scholarly journals Análisis multitemporal de la laguna Suches y del vigor de la vegetación del bofedal de Huaytire, Tacna

2021 ◽  
Vol 20 (1) ◽  
pp. 27-39
Author(s):  
Marianela Sharyley Sanga Franco ◽  
José Francisco Chambe Bahamontes
Keyword(s):  
Landsat 8 ◽  
Landsat 7

La laguna Suches y el bofedal de Huaytire, ubicados en la provincia de Candarave del departamento de Tacna, son ecosistemas importantes que brindan diversos servicios ecosistémicos. En esta investigación, se determinó el cambio del área del espejo de agua de la laguna Suches y el cambio de los valores de NDVI del bofedal de Huaytire entre los años 1975 a 2020, mediante el análisis de imágenes satelitales obtenidas del Servicio Geológico de los Estados Unidos – USGS (Landsat 2, Landsat 5, Landsat 7 y Landsat 8). Se calculó el Índice Normalizado Diferencial de Vegetación (NDVI) y el Índice Diferencial de Agua Normalizado (NDWI). Los resultados evidencian una disminución notable del área de la laguna Suches y del vigor de la vegetación, este último indicando una disminución de la cobertura del bofedal de Huaytire. Aunque, no fue posible establecer una relación causal con los factores que estarían ocasionando tal disminución, la evidencia científica revisada sugiere que el cambio climático, la derivación y extracción de agua superficial y subterránea; así como, el pastoreo de ganado camélido, serían las causantes de los resultados encontrados.

scholarly journals Radiometric Cross Calibration of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+)

2014 ◽  
Vol 6 (12) ◽  
pp. 12619-12638 ◽  
Author(s):  
Nischal Mishra ◽  
Md Haque ◽  
Larry Leigh ◽  
David Aaron ◽  
Dennis Helder ◽  
...  
Keyword(s):  
Thematic Mapper ◽  
Landsat 8 ◽  
Landsat 7 ◽  
Cross Calibration

scholarly journals Performance comparisons of particle swarm optimization, echo state neural network and genetic algorithm for vegetation segmentation

2017 ◽  
Vol 7 (1.1) ◽  
pp. 184
Author(s):  
Rincy Merlin Mathew ◽  
S. Purushothaman ◽  
P. Rajeswari
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Soft Computing ◽  
Particle Swarm ◽  
Band 3 ◽  
Landsat 8 ◽  
Swarm Optimization ◽  
Landsat 7 ◽  
Performance Comparisons

This article presents the implementation of vegetation segmentation by using soft computing methods: particle swarm optimization (PSO), echostate neural network(ESNN) and genetic algorithm (GA). Multispectral image with the required band from Landsat 8 (5, 4, 3) and Landsat 7 (4, 3, 2) are used. In this paper, images from ERDAS format acquired by Landsat 7 ‘Paris.lan’ (band 4, band 3, Band 2) and image acquired from Landsat 8 (band5, band 4, band 3) are used. The soft computing algorithms are used to segment the plane-1(Near infra-red spectra) and plane 2(RED spectra). The monochrome of the two segmented images is compared to present performance comparisons of the implemented algorithms.

scholarly journals Land Cover Change Investigation in the Southern Syrian Coastal Basins During the Past 30-Years Using Landsat Remote Sensing Data

2018 ◽  
Vol 11 (1-2) ◽  
pp. 45-51 ◽  
Author(s):  
Muhannad Hammad ◽  
László Mucsi ◽  
Boudewijn van Leeuwen
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Landsat 7 ◽  
Coastal Basins ◽  
Major Increase ◽  
Temporal And Spatial ◽  
Land Cover Maps ◽  
Conflict Period

Abstract Land cover change and deforestation are important global ecosystem hazards. As for Syria, the current conflict and the subsequent absence of the forest preservation are main reasons for land cover change. This study aims to investigate the temporal and spatial aspects and trends of the land cover alterations in the southern Syrian coastal basins. In this study, land cover maps were made from surface reflectance images of Landsat-5(TM), Landsat-7(ETM+) and Landsat-8(OLI) during May (period of maximum vegetation cover) in 1987, 2002 and 2017. The images were classified into four different thematic classes using the maximum likelihood supervised classification method. The classification results were validated using 160 validation points in 2017, where overall accuracy was 83.75%. Spatial analysis was applied to investigate the land cover change during the period of 30 years for each basin and the whole study area. The results show 262.40 km2 reduction of forest and natural vegetation area during (1987-2017) period, and 72.5% of this reduction occurred during (2002-2017) period due to over-cutting of forest trees as a source of heating by local people, especially during the conflict period. This reduction was particularly high in the Alabrash and Hseen basins with 76.13 and 79.49 km2 respectively, and was accompanied by major increase of agriculture lands area which is attributed to dam construction in these basins which allowed people to cultivate rural lands for subsistence or to enhance their economic situation. The results of this study must draw the relevant authorities’ attention to preserve the remaining forest area.

