Article
Environmental Sciences
Shichen Mu, Kai You, Ting Song, Yajie Li, Lihong Wang, Junzhe Shi
Summary: This article introduces a method for identifying aquatic plants based on remote sensing technology. By constructing a decision tree file, the canopy spectra of eight plants in the Changguangxi Wetland water area were monitored using hyperspectral remote sensing technology, and the effectiveness of this method was demonstrated. The results showed that the spectral characteristics of aquatic plants can be enhanced by calculating spectral indices, thereby improving comparability among different species. The overall recognition accuracy of the constructed decision tree file for eight types of plants reached 85.02%.
ENVIRONMENTAL MONITORING AND ASSESSMENT
(2023)
Article
Environmental Sciences
Huayu Li, Jianhua Wan, Shanwei Liu, Hui Sheng, Mingming Xu
Summary: Efficient methodologies for wetland vegetation mapping are crucial for wetland management and monitoring. This research employed Mahalanobis Distance-based Dynamic Time Warping (MDDTW) using multi-dimensional feature time series to improve the accuracy of wetland vegetation classification. Experimental results in the Yellow River Delta (YRD) showed that the K-Nearest Neighbors (KNN) algorithm based on MDDTW (KNN-MDDTW) achieved high classification accuracy, with an overall accuracy of over 90% and a kappa value exceeding 0.9.
Review
Environmental Sciences
S. Mohammad Mirmazloumi, Armin Moghimi, Babak Ranjgar, Farzane Mohseni, Arsalan Ghorbanian, Seyed Ali Ahmadi, Meisam Amani, Brian Brisco
Summary: This study evaluates the status and trends of wetland studies in Canada using Remote Sensing (RS) technology by reviewing scientific papers published between 1976 and 2020. The analysis shows a rising trend in utilizing multi-source RS datasets and advanced machine learning algorithms for wetland mapping in Canada, with most studies focusing on the province of Ontario. Additionally, pixel-based supervised classifiers were found to be the most popular wetland classification algorithms.
Article
Green & Sustainable Science & Technology
Congju Fu, Baoyin He, Yadong Zhou, Hui Liu, Fan Yang, Jinwen Song, Huiping Cai, Xiaoqin Yang
Summary: This study used remote sensing technology to monitor the spatial-temporal changes of aquatic vegetation in Futou Lake. The results showed that the manual division threshold method performed well in classification, the growth of aquatic vegetation was divided into two stages, and the dominant species were Potamogeton crispus and Trapa.
Review
Environmental Sciences
Gillian S. L. Rowan, Margaret Kalacska
Summary: Submerged aquatic vegetation (SAV) is a critical but understudied component of aquatic ecosystems, which is rapidly changing due to global climate change and human disturbances. Remote sensing (RS) offers efficient and accurate large-scale monitoring for proper SAV management, though its application to underwater ecosystems is complicated by the water column effect and requires careful consideration of sensor selection and data processing methods. Successful use of RS for SAV identification and detection depends on factors such as data quality, resolution, and the specific research goals.
Article
Marine & Freshwater Biology
Brooke M. Conroy, Sarah M. Hamylton, Kristian Kumbier, Jeffrey J. Kelleway
Summary: This study investigated the Coastal Swamp Oak Forest (CSOF) along Australia's east coast using field surveys and remote sensing methods. The results showed that the structure and health of CSOF vegetation are controlled by physico-chemical gradients, and the accelerating sea-level rise may have significant impacts on CSOF.
ESTUARINE COASTAL AND SHELF SCIENCE
(2022)
Article
Environmental Sciences
Xi Jiang, Jiasheng Wang, Xiaoguang Liu, Juan Dai
Summary: The stability of wetlands is threatened by global climate change and human activity. This study focuses on the change in vegetation cover at the estuary of Poyang Lake, exploring the factors influencing this change and providing insights into the dynamic characteristics of vegetation in this area.
