Review
Environmental Sciences
Eldar Kurbanov, Oleg Vorobev, Sergey Lezhnin, Jinming Sha, Jinliang Wang, Xiaomei Li, Janine Cole, Denis Dergunov, Yibo Wang
Summary: This review provides a comprehensive meta-analysis of studies on remotely sensed methods and data used for estimation of forest burnt area, burn severity, post-fire effects, and forest recovery patterns at the global level. It analyzes the characteristics and trends of the research papers, discusses the challenges and advancements of remote sensing techniques in assessing forest burnt area and recovery, and identifies potential opportunities for future research.
Review
Forestry
Abraham Aidoo Borsah, Majid Nazeer, Man Sing Wong
Summary: The increasing level of atmospheric carbon dioxide and its effects on the climate system have become a global environmental issue. Forest ecosystems play a vital role in stabilizing carbon in the atmosphere as a carbon sink and providing habitats for numerous species. Light detection and ranging (LIDAR) technology has revolutionized our understanding of forest structures and enhanced our ability to monitor forest biomass. This paper reviews metrics for forest biomass estimation, discusses methods for selecting metrics for biomass modeling, and addresses assessment criteria for selecting allometric equations for aboveground forest biomass estimations using LIDAR data.
Review
Forestry
Lei Tian, Xiaocan Wu, Yu Tao, Mingyang Li, Chunhua Qian, Longtao Liao, Wenxue Fu
Summary: This article provides an overview of the progress in remote sensing-based forest aboveground biomass (AGB) estimation, including the principles of remote sensing techniques, data sources and methods, sources of uncertainty, and future research directions. Remote sensing plays a crucial role in forest AGB estimation and carbon cycle studies.
Article
Environmental Sciences
Gaia Vaglio Laurin, Nicola Puletti, Clara Tattoni, Carlotta Ferrara, Francesco Pirotti
Summary: Windstorms are a major disturbance factor for European forests, with the 2018 Vaia storm causing significant ecological and financial losses in Italy. Estimating timber loss using satellite remote sensing faces challenges, highlighting the urgent need for a unified national or regional strategy. Remote sensing-based surveys targeting forests are crucial, especially for European countries lacking reliable forest stocks data.
Article
Forestry
Tomasz Hycza, Agnieszka Kaminska, Krzysztof Sterenczak
Summary: This study compared methods for determining the area for which canopy cover is calculated using data from ALS, discussing the differences in accuracy and complexity. The most accurate method was Method 2, while Method 1 was found to be the least accurate option. Accuracy was better in the case of the Kyoto Protocol definition.
Article
Biodiversity Conservation
Nicolas Labriere, Stuart J. Davies, Mathias Disney, Laura Duncanson, Martin Herold, Simon L. Lewis, Oliver L. Phillips, Shaun Quegan, Sassan S. Saatchi, Dmitry G. Schepaschenko, Klaus Scipal, Plinio Sist, Jerome Chave
Summary: This study aims to establish a global forest biomass reference measurement system. To successfully implement this system, uniform data collection and processing standards, inclusive and equitable system establishment and management, as well as mandatory training and involvement of site partners in downstream activities are emphasized.
GLOBAL CHANGE BIOLOGY
(2023)
Article
Environmental Sciences
Meizhi Lin, Qingping Ling, Huiqing Pei, Yanni Song, Zixuan Qiu, Cai Wang, Tiedong Liu, Wenfeng Gong
Summary: The tropical rainforests in Hainan Island, China, are complex in structure with high biomass density, making ground surveys difficult. Remote sensing is a good monitoring method, but the saturation of data from different satellite sensors results in low accuracy.
Review
Environmental Sciences
J. Ashwini John, Melvin S. Samuel, Muthusamy Govarthanan, Ethiraj Selvarajan
Summary: Marine actinobacteria can produce cellulases with high stability, which can convert cellulose into glucose for biofuel production. This review provides a comprehensive overview of various applications of cellulases from marine actinobacteria, including screening for novel cellulase genes, enhancing production, and immobilizing the enzyme for industrial usage.
