Environmental changes monitoring by remote sensing for Danube River Delta, Romania

2003 ◽  
Author(s):  
Maria Zoran
Keyword(s):  
Remote Sensing ◽  
Environmental Changes ◽  
River Delta ◽  
Danube River

THE ROLE OF REMOTE SENSING DATA FOR COASTAL ZONE MONITIORING AND MANAGEMENT (CASE STUDY FOR THE EAST PART OF GULF OF FINLAND)

2017 ◽  
Author(s):  
Nikifor Ostanin ◽  
Nikifor Ostanin
Keyword(s):  
Remote Sensing ◽  
Coastal Zone ◽  
Environmental Changes ◽  
Rapid Development ◽  
Water Environment ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Gulf Of Finland ◽  
Coastal Protection ◽  
Remotely Sensed Data

Coastal zone of the Eastern Gulf of Finland is subjected to essential natural and anthropogenic impact. The processes of abrasion and accumulation are predominant. While some coastal protection structures are old and ruined the problem of monitoring and coastal management is actual. Remotely sensed data is important component of geospatial information for coastal environment research. Rapid development of modern satellite remote sensing techniques and data processing algorithms made this data essential for monitoring and management. Multispectral imagers of modern high resolution satellites make it possible to produce advanced image processing, such as relative water depths estimation, sea-bottom classification and detection of changes in shallow water environment. In the framework of the project of development of new coast protection plan for the Kurortny District of St.-Petersburg a series of archival and modern satellite images were collected and analyzed. As a result several schemes of underwater parts of coastal zone and schemes of relative bathymetry for the key areas were produced. The comparative analysis of multi-temporal images allow us to reveal trends of environmental changes in the study areas. This information, compared with field observations, shows that remotely sensed data is useful and efficient for geospatial planning and development of new coast protection scheme.

Spatio–Temporal Dynamics of Wetland Landscape Patterns Based on Remote Sensing in Yellow River Delta, China

Wetlands ◽  
2014 ◽  
Vol 34 (4) ◽  
pp. 787-801 ◽  
Author(s):  
Guilin Liu ◽  
Luocheng Zhang ◽  
Qian Zhang ◽  
Zipporah Musyimi ◽  
Qinghu Jiang
Keyword(s):  
Remote Sensing ◽  
Temporal Dynamics ◽  
Yellow River ◽  
Yellow River Delta ◽  
River Delta ◽  
Landscape Patterns ◽  
Wetland Landscape ◽  
Spatio Temporal

scholarly journals Holocene environmental changes in Red River delta, Vietnam as inferred from the stable carbon isotopes and C/N ratios

2018 ◽  
Vol 128 (1) ◽  
Author(s):  
Nguyen Tai Tue ◽  
Dang Minh Quan ◽  
Pham Thao Nguyen ◽  
Luu Viet Dung ◽  
Tran Dang Quy ◽  
...  
Keyword(s):  
Carbon Isotopes ◽  
Stable Carbon Isotopes ◽  
Environmental Changes ◽  
River Delta ◽  
Red River ◽  
Stable Carbon ◽  
Red River Delta

scholarly journals MONITORAMENTO DA COBERTURA VEGETAL POR SENSORIAMENTO REMOTO NO SEMIÁRIDO BRASILEIRO ATRAVÉS DE ÍNDICES DE VEGETAÇÃO

Nativa ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 708
Author(s):  
Caio Victor Santos Silva ◽  
Jhon Lennon Bezerra da Silva ◽  
Geber Barbosa De Albuquerque Moura ◽  
Pabrício Marcos Oliveira Lopes ◽  
Cristina Rodrigues Nascimento ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Environmental Changes ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Digital Processing ◽  
Semiarid Region ◽  
Dry Period ◽  
Biophysical Parameters ◽  
Brazilian Semiarid

