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1
Jeon, Gwanggil. "Artificial Intelligence-Based Learning Approaches for Remote Sensing." Remote Sensing 14, no. 20 (October 18, 2022): 5203. http://dx.doi.org/10.3390/rs14205203.
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Garaba, Shungudzemwoyo P., Daniela Voß, Jochen Wollschläger, and Oliver Zielinski. "Modern approaches to shipborne ocean color remote sensing." Applied Optics 54, no. 12 (April 14, 2015): 3602. http://dx.doi.org/10.1364/ao.54.003602.
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Wang, Xuan, Jinglei Yi, Jian Guo, Yongchao Song, Jun Lyu, Jindong Xu, Weiqing Yan, Jindong Zhao, Qing Cai, and Haigen Min. "A Review of Image Super-Resolution Approaches Based on Deep Learning and Applications in Remote Sensing." Remote Sensing 14, no. 21 (October 28, 2022): 5423. http://dx.doi.org/10.3390/rs14215423.
Повний текст джерелаАнотація:
At present, with the advance of satellite image processing technology, remote sensing images are becoming more widely used in real scenes. However, due to the limitations of current remote sensing imaging technology and the influence of the external environment, the resolution of remote sensing images often struggles to meet application requirements. In order to obtain high-resolution remote sensing images, image super-resolution methods are gradually being applied to the recovery and reconstruction of remote sensing images. The use of image super-resolution methods can overcome the current limitations of remote sensing image acquisition systems and acquisition environments, solving the problems of poor-quality remote sensing images, blurred regions of interest, and the requirement for high-efficiency image reconstruction, a research topic that is of significant relevance to image processing. In recent years, there has been tremendous progress made in image super-resolution methods, driven by the continuous development of deep learning algorithms. In this paper, we provide a comprehensive overview and analysis of deep-learning-based image super-resolution methods. Specifically, we first introduce the research background and details of image super-resolution techniques. Second, we present some important works on remote sensing image super-resolution, such as training and testing datasets, image quality and model performance evaluation methods, model design principles, related applications, etc. Finally, we point out some existing problems and future directions in the field of remote sensing image super-resolution.
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Popova, Svetlana Mikhailovna, Valentin Borisovich Uvarov, and Andrey Aleksandrovich Yanik. "Regulation of Remote Sensing of the Earth from Space: International Practice." Международное право, no. 3 (March 2022): 1–27. http://dx.doi.org/10.25136/2644-5514.2022.3.38577.
Повний текст джерелаАнотація:
The article is devoted to the results of the study of international experience in regulating activities in the field of remote sensing of the Earth from space. The institutional and legal approaches of a number of countries and regional associations with a developed remote sensing sector are considered. The purpose is to identify models of regulation and experience useful for russian context. The source base consisted of more than 100 official documents (normative legal acts, strategies, programs, official reports, other materials), as well as academic publications related to the issue under consideration. General scientific research methods, content analysis, formal legal analysis, and comparative legal approaches were used to solve the research tasks. Summary information (on the main regulatory legal acts and institutions regulating remote sensing, features of licensing procedures, approaches to the storage and dissemination of remote sensing data) is presented in tabular form. Authors consider the approaches of states to remote sensing regulation can be described by a limited number of core models (three legal models, two institutional approaches), but international practice differs in a wide variety of details that reflect the specifics of the national context. Authors found the essential similarity of approaches to the regulation of space activities of the two space powers – the Russian Federation and the United States, so the analysis of American failures with the privatization of remote sensing in the late 1970s and 1980s can be useful in determining the ways of development and commercialization of this sector in Russia. The relevance of attention to the international practice of remote sensing regulation is justified by the importance of creating favorable legal mode for the development of this sector in Russia facing the challenges of rapid growth of the market for active Earth observation from space, as well as sharp expansion in the number of users and applications of remote sensing data.
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Lausch, Angela, Michael E. Schaepman, Andrew K. Skidmore, Eusebiu Catana, Lutz Bannehr, Olaf Bastian, Erik Borg, et al. "Remote Sensing of Geomorphodiversity Linked to Biodiversity—Part III: Traits, Processes and Remote Sensing Characteristics." Remote Sensing 14, no. 9 (May 9, 2022): 2279. http://dx.doi.org/10.3390/rs14092279.
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Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed.
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Abdollahi, Abolfazl, Biswajeet Pradhan, Nagesh Shukla, Subrata Chakraborty, and Abdullah Alamri. "Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review." Remote Sensing 12, no. 9 (May 2, 2020): 1444. http://dx.doi.org/10.3390/rs12091444.
Повний текст джерелаАнотація:
One of the most challenging research subjects in remote sensing is feature extraction, such as road features, from remote sensing images. Such an extraction influences multiple scenes, including map updating, traffic management, emergency tasks, road monitoring, and others. Therefore, a systematic review of deep learning techniques applied to common remote sensing benchmarks for road extraction is conducted in this study. The research is conducted based on four main types of deep learning methods, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models. We also compare these various deep learning models applied to remote sensing datasets to show which method performs well in extracting road parts from high-resolution remote sensing images. Moreover, we describe future research directions and research gaps. Results indicate that the largest reported performance record is related to the deconvolutional nets applied to remote sensing images, and the F1 score metric of the generative adversarial network model, DenseNet method, and FCN-32 applied to UAV and Google Earth images are high: 96.08%, 95.72%, and 94.59%, respectively.
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Lukin, V. V., S. K. Abramov, N. N. Ponomarenko, S. S. Krivenko, M. L. Uss, Benoit Vozel, Kacem Chehdi, Karen O. Egiazarian, and J. T. Astola. "APPROACHES TO AUTOMATIC DATA PROCESSING IN HYPERSPECTRAL REMOTE SENSING." Telecommunications and Radio Engineering 73, no. 13 (2014): 1125–39. http://dx.doi.org/10.1615/telecomradeng.v73.i13.10.
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Lin, Li, Liping Di, Chen Zhang, Liying Guo, and Yahui Di. "Remote Sensing of Urban Poverty and Gentrification." Remote Sensing 13, no. 20 (October 9, 2021): 4022. http://dx.doi.org/10.3390/rs13204022.
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In the past few decades, most urban areas in the world have been facing the pressure of an increasing population living in poverty. A recent study has shown that up to 80% of the population of some cities in Africa fall under the poverty line. Other studies have shown that poverty is one of the main contributors to residents’ poor health and social conflict. Reducing the number of people living in poverty and improving their living conditions have become some of the main tasks for many nations and international organizations. On the other hand, urban gentrification has been taking place in the poor neighborhoods of all major cities in the world. Although gentrification can reduce the poverty rate and increase the GDP and tax revenue of cities and potentially bring opportunities for poor communities, it displaces the original residents of the neighborhoods, negatively impacting their living and access to social services. In order to support the sustainable development of cities and communities and improve residents’ welfare, it is essential to identify the location, scale, and dynamics of urban poverty and gentrification, and remote sensing can play a key role in this. This paper reviews, summarizes, and evaluates state-of-the-art approaches for identifying and mapping urban poverty and gentrification with remote sensing, GIS, and machine learning techniques. It also discusses the pros and cons of remote sensing approaches in comparison with traditional approaches. With remote sensing approaches, both spatial and temporal resolutions for the identification of poverty and gentrification have been dramatically increased, while the economic cost is significantly reduced.
