Multi-scale seagrass mapping in satellite data and the use of UAS in accuracy assessment

2018 ◽  
Author(s):  
Despina Makri ◽  
Panagiotis Stamatis ◽  
Michaela Doukari ◽  
Apostolos Papakonstantinou ◽  
Christos Vasilakos ◽  
...  
Keyword(s):  
Satellite Data ◽  
Accuracy Assessment ◽  
Multi Scale ◽  
Seagrass Mapping

scholarly journals Sentinel-1 and Sentinel-2 Data Fusion for Mapping and Monitoring Wetlands

2018 ◽  
Author(s):  
Gordana Kaplan ◽  
Ugur Avdan
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Classification Accuracy ◽  
Accuracy Assessment ◽  
Geographical Information ◽  
Wetland Classification ◽  
Significant Information ◽  
Data Set ◽  
Object Based ◽  
Sentinel 2

Wetlands benefits can be summarized but are not limited to their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Over the past few decades, remote sensing and geographical information technologies has proven to be a useful and frequent applications in monitoring and mapping wetlands. Combining both optical and microwave satellite data can give significant information about the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing data from different sensors, such as radar and optical remote sensing data, can increase the wetland classification accuracy. In this paper we investigate the ability of fusion two fine spatial resolution satellite data, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, for mapping wetlands. As a study area in this paper, Balikdami wetland located in the Anatolian part of Turkey has been selected. Both Sentinel-1 and Sentinel-2 images require pre-processing before their use. After the pre-processing, several vegetation indices calculated from the Sentinel-2 bands were included in the data set. Furthermore, an object-based classification was performed. For the accuracy assessment of the obtained results, number of random points were added over the study area. In addition, the results were compared with data from Unmanned Aerial Vehicle collected on the same data of the overpass of the Sentinel-2, and three days before the overpass of Sentinel-1 satellite. The accuracy assessment showed that the results significant and satisfying in the wetland classification using both multispectral and microwave data. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, with an overall classification accuracy of approximately 90% in the object-based classification. Compared with the high resolution UAV data, the classification results give promising results for mapping and monitoring not just wetlands, but also the sub-classes of the study area. For future research, multi-temporal image use and terrain data collection are recommended.

scholarly journals A Multi-Scale Mapping Approach Based on a Deep Learning CNN Model for Reconstructing High-Resolution Urban DEMs

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1369
Author(s):  
Ling Jiang ◽  
Yang Hu ◽  
Xilin Xia ◽  
Qiuhua Liang ◽  
Andrea Soltoggio ◽  
...  
Keyword(s):  
High Resolution ◽  
Accuracy Assessment ◽  
Cost Effective ◽  
Reconstruction Method ◽  
Low Resolution ◽  
Multi Scale ◽  
Mapping Approach ◽  
Assessment Approach ◽  
Urban Topography ◽  
Complex Features

The scarcity of high-resolution urban digital elevation model (DEM) datasets, particularly in certain developing countries, has posed a challenge for many water-related applications such as flood risk management. A solution to address this is to develop effective approaches to reconstruct high-resolution DEMs from their low-resolution equivalents that are more widely available. However, the current high-resolution DEM reconstruction approaches mainly focus on natural topography. Few attempts have been made for urban topography, which is typically an integration of complex artificial and natural features. This study proposed a novel multi-scale mapping approach based on convolutional neural network (CNN) to deal with the complex features of urban topography and to reconstruct high-resolution urban DEMs. The proposed multi-scale CNN model was firstly trained using urban DEMs that contained topographic features at different resolutions, and then used to reconstruct the urban DEM at a specified (high) resolution from a low-resolution equivalent. A two-level accuracy assessment approach was also designed to evaluate the performance of the proposed urban DEM reconstruction method, in terms of numerical accuracy and morphological accuracy. The proposed DEM reconstruction approach was applied to a 121 km2 urbanized area in London, United Kingdom. Compared with other commonly used methods, the current CNN-based approach produced superior results, providing a cost-effective innovative method to acquire high-resolution DEMs in other data-scarce regions.

scholarly journals The new remote-sensing-derived Swiss glacier inventory: I. Methods

2002 ◽  
Vol 34 ◽  
pp. 355-361 ◽  
Author(s):  
Frank Paul ◽  
Andreas Kääb ◽  
Max Maisch ◽  
Tobias Kellenberger ◽  
Wilfried Haeberli
Keyword(s):  
Satellite Data ◽  
Accuracy Assessment ◽  
Three Dimensional ◽  
Geographical Information ◽  
Glacier Inventory ◽  
Ratio Image ◽  
Digital Elevation ◽  
First Results ◽  
Gis Environment ◽  
Elevation Model

AbstractA new Swiss glacier inventory is to be compiled from satellite data for the year 2000. The study presented here describes two major tasks: an accuracy assessment of different methods for glacier classification with Landsat Thematic Mapper (TM) data and a digital elevation model (DEM); the geographical information system (GIS)-based methods for automatic extraction of individual glaciers from classified satellite data and the computation of three-dimensional glacier parameters (such as minimum, maximum and median elevation or slope and orientation) by fusion with a DEM. First results obtained by these methods are presented in Part II of this paper (Kääb and others, 2002). Thresholding of a ratio image from TM4 and TM5 reveals the best-suited glacier map. The computation of glacier parameters in a GIS environment is efficient and suitable for worldwide application. The methods developed contribute to the U. S. Geological Survey-led Global Land Ice Measurements from Space (GLIMS) project which is currently compiling a global inventory of land ice masses within the framework of global glacier monitoring (Haeberli and others, 2000).

scholarly journals Using Geobia and Data Fusion Approach for Land use and Land Cover Mapping

2016 ◽  
Vol 35 (1) ◽  
pp. 93-104 ◽  
Author(s):  
Piotr Wężyk ◽  
Paweł Hawryło ◽  
Marta Szostak ◽  
Marcin Pierzchalski ◽  
Roeland De Kok
Keyword(s):  
Land Use ◽  
Image Analysis ◽  
Land Cover ◽  
Data Fusion ◽  
Accuracy Assessment ◽  
Multi Scale ◽  
Multi Stage ◽  
Catchment Areas ◽  
Mapping Process ◽  
Fusion Approach

Abstract Land Use and Land Cover (LULC) maps play an important role in an environmental modelling, and for many years efforts have been made to improve and streamline the expensive mapping process. The aim of the study was to create LULC maps of three selected water catchment areas in South Poland using a Geographic Object-Based Image Analysis (GEOBIA) in order to highlight the advantages of this innovative, semi-automatic method of image analysis. the classification workflow included: multi-stage and multi-scale analyses based on a data fusion approach. Input data consisted mainly of BlackBridge (RapidEye) high resolution satellite imagery, although for distinguishing particular LULC classes, additional satellite images (LANDSAT TM5) and GIS-vector data were used. Accuracy assessment of GEOBIA classification results varied from 0.83 to 0.87 (kappa), depending on the specific catchment area. The main recognized advantages of GEOBIA in the case study were: performing of multi-stage and multi-scale image classification using different features for specific LULC classes and the ability to using knowledge-based classification in conjunction with the data fusion approach in an efficient and reliable manner.

Sign in / Sign up

Export Citation Format

Share Document