Article
Remote Sensing
Erika Piaser, Paolo Villa
Summary: The use of machine learning algorithms in different perspectives depends on the quality of reference data, especially in complex environments like wetlands. This study collected an extensive reference dataset of about 400,000 samples from nine different sites and multiple seasons to represent temperate wetland vegetation communities at a continental scale. The performance of selected machine learning classifiers was compared using spectral indices derived from multi-temporal composites of Sentinel-2 as input for detailed wetland vegetation type mapping. The results show that ensemble methods like Random Forest and eXtreme Gradient Boosting generally have higher predictive power, except for Support Vector Machine, which scored the highest overall accuracy and F-score for all target classes. Decreasing the number of input features generally led to classification accuracy losses, more pronounced for Support Vector Machine than for Random Forest, indicating the stronger transferability of Support Vector Machine compared to Random Forest and eXtreme Gradient Boosting.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
Article
Geography, Physical
Han Liu, Tongkui Liao, Yu Wang, Xiaoming Qian, Xiaochen Liu, Chengming Li, Shiwei Li, Zhanlei Guan, Lijue Zhu, Xiaoyuan Zhou, Chong Liu, Tengyun Hu, Ming Luo
Summary: Timely and accurate wetland information is crucial for wetland resource management. However, reliable methods for fine-grained and rapid wetland mapping are still lacking. In this study, a novel multi-stage wetland classification method was proposed, integrating pixel-based and object-based strategies with ensemble learning algorithms and multi-source remote sensing data. The results demonstrated the effectiveness of the proposed method in wetland classification.
GISCIENCE & REMOTE SENSING
(2023)
Article
Environmental Sciences
Yanhui Dai, Lian Feng, Xuejiao Hou, Jing Tang
Summary: An automatic SAV classification algorithm using Landsat imagery was developed in this study, with automatically determined thresholds for key parameters. The algorithm showed high accuracy in classifying SAV in Yangtze Plain lakes and obtaining long-term SAV areal data. It is insensitive to Chl-a concentration in the water column, but has a detection limit below the water surface.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Environmental Sciences
Dong Liu, Shujie Yu, Qitao Xiao, Tianci Qi, Hongtao Duan
Summary: This study used a multilayer back-propagation neural network model to remotely estimate DOC concentrations in eutrophic Lake Taihu and found a mean estimation error of 15.14%. Phytoplankton growth was identified as a significant factor influencing DOC variations, suggesting that terrigenous DOC entering the lake was transformed into other carbon forms.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Multidisciplinary Sciences
Sebastien Rapinel, Lea Panhelleux, Guillaume Gayet, Rachel Vanacker, Blandine Lemercier, Bertrand Laroche, Francois Chambaud, Anis Guelmami, Laurence Hubert-Moy
Summary: This study used remote sensing and field data, combined with artificial intelligence technology, to classify and map wetlands in mainland France. The results show that this approach can accurately reveal the spatial distribution and fuzzy boundaries of wetlands, providing important reference for spatial planning and environmental management.
Article
Environmental Sciences
Xiaole Liu, Guangjun Wang, Yu Shi, Sihai Liang, Jinzhang Jia
Summary: This study investigated the variation in vegetation of the Qinghai-Tibet Plateau permafrost using hyperspectral remote sensing images and a hybrid spectral CNN model. It found that while herbage vegetation increased, the area of noxious weeds expanded rapidly, which may pose a threat to local livestock development. The association of Thermopsis lanceolate tended to spread due to human activities and swamp degradation.
Article
Ecology
Oleksandr Melnyk, Pavlo Manko, Ansgar Brunn
Summary: This article investigates the application of modern open geographic information systems and remote sensing data in forest management tasks in a specific part of Ukraine. The research develops classifiers for forest species based on the unsupervised classification of Sentinel-2 images and the selection of forest species fragments with closed crowns as training data. The accuracy of the supervised classification is evaluated, and it is found that modeling the age groups does not improve the classification result for the test area.
FRONTIERS IN FORESTS AND GLOBAL CHANGE
(2023)
Article
Geosciences, Multidisciplinary
David Olefeldt, Mikael Hovemyr, McKenzie A. Kuhn, David Bastviken, Theodore J. Bohn, John Connolly, Patrick Crill, Eugenie S. Euskirchen, Sarah A. Finkelstein, Helene Genet, Guido Grosse, Lorna Harris, Liam Heffernan, Manuel Helbig, Gustaf Hugelius, Ryan Hutchins, Sari Juutinen, Mark J. Lara, Avni Malhotra, Kristen Manies, A. David McGuire, Susan M. Natali, Jonathan A. O'Donnell, Frans-Jan W. Parmentier, Aleksi Raesaenen, Christina Schaedel, Oliver Sonnentag, Maria Strack, Suzanne E. Tank, Claire Treat, Ruth K. Varner, Tarmo Virtanen, Rebecca K. Warren, Jennifer D. Watts
Summary: The study introduces the BorealArctic Wetland and Lake Dataset (BAWLD) to estimate the distribution of wetlands and lakes in the Arctic region. Using expert assessments and random forest modeling, the dataset provides the distribution of various wetland and lake classes, helping to improve assessments of current and future methane emissions.