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Mehmood, Maryam, Ahsan Shahzad, Bushra Zafar, Amsa Shabbir, and Nouman Ali. "Remote Sensing Image Classification: A Comprehensive Review and Applications." Mathematical Problems in Engineering 2022 (August 2, 2022): 1–24. http://dx.doi.org/10.1155/2022/5880959.
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Remote sensing is mainly used to investigate sites of dams, bridges, and pipelines to locate construction materials and provide detailed geographic information. In remote sensing image analysis, the images captured through satellite and drones are used to observe surface of the Earth. The main aim of any image classification-based system is to assign semantic labels to captured images, and consequently, using these labels, images can be arranged in a semantic order. The semantic arrangement of images is used in various domains of digital image processing and computer vision such as remote sensing, image retrieval, object recognition, image annotation, scene analysis, content-based image analysis, and video analysis. The earlier approaches for remote sensing image analysis are based on low-level and mid-level feature extraction and representation. These techniques have shown good performance by using different feature combinations and machine learning approaches. These earlier approaches have used small-scale image dataset. The recent trends for remote sensing image analysis are shifted to the use of deep learning model. Various hybrid approaches of deep learning have shown much better results than the use of a single deep learning model. In this review article, a detailed overview of the past trends is presented, based on low-level and mid-level feature representation using traditional machine learning concepts. A summary of publicly available image benchmarks for remote sensing image analysis is also presented. A detailed summary is presented at the end of each section. An overview regarding the current trends of deep learning models is presented along with a detailed comparison of various hybrid approaches based on recent trends. The performance evaluation metrics are also discussed. This review article provides a detailed knowledge related to the existing trends in remote sensing image classification and possible future research directions.
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Raju, Manthena Narasimha, Kumaran Natarajan, and Chandra Sekhar Vasamsetty. "Object Recognition in Remote Sensing Images Based on Modified Backpropagation Neural Network." Traitement du Signal 38, no. 2 (April 30, 2021): 451–59. http://dx.doi.org/10.18280/ts.380224.
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In the area of remote sensing, one of the problems is how high-quality remote sensing images are automatically categorized and classified. There have been many suggestions for alternatives. Amongst these, there are drawbacks of approaches focused on low visual and intermediate visual characteristics. This article, therefore, adopts the deep learning method for classifying high-resolution remote sensing picture scenes to learn semantic knowledge. Most of the existing neural network convolution approaches are focused on the model of transfer training and there are comparatively like hidden Marco models, linear fitting methods, the creation of new neural networks based on the latest high-resolution remote sensing picture data sets. But in this paper, we used a modified backpropagation neural network is proposed to detect the objects in images. To test the performance of the proposed model we use two remote sensing data sets benchmark tests were done. The test-precision, precision, reminder, and F1 scores are all fine with the Assist data collection. The precision, precision, reminder, and F1 score are all enhanced on the SIRI-WHU dataset. The proposed system has better precision and robustness compared to the current approaches including the most conventional methods and certain profound learning methods to scene distinguish high-resolution remote sensing pictures.
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Barbotkina, E., Ie Dunaieva, V. Popovych, V. Pashtetsky, V. Terleev, W. Mirschel, and L. Akimov. "Digital approaches in agriculture crop monitoring." IOP Conference Series: Earth and Environmental Science 937, no. 3 (December 1, 2021): 032098. http://dx.doi.org/10.1088/1755-1315/937/3/032098.
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Abstract Implementation of modern technologies for collecting and processing spatial information, primarily Earth remote sensing data, has made it possible to solve a wide range of tasks for specialists in the agricultural industry. The work aim is to assess the state of agricultural crops on the territory of Krymskorozovskoe rural settlement of the Belogorsky district of the Republic of Crimea using materials of Earth remote sensing and modern information technologies. The article reviews the literature on the research topic, studies the most significant works on this theme. The article presents the possibilities of digital information technologies in the framework of solving agricultural problems including creation of maps of fields and database formation, study of the territory relief and the features of its morphological characteristics, prompt identification of changes in agricultural fields, based on the calculation of vegetation indices, with the use of remote sencing; classification and identification of objects by satellite images; forecasting the potential yield of agricultural crops.
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Song, Jieqiong, Jun Li, Hao Chen, and Jiangjiang Wu. "RSMT: A Remote Sensing Image-to-Map Translation Model via Adversarial Deep Transfer Learning." Remote Sensing 14, no. 4 (February 14, 2022): 919. http://dx.doi.org/10.3390/rs14040919.
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Maps can help governments in infrastructure development and emergency rescue operations around the world. Using adversarial learning to generate maps from remote sensing images is an emerging field. As we now know, the urban construction styles of different cities are diverse. The current translation methods for remote sensing image-to-map tasks only work on the specific regions with similar styles and structures to the training set and perform poorly on previously unseen areas. We argue that this greatly limits their use. In this work, we intend to seek a remote sensing image-to-map translation model that approaches the challenge of generating maps for the remote sensing images of unseen areas. Our remote sensing image-to-map translation model (RSMT) achieves universal and general applicability to generate maps over multiple regions by combining adversarial deep transfer training schemes with novel attention-based network designs. Extracting the content and style latent features from remote sensing images and a series of maps, respectively, RSMT generalizes a pattern applied to the remote sensing images of new areas. Meanwhile, we introduce feature map loss and map consistency loss to reinforce generated maps’ precision and geometry similarity. We critically analyze qualitative and quantitative results using widely adopted evaluation metrics through extensive validation and comparisons with previous remote sensing image-to-map approaches. The results of experiment indicate that RSMT can translate remote sensing images to maps better than several state-of-the-art methods.
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Ye, Peng. "Remote Sensing Approaches for Meteorological Disaster Monitoring: Recent Achievements and New Challenges." International Journal of Environmental Research and Public Health 19, no. 6 (March 20, 2022): 3701. http://dx.doi.org/10.3390/ijerph19063701.
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Meteorological disaster monitoring is an important research direction in remote sensing technology in the field of meteorology, which can serve many meteorological disaster management tasks. The key issues in the remote sensing monitoring of meteorological disasters are monitoring task arrangement and organization, meteorological disaster information extraction, and multi-temporal disaster information change detection. To accurately represent the monitoring tasks, it is necessary to determine the timescale, perform sensor planning, and construct a representation model to monitor information. On this basis, the meteorological disaster information is extracted by remote sensing data-processing approaches. Furthermore, the multi-temporal meteorological disaster information is compared to detect the evolution of meteorological disasters. Due to the highly dynamic nature of meteorological disasters, the process characteristics of meteorological disasters monitoring have attracted more attention. Although many remote sensing approaches were successfully used for meteorological disaster monitoring, there are still gaps in process monitoring. In future, research on sensor planning, information representation models, multi-source data fusion, etc., will provide an important basis and direction to promote meteorological disaster process monitoring. The process monitoring strategy will further promote the discovery of correlations and impact mechanisms in the evolution of meteorological disasters.
