Exploring the relationship between Landsat-8/OLI remote sensing reflectance and optically active components in the surface water at the UHE Maua/PR

Remote sensing techniques have been widely used to measure the qualitative parameters of waterbodies. Total suspended solid (TSS) is an important water quality parameter and a surrogate for the water clarity. It can be used as the indicator of sediment in the reservoir, which usually consists of silt, fine sand and microorganisms. This study aimed to utilize the remote sensing technology, in particular Landsat 8 Operational Land Imager (Landsat 8 OLI), to determine the amount of TSS concentration as well as the spatial distribution of TSS concentration in the surface water of the Tri An reservoir. The relationship between field TSS data collected in March, 2020 and the reflectance values of the the Landsat 8 Oli images was investigated. Results showed that there was a strong linear relationshiop between TSS concentration and the reflectance of the red and near infrared reflectance bands from the Landsat 8 Oli (r ranged from 0.58–0.93), in which the ratio of the red band produced the best correlation with the TSS (r = 0.93, with a standard error of 0.6–1.39 mg/L). Based on the linear regression equation, the TSS concentration calculated from the red reflectance values was used for mapping the spatial distribution of TSS in the surface water of the Tri An reservoir. Our results confirmed the accuracy and potential of using the single band from Landsat 8 OLI for mapping the spatial distribution of TSS in the Tri An reservoir.

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Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images

Remote Sensing ◽

10.3390/rs12162587 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2587

Author(s):

Yan Nie ◽

Ying Tan ◽

Yuqin Deng ◽

Jing Yu

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Arid Regions ◽

Drought Index ◽

Dynamic Monitoring ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Rapid Acquisition ◽

Semi Arid ◽

Soil Moisture Inversion

As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.

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Analysis of Landsat-8 OLI imagery for land surface water mapping

Remote Sensing Letters ◽

10.1080/2150704x.2014.960606 ◽

2014 ◽

Vol 5 (7) ◽

pp. 672-681 ◽

Cited By ~ 84

Author(s):

Zhiqiang Du ◽

Wenbo Li ◽

Dongbo Zhou ◽

Liqiao Tian ◽

Feng Ling ◽

...

Keyword(s):

Surface Water ◽

Land Surface ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Land Surface Water ◽

Water Mapping

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FOREST DISTURBANCE ANALYSIS WITH LANDSAT-8 OLI DATA RELATED TO A PARAMETRIC WIND FIELD: A CASE STUDY FOR TYPHOON RAMMASUN (201409)

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-1629-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 1629-1633

Author(s):

C. Tan ◽

W. Fang

Keyword(s):

Remote Sensing ◽

Wind Field ◽

Forest Disturbance ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Dependence Relation ◽

Wind Field Model ◽

Wind Speeds ◽

Sensing Technology ◽

And Function

Forest disturbance induced by tropical cyclone often has significant and profound effects on the structure and function of forest ecosystem. Detection and analysis of post-disaster forest disturbance based on remote sensing technology has been widely applied. At present, it is necessary to conduct further quantitative analysis of the magnitude of forest disturbance with the intensity of typhoon. In this study, taking the case of super typhoon Rammasun (201409), we analysed the sensitivity of four common used remote sensing indices and explored the relationship between remote sensing index and corresponding wind speeds based on pre-and post- Landsat-8 OLI (Operational Land Imager) images and a parameterized wind field model. The results proved that NBR is the most sensitive index for the detection of forest disturbance induced by Typhoon Rammasun and the variation of NBR has a significant linear dependence relation with the simulated 3-second gust wind speed.

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MANEJO, QUALIDADE E DINÂMICA DA DEGRADAÇÃO DE PASTAGENS NA MATA ATLÂNTICA DE MINAS GERAIS - BRASIL

Nativa ◽

10.31413/nativa.v6i4.5542 ◽

2018 ◽

Vol 6 (4) ◽

pp. 370 ◽

Cited By ~ 1

Author(s):

Luís Flávio Pereira ◽

Cecilia Fátima Carlos Ferreira ◽

Ricardo Morato Fiúza Guimarães

Keyword(s):

Remote Sensing ◽

Atlantic Forest ◽

Quality Index ◽

Minas Gerais ◽

Google Earth ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Mata Atlântica ◽

