scholarly journals Remote Sensing of Turbidity in the Tennessee River Using Landsat 8 Satellite

2021 ◽  
Vol 13 (18) ◽  
pp. 3785
Author(s):  
A. K. M. Azad Hossain ◽  
Caleb Mathias ◽  
Richard Blanton
Keyword(s):  
Remote Sensing ◽  
Accuracy Assessment ◽  
The United States ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Water Runoff ◽  
Estimation Model ◽  
Tennessee River ◽  
The World ◽  
Remote Estimation

The Tennessee River in the United States is one of the most ecologically distinct rivers in the world and serves as a great resource for local residents. However, it is also one of the most polluted rivers in the world, and a leading cause of this pollution is storm water runoff. Satellite remote sensing technology, which has been used successfully to study surface water quality parameters for many years, could be very useful to study and monitor the quality of water in the Tennessee River. This study developed a numerical turbidity estimation model for the Tennessee River and its tributaries in Southeast Tennessee using Landsat 8 satellite imagery coupled with near real-time in situ measurements. The obtained results suggest that a nonlinear regression-based numerical model can be developed using Band 4 (red) surface reflectance values of the Landsat 8 OLI sensor to estimate turbidity in these water bodies with the potential of high accuracy. The accuracy assessment of the estimated turbidity achieved a coefficient of determination (R2) value and root mean square error (RMSE) as high as 0.97 and 1.41 NTU, respectively. The model was also tested on imagery acquired on a different date to assess its potential for routine remote estimation of turbidity and produced encouraging results with R2 value of 0.94 and relatively high RMSE.

scholarly journals Retrieval and Evaluation of Chlorophyll-a Concentration in Reservoirs with Main Water Supply Function in Beijing, China, Based on Landsat Satellite Images

2021 ◽  
Vol 18 (9) ◽  
pp. 4419
Author(s):  
Yuequn Lai ◽  
Jing Zhang ◽  
Yongyu Song ◽  
Zhaoning Gong
Keyword(s):  
Remote Sensing ◽  
Water Supply ◽  
Chlorophyll A ◽  
Phosphorus Concentration ◽  
Landsat 8 ◽  
Supply Function ◽  
Estimation Model ◽  
Nitrogen And Phosphorus ◽  
Chlorophyll A Concentration ◽  
Guanting Reservoir

Remote sensing retrieval is an important technology for studying water eutrophication. In this study, Guanting Reservoir with the main water supply function of Beijing was selected as the research object. Based on the measured data in 2016, 2017, and 2019, and Landsat-8 remote sensing images, the concentration and distribution of chlorophyll-a in the Guanting Reservoir were inversed. We analyzed the changes in chlorophyll-a concentration of the reservoir in Beijing and the reasons and effects. Although the concentration of chlorophyll-a in the Guanting Reservoir decreased gradually, it may still increase. The amount and stability of water storage, chlorophyll-a concentration of the supply water, and nitrogen and phosphorus concentration change are important factors affecting the chlorophyll-a concentration of the reservoir. We also found a strong correlation between the pixel values of adjacent reservoirs in the same image, so the chlorophyll-a estimation model can be applied to each other.

scholarly journals Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data

2020 ◽  
Vol 12 (10) ◽  
pp. 1690 ◽  
Author(s):  
Tianyu Hu ◽  
YingYing Zhang ◽  
Yanjun Su ◽  
Yi Zheng ◽  
Guanghui Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Aboveground Biomass ◽  
Mangrove Forest ◽  
Remote Sensing Data ◽  
Dimensional Structure ◽  
Coefficient Of Determination ◽  
Free Access ◽  
Mangrove Forests ◽  
Estimation Model ◽  
Mangrove Biomass

