Articles de revues : « Global environmental change – remote sensing »

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Brewer, T. « Remote Sensing and Global Environmental Change ». Soil Use and Management 28, n^o 2 (4 avril 2012) : 278. http://dx.doi.org/10.1111/j.1475-2743.2012.00395.x.

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Yingshi, Zhao. « Remote Sensing for Global Environmental Change ». National Remote Sensing Bulletin, n^o 3 (1991) : 175–83. http://dx.doi.org/10.11834/jrs.1991028.

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KLEMAS, VICTOR V. « Remote Sensing of Landscape-Level Coastal Environmental Indicators ». Environmental Management 27, n^o 1 (1 janvier 2001) : 47–57. http://dx.doi.org/10.1007/s002670010133.

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Zhang, Xinkai, et Jie Yin. « Application of sea surface temperature remote sensing data in environmental assessment of fishing grounds ». Transactions on Computer Science and Intelligent Systems Research 3 (10 avril 2024) : 110–16. http://dx.doi.org/10.62051/fpa2dm58.

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This article provides a comprehensive discussion of the application and importance of sea surface temperature (SST) remote sensing data in environmental assessment of fisheries. By analysing the collection, processing and practical application of SST data, the article demonstrates how this technology can help scientists and fisheries managers to better understand the impacts of ocean temperature changes on fisheries resources and its role in ensuring the sustainability of global fisheries. The article begins by describing the importance of the marine environment to fishery resources and explains the development of SST remote sensing technology and its application to fishery location and environmental monitoring. The article then discusses in detail the application of SST data in practical fisheries management, in particular how it can help predict fish migration, optimise fishing activities, and assess the impacts of climate change on marine ecosystems. In addition, the article explores the main challenges faced when applying SST data, such as technical limitations, complexity of data interpretation, and unequal access on a global scale. Overall, this article highlights the indispensable role of SST remote sensing data in modern fisheries management, while also pointing out the limitations of its application and future directions. With the continuous advancement of remote sensing technology and the ongoing impact of global climate change, SST data are expected to play an increasingly important role in future fisheries environmental assessment and resource management.

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Balzter, Heiko. « Remote sensing and global environmental change, by Samuel Purkis and Victor Klemas ». International Journal of Geographical Information Science 27, n^o 8 (18 avril 2013) : 1688–89. http://dx.doi.org/10.1080/13658816.2013.780608.

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Lee, Jae K., J. C. Randolph, Kamlesh P. Lulla et Michael R. Helfert. « Interfacing remote sensing and geographic information systems for global environmental change research ». Geocarto International 8, n^o 4 (décembre 1993) : 7–18. http://dx.doi.org/10.1080/10106049309354426.

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Gaulton, Rachel. « Remote sensing and global environmental change, by S. Purkis and V. Klemas ». International Journal of Remote Sensing 34, n^o 2 (18 septembre 2012) : 751–52. http://dx.doi.org/10.1080/01431161.2012.714921.

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Zhang, Hengpan, et Jiahua Li. « Application and progress of water colour remote sensing technology in monitoring chlorophyll concentration changes in seawater ». Transactions on Computer Science and Intelligent Systems Research 3 (10 avril 2024) : 102–9. http://dx.doi.org/10.62051/vag1mq54.

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This paper provides an in-depth discussion on the application and progress of water colour remote sensing technology in monitoring changes in chlorophyll concentration in seawater. The hydrochromatic remote sensing technique uses spectral data acquired by remote sensing satellites and airborne platforms to monitor and analyse the distribution of marine chlorophyll, which is of great significance for understanding and protecting marine ecosystems. The article first introduces the basic principles and development history of this technology, and then discusses in detail its applications in global ocean monitoring, including resource management, environmental assessment, and ecological protection. In the discussion, we highlight the advantages of water colour remote sensing technology in providing large-scale and efficient ocean monitoring, while also pointing out the challenges it faces in terms of data accuracy, atmospheric disturbance handling and algorithm development. In particular, these challenges are gradually being overcome with the application of advanced computational techniques, such as machine learning and artificial intelligence, resulting in significant improvements in monitoring accuracy and efficiency. Finally, this paper looks at the future direction of water colour remote sensing technology, including technological innovation, data integration and interdisciplinary collaboration, highlighting the potential value of this technology in addressing global climate change and marine environmental protection. Through this comprehensive analysis, we have gained a deeper understanding and appreciation of the important role of water colour remote sensing technology in global ocean monitoring and management.

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Sun, Qiong, Chi Zhang, Min Liu et Yongjing Zhang. « Land use and land cover change based on historical space–time model ». Solid Earth 7, n^o 5 (27 septembre 2016) : 1395–403. http://dx.doi.org/10.5194/se-7-1395-2016.

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Abstract. Land use and cover change is a leading edge topic in the current research field of global environmental changes and case study of typical areas is an important approach understanding global environmental changes. Taking the Qiantang River (Zhejiang, China) as an example, this study explores automatic classification of land use using remote sensing technology and analyzes historical space–time change by remote sensing monitoring. This study combines spectral angle mapping (SAM) with multi-source information and creates a convenient and efficient high-precision land use computer automatic classification method which meets the application requirements and is suitable for complex landform of the studied area. This work analyzes the histological space–time characteristics of land use and cover change in the Qiantang River basin in 2001, 2007 and 2014, in order to (i) verify the feasibility of studying land use change with remote sensing technology, (ii) accurately understand the change of land use and cover as well as historical space–time evolution trend, (iii) provide a realistic basis for the sustainable development of the Qiantang River basin and (iv) provide a strong information support and new research method for optimizing the Qiantang River land use structure and achieving optimal allocation of land resources and scientific management.

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Pettorelli, Nathalie, Kamran Safi et Woody Turner. « Satellite remote sensing, biodiversity research and conservation of the future ». Philosophical Transactions of the Royal Society B : Biological Sciences 369, n^o 1643 (26 mai 2014) : 20130190. http://dx.doi.org/10.1098/rstb.2013.0190.

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Assessing and predicting ecosystem responses to global environmental change and its impacts on human well-being are high priority targets for the scientific community. The potential for synergies between remote sensing science and ecology, especially satellite remote sensing and conservation biology, has been highlighted by many in the past. Yet, the two research communities have only recently begun to coordinate their agendas. Such synchronization is the key to improving the potential for satellite data effectively to support future environmental management decision-making processes. With this themed issue, we aim to illustrate how integrating remote sensing into ecological research promotes a better understanding of the mechanisms shaping current changes in biodiversity patterns and improves conservation efforts. Added benefits include fostering innovation, generating new research directions in both disciplines and the development of new satellite remote sensing products.

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Rai, Ram Kumar, Makhan Singh Karada, Riya Mishra, Dheer Agnihotri, Kamal Kishor Patel, Satyendra Thakur et Deepak Singh. « Transformative Role of Remote Sensing in Advancing Horticulture : Optimizing Sustainability, Efficiency and Resilience ». International Journal of Environment and Climate Change 13, n^o 10 (19 septembre 2023) : 3559–67. http://dx.doi.org/10.9734/ijecc/2023/v13i103026.

