Article
Remote Sensing
Xinyu Ding, Qunming Wang, Xiaohua Tong
Summary: In this study, a newly developed spatio-temporal spectral unmixing model is applied for estimating 500 m fractional vegetation cover (FVC), and the accuracy is improved by integrating 250 m features into the original 500 m data. Experimental results show that the proposed method significantly improves the accuracy of FVC mapping, and using training samples extracted at the prediction time for model training is more reliable.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2022)
Article
Environmental Sciences
Alba Viana-Soto, Akpona Okujeni, Dirk Pflugmacher, Mariano Garcia, Inmaculada Aguado, Patrick Hostert
Summary: Mediterranean forests are prone to fires, which can affect their recovery and composition. This study analyzed post-fire vegetation recovery using fractional time series, revealing spatial and temporal patterns of different vegetation types and shifts in composition using a Normalized Difference Tree-Shrub Fraction index.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Lucian Blaga, Dorina Camelia Ilies, Jan A. Wendt, Ioan Rus, Kai Zhu, Lorant Denes David
Summary: This study assessed changes in forest cover in a mountain area in north-west Romania and determined the most efficient methods for extracting and classifying forest areas. The results showed a 9% loss of forest cover over a 17-year period, with an average annual decrease of 33.9 hectares. Support Vector Machine (SVM) and Random Forest (RF) were found to be the best classification methods.
Article
Environmental Sciences
Anca Dabija, Marcin Kluczek, Bogdan Zagajewski, Edwin Raczko, Marlena Kycko, Ahmed H. Al-Sulttani, Anna Tarda, Lydia Pineda, Jordi Corbera
Summary: Land cover information is crucial in European Union spatial management, with the development of the new version CLC+ in progress. Various methods and algorithms are being tested in Catalonia, Poland, and Romania to provide insights and guidance for development.
Article
Ecology
Robin Singh Bhadoria, Manish Kumar Pandey, Pradeep Kundu
Summary: Human intervention causing forest fires hinders nature's ability to recover, leading to climate change consequences that we must take responsibility for and minimize. Mitigating fires by predicting and controlling their spread can be enhanced through machine learning models, like the proposed RVFR model, which achieves higher accuracy in predicting forest fires based on past data.
ECOLOGICAL INFORMATICS
(2021)
Article
Environmental Sciences
Debi Prasad Sahoo, Bhabagrahi Sahoo, Manoj Kumar Tiwari, Goutam Kumar Behera
Summary: This study proposes a novel framework that combines remote sensing images and machine learning algorithms to estimate daily streamflow in the Brahmani River Basin in India. The results show that all developed models can simulate streamflow well, with the SVRFUS model performing the best in reproducing different streamflow regimes. This approach has the potential to be applied in other world-river basins to estimate ecological flow regimes and facilitate aquatic environmental management.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2022)
Article
Computer Science, Artificial Intelligence
Kalaivani Kathirvelu, Asnath Victy Phamila Yesudhas, Sakkaravarthi Ramanathan
Summary: The study aims to monitor surface water and detect changes in lakes for sustainable water resource development. Landsat satellite images of Lake Urmia from 2010, 2000, and 2018 were used for change detection. A novel change detection framework involving pixel level fusion and classification was proposed. Experimental results show that the proposed classifier achieved high accuracy in detecting water area and changes.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Abolfazl Abdollahi, Biswajeet Pradhan, Abdullah Alamri, Chang-Wook Lee
Summary: The primary goal of this research is to evaluate the effectiveness of cloud-based computing services, such as Google Earth Engine (GEE), in classifying multitemporal satellite images and producing high-quality land cover maps for the target year of 2020. The study demonstrates that using GEE and the support vector machine (SVM) approach results in accurate classification. Spectral bands, spectral indices, topographic parameters, and postprocessing techniques also play important roles in improving the quality of land cover maps.