scholarly journals Supraglacial debris cover assessment in the Caucasus Mountains, 1986-2000-2014

2017 ◽  
Author(s):  
Levan G. Tielidze ◽  
Roger D. Wheate ◽  
Stanislav S. Kutuzov ◽  
Kate Doyle ◽  
Ivan I. Lavrentiev
Keyword(s):  
Morphological Type ◽  
Ice Age ◽  
Landsat 8 ◽  
Glacier Area ◽  
Greater Caucasus ◽  
Rock Structure ◽  
Debris Cover ◽  
Landsat 7 ◽  
Caucasus Mountains ◽  
Elevation Model

Abstract. Surpaglacial debris cover plays an increasingly important role impacting on glacier ablation, while there have been limited recent studies for the assessment of debris covered glaciers in the Greater Caucasus mountains. We selected 559 glaciers according to the sections and macroslopes in the Greater Caucasus main watershed range and the Elbrus massif to assess supraglacial debris cover (SDC) for the years 1986, 2000 and 2014. Landsat (Landsat 5 TM, Landsat 7 ETM+, Landsat 8 OLI) and SPOT satellite imagery were analysed to generate glacier outlines using manual and semi-automated methods, along with slope information from a Digital Elevation Model. The study shows there is greater SDC area on the northern than the southern macroslope, and more in the eastern section than the western and central. In 1986-2000-2014, the SDC area increased from 6.4 %-8.2 %-19.4 % on the northern macroslope (apart from the eastern Greater Caucasus section), while on the southern macroslope, SDC increased from 4.0 %-4.9 %-9.2 %. Overall, debris covered glacier numbers increased from 122-143-172 (1986-2000-2014) for 559 selected glaciers. Despite the total glacier area decrease, the SDC glacier area and numbers increased as a function of slope inclination, aspect, glacier morphological type, Little Ice Age (LIA) moraines, rock structure and elevation. The datasets are available for public download at https://doi.pangaea.de/10.1594/PANGAEA.880147.

scholarly journals Komparasi Kemampuan Citra Satelit Landsat Dalam Mengidentifikasi Suhu Permukaan Daratan Di Kota Pekalongan

2020 ◽  
Author(s):  
Trida Ridho Fariz ◽  
Tjaturahono Budi Sanjoto ◽  
Dewi Liesnoor Setyowati
Keyword(s):  
Brightness Temperature ◽  
Landsat 8 ◽  
Landsat 7

Kajian pemetaan suhu permukaan daratan (LST) berbasis citra Landsat sudah sering dilakukan di Indonesia. Tetapi kajian yang membandingkan kemampuan citra satelit Landsat-7 dan Landsat-8 masih jarang dilakukan. Padahal kedua saluran termal pada citra satelit Landsat-7 dan Landsat-8 memiliki kelebihan dan kekurangan masing-masing, sehingga perlu dilakukan kajian untuk membandingkan kemampuan kedua citra satelit tersebut. Penelitian ini bertujuan untuk membandingkan kemampuan band termal antara citra satelit Landsat 7 dengan citra satelit Landsat 8 hanya untuk identifikasi LST, selain itu juga mengetahui perubahannya secara temporal.Data yang digunakan dalam penelitian ini adalah citra satelit Landsat 7 dan Landsat 8. Tahapan analisis data dimulai dengan pengolahan citra satelit untuk suhu perukaan daratan yang terdiri dari kalibrasi radian, koreksi atmosferik, konversi brightness temperature lalu diakhiri dengan konversi suhu permukaan daratan. Setiap peta suhu permukaan daratan dianalisis statistik berupa regresi linier dengan data suhu permukaan daratan hasil pengukuran dilapangan.Hasil penelitian ini menunjukkan bahwa citra satelit Landsat 8 cenderung lebih baik dalam memetakan LST di Kota Pekalongan. Citra satelit Landsat 8 juga digunakan untuk mengidentifikasi perubahan LST di Kota Pekalongan. Kota Pekalongan dalam kurun tahun 2015 sampai 2019 telah terjadi peningkatan suhu sekitar 0,60C. Wilayah yang menngalami perubahan suhu terbsar adalah Kecamatan Pekalongan Selatan.