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The field of horticulture, vital for addressing global challenges like food security and sustainable agriculture, has been revolutionized by remote sensing technology. This comprehensive review explores the transformative impact of remote sensing on horticulture, emphasizing its role in optimizing resource utilization, promoting environmental sustainability, and mitigating the effects of climate change. Remote sensing, encompassing a range of sensors, satellites, and data analysis techniques, enables the collection of critical information from a distance, providing insights into crop health, soil conditions, water availability, and more. Precision agriculture, including the use of GPS and GIS, is integrated with remote sensing to enhance agricultural efficiency while minimizing environmental impacts. Site-Specific Crop Management (SSCM) is highlighted as a key component of precision agriculture, enabled by geospatial technologies, including remote sensing. It discusses how remote sensing systems, with their multispectral and multi-temporal capabilities, support various horticultural applications such as crop yield estimation, abiotic and biotic stress management, crop classification, canopy measurement, crop area estimation, and even crop insurance validation. The use of Geographic Information Systems (GIS) and the Global Positioning System (GPS) in tandem with remote sensing is explored in the context of spatial analysis, mapping, and precise navigation.

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Du, Jinyang, Jennifer D. Watts, Lingmei Jiang, Hui Lu, Xiao Cheng, Claude Duguay, Mary Farina et al. « Remote Sensing of Environmental Changes in Cold Regions : Methods, Achievements and Challenges ». Remote Sensing 11, n^o 16 (20 août 2019) : 1952. http://dx.doi.org/10.3390/rs11161952.

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Cold regions, including high-latitude and high-altitude landscapes, are experiencing profound environmental changes driven by global warming. With the advance of earth observation technology, remote sensing has become increasingly important for detecting, monitoring, and understanding environmental changes over vast and remote regions. This paper provides an overview of recent achievements, challenges, and opportunities for land remote sensing of cold regions by (a) summarizing the physical principles and methods in remote sensing of selected key variables related to ice, snow, permafrost, water bodies, and vegetation; (b) highlighting recent environmental nonstationarity occurring in the Arctic, Tibetan Plateau, and Antarctica as detected from satellite observations; (c) discussing the limits of available remote sensing data and approaches for regional monitoring; and (d) exploring new opportunities from next-generation satellite missions and emerging methods for accurate, timely, and multi-scale mapping of cold regions.

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Yang, Fan, Xiaozhi Men, Yangsheng Liu, Huigeng Mao, Yingnan Wang, Li Wang, Xiran Zhou, Chong Niu et Xiao Xie. « Estimation of Landslide and Mudslide Susceptibility with Multi-Modal Remote Sensing Data and Semantics : The Case of Yunnan Mountain Area ». Land 12, n^o 10 (20 octobre 2023) : 1949. http://dx.doi.org/10.3390/land12101949.

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Landslide and mudslide susceptibility predictions play a crucial role in environmental monitoring, ecological protection, settlement planning, etc. Currently, multi-modal remote sensing data have been used for precise landslide and mudslide disaster prediction with spatial details, spectral information, or terrain attributes. However, features regarding landslide and mudslide susceptibility are often hidden in multi-modal remote sensing images, beyond the features extracted and learnt by deep learning approaches. This paper reports our efforts to conduct landslide and mudslide susceptibility prediction with multi-modal remote sensing data involving digital elevation models, optical remote sensing, and an SAR dataset. Moreover, based on the results generated by multi-modal remote sensing data, we further conducted landslide and mudslide susceptibility prediction with semantic knowledge. Through the comparisons with the ground truth datasets created by field investigation, experimental results have proved that remote sensing data can only enhance deep learning techniques to detect the landslide and mudslide, rather than the landslide and mudslide susceptibility. Knowledge regarding the potential clues about landslide and mudslide, which would be critical for estimating landslide and mudslide susceptibility, have not been comprehensively investigated yet.

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Li, Jinghui, Feng Shao, Qiang Liu et Xiangchao Meng. « Global-Local Collaborative Learning Network for Optical Remote Sensing Image Change Detection ». Remote Sensing 16, n^o 13 (27 juin 2024) : 2341. http://dx.doi.org/10.3390/rs16132341.

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Due to the widespread applications of change detection technology in urban change analysis, environmental monitoring, agricultural surveillance, disaster detection, and other domains, the task of change detection has become one of the primary applications of Earth orbit satellite remote sensing data. However, the analysis of dual-temporal change detection (CD) remains a challenge in high-resolution optical remote sensing images due to the complexities in remote sensing images, such as intricate textures, seasonal variations in imaging time, climatic differences, and significant differences in the sizes of various objects. In this paper, we propose a novel U-shaped architecture for change detection. In the encoding stage, a multi-branch feature extraction module is employed by combining CNN and transformer networks to enhance the network’s perception capability for objects of varying sizes. Furthermore, a multi-branch aggregation module is utilized to aggregate features from different branches, providing the network with global attention while preserving detailed information. For dual-temporal features, we introduce a spatiotemporal discrepancy perception module to model the context of dual-temporal images. Particularly noteworthy is the construction of channel attention and token attention modules based on the transformer attention mechanism to facilitate information interaction between multi-level features, thereby enhancing the network’s contextual awareness. The effectiveness of the proposed network is validated on three public datasets, demonstrating its superior performance over other state-of-the-art methods through qualitative and quantitative experiments.

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Lin, Mingsen, et Yongjun Jia. « Past, Present and Future Marine Microwave Satellite Missions in China ». Remote Sensing 14, n^o 6 (9 mars 2022) : 1330. http://dx.doi.org/10.3390/rs14061330.

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Over the past 60 years, China has made fruitful achievements in the field of ocean microwave remote sensing satellite technology. A long-term plan has now been formulated for the development of Chinese ocean satellites, as well as the construction of a constellation of ocean dynamic environmental and ocean surveillance satellites. These will gradually form China’s ocean monitoring network from space, thereby playing important roles in future ocean resource and environmental monitoring, marine disaster prevention and reduction, and global climate change. In this review manuscript, the developmental history of ocean microwave satellites and the development status of oceanic microwave remote sensing satellites in China are reviewed. In addition, China’s achievements in the field of oceanic microwave remote sensing satellite technology are summarized, and the future development of China’s ocean microwave remote sensing satellite program is analysed.

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Bhattarai, Nishan, et Pradeep Wagle. « Recent Advances in Remote Sensing of Evapotranspiration ». Remote Sensing 13, n^o 21 (23 octobre 2021) : 4260. http://dx.doi.org/10.3390/rs13214260.

