Zhenhao Yin

Accurate mapping of deforestation in regions characterized by complex, heterogeneous landscapes and frequent cloud cover remains a major challenge in remote sensing. This study presents a phenology-informed, spatiotemporal data fusion framework for robust deforestation mapping in North Korea, focusing particularly on hillside fields and unstocked forests—two dominant deforested land cover types in the region. By integrating multi-temporal satellite observations with variables derived from phenological dynamics, our approach effectively distinguishes spectrally similar classes that are otherwise challenging to separate. The Flexible Spatiotemporal Data Fusion Algorithm (FSDAF) was employed to generate high-frequency, Landsat-like time-series from MODIS data, thereby ensuring fine spatial detail alongside temporal consistency. Key classification features—including NDVI, NDSI, NDWI, and snowmelt timing—were identified and ranked using the Random Forest (RF) algorithm. The classification results were validated against reference Landsat imagery, achieving high correlation coefficients (R > 0.8) and structural similarity index values (SSIM > 0.85). The RF-based land cover classification reached an overall accuracy of 86.1% and a Kappa coefficient of 0.837, reflecting strong agreement with ground reference data. Comparative analyses demonstrated that this method outperformed global land cover products, such as MCD12Q1, in capturing the spatial variability and fragmented patterns of deforestation at the regional scale. This research underscores the value of combining spatiotemporal fusion with phenological indicators for accurate, high-resolution deforestation monitoring in data-limited environments, providing practical insights for sustainable forest management and ecological restoration planning.

Several spatiotemporal data fusion methods have been developed to generate continuous fine-resolution satellite imagery using widely available datasets. This study introduces the Residual Distribution-based Spatiotemporal Data Fusion Method (RDSFM), designed to enhance fusion accuracy. RDSFM addresses residuals caused by spatial and temporal variations, utilizing the IR-MAD algorithm to estimate subpixel distribution weights based on multivariate data collected over time. Compared to existing methods, RDSFM offers several key advantages: (1) It accurately predicts seasonal variations in bands such as red and NIR, (2) It effectively handles heterogeneous landscapes and shifting land cover, and (3) It requires only one high-resolution reference image as input, minimizing data requirements. The effectiveness of RDSFM was validated using real satellite images and benchmarked against methods like unmixing-based data fusion (UBDF). Experimental results show that RDSFM successfully captures seasonal changes in coarse-resolution bands, particularly in red and NIR, making it especially useful for vegetation analysis. Additionally, RDSFM demonstrates strong performance in managing heterogeneous landscapes and areas with dynamic land cover, as confirmed by both visual and quantitative assessments.

This study aims to characterize forest cover transitions in North Korea and identify deforested areas that are degraded or at risk of degradation. We used phenological information and random forest classifiers to perform a deforestation classification. We then extracted the two main forest cover loss patterns, sloping farmland (farmland with slope greater than 6 degrees) and unstocked forest (crown cover less than 20%), for the years of 2000, 2010, and 2020. Based on the deforestation map of each year, we analyzed the deforestation dynamics from 1990 to 2020. Forests showed decreases in cover by 27% over the 30-year study period and accounted for 41.5% of the total land area in 2020. Deforestation spread into the core area, which led to severe shrinkage and fragmentation of forests. Unstocked forest and sloping farmland experienced the highest rates of loss among the forestland uses and accounted for 48.9% and 39.3% of the total loss over the study period, respectively. During the study period, 25,128 km², 5346 km², and 6728 km² of forestland was cleared, degraded, and was at risk of degradation or barrenness by artificial repeated fires, respectively. This methodological framework provides a valuable template for areas that are difficult to access, and the deforestation dynamics results can provide a basis for conservation and sustainable management of forest resources.

With the increasing environmental pollution caused by waste polymers, the conversion of polymer components in biomass into valuable products is of great significance for waste management and resource recovery. A two-stage microbial fuel cell (MFC) was used to treat furfural wastewater in this study. The maximum output voltage was 240–250 mV and the power generation time in an operation cycle was 286 h. The degradation efficiency of furfural reached 99–100% (furfural concentration at 300–3000 mg/L) and was slightly reduced to 91% at 7000 mg/L. In addition, the BOD/COD ratio of the furfural wastewater increased from 0.31 to 0.48 after MFC processing. The molecular analysis of the anodic bacterial isolates indicated that the phylogenetic bacterial mixture was dominated by five active anaerobic bacteria with a similarity percentage above 99% for each strain: Burkholderia (B. burdella), Clostridium sensu stricto (Cymbidaceae), Klebsiella (Klebsiella), Ethanoligenens (anaerobic genus), and Acidocella (anaerobic genus); the mixture exhibited good properties to carry out bioelectricity generation in the microbial fuel cell. This indicates that the MFC has effectively degraded furfural for pollutant removal and power generation and is a promising clean method to treat furfural pollution in industry wastewater.

The sensitive detection of heavy metal ions with high toxicity and mobility is extremely important for environmental monitoring. In this study, nitrogen‐doped biochar (N‐BC) was synthesized using waste mushroom bran as a raw material, and nitrogen atoms were successfully doped onto the surface of the biochar via a facile process using an activation‐carbonation strategy. The functional groups on the surface of the biochar were enriched via doping, and the roughness of the biochar surface was significantly improved. Experimental results showed that N‐BC was an efficient sensing material for electroanalysis; therefore, it was used to construct a novel electrochemical sensing platform. The electrochemical response of the biochar‐modified working electrodes was effectively improved for the detection of heavy metals. Under optimized deposition potential, enrichment time, and pH, the N‐BC‐based sensing platform could detect Pb²⁺ and Cd²⁺ separately as well as simultaneously, indicating its significant potential for electrochemical sensing.

The global shortage of freshwater supply has become an imminent problem. The high energy consumption of traditional desalination technology cannot meet the demand for sustainable energy development. Therefore, exploring new energy sources to obtain pure water has become one of the effective ways to solve the freshwater resource crisis. In recent years, solar steam technology which utilizes solar energy as the sole input source for photothermal conversion has shown to be sustainable, low-cost, and environmentally friendly, providing a viable low-carbon solution for freshwater supply. This review summarizes the latest developments in solar steam generators. The working principle of steam technology and the types of heating systems are described. The photothermal conversion mechanisms of different materials are illustrated. Emphasis is placed on describing strategies to optimize light absorption and improve steam efficiency from material properties to structural design. Finally, challenges in the development of solar steam devices are pointed out, aiming to provide new ideas for the development of solar steam devices and alleviate the shortage of freshwater resources.

Ginsenoside is the most important secondary metabolite of ginseng. Natural sources of wild ginseng have been overexploited. Although root culture could reduce the length of the growth cycle of ginseng, the number of ginsenosides is fewer and their contents are lower in adventitious roots of ginseng than that in ginseng cultivated in the field. In this study, we investigated the effects of endophytic bacterial elicitors on biomass and ginsenoside production in adventitious roots cultures of Panax ginseng. Endophyte LB 5-3 as an elicitor could increase biomass and ginsenoside accumulation in ginseng adventitious root culture. After 6 days elicitation with a 10.0 mL of strain LB 5-3, the content of total ginsenoside was 2.026 mg g⁻¹ which was four times more than that in unchallenged roots. The combination of methyl jasmonate and strain LB 5-3 had a negative effect on ginseng adventitious root growth and ginsenoside production. The genomic DNA of strain LB 5-3 was sequenced, and was found to be most closely related to Bacillus altitudinis (KX230132.1). The challenged ginseng adventitious root extracts exerted inhibitory effect against the HepG2 cells, which IC₅₀ value was 0.94 mg mL⁻¹.