Earticle

Recently, intensive research has been conducted to develop innovative methods for diagnosing plant diseases based on hyperspectral technologies. Hyperspectral analysis is a new subject that combines optical spectroscopy and image analysis methods, which makes it possible to simultaneously evaluate both physiological and morphological parameters. Among the physiological and morphological parameters are classifying healthy and diseased plants, assessing the severity of the disease, differentiating the types of pathogens, and identifying the symptoms of biotic stresses at early stages, including during the incubation period, when the symptoms are not visible to the human eye. Plant diseases cause significant economic losses in agriculture around the world as the symptoms of diseases usually appear when the plants are infected severely. Early detection, quantification, and identification of plant diseases are crucial for the targeted application of plant protection measures in crop production. Hence, this can be done by possible applications of hyperspectral sensors and platforms on different scales for disease diagnosis. Further, the main areas of application of hyperspectral sensors in the diagnosis of plant diseases are considered, such as detection, differentiation, and identification of diseases, estimation of disease severity, and phenotyping of disease resistance of genotypes. This review provides a deeper understanding, of basic principles and implementation of hyperspectral sensors that can measure pathogen-induced changes in plant physiology. Hence, it brings together critically assessed reports and evaluations of researchers who have adopted the use of this application. This review concluded with an overview that hyperspectral sensors, as a non-invasive system of measurement can be adopted in early detection, identification, and possible solutions to farmers as it would empower prior intervention to help moderate against decrease in yield and/or total crop loss.

Quantitative evaluation of nutrient status and stoichiometry on the forest floor is a good indicator of litter quality in various vegetation types. This study was conducted to determine the effects of vegetation type on the nutrient concentration and stoichiometry of forest floors at a regional scale. Forest floor samples were collected from four vegetation types of evergreen coniferous forests including Cryptomeria japonica, Chamaecyparis obtusa, evergreen broadleaf, and bamboo spp. forests in southern Korea. The dry weight of the forest floor was higher in the C. japonica and C. obtusa forests than in the evergreen broadleaf and bamboo forests. The mean carbon (C) concentrations of the forest floor were highest in the broadleaf forest, followed by the bamboo forest, C. japonica and C. obtusa forests. Mean nitrogen (N) and phosphorous (P) concentrations in the the coniferous forests were lower than those in the broadleaf and bamboo forests. The mean C:N ratio was the highest in C. obtusa forest (118±25), followed by C. japonica (66±6), evergreen broadleaf (41±1), and bamboo (30±1) forests. However, C:P and N:P ratios were lower in the coniferous forests than in the broadleaf forest indicating that the stoichiometry of the forest floor varies across vegetation types. The C, N, and P stocks on the forest floor were higher in the C. obtusa forest than in the broadleaf or bamboo forests. These results highlight that vegetation type-dependent stoichiometric ratio is an useful indicator for understanding interspecific difference in quality and quantity of the forest floor.

This research aimed to assess the possibility of detecting forest degradation using time-series satellite imagery and three different deep learning-based change detection techniques. The dataset used for the deep learning models was composed of two sets, one based on surface reflectance (SR) spectral information from satellite imagery, combined with Texture Information (GLCM; Gray-Level Co-occurrence Matrix) and terrain information. The deep learning models employed for land cover change detection included image differencing using the Unet semantic segmentation model, multi-encoder Unet model, and multi-encoder Unet++ model. The study found that there was no significant difference in accuracy between the deep learning models for forest degradation detection. Both training and validation accuracies were approximately 89% and 92%, respectively. Among the three deep learning models, the multi-encoder Unet model showed the most efficient analysis time and comparable accuracy. Moreover, models that incorporated both texture and gradient information in addition to spectral information were found to have a higher classification accuracy compared to models that used only spectral information. Overall, the accuracy of forest degradation extraction was outstanding, achieving 98%.

Fire, being primarily a natural phenomenon, is impossible to control, although it is feasible to map the forest fire risk zone, minimizing the frequency of fires. The spread of a fire starting in any stand in a forest can be predicted, given the burning conditions. The natural cover of the land and the safety of the population may be threatened by the spread of forest fires; thus, the prevention of fire damage requires early discovery. Satellite data and geographic information system (GIS) can be used effectively to combine different forest-fire-causing factors for mapping the forest fire risk zone. This study mainly focuses on mapping forest fire risk in the Madikhola watershed. The primary causes of forest fires appear to be human negligence, uncontrolled fire in nearby forests and agricultural regions, and fire for pastoral purposes which were used to evaluate and assign risk values to the mapping process. The majority of fires, according to MODIS events, occurred from December to April, with March recording the highest occurrences. The Risk Zonation Map, which was prepared using LULC, Forest Type, Slope, Aspect, Elevation, Road Proximity, and Proximity to Water Bodies, showed that a High Fire Risk Zone comprised 29% of the Total Watershed Area, followed by a Moderate Risk Zone, covering 37% of the total area. The derived map products are helpful to local forest managers to minimize fire risks within the forests and take proper responses when fires break out. This study further recommends including the fuel factor and other fire-contributing factors to derive a higher resolution of the fire risk map.