JOURNAL OF SENSORS
(2023)
Article
Geography, Physical
Yalan Wang, Giles Foody, Xiaodong Li, Yihang Zhang, Pu Zhou, Yun Du
Summary: In this study, a novel regression-based method called RSWFM is proposed for mapping surface water fractions from Sentinel-2 imagery. It uses a random forest and a synthetic spectral library to achieve high accuracy in mapping small water bodies.
GISCIENCE & REMOTE SENSING
(2023)
Article
Environmental Sciences
Jingru Song, Junhai Gao, Yongbin Zhang, Fuping Li, Weidong Man, Mingyue Liu, Jinhua Wang, Mengqian Li, Hao Zheng, Xiaowu Yang, Chunjing Li
Summary: Coastal wetland soil organic carbon (CW-SOC) is important for soil resource management. The study found significant differences in SOC content among different coastal wetlands, with the content in silty soils being about 1.8 times higher than that in sandy soils. Prediction models based on optimized support vector machine regression and optimized random forest regression accurately predicted the CW-SOC content.
Article
Engineering, Chemical
S. Chehreh Chelgani, H. Nasiri, A. Tohry, H. R. Heidari
Summary: Undoubtedly, hydrocyclones are crucial in powder technology and have a significant impact on process efficiency in plants. However, there is a lack of industrial-scale modeling of hydrocyclones, which can be used to train operators and reduce scale-up errors and lab costs. This study proposes a novel approach using conscious lab (CL) and explainable artificial intelligence (XAI) to fill this gap. The interactions between hydrocyclone variables were explored using the SHapley Additive exPlanations (SHAP) method and a new machine-learning model, CatBoost. The SHAP-CatBoost model successfully captured all the relationships and achieved higher accuracy in predicting O-80 and K-80 compared to other conventional AI methods.
Article
Engineering, Electrical & Electronic
Donghui Chen, Bingyang Wang, Tao Zhang, Zhiyong Chang
Summary: In this study, an electronic nose combined with machine learning algorithms was used to accurately predict pesticides in groundwater, and the concentrations of pesticides were estimated using support vector regression models.
SENSORS AND ACTUATORS A-PHYSICAL
(2023)
Article
Environmental Sciences
Youming Zhang, Na Ta, Song Guo, Qian Chen, Longcai Zhao, Fenling Li, Qingrui Chang
Summary: The use of a fast and accurate unmanned aerial vehicle (UAV) digital camera platform to estimate the leaf area index (LAI) of kiwifruit orchards is significant for growth monitoring, yield estimation, and field management. This study used high-resolution UAV images to extract spectral and textural parameters, which were then used to construct regression models for LAI estimation. The results showed that the model combining texture features had better prediction accuracy compared to the model based solely on spectral indices.
Article
Chemistry, Multidisciplinary
Andrew Rodger, Carsten Laukamp
Summary: In this study, a method is proposed to infer whole rock geochemistry parameters using non-negative matrix functions and random forest regression models. The method can be applied to smart sampling schemes for orebody characterization and budgetary constraints. The experimental results demonstrate that the method is capable of accurately predicting eight geochemical parameters and remains stable and reliable when tested in different spatial locations.
APPLIED SCIENCES-BASEL
(2022)
Article
Environmental Sciences
Mohsen Abbasi Sekkeravani, Ommolbanin Bazrafshan, Hamid Reza Pourghasemi, Arashk Holisaz
Summary: This study prepared a susceptibility map of land subsidence in the central and eastern plains of Fars province in Iran using statistical and machine learning models, identified the main factors influencing land subsidence, and applied logistic regression, random forest, boosting regression tree, and support vector machine models to draw a land subsidence susceptibility zoning map.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Green & Sustainable Science & Technology
Franz Schug, David Frantz, Dominik Wiedenhofer, Helmut Haberl, Doris Virag, Sebastian van der Linden, Patrick Hostert
Summary: This study assessed the dynamics of material stock and its relation to population in Germany using Landsat imagery and a spatial resolution of 30 m. The results showed that material stock and population in Germany grew by 13% and 4% respectively, with highly variable spatial patterns. The reunification of East and West Germany in 1990 led to a rapid growth of material stock per capita in East Germany, despite a decline in population. Possible over- or underestimations of stock growth due to methodological assumptions require further research.
