Structural lineament mapping in a sub-tropical region using Landsat-8/SRTM data: a case study of Deng-Deng area in Eastern Cameroon

2021 ◽  
Vol 14 (23) ◽  
Author(s):  
Donald Hermann Fossi ◽  
Habib Dadjo Djomo ◽  
Jonas Didero Takodjou Wambo ◽  
Sylvestre Ganno ◽  
Amin Beiranvand Pour ◽  
...  
Keyword(s):  
Tropical Region ◽  
Landsat 8 ◽  
Srtm Data ◽  
Eastern Cameroon

Identifying high potential zones of gold mineralization in a sub-tropical region using Landsat-8 and ASTER remote sensing data: A case study of the Ngoura-Colomines goldfield, eastern Cameroon

2020 ◽  
Vol 122 ◽  
pp. 103530 ◽  
Author(s):  
Jonas Didero Takodjou Wambo ◽  
Amin Beiranvand Pour ◽  
Sylvestre Ganno ◽  
Paul D. Asimow ◽  
Basem Zoheir ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Gold Mineralization ◽  
Remote Sensing Data ◽  
High Potential ◽  
Tropical Region ◽  
Landsat 8 ◽  
Sensing Data ◽  
Eastern Cameroon

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 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.

Landsat thermal infrared to detect sub-canopy riparian flooding

2021 ◽  
Author(s):  
Emanuel Storey ◽  
Witold Krajewski ◽  
Efthymios Nikolopoulos
Keyword(s):  
Open Water ◽  
Thermal Infrared ◽  
Short Wave ◽  
Mapping Technique ◽  
Landsat 8 ◽  
Infrared Observations ◽  
Plant Canopies ◽  
Multi Temporal ◽  
Flood Detection

&lt;p&gt;Satellite based flood detection can enhance understanding of risk to humans and infrastructures, geomorphic processes, and ecological effects.&amp;#160; Such application of optical satellite imagery has been mostly limited to the detection of water exposed to sky, as plant canopies tend to obstruct water visibility in short electromagnetic wavelengths.&amp;#160; This case study evaluates the utility in multi-temporal thermal infrared observations from Landsat 8 as a basis for detecting sub-canopy fluvial inundation resulting in ambient temperature change.&lt;/p&gt;&lt;p&gt;We selected three flood events of 2016 and 2019 along sections of the Mississippi, Cedar, and Wapsipinicon Rivers located in Iowa, Minnesota, and Wisconsin, United States.&amp;#160; Classification of sub-canopy water involved logical, threshold-exceedance criteria to capture thermal decline within channel-adjacent vegetated zones.&amp;#160; Open water extent in the floods was mapped based on short-wave infrared thresholds determined parametrically from baseline (non-flooded) observations.&amp;#160; Map accuracy was evaluated using higher-resolution (0.5&amp;#8211;5.0 m) synchronic optical imagery.&lt;/p&gt;&lt;p&gt;Results demonstrate improved ability to detect sub-canopy inundation when thermal infrared change is incorporated: sub-canopy flood class accuracy was comparable to that of open water in previous studies.&amp;#160; The multi-temporal open-water mapping technique yielded high accuracy as compared to similar studies.&amp;#160; This research highlights the utility of Landsat thermal infrared data for monitoring riparian inundation and for validating other remotely sensed and simulated flood maps.&lt;/p&gt;

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