Article
Environmental Studies
David O'Byrne, Altaaf Mechiche-Alami, Anna Tengberg, Lennart Olsson
Summary: This paper focuses on the evaluation of socio-economic impacts of the Great Green Wall Initiative and proposes a framework to measure the well-being effects of sustainable land management interventions. The authors conducted a literature review and analyzed project evaluation reports to improve monitoring and inform the design of future projects.
Review
Plant Sciences
Mona Schreiber, Stefan A. Rensing, Sven B. Gould
Summary: More than half a billion years ago, a lineage of streptophyte algae began terraforming the terrestrial habitat and Earth's atmosphere, providing genes for the subsequent evolution of land plants. Studying molecular adaptations in non-tracheophyte species may help all green life, including crops, better prepare for the challenges of climate change.
TRENDS IN PLANT SCIENCE
(2022)
Article
Green & Sustainable Science & Technology
A. Mirzabaev, M. Sacande, F. Motlagh, A. Shyrokaya, A. Martucci
Summary: The Great Green Wall program aims to restore degraded ecosystems in the Sahel region, with an economic evaluation showing a $1.2 return for every dollar invested in land restoration. It may take up to ten years for land restoration activities to break even from a social perspective, but the study highlights economically attractive and ecologically sustainable activities and locations, despite challenges like lower survival rates of planted trees and grasses, persistence of land degradation drivers, and growing violent conflicts.
NATURE SUSTAINABILITY
(2022)
Article
Computer Science, Hardware & Architecture
Yesaya Tommy Paulus, Gerard Bastiaan Remijn
Summary: This study investigated the usability of various dwell times for selecting visual objects with eye-gaze-based input. The results indicated that dwell times ranging from 250 ms to around 1000 ms were potentially useful for eye-gaze-based object selection.
Review
Chemistry, Physical
Hui Lu, Wenqiang Wu, Zeping He, Xun Han, Caofeng Pan
Summary: In this review, the device types of perovskite-based photodetectors are introduced and the structural characteristics and corresponding device performances are analyzed. The typical construction methods suitable for the fabrication of perovskite photodetector arrays are highlighted, and the current development trends and their applications in image sensing are summarized. Major challenges are presented to guide the development of perovskite photodetector arrays.
NANOSCALE HORIZONS
(2023)
Article
Plant Sciences
Lingjian Wang, Xinggang Tang, Xin Liu, Jinchi Zhang
Summary: With the increase in slope numbers due to social and economic development and the large-scale exploitation of natural resources, the ecological protection and reconstruction of slopes has become a global concern. In this study, eight strategies involving different patented mineral solubilizing microorganisms (MSMs) were explored, and the field application of active permanent greening (APG) based on MSMs was analyzed. The results showed that MSMs significantly improved soil quality and promoted plant growth, with strategy A exhibiting the best performance. The field test confirmed that APG has better greening performance than traditional methods.
FRONTIERS IN PLANT SCIENCE
(2023)
Review
Computer Science, Hardware & Architecture
Shaohua Qi, Xin Ning, Guowei Yang, Liping Zhang, Peng Long, Weiwei Cai, Weijun Li
Summary: This paper comprehensively reviews and classifies the latest developments in deep learning methods for multi-view 3D object recognition, summarizes results on mainstream datasets, provides insightful conclusions, and proposes enlightening future research directions.
Article
Energy & Fuels
Zilong Xia, Yingjie Li, Ruishan Chen, Dhritiraj Sengupta, Xiaona Guo, Bo Xiong, Yilong Niu
Summary: Renewable energy, especially solar energy, is being promoted in many countries as a way to mitigate future energy crises and climate change. China has experienced unprecedented growth in the number and scale of photovoltaic (PV) power stations in the last decade. This study proposes an integrated remote sensing approach to map PV power stations and provides insights into their distribution and ecological impacts. The results show that PV power stations in the northwestern provinces of China have expanded rapidly, with a tendency towards large-scale centralized PV parks. The land used for PV power stations mainly comes from desert and grassland areas. Government policies have played a major role in driving the expansion of PV parks across the country. The findings of this study contribute to the development of industrial standards and environmental regulations for sustainable solar energy development.
