Article
Environmental Sciences
Masoumeh Aghababaei, Ataollah Ebrahimi, Ali Asghar Naghipour, Esmaeil Asadi, Jochem Verrelst
Summary: This study utilized multi-temporal datasets to improve the accuracy of VTs classification in Central Zagros, Iran, with results showing that multi-temporal datasets favored accurate VTs classification. The research highlights the importance of open access cloud-computing platforms like the Google Earth Engine for identifying optimal periods and multi-temporal imagery for VTs classification.
Article
Environmental Sciences
Lei Zhou, Ting Luo, Mingyi Du, Qiang Chen, Yang Liu, Yinuo Zhu, Congcong He, Siyu Wang, Kun Yang
Summary: Machine learning has been explored for automatic identification of construction and demolition waste (C&DW) using remote sensing data sources. By comparing three typical machine learning algorithms, Random Forest (RF) was identified as the optimal method for C&DW identification. Through experimental trials and parameter optimization, the classification accuracy for C&DW was significantly improved.
Article
Environmental Sciences
Jinxi Yao, Ji Wu, Chengzhi Xiao, Zhi Zhang, Jianzhong Li
Summary: Crop extraction and classification are essential in agricultural remote sensing. This study compared traditional machine learning, object-oriented classification, and deep neural networks, proposing a classification framework combining random forest and deep neural network. The spatial and temporal characteristics of crops were analyzed and the RF+DNN method showed better accuracy in crop classification.
Article
Environmental Sciences
Huu-Ty Pham, Hao-Quang Nguyen, Khac-Phuc Le, Thi-Phuong Tran, Nam-Thang Ha
Summary: Wetlands are productive ecosystems that sequester carbon and offer a solution to climate change. Despite advancements in remote sensing, accurate and automatic mapping of wetlands remains challenging due to complex input data. This study proposes a remote sensing approach using Google Earth Engine to automate the extraction of water bodies and mapping of growing lotus in Vietnam. The proposed framework, which utilizes K-Means clustering and machine learning models, achieved high accuracy in water extraction and lotus mapping. The technique has potential for large-scale mapping of other wetland types worldwide.
Article
Environmental Sciences
Maoxin Zhang, Tingting He, Guangyu Li, Wu Xiao, Haipeng Song, Debin Lu, Cifang Wu
Summary: This study conducted an empirical research in the Dexing copper mine, Jiangxi, China, to explore the process of distance and reclamation. Utilizing the Landsat archive on Google Earth Engine, the research detected the disturbance of surface mining in the 1986-2020 period using the CCDC algorithm. The results showed an increase in surface-mining area in the Dexing copper mine, with both mining damage and natural restoration being identified during the studied period. This method provides an innovative perspective for obtaining timely and accurate mining disturbed dynamic information.
Article
Remote Sensing
Tomas Martin Del Valle, Ping Jiang
Summary: Vegetation resources play a crucial role in sustainable development, with monitoring land use and cover being key to their sustainable management. Earth Observation satellites have provided a powerful platform for this task. A classification framework based on random forests and Sentinel data was developed, revealing that certain strategies such as pixel time series features and class-balanced labels can enhance performance but may also lead to tradeoffs between recall and precision.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2022)
Article
Environmental Sciences
Abdelaziz Htitiou, Abdelghani Boudhar, Abdelghani Chehbouni, Tarik Benabdelouahab
Summary: This study automated the extraction of cropland phenological metrics on GEE and used them with machine-learning models to produce high-resolution cropland and crop field-probabilities maps in Morocco. The classification product showed an overall accuracy of 97.86% for the nominal year 2019-2020, and the cropland probabilities maps accurately estimated sub-national SAU areas with an R-value of 0.9.
Article
Environmental Sciences
Kaixiang Yang, Youming Luo, Mengyao Li, Shouyi Zhong, Qiang Liu, Xiuhong Li
Summary: This article introduces a new method for reconstructing Sentinel-2 NDVI and surface reflectance time series, and makes improvements to the traditional discrete cosine transform method. Experimental results show that this method performs better in reconstructing NDVI time series, and can identify and reconstruct cloud-contaminated NDVI and surface reflectance with low RMSE and high R-2.
Article
Environmental Sciences
Hui Liu, Mi Chen, Huixuan Chen, Yu Li, Chou Xie, Bangsen Tian, Chu Wang, Pengfei Ge
Summary: This paper utilizes multiple machine learning algorithms, supported by the Google Earth Engine platform and based on Landsat 8 time series image data, to obtain the spatial variation information of agricultural land in Shandong Province from 2016 to 2020. The results show that the multi-spatial index time series method and the ensemble learning method have higher accuracy in obtaining phenological characteristics of agricultural land and classification.