JOURNAL OF INDUSTRIAL ECOLOGY
(2023)
Article
Biodiversity Conservation
Hendrik Bluhm, Tom A. A. Diserens, Thomas Engleder, Kaja Heising, Marco Heurich, Tomas Janik, Miloslav Jirku, Daniel Klich, Hannes J. J. Koenig, Rafal Kowalczyk, Dries Kuijper, Weronika Maslanko, Frank-Uwe Michler, Wiebke Neumann, Julian Oeser, Wanda Olech, Kajetan Perzanowski, Miroslaw Ratkiewicz, Dusan Romportl, Martin Salek, Tobias Kuemmerle
Summary: This study aimed to assess the opportunities and limitations for range expansions of European bison and moose in Central Europe. It found widespread suitable habitats for these two species, but also identified human pressure and natural barriers that restrict their recolonization. Conservation measures restoring connectivity are needed to allow these large herbivores to expand their historical ranges.
DIVERSITY AND DISTRIBUTIONS
(2023)
Article
Ecology
Xin Zong, Tiejun Wang, Andrew K. Skidmore, Marco Heurich
Summary: This study demonstrates the use of three-dimensional cumulative viewshed in studying animal spatial behavior at a landscape level. The researchers utilized a combined terrestrial and airborne LiDAR technique to measure fine-scale habitat visibility in forested landscapes. The findings reveal the red deer's preference for intermediate habitat visibility and their adaptation of movement rate to fine-scale visibility. This research provides valuable insights into the influence of visibility on animal behavior and highlights the potential of LiDAR in animal ecology and behavior studies.
JOURNAL OF ANIMAL ECOLOGY
(2023)
Article
Ecology
Ana Stritih, Rupert Seidl, Cornelius Senf
Summary: In this study, the horizontal and vertical structure of mountain forests in the European Alps was characterized using spaceborne lidar. Two alternative states of forest structure were identified: short, open-canopy forests and tall, closed-canopy forests. Disturbances played a significant role in transitioning between these states.
Article
Geography, Physical
Simon Koenig, Frank Thonfeld, Michael Foerster, Olena Dubovyk, Marco Heurich
Summary: Bark beetle infestations are a significant forest disturbance agent that has been increasing in frequency and affected areas due to global climate change. This study demonstrates the potential of using multi-sensor time series data from Landsat and Sentinel satellites to detect and monitor bark beetle infestations, with Sentinel-2 providing the best overall results.
GISCIENCE & REMOTE SENSING
(2023)
Review
Ecology
Igor Khorozyan, Marco Heurich
Summary: The Eurasian lynx is an adaptable predator that takes ungulates according to their availability, rather than specializing in hunting hares. The predation on large prey is influenced by the density of prey populations and the forest environment. In the wild, ungulates, particularly roe deer, are the main food source for Eurasian lynx.
Article
Environmental Sciences
Katja Kowalski, Akpona Okujeni, Patrick Hostert
Summary: In this study, a generalized drought monitoring framework for Central European grasslands was developed by combining Sentinel-2 data, field survey information, and spectral unmixing. The study accurately estimated the fractional cover of photosynthetic vegetation, non-photosynthetic vegetation, and soil using a spectral library and multi-temporal Sentinel-2 data. The grassland-specific Normalized Difference Fraction Index (NDFI) was calculated based on the time series data, revealing widespread drought impacts on Central European grasslands during the persistent drought period from 2018 to 2020. The study highlights the value of integrating Sentinel-2 data, field survey information, and spectral unmixing for drought monitoring across grassland gradients in Central Europe.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Infectious Diseases
Janine Rietz, Suzanne T. S. van Beeck Calkoen, Nicolas Ferry, Jens Schlueter, Helena Wehner, Karl-Heinz Schindlatz, Tomas Lackner, Christian von Hoermann, Franz J. Conraths, Jorg Mueller, Marco Heurich
Summary: Because animal carcasses often serve as reservoirs for pathogens, their location and removal are crucial in controlling the spread of diseases. Recent studies have shown that infrared sensors can be used to locate animal carcasses, but little is known about the factors influencing detection success. In this study, we investigated the potential of infrared technology to locate wild boar carcasses, as they play an important role in the spread of African swine fever. Our results showed that the thermal camera accurately measured carcass temperature and that the probability of finding carcasses was influenced by environmental and carcass conditions such as habitat type, air temperature, canopy openness, and decomposition stage.