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Evapotranspiration (ET) plays an important role in coupling the global energy, water, and biogeochemical cycles and explains ecosystem responses to global environmental change. However, quantifying and mapping the spatiotemporal distribution of ET across a large area is still a challenge, which limits our understanding of how a given ecosystem functions under a changing climate. This also poses a challenge to water managers, farmers, and ranchers who often rely on accurate estimates of ET to make important irrigation and management decisions. Over the last three decades, remote sensing-based ET modeling tools have played a significant role in managing water resources and understanding land-atmosphere interactions. However, several challenges, including limited applicability under all conditions, scarcity of calibration and validation datasets, and spectral and spatiotemporal constraints of available satellite sensors, exist in the current state-of-the-art remote sensing-based ET models and products. The special issue on “Remote Sensing of Evapotranspiration II” was launched to attract studies focusing on recent advances in remote sensing-based ET models to help address some of these challenges and find novel ways of applying and/or integrating remotely sensed ET products with other datasets to answer key questions related to water and environmental sustainability. The 13 articles published in this special issue cover a wide range of topics ranging from field- to global-scale analysis, individual model to multi-model evaluation, single sensor to multi-sensor fusion, and highlight recent advances and applications of remote sensing-based ET modeling tools and products.

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G. Pricope, Narcisa, Kerry L. Mapes et Kyle D. Woodward. « Remote Sensing of Human–Environment Interactions in Global Change Research : A Review of Advances, Challenges and Future Directions ». Remote Sensing 11, n^o 23 (26 novembre 2019) : 2783. http://dx.doi.org/10.3390/rs11232783.

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The role of remote sensing and human–environment interactions (HEI) research in social and environmental decision-making has steadily increased along with numerous technological and methodological advances in the global environmental change field. Given the growing inter- and trans-disciplinary nature of studies focused on understanding the human dimensions of global change (HDGC), the need for a synchronization of agendas is evident. We conduct a bibliometric assessment and review of the last two decades of peer-reviewed literature to ascertain what the trends and current directions of integrating remote sensing into HEI research have been and discuss emerging themes, challenges, and opportunities. Despite advances in applying remote sensing to understanding ever more complex HEI fields such as land use/land cover change and landscape degradation, agricultural dynamics, urban geography and ecology, natural hazards, water resources, epidemiology, or paleo HEIs, challenges remain in acquiring and leveraging accurately georeferenced social data and establishing transferable protocols for data integration. However, recent advances in micro-satellite, unmanned aerial systems (UASs), and sensor technology are opening new avenues of integration of remotely sensed data into HEI research at scales relevant for decision-making purposes that simultaneously catalyze developments in HDGC research. Emerging or underutilized methodologies and technologies such as thermal sensing, digital soil mapping, citizen science, UASs, cloud computing, mobile mapping, or the use of “humans as sensors” will continue to enhance the relevance of HEI research in achieving sustainable development goals and driving the science of HDGC further.

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Pastick, Neal J., M. Torre Jorgenson, Scott J. Goetz, Benjamin M. Jones, Bruce K. Wylie, Burke J. Minsley, Hélène Genet, Joseph F. Knight, David K. Swanson et Janet C. Jorgenson. « Spatiotemporal remote sensing of ecosystem change and causation across Alaska ». Global Change Biology 25, n^o 3 (28 mai 2018) : 1171–89. http://dx.doi.org/10.1111/gcb.14279.

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Akin, T., et S. Berberoglu. « MODELING LAND DEGRADATION USING REMOTE SENSING DATA : THE CASE OF SEYHAN BASIN ». International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-1-2023 (15 août 2023) : 449–54. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-1-2023-449-2023.

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Abstract. Land degradation is a global barrier to ecological, economic and sustainable developments. Climate change, natural disasters, human activities may result changes in soil organic carbon content, land productivity and land use/cover. Climate change is accelerating and expanding these degraded areas. If land destruction is not minimized, cause increasing population, inappropriate land use, climate change and rapid depletion of natural resources etc. in the coming years. It is estimated that land degradation and desertification will be the most important environmental problems. Mapping of land degradation using remote sensing techniques; determining sensitive areas for land degradation and taking protection measures; sustainable management of natural resources, ensuring sustainable agricultural production, etc. are the key factors. This study was conducted in the Seyhan basin, which is suffer from soil loss processes, changes in land cover and land use. These indicators are; trends in land productivity dynamics, land cover change and change of soil organic carbon stocks. The data set utilized to reveal the land degradation was including; 1 km resolution Land Productivity from JRC GLOBAL (1999–2013) and 250 m resolution NDVI from MOD13Q1 (2000–2015), Land Cover ESA CCI's with 300 m resolution LC (2000–2015), SOC stock from LUCAS (JRC) with 250 m resolution, 2000–2018 data from CORINE. The land degradation of the Seyhan basin was mapped using the specified land degradation indicators together with the One Out All Out (1OAO) rule.

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Sherbinin, Alex de, Karen Kline et Kal Raustiala. « Remote Sensing Data : Valuable Support for Environmental Treaties ». Environment : Science and Policy for Sustainable Development 44, n^o 1 (janvier 2002) : 20–31. http://dx.doi.org/10.1080/00139150209605589.

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Chen, Wei, Jiage Chen, Yuewu Wan, Xining Liu, Mengya Cai, Jingguo Xu, Hongbo Cui et Mengdie Duan. « Land Cover Classification Based on Multimodal Remote Sensing Fusion ». ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1-2024 (9 mai 2024) : 35–40. http://dx.doi.org/10.5194/isprs-annals-x-1-2024-35-2024.

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Abstract. Global high-precision and high timeliness land cover data is a fundamental and strategic resource for global strategic interest maintenance, global environmental change research, and sustainable development planning. However, due to difficulties in obtaining control and reference information from overseas, a single data source cannot effectively cover, and surface coverage classification faces significant challenges in information extraction. Based on this, this article proposes an intelligent interpretation method for typical elements based on multimodal fusion, starting from the characteristics of domestic remote sensing images. It also develops an optical SAR data conversion and complementarity strategy based on convolutional translation networks, as well as a typical element extraction algorithm. This solves the problems of sparse remote sensing images, limited effective observations, and difficult information recognition, thereby achieving automation of typical element information under dense observation time series High precision extraction and analysis.

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Li, Wantong, Gregory Duveiller, Sebastian Wieneke, Matthias Forkel, Pierre Gentine, Markus Reichstein, Shuli Niu, Mirco Migliavacca et Rene Orth. « Regulation of the global carbon and water cycles through vegetation structural and physiological dynamics ». Environmental Research Letters 19, n^o 7 (1 juillet 2024) : 073008. http://dx.doi.org/10.1088/1748-9326/ad5858.

