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Accurate topography of intertidal mudflats of a fine resolution is fundamental data to further understand the coastal process and achieve targeted coastal management. However, the mapping of mudflat topography is hindered by poor accessibility of muddy environments and a short observation time window caused by periodic tides. Commonly as a revolutionary technique owing to its low cost, flexibility, and quality data, the unmanned aerial vehicle (UAV)-based SfM photogrammetry has been widely applied in coastal areas. The conventional UAV photogrammetric accuracy significantly depends on the number and distribution of ground control points (GCPs), limiting its mapping efficiency. With the increasingly available UAVs with onboard RTK, photogrammetry without GCPs is becoming a promising alternative. However, the ability of this advanced RTK-assisted UAV to capture centimeter-scale elevation changes in intertidal mudflats still remains unclear. For this reason, this paper aims to evaluate the potential of RTK-assisted UAVs in quantifying intertidal topographic changes. The results showed that the RTK-assisted UAV structure-from-motion (SfM) photogrammetry without GCPs could accurately capture fine-scale topographical features such as mudflat gradient and creeks with root-mean-squared errors (RMSE) of ± 3.3 cm, ± 2.8 cm, and ± 4.7 cm on X-, Y-, and vertical directions, respectively. Therefore, this study identified that RTK-assisted UAV photogrammetry could be used to quantify intertidal mudflat morphodynamics and calculate sediment deposition volume within the uncertainty in a more cost-effective way.

Remote sensing

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Remote sensing, especially from satellites, is a source of invaluable data which can be used to generate synoptic information for virtually all parts of the Earth, including the atmosphere, land, and ocean. In the last few decades, such data have evolved as a basis for accurate information about the Earth, leading to a wealth of geoscientific analysis focusing on diverse applications. Geoinformation systems based on remote sensing are increasingly becoming an integral part of the current information and communication society. The integration of remote sensing and geoinformation essentially involves combining data provided from both, in a consistent and sensible manner. This process has been accelerated by technologically advanced tools and methods for remote sensing data access and integration, paving the way for scientific advances in a broadening range of remote sensing exploitations in applications of geoinformation. This volume hosts original research focusing on the exploitation of remote sensing in applications of geoinformation. The emphasis is on a wide range of applications, such as the mapping of soil nutrients, detection of plastic litter in oceans, urban microclimate, seafloor morphology, urban forest ecosystems, real estate appraisal, inundation mapping, and solar potential analysis.

Remote sensing.

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The reprint "Land Administration 2.0" is an extension of the previous reprint "Remote Sensing for Land Administration", another Special Issue in Remote Sensing. This reprint unpacks the responsible use and integration of emerging remote sensing techniques into the domain of land administration, including land registration, cadastre, land use planning, land valuation, land taxation, and land development. The title was chosen as "Land Administration 2.0" in reference to both this Special Issue being the second volume on the topic "Land Administration" and the next-generation requirements of land administration including demands for 3D, indoor, underground, real-time, high-accuracy, lower-cost, and interoperable land data and information.

Remote sensing.

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Understanding forest fire regimes involves characterizing spatial distribution, recurrence, intensity, seasonality, size, and severity. In recent years, knowledge of damage levels can be directly related to the environmental impact of fire and, at the same time, it is a valuable estimator of fire intensity, when the data about it are not available. Remote sensing may be seen as a tool to accurately assess burn severity and to predict the potential effects of forest fires on ecosystems, thus making the prediction of the regeneration of the plant community and the effects on ecosystems easier. This information is basic to facilitate decision-making in the post-fire management of fire-prone ecosystems. Nowadays, there has been intense research activity in relation to burned areas, burn severity, and vegetation regeneration because fires in many areas of the planet are becoming more severe and extensive, and their correct evaluation and follow-up is posing great challenges to current scientists. The current advances in remote sensing and related sciences will allow us to evaluate the damage with greater precision and to know with greater reliability the dynamics of recovery. This reprint contains studies on new remote sensing technologies, new sensors, data collections, and processing methodologies that can be successfully applied in burn severity mapping, vegetation recovery monitoring, and post-fire management of fire-prone ecosystems affected by large fires. We hope this book can help readers become more familiar with this knowledge and foster an increased interest in this field.

Remote sensing.

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This book aims at providing the state-of-the-art on all of the aforementioned tools in different energy applications and at different scales, i.e., urban, regional, national, and even continental for renewable scenarios planning and policy making.

Remote sensing.