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Tsagkatakis, Grigorios, Anastasia Aidini, Konstantina Fotiadou, Michalis Giannopoulos, Anastasia Pentari, and Panagiotis Tsakalides. "Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement." Sensors 19, no. 18 (September 12, 2019): 3929. http://dx.doi.org/10.3390/s19183929.
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Deep Learning, and Deep Neural Networks in particular, have established themselves as the new norm in signal and data processing, achieving state-of-the-art performance in image, audio, and natural language understanding. In remote sensing, a large body of research has been devoted to the application of deep learning for typical supervised learning tasks such as classification. Less yet equally important effort has also been allocated to addressing the challenges associated with the enhancement of low-quality observations from remote sensing platforms. Addressing such channels is of paramount importance, both in itself, since high-altitude imaging, environmental conditions, and imaging systems trade-offs lead to low-quality observation, as well as to facilitate subsequent analysis, such as classification and detection. In this paper, we provide a comprehensive review of deep-learning methods for the enhancement of remote sensing observations, focusing on critical tasks including single and multi-band super-resolution, denoising, restoration, pan-sharpening, and fusion, among others. In addition to the detailed analysis and comparison of recently presented approaches, different research avenues which could be explored in the future are also discussed.
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Scarth, P., J. Armston, N. Flood, R. Denham, L. Collett, F. Watson, B. Trevithick, et al. "OPERATIONAL APPLICATION OF THE LANDSAT TIMESERIES TO ADDRESS LARGE AREA LANDCOVER UNDERSTANDING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3 (August 20, 2015): 571–75. http://dx.doi.org/10.5194/isprsarchives-xl-3-w3-571-2015.
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State Government agencies in northern and eastern Australia and the University of Queensland, Brisbane, have been collaborating through the Joint Remote Sensing Research Program (JRSRP). This has resulted in a significant acceleration in the development and successful operational application of remote sensing methods for the JRSRP members and the various state and national programs and policies which they support. The JRSRP provides an open and collaborative mechanism and governance structure to successfully bring together a unique combination of expertise in image processing, field data collection, and data integration approaches to deliver accurate, repeatable and robust methods for mapping and monitoring Australia’s unique ecosystems. Remote sensing provides spatially- and temporally-comprehensive information about land cover features at a range of scales and often for minimal cost compared to traditional mapping and monitoring approaches. This makes remote sensing a very useful operational mapping and monitoring tool for land managers, particularly in the vast rangelands of Australia. This paper outlines recent developments in remote sensing and modelling products that are being used operationally by JRSRP members to address large area landcover understanding.
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Azarang, Arian, and Nasser Kehtarnavaz. "Image Fusion in Remote Sensing: Conventional and Deep Learning Approaches." Synthesis Lectures on Image, Video, and Multimedia Processing 10, no. 1 (February 18, 2021): 1–93. http://dx.doi.org/10.2200/s01074ed1v01y202101ivm021.
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Khan, Sarwar Shah, Qiong Ran, and Muzammil Khan. "Image pan-sharpening using enhancement based approaches in remote sensing." Multimedia Tools and Applications 79, no. 43-44 (August 29, 2020): 32791–805. http://dx.doi.org/10.1007/s11042-020-09682-z.
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Guanter, Luis, Maximilian Brell, Jonathan C. W. Chan, Claudia Giardino, Jose Gomez-Dans, Christian Mielke, Felix Morsdorf, Karl Segl, and Naoto Yokoya. "Synergies of Spaceborne Imaging Spectroscopy with Other Remote Sensing Approaches." Surveys in Geophysics 40, no. 3 (July 16, 2018): 657–87. http://dx.doi.org/10.1007/s10712-018-9485-z.
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López-Granados, F., M. Jurado-Expósito, J. M. Peña-Barragán, and L. García-Torres. "Using geostatistical and remote sensing approaches for mapping soil properties." European Journal of Agronomy 23, no. 3 (October 2005): 279–89. http://dx.doi.org/10.1016/j.eja.2004.12.003.
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Albrecht, F., T. Blaschke, S. Lang, H. M. Abdulmutalib, G. Szabó, Á. Barsi, C. Batini, et al. "PROVIDING DATA QUALITY INFORMATION FOR REMOTE SENSING APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 15–22. http://dx.doi.org/10.5194/isprs-archives-xlii-3-15-2018.
Повний текст джерелаАнотація:
The availability and accessibility of remote sensing (RS) data, cloud processing platforms and provided information products and services has increased the size and diversity of the RS user community. This development also generates a need for validation approaches to assess data quality. Validation approaches employ quality criteria in their assessment. Data Quality (DQ) dimensions as the basis for quality criteria have been deeply investigated in the database area and in the remote sensing domain. Several standards exist within the RS domain but a general classification – established for databases – has been adapted only recently. For an easier identification of research opportunities, a better understanding is required how quality criteria are employed in the RS lifecycle. Therefore, this research investigates how quality criteria support decisions that guide the RS lifecycle and how they relate to the measured DQ dimensions. Subsequently follows an overview of the relevant standards in the RS domain that is matched to the RS lifecycle. Conclusively, the required research needs are identified that would enable a complete understanding of the interrelationships between the RS lifecycle, the data sources and the DQ dimensions, an understanding that would be very valuable for designing validation approaches in RS.
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Fundisi, Emmanuel, Solomon G. Tesfamichael, and Fethi Ahmed. "Remote sensing of savanna woody species diversity: A systematic review of data types and assessment methods." PLOS ONE 17, no. 12 (December 1, 2022): e0278529. http://dx.doi.org/10.1371/journal.pone.0278529.
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Despite savannas being known for their relatively sparse vegetation coverage compared to other vegetation ecosystems, they harbour functionally diverse vegetation forms. Savannas are affected by climate variability and anthropogenic factors, resulting in changes in woody plant species compositions. Monitoring woody plant species diversity is therefore important to inform sustainable biodiversity management. Remote sensing techniques are used as an alternative approach to labour-intensive field-based inventories, to assess savanna biodiversity. The aim of this paper is to review studies that applied remote sensing to assess woody plant species diversity in savanna environments. The paper first provides a brief account of the spatial distribution of savanna environments around the globe. Thereafter, it briefly defines categorical classification and continuous-scale species diversity assessment approaches for savanna woody plant estimation. The core review section divides previous remote sensing studies into categorical classification and continuous-scale assessment approaches. Within each division, optical, Radio Detection And Ranging (RADAR) and Light Detection and Ranging (LiDAR) remote sensing as applied to savanna woody species diversity is reviewed. This is followed by a discussion on multi-sensor applications to estimate woody plant species diversity in savanna. We recommend that future research efforts should focus strongly on routine application of optical, RADAR and LiDAR remote sensing of physiologically similar woody plant species in savannas, as well as on extending these methodological approaches to other vegetation environments.
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Anderson, K., and H. Croft. "Remote sensing of soil surface properties." Progress in Physical Geography: Earth and Environment 33, no. 4 (August 2009): 457–73. http://dx.doi.org/10.1177/0309133309346644.
