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
Environmental Sciences
Danilo Roberti Alves de Almeida, Eben North Broadbent, Matheus Pinheiro Ferreira, Paula Meli, Angelica Maria Almeyda Zambrano, Eric Bastos Gorgens, Angelica Faria Resende, Catherine Torres de Almeida, Cibele Hummel do Amaral, Ana Paula Dalla Corte, Carlos Alberto Silva, Joao P. Romanelli, Gabriel Atticciati Prata, Daniel de Almeida Papa, Scott C. Stark, Ruben Valbuena, Bruce Walker Nelsonn, Joannes Guillemot, Jean-Baptiste Feret, Robin Chazdon, Pedro H. S. Brancalion
Summary: Remote sensors, particularly UAV-borne lidar and hyperspectral data, offer a promising technology for monitoring forest restoration projects. By combining these two types of data, it is possible to accurately assess vegetation diversity and structure, leading to improved decision-making processes in restoration efforts.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Ecology
Gabriel Guariglia Perez, Vandoir Bourscheidt, Luciano Elsinor Lopes, Juliana Toshie Takata, Patricia Alves Ferreira, Danilo Boscolo
Summary: This study explored the possibility of estimating vegetation height in Atlantic Forest using Sentinel 2 imagery and LiDAR data. The results showed that simple linear models can accurately predict vegetation height, and the model is transferable to new images and locations.
ECOLOGICAL INFORMATICS
(2022)
Article
Environmental Sciences
Marco Balsi, Monica Moroni, Valter Chiarabini, Giovanni Tanda
Summary: An automatic custom-made procedure was developed to identify macroplastic debris loads in coastal and marine environments using hyperspectral imaging from UAVs. The results show successful separate identification of PE and PET objects through post-processing data treatment based on a developed classifier algorithm.
Article
Environmental Sciences
Shuhan Jia, Quanhua Zhao, Yu Li
Summary: This paper presents a new partitioned RP algorithm for reliable and efficient classification of large-size hyperspectral images. By dividing the HSI into multiple sub-HSIs and selecting a projection matrix that maximizes class separability, the proposed algorithm achieves reliable classification results in a short time.
Article
Agriculture, Multidisciplinary
Worasit Sangjan, Rebecca J. Mcgee, Sindhuja Sankaran
Summary: Evaluation of forage quality is crucial for breeding and selecting forage crops, and this study proposes a non-destructive and high-throughput method using spectroscopy to predict the quality traits of field peas. Machine learning models were developed to establish the relationship between quality traits and reflectance spectra data, achieving accurate and timely predictions. This approach can assist breeders and farmers in decision-making for improved livestock health and reduced greenhouse gas emissions.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Environmental Sciences
Sasha J. Kramer, David A. Siegel, Stephane Maritorena, Dylan Catlett
Summary: This study utilized hyperspectral remote sensing data to model phytoplankton pigment composition in the global open ocean. By using optimized principal components regression modeling, thirteen phytoplankton pigments representing five groups were successfully reconstructed. This work advances the development of global spectral models for phytoplankton pigment composition, but more high-quality data is needed for further improvement.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Ecology
Xiaoquan Pan, Jinbao Jiang, Yiming Xiao
Summary: Natural gas is an important clean energy source, but its leakage during transportation can have negative impacts. This study used hyperspectral remote sensing technology to indirectly detect natural gas leakage by analyzing the spectral characteristics of vegetation. The experiment found specific spectral bands sensitive to gas stress, which can be used to identify stressed plants. The proposed index showed promising results in identifying gas-stressed plants.