Article
Engineering, Environmental
Yibin Qiao, Qiang Zhang, Ying Qi, Teng Wan, Lixin Yang, Xin Yu
Summary: Waste classification is an essential aspect of environmental pollution management in modern society. However, existing waste classification models struggle with low-illumination scenes. This study proposes Dark-Waste, a waste classification model, that effectively addresses these challenges and achieves accurate classification in low-light environments. The model combines illumination conversion and an improved ConvNeXt network with YOLOv5, and it has been validated on a self-built dataset. The results demonstrate the model's superior detection performance in low-illumination scenes, highlighting its significance in waste management in complex environments.
RESOURCES CONSERVATION AND RECYCLING
(2023)
Review
Physics, Multidisciplinary
Prasoon Kumar Vinodkumar, Dogus Karabulut, Egils Avots, Cagri Ozcinar, Gholamreza Anbarjafari
Summary: The computer vision, graphics, and machine learning research groups have focused on 3D object recognition. Deep learning approaches have become popular in this field due to their excellent performance in 2D computer vision. Many innovative methods have been proposed and evaluated on benchmark datasets. This study provides a comprehensive assessment of the latest developments in deep learning-based 3D object recognition, covering well-known models and their distinctive qualities.
Article
Construction & Building Technology
Qingling Meng, Jiabing Yang, Yun Zhang, Yilin Yang, Jinbo Song, Jing Wang
Summary: In this study, a rapid and intelligent bridge inspection system was established, which can accurately locate and identify various types of bridge defects, including cracks and spalls, through the combination of robot inspection equipment, deep learning algorithms, and image segmentation algorithms.
JOURNAL OF PERFORMANCE OF CONSTRUCTED FACILITIES
(2023)
Article
Computer Science, Information Systems
Ahmad F. Subahi
Summary: Greening software requirements refers to the incorporation of sustainability principles during the requirements engineering phase of the development life cycle. This can have various effects on software design, such as addressing energy and resource consumption, modularity, maintainability, and adaptability. In this study, a new mechanism is proposed for mapping software nonfunctional requirements to defined dimensions of green software sustainability using the BERT language model. The results demonstrate the effectiveness of this approach in text classification tasks and highlight the importance of domain-specific fine-tuning and transfer learning in requirements engineering for achieving high performance.
Article
Agriculture, Multidisciplinary
Xiaoqiang Zhang, Ying Chen, Jiepeng Jia, Kaiming Kuang, Yubin Lan, Caicong Wu
Summary: Field-road classification that automatically identifies the operation modes of GNSS points plays an important role in the analysis of agricultural vehicles. This study proposed two methods to capture the high-density characteristic in field driving: DBSCAN and an object detection model. The two classification results were combined using the DBI metric. Experimental results showed high accuracy in the field-road classification for wheat and paddy harvesting trajectories.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Agronomy
Quan Xu, Mengting Jin, Peng Guo
Summary: This study proposes a method for high-precision crop classification using time-series UAV images. The spectral and texture features of crops were successfully fused by calculating the separability of samples and deriving texture characteristics. Random forest algorithm achieved the highest accuracy in both pixel-based and object-oriented crop classification. The results provide valuable insights for crop area statistics and precision agriculture management.
Article
Engineering, Chemical
Milica Petrovic, Sasa Rancev, Nena Velinov, Miljana Radovic Vucic, Milan Antonijevic, Goran Nikolic, Aleksandar Bojic
Summary: Microcrystalline alpha-ZnMoO4 catalyst was synthesized by electrodeposition and used in a self-made open air atmospheric pressure pulsating corona plasma reactor for degrading Reactive Black 5 dye. The catalyst enhanced decolourization rate and promoted the generation of center dot OH radical by enhancing decomposition of plasma-generated H2O2.