Mata Atlantica ◽

Forest Biome

Pastagens sob práticas de manejo ineficientes tornam-se degradadas, provocando sérios problemas socioambientais e econômicos. Assim, entender a dinâmica dos sistemas pastoris e suas interações com o meio físico torna-se essencial na busca de alternativas sustentáveis para a agropecuária. Estudou-se manejo, dinâmica anual e interações socioambientais em pastagens de uma bacia hidrográfica no bioma Mata Atlântica em Minas Gerais, Brasil, durante o ano hidrológico 2016/2017. Utilizou-se dados de campo, relatos de agricultores e sensoriamento remoto via imagens LANDSAT 8 OLI e Google Earth Pro®. Foi proposto um índice de qualidade para pastagens da região. As pastagens apresentaram, em média, qualidade moderada. Níveis de degradação foram altos, oscilando de forma quadrática (níveis 2, 4, 5 e IDP) e potencial (nível 1) com a precipitação (p < 0,01), o que sugere que a irrigação possa ser prática eficiente no controle da degradação. Durante o ano, pelo menos 51,27% das pastagens apresentaram algum sinal de degradação, atingindo-se a marca de 91,32%, no período seco. Os resultados sugerem pior qualidade e maiores níveis de degradação de pastagens em terras elevadas e declivosas. Devido às condições socioambientais locais, indica-se o uso de sistemas silvipastoris agroecológicos no manejo das pastagens.Palavras-chave: uso da terra, sensoriamento remoto, relação solo paisagem, Zona da Mata, índice de qualidade. MANAGEMENT, QUALITY AND DEGRADATION DYNAMICS OF PASTURES IN ATLANTIC FOREST BIOME, MINAS GERAIS – BRASIL ABSTRACT:Pastures under inefficient management practices get degraded, leading to serious socioeconomic and environmental issues. That being said, understanding the dynamics of such systems and their interaction with the environment is essential when it comes to looking towards sustainable alternatives for livestock activities. The management, annual dynamics and socio-environmental interactions in pastures in an hydrographic basin located in Atlantic Forest biome, Minas Gerais, Brasil, were studied during the hydrological year of 2016/2017. Field data and farmers reports were utilized, such as remote sensing via images from LANDSAT 8 OLI and Google Earth Pro®. A quality index was proposed for the pastures, which usually presented medium quality. Degradation levels were high, oscillating in a quadratic basis (levels 2, 4, 5 and IDP) and potential (level 1) with precipitation (p < 0,01), which suggests that irrigation might be an efficient practice when it comes to degradation control. During the year, at least 51,27% of pastures have presented signs of degradation, achieving 91,32% in dry periods. The results suggest less quality and bigger degradation levels in pastures located in high and steep areas. Considering the local environmental conditions, agroecological silvopasture systems are recommended regarding the pastures management.Keywords: land use, remote sensing, soil/landscape relationships, Zona da Mata, quality index.

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Water stress coefficient determined by orbital remote sensing techniques

Revista Brasileira de Engenharia Agrícola e Ambiental ◽

10.1590/1807-1929/agriambi.v24n12p847-853 ◽

2020 ◽

Vol 24 (12) ◽

pp. 847-853

Author(s):

Élvis da S. Alves ◽

Roberto Filgueiras ◽

Lineu N. Rodrigues ◽

Fernando F. da Cunha ◽

Catariny C. Aleman

Keyword(s):

Remote Sensing ◽

Water Stress ◽

Irrigation Management ◽

Simple Algorithm ◽

Landsat 8 ◽

Stress Coefficient ◽

Use Efficiency ◽

Remote Sensing Techniques ◽

Water Stress Coefficient ◽

The Relationship

ABSTRACT In regions where the irrigated area is increasing and water availability is reduced, such as the West of the Bahia state, Brazil, the use of techniques that contribute to improving water use efficiency is paramount. One of the ways to improve irrigation is by improving the calculation of actual evapotranspiration (ETa), which among other factors is influenced by soil drying, so it is important to understand this relationship, which is usually accounted for in irrigation management models through the water stress coefficient (Ks). This study aimed to estimate the water stress coefficient (Ks) through information obtained via remote sensing, combined with field data. For this, a study was carried out in the municipality of São Desidério, an area located in western Bahia, using images of the Landsat-8 satellite. Ks was calculated by the relationship between crop evapotranspiration and ETa, calculated by the Simple Algorithm for Evapotranspiration Retrieving (SAFER). The Ks estimated by remote sensing showed, for the development and medium stages, average errors on the order of 5.50%. In the final stage of maize development, the errors obtained were of 23.2%.

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A robust Multi-Band Water Index (MBWI) for automated extraction of surface water from Landsat 8 OLI imagery

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2018.01.018 ◽

2018 ◽

Vol 68 ◽

pp. 73-91 ◽

Cited By ~ 24

Author(s):

Xiaobiao Wang ◽

Shunping Xie ◽

Xueliang Zhang ◽

Cheng Chen ◽

Hao Guo ◽

...

