Article
Forestry
Igor da Silva Narvaes, Joao Roberto dos Santos, Polyanna da Conceicao Bispo, Paulo Mauricio de Alencastro Graca, Ulisses Silva Guimaraes, Fabio Furlan Gama
Summary: We developed a method for estimating above-ground biomass (AGB) from polarimetric SAR images. The model used power and phase-radar attributes, as well as attributes from Touzi decomposition. The proposed model showed good predictive capacity and a positive correlation with the validation results.
Article
Engineering, Electrical & Electronic
Soni Darmawan, Ita Carolita, Rika Hernawati, Dede Dirgahayu, Agustan, Didin Agustian Permadi, Dewi Kania Sari, Widya Suryadini, Dhimas Wiratmoko, Yohanes Kunto
Summary: This study explores the scattering model of oil palm phenology using spaceborne X-, C-, and L-band polarimetric Synthetic Aperture Radar (SAR) imaging. Different scattering characteristics were observed for X-, C-, and L-band polarimetric SAR, with the potential of C-band on TV polarization. The results suggest that the scattering model has potential for identifying oil palm phenology in Indonesia, with future improvements needed for accuracy.
JOURNAL OF SENSORS
(2021)
Article
Environmental Sciences
Roberto Crespo Calvo, Ma Angeles Varo Martinez, Francisco Ruiz Gomez, Antonio Jesus Ariza Salamanca, Rafael M. Navarro-Cerrillo
Summary: This study investigated the forest fuel attributes of a Mediterranean forest type using field data, ALS data, and multispectral satellite data. The study found that ALS data performed well in estimating the biomass of different forest types. The structural complexity of forest fuels was assessed using the LiDAR Height Diversity Index, and the fuel desiccation index was opposite to the fuel quality. Remote sensing is important for characterizing and mapping forest fuel attributes.
Article
Environmental Sciences
Yuzhen Zhang, Jingjing Liu, Wenhao Li, Shunlin Liang
Summary: Feature selection can improve the accuracy of forest aboveground biomass (AGB) prediction and identify important predictors, but its role in AGB estimation has not received sufficient attention. This study quantified the benefits of feature selection in AGB prediction and proposed a stability-heterogeneity-correlation-based ensemble (SHCE) method that outperformed existing FS methods in terms of prediction accuracy and identification of important features.
Article
Ecology
Rakesh Fararoda, R. Suraj Reddy, G. Rajashekar, T. R. Kiran Chand, C. S. Jha, V. K. Dadhwal
Summary: Accurate estimation of spatial above ground biomass in tropical forests is crucial for understanding the global carbon cycle. This study combines field inventory data with optical and microwave images to spatially estimate biomass over Indian forests using a random forest approach. The inclusion of multisource data significantly increases the saturation range and reduces estimation error.
ECOLOGICAL INFORMATICS
(2021)
Article
Environmental Sciences
Wei Chen, Qihui Zheng, Haibing Xiang, Xu Chen, Tetsuro Sakai
Summary: This study utilized PolInSAR technology to estimate forest canopy height based on full-polarized ALOS/PALSAR data, proposing multiple algorithms such as differential DEM, coherent amplitude, coherent phase-amplitude, and RVoG_3 for comparison. By introducing change rate and slope, the estimation model was optimized to improve accuracy in forest canopy height estimation.
Article
Environmental Sciences
Andre Beaudoin, Ronald J. J. Hall, Guillermo Castilla, Michelle Filiatrault, Philippe Villemaire, Rob Skakun, Luc Guindon
Summary: Satellite forest inventories using k-nearest neighbor algorithm combined with Landsat and SAR data can accurately map forest attributes in Canada's northern boreal forests. This study demonstrates the feasibility and effectiveness of optimizing k-NN parameters and feature space for inventory mapping.
Article
Biodiversity Conservation
Daniela Requena Suarez, Danae M. A. Rozendaal, Veronique De Sy, Mathieu Decuyper, Natalia Malaga, Patricia Duran Montesinos, Alexs Arana Olivos, Ricardo De la Cruz Paiva, Christopher Martius, Martin Herold
Summary: Amazonian forests play a vital role as reservoirs of biomass and biodiversity, contributing to climate change mitigation. This study examines the impact of disturbances on forest biomass and biodiversity in the Peruvian Amazon, using tree-level data and remotely sensed monitoring. The results show that disturbance intensity negatively affects tree species richness and biomass recovery. Surprisingly, time since disturbance has a small negative effect on species richness. Approximately 15% of Peruvian Amazonian forests have experienced disturbance since 1984, with an increase in biomass of 4.7 Mg ha(-1) year(-1) during the first 20 years.
