Article
Environmental Sciences
Max J. van Gerrevink, Sander Veraverbeke
Summary: Fire severity, determined by the impact of a fire on the environment, is crucial for modeling fire emissions and planning rehabilitation efforts. The dNBR spectral index outperformed the dNDVI(MID) index in assessing fire severity, displaying stronger relationships with field data and higher optimality values. Future research should further verify the effectiveness of the dNDVI(MID) approach in estimating fire severity over larger areas.
Article
Environmental Sciences
Clement J. F. Delcourt, Alisha Combee, Brian Izbicki, Michelle C. Mack, Trofim Maximov, Roman Petrov, Brendan M. Rogers, Rebecca C. Scholten, Tatiana A. Shestakova, Dave van Wees, Sander Veraverbeke
Summary: This study utilized Sentinel-2 satellite imagery and field data in Northeast Siberian larch-dominated forests to assess fire severity, finding that dNBR can be used to predict fire severity and performs better in mature larch stands. Future research is needed to further refine spaceborne fire severity assessments in the larch forests of Northeast Siberia.
Article
Environmental Sciences
Max J. van Gerrevink, Sander Veraverbeke
Summary: This study evaluates the spectral sensitivity of the dNBR using hyperspectral imagery and identifies the optimal bi-spectral NIR SWIR combination. The best performing combination was found to be bands 63 and 218, which showed a strong relationship with field data and had a median spectral index optimality statistic of 0.31. The hyperspectral sensitivity analysis revealed optimal NIR and SWIR bands for the composition of the dNBR, providing insights for assessing fire severity using hyperspectral data.
Article
Geosciences, Multidisciplinary
Weiwei Wang, Xianli Wang, Wanli Wu, Futao Guo, Jane Park, Guangyu Wang
Summary: This study investigated the burn severity in the Canadian Rocky Mountain region using satellite imagery. It found that fuel type had the most significant influence on burn severity, while topography, vegetation, and climate had equal contributions. The study also predicted the burn severity potential in different areas and highlighted the effectiveness of fire management in local communities.
GEOPHYSICAL RESEARCH LETTERS
(2022)
Article
Forestry
Luc Guindon, Sylvie Gauthier, Francis Manka, Marc-Andre Parisien, Ellen Whitman, Pierre Bernier, Andre Beaudoin, Philippe Villemaire, Rob Skakun
Summary: This study aims to establish a geospatial database of burn severity for wildland fires in Canada from 1985 to 2015 and evaluate seasonal and annual trends in burn severity. The results show that burn severity tends to be lower in spring fires compared to summer fires, both nationally and regionally across different units.
CANADIAN JOURNAL OF FOREST RESEARCH
(2021)
Article
Environmental Sciences
Donato Morresi, Raffaella Marzano, Emanuele Lingua, Renzo Motta, Matteo Garbarino
Summary: Deriving burn severity from multispectral satellite data is crucial to infer environmental change caused by fire, with temporal constraints such as matched acquisition and optimal timing playing a significant role in the variation of burn severity maps. Reflectance composites using Sentinel-2 imagery were found to have the highest overall classification accuracy in producing burn severity maps, offering new opportunities for operational change detection applications.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Jose Maria Costa-Saura, Valentina Bacciu, Claudio Ribotta, Donatella Spano, Antonella Massaiu, Costantino Sirca
Summary: Despite being a natural process, wildfires can have negative impacts on ecosystem services due to global change. However, fire severity has been mostly overlooked in risk analysis, especially at regional levels. Building robust models to predict fire severity is crucial for better decision-making. This study uses machine learning algorithms to predict fire severity using data from Western Italy and Southern France, and highlights the potential usefulness of these tools for risk assessments.