ENVIRONMENTAL RESEARCH
(2022)
Article
Remote Sensing
Rajeev Bhattarai, Parinaz Rahimzadeh-Bajgiran, Aaron Weiskittel, Saeid Homayouni, Tawanda W. Gara, Ryan P. Hanavan
Summary: This study used multiple remote sensing data to model the leaf area index and basal area per ha of red spruce and balsam fir. The results showed that the Random Forest algorithm performed better in modeling. The red-edge spectral vegetation indices played a significant role in the estimation of both leaf area index and basal area per ha. These models are important for evaluating the dynamics of the eastern spruce budworm, as red spruce and balsam fir are its primary host species.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2022)
Review
Energy & Fuels
Nisha Das, Pradip Kumar Jena, Diptymayee Padhi, Mahendra Kumar Mohanty, Gyanaranjan Sahoo
Summary: With the depletion of petroleum derivatives and increasing environmental pollution issues, there has been a growing interest in the study of lignocellulosic biomass as an alternative source of energy. This review focuses on the characterization of different biomass feedstocks and the development of pretreatment technologies for bioethanol production. Various physical, chemical, physicochemical, and biological methods are discussed, which help in overcoming the recalcitrance of lignocellulosic biomass and facilitate the hydrolysis and fermentation process for bioethanol production.
BIOMASS CONVERSION AND BIOREFINERY
(2023)
Review
Environmental Sciences
Zhaobin Wang, Yikun Ma, Yaonan Zhang, Jiali Shang
Summary: The paper provides a comprehensive review of the application of remote sensing technology in grassland monitoring and management. It discusses the estimation methods for various grassland parameters and reviews the applications of remote sensing monitoring, including grassland degradation, grassland use, disaster monitoring, and carbon cycle monitoring. The study suggests that advanced estimation methods and deep learning should be explored in future research.
Article
Environmental Sciences
Zhibin Sun, Wenqi Qian, Qingfeng Huang, Haiyan Lv, Dagui Yu, Qiangxin Ou, Haomiao Lu, Xuehai Tang
Summary: This study estimated the biomass of broad-leaved forests in a nature reserve using machine learning models and remote sensing data. The artificial neural network model showed the highest accuracy and significant temporal variation in biomass was observed from 1998 to 2016. Additionally, correlations were found between biomass change and climate factors.
Review
Chemistry, Physical
Anju Singh, Saroj Raj Kafle, Mukesh Sharma, Beom Soo Kim
Summary: In recent decades, high-efficiency renewable energy progress has been achieved through the utilization of advanced nano-module catalysts. This review article focuses on the synthesis, properties, mechanisms, and applications of carbon dots (CDs) as nanocatalysts in the field of energy storage and conversion. CDs possess unique features such as non-toxicity, biocompatibility, excellent electrocatalytic activity, and dispersibility, making them promising materials for energy-related applications.
Article
Environmental Sciences
Hao Tang, Lei Ma, Andrew Lister, Jarlath O'Neill-Dunne, Jiaming Lu, Rachel L. Lamb, Ralph Dubayah, George Hurtt
Summary: Large-scale airborne lidar data collections can be used to generate high-resolution forest aboveground biomass maps at the state level and beyond, showing potential for forest carbon planning. This study refines a multi-state level forest carbon monitoring framework to address spatial inconsistencies caused by data quality variability. The use of a linear model maintains good prediction accuracy of aboveground biomass density and mitigates problems related to data quality variability, leading to the generation of a consistent 30 m pixel forest aboveground carbon map covering 11 states in the USA.
ENVIRONMENTAL RESEARCH LETTERS
(2021)
Review
Forestry
Jaz Stoddart, Juan Suarez, William Mason, Ruben Valbuena
Summary: Continuous cover forestry is a sustainable management approach that requires new evaluation methods and solutions to challenges through the application of remote sensing techniques.