Mangrove forest ecosystems are distributed at the land–sea interface in tropical and subtropical regions and play an important role in carbon cycles and biodiversity. Accurately mapping global mangrove aboveground biomass (AGB) will help us understand how mangrove ecosystems are affected by the impacts of climatic change and human activities. Light detection and ranging (LiDAR) techniques have been proven to accurately capture the three-dimensional structure of mangroves and LiDAR can estimate forest AGB with high accuracy. In this study, we produced a global mangrove forest AGB map for 2004 at a 250-m resolution by combining ground inventory data, spaceborne LiDAR, optical imagery, climate surfaces, and topographic data with random forest, a machine learning method. From the published literature and free-access datasets of mangrove biomass, we selected 342 surface observations to train and validate the mangrove AGB estimation model. Our global mangrove AGB map showed that average global mangrove AGB density was 115.23 Mg/ha, with a standard deviation of 48.89 Mg/ha. Total global AGB storage within mangrove forests was 1.52 Pg. Cross-validation with observed data demonstrated that our mangrove AGB estimates were reliable. The adjusted coefficient of determination (R2) and root-mean-square error (RMSE) were 0.48 and 75.85 Mg/ha, respectively. Our estimated global mangrove AGB storage was similar to that predicted by previous remote sensing methods, and remote sensing approaches can overcome overestimates from climate-based models. This new biomass map provides information that can help us understand the global mangrove distribution, while also serving as a baseline to monitor trends in global mangrove biomass.

Remote sensing flow velocity of debris-covered glaciers using Landsat 8 data

2015 ◽  
Vol 40 (2) ◽  
pp. 305-321 ◽  
Author(s):  
Lydia Sam ◽  
Anshuman Bhardwaj ◽  
Shaktiman Singh ◽  
Rajesh Kumar
Keyword(s):  
Remote Sensing ◽  
Flow Velocity ◽  
Accuracy Assessment ◽  
Geographical Area ◽  
Surface Flow ◽  
Velocity Estimation ◽  
Surface Velocity ◽  
Landsat 8 ◽  
Glacier Surface ◽  
Glacier Velocity

Changes in ice velocity of a glacier regulate its mass balance and dynamics. The estimation of glacier flow velocity is therefore an important aspect of temporal glacier monitoring. The utilisation of conventional ground-based techniques for detecting glacier surface flow velocity in the rugged and alpine Himalayan terrain is extremely difficult. Remote sensing-based techniques can provide such observations on a regular basis for a large geographical area. Obtaining freely available high quality remote sensing data for the Himalayan regions is challenging. In the present work, we adopted a differential band composite approach, for the first time, in order to estimate glacier surface velocity for non-debris and supraglacial debris covered areas of a glacier, separately. We employed various bandwidths of the Landsat 8 data for velocity estimation using the COSI-Corr (co-registration of optically sensed images and correlation) tool. We performed the accuracy assessment with respect to field measurements for two glaciers in the Indian Himalaya. The panchromatic band worked best for non-debris parts of the glaciers while band 6 (SWIR – short wave infrared) performed best in case of debris cover. We correlated six temporal Landsat 8 scenes in order to ensure the performance of the proposed algorithm on monthly as well as yearly timescales. We identified sources of error and generated a final velocity map along with the flow lines. Over- and underestimates of the yearly glacier velocity were found to be more in the case of slow moving areas with annual displacements less than 5 m. Landsat 8 has great capabilities for such velocity estimation work for a large geographic extent because of its global coverage, improved spectral and radiometric resolutions, free availability and considerable revisit time.

scholarly journals Space-temporal evaluation of biophysical parameters in the High Ipanema watershed by remote sensing

2018 ◽  
Vol 7 (6) ◽  
pp. 357
Author(s):  
Jose Diorgenes Alves Oliveira ◽  
Biancca Correia De Medeiros ◽  
Jhon Lennon Bezerra Da Silva ◽  
Geber Barbosa De Albuquerque Moura ◽  
Frederico Abraão Costa Lins ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Environmental Changes ◽  
The United States ◽  
Semiarid Region ◽  
United States Geological Survey ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Biophysical Parameters ◽  
Environmental Implications

The High Ipanema watershed is located in a semiarid region and because of this, becomes more vulnerable and susceptible to the effects of environmental changes and the degradation process, it has serious economic and socio-environmental implications. In recent years with the advancement of remote sensing based on satellite imagery or other platforms, it has become possible to monitor different and large areas of the various biomes in the world. The objective of this study was to identify changes in the vegetation cover conditions in the Alto Ipanema watershed, using spectral analyzes of Landsat-8 OLI / TIRS satellite images, using remote sensing techniques. Landsat-8 OLI / TIRS satellite images were obtained from the United States Geological Survey – USGS, on 10/12/2013, 14/01/2015 and 12/08/2016, where they were processed from ERDAS IMAGINE® Software, version 9.1. The thematic maps of biophysical parameters were processed by ArcGis® 10.2.2 Software. With the biophysical parameters analyzed, it was found that the northwest portion of the watershed presents a considerable area of exposed soils with indication of a high degree of susceptibility to degradation and that the biophysical parameters evaluated by the SEBAL algorithm are efficient in understanding the dynamics of spatial and temporal areas of semiarid environments.

