scholarly journals Research on Identification and Supervision of Ecological Background of Subtropical Mountainous Highways Based on Multi-source Data

2020 ◽  
Vol 165 ◽  
pp. 02021
Author(s):  
Ming Zhao ◽  
Kai-cheng Hua ◽  
Dong Zhang ◽  
Dong Ni ◽  
Min-min Yuan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Environment ◽  
Remote Sensing Data ◽  
Ecological Indicators ◽  
Mountainous Area ◽  
Land Use Type ◽  
Analysis And Evaluation ◽  
Source Data ◽  
Erosion Index ◽  
Environment Index

Based on the UAV remote sensing method combined with multi-source remote sensing data, the ecological background of the subtropical mountainous area was investigated based on the Huizhou to Qingyuan section of the Shantou-Zhanjiang Expressway. Ecological background evaluation indicators are the theoretical basis to support the analysis and evaluation of this article. The ecological indicators of roads in South China have selected five indicators: vegetation cover index, water environment index, soil erosion index, terrain slope index and land use type index.

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.

scholarly journals Spatiotemporal Differentiation and the Factors of Ecological Vulnerability in the Toutun River Basin Based on Remote Sensing Data

2019 ◽  
Vol 11 (15) ◽  
pp. 4160 ◽  
Author(s):  
Qin Liu ◽  
Tiange Shi
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
River Basin ◽  
Hot Spot ◽  
Remote Sensing Data ◽  
Cold Spot ◽  
Strengthening Effect ◽  
Land Use Type ◽  
Sensing Data ◽  
Ecological Vulnerability

Ecological vulnerability assessment increases the knowledge of ecological status and contributes to formulating local plans of sustainable development. A methodology based on remote sensing data and spatial principal component analysis was introduced to discuss ecological vulnerability in the Toutun River Basin (TRB). Exploratory spatial data analysis and a geo-detector were employed to evaluate the spatial and temporal distribution characteristics of ecological vulnerability and detect the driving factors. Four results were presented: (1) During 2003 and 2017, the average values of humidity, greenness, and heat in TRB increased by 49.71%, 11.63%, and 6.51% respectively, and the average values of dryness decreased by 165.24%. However, the extreme differences in greenness, dryness, and heat tended to be obvious. (2) The study area was mainly dominated by a high and extreme vulnerability grade, and the ecological vulnerability grades showed the distribution pattern that the northern desert area was more vulnerable than the central artificial oasis, and the central artificial oasis was more vulnerable than the southern mountainous area. (3) Ecological vulnerability in TRB showed significant spatial autocorrelation characteristics, and the trend was enhanced. The spatial distribution of hot/cold spots presented the characteristics of “hot spot—cold spot—secondary hot spot—cold spot” from north to south. (4) The explanatory power of each factor of ecological vulnerability was temperature (0.5955) > land use (0.5701) > precipitation (0.5289) > elevation (0.4879) > slope (0.3660) > administrative division (0.1541). The interactions of any two factors showed a non-linear strengthening effect, among which, land use type ∩ elevation (0.7899), land use type ∩ precipitation (0.7867), and land use type ∩ temperature (0.7791) were the significant interaction for ecological vulnerability. Overall, remote sensing data contribute to realizing a quick and objective evaluation of ecological vulnerability and provide valuable information for decision making concerning ecology management and region development.

Determination of grassland use intensity based on multi-temporal remote sensing data and ecological indicators

2017 ◽  
Vol 198 ◽  
pp. 126-139 ◽  
Author(s):  
Marta Gómez Giménez ◽  
Rogier de Jong ◽  
Raniero Della Peruta ◽  
Armin Keller ◽  
Michael E. Schaepman
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Ecological Indicators ◽  
Sensing Data ◽  
Multi Temporal

scholarly journals Assessment of Phytoecological Variability by Red-Edge Spectral Indices and Soil-Landscape Relationships

2019 ◽  
Vol 11 (20) ◽  
pp. 2448 ◽  
Author(s):  
Helena S. K. Pinheiro ◽  
Theresa P. R. Barbosa ◽  
Mauro A. H. Antunes ◽  
Daniel Costa de Carvalho ◽  
Alexis R. Nummer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Remote Sensing Data ◽  
Parent Material ◽  
Mountainous Area ◽  
Kappa Index ◽  
Sensing Data ◽  
Taxonomic Level ◽  
Red Edge ◽  
Geological Conditions