São necessárias medidas que visem à proteção e conservação dos recursos hídricos e naturais de forma rápida e eficiente. As técnicas de sensoriamento remoto são essenciais para o monitoramento ambiental dos recursos no semiárido no espaço e no tempo. Objetivou-se monitorar e analisar à dinâmica da cobertura vegetal através da variabilidade espaço-temporal do albedo da superfície e índices de vegetação em região de Caatinga do semiárido brasileiro por sensoriamento remoto. A área de estudo é o município de Arcoverde, localizado no semiárido de Pernambuco. O estudo foi desenvolvido através de seis imagens orbitais do Landsat-5 do sensor TM. O processamento digital dos parâmetros biofísicos foi realizado pelo algoritmo SEBAL. Os resultados foram analisados através da estatística descritiva e quanto a sua variabilidade. Áreas possivelmente degradadas foram identificadas pelos altos valores de albedo e índices de vegetação significativamente menores, localizadas à sudoeste e noroeste da região. Os índices apresentaram comportamento similares, principalmente no período seco, com baixos valores sendo próximos de zero, áreas afetadas pelo período de seca no semiárido. O SAVI apresentou maior precisão, destacando melhor resposta espectral da vegetação. O sensoriamento remoto promoveu monitoramento espaço-temporal adequado, destacando principalmente o período classificado como climaticamente seco através do albedo e índices de vegetação.Palavras-chave: Caatinga; NDVI; SAVI; mudanças ambientais; SEBAL. MONITORING OF VEGETATION COVER BY REMOTE SENSING IN BRAZILIAN SEMIARID THROUGH VEGETATION INDICES ABSTRACT: Measures are needed aimed at the protection and conservation of water and natural resources quickly and efficiently. Remote sensing techniques are essential for the environmental monitoring of resources in the semiarid region in space and time. Aimed to monitor and analyze the dynamics of vegetation cover through the spatial-temporal variability of the surface albedo and indices of vegetation in the Caatinga region of the Brazilian semiarid by remote sensing. The study area is the municipality of Arcoverde, located in the semiarid of Pernambuco. The study was developed through six orbital images of Landsat-5 of the TM sensor. The digital processing of the biophysical parameters was performed by the SEBAL algorithm. The results were analyzed through descriptive statistics and their variability. Possibly degraded areas were identified by high albedo values and significantly lower vegetation indices, located in the southwest and northwest of the region. The indexes showed similar behavior, mainly in the dry period, with low values being close to zero, areas affected by the dry period in the semiarid. The SAVI presented higher accuracy, highlighting better spectral response of the vegetation. Remote sensing promoted adequate space-time monitoring, highlighting mainly the period classified as climatically dry through the albedo and vegetation indexes.Keywords: Caatinga; NDVI; SAVI; environmental changes; SEBAL.

Applications of ANN for Agriculture Using Remote Sensed Data

2022 ◽  
pp. 1008-1030
Author(s):  
Geetha M. ◽  
Asha Gowda Karegowda ◽  
Nandeesha Rudrappa ◽  
Devika G.
Keyword(s):  
Remote Sensing ◽  
Food Security ◽  
Environmental Changes ◽  
Earth Science ◽  
Yield Estimation ◽  
Government Organizations ◽  
Security Issues ◽  
Time Availability ◽  
Efficient Information ◽  
Indispensable Tool

Ever since the advent of modern geo information systems, tracking environmental changes due to natural and/or manmade causes with the aid of remote sensing applications has been an indispensable tool in numerous fields of geography, most of the earth science disciplines, defense, intelligence, commerce, economics, and administrative planning. Remote sensing is used in science and technology, and through it, an object can be identified, measured, and analyzed without physical presence for interpretation. In India remote sensing has been using since 1970s. One among these applications is the crop classification and yield estimation. Using remote sensing in agriculture for crop mapping, and yield estimation provides efficient information, which is mainly used in many government organizations and the private sector. The pivotal sector for ensuring food security is a major concern of interest in these days. In time, availability of information on agricultural crops is vital for making well-versed decisions on food security issues.

scholarly journals Variable Urbanization Warming Effects across Metropolitans of China and Relevant Driving Factors

