Accuracy assessment of bare soil map of Hungary based on Sentinel satellite data

2021 ◽  
Author(s):  
János Mészáros ◽  
Tünde Takáts ◽  
Mátyás Árvai ◽  
Annamária Laborczi ◽  
Gábor Szatmári ◽  
...  
Keyword(s):  
Soil Texture ◽  
Accuracy Assessment ◽  
Bare Soil ◽  
Google Earth ◽  
Slope Aspect ◽  
Chemical Parameters ◽  
Soil Map ◽  
Optical Images ◽  
Composite Data ◽  
Soil Chemical Parameters

<p>As Earth observation (EO) data is increasing in volume, fast and reliable data-processing tools are also required especially for analyzing large areas with high spatial resolution. Google Earth Engine (GEE) platform provides wide sets of EO imagery and elevation data in a cloud-based processing environment. This research focused on i) the generation of bare soil map of Hungary and ii) the accuracy assessment of created soil maps representing soil texture (clay, sand, silt) and soil chemical parameters (SOC, pH and CaCO<sub>3</sub>).</p><p>In this study Copernicus Sentinel-1 SAR and Sentinel-2 optical images acquired on a mid-term time period between 2017 April and 2020 December were used to generate a median composite. Optical images were filtered for cloud coverage less than 50% and a cloud mask was also implemented on all remaining images. The threshold values for Normalized Difference Vegetation Index and Normalized Burn Ratio indices were 0.55 and 0.35 respectively to differentiate bare soil pixels.</p><p>We tested the prediction accuracy of bare soil composite supplemented by various environmental datasets as additional predictor variables in different scenarios: (i) using solely bare soil composite data (ii) composite data, elevation and its derived parameters (e.g. slope, aspect) (iii) composite data and spectral indices and (iv) all aforementioned data in fusion.</p><p>For validation two types of datasets were used: i) the reference points of the Hungarian Soil Information and Monitoring System with a five-fold cross-validation method and ii) the recently compiled national maps for soil texture and soil chemical parameters.</p><p><strong>Acknowledgment:</strong> Our research was supported by the Hungarian National Research, Development and Innovation Office (NKFIH; K-131820 and K-124290) and by the Scholarship of Human Resource Supporter (NTP-NFTÖ-20-B-0022).</p>

scholarly journals Estimation of Building Heights and DEM Accuracy Assessment Using ICESat-2 Data Products

2021 ◽  
Vol 10 (1) ◽  
pp. 37
Author(s):  
Goddu Pavan Sai Goud ◽  
Ashutosh Bhardwaj
Keyword(s):  
Spatial Data ◽  
Rural Areas ◽  
Urban Areas ◽  
Accuracy Assessment ◽  
Urban Expansion ◽  
Population Expansion ◽  
Cost Effective ◽  
Absolute Error ◽  
Google Earth ◽  
Surface Geometry

The use of remote sensing for urban monitoring is a very reliable and cost-effective method for studying urban expansion in horizontal and vertical dimensions. The advantage of multi-temporal spatial data and high data accuracy is useful in mapping urban vertical aspects like the compactness of urban areas, population expansion, and urban surface geometry. This study makes use of the ‘Ice, cloud, and land elevation satellite-2′ (ICESat-2) ATL 03 photon data for building height estimation using a sample of 30 buildings in three experimental sites. A comparison of computed heights with the heights of the respective buildings from google image and google earth pro was done to assess the accuracy and the result of 2.04 m RMSE was obtained. Another popularly used method by planners and policymakers to map the vertical dimension of urban terrain is the Digital Elevation Model (DEM). An assessment of the openly available DEM products—TanDEM-X and Cartosat-1 has been done over Urban and Rural areas. TanDEM-X is a German earth observation satellite that uses InSAR (Synthetic Aperture Radar Interferometry) technique to acquire DEM while Cartosat-1 is an optical stereo acquisition satellite launched by the Indian Space Research Organization (ISRO) that uses photogrammetric techniques for DEM acquisition. Both the DEMs have been compared with ICESat-2 (ATL-08) Elevation data as the reference and the accuracy has been evaluated using Mean error (ME), Mean absolute error (MAE) and Root mean square error (RMSE). In the case of Greater Hyderabad Municipal Corporation (GHMC), RMSE values 5.29 m and 7.48 m were noted for TanDEM-X 90 and CartoDEM V3 R1 respectively. While the second site of Bellampalli Mandal rural area observed 5.15 and 5.48 RMSE values for the same respectively. Therefore, it was concluded that TanDEM-X has better accuracy as compared to the CartoDEM V3 R1.

