Automatic extraction of yardangs using Landsat 8 and UAV images: A case study in the Qaidam Basin, China

2018 ◽  
Vol 33 ◽  
pp. 53-61 ◽  
Author(s):  
Yanhui Zhao ◽  
Ninghua Chen ◽  
Jianyu Chen ◽  
Chengqing Hu
Keyword(s):  
Qaidam Basin ◽  
Landsat 8 ◽  
Automatic Extraction ◽  
Uav Images

scholarly journals Presenting a Semi-Automatic, Statistically-Based Approach to Assess the Sharpness Level of Optical Images from Natural Targets via the Edge Method. Case Study: The Landsat 8 OLI–L1T Data

2021 ◽  
Vol 13 (8) ◽  
pp. 1593
Author(s):  
Luca Cenci ◽  
Valerio Pampanoni ◽  
Giovanni Laneve ◽  
Carla Santella ◽  
Valentina Boccia
Keyword(s):  
Landsat 8 ◽  
Image Sharpness ◽  
Optical Images ◽  
Data Quality Assessment ◽  
Relevant Role ◽  
Edge Method ◽  
Selection Of ◽  
Statistical Criteria

Developing reliable methodologies of data quality assessment is of paramount importance for maximizing the exploitation of Earth observation (EO) products. Among the different factors influencing EO optical image quality, sharpness has a relevant role. When implementing on-orbit approaches of sharpness assessment, such as the edge method, a crucial step that strongly affects the final results is the selection of suitable edges to use for the analysis. Within this context, this paper aims at proposing a semi-automatic, statistically-based edge method (SaSbEM) that exploits edges extracted from natural targets easily and largely available on Earth: agricultural fields. For each image that is analyzed, SaSbEM detects numerous suitable edges (e.g., dozens-hundreds) characterized by specific geometrical and statistical criteria. This guarantees the repeatability and reliability of the analysis. Then, it implements a standard edge method to assess the sharpness level of each edge. Finally, it performs a statistical analysis of the results to have a robust characterization of the image sharpness level and its uncertainty. The method was validated by using Landsat 8 L1T products. Results proved that: SaSbEM is capable of performing a reliable and repeatable sharpness assessment; Landsat 8 L1T data are characterized by very good sharpness performance.

Delineation of burned arid landscapes using Landsat 8 OLI data: a case study of Karaganda region in Kazakhstan

2021 ◽  
pp. 1-16
Author(s):  
Andrey Karpachevskiy ◽  
Sergey Lednev ◽  
Ivan Semenkov ◽  
Anna Sharapova ◽  
Sultan Nagiyev ◽  
...  
Keyword(s):  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Arid Landscapes ◽  
Karaganda Region

scholarly journals Lineament automatic extraction analysis for Galal Badra river basin using Landsat 8 satellite image

2019 ◽  
Vol 12 (25) ◽  
pp. 44-55 ◽  
Author(s):  
Safaa Sabah Adhab
Keyword(s):  
Program Analysis ◽  
Satellite Image ◽  
Landsat 8 ◽  
Automatic Extraction ◽  
Lineament Density ◽  
Density Analysis ◽  
Lineament Analysis ◽  
Intersection Density ◽  
Surrounding Areas ◽  
The Relationship

This research including lineament automated extraction by using PCI Geomatica program, depending on satellite image and lineament analysis by using GIS program. Analysis included density analysis, length density analysis and intersection density analysis. When calculate the slope map for the study area, found the relationship between the slope and lineament density.The lineament density increases in the regions that have high values for the slope, show that lineament play an important role in the classification process as it isolates the class for the other were observed in Iranian territory, clearly, also show that one of the lineament hit shoulders of Galal Badra dam and the surrounding areas dam. So should take into consideration the lineaments because its plays an important role in the study area.

scholarly journals Estimation of Above-Ground Mangrove Biomass Using Landsat-8 Data- Derived Vegetation Indices: A Case Study in Quang Ninh Province, Vietnam

2021 ◽  
pp. 506-525
Author(s):  
Hai-Hoa Nguyen ◽  
Huy Duc Vu ◽  
Achim Röder
Keyword(s):  
Vegetation Indices ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Estimation Model ◽  
The Status ◽  
Good Potential ◽  
The Mean ◽  
Accuracy Assessments ◽  
Quang Ninh