Article
Agronomy
Juan Cao, Zhao Zhang, Yuchuan Luo, Liangliang Zhang, Jing Zhang, Ziyue Li, Fulu Tao
Summary: This study compared traditional machine learning and three deep learning models for winter wheat yield prediction in major production regions in China, finding that all models performed well at the county level, with DNN and RF having relatively good performance at the field level. The study provides valuable insights for timely and accurate estimation of crop yields, with important implications for food security and agricultural policy-making.
EUROPEAN JOURNAL OF AGRONOMY
(2021)
Article
Geosciences, Multidisciplinary
Yong Piao, Dongkun Lee, Sangjin Park, Ho Gul Kim, Yihua Jin
Summary: This study constructed a forest fire susceptibility map in Gangwon-do, Korea using Google Earth Engine and machine learning algorithms. The results identified slope, human activity, and interference as the important factors affecting forest fire occurrence in the region.
GEOMATICS NATURAL HAZARDS & RISK
(2022)
Article
Geography, Physical
Kueshi Semanou Dahan, Raymond Abudu Kasei, Rikiatu Husseini, Mohammed Y. Said, Md. Mijanur Rahman
Summary: Data processing and climate characterisation for studying the impact of climate on wildfire spread in Ghana are hindered by insufficient and unavailable data, particularly in developing countries. Machine learning, combined with data obtained from various sources, such as CHIRPS, FLDAS, and TerraClimate, is used to analyze the link and contribution of climatic and environmental parameters on wildfire spread in Guinea-savannah (GSZ) and Forest-savannah Mosaic zones (FSZ) in Ghana. The analysis reveals a decrease in rainfall and an increase in temperature in both GSZ and FSZ, with precipitation (PR), reference evapotranspiration (ETR), fire danger index (FDI), and various temperature-related variables playing a significant role in fire spread. The developed codes allow for easy updating and monitoring of climate variability and its impact on fires by researchers and decision-makers.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
Article
Environmental Sciences
Hannah Ferriby, Amir Pouyan Nejadhashemi, Juan Sebastian Hernandez-Suarez, Nathan Moore, Josue Kpodo, Ian Kropp, Rasu Eeswaran, Ben Belton, Mohammad Mahfujul Haque
Summary: Aquaculture in Bangladesh has experienced rapid growth in past decades, contributing significantly to the rural economy. This study proposed six strategies for improving fishpond detection using Sentinel-2 data and Google Earth Engine, with classification and regression trees performing the best among the four machine learning methods studied.
Article
Remote Sensing
Yulin Shangguan, Xiyu Li, Yi Lin, Jinsong Deng, Le Yu
Summary: This study successfully extracted and analyzed the nationwide soybean planting areas in Argentina from 2016 to 2019 using the Google Earth Engine and pixel-based machine learning method random forest. The results showed the importance of NDVI and NIR features in the classification. This research provides an effective method for accurately and rapidly retrieving the soybean planting area.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2022)
Article
Environmental Sciences
Rosa Lasaponara, Nicodemo Abate, Carmen Fattore, Angelo Aromando, Gianfranco Cardettini, Marco Di Fonzo
Summary: This study utilizes Sentinel-2 NDVI time series and Google Earth Engine to detect small-scale land-use/land-cover changes in fire-disturbed environs. The analysis identifies different types of changes and evaluates their reliability.
Article
Multidisciplinary Sciences
Hunter Stanke, Andrew O. Finley, Grant M. Domke, Aaron S. Weed, David W. MacFarlane
Summary: Research has shown that over half of the most abundant tree species in the western United States have experienced population declines in the last two decades, with subalpine tree species showing particularly severe decline, indicating significant changes in forest composition and structure.
NATURE COMMUNICATIONS
(2021)
Article
Ecology
L. E. Nave, K. DeLyser, G. M. Domke, M. K. Janowiak, T. A. Ontl, E. Sprague, B. F. Walters, C. W. Swanston
Summary: The analysis shows that natural factors have a greater control over soil organic carbon stocks than land use and management; harvesting leads to significant decreases in topsoil SOC stocks, but there are exceptions to this trend; land use changes only have limited impacts on SOC storage, primarily reforestation and forest conversion to cultivation.