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Remote sensing is now in a strong position to provide meaningful spatial data for use in soil science investigations. In the last 10 years, advancements in remote sensing techniques and technologies have given rise to a wealth of exciting new research findings in soil-related disciplines. This paper provides a critical insight into the role played by remote sensing in this field, with a specific focus on soil surface monitoring. Two key soil properties are considered in this review, soil surface roughness and moisture, because these two variables have benefited most from recent cutting-edge advances in remote sensing. Of note is the fact that the major recent advancements in spatial assessment of soil structure have emerged from optical remote sensing, while the soil moisture community has benefited from advancements in microwave systems, justifying the focus of this paper in these specific directions. The paper considers the newest techniques within active, passive, optical and microwave remote sensing and concludes by considering future challenges, multisensor approaches and the issue of scale — which is a key cross-disciplinary research question of relevance to soil scientists and remote sensing scientists alike.
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Seidel, Valerie, Daniel Dourte, and Craig Diamond. "Applying Spatial Mapping of Remotely Sensed Data to Valuation of Coastal Ecosystem Services in the Gulf of Mexico." Water 11, no. 6 (June 5, 2019): 1179. http://dx.doi.org/10.3390/w11061179.
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Spatial mapping of remote sensing data tends to be used less when valuing coastal ecosystem services than in other ecosystems. This research project aimed to understand obstacles to the use of remote sensing data in coastal ecosystem valuations, and to educate coastal stakeholders on potential remote sensing data sources and techniques. A workshop program identified important barriers to the adoption of remote sensing data: perceived gaps in spatial and temporal scale, uncertainty about confidence intervals and precision of remote sensing data, and linkages between coastal ecosystem services and values. Case studies that demonstrated the state of the science were used to show methods to overcome the barriers. The case studies demonstrate multiple approaches to valuation that have been used successfully in coastal projects, and validate that spatial mapping of remote sensing data may fill critical gaps, such as cost-effectively generating calibrated historical data.
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Overgaard, J., D. Rosbjerg, and M. B. Butts. "Land-surface modelling in hydrological perspective." Biogeosciences Discussions 2, no. 6 (December 13, 2005): 1815–48. http://dx.doi.org/10.5194/bgd-2-1815-2005.
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Abstract. A comprehensive review of energy-based land-surface modelling, as seen from a hydrological perspective, is provided. We choose to focus on energy-based approaches, because in comparison to the traditional potential evapotranspiration models, these approaches allow for a stronger link to remote sensing and atmospheric modelling. New opportunities for evaluation of distributed land-surface models through application of remote sensing are discussed in detail, and the difficulties inherent in various evaluation procedures are presented. Remote sensing is the only source of distributed data at scales that correspond to hydrological modelling scales. Finally, the dynamic coupling of hydrological and atmospheric models is explored, and the future perspectives of such efforts are discussed.
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Mao, Lijun, Mingshi Li, and Wenjuan Shen. "Remote Sensing Applications for Monitoring Terrestrial Protected Areas: Progress in the Last Decade." Sustainability 12, no. 12 (June 19, 2020): 5016. http://dx.doi.org/10.3390/su12125016.
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Terrestrial protected areas (PAs) play an essential role in maintaining biodiversity and ecological processes worldwide, and the monitoring of PAs is a useful tool in assessing the effectiveness of PA management. Advanced remote sensing technologies have been increasingly used for mapping and monitoring the dynamics of PAs. We review the advances in remote sensing-based approaches for monitoring terrestrial PAs in the last decade and identify four types of studies in this field: land use & land cover and vegetation community classification, vegetation structure quantification, natural disturbance monitoring, and land use & land cover and vegetation dynamic analysis. We systematically discuss the satellite data and methods used for monitoring PAs for the four research objectives. Moreover, we summarize the approaches used in the different types of studies. The following suggestions are provided for future studies: (1) development of remote sensing frameworks for local PA monitoring worldwide; (2) comprehensive utilization of multisource remote sensing data; (3) improving methods to investigate the details of PA dynamics; (4) discovering the driving forces and providing measures for PA management. Overall, the integration of remote sensing data and advanced processing methods can support PA management and decision-making procedures.
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Bennett, Rohan, Peter van Oosterom, Christiaan Lemmen, and Mila Koeva. "Remote Sensing for Land Administration." Remote Sensing 12, no. 15 (August 4, 2020): 2497. http://dx.doi.org/10.3390/rs12152497.
Повний текст джерелаАнотація:
Land administration constitutes the socio-technical systems that govern land tenure, use, value and development within a jurisdiction. The land parcel is the fundamental unit of analysis. Each parcel has identifiable boundaries, associated rights, and linked parties. Spatial information is fundamental. It represents the boundaries between land parcels and is embedded in cadastral sketches, plans, maps and databases. The boundaries are expressed in these records using mathematical or graphical descriptions. They are also expressed physically with monuments or natural features. Ideally, the recorded and physical expressions should align, however, in practice, this may not occur. This means some boundaries may be physically invisible, lacking accurate documentation, or potentially both. Emerging remote sensing tools and techniques offers great potential. Historically, the measurements used to produce recorded boundary representations were generated from ground-based surveying techniques. The approach was, and remains, entirely appropriate in many circumstances, although it can be timely, costly, and may only capture very limited contextual boundary information. Meanwhile, advances in remote sensing and photogrammetry offer improved measurement speeds, reduced costs, higher image resolutions, and enhanced sampling granularity. Applications of unmanned aerial vehicles (UAV), laser scanning, both airborne and terrestrial (LiDAR), radar interferometry, machine learning, and artificial intelligence techniques, all provide examples. Coupled with emergent societal challenges relating to poverty reduction, rapid urbanisation, vertical development, and complex infrastructure management, the contemporary motivation to use these new techniques is high. Fundamentally, they enable more rapid, cost-effective, and tailored approaches to 2D and 3D land data creation, analysis, and maintenance. This Special Issue hosts papers focusing on this intersection of emergent remote sensing tools and techniques, applied to domain of land administration.
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Rahman, Md Shahinoor, and Liping Di. "A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment." Agriculture 10, no. 4 (April 16, 2020): 131. http://dx.doi.org/10.3390/agriculture10040131.
Повний текст джерелаАнотація:
This article reviews case studies which have used remote sensing data for different aspects of flood crop loss assessment. The review systematically finds a total of 62 empirical case studies from the past three decades. The number of case studies has recently been increased because of increased availability of remote sensing data. In the past, flood crop loss assessment was very generalized and time-intensive because of the dependency on the survey-based data collection. Remote sensing data availability makes rapid flood loss assessment possible. This study groups flood crop loss assessment approaches into three broad categories: flood-intensity-based approach, crop-condition-based approach, and a hybrid approach of the two. Flood crop damage assessment is more precise when both flood information and crop condition are incorporated in damage assessment models. This review discusses the strengths and weaknesses of different loss assessment approaches. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat are the dominant sources of optical remote sensing data for flood crop loss assessment. Remote-sensing-based vegetation indices (VIs) have significantly been utilized for crop damage assessments in recent years. Many case studies also relied on microwave remote sensing data, because of the inability of optical remote sensing to see through clouds. Recent free-of-charge availability of synthetic-aperture radar (SAR) data from Sentinel-1 will advance flood crop damage assessment. Data for the validation of loss assessment models are scarce. Recent advancements of data archiving and distribution through web technologies will be helpful for loss assessment and validation.