GLOBAL CHANGE BIOLOGY
(2023)
Article
Environmental Sciences
Maurizio Santoro, Oliver Cartus, Johan E. S. Fransson
Summary: This study revisited the Water Cloud Model (WCM) for estimating forest biomass-related variables, aiming to reduce systematic retrieval errors associated with empirical assumptions in the model by exploring physically-based, Light Detection and Ranging (LiDAR)-aided, model parameterization at a larger scale. The integration of allometries in the WCM effectively reduced estimation errors, demonstrating the potential for providing large-scale estimates of biomass-related variables using L-band backscatter observations.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Remote Sensing
Katsuto Shimizu, Tomohiro Nishizono, Fumiaki Kitahara, Keiko Fukumoto, Hideki Saito
Summary: The integration of TLS and UAV photogrammetry improves the accurate estimation of tree attributes, especially in tree height and stem volume, in managed coniferous forests of Japan.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2022)
Article
Forestry
Erick O. Osewe, Ioan Dutca
Summary: By investigating European beech biomass datasets, it was found that combining variables in allometric models resulted in biased estimates of mean AGB per hectare for European beech trees. For Q values greater than 2.0, the estimation was overestimated by 6.3%, while for Q values less than 2.0, the estimation was underestimated by 3.9%.
Article
Ecology
Usha Mina, A. P. Dimri, Sandhya Farswan
Summary: In Uttarakhand, forest fire events have increased significantly and are influenced by temperature. Forest fires cause a loss of carbon in biomass stock and may disrupt the carbon cycle due to climate change.
Article
Agronomy
Joao Rodrigues da Cunha, Rita de Cassia Alves de Freitas, Djalma Junior de Almeida Taveres Souza, Adriano Venicius Santana Gualberto, Henrique Antunes de Souza, Luiz Fernando Carvalho Leite
Summary: The study evaluated the effects of different monoculture and integrated production systems on soil microbiological attributes and organic carbon in the Cerrado region of Piaui, Brazil. The IPF and CEE systems improved soil attributes, organic carbon, and microbial biomass.
ACTA SCIENTIARUM-AGRONOMY
(2021)
Article
Forestry
Gabriel Marcos Vieira Oliveira, Jose Marcio de Mello, Carlos Rogerio de Mello, Jose Roberto Soares Scolforo, Eder Pereira Miguel, Thiago Campos Monteiro
Summary: The study found that wood density in tropical forests has a high spatial dependence and is closely related to climate variables, showing a continuous gradient distribution. Wood density is correlated with environmental factors such as mean annual precipitation, temperature, and evapotranspiration, and is sensitive to climate change.
JOURNAL OF FORESTRY RESEARCH
(2022)
Article
Environmental Sciences
Aliny Aparecida Dos Reis, Steven E. Franklin, Fausto Weimar Acerbi Junior, Antonio Carlos Ferraz Filho, Jose Marcio de Mello
Summary: This study used DEM data and climate data to estimate productivity in 19 Eucalyptus plantations in Minas Gerais state, Brazil. By using a Random Forest modelling approach, Site Index (SI) and Mean Annual Increment (MAI) were related to geomorphometric variables and macro-climatic measures. The results showed that low productivity sites were the most extensive in the study area and required additional forestry operations to improve productivity and growth.
GEOCARTO INTERNATIONAL
(2022)
Article
Environmental Sciences
Jessica da Silva Costa, Veraldo Liesenberg, Marcos Benedito Schimalski, Raquel Valerio de Sousa, Leonardo Josoe Biffi, Alessandra Rodrigues Gomes, Silvio Luis Rafaeli Neto, Edson Mitishita, Polyanna da Conceicao Bispo
Summary: The study utilized the experimental quad-polarization acquisition mode of ALOS/PALSAR-2 satellite data combined with SENTINEL-2A data to map land cover classes, finding that the addition of polarimetric features significantly improved classification accuracy. However, SENTINEL-2A data alone performed better, suggesting potential for further research to enhance classification accuracy.
Article
Environmental Sciences
Fabio Furlan Gama, Natalia Cristina Wiederkehr, Polyanna da Conceicao Bispo
Summary: This article presents a methodology using Fourier fast transform to filter stripes on radar images caused by ionospheric disturbances. The filtered images were then classified using random forest, showing improved classification performance compared to the original scenes.
