scholarly journals ANALISIS DATA CITRA LANDSAT UNTUK PEMANTAUAN PERUBAHAN GARIS PANTAI KOTA BENGKULU

2017 ◽  
Vol 2 (1) ◽  
pp. 90-100
Author(s):  
Silvy Syukhriani ◽  
Eko Nofridiansyah ◽  
Bambang Sulistyo
Keyword(s):  
Landsat Tm ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat 7 ◽  
Multi Temporal

Penelitian ini bertujuan untuk menganalisis perubahan garis pantai Kota Bengkulu dengan teknologi penginderaan jauh menggunakan data citra Landsat, berdasarkan data multi temporal dengan teknik analisa visual dan digital antara tahun 2006 sampai tahun 2015. Garis pantai adalah batas antara daratan dan lautan yang mempunyai bentuk bervariasi dan dapat berubah dari musim ke musim. Tujuan dari penelitian ini untuk mempermudah dalam memantau perubahan garis pantai Kota Bengkulu dengan teknologi penginderaan jauh menggunakan data citra Landsat-TM, Landsat-7 ETM+ dan Landsat-8 OLI selama 10 tahun dari tahun 2006 sampai tahun 2015. Metode yang digunakan yaitu dengan melakukan digitasi dan tumpang susun (overlay) data citra sehingga diperoleh data perubahan garis pantai, serta pengamatan lapangan sebagai verifikasi hasil. Dari penelitian ini didapatkan bahwa rata-rata luas perubahan garis pantai Kota Bengkulu mengalami abrasi sebesar 19,41 hektar/tahun dan rata-rata luas perubahan garis pantai Kota Bengkulu yang mengalami sedimentasi sebesar 18,7 hektar/tahun. Adapun daerah yang mengalami perubahan garis pantai setiap tahunnya yaitu Muara Sungai Hitam, Muara Kualo, Muara Sungai Jenggalu dan Pelabuhan Pulau Baai. Perubahan Garis Pantai Kota Bengkulu dapat terjadi karena adanya faktor alamiah dan faktor manusia (Antropogenik).

PEMANFAATAN CITRA PENGINDERAN JAUH MULTI-TEMPORAL UNTUK DETEKSI URBAN HEAT ISLAND (UHI) TERHADAP PERUBAHAN PENGGUNAAN LAHAN DI KABUPATEN BULELENG

2020 ◽  
Vol 22 (2) ◽  
pp. 71
Author(s):  
A Sediyo Adi Nugraha ◽  
Dewa Made Atmaja
Keyword(s):  
Maximum Likelihood ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Landsat 8 ◽  
Heat Island ◽  
Landsat 8 Oli ◽  
Landsat 7 ◽  
Multi Temporal ◽  
Urban Heat

Fenomena <em>Urban Heat Island </em>(UHI) sering dipengaruhi oleh kepadatan penduduk dan perubahan penggunaan lahan. Perubahan tesebut memiliki hubungan dengan peningkatan suhu permukaan (<em>Land Surface Temperature</em>/LST) sebagai awal terjadinya UHI. Deteksi perubahan penggunaan lahan dan suhu permukaan dilakukan dari tahun 2000, 2010, dan 2018 pada daerah Kabupaten Buleleng dan berfokus pada Kecamatan Buleleng karena memiliki perubahan lahan terbangun lebih cepat dibandingkan kecamatan lain. Tujuannya untuk mengetahuii bagaimana fenomena UHI itu terjadi akibat dari perubahan penggunaan lahan. Selain itu, seberapa besar peningkatan suhu permukaan selama 18 tahun khususnya di Kecamatan Buleleng dengan mengetahui kondisi ditribusi dan intensitas UHI. Metode yang digunakan dalam deteksi UHI menggunakan citra penginderaan jauh multi-temporal yaitu citra Landsat 7 ETM+ dan citra Landsat 8 OLI/TIRS (<em>The Operational Land Imager and the Thermal Infrared Scanner</em>) sebagai data primer. Pengolahan data akan berfokus pada ekstraksi suhu permukaan dengan metode <em>Split-Windows Algorithm Sobrino </em>(SWA-S) untuk Landsat 8 dan metode <em>Brightness Temperature Emissivity Correction</em> untuk Landsat 7, kemudian <em>Maximum Likelihood</em> sebagai metode penggunaan lahan. Hasil pengolahan menunjukkan bahwa penggunaan metode yang berbeda memberikan dampak terhadap fenomena UHI. Perbedaan suhu selama 18 tahun sebesar sebesar ±5°C hal itu dipengaruhi dari kondisi awan dan bayangan. Perubahan penggunaan lahan dari tahun 2000 hingga 2018 terdapat peningkatan lahan terbangun di Kecamatan Buleleng dan peningkatan suhu permukan sebesar 2°-7°C dari lahan terbangun. Fenomena UHI untuk distribusi dan instensitas UHI terjadi di daerah pusat perkotaan dan kenaikan intensitas UHI sebesar 1.75°C. kesimpulannya bahwa perubahan lahan terbangun memberikan dampak kenaikan suhu permukaan dan menyebabkan fenomena UHI.