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Abstract Vegetation plays an essential role in regulating carbon and water cycles, e.g. by taking up atmospheric CO2 through photosynthesis and by transferring soil water to the atmosphere through transpiration. Vegetation function is shaped by its structure and physiology: vegetation structure is determined by the amount of materials for plants and how it is organised in space and time, while vegetation physiology controls the instantaneous response of vegetation function to environmental conditions. Recognizing and disentangling these aspects of vegetation is key to understanding and predicting the response of the terrestrial biosphere to global change. This is now possible, as comprehensive measurements from Earth observations, both from satellites and the ground, provide invaluable data and information. This review introduces and describes vegetation structure and physiology, and summarises, compares, and contextualises recent literature to illustrate the state of the art in monitoring vegetation dynamics, quantifying large-scale vegetation physiology, and investigating vegetation regulation on the changes of global carbon and water fluxes. This includes results from remote sensing, in-situ measurements, and model simulations, used either to study the response of vegetation structure and physiology to global change, or to study the feedback of vegetation to global carbon and water cycles. We find that observation-based work is underrepresented compared with model-based studies. We therefore advocate further work to make better use of remote sensing and in-situ measurements, as they promote the understanding of vegetation dynamics from a fundamental data-driven perspective. We highlight the usefulness of novel and increasing satellite remote sensing data to comprehensively investigate the structural and physiological dynamics of vegetation on the global scale, and to infer their influence on the land carbon sink and terrestrial evaporation. We argue that field campaigns can and should complement large-scale analyses together with fine spatio-temporal resolution satellite remote sensing to infer relevant ecosystem-scale processes.

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Wang, Qizhi, Maofang Gao et Huijie Zhang. « Agroecological Efficiency Evaluation Based on Multi-Source Remote Sensing Data in a Typical County of the Tibetan Plateau ». Land 11, n^o 4 (10 avril 2022) : 561. http://dx.doi.org/10.3390/land11040561.

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Evaluating agricultural ecology can help us to understand regional environmental status and contribute to the sustainable development of agricultural ecosystems. Furthermore, the results of eco-environmental assessment can provide data support for policy-making and agricultural production. The application of multi-source remote-sensing technology has the advantages of being fast, accurate and wide ranging. It can reveal the status of regional ecological environments, and is of great significance to monitoring their quality. In this paper, an agroecological efficiency evaluation model was constructed by combining remote sensing data and ecological index (EI). Multi-source remote-sensing data were used to obtain the evaluation index. Indicators collected from satellites, such as biological richness, vegetation cover, water network density, land stress, and pollution load, were used to quantitatively evaluate the agroecological efficiency of Rangtang County in the Tibetan Plateau. The results showed that the EI of Rangtang County increased from 61.77 to 65.10 during 2000–2020, which means that the eco-environmental quality of this area was good, and it has shown an obviously improving trend over the past 20 years. Rangtang County has converted more than 30 km²of grassland into woodland over the past 20 years. Climate change and human activities have had combined effects on the ecological environment of this area. The change in ecological environment quality is greatly affected by human disturbance. Policymakers should continue setting up nature reserves and should implement the policy of returning farmland to forests. Unreasonable grazing and rational allocation of land resources are still critical points of concern for future ecological environment construction. EI, combined with remote sensing and statistical data, is proven to be able to reasonably represent changes in ecological environment in Rangtang County, thus providing more possibilities for ecological evaluation on the Tibetan Plateau, and even the whole world.

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Alavipanah, Seyed Kazem, Mohammad Karimi Firozjaei, Amir Sedighi, Solmaz Fathololoumi, Saeid Zare Naghadehi, Samiraalsadat Saleh, Maryam Naghdizadegan et al. « The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing : A Review ». Land 11, n^o 11 (12 novembre 2022) : 2025. http://dx.doi.org/10.3390/land11112025.

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In remote sensing (RS), shadows play an important role, commonly affecting the quality of data recorded by remote sensors. It is, therefore, of the utmost importance to detect and model the shadow effect in RS data as well as the information that is obtained from them, particularly when the data are to be used in further environmental studies. Shadows can generally be categorized into four types based on their sources: cloud shadows, topographic shadows, urban shadows, and a combination of these. The main objective of this study was to review the recent literature on the shadow effect in remote sensing. A systematic literature review was employed to evaluate studies published since 1975. Various studies demonstrated that shadows influence significantly the estimation of various properties by remote sensing. These properties include vegetation, impervious surfaces, water, snow, albedo, soil moisture, evapotranspiration, and land surface temperature. It should be noted that shadows also affect the outputs of remote sensing processes such as spectral indices, urban heat islands, and land use/cover maps. The effect of shadows on the extracted information is a function of the sensor–target–solar geometry, overpass time, and the spatial resolution of the satellite sensor imagery. Meanwhile, modeling the effect of shadow and applying appropriate strategies to reduce its impacts on various environmental and surface biophysical variables is associated with many challenges. However, some studies have made use of shadows and extracted valuable information from them. An overview of the proposed methods for identifying and removing the shadow effect is presented.

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Hereher, Mohamed E. « Climate Change during the Third Millennium—The Gulf Cooperation Council Countries ». Sustainability 14, n^o 21 (31 octobre 2022) : 14181. http://dx.doi.org/10.3390/su142114181.

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The Gulf Cooperation Council (GCC) is a union occurring in the arid subtropical belt of the world. Contemporary climate change is a serious environmental issue at the regional and global levels. The main purpose of this study was to investigate the changes and trends in the regional climate in the GCC countries in terms of the land surface temperatures and surface anomalies, precipitation, and sea surface temperatures over the first two decades of this millennium. Research data exclusively relied on satellite remote sensing. Spatial, statistical, and cartographic analyses were performed to extract, manipulate, and display thematic maps reflecting the changes and trends of the regional climate. The results showed that notable climate changes were obvious and frequent throughout the GCC countries, with symptoms influencing the atmosphere, lithosphere, and the hydrosphere of the region. However, a prominent gradient in the severity of climate change occurred from north to south. Remarkably, serious impacts were observed in Kuwait and eastern Saudi Arabia, while the least effects were recorded in Oman. The study denotes the competence of remote sensing for monitoring regional climate change.

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Wang, Haijun, Peihao Peng, Xiangdong Kong, Tingbin Zhang et Guihua Yi. « Vegetation dynamic analysis based on multisource remote sensing data in the east margin of the Qinghai-Tibet Plateau, China ». PeerJ 7 (13 décembre 2019) : e8223. http://dx.doi.org/10.7717/peerj.8223.

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This study focuses on the vegetation dynamic caused by global environmental change in the eastern margin of the Qinghai-Tibet Plateau (EMQTP). The Qinghai–Tibet Plateau (QTP) is one of the most sensitive areas responding to global environmental change, particularly global climate change, and has been recognized as a hotspot for coupled studies on changes in global terrestrial ecosystems and global climates. An important component of terrestrial ecosystems, vegetation dynamic has become a key issue in global environmental change, and numerous case studies have been conducted on vegetation dynamic trends using multi-source data and multi-scale methods across different study periods. The EMQTP is regarded as a transitional area located between the QTP and the Sichuan basin, and has special geographical and climatic conditions. Although this area is ecologically fragile and sensitive to climate change, few studies about vegetation dynamics have been carried out in this area. Thus, in this study, we used long-term series datasets of GIMMS 3g NDVI and VGT/PROBA-V NDVI to analyze the vegetation dynamics and phenological changes from 1982 to 2018. Validation was performed based on Landsat NDVI and Vegetation Index & Phenology (VIP) data. The results reveal that the year 1998 was a vital turning point in the start of growing season (SGS) in vegetation ecosystems. Before this turning point, the SGS had an average slope of 9.2 days/decade, and after, the average slope was 3.9 days/decade. The length of growing season (LGS) was slightly prolonged between 1982 to 2015. Additionally, the largest national alpine wetland grassland experienced significant vegetation degradation; in autumn, the degraded area accounted for 63.4%. Vegetation degradation had also appeared in the arid valleys of the Yalong River and the Jinsha River. Through validation analysis, we found that the main causes of vegetation degradation are the natural degradation of wetland grassland and human activities, specifically agricultural development and residential area expansion.