Article
Environmental Sciences
Ana Maria Pacheco-Pascagaza, Yaqing Gou, Valentin Louis, John F. Roberts, Pedro Rodriguez-Veiga, Polyanna da Conceicao Bispo, Fernando D. B. Espirito-Santo, Ciaran Robb, Caroline Upton, Gustavo Galindo, Edersson Cabrera, Indira Paola Pachon Cendales, Miguel Angel Castillo Santiago, Oswaldo Carrillo Negrete, Carmen Meneses, Marco Iniguez, Heiko Balzter
Summary: The commitment by over 100 governments to end deforestation by 2030 requires more effective forest monitoring systems. Near real-time change detection of forest cover loss enables timely monitoring and responses, and the Copernicus Sentinel-2 satellites provide suitable data for this purpose. The proposed NRT change detection system based on these satellites has been evaluated and proven to accurately detect forest cover loss and other vegetation changes. It is also scalable and adaptable to larger regions and countries.
Article
Environmental Sciences
Anna Moore, Emma Ashworth, Carla Mason, Joao Santos, Rosie Mansfield, Emily Stapley, Jessica Deighton, Neil Humphrey, Nick Tait, Daniel Hayes
Summary: The substantial time spent in schools by children and young people makes them important sites for testing and implementing prevention and early intervention programs. However, schools are complex settings and maintaining school engagement in research trials can be challenging, leading to high attrition rates. This commentary presents insights from two large-scale mental health intervention trials in English schools, exploring barriers and challenges to school engagement in promotion or early intervention research and offering detailed recommendations for other researchers.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2022)
Article
Multidisciplinary Sciences
Rosie Mansfield, Joao Santos, Jessica Deighton, Daniel Hayes, Tjasa Velikonja, Jan R. Boehnke, Praveetha Patalay
Summary: This study investigated the impact of COVID-19 on adolescent mental health using a natural experimental design within two ongoing cluster randomized controlled trials. Results showed that adolescents exposed to COVID-19 had higher depressive symptoms and lower life satisfaction, with no effect on externalizing difficulties. Exploratory analyses suggest that the negative impact of the pandemic on adolescent mental health may have been greater for females than males.
ROYAL SOCIETY OPEN SCIENCE
(2022)
Article
Forestry
Gabriel Fernandes Bueno, Emanuel Arnoni Costa, Cesar Augusto Guimaraes Finger, Veraldo Liesenberg, Polyanna da Conceicao Bispo
Summary: This study aimed to predict the crown diameter of open-growing trees using statistical models and found that artificial neural networks performed better than other techniques. These models can help assess the crown dynamics of species and support decision making in silvicultural practices and other related activities in the Cerrado biome.
Article
Forestry
Lorena Oliveira Barbosa, Emanuel Arnoni Costa, Cristine Tagliapietra Schons, Cesar Augusto Guimaraes Finger, Veraldo Liesenberg, Polyanna da Conceicao Bispo
Summary: This research aimed to develop statistical models using Artificial Neural Networks (ANNs) to predict the basal area increment (BAI) for Araucaria angustifolia. The study found that tree size and competition were the most significant factors influencing A. angustifolia's BAI. The spatial distribution of competing trees and the type of competition also contributed significantly to the representation of competitive status. Therefore, both intra-specific and inter-specific competition are important factors to consider.
Article
Forestry
Emanuel Arnoni Costa, Andre Felipe Hess, Cesar Augusto Guimaraes Finger, Cristine Tagliapietra Schons, Danieli Regina Klein, Lorena Oliveira Barbosa, Geedre Adriano Borsoi, Veraldo Liesenberg, Polyanna da Conceicao Bispo
Summary: The application of artificial intelligence in forestry shows great potential through evaluating the performance of different neural network models in estimating tree height, especially utilizing ANN technology to integrate categorical and numerical variables for modeling. It is important to pay attention to data normalization, the number of neurons in the hidden layer, and the selection of activation functions during model building to ensure accuracy and avoid overfitting.