SEPARATION AND PURIFICATION TECHNOLOGY
(2021)
Article
Ecology
Qian Li, Yuemin Yue, Siyu Liu, Martin Brandt, Zhengchao Chen, Xiaowei Tong, Kelin Wang, Jingyi Chang, Rasmus Fensholt
Summary: Mapping forests with low cost high-resolution satellite images based on crown structure allows for accurate characterization of forest classes, supporting sustainable forest management and restoration activities.
REMOTE SENSING IN ECOLOGY AND CONSERVATION
(2023)
Article
Geosciences, Multidisciplinary
Lei Fan, Jean-Pierre Wigneron, Philippe Ciais, Jerome Chave, Martin Brandt, Stephen Sitch, Chao Yue, Ana Bastos, Xin Li, Yuanwei Qin, Wenping Yuan, Dmitry Schepaschenko, Liudmila Mukhortova, Xiaojun Li, Xiangzhuo Liu, Mengjia Wang, Frederic Frappart, Xiangming Xiao, Jingming Chen, Mingguo Ma, Jianguang Wen, Xiuzhi Chen, Hui Yang, Dave van Wees, Rasmus Fensholt
Summary: Siberian forests have been considered an important carbon sink, but severe droughts and fire disturbances may have affected their carbon dynamics. Limited forest inventories have led to uncertainties in the carbon balance. This study analyzed microwave observations from 2010 to 2019 and found that the carbon balance of Siberian forests was close to neutral, with a small carbon sink. Fire and drought caused significant losses of live above-ground carbon, contrasting with the greening trends in leaf area index. This highlights the vulnerability of large forest carbon stores in Siberia to climate-induced disturbances.
Article
Environmental Sciences
Yujie Dou, Feng Tian, Jean-Pierre Wigneron, Torbern Tagesson, Jinyang Du, Martin Brandt, Yi Liu, Linqing Zou, John S. Kimball, Rasmus Fensholt
Summary: Vegetation optical depth (VOD) from satellite passive microwave sensors has been used to monitor aboveground biomass carbon dynamics. However, uncertainty in the relationship between VOD and biomass carbon arises from changes in water stress and moisture content. This study evaluated the reliability of using VOD from different frequencies and temporal aggregation methods for estimating decadal biomass carbon dynamics at the global scale.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Environmental Sciences
Maurice Mugabowindekwe, Martin Brandt, Jerome Chave, Florian Reiner, David L. L. Skole, Ankit Kariryaa, Christian Igel, Pierre Hiernaux, Philippe Ciais, Ole Mertz, Xiaoye Tong, Sizhuo Li, Gaspard Rwanyiziri, Thaulin Dushimiyimana, Alain Ndoli, Valens Uwizeyimana, Jens-Peter Barnekow Lilleso, Fabian Gieseke, Compton J. J. Tucker, Sassan Saatchi, Rasmus Fensholt
Summary: This study proposes an approach to map the carbon stock of each individual tree at the national scale of Rwanda using aerial imagery from 2008 and deep learning. The results show that 72% of the mapped trees are located in farmlands and savannas, and 17% in plantations, accounting for 48.6% of the national aboveground carbon stocks. Natural forests cover 11% of the total tree count and 51.4% of the national carbon stocks, with an overall carbon stock uncertainty of 16.9%. The mapping of all trees is crucial for effective planning and monitoring of restoration activities, as well as optimization of carbon sequestration, biodiversity, and economic benefits.