CURRENT FORESTRY REPORTS
(2023)
Article
Geosciences, Multidisciplinary
Seunghee Lee, Seohui Park, Myong-In Lee, Ganghan Kim, Jungho Im, Chang-Keun Song
Summary: This study used a machine learning algorithm to estimate ground-level particulate matter (PM) and applied it to a weather forecasting model. Initializing the model with the new analysis data significantly reduced analysis error and improved forecast skill. The synergistic use of data assimilation and machine learning can maximize the effectiveness of satellite-based air quality forecasts at the ground.
GEOPHYSICAL RESEARCH LETTERS
(2022)
Article
Environmental Sciences
Sihun Jung, Cheolhee Yoo, Jungho Im
Summary: A novel two-step data fusion framework was proposed to generate high-resolution seamless daily sea surface temperature (SST) products from multi-satellite data sources, and its performance was evaluated over the Kuroshio Extension in the Northwest Pacific. The results showed promising results and the superiority of the proposed approach using multi-satellite data sources.
Article
Environmental Sciences
You-Hyun Baek, Il-Ju Moon, Jungho Im, Juhyun Lee
Summary: A novel TC size estimation model using CNN based on geostationary satellite infrared images was developed. The multi-task model performed better than the single-task model, and the inclusion of TC intensity information further improved the performance. The results suggest that this CNN model using satellite images may be a powerful tool for estimating TC sizes.
Article
Environmental Sciences
Dongjin Cho, Dukwon Bae, Cheolhee Yoo, Jungho Im, Yeonsu Lee, Siwoo Lee
Summary: This study proposed a novel approach to reconstruct all-sky 1 km MODIS LST in South Korea during the summer seasons, considering the cloud effects on LST. The reconstructed LST showed higher accuracy compared to the LDAPS model, and the cloud cover information improved the LST estimation accuracy under cloudy conditions.
Article
Meteorology & Atmospheric Sciences
Dongjin Cho, Cheolhee Yoo, Bokyung Son, Jungho Im, Donghyuck Yoon, Dong-Hyun Cha
Summary: This study compared different post-processing models and proposed a novel multi-model ensemble method based on skill scores for predicting next-day maximum air temperature in South Korea and Seoul. The results showed that the convolutional neural network performed well among individual models and had lower root mean square error when utilizing surrounding spatial information. The proposed multi-model ensemble method demonstrated more reliable and robust results compared to the individual models.
WEATHER AND CLIMATE EXTREMES
(2022)
Article
Environmental Sciences
Man Wang, Jungho Im, Yinghui Zhao, Zhen Zhen
Summary: This study explores the non-destructive estimation of individual tree aboveground biomass using unmanned aerial vehicle and terrestrial LiDAR data. The results show that the hierarchical Bayesian method and multi-platform LiDAR data provide a potential solution for accurate individual tree AGB modeling with small sample sizes.
Article
Environmental Sciences
Yeji Shin, Juhyun Lee, Jungho Im, Seongmun Sim
Summary: In this study, a geostationary-satellite-based approach for estimating the center of tropical cyclones (TCs) is proposed. The approach utilizes a score matrix (SCM) and an enhanced logarithmic spiral band (LSB) to determine an accurate TC center. The experimental results show that the proposed method outperforms existing approaches, particularly in detecting strong TCs.
Article
Geography, Physical
Young Jun Kim, Daehyeon Han, Eunna Jang, Jungho Im, Taejun Sung
Summary: Salinity is a critical factor that affects the circulation and heat transport of oceans. Satellite remote sensing has been used to monitor sea surface salinity (SSS) since 2009. This study reviews the methods of retrieving SSS from satellites and provides guidelines for future research. The limitations and challenges of satellite-derived SSS are also discussed.
