Characterizing the May 2015 Karayaylak Glacier surge in the eastern Pamir Plateau using remote sensing

ABSTRACTWe investigate an internal surge of Karayaylak Glacier, which was reported by the media in May 2015. To differentiate the May 2015 glacier surge from other glacier advances, we surveyed changes in velocity, crevasses and glacier area using Landsat 8 OLI L1T, ZY-1-02C and Gaofen-1 images from October 2014 to July 2015. The velocity, measured by automatic feature extraction and tracking during the active phase, was 10–100 times the velocity during the quiescent phase, with a maximum of (20.2 ± 0.9) m d−1 (mean ± standard error) from 8 to 15 May 2015 in the west branch of the glacier. The surge initiation and termination took place from 13 April to 16 June 2015. Ice in the west branch (length, 7 km; area, 6.8 km2) of Karayaylak Glacier accelerated down to the east branch, leading to the development of crevasses and ice covering an additional 0.1 km2 of summer pasture on the northwestern side. However, we detected no advance of the glacier's terminus.

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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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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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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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Improved SRGAN for Remote Sensing Image Super-Resolution Across Locations and Sensors

Remote Sensing ◽

10.3390/rs12081263 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1263 ◽

Cited By ~ 1

Author(s):

Yingfei Xiong ◽

Shanxin Guo ◽

Jinsong Chen ◽

Xinping Deng ◽

Luyi Sun ◽

...

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Spatial Resolution ◽

Super Resolution ◽

Land Cover Classification ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Remote Sensing Images ◽

The Cross ◽

Model Training

Detailed and accurate information on the spatial variation of land cover and land use is a critical component of local ecology and environmental research. For these tasks, high spatial resolution images are required. Considering the trade-off between high spatial and high temporal resolution in remote sensing images, many learning-based models (e.g., Convolutional neural network, sparse coding, Bayesian network) have been established to improve the spatial resolution of coarse images in both the computer vision and remote sensing fields. However, data for training and testing in these learning-based methods are usually limited to a certain location and specific sensor, resulting in the limited ability to generalize the model across locations and sensors. Recently, generative adversarial nets (GANs), a new learning model from the deep learning field, show many advantages for capturing high-dimensional nonlinear features over large samples. In this study, we test whether the GAN method can improve the generalization ability across locations and sensors with some modification to accomplish the idea “training once, apply to everywhere and different sensors” for remote sensing images. This work is based on super-resolution generative adversarial nets (SRGANs), where we modify the loss function and the structure of the network of SRGANs and propose the improved SRGAN (ISRGAN), which makes model training more stable and enhances the generalization ability across locations and sensors. In the experiment, the training and testing data were collected from two sensors (Landsat 8 OLI and Chinese GF 1) from different locations (Guangdong and Xinjiang in China). For the cross-location test, the model was trained in Guangdong with the Chinese GF 1 (8 m) data to be tested with the GF 1 data in Xinjiang. For the cross-sensor test, the same model training in Guangdong with GF 1 was tested in Landsat 8 OLI images in Xinjiang. The proposed method was compared with the neighbor-embedding (NE) method, the sparse representation method (SCSR), and the SRGAN. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were chosen for the quantitive assessment. The results showed that the ISRGAN is superior to the NE (PSNR: 30.999, SSIM: 0.944) and SCSR (PSNR: 29.423, SSIM: 0.876) methods, and the SRGAN (PSNR: 31.378, SSIM: 0.952), with the PSNR = 35.816 and SSIM = 0.988 in the cross-location test. A similar result was seen in the cross-sensor test. The ISRGAN had the best result (PSNR: 38.092, SSIM: 0.988) compared to the NE (PSNR: 35.000, SSIM: 0.982) and SCSR (PSNR: 33.639, SSIM: 0.965) methods, and the SRGAN (PSNR: 32.820, SSIM: 0.949). Meanwhile, we also tested the accuracy improvement for land cover classification before and after super-resolution by the ISRGAN. The results show that the accuracy of land cover classification after super-resolution was significantly improved, in particular, the impervious surface class (the road and buildings with high-resolution texture) improved by 15%.

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Estimation of PM10 Distribution using Landsat5 and Landsat8 Remote Sensing

Proceedings ◽

10.3390/proceedings2231430 ◽

2018 ◽

Vol 2 (23) ◽

pp. 1430

Author(s):

V. M. Fernández-Pacheco ◽

C. A. López-Sánchez ◽

E. Álvarez-Álvarez ◽

M. J. Suárez López ◽

L. García-Expósito ◽

...

Keyword(s):

Remote Sensing ◽

Air Pollutants ◽

Satellite Images ◽

Satellite Image ◽

Remote Sensing Image ◽

Regional Studies ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Landsat 5 Tm ◽

Source Of Information

Air pollution is one of the major environmental problems, especially in industrial and highly populated areas. Remote sensing image is a rich source of information with many uses. This paper is focused on estimation of air pollutants using Landsat-5 TM and Landsat-8 OLI satellite images. Particulate Matter with particle size less than 10 microns (PM10) is estimated for the study area of Principado de Asturias (Spain). When a satellite records the radiance of the surface received at sensor, does not represent the true radiance of the surface. A noise caused by Aerosol and Particulate Matters attenuate that radiance. In many applications of remote sensing, that noise called path radiance is removed during pre-processing. Instead, path radiance was used to estimate the PM10 concentration in the air. A relationship between the path radiance and PM10 measurements from ground stations has been established using Random Forest (RF) algorithm and a PM10 map was generated for the study area. The results show that PM10 estimation through satellite image is an efficient technique and it is suitable for local and regional studies.

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Thermal Remote Sensing: A tool to Determine Temporal Land Surface Temperature in Hawassa City, Ethiopia

10.21203/rs.3.rs-63046/v1 ◽

2020 ◽

Author(s):

Mikias Biazen Molla

Keyword(s):

Remote Sensing ◽

Standard Deviation ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Urban Expansion ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Thermal Remote Sensing ◽

The City

Abstract This investigation was conducted for the estimation of the temporal land surface temperature value using thermal remote sensing of Landsat-8 (OLI) Data in Hawassa City Administration, Ethiopia. Satellite datasets of Landsat-7 (ETM+) for 22nd March 2002 and Landsat-8 (OLI) of 22nd March 2019 were taken for this study. Different algorisms were used to estimate the Normalized Difference Vegetation Index threshold from the Red and Near-Infrared band and the ground earth's surface emissivity esteem is legitimately recovered from the thermal infrared by coordinating with the outcome got from MODIS information. The land use land cover map of the city was prepared with better accuracy using the on-screen classification technique. The spatial distribution of surface temperature of the city range from 6.62°C to 22.54°C with a mean of 14.58°C and a standard deviation of 11.25 in the year of march 22nd 2002. The LST result derived from Landsat 8 for March 22nd, 2019, ranges from 11.97°C to 35.5°C with a mean of 23.735 °C and a standard deviation of 16.64. In both years the higher LST values correspond to built-up/settlement and bare/open lands of the city; whereas, lower LST values were observed in vegetation (trees/woodlot, shrubs, and grass forested) area. Urban expansion (built-up area roads, and another impervious surface), decline in vegetation levels due to deforestation and increasing population density. Increasing an evergreen tree and green space coverage, design and develop city parks and rehabilitate the existing degraded natural environments are among the recommended strategy to reduce the rate of LST.

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