NATURE CLIMATE CHANGE
(2023)
Article
Multidisciplinary Sciences
Compton Tucker, Martin Brandt, Pierre Hiernaux, Ankit Kariryaa, Kjeld Rasmussen, Jennifer Small, Christian Igel, Florian Reiner, Katherine Melocik, Jesse Meyer, Scott Sinno, Eric Romero, Erin Glennie, Yasmin Fitts, August Morin, Jorge Pinzon, Devin McClain, Paul Morin, Claire Porter, Shane Loeffler, Laurent Kergoat, Bil-Assanou Issoufou, Patrice Savadogo, Jean-Pierre Wigneron, Benjamin Poulter, Philippe Ciais, Robert Kaufmann, Ranga Myneni, Sassan Saatchi, Rasmus Fensholt
Summary: We assessed the distribution, density, cover, size, mass, and carbon content of over 9.9 billion trees in the semi-arid sub-Saharan Africa north of the Equator using satellite data, machine learning, and high-performance computing. The average carbon stocks of individual trees ranged from 0.54 Mg C ha(-1) and 63 kg C tree(-1) in the arid zone to 3.7 Mg C ha(-1) and 98 kg tree(-1) in the sub-humid zone. Overall, we estimated the total carbon for our study area to be 0.84 (& PLUSMN;19.8%) Pg C. Comparisons with previous simulation studies showed discrepancies in tree density and carbon stocks. We provide a linked database for scientists, policymakers, practitioners, and farmers to estimate farmland tree carbon stocks.
Article
Multidisciplinary Sciences
Florian Reiner, Martin Brandt, Xiaoye Tong, David Skole, Ankit Kariryaa, Philippe Ciais, Andrew Davies, Pierre Hiernaux, Jerome Chave, Maurice Mugabowindekwe, Christian Igel, Stefan Oehmcke, Fabian Gieseke, Sizhuo Li, Siyu Liu, Sassan Saatchi, Peter Boucher, Jenia Singh, Simon Taugourdeau, Morgane Dendoncker, Xiao-Peng Song, Ole Mertz, Compton J. Tucker, Rasmus Fensholt
Summary: The continuous monitoring of trees is crucial for sustainable land management, but current systems lack consistent coverage. This study uses high-resolution imagery from the PlanetScope nanosatellite constellation to map tree cover in Africa, revealing that 29% of trees are found outside traditionally classified forest areas. This accurate mapping at the individual tree level has the potential to redefine land use impacts and contribute to natural climate solutions.
NATURE COMMUNICATIONS
(2023)
Article
Multidisciplinary Sciences
Wenmin Zhang, Guy Schurgers, Josep Penuelas, Rasmus Fensholt, Hui Yang, Jing Tang, Xiaowei Tong, Philippe Ciais, Martin Brandt
Summary: The impact of tropical temperature fluctuations on the growth rate of atmospheric CO(2) is no longer significant in recent decades. This is primarily due to increased precipitation, which has weakened the link between the carbon cycle and tropical temperature variation.
NATURE COMMUNICATIONS
(2023)
Article
Pharmacology & Pharmacy
Alfonso T. Garcia-Sosa
Summary: As the relevance of data-driven discovery escalates, meticulous scrutiny of datasets utilizing principles like Benford's Law to enhance data integrity and guide resource allocation is anticipated. Addressing critical aspects such as bias mitigation, algorithm effectiveness, data stewardship, effects, and fraud prevention is essential in the data-driven era of the pharmaceutical and medical industries. Harnessing Benford's Law and other statistical tests in drug discovery provides a potent strategy to detect data anomalies, fill data gaps, and enhance dataset quality.
EXPERT OPINION ON DRUG DISCOVERY
(2023)
Article
Geosciences, Multidisciplinary
Wenmin Zhang, Rasmus Fensholt, Martin Brandt
Summary: This study develop data-driven models to predict woody cover in Africa and find that woody cover can be accurately modeled using Random Forest. The simulations based on CMIP6 precipitation data project an overall increase in woody cover at the continental scale by 2100. However, this increase is mainly observed in regions with annual precipitation less than 1,600 mm, while higher rainfall areas are predicted to experience a decrease in woody cover. These results suggest that climate change may lead to changes in the functioning of dryland ecosystems and a loss of carbon stocks in humid areas.
GEOPHYSICAL RESEARCH LETTERS
(2023)
Article
Geosciences, Multidisciplinary
Hui Yang, Philippe Ciais, Frederic Frappart, Xiaojun Li, Martin Brandt, Rasmus Fensholt, Lei Fan, Sassan Saatchi, Simon Besnard, Zhu Deng, Simon Bowring, Jean-Pierre Wigneron
Summary: Changes in terrestrial carbon storage under environmental and land-use changes are crucial for regional and global carbon budgets. This study used L-band microwave vegetation optical depth to generate global maps of annual live vegetation biomass, and found that boreal and temperate forests contribute the most to the global carbon sink, while wet tropical forests serve as small carbon sources. Additionally, the study revealed that tropical deforested and degraded old-growth forests are nearly carbon neutral, while young and middle-aged forests in temperate and boreal regions are the largest sinks.