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Pham, Tien, Naoto Yokoya, Dieu Bui, Kunihiko Yoshino, and Daniel Friess. "Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges." Remote Sensing 11, no. 3 (January 22, 2019): 230. http://dx.doi.org/10.3390/rs11030230.
Повний текст джерелаАнотація:
The mangrove ecosystem plays a vital role in the global carbon cycle, by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, mangroves have been lost worldwide, resulting in substantial carbon stock losses. Additionally, some aspects of the mangrove ecosystem remain poorly characterized compared to other forest ecosystems due to practical difficulties in measuring and monitoring mangrove biomass and their carbon stocks. Without a quantitative method for effectively monitoring biophysical parameters and carbon stocks in mangroves, robust policies and actions for sustainably conserving mangroves in the context of climate change mitigation and adaptation are more difficult. In this context, remote sensing provides an important tool for monitoring mangroves and identifying attributes such as species, biomass, and carbon stocks. A wide range of studies is based on optical imagery (aerial photography, multispectral, and hyperspectral) and synthetic aperture radar (SAR) data. Remote sensing approaches have been proven effective for mapping mangrove species, estimating their biomass, and assessing changes in their extent. This review provides an overview of the techniques that are currently being used to map various attributes of mangroves, summarizes the studies that have been undertaken since 2010 on a variety of remote sensing applications for monitoring mangroves, and addresses the limitations of these studies. We see several key future directions for the potential use of remote sensing techniques combined with machine learning techniques for mapping mangrove areas and species, and evaluating their biomass and carbon stocks.
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Lentile, Leigh B., Zachary A. Holden, Alistair M. S. Smith, Michael J. Falkowski, Andrew T. Hudak, Penelope Morgan, Sarah A. Lewis, Paul E. Gessler, and Nate C. Benson. "Remote sensing techniques to assess active fire characteristics and post-fire effects." International Journal of Wildland Fire 15, no. 3 (2006): 319. http://dx.doi.org/10.1071/wf05097.
Повний текст джерелаАнотація:
Space and airborne sensors have been used to map area burned, assess characteristics of active fires, and characterize post-fire ecological effects. Confusion about fire intensity, fire severity, burn severity, and related terms can result in the potential misuse of the inferred information by land managers and remote sensing practitioners who require unambiguous remote sensing products for fire management. The objective of the present paper is to provide a comprehensive review of current and potential remote sensing methods used to assess fire behavior and effects and ecological responses to fire. We clarify the terminology to facilitate development and interpretation of comprehensible and defensible remote sensing products, present the potential and limitations of a variety of approaches for remotely measuring active fires and their post-fire ecological effects, and discuss challenges and future directions of fire-related remote sensing research.
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Hunt, David A., Karyn Tabor, Jennifer H. Hewson, Margot A. Wood, Louis Reymondin, Kellee Koenig, Mikaela Schmitt-Harsh, and Forrest Follett. "Review of Remote Sensing Methods to Map Coffee Production Systems." Remote Sensing 12, no. 12 (June 25, 2020): 2041. http://dx.doi.org/10.3390/rs12122041.
Повний текст джерелаАнотація:
The coffee sector is working towards sector-wide commitments for sustainable production. Yet, knowledge of where coffee is cultivated and its environmental impact remains limited, in part due to the challenges of mapping coffee using satellite remote sensing. We recognize the urgency to capitalize on recent technological advances to improve remote sensing methods and generate more accurate, reliable, and scalable approaches to coffee mapping. In this study, we provide a systematic review of satellite-based approaches to mapping coffee extent, which produced 43 articles in the peer-reviewed and gray literature. We outline key considerations for employing effective approaches, focused on the need to balance data affordability and quality, classification complexity and accuracy, and generalizability and site-specificity. We discuss research opportunities for improved approaches by leveraging the recent expansion of diverse satellite sensors and constellations, optical/Synthetic Aperture Radar data fusion approaches, and advances in cloud computing and deep learning algorithms. We highlight the need for differentiating between production systems and the need for research in important coffee-growing geographies. By reviewing the range of techniques successfully used to map coffee extent, we provide technical recommendations and future directions to enable accurate and scalable coffee maps.
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Thuestad, Alma Elizabeth, Ole Risbøl, Jan Ingolf Kleppe, Stine Barlindhaug, and Elin Rose Myrvoll. "Archaeological Surveying of Subarctic and Arctic Landscapes: Comparing the Performance of Airborne Laser Scanning and Remote Sensing Image Data." Sustainability 13, no. 4 (February 10, 2021): 1917. http://dx.doi.org/10.3390/su13041917.
Повний текст джерелаАнотація:
What can remote sensing contribute to archaeological surveying in subarctic and arctic landscapes? The pros and cons of remote sensing data vary as do areas of utilization and methodological approaches. We assessed the applicability of remote sensing for archaeological surveying of northern landscapes using airborne laser scanning (LiDAR) and satellite and aerial images to map archaeological features as a basis for (a) assessing the pros and cons of the different approaches and (b) assessing the potential detection rate of remote sensing. Interpretation of images and a LiDAR-based bare-earth digital terrain model (DTM) was based on visual analyses aided by processing and visualizing techniques. 368 features were identified in the aerial images, 437 in the satellite images and 1186 in the DTM. LiDAR yielded the better result, especially for hunting pits. Image data proved suitable for dwellings and settlement sites. Feature characteristics proved a key factor for detectability, both in LiDAR and image data. This study has shown that LiDAR and remote sensing image data are highly applicable for archaeological surveying in northern landscapes. It showed that a multi-sensor approach contributes to high detection rates. Our results have improved the inventory of archaeological sites in a non-destructive and minimally invasive manner.
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Pizani, Fernanda, Philippe Maillard, and Camila Costa Amorim. "The Estimation of Water Quality Parameters in Lentic Environments Through Remote Sensing Technologies: a Review of the Past Two Decades." Revista Brasileira de Cartografia 74, no. 3 (September 5, 2022): 729–54. http://dx.doi.org/10.14393/rbcv74n3-65357.
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The use of remote sensing technology applied to measure the quality of continental waters has grown exponentially since the turn of the century. Using different sensors on board satellites or airborne platforms, the estimation of water quality parameters has been carried out through both empirical and analytical approaches. This work aims to review the specific scientific production of the last two decades to assess how the evolution of the sensors and platforms have affected the potential and the limitations of remote sensing technologies to estimate water quality parameters in lakes and reservoirs. The study also focuses on the accuracy of remote sensing techniques for the major optically active parameters: chlorophyll-a and phycocyanin, Secchi disk depth and turbidity. The article is subdivided by sections dedicated to each of these parameters. A review of remote sensing platforms and sensors precedes the parameters sections. The past 20 years have brought a large body of articles on how remote sensing data can be used to estimate these parameters. Empirical methods dominate overwhelmingly with a four to one proportion over analytical approaches. Environmental factors such as season, complexity of water and concentration loads appear to exert a strong control over the quality of the results. Recent platforms and sensors have brought noticeable improvements over results achieved in this period.