Article
Forestry
Laryssa Demetrio, Andre Felipe Hess, Alex Nascimento de Sousa, Emanuel Arnoni Costa, Veraldo Liesenberg, Mauricio Jean Freisleben, Marcos Benedito Schimalski, Cesar Augusto Guimaraes Finger, Noe dos Santos Ananias Hofico, Polyanna da Conceicao Bispo
Summary: Understanding the correlation between reproductive structures and dendro/morphometric variables of Araucaria angustifolia tree species is crucial for its conservation and sustainable forest management. By visually counting and measuring variables, it was found that the morphometric variables are correlated with male strobili production, indicating the importance of vitality, dimension, density, growth space, and position of the tree in the forest.
Article
Forestry
Igor da Silva Narvaes, Joao Roberto dos Santos, Polyanna da Conceicao Bispo, Paulo Mauricio de Alencastro Graca, Ulisses Silva Guimaraes, Fabio Furlan Gama
Summary: We developed a method for estimating above-ground biomass (AGB) from polarimetric SAR images. The model used power and phase-radar attributes, as well as attributes from Touzi decomposition. The proposed model showed good predictive capacity and a positive correlation with the validation results.
Article
Forestry
Cesar Augusto Guimaraes Finger, Emanuel Arnoni Costa, Andre Felipe Hess, Veraldo Liesenberg, Polyanna da Conceicao Bispo
Summary: Brazilian pine, a significant tree species in Brazil, is endangered due to uncontrolled exploitation and requires sustainable cultivation and conservation research.
Article
Environmental Sciences
Dazhou Ping, Ricardo Dalagnol, Lenio Soares Galvao, Bruce Nelson, Fabien Wagner, David M. M. Schultz, Polyanna da C. Bispo
Summary: This study aimed to track vegetation recovery after blowdown events in the Amazon Forest, using Landsat-8 and PlanetScope NICFI satellite imagery. The results showed that the NICFI data provided more accurate characterization of post-disturbance vegetation recovery, and after approximately 15 months, the non-photosynthetic vegetation and green vegetation values recovered to pre-blowdown levels. These findings provide valuable insights into the dynamics of vegetation recovery following blowdown events.
Article
Medicine, Research & Experimental
Annie O'Brien, Suzanne Hamilton, Neil Humphrey, Pamela Qualter, Jan R. Boehnke, Joao Santos, Ola Demkowicz, Margarita Panayiotou, Alex Thompson, Jennifer Lau, Lauren Burke, Yizhuo Lu
Summary: This protocol describes a two-arm parallel cluster randomised controlled trial to investigate the impact of a universal social and emotional learning intervention on a range of outcomes among English primary school pupils aged 9-11 years.
Letter
Ecology
Polyanna da Conceicao Bispo, Michelle C. A. Picoli, Beatriz Schwantes Marimon, Ben Hur Marimon Jr, Carlos A. Peres, Imma Oliveras Menor, Daniel E. Silva, Flavia de Figueiredo Machado, Ane A. C. Alencar, Claudio A. de Almeida, Liana O. Anderson, Luiz E. O. C. Aragao, Fabio Marcelo Breunig, Mercedes Bustamante, Ricardo Dalagnol, Jose Alexandre F. Diniz-Filho, Laerte G. Ferreira, Manuel E. Ferreira, Gilberto Fisch, Lenio Soares Galvao, Angelica Giarolla, Alessandra Rodrigues Gomes, Paulo de Marco Jr, Tahisa N. Kuck, Caroline E. R. Lehmann, Murilo Ruv Lemes, Veraldo Liesenberg, Rafael Loyola, Marcia N. Macedo, Flavia de Souza Mendes, Sabrina do Couto de Miranda, Douglas C. Morton, Yhasmin M. Moura, Johan A. Oldekop, Mario B. Ramos-Neto, Thais M. Rosan, Sassan Saatchi, Edson E. Sano, Carlota Segura-Garcia, Julia Z. Shimbo, Thiago S. F. Silva, Diego P. Trevisan, Barbara Zimbres, Natalia C. Wiederkehr, Celso H. L. Silva-Junior
NATURE ECOLOGY & EVOLUTION
(2023)
Article
Psychology, Clinical
Margarita Panayiotou, Joao Santos, Louise Black, Neil Humphrey
Summary: The Social Skills Improvement System (SSIS) has been widely used in measuring social skills in children for over a decade, but evidence of its structural validity is lacking. This study found problematic fit and poor discriminant validity for the original seven-factor and more recent five-factor structure of SSIS. Using exploratory graph analysis and bifactor-(S - 1) modeling, a four-factor structure with a general factor defined by empathy and prosocial skills was supported.