Monitor Mangrove Forest Dynamics from Multi-temporal Landsat 8-OLI Images in the Southern Coast of Sancti Spíritus Province (Cuba)

2021 ◽  
pp. 169-182
Author(s):  
Ernesto Marcheggiani ◽  
Andrea Galli ◽  
Osmany Ceballo Melendres ◽  
Ben Somers ◽  
Julio P. García-Lahera ◽  
...  
Keyword(s):  
Forest Dynamics ◽  
Mangrove Forest ◽  
Landsat 8 ◽  
Southern Coast ◽  
Landsat 8 Oli ◽  
Multi Temporal

scholarly journals Mapping Winter Wheat with Combinations of Temporally Aggregated Sentinel-2 and Landsat-8 Data in Shandong Province, China

2020 ◽  
Vol 12 (12) ◽  
pp. 2065 ◽  
Author(s):  
Feng Xu ◽  
Zhaofu Li ◽  
Shuyu Zhang ◽  
Naitao Huang ◽  
Zongyao Quan ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Google Earth ◽  
Shandong Province ◽  
Sensor Data ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Multi Temporal ◽  
Wheat Development ◽  
Development Phases ◽  
Sentinel 2

Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer’s and user’s accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 data were combined. As our results also revealed, it was always good to classify multi-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase’s and reviving phase’s data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical satellite data (Landsat-8 OLI and Sentinel-2 MSI) and prioritize key winter wheat development phases for accurate mapping winter wheat planting areas across China and elsewhere.

scholarly journals DINÂMICA DO VOLUME, BIOMASSA E CARBONO NA MATA ATLÂNTICA POR FERRAMENTA DE DETECÇÃO DE MUDANÇAS

Nativa ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 437
Author(s):  
Ayrton Machado ◽  
Ana Paula Marques Martins ◽  
Carlos Roberto Sanquetta ◽  
Ana Paula Dalla Corte ◽  
Jaime Wojciechowski ◽  
...  
Keyword(s):  
River Basin ◽  
Atlantic Forest ◽  
Landsat 8 ◽  
South American ◽  
Landsat 8 Oli ◽  
Mata Atlântica ◽  
Landsat 7 ◽  
Mata Atlantica ◽  
Year 2000 ◽  
Iguaçu River

A Mata Atlântica é reconhecida internacionalmente como uma das maiores e mais importantes florestas tropicais do continente sul-americano e além de sua importância para a biodiversidade, esse Bioma exerce importante função no ciclo de carbono. O objetivo deste trabalho foi desenvolver e aplicar uma rotina de detecção de mudanças dos estoques de volume, biomassa e carbono de 2000 a 2015 na Bacia do Rio Iguaçu, Estado do Paraná. Foram utilizadas imagens Landsat-7 ETM+ para o ano 2000 e Landsat-8 OLI para o ano de 2015 totalizando dez cenas para cada período. Foi desenvolvido uma rotina em Python e implementado no Software ArcGIS 10.4 para realizar a automatização de um processo de cálculo de estimativa de volume, biomassa e carbono para os remanescentes de vegetação natural. Houve acréscimo de 15,21% em volume, 14,95% em biomassa, 14,96% em carbono não considerando os estágios sucessionais nem subdivisão por fitofisionomia na bacia do Rio Iguaçu.  Desta forma, concluiu-se que a região de estudo está colaborando de forma positiva para a remoção de dióxido de carbono da atmosfera.Palavras-chave: bacia do rio Iguaçu; mudanças climáticas; sequestro de carbono. DYNAMICS OF VOLUME, BIOMASS AND CARBON IN THE ATLANTIC FOREST BY A CHANGE DETECTION TOOL ABSTRACT: The Atlantic Forest is recognized internationally as one of the largest and most important tropical forests in the South American continent and besides its importance for biodiversity, this biome plays important role in the carbon cycle. The objective of this work was to develop and apply a routine of detection of changes in volume, biomass and carbon stocks from 2000 to 2015 in the Iguaçu River Basin, State of Paraná. They were used Landsat-7 ETM+ images for the year 2000 and Landsat-8 OLI images for the year 2015 totaling ten images for each period. A routine was developed in Python and implemented in ArcGIS 10.4 Software to perform the automation of a calculation process of volume, biomass and carbon estimation for the remnants of natural vegetation. There was an increase of 15.21% in volume, 14.95% in biomass, 14.96% in carbon, not considering successional stages nor subdivision by phytophysiognomy in the Iguaçu River basin. Thus concludes that the region of study is collaborating in a positive way for the removal of carbon dioxide from the atmosphere.Keywords: Iguaçu river basin; climate changes; carbon sequestration.

