ANALYSIS OF LANDSAT 8 SATELLITE IMAGERY TO IDENTIFY POTENTIAL OF SPRING (Case Study: District Bojonegoro)

Seribu Islands is one of the marine tourism destinations in Jakarta. The high level of tourism in the Seribu Islands can be a threat to shallow water seabed profile habitat. Therefore, monitoring of changes in shallow water seabed profile habitat is needed so the sustainability can be monitored. This study aimed to determine changes in the shallow water seabed profile on Karya Island, Semak Daun Island, and Gosong Balik Layar in 2016 and 2018 based on Landsat 8 Satellite Imagery. Methods of this research used satellite image pre-processing, image classification, field survey, image reclassification, and accuracy assessment. The results showed that the coral area had decrease trend, while the area of Seagrass mix Seaweed had increased. The result of this classification had an accuracy value of 71.52%. Keywords: remote sensing, multispectral imagery, Lyzenga, benthic habitat, Seribu Island

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Trophic state assessment of a freshwater Himalayan lake using Landsat 8 OLI satellite imagery: A case study of Wular Lake, Jammu and Kashmir (India)

Earth and Space Science ◽

10.1029/2021ea001653 ◽

2022 ◽

Author(s):

Fayma Mushtaq ◽

Mili Ghosh Nee Lala ◽

Afaan Gulzar Mantoo

Keyword(s):

Satellite Imagery ◽

Trophic State ◽

State Assessment ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Jammu And Kashmir ◽

Wular Lake

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Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽

10.3390/geosciences11080312 ◽

2021 ◽

Vol 11 (8) ◽

pp. 312

Author(s):

Barbara Wiatkowska ◽

Janusz Słodczyk ◽

Aleksandra Stokowska

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Satellite Imagery ◽

Urban Areas ◽

Agricultural Land ◽

Urban Expansion ◽

Landsat 8 ◽

Rate Of Increase ◽

Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

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Night‐time lights satellite imagery reveals hotspots of second home mobility in rural Russia (a case study of Yaroslavl oblast)

Regional Science Policy & Practice ◽

10.1111/rsp3.12441 ◽

2021 ◽

Author(s):

Alexander Sheludkov ◽

Alexandra Starikova

Keyword(s):

Satellite Imagery ◽

Yaroslavl Oblast ◽

Night Time ◽

Second Home

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Identification of Agricultural Management Zones Through Clustering Algorithms with Thermal and Multispectral Satellite Imagery

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488517400062 ◽

2017 ◽

Vol 25 (Suppl. 1) ◽

pp. 121-140

Author(s):

R. B. Arango ◽

A. M. Campos ◽

E. F. Combarro ◽

E. R. Canas ◽

I. Díaz

Keyword(s):

Satellite Imagery ◽

Precision Agriculture ◽

Clustering Algorithms ◽

Economic Benefits ◽

Agricultural Management ◽

Landsat 8 ◽

Special Equipment ◽

Management Zones ◽

Ndvi Index ◽

Cost Efficient

Precision Agriculture entails the appropriate management of the inherent variability of soil and crops, resulting in an increase of economic benefits and a reduction of environmental impact. However, site-specific treatments require maps of the soil variability to identify areas of land that share similar properties. In order to produce these maps, we propose a cost-efficient method that combines clustering algorithms with publicly available satellite imagery. The method does not require exploring the parcels with any special equipment or taking samples of the soil for laboratory analysis. The proposed method was tested in a case study for three vineyard parcels with topographical dissimilarities. The study compares different spectral and thermal bands from the Landsat 8 satellite as well as vegetation and moisture indices to determine which one produces the best clustering. The experimental results seem promising for identification of agricultural management zones. The findings suggest that thermal bands produce better clustering than those based on the NDVI index.

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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

Remote Sensing ◽

10.3390/rs13081593 ◽

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.

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