scholarly journals Spatial Analysis of Coastline Change by Remote Sensing in the North Coast of Karawang Regency

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Roberto Pasaribu ◽  
Firman Agus H. ◽  
Liliek Soeprijadi
Keyword(s):  
Shoreline Change ◽  
Rate Of Change ◽  
North Coast ◽  
Landsat 8 ◽  
Northern Coast ◽  
Shoreline Changes ◽  
The Road ◽  
Coastline Change ◽  
The North ◽  
Landsat 7

<p><em>The existence of the coast in the northern part of Karawang Regency is very worrying. Seawater<strong> </strong>that was far up to tens of meters from the side of the road, is now on the lip of the road, even some parts of the road are cut off due to abrasion of seawater. Some villages were affected by abrasion erosion. One of the effects of damage due to abrasion and sedimentation is the occurrence of shoreline changes. This change in coastline will affect people's lives and spatial planning for the development of the area. For this reason, this study aims to determine the extent and rate of shoreline changes that occurred on the coast of Karawang Regency in the periods of 1989, 1995, 2001, 2005, 2009, 2016, and 2018. The shoreline data was obtained from the extraction of Landsat 3 MSS, Landsat 5 TM, Landsat 7 ETM +, and Landsat 8 </em><em>OLI</em>-<em>TIRS after the NDWI process was previously carried out. While the rate of change is calculated at 6 sample point locations scattered along the northern coast of Karawang Regency. The results showed that the largest area damaged by abrasion occurred in Sedari Village covering an area of 166.802 hectares, and the area formed by the largest sedimentation occurred in Muara Cilamaya Village at 276,318 hectares. Meanwhile, the fastest rate of shoreline change due to abrasion occurred in Sukajaya Village at 10 meters </em>/<em>year, while the slowest in Sedari Village at 3.77 meters / year. The fastest sedimentation process in Muara Cimalaya Village is 4.5 meters / year, while the late one in Tanjung Pakis Village is 3.09 meters / year.</em><em></em></p><p><strong><em>Keywords: </em></strong><em>Abra</em><em>sion, Accretion, Coastline Changes, Karawang</em><em></em></p>

Monitoreo del proceso de afectación del páramo andino venezolano a través de un análisis multitemporal

2021 ◽  
Vol 62 (2) ◽  
pp. 298-314
Author(s):  
Jesús Araujo ◽  
Yolanda Molina
Keyword(s):  
Landsat 8 ◽  
Landsat 7

La investigación midió el proceso de afectación del páramo andino venezolano mediante un análisis multitemporal del indicador ‘cobertura vegetal de páramo andino intervenida’, usando imágenes Landsat 7 de los años 2006 y 2012, y Landsat 8 del año 2016, que abarcan el municipio Rangel del estado Mérida, como parte de un sistema de indicadores de gestión diseñado en el marco del Plan de Acción para la Conservación del Páramo Andino Venezolano. Los resultados muestran una disminución de 538,69 ha de superficie de páramo andino, a consecuencia de intervenciones entre los años 2006 y 2016. Adicionalmente, se identificó un incremento de 426,08 ha de la superficie de selva nublada montano alta, ocupando áreas previamente dominadas por vegetación de páramo, debido al avance altitudinal de los bosques andinos en las vertientes húmedas posiblemente como consecuencia del cambio climático.

scholarly journals Studi Laju Perubahan Garis Pantai di Pesisir Tenggara Bali Menggunakan Citra Satelit Landsat (Studi Kasus Kabupaten Gianyar dan Klungkung)

2017 ◽  
Vol 3 (2) ◽  
pp. 204
Author(s):  
I Nengah Jaya Nugraha ◽  
I Wayan Gede Astawa Karang ◽  
I Gusti Bagus Sila Dharma
Keyword(s):  
Landsat Imagery ◽  
Shoreline Change ◽  
Rate Of Change ◽  
Landsat 8 ◽  
Band Ratio ◽  
Preliminary Information ◽  
Landsat 7 ◽  
Combination Of Methods ◽  
Analysis System ◽  
The Impact