There is a relation of vegetation physiognomies with soil and geological conditions that can be represented spatially with the support of remote sensing data. The goal of this research was to map vegetation physiognomies in a mountainous area by using Sentinel-2 Multispectral Instrument (MSI) data and morphometrical covariates through data mining techniques. The research was based on red-edge (RE) bands, and indices, to classify phytophysiognomies at two taxonomic levels. The input data was pixel sampled based on field sample sites. Data mining procedures comprised covariate selection and supervised classification through the Random Forest model. Results showed the potential of bands 3, 5, and 6 to map phytophysiognomies for both seasons, as well as Green Chlorophyll (CLg) and SAVI indices. NDVI indices were important, particularly those calculated with bands 6, 7, 8, and 8A, which were placed at the RE position. The model performance showed reasonable success to Kappa index 0.72 and 0.56 for the first and fifth taxonomic level, respectively. The model presented confusion between Broadleaved dwarf-forest, Parkland Savanna, and Bushy grassland. Savanna formations occurred variably in the area while Bushy grasslands strictly occur in certain landscape positions. Broadleaved forests presented the best performance (first taxonomic level), and among its variation (fifth level) the model could precisely capture the pattern for those on deep soils from gneiss parent material. The approach was thus useful to capture intrinsic soil-plant relationships and its relation with remote sensing data, showing potential to map phytophysiognomies in two distinct taxonomic levels in poorly accessible areas.

Coupling remote sensing data and eco-hydrological model to evaluate Non-point source pollution risk for water resource management

2014 ◽  
Vol 11 (2) ◽  
pp. 157-162
Author(s):  
Shudong Wang ◽  
Lifu Zhang ◽  
Xia Zhang ◽  
Wanqing Li ◽  
Tong Shuai ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Risk Assessment ◽  
Point Source ◽  
Catchment Area ◽  
Water Environment ◽  
Remote Sensing Data ◽  
Source Model ◽  
Point Source Pollution ◽  
Pollution Risk ◽  
Non Point Source Pollution

Non-point source pollution risk assessment for surface drinking water catchments is an important basis and premise for the scientific management over water environment, while remote sensing technology may timely find the spatial distribution pattern and variation of risk. Coupling the Non-point source model and remote sensing data is a potential method for the water environment risk assessment. The dual Non-point source model independently developed by China is chosen to study its practical applicability in the experimental catchment area of Hebei Yuecheng Reservoir in combination with the remote sensing and GIS data, and to study the spatial distribution pattern of the Non-point source Phosphorus (P) pollution generated by the spatial landuse. The result shows that:(1) the coupled model is well adapted to the catchment area of Hebei Yuecheng Reservoir, and the simulated Non-point source P load is strongly related to the observation data of the hydrologic stations such as Liujiazhuang, Guantai and etc.; (2) The disorderly development of social economy is the main cause of Non-point source pollution, and the farmlands, urban and rural residential areas in the catchment area are the major risk sources of Non-point source pollution; (3) the two assessment units, catchment unit and administration unit, are employed in this study. They are complementary to each other, which is convenient for management because they can reflect not only the P risk distribution but also the specific location of the administration areas within the risk area.

УСОВЕРШЕНСТВОВАННАЯ МЕТОДИКА ПОДГОТОВКИ КОМПЬЮТЕРНОЙ МОДЕЛИ ГИДРОМЕЛИОРАТИВНОЙ СИСТЕМЫ

2020 ◽  
pp. 6
Author(s):  
A.A. Talyzov
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Computer Model ◽  
Irrigation System ◽  
Remote Sensing Data ◽  
Geographic Information ◽  
Surface Design ◽  
Source Data ◽  
Reclamation System

Целью данного исследования являлось создание усовершенствованной методики обработки исходных данных при подготовке компьютерной модели гидромелиоративной системы. Предлагаемая методика основана на использовании современных методов обработки данных, основанных на использовании географической информационной системы (ГИС), а также дополнительных программ, предназначенных для и формирования численной гидравлической модели. Описана методика совместного использования картографических материалов, данных дистанционного зондирования поверхности земли, проектных материалов и данных обследования объекта на этапе построения компьютерной модели мелиоративной системы.The aim of this research was development of an improved methodology for processing source data in the preparation of a computer model of irrigation system. The proposed methodology is based on the use of modern data processing methods based on the use of a geographic information system (GIS), as well as additional programs designed to form a numerical hydraulic model. The technique of using of cartographic materials, remote sensing data of the earths surface, design materials and survey data at the stage of constructing a computer model of the reclamation system is described.