ECOLOGICAL APPLICATIONS
(2021)
Review
Plant Sciences
Songlin Fei, Stephanie N. Kivlin, Grant M. Domke, Insu Jo, Elizabeth A. LaRue, Richard P. Phillips
Summary: First principles predict that there is a relationship between plant and mycorrhizal fungal diversity, but this relationship is inconsistent on larger scales, likely due to different relationships between different mycorrhizal fungal guilds and plant diversity, scale dependency, and lack of coordinated sampling efforts. Understanding the coupling between plant and mycorrhizal fungal diversity across scales is important for predicting the ecosystem consequences of species gains and losses.
Article
Ecology
Lucas E. Nave, Kendall DeLyser, Grant M. Domke, Scott M. Holub, Maria K. Janowiak, Brian Kittler, Todd A. Ontl, Eric Sprague, Eric B. Sucre, Brian F. Walters, Christopher W. Swanston
Summary: This article uses meta-analysis and observational databases to assess the impact of disturbance and management on soil organic carbon (SOC) stocks. The results indicate that vegetation, climate, and topography are the main controlling factors of SOC stocks. Increased warming, drying, wildfires, and forest regeneration failure pose significant risks to SOC stocks in the Pacific Northwest region. Wildfires decrease SOC stocks throughout the soil profile, while other management practices have minimal impact on SOC stocks.
ECOLOGICAL APPLICATIONS
(2022)
Article
Ecology
Jonathan Knott, Grant Domke, Christopher Woodall, Brian Walters, Michael Jenkins, Songlin Fei
Summary: This study analyzed two decades of forest plot data in the Great Lakes region of the eastern U.S. and found that shifts in forest communities have important implications for carbon dynamics. The carbon content of live trees varied with different communities, while the carbon content of standing dead trees was influenced by community composition and stand structure.
Article
Environmental Sciences
Lucia A. Fitts, Grant M. Domke, Matthew B. Russell
Summary: Forest disturbances play a critical role in ecosystem dynamics. This study compared different methods for quantifying disturbances at individual tree and condition-level scales, and provided a methodology for selecting an appropriate disturbance variable. The results showed that the choice of disturbance variable significantly affected the magnitude of disturbances. These findings are important for measuring disturbance magnitude, forest management plans, and carbon stock reports under future global change scenarios.
ENVIRONMENTAL MONITORING AND ASSESSMENT
(2022)
Article
Environmental Sciences
Yifan Yu, Sassan Saatchi, Grant M. Domke, Brian Walters, Christopher Woodall, Sangram Ganguly, Shuang Li, Subodh Kalia, Taejin Park, Ramakrishna Nemani, Stephen C. Hagen, Lindsay Melendy
Summary: Integrating satellite observations with forest inventory data can improve the accuracy and precision of estimating forest carbon stocks and changes.
ENVIRONMENTAL RESEARCH LETTERS
(2022)
Article
Agronomy
Courtney L. Giebink, Grant M. Domke, Rosie A. Fisher, Kelly A. Heilman, David J. P. Moore, R. Justin DeRose, Margaret E. K. Evans
Summary: There is great hope for forest ecosystems to contribute to greenhouse gas emission reduction targets and limit global warming. However, the current policy and ecology surrounding forest-based natural climate solutions (NCS), particularly in temperate forests of the United States, have gaps in carbon accounting and a lack of understanding between ambitions and forest ecology. Improved use of data models can help in better assessing and anticipating forest-based climate mitigation.
Article
Geography, Physical
Qiang Zhou, George Xian, Josephine Horton, Danika Wellington, Grant Domke, Roger Auch, Congcong Li, Zhe Zhu
Summary: Forests cover about one-third of the land area of the conterminous United States (CONUS) and play a crucial role in offsetting carbon emissions and supporting local economies. The demand for information on forest regrowth and recovery following disturbances has increased, particularly for cost-effective nature-based climate solutions. However, mapping the tree regrowth duration at an annual time interval and high resolution remains challenging.
GISCIENCE & REMOTE SENSING
(2022)
Article
Multidisciplinary Sciences
Mahendra Doraisami, Rosalyn Kish, Nicholas J. Paroshy, Grant M. Domke, Sean C. Thomas, Adam R. Martin
Summary: The study describes the largest global database of woody tissue carbon concentration, which includes 3,676 individual records from 864 tree species. This database provides important information for forest carbon estimation and for studying the extent, causes, and consequences of variation in wood chemical traits among species and biomes.
Article
Environmental Sciences
Nancy F. Sonti, Rachel Riemann, Miranda H. Mockrin, Grant M. Domke
Summary: The wildland-urban interface (WUI) is the fastest-growing land use type in the United States, and it is important to understand how this development affects the landscape and structure of WUI forests. Research found that WUI forests have higher carbon storage but lower structural diversity compared to non-WUI forests, which may impact forest regeneration and other ecological functions.
