scholarly journals METHODOLOGY DEVELOPMENT FOR CALCULATING THE AREA OF GREENERY IN A CITY, USING REMOTE SENSING DATA

2021 ◽  
pp. 170-181
Author(s):  
Olga S. Sergeeva ◽  
◽  
Semen P. Pirozhkov ◽  
Keyword(s):  
Remote Sensing ◽  
Urban Environment ◽  
Remote Sensing Data ◽  
Green Spaces ◽  
Space Images ◽  
Important Indicator ◽  
Sensing Data ◽  
Environment Quality ◽  
Gis Technologies ◽  
Green Areas

This article discusses the possibility of using Earth remote sensing data and GIS technologies for assessing the area of green spaces in a city. Green areas are an important indicator of the urban environment quality. Quantitative information on such areas is necessary to calculate the total index of the urban environment quality, which is provided annually to the state statistics authorities. Various methods of obtaining such data are possible, including by decoding orthophotomaps, aerial photography, and mobile laser scanning. GIS technologies provide ample opportunities in this area: they allow one to create electronic maps, attributive databases, and maintain up-to-date information. The paper provides an example of using space images to calculate green areas in one of the microdistricts in the city of Perm. We describe the technique for recalculating the number of trees in a landscaping area; assess the planting areas of general and limited use and the total area of green spaces in the microdistrict; calculate the share of green areas and the greening level of the microdistrict, which are necessary for calculating the urban environment quality index. The technique proposed in this work can significantly reduce the time and labor costs for finding indicators of the urban environment greening.

Monitoring the urban environment quality in the city of Johannesburg using remote sensing data

2020 ◽  
Vol 171 ◽  
pp. 103969
Author(s):  
Marko Freddy Mudede ◽  
Solomon W. Newete ◽  
Khaled Abutaleb ◽  
Nsalambi Nkongolo
Keyword(s):  
Remote Sensing ◽  
Urban Environment ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Environment Quality ◽  
The City

scholarly journals Mapping Dragon Fruit Croplands from Space Using Remote Sensing of Artificial Light at Night

2020 ◽  
Vol 12 (24) ◽  
pp. 4139
Author(s):  
Ruirui Wang ◽  
Wei Shi ◽  
Pinliang Dong
Keyword(s):  
Remote Sensing ◽  
Infrared Imaging ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Important Indicator ◽  
Artificial Lighting ◽  
Sensing Data ◽  
Dragon Fruit ◽  
Positive Correlations

The nighttime light (NTL) on the surface of Earth is an important indicator for the human transformation of the world. NTL remotely sensed data have been widely used in urban development, population estimation, economic activity, resource development and other fields. With the increasing use of artificial lighting technology in agriculture, it has become possible to use NTL remote sensing data for monitoring agricultural activities. In this study, National Polar Partnership (NPP)-Visible Infrared Imaging Radiometer Suite (VIIRS) NTL remote sensing data were used to observe the seasonal variation of artificial lighting in dragon fruit cropland in Binh Thuan Province, Vietnam. Compared with the statistics of planted area, area having products and production of dragon fruit by district in the Statistical Yearbook of Binh Thuan Province 2018, values of the mean and standard deviation of NTL brightness have significant positive correlations with the statistical data. The results suggest that the NTL remotely sensed data could be used to reveal some agricultural productive activities such as dragon fruits production accurately by monitoring the seasonal artificial lighting. This research demonstrates the application potential of NTL remotely sensed data in agriculture.

Ecological environment quality assessment based on remote sensing data for land consolidation

2019 ◽  
Vol 239 ◽  
pp. 118126 ◽  
Author(s):  
Wei Shan ◽  
Xiaobin Jin ◽  
Jie Ren ◽  
Yongcai Wang ◽  
Zhigang Xu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Quality Assessment ◽  
Remote Sensing Data ◽  
Land Consolidation ◽  
Ecological Environment ◽  
Sensing Data ◽  
Environment Quality

scholarly journals Mapping Functional Urban Green Types Using High Resolution Remote Sensing Data

2020 ◽  
Vol 12 (5) ◽  
pp. 2144
Author(s):  
Jeroen Degerickx ◽  
Martin Hermy ◽  
Ben Somers
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Green Infrastructure ◽  
Remote Sensing Data ◽  
Hyperspectral Data ◽  
Airborne Lidar ◽  
Green Spaces ◽  
Human Society ◽  
Urban Green ◽  
Sensing Data

Urban green spaces are known to provide ample benefits to human society and hence play a vital role in safeguarding the quality of life in our cities. In order to optimize the design and management of green spaces with regard to the provisioning of these ecosystem services, there is a clear need for uniform and spatially explicit datasets on the existing urban green infrastructure. Current mapping approaches, however, largely focus on large land use units (e.g., park, garden), or broad land cover classes (e.g., tree, grass), not providing sufficient thematic detail to model urban ecosystem service supply. We therefore proposed a functional urban green typology and explored the potential of both passive (2 m-hyperspectral and 0.5 m-multispectral optical imagery) and active (airborne LiDAR) remote sensing technology for mapping the proposed types using object-based image analysis and machine learning. Airborne LiDAR data was found to be the most valuable dataset overall, while fusion with hyperspectral data was essential for mapping the most detailed classes. High spectral similarities, along with adjacency and shadow effects still caused severe confusion, resulting in class-wise accuracies <50% for some detailed functional types. Further research should focus on the use of multi-temporal image analysis to fully unlock the potential of remote sensing data for detailed urban green mapping.