This study aimed to map the status of mangrove forests over the coasts of Hai Ha District and Mong Cai City in Quang Ninh Province by using 2019 Landsat-8 imagery. It then developed the AGB estimation model of mangrove forests based on the AGB estimation-derived plots inventory and vegetation indices-derived from Landsat-8 data. As results, there were five land covers identified, including mangrove forests, other vegetation, wetlands, built-up, and water, with the overall accuracy assessments of 80.0% and Kappa coefficient of 0.74. The total extent of mangrove forests was estimated at 4291.2 ha. The best AGB estimation model that was selected to estimate the AGB and AGC of mangrove forests for the whole coasts of Hai Ha District and Mong Cai City is AGB= 30.38 + 911.95*SAVI (R2=0.924, PValue <0.001). The model validation assessment has confirmed that the selected AGB model can be applied to Hai Ha and Mong Cai coasts with the mean difference between AGB observed and AGB predicted at 16.0 %. This satisfactory AGB model also suggests a good potential for AGB and AGC mapping, which offer the carbon trading market in the study site. As the AGB model selected, the total AGB and AGC of mangrove forests were estimated at about 14,600,000 tons and 6,868,076 tons with a range of from 94.0 - 432.0 tons ha-1, from 44.2 - 203.02 tons ha-1, respectively. It also suggests that the newly-developed AGB model of mangrove forests can be used to estimate AGC stocks and carbon sequestration of mangrove forests for C-PFES in over the coasts of Hai Ha District and Mong Cai City, which is a very importantly financial source for mangrove forest managers, in particular for local mangrove protectors.

scholarly journals True-orthophoto generation from UAV images: Implementation of a combined photogrammetric and computer vision approach

2014 ◽  
Vol II-5 ◽  
pp. 57-63 ◽  
Author(s):  
L. Barazzetti ◽  
R. Brumana ◽  
D. Oreni ◽  
M. Previtali ◽  
F. Roncoroni
Keyword(s):  
3D Model ◽  
Bundle Block Adjustment ◽  
Dense Matching ◽  
Automated Data Processing ◽  
Feature Based ◽  
Correlation Techniques ◽  
Uav Images ◽  
Image Orientation ◽  
Santa Maria

This paper presents a photogrammetric methodology for true-orthophoto generation with images acquired from UAV platforms. The method is an automated multistep workflow made up of three main parts: (i) image orientation through feature-based matching and collinearity equations / bundle block adjustment, (ii) dense matching with correlation techniques able to manage multiple images, and true-orthophoto mapping for 3D model texturing. It allows automated data processing of sparse blocks of convergent images in order to obtain a final true-orthophoto where problems such as self-occlusions, ghost effects, and multiple texture assignments are taken into consideration. <br><br> The different algorithms are illustrated and discussed along with a real case study concerning the UAV flight over the Basilica di Santa Maria di Collemaggio in L'Aquila (Italy). The final result is a rigorous true-orthophoto used to inspect the roof of the Basilica, which was seriously damaged by the earthquake in 2009.

A Study on The Use of UAV Images to Improve the Separation Accuracy of Agricultural Land Areas

2021 ◽  
Author(s):  
Fahime Arabi Aliabad ◽  
Hamid Reza Ghafarian Malamiri ◽  
Saeed Shojaei
Keyword(s):  
Spatial Resolution ◽  
Agricultural Land ◽  
Principal Component ◽  
Landsat Image ◽  
Fuzzy Artmap ◽  
Landsat 8 ◽  
Pass Filter ◽  
Tree Methods ◽  
Uav Images ◽  
High Pass

Abstract Classifying satellite images with medium spatial resolution such as Landsat, it is usually difficult to distinguish between plant species, and it is impossible to determine the area covered with weeds. In this study, a Landsat 8 image along with UAV images was used to separate pistachio cultivars and separate weed from trees. In order to use the high spatial resolution of UAV images, image fusion was carried out through high-pass filter, wavelet, principal component transformation, BROVEY, IHS and Gram Schmidt methods, and ERGAS, RMSE and correlation criteria were applied to assess their accuracy. The results represented that the wavelet method with R2, RMSE and ERGAS 0.91, 12.22 cm and 2.05 respectively had the highest accuracy in combining these images. Then, images obtained by this method were chosen with a spatial resolution of 20 cm for classification. Different classification methods including unsupervised method, maximum likelihood, minimum distance, fuzzy artmap, perceptron and tree methods were evaluated. Moreover, six soil classes, Ahmad Aghaei, Akbari, Kalleh Ghoochi, Fandoghi and a mixing class of Kalleh Ghoochi and Fandoghi were applied and also three classes of soil, pistachio tree and weeds were extracted from the trees. The results demonstrated that the fuzzy artmap method had the highest accuracy in separating weeds from trees, differentiating various pistachio cultivars with Landsat image and also classification with combined image and had 0.87, 0.79 and 0.87 kappa coefficients respectively. The comparison between pistachio cultivars through Landsat image and combined image showed that the validation accuracy obtained from harvest has raised by 17% because of combination of images. The results of this study indicated that the combination of UAV and Landsat 8 images affects well to separate pistachio cultivars and determine the area covered with weeds.

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