Article
Plant Sciences
Brandon L. Giddey, Johan A. Baard, Tineke Kraaij
Summary: This study validated the accuracy of dNBR derived from Sentinel 2 images as an index of fire severity in Afrotemperate forest through field observations. The strong linear relationship between dNBR and stem fire severity confirmed its accuracy in measuring fire severity. In the investigated fire, 67% of Afrotemperate forest burnt at low severity, 21% at medium severity, and 12% at high severity, providing valuable information for further investigation of fire regimes and ecology.
SOUTH AFRICAN JOURNAL OF BOTANY
(2022)
Article
Geography, Physical
Saeid Gholinejad, Elahe Khesali
Summary: The extraction of burn severity information through satellite images and spectral indices plays a crucial role in addressing fire crises. An automatic procedure based on change-point analysis is proposed for processing fire images to detect burn severity levels. Experimental results demonstrate the high efficiency of this method.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2021)
Article
Ecology
Matthew J. Reilly, Aaron Zuspan, Zhiqiang Yang
Summary: This study introduces a remote sensing approach for mapping delayed mortality after wildfires. The method based on the decline in normalized burn ratio (NBR) accurately predicts the presence of delayed mortality. The results show that delayed mortality significantly affects the post-fire landscape patterns of burn severity, with variations among different forest types and environmental conditions.
Article
Forestry
Cunyong Ju, Tijiu Cai, Wenhong Li, Ge Sun, Chengliang Lei, Xueying Di, Xiuling Man
Summary: In this study, a multivariate PLSR model was developed to better interpret the variance of burn severity, outperforming single variable models. The new model should assist in understanding forest burn severity patterns and aid in vegetation restoration efforts in the region.
JOURNAL OF FORESTRY RESEARCH
(2021)
Article
Environmental Sciences
Flavie Pelletier, Bianca N. Eskelson, Vicente J. Monleon, Yi-Chin Tseng
Summary: Accurate assessment of burn severity after wildfires is crucial as their frequency and size increase. Remotely-sensed imagery allows for rapid assessment, but requires field validation. This study used ground-based inventory data and remotely-sensed data to determine the best matching methods for burn severity assessments.
Article
Geosciences, Multidisciplinary
Esteban Alonso-Gonzalez, Victor Fernandez-Garcia
Summary: The study presented the first global burn severity database (MOSEV database) based on MODIS data, comparing it with Landsat-8 scenes with 30-meter resolution, showing a high correlation between the two datasets. The database is structured according to the MODIS tiling system and is freely downloadable.
EARTH SYSTEM SCIENCE DATA
(2021)
Article
Multidisciplinary Sciences
David E. Rother, Fernando De Sales, Doug Stow, Joe McFadden
Summary: Burn severity has significant effects on postfire vegetation recovery and boundary-layer climate. The study found that high severity fires resulted in the greatest reduction in vegetation, but also the fastest recovery rate. However, after five years, neither land surface temperature nor vegetation index returned to prefire levels.
Article
Forestry
Larissa L. Yocom, Jeff Jenness, Peter Z. Fule, Andrea E. Thode
Summary: Reburned areas tend to have lower severity in initial fires, and different vegetation types show varying fire severity in initial fires and reburns. Considering wildfires as fuel treatments may be beneficial for certain forest ecosystems.
Article
Environmental Sciences
Adrianna C. Foster, Jacquelyn K. Shuman, Brendan M. Rogers, Xanthe J. Walker, Michelle C. Mack, Laura L. Bourgeau-Chavez, Sander Veraverbeke, Scott J. Goetz
Summary: Forest characteristics, structure, and dynamics in the North American boreal region are influenced by wildfire intensity, severity, and frequency. Increasing temperatures may result in more intense and frequent fires, but an increase in deciduous forest cover could decrease flammability. A forest model was used to analyze the bottom-up (fuels) and top-down (climate) controls on fire activity and project future dynamics. The model showed good agreement with observations and predicted changes in biomass and fire probability.