ENVIRONMENTAL RESEARCH LETTERS
(2023)
Review
Ecology
Jonathan A. Knott, Greg C. Liknes, Courtney L. Giebink, Sungchan Oh, Grant M. Domke, Ronald E. McRoberts, Valquiria F. Quirino, Brian F. Walters
Summary: Large-scale ecological sampling networks aim to collect in situ data for various purposes, but the issue of outliers arising in data harmonization is often overlooked. This paper reviews the sources of outliers and their impact on estimates of above-ground biomass population parameters using a case study. The study shows that the inclusion or removal of outliers can lead to substantial differences in biomass estimates, highlighting the importance of proper use of field-collected and remotely sensed data in geospatial data harmonization.
METHODS IN ECOLOGY AND EVOLUTION
(2023)
Article
Ecology
Brendan R. Quirion, Grant M. Domke, Brian F. Walters, Gary M. Lovett, Joseph E. Fargione, Leigh Greenwood, Kristina Serbesoff-King, John M. Randall, Songlin Fei
Summary: Research indicates that forests' carbon sequestration capacity is reduced by insect and disease disturbances, leading to a decrease in overall carbon sequestration ability. Strengthened international trade policies, phytosanitary standards, and improved forest management have the potential to protect forests and their natural contribution to climate change mitigation.
FRONTIERS IN FORESTS AND GLOBAL CHANGE
(2021)
Article
Environmental Sciences
Toshimi Nakajima, Mao Kuragano, Makoto Yamada, Ryo Sugimoto
Summary: This study compared the contribution of submarine groundwater discharge (SGD) to river nutrient budgets at nearshore and embayment scales, and found that SGD-derived nutrients become more important at larger spatial scales.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Fan Liu, Lei Zhang, Chongyang Zhang, Ziguang Chen, Jingguang Li
Summary: NO2 emissions from wall-mounted gas stoves used for household heating have become a significant source of indoor pollution in Chinese urban areas. The high indoor concentration of NO2 poses potential health risks to residents. It is urgently necessary to establish relevant regulations and implement emission reduction technologies to reduce NO2 emissions from wall-mounted gas stoves.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Letter
Environmental Sciences
Hans Peter H. Arp, Raoul Wolf, Sarah E. Hale, Sivani Baskaran, Juliane Gluege, Martin Scheringer, Xenia Trier, Ian T. Cousins, Harrie Timmer, Roberta Hofman-Caris, Anna Lennquist, Andre D. Bannink, Gerard J. Stroomberg, Rosa M. A. Sjerps, Rosa Montes, Rosario Rodil, Jose Benito Quintana, Daniel Zahn, Herve Gallard, Tobias Mohr, Ivo Schliebner, Michael Neumann
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Philomina Onyedikachi Peter, Binessi Edouard Ifon, Francois Nkinahamira, Kayode Hassan Lasisi, Jiangwei Li, Anyi Hu, Chang-Ping Yu
Summary: This study investigates the relationship between dissolved organic matter (DOM) and Rare Earth Elements (REEs) in sediments from Yundang Lagoon, China. The results show four distinct fluorescent components, with protein-like substances being the most prevalent. Additionally, the total fluorescence intensity and LREE concentrations exhibit a synchronized increase from Outer to Inner to Songbai Lake core sediments. The findings demonstrate a strong correlation between DOM content and pollution levels.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Surya Gupta, Pasquale Borrelli, Panos Panagos, Christine Alewell
Summary: The objective of this study is to incorporate soil hydraulic properties into the erodibility factor (K) of USLE-type models. By modifying and improving the existing equations for soil texture and permeability, the study successfully included information on saturated hydraulic conductivity (Ksat) into the calculation of K factor. Using the Random Forest machine learning algorithm, two independent K factor maps with different spatial resolutions were generated. The results show that the decrease in K factor values has a positive impact on the modeling of soil erosion rates.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Jesmin Akter, Wendy J. M. Smith, Yawen Liu, Ilho Kim, Stuart L. Simpson, Phong Thai, Asja Korajkic, Warish Ahmed
Summary: The choice of workflow in wastewater surveillance has a significant impact on SARS-CoV-2 concentrations, while having minimal effects on HF183 and no effect on HAdV 40/41 concentrations. Certain components in the workflow can be interchangeable, but factors such as buffer type, chloroform, and homogenization speed can affect the recovery of viruses and bacteria.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Yu Luo, Xueting Yang, Diwei Wang, Hongmei Xu, Hongai Zhang, Shasha Huang, Qiyuan Wang, Ningning Zhang, Junji Cao, Zhenxing Shen