scholarly journals URBAN SCENE CLASSIFICATION USING FEATURES EXTRACTED FROM PHOTOGRAMMETRIC POINT CLOUDS ACQUIRED BY UAV

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 511-518 ◽  
Author(s):  
G. G. Pessoa ◽  
R. C. Santos ◽  
A. C. Carrilho ◽  
M. Galo ◽  
A. Amorim
Keyword(s):  
Point Clouds ◽  
Bare Soil ◽  
Urban Environments ◽  
Data Sources ◽  
Classification Error ◽  
Error Matrix ◽  
Urban Scenes ◽  
Optical Images ◽  
Combining Data ◽  
Quantitative Analyses

<p><strong>Abstract.</strong> Images and LiDAR point clouds are the two major data sources used by the photogrammetry and remote sensing community. Although different, the synergy between these two data sources has motivated exploration of the potential for combining data in various applications, especially for classification and extraction of information in urban environments. Despite the efforts of the scientific community, integrating LiDAR data and images remains a challenging task. For this reason, the development of Unmanned Aerial Vehicles (UAVs) along with the integration and synchronization of positioning receivers, inertial systems and off-the-shelf imaging sensors has enabled the exploitation of the high-density photogrammetric point cloud (PPC) as an alternative, obviating the need to integrate LiDAR and optical images. This study therefore aims to compare the results of PPC classification in urban scenes considering radiometric-only, geometric-only and combined radiometric and geometric data applied to the Random Forest algorithm. For this study the following classes were considered: buildings, asphalt, trees, grass, bare soil, sidewalks and power lines, which encompass the most common objects in urban scenes. The classification procedure was performed considering radiometric features (Green band, Red band, NIR band, NDVI and Saturation) and geometric features (Height – nDSM, Linearity, Planarity, Scatter, Anisotropy, Omnivariance and Eigenentropy). The quantitative analyses were performed by means of the classification error matrix using the following metrics: overall accuracy, recall and precision. The quantitative analyses present overall accuracy of 0.80, 0.74 and 0.98 for classification considering radiometric, geometric and both data combined, respectively.</p>

scholarly journals Landslides susceptibility modelling using Multivariate Logistic Regression Model in the Sahla Watershed in Northern Morocco

2021 ◽  
Vol 33 ◽  
Author(s):  
Mohammed El-Fengour ◽  
Hanifa El Motaki ◽  
Aissa El Bouzidi
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Google Earth ◽  
Slope Aspect ◽  
Multivariate Logistic Regression Model ◽  
Susceptibility Map ◽  
Potential Hazard ◽  
Topographic Wetness Index ◽  
Landslide Occurrence

This study aimed to assess landslide susceptibility in the Sahla watershed in northern Morocco. Landslides hazard is the most frequent phenomenon in this part of the state due to its mountainous precarious environment. The abundance of rainfall makes this area suffer mass movements led to a notable adverse impact on the nearby settlements and infrastructures. There were 93 identified landslide scars. Landslide inventories were collected from Google Earth image interpretations. They were prepared out of landslide events in the past, and future landslide occurrence was predicted by correlating landslide predisposing factors. In this paper, landslide inventories are divided into two groups, one for landslide training and the other for validation. The Landslide Susceptibility Map (LSM) is prepared by Logistic Regression (LR) Statistical Method. Lithology, stream density, land use, slope curvature, elevation, topographic wetness index, slope aspect, and slope angle were used as conditioning factors. The Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) was employed to examine the performance of the model. In the analysis, the LR model results in 96% accuracy in the AUC. The LSM consists of the predicted landslide area. Hence it can be used to reduce the potential hazard linked with the landslides in the Sahla watershed area in Rif Mountains in northern Morocco.

scholarly journals EFFECTS OF SPATIAL VARIABILITY OF SOIL CHEMICAL PARAMETERS ON TIFTON 85 GRASS YIELD1

2020 ◽  
Vol 33 (1) ◽  
pp. 236-245
Author(s):  
EUDOCIO RAFAEL OTAVIO DA SILVA ◽  
MURILO MACHADO DE BARROS ◽  
MARCOS GERVASIO PEREIRA ◽  
JOÃO HENRIQUE GAIA GOMES ◽  
STEPHANY DA COSTA SOARES
Keyword(s):  
Decision Making ◽  
Spatial Variability ◽  
Dry Matter ◽  
Irregular Sampling ◽  
Descriptive Statistics ◽  
Dry Matter Yield ◽  
Chemical Parameters ◽  
Tropical Pastures ◽  
Chemical Attributes ◽  
Soil Chemical Parameters