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Wentz, Elizabeth, Sharolyn Anderson, Michail Fragkias, Maik Netzband, Victor Mesev, Soe Myint, Dale Quattrochi, Atiqur Rahman et Karen Seto. « Supporting Global Environmental Change Research : A Review of Trends and Knowledge Gaps in Urban Remote Sensing ». Remote Sensing 6, n^o 5 (30 avril 2014) : 3879–905. http://dx.doi.org/10.3390/rs6053879.

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Tawfik, Zaki S., et Alaa H. Al-Hamami. « Roles of Climate Change and Environmental Sustainability ». Journal Port Science Research 6, special (1 mars 2024) : 81–89. http://dx.doi.org/10.36371/port.2023.special.12.

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Climate change is widely acknowledged as a major global challenge with serious environmental, economic, and social repercussions. Iraq is ranked as the world's fifth greatest vulnerable country. The country is facing serious challenges, such as rising temperatures, decreasing rainfall, alkalinity, and the latest dust storms. Given this, combating climate change is an urgent and profound task for Iraq, a complex and difficult mission that will span several generations. To confront the challenge of climate change, a comprehensive climate change mitigation and adaptation framework covering as many climate-related topics as possible and connecting as many interested parties as possible across Iraq is required. It is critical to weather patterns topics such as climate-induced impact assessment, environmental vulnerability, and adaptability and sequels, climate change. Our objective use remote sensing and GIS are to mitigation and adaptation strategies to hold the sustainable weather and an improved understanding of climate, climate-related impacts, and remedies.

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Kacic, Patrick, et Claudia Kuenzer. « Forest Biodiversity Monitoring Based on Remotely Sensed Spectral Diversity—A Review ». Remote Sensing 14, n^o 21 (26 octobre 2022) : 5363. http://dx.doi.org/10.3390/rs14215363.

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Forests are essential for global environmental well-being because of their rich provision of ecosystem services and regulating factors. Global forests are under increasing pressure from climate change, resource extraction, and anthropologically-driven disturbances. The results are dramatic losses of habitats accompanied with the reduction of species diversity. There is the urgent need for forest biodiversity monitoring comprising analysis on α, β, and γ scale to identify hotspots of biodiversity. Remote sensing enables large-scale monitoring at multiple spatial and temporal resolutions. Concepts of remotely sensed spectral diversity have been identified as promising methodologies for the consistent and multi-temporal analysis of forest biodiversity. This review provides a first time focus on the three spectral diversity concepts “vegetation indices”, “spectral information content”, and “spectral species” for forest biodiversity monitoring based on airborne and spaceborne remote sensing. In addition, the reviewed articles are analyzed regarding the spatiotemporal distribution, remote sensing sensors, temporal scales and thematic foci. We identify multispectral sensors as primary data source which underlines the focus on optical diversity as a proxy for forest biodiversity. Moreover, there is a general conceptual focus on the analysis of spectral information content. In recent years, the spectral species concept has raised attention and has been applied to Sentinel-2 and MODIS data for the analysis from local spectral species to global spectral communities. Novel remote sensing processing capacities and the provision of complementary remote sensing data sets offer great potentials for large-scale biodiversity monitoring in the future.

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Jiao, Zehua. « The Application of Remote Sensing Techniques in Ecological Environment Monitoring ». Highlights in Science, Engineering and Technology 81 (26 janvier 2024) : 449–55. http://dx.doi.org/10.54097/7dqegz64.

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As global environmental challenges continue to escalate, remote sensing technology has become an indispensable tool for researching, monitoring, and managing these issues. This article aims to summarize the principles of various remote sensing technologies, their primary application areas, and their crucial role in ecological and environmental conservation for sustainable development. Remote sensing technology offers a diverse array of data from multiple sources and scales, facilitating the monitoring of natural resource changes, studying ecosystem health, and assessing the impact of human activities on the environment. In-depth exploration of the latest advancements and application cases in remote sensing includes: (1) Vegetation: Applications in vegetation monitoring encompass the use of technologies such as unmanned aerial vehicle (UAV) remote sensing and laser radar (LiDAR) for monitoring forest health, vegetation cover, and changes. These applications support forest management and ecological research. (2) Water Bodies: Thermal infrared remote sensing and satellite remote sensing are utilized to monitor water quality, water level fluctuations, and aid in water resource management, addressing water resource challenges. (3) Soil: Remote sensing technology is employed in soil applications, utilizing multispectral and synthetic aperture radar (SAR) techniques for soil property assessment, soil moisture analysis, and soil erosion prediction. These applications contribute to agricultural and land management decisions, and provide suitable remote sensing technologies for the current trends in the ecological field, serving as a reference for monitoring methods.

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Mohammed, Zainab T., Riyad H. Al-Anbari et Oday Z. Jasim. « Air Temperature Modelling Depended on Remote Sensing Techniques ». Engineering and Technology Journal 38, n^o 3A (25 mars 2020) : 352–60. http://dx.doi.org/10.30684/etj.v38i3a.398.

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Air temperature (T air) near the land surface is a fundamental descriptor of physical environmental conditions and one of the most widely used climatic variables in global change studies. In this study, the researcher trying to suggest a model for estimating air temperature in summer season for any region through integrating of Iraqi Agrometeorological network daily (T air) with the moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST), Duration Day Length (DDL) and Digital Elevation Model (DEM). In this model, using satellite images for the study area and data of air temperature for four weather stations located in Babylon governorate from 1- June to 30- September on year 2017 for modeling and accuracy assessment air temperature estimation. The standard error of this model is 1.72887° C, and the correlation equal to 0.69698.

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Liu, Zhi Bo, et Pei Ji Shi. « Ecological Environment Effect Calculation of Land Use Change Based on Geography Information System ». Applied Mechanics and Materials 707 (décembre 2014) : 228–31. http://dx.doi.org/10.4028/www.scientific.net/amm.707.228.

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With the rapid development of social and economy, land use/cover change (LUCC) has been regard as a critical effect of global environment change. Researches on LUCC convert from single factor influence to overall research on the effects of regional ecological environment. Research Scope of study area gradually changes from the ecological fragile areas in the western to developed areas. This paper summarized the main methods of effect of land use change on the evaluate of eco-environment research. The results show that: environmental effects of land use/cover change improved in the long term; the rapid progress of remote sensing technology and geographic information system make environmental effects of land use/cover change more convenient.

