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
Junli Wang, Guifa Chen, Zishi Fu, Hongxia Qiao, Fuxing Liu
Summary: The study found that wetland nitrogen removal is affected during the plant wilting period, and balancing the tradeoff between nitrogen removal and wetland sustainability can be achieved by choosing the optimal harvest time. Results showed that late harvest time reduces nitrogen removal, and the impact of harvest time on plant nutrient response varies in different years.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2021)
Article
Environmental Sciences
Connor J. Anderson, Daniel Heins, Keith C. Pelletier, Julia L. Bohnen, Joseph F. Knight
Summary: This study tested the utility of RGB UAS imagery for mapping Phragmites in two locations in the U.S. The highest overall classification accuracy of 90% was achieved when pairing the UAS imagery with a UAS-derived CHM. The inclusion of RADARSAT-2 HH polarization and commercial satellite stereo retrievals slightly reduced classification accuracy.
Article
Environmental Sciences
Mahdi Hasanlou, Reza Shah-Hosseini, Seyd Teymoor Seydi, Sadra Karimzadeh, Masashi Matsuoka
Summary: This study introduces a new method for unsupervised damage detection using Sentinel imagery, consisting of two main phases: built-up extraction and damage detection. By utilizing Sentinel-2 and Sentinel-1 imagery, the proposed method is able to accurately and quickly detect damages to buildings caused by earthquakes.
Article
Green & Sustainable Science & Technology
Panpan Cui, Fangli Su, Fang Zhou
Summary: This study investigated the response of Phragmites australis populations to inundation depth and its impact on clonal plants' phenotypic variability. The results showed a negative correlation between inundation depth and shoot height, leaf length, leaf width, leaf biomass, and panicle length. Leaf parameters exhibited a higher coefficient of variation and played a crucial role in the action of plants during floods. Population differentiation was consistent with geographical distance and morphological similarity.
Article
Remote Sensing
Masoumeh Hamidi, Abdolreza Safari, Saeid Homayouni
Summary: This study introduces two Auto-Encoder-based learning schemes to enhance the stability of remotely sensed data classification for crop mapping by utilizing spatio-temporal features. The ensemble strategy showed the best performance in experiments, achieving the highest accuracy.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2021)
Article
Agronomy
Masoumeh Hamidi, Abdolreza Safari, Saeid Homayouni, Hadiseh Hasani
Summary: Accurate crop mapping is crucial in agricultural applications, but it is challenging due to the variabilities in spectral, spatial, and temporal characteristics. This study introduces a deep learning framework, called Guided Filtered Sparse Auto-Encoder (GFSAE), to improve the accuracy of crop mapping by incorporating field boundary information. The evaluation on two high-resolution image datasets shows that GFSAE achieves impressive improvements in terms of accuracy metrics compared to the traditional Sparse Auto Encoder (SAE).
Article
Environmental Sciences
Guillermo Siesto, Marcos Fernandez-Sellers, Adolfo Lozano-Tello
Summary: The method proposed in the study uses satellite data and deep convolutional neural networks to classify crops while considering images from multiple dates, allowing for cost-efficient monitoring and classification of various crop types.
Article
Environmental Sciences
Kai O. Bergmueller, Mark C. Vanderwel
Summary: This study used spectral information from UAV imagery to predict tree mortality in 38 forest stands in western Canada. The inclusion of multispectral indices improved the prediction accuracy, and different tree species had varying levels of prediction performance. However, all models tended to overpredict tree mortality.
Article
Environmental Sciences
J. M. Carricondo, J. V. Oliver-Villanueva, J. V. Turegano, J. A. Gonzalez, J. Mengual
Summary: Continuous phosphorus discharges in bodies of water, generated by human activities, produce contaminated water and eutrophication. Efficient and low-cost systems are necessary to remove phosphorus. Generating renewable energy from biomass waste, and using resulting ash for phosphorus removal, is important. Reed ash has been proven to effectively improve water quality.
ENVIRONMENTAL TECHNOLOGY
(2021)
Article
Biodiversity Conservation
Huijun Qin, Liang Jiao, Fang Li, Yi Zhou
Summary: This study investigates the differences in adaptive strategies of Phragmites australis in different coverage areas and identifies soil water content and soil salt content as key factors driving the changes in physiological, morphological, and behavioral adaptations of the plant.
