Cerebellar parallel fiber-Purkinje cell (PF-PC) long-term synaptic plasticity is important for the formation and stability of cerebellar neuronal circuits, and provides substrates for motor learning and memory. We previously reported both presynaptic long-term potentiation (LTP) and long-term depression (LTD) in cerebellar PF-PC synapses in vitro. However, the expression and mechanisms of cerebellar PF-PC synaptic plasticity in the cerebellar cortex in vivo are poorly understood. In the present study, we studied the properties of 4 Hz stimulation-induced PF-PC presynaptic long-term plasticity using in vivo the whole-cell patch-clamp recording technique and pharmacological methods in urethane-anesthetised mice. Our results demonstrated that 4 Hz PF stimulation induced presynaptic LTD of PF-PC synaptic transmission in the intact cerebellar cortex in living mice. The PF-PC presynaptic LTD was attenuated by either the N-methyl-D-aspartate receptor antagonist, D-aminophosphonovaleric acid, or the group 1 metabotropic glutamate receptor antagonist, JNJ16259685, and was abolished by combined D-aminophosphonovaleric acid and JNJ16259685, but enhanced by inhibition of nitric oxide synthase. Blockade of cannabinoid type 1 receptor activity abolished the PF-PC LTD and revealed a presynaptic PF-PC LTP. These data indicate that both endocannabinoids and nitric oxide synthase are involved in the 4 Hz stimulation-induced PF-PC presynaptic plasticity, but the endocannabinoid-dependent PF-PC presynaptic LTD masked the nitric oxide-mediated PF-PC presynaptic LTP in the cerebellar cortex in urethane-anesthetised mice.

Remote sensing (RS) has been used to monitor inaccessible regions. It is considered a useful technique for deriving important environmental information from inaccessible regions, especially North Korea. In this study, we aim to develop a tree species classification model based on RS and machine learning techniques, which can be utilized for classification in North Korea. Two study sites were chosen, the Korea National Arboretum (KNA) in South Korea and Mt. Baekdu (MTB; a.k.a., Mt. Changbai in Chinese) in China, located in the border area between North Korea and China, and tree species classifications were examined in both regions. As a preliminary step in developing a classification algorithm that can be applied in North Korea, common coniferous species at both study sites, Korean pine (Pinus koraiensis) and Japanese larch (Larix kaempferi), were chosen as targets for investigation. Hyperion data have been used for tree species classification due to the abundant spectral information acquired from across more than 200 spectral bands (i.e., hyperspectral satellite data). However, it is impossible to acquire recent Hyperion data because the satellite ceased operation in 2017. Recently, Sentinel-2 satellite multispectral imagery has been used in tree species classification. Thus, it is necessary to compare these two kinds of satellite data to determine the possibility of reliably classifying species. Therefore, Hyperion and Sentinel-2 data were employed, along with machine learning techniques, such as random forests (RFs) and support vector machines (SVMs), to classify tree species. Three questions were answered, showing that: (1) RF and SVM are well established in the hyperspectral imagery for tree species classification, (2) Sentinel-2 data can be used to classify tree species with RF and SVM algorithms instead of Hyperion data, and (3) training data that were built in the KNA cannot be used for the tree classification of MTB. Random forests and SVMs showed overall accuracies of 0.60 and 0.51 and kappa values of 0.20 and 0.00, respectively. Moreover, combined training data from the KNA and MTB showed high classification accuracies in both regions; RF and SVM values exhibited accuracies of 0.99 and 0.97 and kappa values of 0.98 and 0.95, respectively.

Climate change and global rapid agricultural expansion have drastically reduced the area of wetlands globally recently, so that the ecosystem functions of wetlands have been impacted severely. Therefore, this study integrated the land use data and the integrated valuation of ecosystem services and tradeoffs (InVEST) model to evaluate the impacts of the land-use change (LUC) on wetland ecosystem services (ES) from 1976 to 2016 in the Tumen River Basin (TRB). Results reveal that the area of wetlands in TRB had decreased by 22.39% since 1976, mainly due to the rapid conversion of wetlands to dry fields and construction lands, and the LUC had induced notable geospatial changes in wetland ES consequently. A marked decrease in carbon storage and water yield was observed, while the habitat quality was enhanced slightly. Specifically, the conversion of rivers and paddy fields to ponds and reservoirs were the main reasons for the increase in habitat quality and caused the habitat quality to increase by 0.09. The conversion of marshes to lakes, paddy fields, grasslands, dry fields, and artificial surfaces were the key points for the decline in carbon storage; the conversion of marshes to lakes (5.38 km2) and reservoir ponds (1.69 km2) were the dominant factors driving the losses of water yield. According to our results, we should center on the conservation of wetlands and rethink the construction of the land use. The findings are expected to provide a theoretical reference and basis for promoting environmental protection in TRB and the construction of ecological civilization in border areas.

The most recent forest-type map of the Korean Peninsula was produced in 1910. That of South Korea alone was produced since 1972; however, the forest type information of North Korea, which is an inaccessible region, is not known due to the separation after the Korean War. In this study, we developed a model to classify the five dominant tree species in North Korea (Korean red pine, Korean pine, Japanese larch, needle fir, and Oak) using satellite data and machine-learning techniques. The model was applied to the Gwangneung Forest area in South Korea; the Mt. Baekdu area of China, which borders North Korea; and to Goseong-gun, at the border of South Korea and North Korea, to evaluate the model’s applicability to North Korea. Eighty-three percent accuracy was achieved in the classification of the Gwangneung Forest area. In classifying forest types in the Mt. Baekdu area and Goseong-gun, even higher accuracies of 91% and 90% were achieved, respectively. These results confirm the model’s regional applicability. To expand the model for application to North Korea, a new model was developed by integrating training data from the three study areas. The integrated model’s classification of forest types in Goseong-gun (South Korea) was relatively accurate (80%); thus, the model was utilized to produce a map of the predicted dominant tree species in Goseong-gun (North Korea).