VIP-BP model for retrieving chlorophyll a concentration in the river by using remote sensing data

2015 ◽  
Vol 52 (2) ◽  
pp. 136-150
Author(s):  
Xiao Xiao ◽  
Biyu Song ◽  
Xiongfei Wen ◽  
Dengzhong Zhao ◽  
Xuejun Cheng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Remote Sensing Data ◽  
Band Model ◽  
Retrieval Model ◽  
Important Indicator ◽  
Sensing Data ◽  
Spectrum Transformation ◽  
Bp Model ◽  
Chla Concentration

Chlorophyll a (Chla) is an important indicator of phytoplankton biomass in waters, and its concentration can reflect the degree of eutrophication. This paper is aimed to develop a highly accurate and universally applicable retrieval model for the concentration of Chla in rivers using remote sensing data. Taking the middle and lower reaches of the Han River as the study area, the Chla retrieval model (VIP-BP model) is established by combining the Variable Importance Projection Index and BP neural algorithm and then calibrated by the measured data from 2012 to 2013. This model uses the VIP index for selection of the appropriate spectrum transformation form and input bands. Then, the BP neural network algorithm is integrated to estimate Chla concentration. After validation and comparison with the three-band model, the results suggest that the VIP-BP model could more accurately and really reflect the changes in Chla concentration than the three-band model in the study area. When Chla concentration decreases, the retrieval error of both models increases, while the error of the VIP-BP model is significantly lower than that of the three-band model, which indicates that the VIP-BP model is more stable and preferred.

scholarly journals Application of GIS Technologies to Improve the Efficiency of HPP Cascades Simulation Model

2020 ◽  
pp. 732-744
Author(s):  
Anna Yu. Arestova ◽  
Sergey V. Mitrofanov ◽  
Anastasiya G. Rusina ◽  
Alexey A. Kolesnikov
Keyword(s):  
Remote Sensing ◽  
Simulation Model ◽  
Block Diagram ◽  
Remote Sensing Data ◽  
Hydroelectric Power ◽  
Calculation Algorithm ◽  
Earth Remote Sensing ◽  
Sensing Data ◽  
Gis Technologies ◽  
Power Stations

The paper presents the calculation algorithm for water-energy regime of a hydroelectric power stations cascade. The block diagram of the algorithm is given for implementing the simulation model and automating the process of regime calculating. The proposed algorithm allows to evaluate the efficiency of the cascade operation, as well as to optimize the regime according to various criteria. Additionally, an option is proposed to integrate GIS monitoring data into the calculation algorithm. The sources and characteristics of Earth remote sensing data that can be used to build the model are presented

scholarly journals SOFTWARE FOR AUTOMATION OF SPACE IMAGE RECOGNITION

2021 ◽  
Vol 6 ◽  
pp. 207-212
Author(s):  
Sergey A. Myasoedov
Keyword(s):  
Remote Sensing ◽  
Image Recognition ◽  
Remote Sensing Data ◽  
Urban Infrastructure ◽  
Space Images ◽  
Automated Recognition ◽  
Data Services ◽  
Sensing Data ◽  
Space Image

The article discusses the main universal systems for working with remote sensing data - services that provide software for automated recognition of urban infrastructure objects in space images. The functions that these products can perform and their structure are considered.

scholarly journals APPLICATION OF REMOTE SENSING DATA TO THE ESTIMATION OF ANTHROPOGENOUS IMPACT ON ECOSYSTEMS (ON THE EXAMPLE WESTERN MONGOLIA)

2019 ◽  
Vol 6 (2) ◽  
pp. 128-133
Author(s):  
Liyana Abdrashitova ◽  
Alexander Chermoshentsev
Keyword(s):  
Remote Sensing ◽  
Anthropogenic Impact ◽  
Remote Sensing Data ◽  
Arid Areas ◽  
Space Images ◽  
Sensing Data ◽  
Western Mongolia ◽  
Automated Interpretation ◽  
Thematic Processing ◽  
Data Processing Methods

The article is devoted to the use of remote sensing data for assessing the anthropogenic impact on ecosystems on the example of arid areas. It describes main advantages and problems of the data processing methods, including automated interpretation of images. The technique of thematic processing of a time series of space images is proposed.

scholarly journals Research and forecasting of landslide phenomena of the Dniper zone of Kyiv based on the remote sensing data and geomorfological studies

2017 ◽  
pp. 10-16
Author(s):  
Nataliia Pazynych
Keyword(s):  
Remote Sensing ◽  
Digital Elevation Models ◽  
Remote Sensing Data ◽  
Space Images ◽  
Sensing Data ◽  
Digital Elevation ◽  
The Right

The article presents the results of the investigation of landslides in the right bank of the Kyiv, on the basis of space images, digital elevation models using two geomorphological methods. The result of the complexization of geomorphological methods was the compilation of a synthetic map of dynamic relief plastics, which reflects the structure of linear and area elements of the relief. The conducted comparison of geomorphological constructions with landslide bodies allowed to identify zones and areas of increased danger of landslide formation.

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