ECOLOGICAL INDICATORS
(2022)
Article
Green & Sustainable Science & Technology
Shweta Yadav, Jhalesh Kumar, Sandeep Kumar Malyan, Rajesh Singh, Omkar Singh, Vikas Chandra Goyal, Jyoti Singh, Ritika Negi
Summary: Floating treatment wetlands (FTWs) are an innovative and economical way of wastewater treatment using hydroponically grown emergent plants. This study evaluated the performance of FTWs using Canna indica and Phragmites australis for municipal wastewater treatment. Results showed that Phragmites australis performed well in organic matter removal, while Canna indica was effective in nutrient removal, particularly NO3- from wastewater.
Article
Environmental Sciences
Sara Perez-Carabaza, Oisin Boydell, Jerome O'Connell
Summary: This study aims to propose a habitat mapping solution that combines CNNs with high spatial, spectral, and multitemporal unmanned aerial vehicle image data for monitoring threatened habitats in European environmental policies. The proposed CNN-based method shows a high level of classification accuracy in the experiment conducted in the Maharees region of Ireland.
JOURNAL OF APPLIED REMOTE SENSING
(2021)
Article
Agronomy
Patricia Lopez-Garcia, Diego Intrigliolo, Miguel A. Moreno, Alejandro Martinez-Moreno, Jose Fernando Ortega, Eva Pilar Perez-Alvarez, Rocio Ballesteros
Summary: The development of unmanned aerial vehicles (UAVs) and light sensors has introduced new approaches for high-resolution remote sensing applications. The use of cameras onboard UAVs, including multispectral and conventional RGB sensors, can provide valuable data for determining plant water status and managing irrigation. The study conducted in a vineyard in Spain showed that visible domain information was highly related to the water stress integral during the 2018 season, while multispectral artificial neural networks (ANNs) performed slightly better in subsequent seasons. The differences in spatial resolution and radiometric quality between RGB and multispectral geomatic products explained the good model performances with each type of data.
Article
Environmental Sciences
Yanchao Zhang, Wen Yang, Ying Sun, Christine Chang, Jiya Yu, Wenbo Zhang
Summary: This study examined the fusion of spectral bands information and vegetation indices for almond plantation classification using different machine learning algorithms. It was found that spectral information can be used for ground classification, with SVM performing the best among the algorithms tested. The combination of multispectral bands and vegetation indices can improve classification accuracy, with specific vegetation indices like NDEGE, NDVIG, and NDVGE showing consistent performance in enhancing accuracy.
Article
Biodiversity Conservation
A. G. Besnard, A. Davranche, S. Maugenest, J. B. Bouzille, A. Vian, J. Secondi
ECOLOGICAL INDICATORS
(2015)
Article
Multidisciplinary Sciences
Jean Secondi, Valentin Dupont, Aurelie Davranche, Nathalie Mondy, Thierry Lengagne, Marc Thery
Article
Geography, Physical
Aurelie Davranche, Gaetan Lefebvre, Brigitte Poulin
PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING
(2009)
Article
Environmental Sciences
Aurelie Davranche, Gaetan Lefebvre, Brigitte Poulin
REMOTE SENSING OF ENVIRONMENT
(2010)
Article
Environmental Sciences
Aurelie Davranche, Brigitte Poulin, Gaetan Lefebvre
REMOTE SENSING OF ENVIRONMENT
(2013)
Article
Environmental Sciences
Gaetan Lefebvre, Aurelie Davranche, Loic Willm, Julie Campagna, Lauren Redmond, Clement Merle, Anis Guelmami, Brigitte Poulin
Article
Ecology
Jean Secondi, Aurelie Davranche, Marc Thery, Nathalie Mondy, Thierry Lengagne
GLOBAL ECOLOGY AND BIOGEOGRAPHY
(2020)
Article
Environmental Sciences
Ruonan Chen, Liangyun Liu, Xinjie Liu, Zhunqiao Liu, Lianhong Gu, Uwe Rascher
Summary: This study presents methods to accurately estimate sub-daily GPP from SIF in evergreen needleleaf forests and demonstrates that the interactions among light, canopy structure, and leaf physiology regulate the SIF-GPP relationship at the canopy scale.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Daniel L. Goldberg, Madankui Tao, Gaige Hunter Kerr, Siqi Ma, Daniel Q. Tong, Arlene M. Fiore, Angela F. Dickens, Zachariah E. Adelman, Susan C. Anenberg
Summary: A novel method is applied in this study to directly use satellite data to evaluate the spatial patterns of urban NOx emissions inventories. The results show that the 108 spatial surrogates used by NEMO are generally appropriate, but there may be underestimation in areas with dense intermodal facilities and overestimation in wealthy communities.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Zhuoyue Hu, Xiaoyan Li, Liyuan Li, Xiaofeng Su, Lin Yang, Yong Zhang, Xingjian Hu, Chun Lin, Yujun Tang, Jian Hao, Xiaojin Sun, Fansheng Chen