EARTH SYSTEM SCIENCE DATA
(2021)
Article
Agriculture, Multidisciplinary
Qitao Xiao, Cheng Hu, Xiaohong Gu, Qingfei Zeng, Zhenjing Liu, Wei Xiao, Mi Zhang, Zhenghua Hu, Wei Wang, Juhua Luo, Yinguo Qiu, Xuhui Lee, Hongtao Duan
Summary: Freshwater aquaculture is a major source of nitrous oxide emissions in agricultural industries. A study found that the indirect N2O emissions from lake aquaculture were over one order of magnitude higher than from open water. The ratio of carbon to nitrogen resulting from feed application was identified as an important factor contributing to the increased N2O production efficiency in aquaculture farms.
AGRICULTURE ECOSYSTEMS & ENVIRONMENT
(2023)
Article
Engineering, Environmental
Dong Liu, Zhandong Sun, Ming Shen, Liqiao Tian, Shujie Yu, Xintong Jiang, Hongtao Duan
Summary: A new method has been developed to remotely observe the three-dimensional distribution of particulate organic carbon (POC) storage in shallow eutrophic lakes using satellite data. This study is significant for understanding the carbon cycle in such lakes.
Article
Environmental Sciences
Yuanshan Liao, Haijin Lan, Xinyue Zhang, Zhenjing Liu, Mi Zhang, Zhenghua Hu, Hongtao Duan, Qitao Xiao
Summary: Lakes are important sources of atmospheric methane, and the emissions from the river inlet region are less studied. Field measurements at Lake Taihu over six years show that the river inlet region is a hot spot of CH4 emission, with a seven times higher annual mean value compared to the pelagic region. The variability of CH4 emission is linked to pollution loadings and CH4-rich water in the inflowing river.
Article
Environmental Sciences
Tianci Qi, Ming Shen, Tiit Kutser, Qitao Xiao, Zhigang Cao, Jinge Ma, Juhua Luo, Dong Liu, Hongtao Duan
Summary: Satellite observations can effectively reduce uncertainties in CO2 emission estimations compared to insufficient field data. However, developing remote sensing-based models for mapping cCO2 concentrations in lakes at regional or global scales remains a significant challenge. In this study, we developed a cCO2 estimation model using Sentinel-3-derived lake environmental variables and field cCO2 data from 16 lakes in Eastern China. The model showed high performance in calibration and validation, and the spatial and temporal dynamics of dissolved CO2 concentrations in 113 lakes were successfully mapped using Sentinel-3 data. The study provides important insights into CO2 emissions from meso-eutrophic lakes and highlights the potential of satellite remote sensing in expanding the coverage of lake CO2.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Environmental Sciences
Juhua Luo, Guigao Ni, Yunlin Zhang, Kang Wang, Ming Shen, Zhigang Cao, Tianci Qi, Qitao Xiao, Yinguo Qiu, Yongjiu Cai, Hongtao Duan
Summary: This study presents a novel three-step classification algorithm based on Landsat imagery for the identification and monitoring of lake vegetation and algal bloom. The algorithm was validated on 22 lakes in the middle and lower reaches of the Yangtze River and applied to Landsat data from 1985 to 2021. The results show a significant decrease in lake vegetation and an increase in algal bloom in the study area, indicating an ongoing transition from a macrophyte-dominated state to a phytoplankton-dominated state.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Environmental Sciences
Yinguo Qiu, Yaqin Jiao, Juhua Luo, Zhenyu Tan, Linsheng Huang, Jinling Zhao, Qitao Xiao, Hongtao Duan
Summary: This paper proposes a novel rapid reconstruction scheme for water regions in 3D models of oblique photography, which can achieve fast and accurate reconstruction. Experimental results show that this scheme can improve the current UAV oblique photography 3D modeling technique and expand its application in twin watershed, twin city, and other areas.