Erosion and abrasion are the events that led to the beach shoreline position changes. The impact of climate change is the rise in sea level also causes changes in the coastline. South East coast of Bali, especially along the coast Gianyar and Klungkung changing coastline. This study aims to identify and calculate the rate of shoreline change in Gianyar and Klungkung from 1995 to 2015. The study was a preliminary information shoreline change and do not analyze the causes such as tides, currents, waves, and wind. The method used remote sensing analysis with the extraction of the coastline from the Landsat 5 satellite images in 1995, Landsat 7 in 2005, and Landsat 8 2015. Landsat imagery analyzed by a combination of methods approach the threshold and band ratio of wave infrared and green. Image processing using software Quantum GIS 2.8 and System for Automated Geoscientific Analyses (SAGA) GIS 2.2, extention Digintal Shoreline Analysis System (DSAS) to make calculations transect coastline. The results of the analysis of overlaying identify coastline in Gianyar and Klungkung change at a rate that varies every village. The rate of change of coastline in Gianyar due to accretion between 0.5096 - 8.6074 m / yr, while due to erosion between -3.7343 to -1.3201 m / yr. The rate of change in Klungkung regency coastline due to accretion between 0.6337 - 2.6875 m / yr, while due to erosion between -8.8795 to -0.8833 m / yr.

Comparison of Landsat 8 and Landsat 7 for regional measurements of CDOM and water clarity in lakes

2016 ◽  
Vol 185 ◽  
pp. 119-128 ◽  
Author(s):  
Leif G. Olmanson ◽  
Patrick L. Brezonik ◽  
Jacques C. Finlay ◽  
Marvin E. Bauer
Keyword(s):  
Water Clarity ◽  
Landsat 8 ◽  
Landsat 7

scholarly journals Combining Multi-Sensor Satellite Imagery to Improve Long-Term Monitoring of Temporary Surface Water Bodies in the Senegal River Floodplain

2020 ◽  
Vol 12 (19) ◽  
pp. 3157
Author(s):  
Andrew Ogilvie ◽  
Jean-Christophe Poussin ◽  
Jean-Claude Bader ◽  
Finda Bayo ◽  
Ansoumana Bodian ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Bodies ◽  
Landsat 8 ◽  
Site Specific ◽  
Senegal River ◽  
Landsat 7 ◽  
Long Term Monitoring ◽  
Term Monitoring ◽  
Sentinel 2

Accurate monitoring of surface water bodies is essential in numerous hydrological and agricultural applications. Combining imagery from multiple sensors can improve long-term monitoring; however, the benefits derived from each sensor and the methods to automate long-term water mapping must be better understood across varying periods and in heterogeneous water environments. All available observations from Landsat 7, Landsat 8, Sentinel-2 and MODIS over 1999–2019 are processed in Google Earth Engines to evaluate and compare the benefits of single and multi-sensor approaches in long-term water monitoring of temporary water bodies, against extensive ground truth data from the Senegal River floodplain. Otsu automatic thresholding is compared with default thresholds and site-specific calibrated thresholds to improve Modified Normalized Difference Water Index (MNDWI) classification accuracy. Otsu thresholding leads to the lowest Root Mean Squared Error (RMSE) and high overall accuracies on selected Sentinel-2 and Landsat 8 images, but performance declines when applied to long-term monitoring compared to default or site-specific thresholds. On MODIS imagery, calibrated thresholds are crucial to improve classification in heterogeneous water environments, and results highlight excellent accuracies even in small (19 km2) water bodies despite the 500 m spatial resolution. Over 1999–2019, MODIS observations reduce average daily RMSE by 48% compared to the full Landsat 7 and 8 archive and by 51% compared to the published Global Surface Water datasets. Results reveal the need to integrate coarser MODIS observations in regional and global long-term surface water datasets, to accurately capture flood dynamics, overlooked by the full Landsat time series before 2013. From 2013, the Landsat 7 and Landsat 8 constellation becomes sufficient, and integrating MODIS observations degrades performance marginally. Combining Landsat and Sentinel-2 yields modest improvements after 2015. These results have important implications to guide the development of multi-sensor products and for applications across large wetlands and floodplains.

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