scholarly journals ENVIRONMENTAL IMPACT OF RAVINE DEGRADATION ON SOCIO-ECONOMIC DEVELOPMENT IN NSUKARA OFFOT IN UYO LGA, AKWA IBOM STATE

2015 ◽  
pp. 209-220
Author(s):  
MO Okereke ◽  
AK Uchua ◽  
JJ Essien ◽  
JE Ezugwu
Keyword(s):  
Economic Development ◽  
Near Infrared ◽  
Research Work ◽  
Gully Erosion ◽  
Morphological Evidence ◽  
Landsat 8 ◽  
Water Runoff ◽  
The World ◽  
Socio Economic Development ◽  
The Impact

Ravine degradation is one of the major environmental threats throughout the world and affects multiple soils and land functions. There is ample physical evidence of severe gully erosion occurring in different parts of the world. Gullies are one of the few sources of morphological evidence in the landscape showing the intensity of soil erosion in the area, reflecting the impact of environmental change (especially due to interactions between geomorphological features, changes in land use, and extreme climatic events). The impacts of ravine erosion in Akwa Ibom State, Uyo in particular are enormous and are still creating a lot of menace ranging from loss of farmlands and properties, a threat to vegetation, effect on life among others. This paper discusses the use of space-based techniques to assess the impact of ravine erosion and its effects on socio-economic development of Nsukara Offot in Uyo L.G.A. In the study, the shapefile of Uyo was used to clip the study area from Landsat 8 (2018) in a GIS environment, the extracted images were processed using ArcGIS 10.4, Likelihood classification was carried out for 3 spectral bands corresponding to Band 5, Band 4 and Band 3 combination Near infrared, red, and green (NIR, R, G). Spot 5 Image was also used to identify the interesting features in the area that is valuable for this research work. The features digitized were Built-up areas, Ravine, and Roads. The ArcGIS software version 10.4 was used to buffer the distance from the ravine to the different structures, other facilities, and one of the major roads in the area. Results from the field observations and measurements showed that the width of the ravine is 8m, depth is 13m while the length is about 100m. The distance of the ravine from the nearest building which is Ray Field International Secondary School, Uyo is 10m away from the school gate and 5m from the road. This shows a rapid encroachment to the facility and poses a hazard to both human and infrastructure. The study shows that the ravine occurred as a result of inappropriate channeling of water runoff in unprotected land thereby washing away the soil along the drainage line. GIS is a valuable tool in monitoring morphology while the results of the study can be used for planning for further monitoring, gully erosion control, and management.

scholarly journals CLASSIFICATION AND ASSESSMENT OF THE LAND USE – LAND COVER CHANGES IN JODHPUR CITY USING REMOTE SENSING TECHNOLOGIES

2018 ◽  
Vol XLII-5 ◽  
pp. 767-771 ◽  
Author(s):  
M. A. Saharan ◽  
N. Vyas ◽  
S. L. Borana ◽  
S. K. Yadav
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Assessment Method ◽  
Land Cover Changes ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Rock Outcrops ◽  
Barren Land

<p><strong>Abstract.</strong> Land Use – Land Cover (LULC) classification mapping is an important tool for management of natural resources of an area. The remote sensing technology in recent times has been used in monitoring the changing patterns of land use-land cover. The aim of the study is to monitor the LULC changes in Jodhpur city over the period 1990–2018. Satellite imagery of Landsat 8 OLI (June, 2018) &amp;amp; Landsat TM (Oct, 1990) were used for classification analysis. Supervised classification-maximum likelihood algorithm is used in ENVI software to detect land use land cover changes. Five LULC categories were used, namely- urban area, mining area, vegetation, water bodies and other area (Rock outcrops and barren land). The LULC classified maps of two different periods i.e. 2018 and 1990 were generated on 1<span class="thinspace"></span>:<span class="thinspace"></span>50,000 scale. The accuracy assessment method was used to measure the accuracy of classified maps. This study shall be of good assistance to the town planners of Jodhpur city for the purpose of the sustainable development as per the master plan 2031.</p>

scholarly journals Fertility and Ecology Assessment of 100 Year Old Rare Indigenous Spider Web Shape Paddy Field in Semiarid Ecosystem of Flores Island Using NDVI