TRANSBOUNDARY AND EMERGING DISEASES
(2023)
Article
Ecology
Lisa Mandl, Ana Stritih, Rupert Seidl, Christian Ginzler, Cornelius Senf
Summary: The launch of NASA's GEDI mission in 2018 provides new opportunities for describing forest ecosystems across large scales. The study quantified GEDI's potential to estimate forest structure in mountain landscapes and found a high agreement between GEDI and ALS at the landscape level. The research highlights the importance of GEDI for ecosystem dynamics and management.
REMOTE SENSING IN ECOLOGY AND CONSERVATION
(2023)
Article
Multidisciplinary Sciences
Franz Schug, Dominik Wiedenhofer, Helmut Haberl, David Frantz, Doris Virag, Sebastian van der Linden, Patrick Hostert
Summary: This study provides high-resolution maps of material stocks in buildings and infrastructures in Austria, showing a 33-year time series. These data are important for studies on societal resource use, transport studies, and land system science.
Article
Biodiversity Conservation
Rudolf Reiner, Rupert Seidl, Sebastian Seibold, Cornelius Senf
Summary: As climate change intensifies, forest disturbances caused by increased demand for timber are on the rise. These disturbances create transient edges within forests, which can have significant effects on the habitat quality of forest-dwelling species.
JOURNAL OF APPLIED ECOLOGY
(2023)
Review
Ecology
Stefano Palmero, Joe Premier, Stephanie Kramer-Schadt, Pedro Monterroso, Marco Heurich
Summary: Robust monitoring is crucial for successful conservation planning, especially for elusive and low-density species like felids. This study examined the impact of sampling designs on the precision of population density estimates for territorial felids. Analysis of 137 camera-trapping and spatial capture-recapture studies revealed that the number of individuals captured, recapture frequency, and capture probability are the most important variables affecting precision. Guidelines for future studies and a reporting protocol were provided to improve the reproducibility and comparability of spatial capture-recapture research.
Article
Hospitality, Leisure, Sport & Tourism
Stefanie Doeringer, Florian Porst, Lena Stumpf, Marco Heurich
Summary: This study examined the effects of visitation numbers, perceived encounters, and expectations on perceived crowding in the Bavarian Forest National Park. The results showed that measured visitor density is a reliable indicator of perceived crowding, moderated by site-specific conditions. The calculated thresholds provide a valuable tool to inform the public about peak visiting times in advance.
Article
Ecology
Mauro Hermann, Matthias Rothlisberger, Arthur Gessler, Andreas Rigling, Cornelius Senf, Thomas Wohlgemuth, Heini Wernli
Summary: Forest dieback in Europe has intensified and expanded, influenced by meteorological variations of temperature and precipitation. This study quantitatively investigates the meteorological history preceding events of reduced forest greenness and identifies the impact of the hottest summer on record in 2022, negatively affecting 37% of temperate and Mediterranean forest regions. The findings highlight the importance of understanding the forest-meteorology interaction for forest dieback in a changing climate.
Article
Biodiversity Conservation
Suzanne T. S. van Beeck Calkoen, Dries P. J. Kuijper, Marco Apollonio, Lena Blondel, Carsten F. Dormann, Ilse Storch, Marco Heurich
Summary: This study provides evidence for the dominant role played by humans (i.e. hunting, land-use activities) relative to large carnivores in reducing red deer density across European human-dominated landscapes. These findings suggest that when we would like large carnivores to exert numeric effects, we should focus on minimizing human impacts to allow the ecological impacts of large carnivores on ecosystem functioning.
JOURNAL OF APPLIED ECOLOGY
(2023)
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)