated from 156 Brassica rapa core collections, and classification analyses were carried out using four different convolutional neural network architectures. The results of lateral view data showed higher accuracy compared with top view data. Furthermore, the relatively low accuracy of ResNet50 architecture suggested that definition and estimation of similarity index of phenotypic data were required before the selection of deep learning architectures.

Size-dependent variation in leaf functional traits and nitrogen allocation trade-offs in Robinia pseudoacacia and Cornus controversa.

Tree species vary in how they invest resources to different functions throughout their life histories, and investigating the detailed patterns of ontogenetic changes in key functional traits will aid in predicting forest dynamics and ecosystem processes. In this context, we investigated size-dependent changes in key leaf functional traits and nitrogen (N) allocation trade-offs in black locust (Robinia pseudoacacia L., an N-fixing pioneer species) and giant dogwood (Cornus controversa Hemsl., a mid-successional species), which have different life-history strategies, especially in their light use. We found that the leaf mass per area and leaf carbon concentrations increased linearly with tree size (diameter at breast height, DBH), whereas leaf N concentrations decreased nonlinearly, with U- and hump-shaped patterns in black locust and giant dogwood, respectively. We also discovered large differences in N allocation between the two species. The fraction of leaf N invested in cell walls was much higher in black locust than in giant dogwood, while the opposite was true for the light harvesting N fraction. Furthermore, these fractions were related to DBH to varying degrees: the cell wall N fraction increased with DBH for both species, whereas the light harvesting N fraction of giant dogwood decreased nonlinearly and that of black locust remained constant. Instead, black locust reduced the fraction of leaf N invested in other N pools, resulting in a smaller fraction compared to that of giant dogwood. On the other hand, both species had similar fraction of leaf N invested in ribulose-1,5-bisphosphate carboxylase/oxygenase across tree size. This study indicated that both species increased leaf mechanical toughness through characteristic changes in N allocation trade-offs over the lifetimes of the trees.

The biocompatibility of SLA-treated titanium implants.

The titanium implant surface was sandblasted with large grits and acid etched (SLA) to increase the implant surface for osseointegration. The topography of the titanium surface was investigated with scanning electron microscopy (SEM) and a profilometer. The SLA implant demonstrated uniform small micro pits (1-2 microm in diameter). The values of average roughness (R(a)) and maximum height (R(t)) were 1.19 microm and 10.53 microm respectively after sandblasting and the acid-etching treatment. In the cell-surface interaction study, the human osteoblast cells grew well in vitro. The in vivo evaluation of the SLA implant placed in rabbit tibia showed good bone-to-implant contact (BIC) with a mean value of 29% in total length of the implant. In the short-term clinical study, SLA implants demonstrated good clinical performance, maintaining good crestal bone height.