Article
Environmental Sciences
Zhigang Cao, Chuanmin Hu, Ronghua Ma, Hongtao Duan, Miao Liu, Steven Loiselle, Kaishan Song, Ming Shen, Dong Liu, Kun Xue
Summary: This study developed a machine learning model to generate SPM time series in 269 lakes across China from 2002 to 2021. The study found that SPM concentrations showed a significant decrease in the 21st century, with different changing patterns in different climate zones and ecoregions.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Environmental Sciences
Jinge Ma, Steven Loiselle, Zhigang Cao, Tianci Qi, Ming Shen, Juhua Luo, Kaishan Song, Hongtao Duan
Summary: Under the influence of climate warming and human activities, large lakes worldwide have experienced an increase in eutrophication and algal blooms. This study utilizes daily satellite observations to develop an algorithm that accurately identifies the spatiotemporal distribution of algal bloom dynamics in large lakes. The findings show positive trends in bloom area, frequency, and an earlier bloom time, with climate factors and human activities identified as key drivers.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Engineering, Environmental
Ming Shen, Zhigang Cao, Liqiang Xie, Yanyan Zhao, Tianci Qi, Kaishan Song, Lili Lyu, Dian Wang, Jinge Ma, Hongtao Duan
Summary: Cyanobacterial blooms and the release of algal toxins pose a serious threat to the safety of drinking water sources. However, the monitoring and evaluation of algal toxins in lake water have not been carried out regularly. This study developed a remote sensing scheme based on satellite data to assess the risk of algal toxins and found that most large lakes in eastern China had experienced high risk at least once. Fortunately, the frequency of high human health risks in terms of lake areas was low, indicating the potential to set drinking water intakes in most waters while reducing cyanobacterial blooms.
Article
Remote Sensing
Zhenyu Tan, Chen Yang, Yinguo Qiu, Wei Jia, Chenxi Gao, Hongtao Duan
Summary: This paper proposes a novel machine learning approach that can effectively extract harmful algal blooms (HABs) from images captured under various shooting poses. The approach was applied in Lake Chaohu and consistently reports the real-time status of HABs along the bank.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
Article
Spectroscopy
Jiang Xin-tong, Xiao Qi-tao, Li Yi-min, Liao Yuan-shan, Liu Dong, Duan Hong-tao
Summary: Lake Bosten, the largest inland freshwater lake in the northwest arid zone of China, has been heavily impacted by human activities and wastewater discharge, affecting the lake ecosystem and drinking water safety. The study analyzed the three-dimensional fluorescence spectra of coloured dissolved organic matter (CDOM) and found three fractions of CDOM in Lake Bosten. The influence of river input on Lake Bosten's dissolved organic matter (DOM) varied between seasons and correlated with the change in river water quality.
SPECTROSCOPY AND SPECTRAL ANALYSIS
(2023)
Article
Environmental Sciences
Yuanshan Liao, Qitao Xiao, Yimin Li, Chen Yang, Junli Li, Hongtao Duan
Summary: Saline lakes are integral components of the global carbon cycle and play a significant role in greenhouse gas emissions. This study focuses on Bosten Lake, an inland saline lake in China, and reveals that it is a significant source of atmospheric carbon emissions. The emissions are influenced by temporal variations in salinity and trophic state. Additionally, spatial heterogeneity in carbon emissions is driven by exogenous inputs. This study provides valuable insights into greenhouse gas emissions from saline lakes in arid regions and contributes to a better understanding of the carbon cycle in different types of lakes.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Remote Sensing
Zhuting Tan, Zhengyu Tan, Juhua Luo, Hongtao Duan
Summary: This study proposes a new method for cotton sample selection and employs machine learning to effectively identify long time series cotton planting areas at a 30-meter resolution scale. The study uses Bortala and Shuanghe in Xinjiang, China as case studies to demonstrate the approach. The results show that the method can achieve high accuracy and reveal the spatiotemporal distribution characteristics of cotton planting areas.
GEO-SPATIAL INFORMATION SCIENCE
(2023)
Article
Biodiversity Conservation
Yinguo Qiu, Hao Liu, Fuzhang Liu, Dexin Li, Chengzhao Liu, Weixin Liu, Jiacong Huang, Qitao Xiao, Juhua Luo, Hongtao Duan
Summary: This study developed a novel framework that can timely and accurately grasp both present conditions and accumulation risks of harmful algal blooms (HABs) in nearshore areas of lakes. By using quantitative monitoring and simulation modeling, the framework showed high value in monitoring and emergency prevention of HABs.
ECOLOGICAL INDICATORS
(2023)
Article
Biodiversity Conservation
Yongcui Lan, Jinliang Wang, Qianwei Liu, Fang Liu, Lanfang Liu, Jie Li, Mengjia Luo
Summary: This study focuses on the five major plateau lake basins in central Yunnan, China, and constructs an ecological security pattern using the source-resistance surface-corridor-pinch point framework. The study simulates land use/cover change in the region and identifies early warning regions where future urban expansion poses a threat to current ecological source areas and corridors.