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Zhou, Yuyu, Qihao Weng et Ni-Bin Chang. « Special Section Guest Editorial : Advances in Remote Sensing for Monitoring Global Environmental Changes ». Journal of Applied Remote Sensing 6, n^o 1 (18 décembre 2012) : 061799. http://dx.doi.org/10.1117/1.jrs.6.061799.

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Sogno, Patrick, Igor Klein et Claudia Kuenzer. « Remote Sensing of Surface Water Dynamics in the Context of Global Change—A Review ». Remote Sensing 14, n^o 10 (21 mai 2022) : 2475. http://dx.doi.org/10.3390/rs14102475.

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Inland surface water is often the most accessible freshwater source. As opposed to groundwater, surface water is replenished in a comparatively quick cycle, which makes this vital resource—if not overexploited—sustainable. From a global perspective, freshwater is plentiful. Still, depending on the region, surface water availability is severely limited. Additionally, climate change and human interventions act as large-scale drivers and cause dramatic changes in established surface water dynamics. Actions have to be taken to secure sustainable water availability and usage. This requires informed decision making based on reliable environmental data. Monitoring inland surface water dynamics is therefore more important than ever. Remote sensing is able to delineate surface water in a number of ways by using optical as well as active and passive microwave sensors. In this review, we look at the proceedings within this discipline by reviewing 233 scientific works. We provide an extensive overview of used sensors, the spatial and temporal resolution of studies, their thematic foci, and their spatial distribution. We observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. Multiple global analysis-ready products are available for investigating surface water area dynamics, but so far none offer high spatial and temporal resolution.

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Gu, Zhujun, et Maimai Zeng. « The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection : Review and Perspectives ». Sustainability 16, n^o 1 (28 décembre 2023) : 274. http://dx.doi.org/10.3390/su16010274.

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The integration of Artificial Intelligence (AI) and Satellite Remote Sensing in Land Cover Change Detection (LCCD) has gained increasing significance in scientific discovery and research. This collaboration accelerates research efforts, aiding in hypothesis generation, experiment design, and large dataset interpretation, providing insights beyond traditional scientific methods. Mapping land cover patterns at global, regional, and local scales is crucial for monitoring the dynamic world, given the significant impact of land cover distribution on climate and environment. Satellite remote sensing is an efficient tool for monitoring land cover across vast spatial extents. Detection of land cover change through satellite remote sensing images is critical in influencing ecological balance, climate change mitigation, and urban development guidance. This paper conducts a comprehensive review of LCCD using remote sensing images, encompassing exhaustive examination of satellite remote sensing data types and contemporary methods, with a specific focus on advanced AI technology applications. Furthermore, the study delves into the challenges and potential solutions in the field of LCCD, providing a comprehensive overview of the state of the art, offering insights for future research and practical applications in this domain.

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Campbell, Anthony D., Temilola Fatoyinbo, Sean P. Charles, Laura L. Bourgeau-Chavez, Joaquim Goes, Helga Gomes, Meghan Halabisky et al. « A review of carbon monitoring in wet carbon systems using remote sensing ». Environmental Research Letters 17, n^o 2 (1 février 2022) : 025009. http://dx.doi.org/10.1088/1748-9326/ac4d4d.

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Abstract Carbon monitoring is critical for the reporting and verification of carbon stocks and change. Remote sensing is a tool increasingly used to estimate the spatial heterogeneity, extent and change of carbon stocks within and across various systems. We designate the use of the term wet carbon system to the interconnected wetlands, ocean, river and streams, lakes and ponds, and permafrost, which are carbon-dense and vital conduits for carbon throughout the terrestrial and aquatic sections of the carbon cycle. We reviewed wet carbon monitoring studies that utilize earth observation to improve our knowledge of data gaps, methods, and future research recommendations. To achieve this, we conducted a systematic review collecting 1622 references and screening them with a combination of text matching and a panel of three experts. The search found 496 references, with an additional 78 references added by experts. Our study found considerable variability of the utilization of remote sensing and global wet carbon monitoring progress across the nine systems analyzed. The review highlighted that remote sensing is routinely used to globally map carbon in mangroves and oceans, whereas seagrass, terrestrial wetlands, tidal marshes, rivers, and permafrost would benefit from more accurate and comprehensive global maps of extent. We identified three critical gaps and twelve recommendations to continue progressing wet carbon systems and increase cross system scientific inquiry.

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Ding, Ying, Huihui Feng et Bin Zou. « Remote Sensing-Based Estimation on Hydrological Response to Land Use and Cover Change ». Forests 13, n^o 11 (24 octobre 2022) : 1749. http://dx.doi.org/10.3390/f13111749.

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Hydrological processes are an important driving force of environmental pollutant variation that has aroused global concern. Land use and cover change (LUCC) strongly affects hydrological processes. Remote sensing technology has played an increasingly important role in studying the relationship between LUCC and hydrological processes. This study summarizes the progress of hydrological responses to LUCC. Overall, remote sensing can provide spatially continuous data of land cover and hydrological variables. With the aid of the retrieved data sets, the effects of LUCC on hydrological processes can be evaluated via correlation analysis, multiple regression method, experimental watershed approach and trajectory-based approaches. However, due to the high complexity of geographical systems, it is difficult to quantitatively separate the actual components of the influence of LUCC. The heterogeneous surface properties also lead to various results at different spatial and temporal scales. Future research should meet the challenges in data estimation, research methodology and analysis process.

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Mukai, Sonoyo, Itaru Sano et Makiko Nakata. « Improved Algorithms for Remote Sensing-Based Aerosol Retrieval during Extreme Biomass Burning Events ». Atmosphere 12, n^o 3 (20 mars 2021) : 403. http://dx.doi.org/10.3390/atmos12030403.

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This study proposed an aerosol characterization process using satellites for severe biomass burning events. In general, these severely hazy cases are labeled as “undecided” or “hazy.” Because atmospheric aerosols are significantly affected by factors such as air quality, global climate change, local environmental risk, and human and biological health, efficient and accurate algorithms for aerosol retrieval are required for global satellite data processing. Our previous classification of aerosol types was based primarily on near-ultraviolet (UV) data, which facilitated subsequent aerosol retrieval. In this study, algorithms for aerosol classification were expanded to events with serious biomass burning aerosols (SBBAs). Once a biomass burning event is identified, the appropriate radiation simulation method can be applied to characterize the SBBAs. The second-generation global imager (SGLI) on board the Japanese mission JAXA/Global Change Observation Mission-Climate contains 19 channels, including red (674 nm) and near-infrared (869 nm) polarization channels with a high resolution of 1 km. Using the large-scale wildfires in Kalimantan, Indonesia in 2019 as an example, the complementarity between the polarization information and the nonpolarized radiance measurements from the SGLI was demonstrated to be effective in radiation simulations for biomass burning aerosol retrieval. The retrieved results were verified using NASA/AERONET ground-based measurements, and then compared against JAXA/SGLI/L2-version-1 products, and JMA/Himawari-8/AHI observations.