ECOLOGICAL INFORMATICS
(2022)
Editorial Material
Environmental Sciences
Yongguang Zhang, Mirco Migliavacca, Josep Penuelas, Weimin Ju
Summary: This article introduces the recent advances in remote sensing of plant traits and functions, including the shift from monitoring structural parameters to functional traits and the use of hyperspectral techniques to monitor vegetation status across various scales. The eight papers mentioned in this editorial focus on developing new remote sensing techniques and algorithms for retrieving plant functional traits, which will help improve the estimation of vegetation processes such as photosynthesis, water cycle, and carbon cycle.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Environmental Sciences
Glenn M. Suir, Sam Jackson, Christina Saltus, Molly Reif
Summary: Monitoring and modeling coastal vegetation and wetland systems is a challenge, but remote sensing applications can provide alternative and complementary approaches. This study used airborne LiDAR and hyperspectral imagery to quantify coastal dune vegetation characteristics and assess landscape-level trends. The findings showed that areas with the greatest decreases in vegetation metric values were associated with direct tropical storm energies, while those with the greatest increases were in areas where overwashed sediments were distributed. The methodologies developed in this study narrow existing gaps in dune vegetation data and improve future predictions of nearshore dynamics and disturbance impacts.
Article
Plant Sciences
Mia T. Wavrek, Eric Carr, Sharon Jean-Philippe, Michael L. McKinney
Summary: We used drone remote sensing to analyze the relationship between field-collected forest health indicators and four Vegetative Indices (VI) in order to improve conservation management of urban forests. Our findings showed that the calculated VI values from drone imagery were significantly related to ecological concerns, forest composition, and equitability. Despite the limitations of the small number of plots, our results indicate the potential for drone remote sensing as a low-cost and efficient tool for urban forest management.
URBAN FORESTRY & URBAN GREENING
(2023)
Article
Biodiversity Conservation
Xiaoai Dai, Haipeng Feng, Lixiao Xiao, Jiayun Zhou, Zekun Wang, Junjun Zhang, Tianzhang Fu, Yunfeng Shan, Xianhua Yang, Yakang Ye, Li Xu, Xiaoli Jiang, Shibo Fang, Yuanzhi Yao
Summary: Mining activities in mining cities cause significant ecological stresses to the environment, threatening the health of vegetation and human. This study used hyperspectral remote sensing data to assess the ecological vulnerability of Panzhihua city, a representative mining city in China. The results showed that hyperspectral imaging performed better in precision and concentration, allowing more accurate monitoring of vegetation growth and restoration, and providing guidance for ecological conservation measures.
ECOLOGICAL INDICATORS
(2022)
Article
Ecology
Aaron G. Kamoske, Kyla M. Dahlin, Quentin D. Read, Sydne Record, Scott C. Stark, Shawn P. Serbin, Phoebe L. Zarnetske
Summary: Rapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Recently, remote sensing platforms utilizing high spectral resolution and lidar data have provided an opportunity to study biodiversity patterns across space and time.
GLOBAL ECOLOGY AND BIOGEOGRAPHY
(2022)
Article
Environmental Sciences
Jan Hanus, Lukas Slezak, Tomas Fabianek, Lukas Fajmon, Tomas Hanousek, Ruzena Janoutova, Daniel Kopkane, Jan Novotny, Karel Pavelka, Miroslav Pikl, Frantisek Zemek, Lucie Homolova
Summary: FLIS is a multi-sensor platform that integrates optical, thermal, and laser scanning remotely sensed data to study terrestrial ecosystems. It provides spectral data, landscape orography, and 3D structure information, allowing for the assessment of vegetation ecosystems and the study of thermal behavior in urban systems.
Article
Environmental Sciences
Xiaoping Wang, Jingming Shi, Chenfeng Wang, Chao Gao, Fei Zhang
Summary: This study uses remote sensing inversion and mapping techniques to estimate forest stand age, taking into account the remote sensing mechanism of vegetation indices and the physiological function and canopy structure of the forest. Multiple linear regression and random forest models are used for the estimation, and the accuracy of the models is evaluated. The results show that the reflectance of the canopy decreases with the increase of forest stand age, and the relationship between forest stand age and red edge is the most significant. The random forest model has a higher accuracy in estimating forest stand age.