ENVIRONMENTAL RESEARCH LETTERS
(2022)
Article
Agronomy
Willem W. Verstraeten, Rostislav Kouznetsov, Lucie Hoebeke, Nicolas Bruffaerts, Mikhail Sofiev, Andy W. Delcloo
Summary: Airborne birch pollen can cause allergies and have a negative impact on public health. By using a modeling approach, researchers have reconstructed and predicted the spatial distribution of birch pollen levels in Belgium from 1982 to 2019. The study shows that the amount of birch pollen in the air has been increasing, with climate change and variations in pollen emission sources playing a significant role.
AGRICULTURAL AND FOREST METEOROLOGY
(2022)
Article
Environmental Sciences
Maaike Izeboud, Stef Lhermitte
Summary: Areas of structural damage in Antarctic ice shelves weaken them mechanically, making them prone to disintegration and retreat. Studying the development of damage and its impact on ice sheet dynamics is crucial for understanding ice shelf stability and sea level rise. However, quantifying damage accurately is challenging due to various factors, such as complex surface conditions, variable imagery quality, and limited mapping options.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Environmental Sciences
J. Melchior van Wessem, Michiel R. R. van den Broeke, Bert Wouters, Stef Lhermitte
Summary: The study estimates the temperature thresholds for melt ponding over Antarctic ice shelves and reveals that cold and dry ice shelves are more vulnerable than expected. The commonly used -5 degrees C temperature threshold may not be applicable to all Antarctic ice shelves. The warming thresholds for melt pond formation are found to be highly variable and dependent on snow accumulation, suggesting that many ice shelves, even cold ones, may reach these thresholds by the end of the century.
NATURE CLIMATE CHANGE
(2023)
Article
Environmental Sciences
Willem W. Verstraeten, Nicolas Bruffaerts, Rostislav Kouznetsov, Letty de Weger, Mikhail Sofiev, Andy W. Delcloo
Summary: Changes in climate and land-use can lead to increased emission of allergenic pollen, causing a rise in respiratory allergies. A research study using the SILAM model found that the increase in birch pollen concentrations is associated with increasing radiation, decreasing precipitation, and decreasing wind speed, while the decrease in grass pollen concentrations is driven by a decreasing trend in grass pollen sources and decreasing precipitation.
ATMOSPHERIC ENVIRONMENT
(2023)
Article
Meteorology & Atmospheric Sciences
Marijn van der Meer, Sophie de Roda Husman, Stef Lhermitte
Summary: This study assesses the potential of using a cost-efficient machine learning alternative to dynamical downscaling in regional climate models (RCMs). The results show that a deep learning RCM-emulator can learn the proper GCM to RCM downscaling function while working directly with GCM data, and it presents a significant computational gain compared to an RCM simulation.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2023)
Article
Multidisciplinary Sciences
Yuquan Qu, Diego G. Miralles, Sander Veraverbeke, Harry Vereecken, Carsten Montzka
Summary: In many parts of the world, conditions for wildfires are increasing. This study examines the impact of weather and fuel conditions on wildfires and finds that weather plays a larger role than fuel, especially in tropical rainforests, mid-latitudes, and Siberian boreal forests. Fuel conditions are more dominant in North American and European boreal forests, as well as African and Australian savannahs. The study also highlights the complementary predictability of weather and fuel conditions for wildfire forecasting, with seasonal or interannual predictions feasible in areas where fuel conditions dominate.
NATURE COMMUNICATIONS
(2023)
Article
Geography, Physical
Diana Francis, Ricardo Fonseca, Kyle S. Mattingly, Stef Lhermitte, Catherine Walker
Summary: Pine Island Glacier has experienced increased ice loss, with Foehn winds being an important factor through increased surface sublimation. The impact of blowing snow and melting is relatively small. Therefore, atmospheric forcing plays a crucial role in the ice mass balance.