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Long, Lin, Yuanyuan Chen, Shaojun Song, Xiaoli Zhang, Xiang Jia, Yagang Lu et Gao Liu. « Remote Sensing Monitoring of Pine Wilt Disease Based on Time-Series Remote Sensing Index ». Remote Sensing 15, n^o 2 (6 janvier 2023) : 360. http://dx.doi.org/10.3390/rs15020360.

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Under the strong influence of climate change and human activities, the frequency and intensity of disturbance events in the forest ecosystem both show significant increasing trends. Pine wood nematode (Bursapherenchus xylophilus, PWN) is one of the major alien invasive species in China, which has rapidly infected the forest and spread. In recent years, its tendency has been to spread from south to north, causing serious losses to Pinus and non-Pinus coniferous forests. It is urgent to carry out remote sensing monitoring and prediction of pine wilt disease (PWD). Taking Anhui Province as the study area, we applied ground survey, satellite-borne optical remote sensing imagery and environmental factor statistics, relying on the Google Earth Engine (GEE) platform to build a new vegetation index NDFI based on time-series Landsat images to extract coniferous forest information and used a random forest classification algorithm to build a monitoring model of the PWD infection stage. The results show that the proposed NDFI differentiation threshold classification method can accurately extract the coniferous forest range, with the overall accuracy of 87.75%. The overall accuracy of the PWD monitoring model based on random forest classification reaches 81.67%, and the kappa coefficient is 0.622. High temperature and low humidity are conducive to the survival of PWN, which aggravates the occurrence of PWD. Under the background of global warming, the degree of PWD in Anhui Province has gradually increased, and has transferred from the southwest and south to the middle and northeast. Our results show that PWD monitoring and prediction at a regional scale can be realized by using long time-series multi-source remote sensing data, NDFI index can accurately extract coniferous forest information and grasp disease information in a timely manner, which is crucial for effective monitoring and control of PWD.

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Sidman, Gabriel, Sydney Fuhrig et Geeta Batra. « The Use of Remote Sensing Analysis for Evaluating the Impact of Development Projects in the Yellow Sea Large Marine Ecosystem ». Sustainability 12, n^o 9 (30 avril 2020) : 3628. http://dx.doi.org/10.3390/su12093628.

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Remote sensing has long been valued as a data source for monitoring environmental indicators and detecting trends in ecosystem stress from anthropogenic causes such as deforestation, river dams and air and water pollution. More recently, remote sensing analyses have been applied to evaluate the impacts of environmental projects and programs on reducing environmental stresses. Such evaluation has focused primarily on the change in above-surface vegetation such as forests. This study uses remote sensing ocean color products to evaluate the impact on reducing marine pollution of the Global Environment Facility’s (GEF) portfolio of projects in the Yellow Sea Large Marine Ecosystem. Chlorophyll concentration was derived from satellite images over a time series from the 1990s, when GEF projects began, until the present. Results show a 50% increase in chlorophyll until 2011 followed by a 34% decrease until 2019, showing a potential delayed effect of pollution control efforts. The rich time series data is a major advantage to using geospatial analysis for evaluating the impacts of environmental interventions on marine pollution. However, one drawback to the method is that it provides insights into correlations but cannot attribute the results to any particular cause, such as GEF interventions.

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Musse, Monica Alejandra, Daniel Alberto Barona et Luis Marino Santana Rodriguez. « Urban environmental quality assessment using remote sensing and census data ». International Journal of Applied Earth Observation and Geoinformation 71 (septembre 2018) : 95–108. http://dx.doi.org/10.1016/j.jag.2018.05.010.

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Arimjaya, I. Wayan Gede Krisna, et Muhammad Dimyati. « Remote sensing and geographic information systems technics for spatial-based development planning and policy ». International Journal of Electrical and Computer Engineering (IJECE) 12, n^o 5 (1 octobre 2022) : 5073. http://dx.doi.org/10.11591/ijece.v12i5.pp5073-5083.

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Indonesia's land-use and land-cover change (LULCC) is a global concern. The relocation plan of the capital city of Indonesia to East Kalimantan will be becoming an environmental issue. Knowing the latest land cover change modeling and prediction research is essential for fundamental knowledge in spatial planning and policies for regional development. Five articles related to integrated technology of geographic information systems (GIS) and remote sensing for spatial modeling were reviewed and compared using nine variables: title, journal (ranks), keywords, objectives, data sources, variables, location, method, and main findings. The results show that the variables that significantly affect LULCC are height, slope, distance from the road, and distance from the built-up area. The artificial neural network-based cellular automata (ANN-CA) method could be the best approach to model the LULCC. Furthermore, by the current availability of global multi-temporal and multi-sensor remote sensing data, the LULCC modeling study can be limitless

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Wang, Qiang, Jinping Wang, Mingmei Xue et Xifeng Zhang. « Characteristics and Trends of Ocean Remote Sensing Research from 1990 to 2020 : A Bibliometric Network Analysis and Its Implications ». Journal of Marine Science and Engineering 10, n^o 3 (6 mars 2022) : 373. http://dx.doi.org/10.3390/jmse10030373.

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The ocean is of great significance in the climate system, global resources and strategic decision making. With the continuous improvement in remote sensing technology, ocean remote sensing research has increasingly become an important topic for resource development and environmental protection. This paper uses bibliometric analysis method and VOSviewer visual software to conduct analysis. The analysis focuses on the period from 1990 to 2020. The analysis results show that articles have been steadily increasing over the past two decades. Scholars and researchers form the United States, China and Europe (mainly Western European countries), as well as NASA, Chinese Academy of Sciences and NOAA have bigger influence in this field to some extent. Among them, the United States and NASA holds the core leading position. Moreover, global cooperation in this field presents certain characteristics of geographical distribution. This study also reveals journals that include the most publications and subject categories that are highly relevant to related fields. Cluster analysis shows that remote sensing, ocean color, MODIS (or Moderate Resolution Imaging Spectroradiometer), chlorophy, sea ice and climate change are main research hotspots. In addition, in the context of climate warming, researchers have improved monitoring technology for remote sensing to warn and protect ocean ecosystems in hotspots (the Arctic and Antarctica). The valuable results obtained from this study will help academic professionals keep informed of the latest developments and identify future research directions in the field related to ocean remote sensing.

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Lucas, Neil S., et Paul J. Curran. « Forest ecosystem simulation modelling : the role of remote sensing ». Progress in Physical Geography : Earth and Environment 23, n^o 3 (septembre 1999) : 391–423. http://dx.doi.org/10.1177/030913339902300304.

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In recent years forest ecosystems have come under increasing pressure from environmental changes such as global warming and the impacts of pollution. Recent research has indicated that computer-simulation models driven by remotely sensed estimates of key variables may be used to assess the spatial impact of global environment changes on forest processes. This article begins with a discussion of key issues related to driving such models with remotely sensed estimates of these key variables. The article then outlines an investigation that examined whether a general ecosystem simulation model (FOREST-BGC), driven by remotely sensed and meteorological data, could be used to estimate forest processes for a Sitka spruce ( Picea sitchensis) plantation in mid-Wales.