Summary: This paper proposes a whisk-broom imaging method using a long-linear-array detector and high-precision scanning mirror to achieve high-resolution and wide-swath thermal infrared data. The method has been implemented in the SDGs satellite and has shown promising test results.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Dandan Wang, Leiqiu Hu, James A. Voogt, Yunhao Chen, Ji Zhou, Gaijing Chang, Jinling Quan, Wenfeng Zhan, Zhizhong Kang
Summary: This study evaluates different schemes for determining model coefficients to quantify and correct the anisotropic impact from remote sensing LST for urban applications. The schemes have consistent results and accurately estimate parameter values, facilitating the broadening of parametric models.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Jamie Tolan, Hung - Yang, Benjamin Nosarzewski, Guillaume Couairon, Huy V. Vo, John Brandt, Justine Spore, Sayantan Majumdar, Daniel Haziza, Janaki Vamaraju, Theo Moutakanni, Piotr Bojanowski, Tracy Johns, Brian White, Tobias Tiecke, Camille Couprie
Summary: Vegetation structure mapping is crucial for understanding the global carbon cycle and monitoring nature-based approaches to climate adaptation and mitigation. This study presents the first high-resolution canopy height maps for California and Sao Paulo, achieved through the use of very high resolution satellite imagery and aerial lidar data. The maps provide valuable tools for forest structure assessment and land use monitoring.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Regina Eckert, Steffen Mauceri, David R. Thompson, Jay E. Fahlen, Philip G. Brodrick
Summary: In this paper, a mathematical framework is proposed to improve the retrieval of surface reflectance and atmospheric parameters by leveraging the expected spatial smoothness of the atmosphere. Experimental results show that this framework can reduce the surface reflectance retrieval error and surface-related biases.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Chongya Jiang, Kaiyu Guan, Yizhi Huang, Maxwell Jong
Summary: This study presents the Field Rover method, which uses vehicle-mounted cameras to collect ground truth data on crop harvesting status. The machine learning approach and remote sensing technology are employed to upscale the results to a regional scale. The accuracy of the remote sensing method in predicting crop harvesting dates is validated through comparison with satellite data.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Oksana V. Lunina, Anton A. Gladkov, Alexey V. Bochalgin
Summary: In this study, an unmanned aerial vehicle (UAV) was used to detect and map surface discontinuities with displacements of a few centimeters, indicating the presence of initial geological deformations. The study found that sediments of alluvial fans are susceptible to various tectonic and exogenous deformational processes, and the interpretation of ultra-high resolution UAV images can help recognize low-amplitude brittle deformations at an early stage. UAV surveys are critical for discerning neotectonic activity and its related hazards over short observation periods.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Feng Zhao, Weiwei Ma, Jun Zhao, Yiqing Guo, Mateen Tariq, Juan Li
Summary: This study presents a data-driven approach to reconstruct the terrestrial SIF spectrum using measurements from the TROPOMI instrument on Sentinel-5 precursor mission. The reconstructed SIF spectrum shows improved spatiotemporal distributions and demonstrates consistency with other datasets, indicating its potential for better understanding of the ecosystem function.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Stephen Stehman, John E. Wagner
Summary: This article investigates optimal sample allocation in stratified random sampling for estimation of accuracy and proportion of area in applications where the target class is rare. The study finds that precision of estimated accuracy has a stronger impact on sample allocation than estimation of proportion of area, and the trade-offs among these estimates become more pronounced as the target class becomes rarer. The results provide quantitative evidence to guide sample allocation decisions in specific applications.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Jingyao Zheng, Tianjie Zhao, Haishen Lu, Defu Zou, Nemesio Rodriguez-Fernandez, Arnaud Mialon, Philippe Richaume, Jianshe Xiao, Jun Ma, Lei Fan, Peilin Song, Yonghua Zhu, Rui Li, Panpan Yao, Qingqing Yang, Shaojie Du, Zhen Wang, Zhiqing Peng, Yuyang Xiong, Zanpin Xing, Lin Zhao, Yann Kerr, Jiancheng Shi
Summary: Soil moisture and freeze/thaw (F/T) play a crucial role in water and heat exchanges at the land-atmosphere interface. This study reports the establishment of a wireless sensor network for soil moisture and temperature over the permafrost region of Tibetan Plateau. Satellite-based surface soil moisture (SSM) and F/T products were evaluated using ground-based measurements. The results show the reliability of L-band passive microwave SSM and F/T products, while existing F/T products display earlier freezing and later thawing, leading to unsatisfactory accuracy.
REMOTE SENSING OF ENVIRONMENT
(2024)