2020 ◽  
Author(s):  
Andri Wibowo
Keyword(s):  
Remote Sensing ◽  
Indigenous People ◽  
Paddy Field ◽  
Landsat 8 ◽  
Rectangular Shape ◽  
Spider Web ◽  
Semiarid Environment ◽  
Circular Field ◽  
The World ◽  
Semiarid Ecosystem

Paddy field is an old agriculture practice that very common especially in Asia. The earliest paddy field found dated back to 4330 BC. Most paddy fields in the world are having rectangular shapes. Whereas, in Flores island, indigenous people have developed a spider web or circular paddy field instead of regular rectangular shape and this driven by culture and local wisdom. In here, the objectives of this study are to assess the characteristic, ecology and fertility of circular paddy field compared to common rectangular shape. Fertility values were assessed using Landsat 8 remote sensing with RGB combination of NIR, SWIR 1 and blue. The study site was paddy field within Flores island. The result shows that spider web paddy field appeared in many sizes, number, altitude, ecosystem and terrain. Remote sensing result confirms that the fertility of circular paddy field is similar to the rectangular shape. Likewise, circular field has higher NDVI than rectangular field. Considering semiarid environment, limited labor and resources in Flores island, circular paddy field shape can allow the use of pivot irrigation that more efficient.

scholarly journals IRANIAN LAND COVER MAPPING USING LANDSAT-8 IMAGERY AND RANDOM FOREST ALGORITHM

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 77-81
Author(s):  
M. Amani ◽  
A. Ghorbanian ◽  
S. Mahdavi ◽  
A. Mohammadzadeh
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Land Cover ◽  
Large Scale ◽  
Accuracy Assessment ◽  
Land Cover Classification ◽  
Google Earth ◽  
Machine Learning Techniques ◽  
Land Cover Mapping ◽  
Landsat 8

Abstract. Land cover classification is important for various environmental assessments. The opportunity of imaging the Earth’s surface makes remote sensing techniques efficient approaches for land cover classification. The only country-wide land cover map of Iran was produced by the Iranian Space Agency (ISA) using low spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and a basic classification method. Thus, it is necessary to produce a more accurate map using advanced remote sensing and machine learning techniques. In this study, multi-temporal Landsat-8 data (1,321 images) were inserted into a Random Forest (RF) algorithm to classify the land cover of the entire country into 13 categories. To this end, all steps, including pre-processing, classification, and accuracy assessment were implemented in the Google Earth Engine (GEE) platform. The overall classification accuracy and Kappa Coefficient obtained from the Iran-wide map were 74% and 0.71, respectively, indicating the high potential of the proposed method for large-scale land cover mapping.

scholarly journals SENSORIAMENTO REMOTO APLICADO A MODELAGEM DE PRODUTIVIDADE DA CULTURA DA CANA-DE-AÇÚCAR

2019 ◽  
Vol 34 (2) ◽  
pp. 263-270
Author(s):  
Victor Costa Leda ◽  
Aline Kuramoto Golçalves ◽  
Natalia da Silva Lima
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Sao Paulo ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
São Paulo ◽  
Vegetation Indexes ◽  
Production Area ◽  
Remote Sensing Techniques ◽  
Sugarcane Productivity