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Turner, David P., Greg Koerper, Hermann Gucinski, Charles Peterson et Robert K. Dixon. « Monitoring global change : Comparison of forest cover estimates using remote sensing and inventory approaches ». Environmental Monitoring and Assessment 26-26, n^o 2-3 (juillet 1993) : 295–305. http://dx.doi.org/10.1007/bf00547506.

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Garcia-Lozano, Carla, Laura Olivas, Rosa Olivella et Anna Peliova. « Edusat : remote sensing as an educational resource. The use of data from the Copernicus program as an innovative teaching method for students, teachers, and researchers ». AGILE : GIScience Series 2 (4 juin 2021) : 1–6. http://dx.doi.org/10.5194/agile-giss-2-26-2021.

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Abstract. The intensification in recent decades of scientific evidence on climate change and on the degradation of natural systems has led to increasing public awareness about the environment. In recent times, this commitment to respecting the natural environment has emerged strongly among young people. Through various platforms, entities, and slogans, students from all over the world, and belonging to different disciplines, are coming together to defend their right to have a planet that enjoys good environmental health.The platform Edusat presented in this article aims to provide young people with empirical and quantitative learning tools to strengthen their ecology message. By means of remote sensing and through the data generated by the Copernicus program, an educational resource that analyzes the consequences of global environmental change is presented. In this context, remote sensing is a technological and transdisciplinary resource that provides young people with scientific arguments to censure the current relationship between human societies and nature.

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Yamasaki, Eri, Florian Altermatt, Jeannine Cavender-Bares, Meredith C. Schuman, Debra Zuppinger-Dingley, Irene Garonna, Fabian D. Schneider et al. « Genomics meets remote sensing in global change studies : monitoring and predicting phenology, evolution and biodiversity ». Current Opinion in Environmental Sustainability 29 (décembre 2017) : 177–86. http://dx.doi.org/10.1016/j.cosust.2018.03.005.

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Rastogi, Anshu, Subhajit Bandopadhyay, Marcin Stróżecki et Radosław Juszczak. « Monitoring the Impact of Environmental Manipulation on Peatland Surface by Simple Remote Sensing Indices ». ITM Web of Conferences 23 (2018) : 00030. http://dx.doi.org/10.1051/itmconf/20182300030.

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The behaviour of nature depends on the different components of climates. Among these, temperature and rainfall are two of the most important components which are known to change plant productivity. Peatlands are among the most valuable ecosystems on the Earth, which is due to its high biodiversity, huge soil carbon storage, and its sensitivity to different environmental factors. With the rapid growth in industrialization, the climate change is becoming a big concern. Therefore, this work is focused on the behaviour of Sphagnum peatland in Poland, subjected to environment manipulation. Here it has been shown how a simple reflectance based technique can be used to assess the impact of climate change on peatland. The experimental setup consists of four plots with two kind of manipulations (control, warming, reduced precipitation, and a combination of warming and reduced precipitation). Reflectance data were measured twice in August 2017 under a clear sky. Vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI), near-infrared reflectance of vegetation (NIRv), MERIS terrestrial chlorophyll index (MTCI), Green chlorophyll index (CIgreen), Simple Ration (SR), and Water Band Index (WBI) were calculated to trace the impact of environmental manipulation on the plant community. Leaf Area Index of vascular plants was also measured for the purpose to correlate it with different VIs. The observation predicts that the global warming of 1°C may cause a significant change in peatland behaviour which can be tracked and monitored by simple remote sensing indices.

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Xu, Haowei, Hao Duan, Qiuju Li et Chengxin Han. « Identification of Actual Irrigated Areas in Tropical Regions Based on Remote Sensing Evapotranspiration ». Atmosphere 15, n^o 4 (16 avril 2024) : 492. http://dx.doi.org/10.3390/atmos15040492.

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Amidst global climate change and unsustainable human exploitation of water resources, water has emerged as a critical factor constraining global agricultural food production and ecological environments. Particularly in agricultural powerhouses like China, irrigation water accounts for a significant portion of freshwater resource utilization. However, the inefficiency of irrigation water usage has become a weak link in water resource management. To better assess irrigation water efficiency, an accurate estimation of regional irrigated areas is urgently needed. This study proposes a method for identifying actual irrigated areas based on remote sensing-derived evapotranspiration (ET) to address the challenge of accurately interpreting irrigated areas in tropical regions. Using Yunnan Province’s Yuanmou irrigation district as a case study, this research combined ground monitoring data and remote sensing data to identify actual irrigated areas through ET inversion and downscaling methods using the Penman–Monteith–Leuning (PML) model. In 2023, the total irrigated area interpreted from remote sensing in the study area was approximately 15,000 hm2, with a comparison against validation points revealing an extraction error of 16%. The small error indicates that this method can effectively enhance the reliability of monitoring actual irrigated areas, thus providing valuable data support for agricultural irrigation water management.

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Selsam, Peter, Jan Bumberger, Thilo Wellmann, Marion Pause, Ronny Gey, Erik Borg et Angela Lausch. « Ecosystem Integrity Remote Sensing—Modelling and Service Tool—ESIS/Imalys ». Remote Sensing 16, n^o 7 (25 mars 2024) : 1139. http://dx.doi.org/10.3390/rs16071139.

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One of the greatest challenges of our time is monitoring the rapid environmental changes taking place worldwide at both local and global scales. This requires easy-to-use and ready-to-implement tools and services to monitor and quantify aspects of bio- and geodiversity change and the impact of land use intensification using freely available and global remotely sensed data, and to derive remotely sensed indicators. Currently, there are no services for quantifying both raster- and vector-based indicators in a “compact tool”. Therefore, the main innovation of ESIS/Imalys is having a remote sensing (RS) tool that allows for RS data processing, data management, and continuous and discrete quantification and derivation of RS indicators in one tool. With the ESIS/Imalys project (Ecosystem Integrity Remote Sensing—Modelling and Service Tool), we try to present environmental indicators on a clearly defined and reproducible basis. The Imalys software library generates the RS indicators and remote sensing products defined for ESIS. This paper provides an overview of the functionality of the Imalys software library. An overview of the technical background of the implementation of the Imalys library, data formats and the user interfaces is given. Examples of RS-based indicators derived using the Imalys tool at pixel level and at zone level (vector level) are presented. Furthermore, the advantages and disadvantages of the Imalys tool are discussed in detail in order to better assess the value of Imalys for users and developers. The applicability of the indicators will be demonstrated through three ecological applications, namely: (1) monitoring landscape diversity, (2) monitoring landscape structure and landscape fragmentation, and (3) monitoring land use intensity and its impact on ecosystem functions. Despite the integration of large amounts of data, Imalys can run on any PC, as the processing and derivation of indicators has been greatly optimised. The Imalys source code is freely available and is hosted and maintained under an open source license. Complete documentation of all methods, functions and derived indicators can be found in the freely available Imalys manual. The user-friendliness of Imalys, despite the integration of a large amount of RS data, makes it another important tool for ecological research, modelling and application for the monitoring and derivation of ecosystem indicators from local to global scale.

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