Article
Ecology
Stefano Potter, Sol Cooperdock, Sander Veraverbeke, Xanthe Walker, Michelle C. Mack, Scott J. Goetz, Jennifer Baltzer, Laura Bourgeau-Chavez, Arden Burrell, Catherine Dieleman, Nancy French, Stijn Hantson, Elizabeth E. Hoy, Liza Jenkins, Jill F. Johnstone, Evan S. Kane, Susan M. Natali, James T. Randerson, Merritt R. Turetsky, Ellen Whitman, Elizabeth Wiggins, Brendan M. Rogers
Summary: Fire is a major disturbance in Alaskan and Canadian boreal ecosystems, releasing significant amounts of carbon into the atmosphere. The increase in burned area and carbon emissions due to climate change can potentially shift the region from a carbon sink to a carbon source. Therefore, it is crucial to monitor the changes in burned area and fire carbon emissions over time.
Article
Engineering, Electrical & Electronic
Sophie de Roda Husman, Zhongyang Hu, Bert Wouters, Peter Kuipers Munneke, Sanne Veldhuijsen, Stef Lhermitte
Summary: Surface melt is an important factor contributing to ice shelf disintegration and mass loss in Antarctica. Satellite remote sensing is used to monitor surface melt and enhance our understanding of ice shelf stability. However, different sensors observe surface melt differently, leading to significant inconsistencies in derived melt estimates. In this study, we compared melt detection results from four commonly used remote sensing sensors and found large differences in detected melt between the sensors. We argue that different sensors can complement each other and improve the detection of surface melt in Antarctica.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Multidisciplinary Sciences
Romy Hulskamp, Arjen Luijendijk, Bas van Maren, Antonio Moreno-Rodenas, Floris Calkoen, Etienne Kras, Stef Lhermitte, Stefan Aarninkhof
Summary: This study presents a global assessment of the distribution and change of muddy coasts using satellite imagery and coastal geospatial datasets. The findings show that 14% of the world's ice-free coastline is muddy, with the majority experiencing erosion rates exceeding 1 meter per year over the past three decades.
NATURE COMMUNICATIONS
(2023)
Article
Remote Sensing
Yanxi Li, Rui Chen, Binbin He, Sander Veraverbeke
Summary: This study integrated optical data and synthetic aperture radar (SAR) data to estimate foliage fuel load (FFL) by analyzing spatiotemporal features. The results showed that both SAR and optical data contributed significantly to FFL estimation, with the best performance achieved when the two data sources were combined. Additionally, temporal features were found to be more important predictors of FFL than spatial features.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2022)
Article
Geosciences, Multidisciplinary
Dave van Wees, Guido R. van der Werf, James T. Randerson, Brendan M. Rogers, Yang Chen, Sander Veraverbeke, Louis Giglio, Douglas C. Morton
Summary: In fire emission models, the spatial resolution of the modelling framework and satellite data have a considerable impact on emission estimates. Using a 500 m resolution model based on MODIS data, global average carbon emissions from fire were calculated to be 2.1 Pg C yr(-1) during 2002-2020. Fire-related forest loss accounted for a significant portion of global burned area and emissions, indicating higher fuel consumption in forest fires compared to the global average. Soil organic carbon combustion in the boreal region and tropical peatlands also contributed to global emissions.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2022)
Article
Ecology
Clement Jean Frederic Delcourt, Sander Veraverbeke
Summary: This study presents values of mean squared diameter and specific gravity that can be used to calculate fine dead and downed woody debris loads in Cajander larch forests in northeast Siberia. These values provide important references for accurately estimating aboveground biomass in the region.
Article
Environmental Studies
Jasper Dijkstra, Tracy Durrant, Jesus San-Miguel-Ayanz, Sander Veraverbeke
Summary: This study developed random forest models to predict the occurrence and burned area of anthropogenic and lightning fires in Europe, and found that the majority of fires and burned area in Europe are caused by human activities. However, lightning plays a significant role in the remote northern regions of Scandinavia.
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)