Modelling the surface circulation and thermal structure of a tropical reservoir using three-dimensional hydrodynamic lake model and remote-sensing data

With the growing development of remote sensors, huge volumes of remote sensing data are being utilized in related applications, bringing new challenges to the efficiency and capability of processing huge datasets. Spatiotemporal remote sensing data fusion can restore high spatial and high temporal resolution remote sensing data from multiple remote sensing datasets. However, the current methods require long computing times and are of low efficiency, especially the newly proposed deep learning-based methods. Here, we propose a fast three-dimensional convolutional neural network-based spatiotemporal fusion method (STF3DCNN) using a spatial-temporal-spectral dataset. This method is able to fuse low-spatial high-temporal resolution data (HTLS) and high-spatial low-temporal resolution data (HSLT) in a four-dimensional spatial-temporal-spectral dataset with increasing efficiency, while simultaneously ensuring accuracy. The method was tested using three datasets, and discussions of the network parameters were conducted. In addition, this method was compared with commonly used spatiotemporal fusion methods to verify our conclusion.

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Analysis of tectonic-controlled fluvial morphology and sedimentary processes of the western Amazon Basin: an approach using satellite images and digital elevation model

Anais da Academia Brasileira de Ciências ◽

10.1590/s0001-37652007000400010 ◽

2007 ◽

Vol 79 (4) ◽

pp. 693-711 ◽

Cited By ~ 25

Author(s):

Clauzionor L. Silva ◽

Norberto Morales ◽

Alvaro P. Crósta ◽

Solange S. Costa ◽

Jairo R. Jiménez-Rueda

Keyword(s):

Remote Sensing ◽

Digital Elevation Model ◽

Amazon Basin ◽

Three Dimensional ◽

Remote Sensing Data ◽

Sedimentary Processes ◽

Sensing Data ◽

Fluvial Morphology ◽

Digital Elevation ◽

Elevation Model

An investigation of the tectonic controls of the fluvial morphology and sedimentary processes of an area located southwest of Manaus in the Amazon Basin was conducted using orbital remote sensing data. In this region, low topographic gradients represent a major obstacle for morphotectonic analysis using conventional methods. The use of remote sensing data can contribute significantly to overcome this limitation. In this instance, remote sensing data comprised digital elevation model (DEM) acquired by the Shuttle Radar Topographic Mission (SRTM) and Landsat Thematic Mapper images. Advanced image processing techniques were employed for enhancing the topographic textures and providing a three-dimensional visualization, hence allowing interpretation of the morphotectonic elements. This led to the recognition of main tectonic compartments and several morphostructural features and landforms related to the neotectonic evolution of this portion of the Amazon Basin. Features such as fault scarps, anomalous drainage patterns, aligned ridges, spurs and valleys, are expressed in the enhanced images as conspicuous lineaments along NE-SW, NW-SE, E-W and N-S directions. These features are associated to the geometry of alternated horst and graben structures, the latter filled by recent sedimentary units. Morphotectonic interpretation using this approach has proven to be efficient and permitted to recognize new tectonic features that were named Asymmetric Ariaú Graben, Rombohedral Manacapuru Basin and Castanho-Mamori Graben.

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Visual Digital Forest Model Based on a Remote Sensing Data and Forest Inventory Data

Remote Sensing ◽

10.3390/rs13204092 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4092

Author(s):

Marsel Vagizov R. ◽

Eugenie Istomin P. ◽

Valerie Miheev L. ◽

Artem Potapov P. ◽

Natalya Yagotinceva V.

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Forest Inventory ◽

Three Dimensional ◽

Remote Sensing Data ◽

Digital Model ◽

Inventory Data ◽

Forest Model ◽

Sensing Data ◽

Forest Inventory Data

This article discusses the process of creating a digital forest model based on remote sensing data, three-dimensional modeling, and forest inventory data. Remote sensing data of the Earth provide a fundamental tool for integrating subsequent objects into a digital forest model, enabling the creation of an accurate digital model of a selected forest quarter by using forest inventory data in educational and experimental forestry, and providing a valuable and extensive database of forest characteristics. The formalization and compilation of technologies for connecting forest inventory databases and remote sensing data with the construction of three-dimensional tree models for a dynamic display of changes in forests provide an additional source of data for obtaining new knowledge. The quality of forest resource management can be improved by obtaining the most accurate details of the current state of forests. Using machine learning and regression analysis methods as part of a digital model, it is possible to visually assess the course of planting growth, changes in species composition, and other morphological characteristics of forests. The goal of digital, interactive forest modeling is to create virtual simulations of the future status of forests using a combination of predictive forest inventory models and machine learning technology. The research findings provide a basic idea and technique for developing local digital forest models based on remote sensing and data integration technologies.

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Methodology for geographical data evolution: three-dimensional particle-based real-time snow simulation with remote-sensing data

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.8.084598 ◽

2013 ◽

Vol 8 (1) ◽

pp. 084598 ◽

Cited By ~ 8

Author(s):

Jian Tan ◽

Xiangtao Fan ◽

Yingchao Ren

Keyword(s):

Remote Sensing ◽

Real Time ◽

Three Dimensional ◽

Remote Sensing Data ◽

Geographical Data ◽

Sensing Data ◽

Data Evolution

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Current Trends in Forest Ecological Applications of Three-Dimensional Remote Sensing: Transition from Experimental to Operational Solutions?

Forests ◽

10.3390/f10100891 ◽

2019 ◽

Vol 10 (10) ◽

pp. 891

Author(s):

Hooman Latifi ◽

Ruben Valbuena

Keyword(s):

Remote Sensing ◽

Forest Ecology ◽

Three Dimensional ◽

Remote Sensing Data ◽

Structure And Function ◽

Small Scale ◽

Sensing Data ◽

Composition Structure ◽

And Function ◽

Novel Applications

The alarming increase in the magnitude and spatiotemporal patterns of changes in composition, structure and function of forest ecosystems during recent years calls for enhanced cross-border mitigation and adaption measures, which strongly entail intensified research to understand the underlying processes in the ecosystems as well as their dynamics. Remote sensing data and methods are nowadays the main complementary sources of synoptic, up-to-date and objective information to support field observations in forest ecology. In particular, analysis of three-dimensional (3D) remote sensing data is regarded as an appropriate complement, since they are hypothesized to resemble the 3D character of most forest attributes. Following their use in various small-scale forest structural analyses over the past two decades, these sources of data are now on their way to be integrated in novel applications in fields like citizen science, environmental impact assessment, forest fire analysis, and biodiversity assessment in remote areas. These and a number of other novel applications provide valuable material for the Forests special issue “3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function”, which shows the promising future of these technologies and improves our understanding of the potentials and challenges of 3D remote sensing in practical forest ecology worldwide.

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