scholarly journals Comparison between Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) Assessment of Vegetation Indices

2017 ◽  
Vol 1 (2) ◽  
pp. 355-366
Author(s):  
E. O. Makinde ◽  
A. D. Obigha
Keyword(s):  
Statistical Analysis ◽  
Land Cover ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Thematic Mapper ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Water Index ◽  
Landsat 7 ◽  
Index 2

The Landsat system has contributed significantly to the understanding of the Earth observation for over forty years. Since May 2013, data from Landsat 8 has been available online for download, with substantial differences from its predecessors, having an extended number of spectral bands and narrower bandwidths. The objectives of this research were majorly to carry out a cross comparison analysis between vegetation indices derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) and also performed statistical analysis on the results derived from the vegetation indices. Also, this research carried out a change detection on four land cover classes present within the study area, as well as projected the land cover for year 2030. The methods applied in this research include, carrying out image classification on the Landsat imageries acquired between 1984 – 2016 to ascertain the changes in the land cover types, calculated the mean values of differenced vegetation indices derived from the four land covers between Landsat 7 ETM+ and Landsat 8 OLI. Statistical analysis involving regression and correlation analysis were also carried out on the vegetation indices derived between the two sensors, as well as scatter plot diagrams with linear regression equation and coefficients of determination (R2). The results showed no noticeable differences between Landsat 7 and Landsat 8 sensors, which demonstrates high similarities. This was observed because Global Environmental Monitoring Index (GEMI), Improved Modified Triangular Vegetation Index 2 (MTVI2), Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Leaf Area Index (LAI) and Land Surface Water Index (LSWI) had smaller standard deviations. However, Renormalized Difference Vegetation Index (RDVI), Anthocyanin Reflectance Index 1 (ARI1) and Anthocyanin Reflectance Index 2 (ARI2) performed relatively poorly because their standard deviations were high. the correlation analysis of the vegetation indices that both sensors had a very high linear correlation coefficient with R2 greater than 0.99. It was concluded from this research that Landsat 7 ETM+ and Landsat 8 OLI can be used as complimentary data.

scholarly journals ANALYSIS OF RELATIONSHIP BETWEEN URBAN HEAT ISLAND EFFECT AND LAND USE/COVER TYPE USING LANDSAT 7 ETM+ AND LANDSAT 8 OLI IMAGES

2016 ◽  
Vol XLI-B8 ◽  
pp. 821-828
Author(s):  
N. Aslan ◽  
D. Koc-San
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Vegetation Index ◽  
Irrigated Agriculture ◽  
Time Interval ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat Images ◽  
Study Results ◽  
Landsat 7

The main objectives of this study are (i) to calculate Land Surface Temperature (LST) from Landsat imageries, (ii) to determine the UHI effects from Landsat 7 ETM+ (June 5, 2001) and Landsat 8 OLI (June 17, 2014) imageries, (iii) to examine the relationship between LST and different Land Use/Land Cover (LU/LC) types for the years 2001 and 2014. The study is implemented in the central districts of Antalya. Initially, the brightness temperatures are retrieved and the LST values are calculated from Landsat thermal images. Then, the LU/LC maps are created from Landsat pan-sharpened images using Random Forest (RF) classifier. Normalized Difference Vegetation Index (NDVI) image, ASTER Global Digital Elevation Model (GDEM) and DMSP_OLS nighttime lights data are used as auxiliary data during the classification procedure. Finally, UHI effect is determined and the LST values are compared with LU/LC classes. The overall accuracies of RF classification results were computed higher than 88&thinsp;% for both Landsat images. During 13-year time interval, it was observed that the urban and industrial areas were increased significantly. Maximum LST values were detected for dry agriculture, urban, and bareland classes, while minimum LST values were detected for vegetation and irrigated agriculture classes. The UHI effect was computed as 5.6&thinsp;&deg;C for 2001 and 6.8&thinsp;&deg;C for 2014. The validity of the study results were assessed using MODIS/Terra LST and Emissivity data and it was found that there are high correlation between Landsat LST and MODIS LST data (r<sup>2</sup>&thinsp;=&thinsp;0.7 and r<sup>2</sup>&thinsp;=&thinsp;0.9 for 2001 and 2014, respectively).

scholarly journals Spatial Monitoring of Urban Development Direction using Landsat 7 ETM+ and Landsat 8 OLI/TIRS in Balikpapan City, Indonesia