The Japanese larch (Larix kaempferi [Lamb.] Carrière) is a major timber species in Korea. However, studies on bucking rates and merchantable logs of this species are insufficient in South Korea. To bridge these gaps, in this study, the bucking rate of Japanese larch (Larix kaempferi [Lamb.] Carrière) was computed and the number of long logs and merchantable log volumes were analyzed. Sample trees were bucked according to the log grade for trading, and collected from a forest field in Gangwon Province. The bucking rate of all Japanese larch logs was ＞89%. The highly profitable 2-4 logs of 3.6 m length from trees with ≤30 cm diameter at breast height (DBH) and 5-6 logs with ≥34 cm DBH were produced. The bucking rate of long logs was ＞84%; thus, Japanese larch was found to be suitable for the supply of high-grade timber. Additionally, to follow reasonable wood supply plans, merchantable volume tables were offered based on 3.6 m-long number of logs and small-end diameter classes. Understanding the proportion of merchantable log volumes, bucking rates, and the number of long and short logs has large-scale applications in practical forestry.

Cadmium (Cd) is non-essential heavy metal that negatively affects plant metabolism. Nitric oxide (NO) is an increasingly important molecule for plant metabolism that makes signaling. In this study, it was aimed to investigate the alleviating effect of sodium nitroprusside (SNP) application as NO donor in white poplar (Populus alba) under Cd stress conditions. SNP and without SNP treatments increased the Cd accumulation in root tissue. While photosynthetic pigments (Chl a, Chl b, Chl a+b, and carotenoid) content decreased by only Cd application, SNP+Cd application decreased the rate of photosynthetic pigments reduction. When the results of Cd and Cd+SNP applications were evaluated for mineral (Fe, Zn, Mn and Cu) uptake, it was found that the positive effect of SNP was heterogeneously affected. Depending on SNP application, it was found that malondialdehyde (MDA) amount decreased in leaf in 100 μM Cd applications while hydrogen peroxide (H2O2) amount decreased in 100 and 500 μM Cd applications. When antioxidant enzyme activities were examined, it was found that catalase (CAT) and ascorbate peroxidase (APX) enzyme activities increased with 100 μM SNP applications under all Cd applications. As a result, it was found that SNP application under Cd stress generally supports physiological processes positively in white poplar, suggesting that NO molecule plays important alleviating roles in plant metabolism.

Alnus japonica is indigenous to korea and usually grows in wet lowlands and damp regions in the mountains. Oregonin, is known as a representative compound of the diarylheptanoid mainly found in Alnus species and has been reported to have anti-inflammatory and antioxidant effects. In this experiment, we conducted a study on the presence of orogonin in the native Alnus japonica from the Baekdudaegan mountain range in Korea. We collected a total of 30 samples from Chuncheon, Yangyang, and Jeongseon and conducted experiments. In the HPLC analysis, we confirmed that oregonin was detected in all samples. After the quantitative analysis, samples with the highest content from each region were selected and LC-MS/MS experiments were performed. As a result, the same molecular weight as oregonin, 477.2 m/z, was confirmed in all samples.

Mushroom is very important to rural dwellers due to the various economic, health and nutritional benefits that can be derived from its consumption, sales and utilization. Hence, this experiment was carried out with a view to evaluating the influence of locally available substrate i.e., sawdust of different wood species on the cultivation of Pleurotus ostreatus (oyster mushroom). Pleurotus ostreatus was grown on sawdust (indigenous and exotic species) using two pretreatment methods, which included hot water treatment to remove (a water-soluble extract) and non-extracted sawdusts (substrate). The result of this study showed that there was a significant difference in the effect of different wood species selected. Parkia biglobosa produced a better yield (71.06 g), followed by Anogeissus leiocarpus (53.17 g) and Gmelina arborea (51.39 g) in a non-treated unextracted states. In the treated samples, Parkia biglobosa has the highest yield (50.61 g) followed by Anogeissus leiocarpus (49.28 g) and Gmelina arborea (45.00 g). Thus, Parkia biglobosa and Anogeissus leiocarpus produced a higher yield of mushroom either in a treated or non-treated state and they could be recommended for growing oyster mushroom (Pleurotus ostreatus).