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.

Identification of Dominant Factors Affecting Soil Erosion and Water Yield within Key Ecological Functional Areas

2020 ◽  
Author(s):  
Jiangbo Gao ◽  
Yuan Jiang
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Spatial Distribution ◽  
Soil Erosion ◽  
Spatial Heterogeneity ◽  
Remote Sensing Data ◽  
Vegetation Coverage ◽  
Water Yield ◽  
Land Use Type ◽  
Functional Areas

<p>Soil conservation and water retention are important metrics for designating key ecological functional areas. However, research on the quantitative identification of dominant environmental factors in different ecological functional areas remains relatively inadequate, which is unfavorable for zone-based management of key ecological functional areas. This paper presents a case study of Beijing’s key ecological functional areas. In order to objectively reflect the ecological characteristics of key ecological functional areas in Beijing which is mainly dominated by mountainous areas, the application of remote sensing data about high resolution is important for the improvement of model calculation and spatial heterogeneity. Based on multi-source remote sensing data, meteorological and soil observations, soil erosion and water yield were calculated using the Revised Universal Soil Loss Equation (RUSLE) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Combining the influencing factors, including slope, precipitation, land use type, vegetation coverage, geomorphological type and elevation, a quantitative attribution analysis was performed on soil erosion and water yield in Beijing’s key ecological functional areas using the geographical detector. The power of each influencing factor and their interaction factors in explaining the spatial distribution of soil erosion or water yield varied significantly among different key ecological function areas. Vegetation coverage was the dominant factor affecting soil erosion in Beijing’s key ecological function areas, explaining greater than 30% of its spatial heterogeneity. Land use type can explain the spatial heterogeneity of water yield more than 60%. In addition, the combination of vegetation coverage and slope was found to significantly enhance the spatial distribution of soil erosion (>55% in various key ecological functional areas). The superposition of land use type and slope explained greater than 70% of the spatial distribution for water yield in key ecological functional areas. The geographical detector results indicated that the high soil erosion risk areas and high water yield areas varied significantly among different ecological functional areas. Thus, in efforts to enhance key ecological functional areas protection, focus should be placed on the spatial heterogeneity of soil erosion and water yield in different ecological functional areas.</p>

scholarly journals Yield estimation of the 2020 Beirut explosion using open access waveform and remote sensing data

2021 ◽  
Author(s):  
Christoph Pilger ◽  
Peter Gaebler ◽  
Patrick Hupe ◽  
Andre Kalia ◽  
Felix Schneider ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Source Characterization ◽  
Social Relevance ◽  
Radar Remote Sensing ◽  
Site Analysis ◽  
Sensing Data ◽  
International Monitoring ◽  
Source Data ◽  
The City

<p>We report on a multi-technique analysis using publicly available data for investigating the huge, accidental explosion that struck the city of Beirut, Lebanon, on August 4, 2020. Its devastating shock wave led to thousands of injured with more than two hundred fatalities and caused immense damage to buildings and infrastructure. Our combined analysis of seismological, hydroacoustic, infrasonic and radar remote sensing data allows us to characterize the source as well as to estimate the explosive yield. The latter ranges between 0.8 and 1.1 kt TNT (kilotons of trinitrotoluene) equivalent and is plausible given the reported 2.75 kt of ammonium nitrate as explosive source. Data from the International Monitoring System of the CTBTO are used for infrasound array detections. Seismometer data from GEOFON and IRIS complement the source characterization based on seismic and acoustic signal recordings, which propagated in solid earth, water and air. Copernicus Sentinel data serve for radar remote sensing and damage estimation. As there are strict limitations for an on-site analysis of this catastrophic explosion, our presented approach based on openly accessible data from global station networks and satellite missions is of high scientific and social relevance that furthermore is transferable to other explosions.</p>

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