Article
Environmental Sciences
Jean-Luc Widlowski, Corrado Mio, Mathias Disney, Jennifer Adams, Ioannis Andredakis, Clement Atzberger, James Brennan, Lorenzo Busetto, Michael Chelle, Guido Ceccherini, Roberto Colombo, Jean-Francois Cote, Alo Eenmaee, Richard Essery, Jean-Philippe Gastellu-Etchegorry, Nadine Gobron, Eloi Grau, Vanessa Haverd, Lucie Homolova, Huaguo Huang, Linda Hunt, Hideki Kobayashi, Benjamin Koetz, Andres Kuusk, Joel Kuusk, Mait Lang, Philip E. Lewis, Jennifer L. Lovell, Zbynek Malenovsky, Michele Meroni, Felix Morsdorf, Matti Mottus, Wenge Ni-Meister, Bernard Pinty, Miina Rautiainen, Martin Schlerf, Ben Somers, Jan Stuckens, Michel M. Verstraete, Wenze Yang, Feng Zhao, Terenzio Zenone
REMOTE SENSING OF ENVIRONMENT
(2015)
Article
Environmental Sciences
Qiang Wang, Yong Pang, Zengyuan Li, Guoqing Sun, Erxue Chen, Wenge Ni-Meister
Article
Meteorology & Atmospheric Sciences
Eric Kutter, Chuixiang Yi, George Hendrey, Heping Liu, Timothy Eaton, Wenge Ni-Meister
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
(2017)
Article
Environmental Sciences
Wenge Ni-Meister, Wenze Yang, Shihyan Lee, Alan H. Strahler, Feng Zhao
REMOTE SENSING OF ENVIRONMENT
(2018)
Article
Engineering, Electrical & Electronic
Bin Peng, Jiancheng Shi, Wenge Ni-Meister, Tianjie Zhao, Dabin Ji
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2014)
Article
Geography, Physical
Gordon M. Green, Sean C. Ahearn, Wenge Ni-Meister
PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING
(2013)
Article
Environmental Sciences
Feng Zhao, Xiaoyuan Yang, Alan H. Strahler, Crystal L. Schaaf, Tian Yao, Zhuosen Wang, Miguel O. Roman, Curtis E. Woodcock, Wenge Ni-Meister, David L. B. Jupp, Jenny L. Lovell, Darius S. Culvenor, Glenn J. Newnham, Hao Tang, Ralph O. Dubayah
REMOTE SENSING OF ENVIRONMENT
(2013)
Article
Environmental Sciences
Linda I. Pistolesi, Wenge Ni-Meister, Kyle C. McDonald
WETLANDS ECOLOGY AND MANAGEMENT
(2015)
Article
Environmental Sciences
Sungho Choi, Xiliang Ni, Yuli Shi, Sangram Ganguly, Gong Zhang, Hieu V. Duong, Michael A. Lefsky, Marc Simard, Sassan S. Saatchi, Shihyan Lee, Wenge Ni-Meister, Shilong Piao, Chunxiang Cao, Ramakrishna R. Nemani, Ranga B. Myneni
Article
Environmental Sciences
Qiang Wang, Wenge Ni-Meister
Correction
Environmental Sciences
Qiang Wang, Wenge Ni-Meister
Article
Engineering, Environmental
Bibhash Nath, Wenge Ni-Meister, Runti Choudhury
Summary: The rapid expansion of urban environments has impacted hydrological cycles, biogeochemical processes, and the environmental sustainability of natural resources, resulting in a decline in groundwater levels. Despite the doubling of urban built areas over 30 years, urbanization has negatively affected vegetation, fallow land, and wetlands.
GROUNDWATER FOR SUSTAINABLE DEVELOPMENT
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Wenge Ni-Meister, Shihyal Lee
IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
(2020)
Proceedings Paper
Engineering, Electrical & Electronic
Wenge Ni-Meister
2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
(2016)