GISCIENCE & REMOTE SENSING
(2023)
Article
Geography, Physical
Yoojin Kang, Eunna Jang, Jungho Im, Chungeun Kwon
Summary: This study proposed a deep learning-based forest fire detection algorithm that effectively reduced detection latency and false alarms. By combining input features, the research demonstrated that temporal and spatial information contributed to improving the accuracy of machine learning techniques for fire detection.
GISCIENCE & REMOTE SENSING
(2022)
Article
Environmental Sciences
Seonyoung Park, Jaese Lee, Jongmin Yeom, Eunkyo Seo, Jungho Im
Summary: Drought has a significant impact on the economy of a region, and its severity is determined by the level of infrastructure in the affected area. This study compared drought indices in North Korea and South Korea to understand the differences in agricultural infrastructure and their impact on drought. The findings suggest that different agricultural systems result in different drought impacts.
Article
Environmental Sciences
Hyunyoung Choi, Seonyoung Park, Yoojin Kang, Jungho Im, Sanghyeon Song
Summary: Many studies have explored the use of satellite data, specifically aerosol optical depth (AOD), to produce real-time ground-level information on PM2.5. Machine learning-based research has emerged to overcome the computational demands of these techniques. In this study, ML techniques were used to estimate ground-level PM2.5 concentrations in South Korea using top-of-atmosphere reflectance from the GOCI-I and GOCI-II satellites. Three ML techniques and three input feature schemes were examined, with the results showing that LGBM performed the best. The use of GOCI-II-based models with its higher spatial resolution and TOA reflectance data reduced the missing rate of estimated PM2.5 concentrations by up to 50%.
ENVIRONMENTAL POLLUTION
(2023)
Article
Geography, Physical
Daehyeon Han, Minki Choo, Jungho Im, Yeji Shin, Juhyun Lee, Sihun Jung
Summary: Skillful quantitative precipitation nowcasting is important for predicting short-term intense precipitation. This study proposes an improved radar-based QPN model using the simpler yet better video prediction model and demonstrates its superior performance in independent evaluations. It shows robust performance under different precipitation conditions.
GISCIENCE & REMOTE SENSING
(2023)
Article
Meteorology & Atmospheric Sciences
Jinhyeok Yu, Chul H. Song, Dogyeong Lee, Sojin Lee, Hyun S. Kim, Kyung M. Han, Seohui Park, Jungho Im, Soon-Young Park, Moongu Jeon, Vincent-Henri Peuch, Pablo E. Saide, Gregory R. Carmichael, Jeeho Kim, Jhoon Kim, Chang-Keun Song, Jung-Hun Woo, Seong-Hyun Ryu
Summary: Concentrations of PM2.5 and PM10 in the environment have become a serious global environmental problem, causing deaths and economic losses. This study demonstrates that by combining information from ground observation networks, GEO satellite sensor, and an advanced air quality modeling system, the short-term predictability of PM2.5 over South Korea can be greatly improved. The study highlights the challenges of using LEO satellite observations and shows that the advanced modeling system with synergistically-combined information can achieve substantial enhancements in PM2.5 predictability.
NPJ CLIMATE AND ATMOSPHERIC SCIENCE
(2023)
Article
Engineering, Electrical & Electronic
Seongmun Sim, Jungho Im
Summary: This study constructed machine-learning models for continuous ocean-fog detection based on the infrared channels of Himawari-8 satellite. The proposed model achieved high accuracy in both daytime and nighttime, accurately distinguishing between ocean-fog, clear skies, and clouds. The research findings can be utilized to improve operational ocean-fog detection and forecasting.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Remote Sensing
Siwoo Lee, Cheolhee Yoo, Jungho Im, Dongjin Cho, Yeonsu Lee, Dukwon Bae
Summary: This study proposes a hybrid analytical method to improve the detection of urban heat island effect by considering the dynamic structural changes of cities both horizontally and vertically. Temporal LCZ maps were constructed using a convolutional neural network, and downscaled models were developed to observe the detailed surface energy flux. A filtering method was employed to improve the reliability of the results.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
