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
Hongyi Zhang, Yong Liu, Xinghua Li, Ruitao Feng, Yuting Gong, Yazhen Jiang, Xiaobin Guan, Shuang Li
Summary: Urban ecological environment assessment is crucial for urban sustainable development and environment protection. This study developed an improved urban ecological comfort index based on remote sensing indicators. The results showed that the urban ecological environment has improved in the past decade, with a positive correlation with economic development, but degradation was observed in areas of urban expansion.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2023)
Article
Environmental Sciences
Subrina Tahsin, Stephen C. Medeiros, Arvind Singh
Summary: This study proposes a method to generate synthetic NDVI images using a virtual constellation, which effectively mitigates the impact of cloud contamination on vegetation monitoring data, showing high accuracy and reliability.
Article
Environmental Sciences
Yuxin Zhao, Dehua Mao, Dongyou Zhang, Zongming Wang, Baojia Du, Hengqi Yan, Zhiqiang Qiu, Kaidong Feng, Jingfa Wang, Mingming Jia
Summary: In this study, the distribution of Phragmites australis in the Momoge Ramsar Wetland site was successfully mapped using the random forest method and Sentinel-1/2 images. Multiple linear regression models were used to estimate the aboveground biomass of Phragmites australis. The findings highlight the significance of the Sentinel-2 red-edge band in improving the accuracy of biomass estimation.
Article
Environmental Sciences
Jianjun Lyu, Ying Hu, Shuliang Ren, Yao Yao, Dan Ding, Qingfeng Guan, Liufeng Tao
Summary: This study proposed a new framework for extracting tailings pond margins using deep learning and the random forest algorithm, achieving high accuracy and efficiency. By creating an open source dataset and conducting empirical analysis in Tongling city, the study significantly improved extraction efficiency and accuracy.
Article
Computer Science, Artificial Intelligence
Yasin Ul Haq, Muhammad Shahbaz, H. M. Shahzad Asif, Ali Al-Laith, Wesam Alsabban, Muhammad Haris Aziz
Summary: Soil study is crucial for crop cultivation, and traditional soil identification methods are time-consuming. This study proposes a model that utilizes remote sensing data to accurately identify soil types. The experimental results show that the Random Forest and Logistic Model Tree techniques perform well, providing valuable insights for agricultural planning and increased crop yield.
PEERJ COMPUTER SCIENCE
(2022)
Article
Engineering, Electrical & Electronic
Meisam Amani, Brian Brisco, Sahel Mahdavi, Arsalan Ghorbanian, Armin Moghimi, Evan R. DeLancey, Michael Merchant, Raymond Jahncke, Lee Fedorchuk, Amy Mui, Thierry Fisette, Mohammad Kakooei, Seyed Ali Ahmadi, Brigitte Leblon, Armand LaRocque
Summary: The first Canadian wetland inventory (CWI) map was produced in 2019 using Landsat data and the Google Earth Engine (GEE) platform, with a 71% overall accuracy. However, limitations such as low-quality training samples were identified, prompting solutions like incorporating reliable in situ data and using object-based classification methods to improve accuracy. Valuable feedback on the map's accuracy was received, highlighting the importance of improving the CWI map for future applications.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Environmental Sciences
Masoud Mahdianpari, Jean Elizabeth Granger, Fariba Mohammadimanesh, Sherry Warren, Thomas Puestow, Bahram Salehi, Brian Brisco
Summary: This study aims to produce the first high-resolution wetland map of the City of St. John's in Canada using advanced machine learning algorithms, very high-resolution satellite imagery, and airborne LiDAR technology. By applying an object-based random forest algorithm to features extracted from WorldView-4, GeoEye-1, and LiDAR data, the study characterizes five wetland classes within an urban area with an overall accuracy of 91.12% and produces wetland surface water flow connectivity using LiDAR data.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2021)
Article
Plant Sciences
Ru Zhang, Mingxu Zhang, Yumei Yan, Yuan Chen, Linlin Jiang, Xinxin Wei, Xiaobo Zhang, Huanting Li, Minhui Li
Summary: This study evaluated the impact of environmental factors on the Astragalus mongholicus industry and used MaxEnt to simulate its suitable distribution. The results showed that precipitation and temperature were the most important factors influencing its growth. The analysis of image features revealed that the planting areas of A. mongholicus were consistent with the predicted suitable areas. Additionally, the study investigated the planting, processing, and sales of A. mongholicus, and analyzed the roles of stakeholders in the value chains.
FRONTIERS IN PLANT SCIENCE
(2022)
Article
Environmental Sciences
Sixue Shi, Yu Chang, Yuehui Li, Yuanman Hu, Miao Liu, Jun Ma, Zaiping Xiong, Ding Wen, Binglun Li, Tingshuang Zhang
Summary: This study mapped the degradation process of wetlands over the past few decades and their dynamics in 2018 using satellite data of different resolutions, and analyzed the impact of historical land use on current wetland changes. The results indicate that the new classification method is the most accurate, providing more detailed scientific guidance for wetland managers.
Article
Geography, Physical
Yansheng Li, Bo Dang, Yongjun Zhang, Zhenhong Du
Summary: This paper summarizes the challenges, opportunities, and methods in water body classification from high-resolution optical remote sensing imagery. It introduces practical applications and benchmarks for evaluation. The research provides a fuller understanding of water body classification and suggests future research directions.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)
Article
Geosciences, Multidisciplinary
Sanaz Zare, Ali Abtahi, Seyed Rashid Fallah Shamsi, Philippe Lagacherie
Summary: This study fills the gap in comparing and developing methods for integrating new sources of soil data for DSM by mapping electrical conductivities using real measurements and EM38MK2 measurements. The results show the utility of EM38MK2 data as surrogate input data for mapping soil salinity, with regression co-kriging identified as the best integration method. The impact of EM38MK2 data on performance gains increases as the sizes of real soil salinity measurements decrease, indicating a promising way to tackle constraints of DSM in areas where soil sensing as alternative data is accessible.