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Lu, Sha, Arnold Heemink, Hai Xiang Lin, Arjo Segers, and Guangliang Fu. "Evaluation Criteria on the Design for Assimilating Remote Sensing Data Using Variational Approaches." Monthly Weather Review 145, no. 6 (May 11, 2017): 2165–75. http://dx.doi.org/10.1175/mwr-d-16-0289.1.
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Abstract Remote sensing, as a powerful tool for monitoring atmospheric phenomena, has been playing an increasingly important role in inverse modeling. Remote sensing instruments measure quantities that often combine several state variables as one. This creates very strong correlations between the state variables that share the same observation variable. This may cause numerical problems resulting in a low convergence rate or inaccurate estimates in gradient-based variational assimilation if improper error statistics are used. In this paper, two criteria or scoring rules are proposed to quantify the numerical robustness of assimilating a specific set of remote sensing observations and to quantify the reliability of the estimates of the parameters. The criteria are derived by analyzing how the correlations are created via shared observation data and how they may influence the process of variational data assimilation. Experimental tests are conducted and show a good level of agreement with theory. The results illustrate the capability of the criteria to indicate the reliability of the assimilation process. Both criteria can be used with observing system simulation experiments (OSSEs) and in combination with other verification scores.
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Zhang, Xin, Liangxiu Han, Lianghao Han, and Liang Zhu. "How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?" Remote Sensing 12, no. 3 (January 28, 2020): 417. http://dx.doi.org/10.3390/rs12030417.
Повний текст джерелаАнотація:
Land cover information plays an important role in mapping ecological and environmental changes in Earth’s diverse landscapes for ecosystem monitoring. Remote sensing data have been widely used for the study of land cover, enabling efficient mapping of changes of the Earth surface from Space. Although the availability of high-resolution remote sensing imagery increases significantly every year, traditional land cover analysis approaches based on pixel and object levels are not optimal. Recent advancement in deep learning has achieved remarkable success on image recognition field and has shown potential in high spatial resolution remote sensing applications, including classification and object detection. In this paper, a comprehensive review on land cover classification and object detection approaches using high resolution imagery is provided. Through two case studies, we demonstrated the applications of the state-of-the-art deep learning models to high spatial resolution remote sensing data for land cover classification and object detection and evaluated their performances against traditional approaches. For a land cover classification task, the deep-learning-based methods provide an end-to-end solution by using both spatial and spectral information. They have shown better performance than the traditional pixel-based method, especially for the categories of different vegetation. For an objective detection task, the deep-learning-based object detection method achieved more than 98% accuracy in a large area; its high accuracy and efficiency could relieve the burden of the traditional, labour-intensive method. However, considering the diversity of remote sensing data, more training datasets are required in order to improve the generalisation and the robustness of deep learning-based models.
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Wang, Zhaobin, Yikun Ma, Yaonan Zhang, and Jiali Shang. "Review of Remote Sensing Applications in Grassland Monitoring." Remote Sensing 14, no. 12 (June 17, 2022): 2903. http://dx.doi.org/10.3390/rs14122903.
Повний текст джерелаАнотація:
The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This paper provides a comprehensive review of the latest remote sensing estimation methods for some critical grassland parameters, including above-ground biomass, primary productivity, fractional vegetation cover, and leaf area index. Then, the applications of remote sensing monitoring are also reviewed from the perspective of their use of these parameters and other remote sensing data. In detail, grassland degradation and grassland use monitoring are evaluated. In addition, disaster monitoring and carbon cycle monitoring are also included. Overall, most studies have used empirical models and statistical regression models, while the number of machine learning approaches has an increasing trend. In addition, some specialized methods, such as the light use efficiency approaches for primary productivity and the mixed pixel decomposition methods for vegetation coverage, have been widely used and improved. However, all the above methods have certain limitations. For future work, it is recommended that most applications should adopt the advanced estimation methods rather than simple statistical regression models. In particular, the potential of deep learning in processing high-dimensional data and fitting non-linear relationships should be further explored. Meanwhile, it is also important to explore the potential of some new vegetation indices based on the spectral characteristics of the specific grassland under study. Finally, the fusion of multi-source images should also be considered to address the deficiencies in information and resolution of remote sensing images acquired by a single sensor or satellite.
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Davis, Dylan S. "Geographic Disparity in Machine Intelligence Approaches for Archaeological Remote Sensing Research." Remote Sensing 12, no. 6 (March 12, 2020): 921. http://dx.doi.org/10.3390/rs12060921.
Повний текст джерелаАнотація:
A vast majority of the archaeological record, globally, is understudied and increasingly threatened by climate change, economic and political instability, and violent conflict. Archaeological data are crucial for understanding the past, and as such, documentation of this information is imperative. The development of machine intelligence approaches (including machine learning, artificial intelligence, and other automated processes) has resulted in massive gains in archaeological knowledge, as such computational methods have expedited the rate of archaeological survey and discovery via remote sensing instruments. Nevertheless, the progression of automated computational approaches is limited by distinct geographic imbalances in where these techniques are developed and applied. Here, I investigate the degree of this disparity and some potential reasons for this imbalance. Analyses from Web of Science and Microsoft Academic searches reveal that there is a substantial difference between the Global North and South in the output of machine intelligence remote sensing archaeology literature. There are also regional imbalances. I argue that one solution is to increase collaborations between research institutions in addition to data sharing efforts.
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Kotivuori, Eetu, Matti Maltamo, Lauri Korhonen, Jacob L. Strunk, and Petteri Packalen. "Prediction error aggregation behaviour for remote sensing augmented forest inventory approaches." Forestry: An International Journal of Forest Research 94, no. 4 (March 24, 2021): 576–87. http://dx.doi.org/10.1093/forestry/cpab007.
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Abstract In this study we investigated the behaviour of aggregate prediction errors in a forest inventory augmented with multispectral Airborne Laser Scanning and airborne imagery. We compared an Area-Based Approach (ABA), Edge-tree corrected ABA (EABA) and Individual Tree Detection (ITD). The study used 109 large 30 × 30 m sample plots, which were divided into four 15 × 15 m subplots. Four different levels of aggregation were examined: all four subplots (quartet), two diagonal subplots (diagonal), two edge-adjacent subplots (adjacent) and subplots without aggregation. We noted that the errors at aggregated levels depend on the selected predictor variables, and therefore, this effect was studied by repeating the variable selection 200 times. At the subplot level, EABA provided the lowest mean of root mean square error ($\overline{\mathrm{RMSE}}$) values of 200 repetitions for total stem volume (EABA 21.1 percent, ABA 23.5 percent, ITD 26.2 percent). EABA also fared the best for diagonal and adjacent aggregation ($\overline{\mathrm{RMSE}}$: 17.6 percent, 17.4 percent), followed by ABA ($\overline{\mathrm{RMSE}}$: 19.3 percent, 18.2 percent) and ITD ($\overline{\mathrm{RMSE}}$: 21.8, 21.9 percent). Adjacent subplot errors of ABA were less correlated than errors of diagonal subplots, which resulted also in clearly lower RMSEs for adjacent subplots. This appears to result from edge tree effects, where omission and commission errors cancel for trees leaning from one subplot into the other. The best aggregate performance was achieved at the quartet level, as expected from fundamental properties of variance. ABA and EABA had similar RMSEs at the quartet level ($\overline{\mathrm{RMSE}}$ 15.5 and 15.3 percent), with poorer ITD performance ($\overline{\mathrm{RMSE}}$ 19.4 percent).