SENSORIAMENTO REMOTO APLICADO A MODELAGEM DE PRODUTIVIDADE DA CULTURA DA CANA-DE-AÇÚCAR   VICTOR COSTA LEDA1, ALINE KURAMOTO GOLÇALVES2, NATALIA DA SILVA LIMA3   1 Departamento de Solos e Recursos Ambientais, Universidade Paulista “Júlio de Mesquita Filho” – Unesp, Fazenda Experimental Lageado, Avenida Universitária, nº 3780, Altos do Paraíso, CEP 18610-034, Botucatu, São Paulo, Brasil, [email protected]. 2 Departamento de Solos e Recursos Ambientais, Universidade Paulista “Júlio de Mesquita Filho” – Unesp, Fazenda Experimental Lageado, Avenida Universitária, nº 3780, Altos do Paraíso, CEP 18610-034, Botucatu, São Paulo, Brasil, [email protected]. 3 Departamento de Solos e Recursos Ambientais, Universidade Paulista “Júlio de Mesquita Filho” – Unesp, Fazenda Experimental Lageado, Avenida Universitária, nº 3780, Altos do Paraíso, CEP 18610-034, Botucatu, São Paulo, Brasil, [email protected].   RESUMO: O trabalho objetivou modelar as correlações de produtividade da cana-de-açúcar com índices de vegetação obtidos por meio de análise de imagens orbitais. Para análise, foram elaborados modelos matemáticos que expliquem a produtividade da cana-de-açúcar por meio das técnicas de geoprocessamento e sensoriamento remoto. O experimento foi realizado na área de produção comercial da Agrícola Rio Claro, parceira do grupo Zilor, que está localizada nos municípios de Lençóis Paulista e Pratânia, SP. A área ocupa aproximadamente 6000 ha, com altimetrias variando entre 600 e 700 m. Foi constatado que as modelagens foram satisfatórias, variando o coeficiente de determinação entre 0,15 a 0,97, sendo que, em períodos de colheita com elevados coeficientes de determinação, podem geralmente ser encontradas áreas de forma aglomerada, o que sugere uma menor incidência de variáveis. Enquanto áreas que apresentaram coeficientes de determinação baixos, podem ser explicadas devido a fatores como, dispersão dos talhões na área, classes de solo, precipitação e variedades da cultura, provavelmente distintos.   Palavras-chaves: índices de vegetação, Landsat 8, regressão linear múltipla.   REMOTE SENSING FOR THE SUGARCANE PRODUCTIVITY MODELING   ABSTRACT: The aim of this study was to model the sugarcane productivity correlations with vegetation indexes obtained through orbital image analysis. From the analysis was elaborated      mathematical models to explain sugarcane productivity through geoprocessing and remote sensing techniques. The experiment was carried out in the commercial production area of Agrícola Rio Claro, a partner of the Zilor group, located in the municipalities of Lençóis Paulista and Pratânia, SP, with approximately 6,000 hectares, with altimetry varying between 600 and 700 meters. It was verified that the modeling was satisfactory, varying the coefficient of determination between 0,15 and 0,97. Once      in periods with high determination coefficients, areas of agglomerated form can usually be found, which suggests a lower incidence of variables. While, in periods with low determination coefficients, can be explain due to listed factors that occurred as dispersion of the stands in the area, classes of soil, precipitation and probably different varieties of the crop.   Keywords: vegetation index, landsat8, multiple linear regression.

scholarly journals Remote sensing vegetation index methods to evaluate changes in greenness and evapotranspiration in riparian vegetation in response to the Minute 319 environmental pulse flow to Mexico

2018 ◽  
Vol 380 ◽  
pp. 45-54 ◽  
Author(s):  
Pamela L. Nagler ◽  
Christopher J. Jarchow ◽  
Edward P. Glenn
Keyword(s):  
Remote Sensing ◽  
Gulf Of California ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Colorado River ◽  
Environmental Flows ◽  
The United States ◽  
Landsat 8 ◽  
Pulse Flow ◽  
The Us

Abstract. During the spring of 2014, 130 million m3 of water were released from the United States' Morelos Dam on the lower Colorado River to Mexico, allowing water to reach the Gulf of California for the first time in 13 years. Our study assessed the effects of water transfer or ecological environmental flows from one nation to another, using remote sensing. Spatial applications for water resource evaluation are important for binational, integrated water resources management and planning for the Colorado River, which includes seven basin states in the US plus two states in Mexico. Our study examined the effects of the historic binational experiment (the Minute 319 agreement) on vegetative response along the riparian corridor. We used 250 m Moderate Resolution Imaging Spectroradiometer (MODIS), Enhanced Vegetation Index (EVI) and 30 m Landsat 8 satellite imagery to track evapotranspiration (ET) and the normalized difference vegetation index (NDVI). Our analysis showed an overall increase in NDVI and evapotranspiration (ET) in the year following the 2014 pulse, which reversed a decline in those metrics since the last major flood in 2000. NDVI and ET levels decreased in 2015, but were still significantly higher (P < 0.001) than pre-pulse (2013) levels. Preliminary findings show that the decline in 2015 persisted into 2016 and 2017. We continue to analyse results for 2018 in comparison to short-term (2013–2018) and long-term (2000–2018) trends. Our results support the conclusion that these environmental flows from the US to Mexico via the Minute 319 “pulse” had a positive, but short-lived (1 year), impact on vegetation growth in the delta.

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