Article
Engineering, Electrical & Electronic
Masoud Mahdianpari, Brian Brisco, Jean Granger, Fariba Mohammadimanesh, Bahram Salehi, Saeid Homayouni, Laura Bourgeau-Chavez
Summary: The development of the Canadian Wetland Inventory Map has improved over several generations, with the latest refinement achieving an average accuracy of 90.53%. By integrating additional environmental and remote sensing datasets, the inventory map has reduced overestimation of wetlands and increased accuracy across all ecozones. The importance of multisource data and adequate test and train data for wetland classification at a countrywide scale has been highlighted.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Engineering, Electrical & Electronic
Meisam Amani, Sahel Mahdavi, Mohammad Kakooei, Arsalan Ghorbanian, Brian Brisco, Evan Delancey, Souleymane Toure, Eugenio Landeiro Reyes
Summary: This study investigated wetland trends in Alberta over the past 37 years, finding that wetland loss was mainly due to conversion to Forest and Grassland/Shrubland, while wetland gain was primarily due to conversion from the Forest class to wetlands. The results will assist policymakers in adjusting necessary policies to mitigate potential wetland changes caused by anthropogenic activities and climate-related events.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Environmental Sciences
Yan Jin, Rui Liu, Haoyu Fan, Pengdu Li, Yaojie Liu, Yan Jia
Summary: This article presents a stepwise downscaling approach using random forest regression kriging technique to downscale census data to multi-resolution gridded population datasets. Population distribution maps at different spatial resolutions were generated and compared with other downscaling methods and population products using Nanjing, China as the experimental case. The results showed that the proposed approach produced gridded population maps with higher accuracy and more accurate details of population distribution, resulting in a significant reduction in mean absolute error and root mean squared error values.
Article
Geochemistry & Geophysics
R. Ivan Blanco, G. Melodie Naja, Rosanna G. Rivero, Rene M. Price
APPLIED GEOCHEMISTRY
(2013)
Article
Agronomy
Juliana Corrales, G. Melodie Naja, Rosanna G. Rivero, Fernando Miralles-Wilhelm, Mahadev G. Bhat
IRRIGATION AND DRAINAGE
(2013)
Article
Chemistry, Analytical
Yirgalem Chebud, Ghinwa M. Naja, Rosanna Rivero
JOURNAL OF ENVIRONMENTAL MONITORING
(2011)
Article
Environmental Sciences
R. G. Rivero, S. Grunwald, M. W. Binford, T. Z. Osborne
REMOTE SENSING OF ENVIRONMENT
(2009)
Article
Environmental Sciences
Juliana Corrales, Ghinwa M. Naja, Catherine Dziuba, Rosanna G. Rivero, William Orem
SCIENCE OF THE TOTAL ENVIRONMENT
(2011)
Article
Soil Science
Jongsung Kim, Sabine Grunwald, Rosanna G. Rivero, Rick Robbins
SOIL SCIENCE SOCIETY OF AMERICA JOURNAL
(2012)
Article
Environmental Sciences
Ghinwa M. Naja, Rosanna Rivero, Stephen E. Davis, Thomas Van Lent
WATER AIR AND SOIL POLLUTION
(2011)
Article
Environmental Sciences
Yirgalem Chebud, Ghinwa M. Naja, Rosanna G. Rivero, Assefa M. Melesse
WATER AIR AND SOIL POLLUTION
(2012)
Article
Environmental Sciences
Xavier Zapata-Rios, Rosanna G. Rivero, Ghinwa M. Naja, Pierre Goovaerts
WATER RESOURCES RESEARCH
(2012)
Article
Environmental Sciences
Xavier Zapata-Rios, Rosanna G. Rivero, Ghinwa M. Naja, Pierre Goovaerts
WATER RESOURCES RESEARCH
(2013)
Editorial Material
Environmental Sciences
Xavier Zapata-Rios, Rosanna G. Rivero, Ghinwa M. Naja, Pierre Goovaerts
WATER RESOURCES RESEARCH
(2014)
Article
Environmental Sciences
Mariana B. A. Fragomeni, Sergio Bernardes, J. Marshall Shepherd, Rosanna Rivero
Article
Ecology
Elsa M. Ordway, Andrew J. Elmore, Sonja Kolstoe, John E. Quinn, Rachel Swanwick, Megan Cattau, Dylan Taillie, Steven M. Guinn, K. Dana Chadwick, Jeff W. Atkins, Rachael E. Blake, Melissa Chapman, Kelly Cobourn, Tristan Goulden, Matthew R. Helmus, Kelly Hondula, Carrie Hritz, Jennifer Jensen, Jason P. Julian, Yusuke Kuwayama, Vijay Lulla, Donal O'Leary, Donald R. Nelson, Jonathan P. Ocon, Stephanie Pau, Guillermo E. Ponce-Campos, Carlos Portillo-Quintero, Narcisa G. Pricope, Rosanna G. Rivero, Laura Schneider, Meredith Steele, Mirela G. Tulbure, Matthew A. Williamson, Cyril Wilson
Summary: In the 21st century, human-environment interactions will expose both systems to risks and opportunities, requiring large volumes of multi-resolution information for analysis. Factors such as climate change, land-use change affect regions differently, and scarcity of relevant data can hinder research progress.
Article
Geography
Rosanna G. Rivero, Betty J. Grizzle, Mehrnoosh Mahmoudi, Christopher McVoy, G. Melodie Naja, Thomas Van Lent
ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS
(2020)