2020 ◽  
Vol 12 (9) ◽  
pp. 1500 ◽  
Author(s):  
Qiang Zhang ◽  
Zixuan Wu ◽  
Huiqian Yu ◽  
Xiudi Zhu ◽  
Zexi Shen
Keyword(s):  
Remote Sensing ◽  
Spatial Scales ◽  
River Delta ◽  
Driving Factors ◽  
Landsat 8 ◽  
Temporal Scales ◽  
Linear Relations ◽  
Urban Agglomerations ◽  
Spatial And Temporal Scales ◽  
Urbanized Region

Urbanization is mainly characterized by the expansion of impervious surface (IS) and hence modifies hydrothermal properties of the urbanized areas. This process results in rising land surface temperature (LST) of the urbanized regions, i.e., urban heat island (UHI). Previous studies mainly focused on relations between LST and IS over individual city. However, because of the spatial heterogeneity of UHI from individual cities to urban agglomerations and the influence of relevant differences in climate background across urban agglomerations, the spatial-temporal scale independence of the IS-LST relationship still needs further investigation. In this case, based on Landsat-8 Operational Land Imager and Thermal Infrared Sensor (Landsat 8 OLI/TIRS) remote sensing image and multi-source remote sensing data, we extracted IS using VrNIR-BI (Visible red and NIR-based built-up Index) and calculated IS density across three major urban agglomerations across eastern China, i.e., the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) to investigate the IS-LST relations on different spatial and temporal scales and clarify the driving factors of LST. We find varying warming effects of IS on LST in diurnal and seasonal sense at different time scales. Specifically, the IS has stronger impacts on increase of LST during daytime than during nighttime and stronger impacts on increase of LST during summer than during winter. On different spatial scales, more significant enhancing effects of IS on LST can be observed across individual city than urban agglomerations. The Pearson correlation coefficient (r) between IS and LST at the individual urbanized region can be as high as 0.94, indicating that IS can well reflect LST changes within individual urbanized region. However, relationships between IS and LST indicate nonlinear effects of IS on LST. Because of differences in spatial scales, latitudes, and local climates, we depicted piecewise linear relations between IS and LST across BTH when the IS density was above 10% to 17%. Meanwhile, linear relations still stand between IS density and LST across YRD and PRD. Besides, the differences in the IS-LST relations across urban agglomeration indicate more significant enhancing effects of IS on LST across PRD than YRD and BTH. These findings help to enhance human understanding of the warming effects of urbanization or UHI at different spatial and temporal scales and is of scientific and practical merits for scientific urban planning.

scholarly journals Estimation of Cargo Handling Capacity of Coastal Ports in China Based on Panel Model and DMSP-OLS Nighttime Light Data

2019 ◽  
Vol 11 (5) ◽  
pp. 582 ◽  
Author(s):  
Aoshuang Liu ◽  
Ye Wei ◽  
Bailang Yu ◽  
Wei Song
Keyword(s):  
Remote Sensing ◽  
Statistical Data ◽  
Estimation Method ◽  
River Delta ◽  
Important Indicator ◽  
Strong Basis ◽  
Cargo Handling ◽  
Panel Model ◽  
Nighttime Light Data ◽  
Nighttime Light

The cargo handling capacity of a port is the most basic and important indicator of port size. Based on the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime light data and panel model, this study attempts to estimate the cargo handling capacity of 28 coastal ports in China using satellite remote sensing. The study confirmed that there is a very close correlation between DMSP-OLS nighttime light data and the cargo handling capacity of the ports. Based on this correlation, the panel data model was established for remote sensing-based estimation of cargo handling capacity at the port and port group scales. The test results confirm that the nighttime light data can be used to accurately estimate the cargo handling capacity of Chinese ports, especially for the Yangtze River Delta Port Group, Pearl River Delta Port Group, Southeast Coastal Port Group, and Southwest Coastal Port Group that possess huge cargo handling capacities. The high accuracy of the model reveals that the remote sensing analysis method can make up for the lack of statistical data to a certain extent, which helps to scientifically analyze the spatiotemporal dynamic changes of coastal ports, provides a strong basis for decision-making regarding port development, and more importantly provides a convenient estimation method for areas that have long lacked statistical data on cargo handling capacity.

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