scholarly journals Accuracy Enhancement for Land Cover Classification Using LiDAR and Multitemporal Sentinel 2 Images in a Forested Watershed

Proceedings ◽  
2018 ◽  
Vol 2 (20) ◽  
pp. 1280 ◽  
Author(s):  
Laura Fragoso-Campón ◽  
Elia Quirós ◽  
Julián Mora ◽  
José Antonio Gutiérrez ◽  
Pablo Durán-Barroso
Keyword(s):  
Land Cover ◽  
Vegetation Cover ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Land Cover Classification ◽  
Lidar Data ◽  
Accuracy Enhancement ◽  
Multispectral Data ◽  
Sentinel 2

Mapping land cover with high accuracy has become a reality with the application of current remote sensing techniques. Due to the specific spectral response of the vegetation, soil and vegetation indices are adequate tools to help in the discrimination of land uses. Additionally, the accuracy of satellite imagery classification can be improved using multitemporal series combined with LiDAR data. This datafusion takes advantage of the information provided by LiDAR for the vegetation cover density, and the capability of multispectral data to detect the type of vegetation. The main goal of this study is to analyze the accuracy enhancement in land cover classification of two forested watersheds when using datafusion of annual time series of Sentinel-2 images complemented with low density LiDAR. The obtained results show that overall accuracy is better if LiDAR data is included in the classification. This improvement can be a significant issue in land cover classification of forest watershed due to relationship and influence that vegetation cover has on runoff estimation.

scholarly journals Multispectral LiDAR data for land cover classification of urban areas

2021 ◽  
Author(s):  
Salem Morsy ◽  
Ahmed Shaker ◽  
Ahmed El-Rabbany
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Energy Intensity ◽  
Vegetation Indices ◽  
Land Cover Classification ◽  
Lidar Data ◽  
Radiometric Correction ◽  
Correction Model ◽  
Normalized Difference Vegetation Indices

Airborne Light Detection And Ranging (LiDAR) systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity) from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood lassifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy. Keywords: multispectral LiDAR; land cover; ground filtering; NDVI; radiometric correction

scholarly journals Multispectral LiDAR data for land cover classification of urban areas

2021 ◽  
Author(s):  
Salem Morsy ◽  
Ahmed Shaker ◽  
Ahmed El-Rabbany
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Energy Intensity ◽  
Vegetation Indices ◽  
Land Cover Classification ◽  
Lidar Data ◽  
Radiometric Correction ◽  
Correction Model ◽  
Normalized Difference Vegetation Indices

Airborne Light Detection And Ranging (LiDAR) systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity) from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood lassifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy. Keywords: multispectral LiDAR; land cover; ground filtering; NDVI; radiometric correction

Post-processing tools for land cover classification of Sentinel-2

2019 ◽  
Author(s):  
Ewa Gromny ◽  
Stanisław Lewiński ◽  
Marcin Rybicki ◽  
Radosław Malinowski ◽  
Michał Krupiński ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Post Processing ◽  
Sentinel 2

scholarly journals SYNTHETIC APERTURE RADAR (SAR) BASED CLASSIFIERS FOR LAND APPLICATIONS IN GERMANY

2016 ◽  
Vol XLI-B1 ◽  
pp. 1187-1193 ◽  
Author(s):  
G. Suresh ◽  
R. Gehrke ◽  
T. Wiatr ◽  
M. Hovenbitzer
Keyword(s):  
Land Cover ◽  
Synthetic Aperture Radar ◽  
Land Cover Classification ◽  
Classification Systems ◽  
Synthetic Aperture ◽  
Optical Data ◽  
Sar Images ◽  
Sentinel 2 ◽  
Aperture Radar

Land cover information is essential for urban planning and for land cover change monitoring. This paper presents an overview of the work conducted at the Federal Agency for Cartography and Geodesy (BKG) with respect to Synthetic Aperture Radar (SAR) based land cover classification. Two land cover classification approaches using SAR images are reported in this paper. The first method involves a rule-based classification using only SAR backscatter intensity while the other method involves supervised classification of a polarimetric composite of the same SAR image. The LBM-DE has been used for training and validation of the SAR classification results. Images acquired from the Sentinel-1a satellite are used for classification and the results have been reported and discussed. The availability of Sentinel-1a images that are weather and daylight independent allows for the creation of a land cover classification system that can be updated and validated periodically, and hence, be used to assist other land cover classification systems that use optical data. With the availability of Sentinel-2 data, land cover classification combining Sentinel-1a and Sentinel-2 images present a path for the future.

scholarly journals Land/Water Discrimination And Land Cover Classification Using Multispectral Airborne Lidar Data