ECOLOGICAL INDICATORS
(2024)
Article
Biodiversity Conservation
Pingping Huang, Feng Zhao, Bailing Zhou, Kuidong Xu
Summary: This study investigates the distribution of benthic microeukaryotes in the China Seas and finds that they can stride over the ecological barrier of 32 degrees N. The study also highlights the significant influence of depth, temperature, and latitude on communities in the China Seas.
ECOLOGICAL INDICATORS
(2024)
Article
Biodiversity Conservation
Federico Morelli, Yanina Benedetti, Jesse Stanford, Leszek Jerzak, Piotr Tryjanowski, Paolo Perna, Riccardo Santolini
Summary: Species distribution models (SDMs) are numerical tools used for predicting species' spatial distribution. This study found that ecological characteristics, such as habitat specialization, play a role in improving the accuracy of SDMs.
ECOLOGICAL INDICATORS
(2024)
Article
Biodiversity Conservation
Xiaoxuan Wu, Hang Liu, Wei Liu
Summary: Global climate change, urbanization, and economic development have increased the need for sustainable human development, urban ecological governance, and low-carbon energy transformation. This study analyzes the green ecological transition in Chengdu based on panel data from 2010 to 2020, exploring its spatiotemporal evolution and key factors. The results show an overall upward trend in Chengdu's green ecological development and positive spatial autocorrelation in certain districts.
ECOLOGICAL INDICATORS
(2024)
Article
Biodiversity Conservation
Castaldi Simona, Formicola Nicola, Mastrocicco Micol, Morales Rodriguez Carmen, Morelli Raffaella, Prodorutti Daniele, Vannini Andrea, Zanzotti Roberto
Summary: Sustainable agricultural practices are increasingly important for global and national environmental policies and economy. This study compared the sustainability of grape production under integrated and organic management using multiple indicators. The results showed that organic management was more beneficial for most environmental aspects of the agroecosystem compared to integrated management, without affecting grape yield.
ECOLOGICAL INDICATORS
(2024)
Article
Biodiversity Conservation
Gaia Vaglio Laurin, Alexander Cotrina-Sanchez, Luca Belelli-Marchesini, Enrico Tomelleri, Giovanna Battipaglia, Claudia Cocozza, Francesco Niccoli, Jerzy Piotr Kabala, Damiano Gianelle, Loris Vescovo, Luca Da Ros, Riccardo Valentini
Summary: Phenology monitoring is important for understanding forest functioning and climate impacts. This research compares the phenological behavior of European beech forests using Tree-Talker (TT+) and Sentinel 2 satellite data. The study finds differences in the information derived by the two sensor types, particularly in terms of season length, phenology changepoints, and leaf period variability. TT+ with its higher temporal resolution demonstrates precision in capturing the phenological changepoints, especially when satellite image availability is limited.
ECOLOGICAL INDICATORS
(2024)
Article
Biodiversity Conservation
Huanhuan Pan, Ziqiang Du, Zhitao Wu, Hong Zhang, Keming Ma
Summary: The land use and cover changes resulting from coal mining activities and ecological restoration have had a significant impact on ecosystem services in mining areas. This study investigates the relationship between ecosystem services and land use intensity in coal mining areas, emphasizing the importance of understanding this interdependence for balanced human-land system development. The research examines the evolving relationship across different reclamation stages in Shanxi, China, using a coupling coordination degree model. The findings suggest the need for timely and judicious reclamation of coalfields, considering the land's bearing capacity.
ECOLOGICAL INDICATORS
(2024)
Article
Biodiversity Conservation
Jingjuan He, Yijun Shi, Lihua Xu, Zhangwei Lu, Mao Feng
Summary: This study examines the spatial interplay between changes in the blue-green spatial distribution and modifications in land surface temperature grades in Shanghai. The findings reveal that the transformation of the blue-green spatial pattern differs between different sectors of the city, and the impact on the thermal environment varies spatially.
ECOLOGICAL INDICATORS
(2024)
Article
Biodiversity Conservation
Yi Xu, Di Zhang, Junqiang Lin, Qidong Peng, Xiaohui Lei, Tiantian Jin, Jia Wang, Ruifang Yuan
Summary: This study analyzed the response relationship between phytoplankton growth and water environmental parameters in the Middle Route of the South-to-North Water Diversion Project in China using long-term monitoring data and machine learning models. The results revealed the differences between monitoring sites and identified the key parameters that affect phytoplankton growth.
ECOLOGICAL INDICATORS
(2024)
