Combining Argo and remote-sensing data to estimate the ocean three-dimensional temperature fields—a first approach based on simulated observations

2004 ◽  
Vol 46 (1-4) ◽  
pp. 85-98 ◽  
Author(s):  
S. Guinehut ◽  
P.Y. Le Traon ◽  
G. Larnicol ◽  
S. Philipps
Keyword(s):  
Remote Sensing ◽  
Three Dimensional ◽  
Remote Sensing Data ◽  
Temperature Fields ◽  
Sensing Data

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

2013 ◽  
Vol 28 (4) ◽  
pp. 516-525 ◽  
Author(s):  
Marcelo Pedroso Curtarelli ◽  
Enner Alcântara ◽  
Camilo Daleles Rennó ◽  
Arcilan Trevenzoli Assireu ◽  
Marie Paule Bonnet ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Thermal Structure ◽  
Three Dimensional ◽  
Remote Sensing Data ◽  
Tropical Reservoir ◽  
Sensing Data ◽  
Surface Circulation ◽  
Lake Model

scholarly journals CARTOGRAPHIC MODELING OF LAND SURFACE TEMPERATURE FIELDS OF THE NOVOSIBIRSK REGION ACCORDING TO REMOTE SENSING DATA

2021 ◽  
Vol 4 (1) ◽  
pp. 45-51
Author(s):  
Polina V. Voronina ◽  
Igor A. Pestunov ◽  
Svetlana Ya. Kudryashova
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Temperature Regime ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Temperature Fields ◽  
Sensing Data ◽  
Novosibirsk Region ◽  
Cartographic Modeling

The results of the study of the land surface temperature regime of the Novosibirsk region for cartographic modeling based on satellite sensing data are presented.

scholarly journals A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset

2020 ◽  
Vol 12 (23) ◽  
pp. 3888
Author(s):  
Mingyuan Peng ◽  
Lifu Zhang ◽  
Xuejian Sun ◽  
Yi Cen ◽  
Xiaoyang Zhao
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Temporal Resolution ◽  
Three Dimensional ◽  
Remote Sensing Data ◽  
High Temporal Resolution ◽  
Fusion Method ◽  
Sensing Data ◽  
Resolution 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.

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

2007 ◽  
Vol 79 (4) ◽  
pp. 693-711 ◽  
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.

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

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.

Sign in / Sign up

Export Citation Format

Share Document