2021 ◽  
Author(s):  
Salem Wagih Salem Morsy
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Region Growing ◽  
Point Clouds ◽  
Land Cover Classification ◽  
Lidar Data ◽  
Coastal Zones ◽  
532 Nm ◽  
Land Water

Multispectral airborne Light Detection And Ranging (LiDAR) systems are currently available. Optech Titan is an example of these systems, which acquires LiDAR point clouds at three independent wavelengths (1550, 1064 and 532 nm) from Earth’s surface. This dissertation aims to use the radiometric information (i.e., intensity) of the Optech Titan LiDAR data along with the geometric information (e.g., height) for land/water discrimination in coastal zones and land cover classification of urban areas. A set of point features based on elevation, intensity, and geometry was extracted and evaluated for land/water discrimination in coastal zones. In addition, an automated land/water discrimination approach based on seeded region growing algorithm was presented. Two data subsets were tested at Lake Ontario and Tobermory Harbour in Ontario, Canada. The elevation and geometry-based features achieved average overall accuracies of 72.8% - 83.3% and 69.9% -74.4%, respectively, while the intensity-based features achieved an average overall accuracy of 59.0% - 63.4%. The region growing method achieved an average overall accuracy of more than 99%, and the automation of this method is restricted by having double returns from water bodies at the 532 nm wavelength. A hierarchal point-based classification approach was presented for land cover classification of urban areas. The collected point clouds at the three wavelengths were first merged and three intensity values were estimated for each LiDAR point, followed by three-level classification approach. First, a ground filtering method was applied to separate non-ground from ground points. Second, three normalized difference vegetation indices (NDVIs) were computed, followed by NDVIs’ histograms construction. A multivariate Gaussian decomposition (MVGD) was then used to divide those histograms into buildings or trees from non-ground and roads or grass from ground points. Third, classes such as power lines, swimming pools and different types of trees were labeled based on their spectral characteristics. Three data subsets were tested representing different complexity of urban areas in Oshawa, Ontario, Canada. It is shown that the presented approach has achieved an overall accuracy up to 93.0%, which increased to more than 99% by considering the spatial coherence of the LiDAR point clouds.

scholarly journals Continuous Monitoring of the Flooding Dynamics in the Albufera Wetland (Spain) by Landsat-8 and Sentinel-2 Datasets

2021 ◽  
Vol 13 (17) ◽  
pp. 3525
Author(s):  
Carmela Cavallo ◽  
Maria Nicolina Papa ◽  
Massimiliano Gargiulo ◽  
Guillermo Palau-Salvador ◽  
Paolo Vezza ◽  
...  
Keyword(s):  
Land Cover ◽  
Continuous Monitoring ◽  
Open Water ◽  
Bare Soil ◽  
Landsat 8 ◽  
Multispectral Data ◽  
Spatially Extended ◽  
Moderate Resolution ◽  
Sentinel 2

Satellite data are very useful for the continuous monitoring of ever-changing environments, such as wetlands. In this study, we investigated the use of multispectral imagery to monitor the winter evolution of land cover in the Albufera wetland (Spain), using Landsat-8 and Sentinel-2 datasets. With multispectral data, the frequency of observation is limited by the possible presence of clouds. To overcome this problem, the data acquired by the two missions, Landsat-8 and Sentinel-2, were jointly used, thus roughly halving the revisit time. The varied types of land cover were grouped into four classes: (1) open water, (2) mosaic of water, mud and vegetation, (3) bare soil and (4) vegetated soil. The automatic classification of the four classes was obtained through a rule-based method that combined the NDWI, MNDWI and NDVI indices. Point information, provided by geo-located ground pictures, was spatially extended with the help of a very high-resolution image (GeoEye-1). In this way, surfaces with known land cover were obtained and used for the validation of the classification method. The overall accuracy was found to be 0.96 and 0.98 for Landsat-8 and Sentinel-2, respectively. The consistency evaluation between Landsat-8 and Sentinel-2 was performed in six days, in which acquisitions by both missions were available. The observed dynamics of the land cover were highly variable in space. For example, the presence of the open water condition lasted for around 60–80 days in the areas closest to the Albufera lake and progressively decreased towards the boundaries of the park. The study demonstrates the feasibility of using moderate-resolution multispectral images to monitor land cover changes in wetland environments.

Land-cover classification of multispectral LiDAR data using CNN with optimized hyper-parameters

2020 ◽  
Vol 166 ◽  
pp. 241-254 ◽  
Author(s):  
Suoyan Pan ◽  
Haiyan Guan ◽  
Yating Chen ◽  
Yongtao Yu ◽  
Wesley Nunes Gonçalves ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Lidar Data
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