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Palubinskas, G. "PAN-SHARPENING APPROACHES BASED ON UNMIXING OF MULTISPECTRAL REMOTE SENSING IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 693–702. http://dx.doi.org/10.5194/isprs-archives-xli-b7-693-2016.
Повний текст джерелаАнотація:
Model based analysis or explicit definition/listing of all models/assumptions used in the derivation of a pan-sharpening method allows us to understand the rationale or properties of existing methods and shows a way for a proper usage or proposal/selection of new methods ‘better’ satisfying the needs of a particular application. Most existing pan-sharpening methods are based mainly on the two models/assumptions: spectral consistency for high resolution multispectral data (physical relationship between multispectral and panchromatic data in a high resolution scale) and spatial consistency for multispectral data (so-called Wald’s protocol first property or relationship between multispectral data in different resolution scales). Two methods, one based on a linear unmixing model and another one based on spatial unmixing, are described/proposed/modified which respect models assumed and thus can produce correct or physically justified fusion results. Earlier mentioned property ‘better’ should be measurable quantitatively, e.g. by means of so-called quality measures. The difficulty of a quality assessment task in multi-resolution image fusion or pan-sharpening is that a reference image is missing. Existing measures or so-called protocols are still not satisfactory because quite often the rationale or assumptions used are not valid or not fulfilled. From a model based view it follows naturally that a quality assessment measure can be defined as a combination of error model residuals using common or general models assumed in all fusion methods. Thus in this paper a comparison of the two earlier proposed/modified pan-sharpening methods is performed. Preliminary experiments based on visual analysis are carried out in the urban area of Munich city for optical remote sensing multispectral data and panchromatic imagery of the WorldView-2 satellite sensor.
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Sibley, Adam M., Patricio Grassini, Nancy E. Thomas, Kenneth G. Cassman, and David B. Lobell. "Testing Remote Sensing Approaches for Assessing Yield Variability among Maize Fields." Agronomy Journal 106, no. 1 (January 2014): 24–32. http://dx.doi.org/10.2134/agronj2013.0314.
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Palubinskas, G. "PAN-SHARPENING APPROACHES BASED ON UNMIXING OF MULTISPECTRAL REMOTE SENSING IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 693–702. http://dx.doi.org/10.5194/isprsarchives-xli-b7-693-2016.
Повний текст джерелаАнотація:
Model based analysis or explicit definition/listing of all models/assumptions used in the derivation of a pan-sharpening method allows us to understand the rationale or properties of existing methods and shows a way for a proper usage or proposal/selection of new methods ‘better’ satisfying the needs of a particular application. Most existing pan-sharpening methods are based mainly on the two models/assumptions: spectral consistency for high resolution multispectral data (physical relationship between multispectral and panchromatic data in a high resolution scale) and spatial consistency for multispectral data (so-called Wald’s protocol first property or relationship between multispectral data in different resolution scales). Two methods, one based on a linear unmixing model and another one based on spatial unmixing, are described/proposed/modified which respect models assumed and thus can produce correct or physically justified fusion results. Earlier mentioned property ‘better’ should be measurable quantitatively, e.g. by means of so-called quality measures. The difficulty of a quality assessment task in multi-resolution image fusion or pan-sharpening is that a reference image is missing. Existing measures or so-called protocols are still not satisfactory because quite often the rationale or assumptions used are not valid or not fulfilled. From a model based view it follows naturally that a quality assessment measure can be defined as a combination of error model residuals using common or general models assumed in all fusion methods. Thus in this paper a comparison of the two earlier proposed/modified pan-sharpening methods is performed. Preliminary experiments based on visual analysis are carried out in the urban area of Munich city for optical remote sensing multispectral data and panchromatic imagery of the WorldView-2 satellite sensor.
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Chu, Duo. "MODIS remote sensing approaches to monitoring soil moisture in Tibet, China." Remote Sensing Letters 9, no. 12 (October 3, 2018): 1148–57. http://dx.doi.org/10.1080/2150704x.2018.1516308.
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Bouman, B. A. M. "Crop modelling and remote sensing for yield prediction." Netherlands Journal of Agricultural Science 43, no. 2 (June 1, 1995): 143–61. http://dx.doi.org/10.18174/njas.v43i2.573.
Повний текст джерелаАнотація:
Methods for the application of crop growth models, remote sensing and their integrative use for yield forecasting and prediction are presented. First, the general principles of crop growth models are explained. When crop simulation models are used on regional scales, uncertainty and spatial variation in model parameters can result in broad bands of simulated yield. Remote sensing can be used to reduce some of this uncertainty. With optical remote sensing, standard relations between the Weighted Difference Vegetation Index and fraction ground cover and LAI were established for a number of crops. The radar backscatter of agricultural crops was found to be largely affected by canopy structure, and, for most crops, no consistent relationships with crop growth indicators were established. Two approaches are described to integrate remote sensing data with crop growth models. In the first one, measures of light interception (ground cover, LAI) estimated from optical remote sensing are used as forcing function in the models. In the second method, crop growth models are extended with remote sensing sub-models to simulate time-series of optical and radar remote sensing signals. These simulated signals are compared to measured signals, and the crop growth model is re-calibrated to match simulated with measured remote sensing data. The developed methods resulted in increased accuracy in the simulation of crop growth and yield of wheat and sugar beet in a number of case-studies.
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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, no. 16 (August 20, 2019): 1952. http://dx.doi.org/10.3390/rs11161952.
Повний текст джерелаАнотація:
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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Raju, Manthena Narasimha, Kumaran Natarajan, and Chandra Sekhar Vasamsetty. "Remote Sensing Image Classification Using CNN-LSTM Model." Revue d'Intelligence Artificielle 36, no. 1 (February 28, 2022): 147–53. http://dx.doi.org/10.18280/ria.360117.
Повний текст джерелаАнотація:
The image classification of remote sensing (RS) plays a significant role in earth observation technology using RS data, extensively used in the military and civic sectors. However, the RS image classification confronts substantial scientific and practical difficulties because of RS data features, such as high dimensionality and relatively limited quantities of labeled examples accessible. In recent years, as new methods of deep learning (DL) have emerged, RS image classification approaches using DL have made significant advances, providing new possibilities for RS image classification research and development. Most of the researchers are using CNN to classify remote sensing images, but CNN alone problem with sequence data processing. But to get some sense out of the classification of remote sensing images. To avoid this in this paper, we use the CNN-LSTM model. The model performed ineffective classification of remote sensing images; the experimental results show that the proposed model is effective in classifying remote sensing images.