2020 ◽  
Vol 9 (4) ◽  
pp. 2703-2711
Keyword(s):  
Urban Development ◽  
Urban Areas ◽  
Control Function ◽  
Temporal Analysis ◽  
Bare Soil ◽  
Landsat 8 ◽  
Vegetation Density ◽  
Landsat 8 Oli ◽  
Density Class ◽  
Landsat 7

The development of urban areas in the city of Balikpapan increases over time and is characterized by increasing population. The growth and development of urban areas needs to be monitored so that the control function on area spatial can be implemented. This research aims to determine the direction of urban areas and measure the density of the built-up as a leading indicator of the development of urban areas in Balikpapan. The method used in this study is the multispasio-temporal analysis of remote sensing data of Landsat 7 ETM+ and Landsat 8 OLI/TIRS which contain a combination of spectral transformation, classification supervised Maximum Likelihood, accuracy assessment and statistical analysis. The results showed the trend of urban development from 2001 to 2019 towards east and northeast with the highest built-up density located in the sub-district of Balikpapan Tengah by 82.07% and followed by the sub-district of Balikpapan Kota by 76.94%. The largest land conversion took place on the bare soil with low vegetation density class to be vegetation with the converted area of 7095.91 ha or approximately 14.10% followed by the bare soil with low vegetation density class to be built-up with the converted area of 5826.86 ha or about 11.58% of the total area of Balikpapan city during the period from 2001 to 2019. The accuracy of urban development map in 2001 reaches 92.39 % and the year 2019 reaches 95.69 %, while the accuracy of land cover map in 2001 reaches 85.57% and the year 2019 reaches 87.28 %.

scholarly journals Análise geoespacial do processo de desmatamento da Caatinga no município de Catolé do Rocha – PB

2015 ◽  
Vol 10 (2) ◽  
pp. 239-244
Author(s):  
R. A. Oliveira ◽  
J. S. Almeida ◽  
W. F. Melo ◽  
A. B. A. Andrade ◽  
W. F. Mello
Keyword(s):  
Temporal Analysis ◽  
Geospatial Analysis ◽  
Boolean Logic ◽  
Landsat 8 ◽  
Programming Environment ◽  
Average Performance ◽  
Analysis Process ◽  
Landsat 7 ◽  
Multi Temporal ◽  
Dense Coverage

Objetivou-se analisar o processo de desmatamento no município de Catolé do Rocha-PB. Com o presente trabalho estabelecemos uma análise multitemporal das mudanças no uso e ocupação do solo no município de Catolé do Rocha entre os anos de 2005 e 2013. Foram utilizadas imagens do sensor TM Landsat-7, ano 2005 Bandas 3, 4, 5, ponto 216 e órbita 064, e Landsat-8 Banda 8, ponto 216, órbita 064, (georreferenciada). No ano de 2013 as imagens foram corrigidas no Regeemy 0.2.43, e processadas, filtradas e classificadas no SPRING 5.2, a classificação foi efetuada pelo método pixel a pixel, foi obtido 9 amostras para cada classe, com desempenho médio acima de 90%. A imagem resultante da sobreposição dos planos de informação foi obtida por cruzamento usando lógica booleana, no ambiente de programação em LEGAL. A vegetação manteve os níveis degradação principalmente sobtre as regiões de classes (Caatinga Estépica Florestada Mantida e Caatinga Estépica Arborizada Mantida), representando respectivamente, 14,7% e 27,0%. Isso descreve os elevados níveis de degradação que as atividades impõem sobre a paisagem. Também pode-se observar nessas áreas, condições de raleamento da cobertura mais densa e aumento da cobertura menos densa. Caracterizando um aumento do processo de degradação da vegetação natural. Geospatial analysis process of deforestation Caatinga in the municipality of Catolé do Rocha – PBAbstract: This study aimed to analyze the process of deforestation in the municipality of Catolé do Rocha-PB. The present work established a multi-temporal analysis of changes in the use and occupation of land in the municipality Catolé do Rocha between the years 2005 and 2013. They were used sensor images TM Landsat-7, 2005 Bands 3, 4, 5, point 216 and orbit 064, and Landsat-8 Band 8, paragraph 216, orbiting 064 (georeferenced), 2013, The images were corrected in Regeemy 0.2.43, and processed, filtered and sorted in SPRING 5.2, the rating was performed by the pixel-by-pixel method, 9 samples was obtained for each class, with an average performance above 90%. The resulting overlay of information layers was obtained by crossing using Boolean logic, at Legal in programming environment. The vegetation degradation levels remained mainly on the classes of regions (Caatinga Caatinga and Maintained Forested Steppe Steppe Tree Maintained), representing respectively 14.7% and 27.0%. This describes the high levels of degradation that activities impose on the landscape. It can also be observed in these areas, thinning conditions of denser coverage and increased less dense coverage. Featuring an increased degradation of natural vegetation process.

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