ABSTRACT Studies on spatial variability of soil attributes of tropical pastures gather information that can assist in decision making about managements of these soils. The objective of the present study was to evaluate the spatial variability of soil chemical attributes and their effects on grass yield of Tifton 85. The experiment was carried out in an area of 3.91 ha at the Feno Rio Farm of the Federal Rural University of Rio de Janeiro, Seropédica, RJ, Brazil. Soils of the 0-0.20 and 0.20-0.40 m layers were sampled considering an irregular sampling mesh, making a total of 50 georeferenced points. The parameters evaluated were: the soil chemical attributes pH, Al+3, Ca+2, Mg+2, Na+, K+, P, H+Al, and total organic carbon (TOC); and the Tifton 85 dry matter yield (DMY). The results of these parameters were subjected to descriptive statistics, linear correlation, and geostatistics, and maps were developed for the analyses. Regions with grass yields different from the general mean were found in the area, which presented mean grass yield of 2248 kg ha-1. The soil chemical parameters Na+, Ca+2, TOC, and H+Al were significantly correlated with DMY, confirming that they are important and affect the Tifton 85 grass yield. The mapping of the Tifton 85 cycle is important for understanding the variability of DMY. The investigation of areas with different productive potentials should be followed by development of maps of soil chemical attributes to correlate and understand the ratios that may be involved with these variations.

scholarly journals ACCURACY ASSESSMENT OF 3D MODELS GENERATED FROM GOOGLE STREET VIEW IMAGERY

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 181-188 ◽  
Author(s):  
N. Bruno ◽  
R. Roncella
Keyword(s):  
Accuracy Assessment ◽  
Average Distance ◽  
3D Models ◽  
Google Earth ◽  
Google Street View ◽  
3D City Model ◽  
Software Packages ◽  
Street View ◽  
Panoramic Images ◽  
City Model

<p><strong>Abstract.</strong> Google Street View is a technology implemented in several Google services/applications (e.g. Google Maps, Google Earth) which provides the user, interested in viewing a particular location on the map, with panoramic images (represented in equi-rectangular projection) at street level. Generally, consecutive panoramas are acquired with an average distance of 5&amp;ndash;10<span class="thinspace"></span>m and can be compared to a traditional photogrammetric strip and, thus, processed to reconstruct portion of city at nearly zero cost. Most of the photogrammetric software packages available today implement spherical camera models and can directly process images in equi-rectangular projection. Although many authors provided in the past relevant works that involved the use of Google Street View imagery, mainly for 3D city model reconstruction, very few references can be found about the actual accuracy that can be obtained with such data. The goal of the present work is to present preliminary tests (at time of writing just three case studies has been analysed) about the accuracy and reliability of the 3D models obtained from Google Street View panoramas.</p>

scholarly journals Object-Based Mapping of Gullies Using Optical Images: A Case Study in the Black Soil Region, Northeast of China

2020 ◽  
Vol 12 (3) ◽  
pp. 487 ◽  
Author(s):  
Biwei Wang ◽  
Zengxiang Zhang ◽  
Xiao Wang ◽  
Xiaoli Zhao ◽  
Ling Yi ◽  
...  
Keyword(s):  
Regional Scale ◽  
Black Soil ◽  
Google Earth ◽  
Gully Erosion ◽  
Image Feature ◽  
Gradient Boosting ◽  
Light Gradient ◽  
Optical Images ◽  
Black Soil Region ◽  
Object Based

Gully erosion is a widespread natural hazard. Gully mapping is critical to erosion monitoring and the control of degraded areas. The analysis of high-resolution remote sensing images (HRI) and terrain data mixed with developed object-based methods and field verification has been certified as a good solution for automatic gully mapping. Considering the availability of data, we used only open-source optical images (Google Earth images) to identify gully erosion through image feature modeling based on OBIA (Object-Based Image Analysis) in this paper. A two-end extrusion method using the optimal machine learning algorithm (Light Gradient Boosting Machine (LightGBM)) and eCognition software was applied for the automatic extraction of gullies at a regional scale in the black soil region of Northeast China. Due to the characteristics of optical images and the design of the method, unmanaged gullies and gullies harnessed in non-forest areas were the objects of extraction. Moderate success was achieved in the absence of terrain data. According to independent validation, the true overestimation ranged from 20% to 30% and was mainly caused by land use types with high erosion risks, such as bare land and farm lanes being falsely classified as gullies. An underestimation of less than 40% was adjacent to the correctly extracted gullied areas. The results of extraction in regions with geographical object categories of a low complexity were usually more satisfactory. The overall performance demonstrates that the present method is feasible for gully mapping at a regional scale, with high automation, low cost, and acceptable accuracy.

scholarly journals Soil Texture Effects on Surface Resistance to Bare‐Soil Evaporation

2018 ◽  
Vol 45 (19) ◽  
pp. 10,398-10,405 ◽  
Author(s):  
P. Lehmann ◽  
O. Merlin ◽  
P. Gentine ◽  
D. Or
Keyword(s):  
Soil Texture ◽  
Surface Resistance ◽  
Bare Soil ◽  
Soil Evaporation ◽  
Bare Soil Evaporation
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