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Barsi, Á., Zs Kugler, I. László, Gy Szabó, and H. M. Abdulmutalib. "ACCURACY DIMENSIONS IN REMOTE SENSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 61–67. http://dx.doi.org/10.5194/isprs-archives-xlii-3-61-2018.
Повний текст джерелаАнотація:
The technological developments in remote sensing (RS) during the past decade has contributed to a significant increase in the size of data user community. For this reason data quality issues in remote sensing face a significant increase in importance, particularly in the era of Big Earth data. Dozens of available sensors, hundreds of sophisticated data processing techniques, countless software tools assist the processing of RS data and contributes to a major increase in applications and users. In the past decades, scientific and technological community of spatial data environment were focusing on the evaluation of data quality elements computed for point, line, area geometry of vector and raster data. Stakeholders of data production commonly use standardised parameters to characterise the quality of their datasets. Yet their efforts to estimate the quality did not reach the general end-user community running heterogeneous applications who assume that their spatial data is error-free and best fitted to the specification standards. The non-specialist, general user group has very limited knowledge how spatial data meets their needs. These parameters forming the external quality dimensions implies that the same data system can be of different quality to different users. The large collection of the observed information is uncertain in a level that can decry the reliability of the applications.<br> Based on prior paper of the authors (in cooperation within the Remote Sensing Data Quality working group of ISPRS), which established a taxonomy on the dimensions of data quality in GIS and remote sensing domains, this paper is aiming at focusing on measures of uncertainty in remote sensing data lifecycle, focusing on land cover mapping issues. In the paper we try to introduce how quality of the various combination of data and procedures can be summarized and how services fit the users’ needs.<br> The present paper gives the theoretic overview of the issue, besides selected, practice-oriented approaches are evaluated too, finally widely-used dimension metrics like Root Mean Squared Error (RMSE) or confusion matrix are discussed. The authors present data quality features of well-defined and poorly defined object. The central part of the study is the land cover mapping, describing its accuracy management model, presented relevance and uncertainty measures of its influencing quality dimensions. In the paper theory is supported by a case study, where the remote sensing technology is used for supporting the area-based agricultural subsidies of the European Union, in Hungarian administration.
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Cardoso-Fernandes, Joana, Ana C. Teodoro, Alexandre Lima, Mônica Perrotta, and Encarnación Roda-Robles. "Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives." Applied Sciences 10, no. 5 (March 5, 2020): 1785. http://dx.doi.org/10.3390/app10051785.
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Optical and thermal remote sensing data have been an important tool in geological exploration for certain deposit types. However, the present economic and technological advances demand the adaptation of the remote sensing data and image processing techniques to the exploration of other raw materials like lithium (Li). A bibliometric analysis, using a systematic review approach, was made to understand the recent interest in the application of remote sensing methods in Li exploration. A review of the application studies and developments in this field was also made. Throughout the paper, the addressed topics include: (i) achievements made in Li exploration using remote sensing methods; (ii) the main weaknesses of the approaches; (iii) how to overcome these difficulties; and (iv) the expected research perspectives. We expect that the number of studies concerning this topic will increase in the near future and that remote sensing will become an integrated and fundamental tool in Li exploration.
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Martínez-Vicente, Víctor, James R. Clark, Paolo Corradi, Stefano Aliani, Manuel Arias, Mathias Bochow, Guillaume Bonnery, et al. "Measuring Marine Plastic Debris from Space: Initial Assessment of Observation Requirements." Remote Sensing 11, no. 20 (October 21, 2019): 2443. http://dx.doi.org/10.3390/rs11202443.
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Sustained observations are required to determine the marine plastic debris mass balance and to support effective policy for planning remedial action. However, observations currently remain scarce at the global scale. A satellite remote sensing system could make a substantial contribution to tackling this problem. Here, we make initial steps towards the potential design of such a remote sensing system by: (1) identifying the properties of marine plastic debris amenable to remote sensing methods and (2) highlighting the oceanic processes relevant to scientific questions about marine plastic debris. Remote sensing approaches are reviewed and matched to the optical properties of marine plastic debris and the relevant spatio-temporal scales of observation to identify challenges and opportunities in the field. Finally, steps needed to develop marine plastic debris detection by remote sensing platforms are proposed in terms of fundamental science as well as linkages to ongoing planning for satellite systems with similar observation requirements.
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Huang, Wendong, Zhengwu Yuan, Aixia Yang, Chan Tang, and Xiaobo Luo. "TAE-Net: Task-Adaptive Embedding Network for Few-Shot Remote Sensing Scene Classification." Remote Sensing 14, no. 1 (December 28, 2021): 111. http://dx.doi.org/10.3390/rs14010111.
Повний текст джерелаАнотація:
Recently, approaches based on deep learning are quite prevalent in the area of remote sensing scene classification. Though significant success has been achieved, these approaches are still subject to an excess of parameters and extremely dependent on a large quantity of labeled data. In this study, few-shot learning is used for remote sensing scene classification tasks. The goal of few-shot learning is to recognize unseen scene categories given extremely limited labeled samples. For this purpose, a novel task-adaptive embedding network is proposed to facilitate few-shot scene classification of remote sensing images, referred to as TAE-Net. A feature encoder is first trained on the base set to learn embedding features of input images in the pre-training phase. Then in the meta-training phase, a new task-adaptive attention module is designed to yield the task-specific attention, which can adaptively select informative embedding features among the whole task. In the end, in the meta-testing phase, the query image derived from the novel set is predicted by the meta-trained model with limited support images. Extensive experiments are carried out on three public remote sensing scene datasets: UC Merced, WHU-RS19, and NWPU-RESISC45. The experimental results illustrate that our proposed TAE-Net achieves new state-of-the-art performance for few-shot remote sensing scene classification.
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Lazarowska, Agnieszka. "Review of Collision Avoidance and Path Planning Methods for Ships Utilizing Radar Remote Sensing." Remote Sensing 13, no. 16 (August 18, 2021): 3265. http://dx.doi.org/10.3390/rs13163265.
Повний текст джерелаАнотація:
The paper presents a comparative analysis of recent collision avoidance and real-time path planning algorithms for ships. Compared methods utilize radar remote sensing for target ships detection. Different recently introduced approaches are briefly described and compared. An emphasis is put on input data reception using a radar as a remote sensing device applied in order to detect moving obstacles such as encountered ships. The most promising methods are highlighted and their advantages and limitations are discussed. Concluding remarks include proposals of further research directions in the development of collision avoidance methods utilizing radar remote sensing.
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Vozel, Benoit, Vladimir Lukin, and Joan Serra-Sagristà. "Editorial to Special Issue “Remote Sensing Data Compression”." Remote Sensing 13, no. 18 (September 17, 2021): 3727. http://dx.doi.org/10.3390/rs13183727.
Повний текст джерелаАнотація:
A huge amount of remote sensing data is acquired each day, which is transferred to image processing centers and/or to customers. Due to different limitations, compression has to be applied on-board and/or on-the-ground. This Special Issue collects 15 papers dealing with remote sensing data compression, introducing solutions for both lossless and lossy compression, analyzing the impact of compression on different processes, investigating the suitability of neural networks for compression, and researching on low complexity hardware and software approaches to deliver competitive coding performance.