Summary: Atmospheric PM2.5, which can generate reactive oxygen species (ROS), is associated with cardiorespiratory morbidity and mortality. The study found that both the mass concentration of PM2.5 and the DTT activity were higher during the heating season than during the nonheating season. Combustion sources were the primary contributors to DTT activity during the heating season, while secondary formation dominated during the nonheating season. The study also revealed that biomass burning had the highest inherent oxidation potential among all sources investigated.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Erin L. Murphy, Leah R. Gerber, Chelsea M. Rochman, Beth Polidoro
Summary: Plastic pollution has devastating consequences for marine organisms. This study uses a trait-based framework to develop a vulnerability index for marine mammals, seabirds, and sea turtles in Hawai'i. The index ranks 63 study species based on their vulnerability to macroplastic pollution, providing valuable information for species monitoring and management priorities.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Kenji Maurice, Amelia Bourceret, Sami Youssef, Stephane Boivin, Liam Laurent-Webb, Coraline Damasio, Hassan Boukcim, Marc-Andre Selosse, Marc Ducousso
Summary: Growing pressure from climate change and agricultural land use is destabilizing soil microbial community interactions. Little is known about microbial community resistance and adaptation to disturbances, hindering our understanding of recovery latency and implications for ecosystem functioning. This study found that anthropic disturbance and natural disturbance have different effects on the topology and stability of soil microbial networks.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Yunhao Li, Yali Feng, Haoran Li, Yisong Yao, Chenglong Xu, Jinrong Ju, Ruiyu Ma, Haoyu Wang, Shiwei Jiang
Summary: Deep-sea mining poses a serious threat to marine ecosystems and human health by disturbing sediment and transmitting metal ions through the food chain. This study developed a new regenerative adsorption material, OMN@SA, which effectively removes metal ions. The adsorption mechanism and performance of the material for metal ion fixation were investigated.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Antonio Medici, Margherita Lavorgna, Marina Isidori, Chiara Russo, Elena Orlo, Giovanni Luongo, Giovanni Di Fabio, Armando Zarrelli
Summary: Valsartan, a widely used antihypertensive drug, has been detected in high concentrations in surface waters due to its unchanged excretion and incomplete degradation in wastewater treatment plants. This study investigated the degradation of valsartan and identified 14 degradation byproducts. The acute and chronic toxicity of these byproducts were evaluated in key organisms in the freshwater trophic chain.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Jiang Lin, Lianbao Chi, Qing Yuan, Busu Li, Mingbao Feng
Summary: This study investigated the photodegradation behavior and product formation of two representative pharmaceuticals in simulated estuary water. The study found that the formed transformation products of these pharmaceuticals have potential toxicity on marine organisms, including oxidative stress and damage to cellular components.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Hua Fang, Dongdong Jiang, Ye He, Siyi Wu, Yuehong Li, Ziqi Zhang, Haoting Chen, Zixin Zheng, Yan Sun, Wenxiang Wang
Summary: This study revealed that exposure to lower levels of air pollutants led to decreased pregnancy rates, with PM10, NO2, SO2, and CO emerging as the four most prominent pollutants. Individuals aged 35 and above exhibited heightened susceptibility to pollutants.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Ali Shaan Manzoor Ghumman, Rashid Shamsuddin, Amin Abbasi, Mohaira Ahmad, Yoshiaki Yoshida, Abdul Sami, Hamad Almohamadi
Summary: In this study, inverse vulcanized polysulfides (IVP) were synthesized by reacting molten sulfur with 4-vinyl benzyl chloride, and then functionalized using N-methyl D-glucamine (NMDG). The functionalized IVP showed a high mercury adsorption capacity and a machine learning model was developed to predict the amount of mercury removed. Furthermore, the functionalized IVP can be regenerated and reused, providing a sustainable and cost-effective adsorbent.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Rita Bonfiglio, Renata Sisto, Stefano Casciardi, Valeria Palumbo, Maria Paola Scioli, Erica Giacobbi, Francesca Servadei, Gerry Melino, Alessandro Mauriello, Manuel Scimeca
Summary: This study investigated the presence of aluminum in human colon cancer samples and its potential association with biological processes involved in cancer progression. Aluminum was found in tumor areas of 24% of patients and was associated with epithelial to mesenchymal transition (EMT) and cell death. Additional analyses revealed higher tumor mutational burden and mutations in genes related to EMT and apoptosis in aluminum-positive colon cancers. Understanding the molecular mechanisms of aluminum toxicity may improve strategies for the management of colon cancer patients.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