Article
Environmental Sciences
Xiaowei Tong, Martin Brandt, Yuemin Yue, Xiaoxin Zhang, Rasmus Fensholt, Philippe Ciais, Kelin Wang, Siyu Liu, Wenmin Zhang, Chen Mao, Martin Rudbeck Jepsen
Summary: Using high-resolution satellite data from 1986 to 2020, our analysis reveals the complex spatiotemporal patterns of southern China's forest transition. We find that the surge in forest area around 2010 is due to the growth of trees planted after 2000, which formed dense forests about a decade later. Our study documents the densification and expansion of forests in a country that had been largely deforested three decades ago.
COMMUNICATIONS EARTH & ENVIRONMENT
(2023)
Article
Environmental Sciences
Min Chen, Zhen Qian, Niklas Boers, Anthony J. Jakeman, Albert J. Kettner, Martin Brandt, Mei-Po Kwan, Michael Batty, Wenwen Li, Rui Zhu, Wei Luo, Daniel P. Ames, C. Michael Barton, Susan M. Cuddy, Sujan Koirala, Fan Zhang, Carlo Ratti, Jian Liu, Teng Zhong, Junzhi Liu, Yongning Wen, Songshan Yue, Zhiyi Zhu, Zhixin Zhang, Zhuo Sun, Jian Lin, Zaiyang Ma, Yuanqing He, Kai Xu, Chunxiao Zhang, Hui Lin, Guonian Lue
Summary: Methods to integrate Earth system modelling with deep learning offer promise for advancing understanding of Earth processes. This Perspective explores the development and applications of hybrid Earth system modelling, a framework that integrates neural networks into ESM throughout the modelling lifecycle. Yet existing hybrid ESMs largely have deep neural networks incorporated only during the initial stage of model development.
NATURE REVIEWS EARTH & ENVIRONMENT
(2023)
Article
Green & Sustainable Science & Technology
Qirui Li, Cyrus Samimi
Summary: This study aims to examine human mobility in Sub-Saharan Africa and its relationship with climatic and socioeconomic factors. The results indicate that seven out of forty countries have high mobility, while most experienced a decline in permanent migration. Climate and socioeconomic conditions have significant effects on mobility, but the effects differ between temporary moves and permanent migration.
Article
Environmental Sciences
Yanbiao Xi, Wenmin Zhang, Martin Brandt, Qingjiu Tian, Rasmus Fensholt
Summary: This study successfully predicted tree species diversity in a mixed broadleaf-conifer forest in northeast China using machine learning and multi-temporal Sentinel-1/2 data. The results show the important roles of air temperature and soil fertility in governing the spatial variability of tree species diversity.
SCIENCE OF REMOTE SENSING
(2023)
Article
Multidisciplinary Sciences
Sizhuo Li, Martin Brandt, Rasmus Fensholt, Ankit Kariryaa, Christian Igel, Fabian Gieseke, Thomas Nord-Larsen, Stefan Oehmcke, Ask Holm Carlsen, Samuli Junttila, Xiaoye Tong, Alexandre d'Aspremont, Philippe Ciais
Summary: Sustainable tree resource management is crucial for mitigating climate warming, fostering a green economy, and protecting habitats. In this study, a deep learning-based framework was developed to provide location, crown area, and height information of individual overstory trees from aerial images at the country scale. The results showed a low bias in identifying large trees and highlighted the significant contribution of trees outside forests to total tree cover, which is often overlooked in national inventories. However, the framework had a higher bias when evaluating all taller trees, including small or understory trees that are undetectable. Moreover, the framework was easily transferable to data from Finland, despite different data sources.