Keyword(s):

Surface Water ◽

Landsat 8 ◽

Automated Extraction ◽

Landsat 8 Oli ◽

Water Index ◽

Multi Band

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Penentuan Luas Area Sawah Kabupaten Sleman Pada Citra Landsat 8 Operational Land Imager (OLI)

Respati ◽

10.35842/jtir.v13i3.253 ◽

2018 ◽

Vol 13 (3) ◽

Author(s):

Sulidar Fitri ◽

Novi Nurjanah

Keyword(s):

Remote Sensing ◽

Land Use ◽

Support Vector Machine ◽

Rice Field ◽

Image Data ◽

Rice Fields ◽

Support Vector ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Learning Machine

INTISARITeknologi penginderaan jauh sangat baik dijadikan data pembuatan peta penggunaan lahan, karena kebutuhan pemetaan semakin tinggi terutama untuk mendeteksi perubahan penggunaan lahan terutama untuk penentuan luas area khususnya sawah di kabupaten Sleman. Untuk mendapatkan informasi luasan area sawah dari interpretasi citra landsat-8 OLI (Operational Land Imager) diperlukan metode khusus, terutama untuk pengolahan data citra penginderaan jauh secara digital. Salah satu metode pengolahan citra penginderaan jauh adalah metode Support Vector Machine (SVM). Metode SVM merupakan metode learning machine (Pembelajaran mesin) yang dapat mengklasifikasikan pola serta mengenali pola dari inputan atau contoh data yang diberikan dan juga termasuk ke dalam supervised learning. Hasil area sawah yang didapati dari citra Landsat 8 OLI dengan pengolahan metode SVM didapati berada di 18 kecamatan dala Kabupaten Sleman. Luasan tertinggi ada di kecamatan Ngaglik dengan 19,78 KM2 dan terendah di kecamatan Turi seluas 2,14 KM2. Nilai keseluruhan akurasi yang didapat untuk kelas lahan sawah dan area non sawah adalah adalah 53%.Kata kunci— Landsat-8 OLI, SVM, Data Citra, Geospasial, Luas Area Sawah ABSTRACTRemote sensing technology is very well used as a data for making land use maps, because mapping needs are increasingly high especially for detecting land use changes, especially for determining the area, especially rice fields in Sleman district. To get information about the area of the rice fields from the interpretation of Landsat-8 OLI (Operational Land Imager), special methods are needed, especially for processing remote sensing image data digitally. One method of processing remote sensing images is the Support Vector Machine (SVM) method. The SVM method is a learning machine method that can classify patterns and recognize patterns from input or sample data provided and also includes supervised learning. The results of the rice field that were found from the Landsat 8 OLI image by processing the SVM method were found in 18 sub-districts in Sleman Regency. The highest area is in Ngaglik sub-district with 19.78 KM2 and the lowest in Turi sub-district is 2.14 KM2. The overall value of the accuracy obtained for the class of rice field and non-rice field is 53%.Kata kunci— Landsat-8 OLI, SVM, Image Data, Geospatial, Area of Rice Fields

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Application of optical and radar data for lineaments mapping in Kerdous inlier of the Anti Atlas belt, Morocco

10.5194/egusphere-egu21-7895 ◽

2021 ◽

Author(s):

Amine Jellouli ◽

Abderrazak El Harti ◽

Zakaria Adiri ◽

Mohcine Chakouri ◽

Jaouad El Hachimi ◽

...

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Radar Data ◽

Ore Deposits ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Alos Palsar ◽

Geological Map ◽

Color Composite ◽

Manual Extraction

Lineament mapping is an important step for lithological and hydrothermal alterations mapping. It is considered as an efficient research task which can be a part of structural investigation and mineral ore deposits identification. The availability of optical as well as radar remote sensing data, such as Landsat 8 OLI, Terra ASTER and ALOS PALSAR data, allows lineaments mapping at regional and national scale. The accuracy of the obtained results depends strongly on the spatial and spectral resolution of the data. The aim of this study was to compare Landsat 8 OLI, Terra ASTER, and radar ALOS PALSAR satellite data for automatic and manual lineaments extraction. The module Line of PCI Geomatica software was applied on PC1 OLI, PC3 ASTER and HH and HV polarization images to automatically extract geological lineaments. However, the manual extraction was achieved using the RGB color composite of the directional filtered images N - S (0&#176;), NE - SW (45&#176;) and E - W (90&#176;) of the OLI panchromatic band 8. The obtained lineaments from automatic and manual extraction were compared against the faults and photo-geological lineaments digitized from the existing geological map of the study area. The extracted lineaments from PC1 OLI and ALOS PALSAR polarizations images showed the best correlation with faults and photo-geological lineaments. The results indicate that the lineaments extracted from HH and HV polarizations of ALOS PALSAR radar data used in this study, with 1499 and 1507 extracted lineaments, were more efficient for structural lineament mapping, as well as the PC1 OLI image with 1057 lineaments.Keywords Remote Sensing . OLI. ALOS PALSAR . ASTER . Kerdous Inlier . Anti Atlas

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