Development of urban green space monitoring technique with remote sensing and its application

2016 ◽  
Author(s):  
Cristian Gradinaru ◽  
Ioan Sorin Herban ◽  
Cristian Gabor
Keyword(s):  
Remote Sensing ◽  
Green Space ◽  
Urban Green Space ◽  
Urban Green ◽  
Monitoring Technique

scholarly journals A Novel Intelligent Classification Method for Urban Green Space Based on High-Resolution Remote Sensing Images

2020 ◽  
Vol 12 (22) ◽  
pp. 3845
Author(s):  
Zhiyu Xu ◽  
Yi Zhou ◽  
Shixin Wang ◽  
Litao Wang ◽  
Feng Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Classification Accuracy ◽  
Green Space ◽  
Urban Green Space ◽  
Classification Method ◽  
Deciduous Trees ◽  
Remote Sensing Images ◽  
Urban Green

The real-time, accurate, and refined monitoring of urban green space status information is of great significance in the construction of urban ecological environment and the improvement of urban ecological benefits. The high-resolution technology can provide abundant information of ground objects, which makes the information of urban green surface more complicated. The existing classification methods are challenging to meet the classification accuracy and automation requirements of high-resolution images. This paper proposed a deep learning classification method for urban green space based on phenological features constraints in order to make full use of the spectral and spatial information of green space provided by high-resolution remote sensing images (GaoFen-2) in different periods. The vegetation phenological features were added as auxiliary bands to the deep learning network for training and classification. We used the HRNet (High-Resolution Network) as our model and introduced the Focal Tversky Loss function to solve the sample imbalance problem. The experimental results show that the introduction of phenological features into HRNet model training can effectively improve urban green space classification accuracy by solving the problem of misclassification of evergreen and deciduous trees. The improvement rate of F1-Score of deciduous trees, evergreen trees, and grassland were 0.48%, 4.77%, and 3.93%, respectively, which proved that the combination of vegetation phenology and high-resolution remote sensing image can improve the results of deep learning urban green space classification.

scholarly journals Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Nhat-Duc Hoang ◽  
Xuan-Linh Tran
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Use ◽  
Green Space ◽  
Urban Land ◽  
Urban Green Space ◽  
Urban Land Use ◽  
Urban Green ◽  
Marine Predators ◽  
Sentinel 2

Information regarding the current status of urban green space is crucial for urban land-use planning and management. This study proposes a remote sensing and data-driven solution for urban green space detection at regional scale via employment of state-of-the-art metaheuristic and machine learning approaches. Remotely sensed data obtained from Sentinel 2 satellite in the study area of Da Nang city (Vietnam) are used to construct and verify an intelligent model that hybridizes Marine Predators Algorithm (MPA) and support vector machines (SVM). SVM are employed to generalize a decision boundary that separates features characterizing statistical measurements of remote sensing data into two categories of “green space” and “nongreen space”. The MPA metaheuristic is used to optimize the SVM training phase by identifying an appropriate set of the SVM’s hyperparameters including the penalty coefficient and the kernel function parameter. Experimental results show that the proposed model which processes information provided by all of the Sentinel 2 satellite’s spectral bands can deliver a better performance than those obtained from the model based on vegetation indices. With a good classification accuracy rate of roughly 93%, an F1 score = 0.93, and an area under the receiver operating characteristic = 0.98, the newly developed model is a promising tool to assist local authority to obtain up-to-date information on urban green space and develop plans of sustainable urban land use.

scholarly journals Social functional mapping of urban green space using remote sensing and social sensing data

2018 ◽  
Vol 146 ◽  
pp. 436-452 ◽  
Author(s):  
Wei Chen ◽  
Huiping Huang ◽  
Jinwei Dong ◽  
Yuan Zhang ◽  
Yichen Tian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Green Space ◽  
Urban Green Space ◽  
Functional Mapping ◽  
Urban Green ◽  
Social Sensing ◽  
Sensing Data

scholarly journals THE CITY OF ADDIS ABABA FROM ‘FOREST CITY’ TO ‘URBAN HEAT ISLAND’: AN ANALYSIS OF URBAN GREEN SPACE DYNAMICS USING REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEM

2017 ◽  
Vol 10 (2) ◽  
pp. 254-262
Author(s):  
Mathias Tesfaye Abebe ◽  
Tebarek Lika Megento
Keyword(s):  
Remote Sensing ◽  
Green Space ◽  
Addis Ababa ◽  
Urban Green Space ◽  
Green Spaces ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Gis Tools ◽  
Remote Sensing Techniques ◽  
Space Dynamics

The unprecedented rate of urban growth in developing countries causes various problems such as deficiency in public infrastructure services, lack of green spaces and inadequate service provisions. This study applies GIS tools and remote sensing techniques to assess the effects of urban development on urban green space in Ethiopia’s capital. Spatial and non-spatial datasets were collected from different organizations and processed using GIS tools and remote sensing techniques for land use/ land cover classification and analysis. The analysis demonstrated shrinking of urban green spaces- plantations, forestland, grassland and cultivated land (at annual rates of 5.9%, 3.3%, 5.4% and 3.7 % respectively) by 82.1%, 62.1%, 78.8 and 65.8 % respectively during the past three decades (1986-2015) whereas built-up and transport areas increased at annual rate of 5.7% and 1.3% and consumed 419% and 47% of the city’s total area respectively.

scholarly journals THE CITY OF ADDIS ABABA FROM ‘FOREST CITY’ TO ‘URBAN HEAT ISLAND’: AN ANALYSIS OF URBAN GREEN SPACE DYNAMICS USING REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEM

2017 ◽  
Vol 10 (2) ◽  
pp. 254-262 ◽  
Author(s):  
Mathias Tesfaye Abebe ◽  
Tebarek Lika Megento
Keyword(s):  
Remote Sensing ◽  
Green Space ◽  
Addis Ababa ◽  
Urban Green Space ◽  
Green Spaces ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Gis Tools ◽  
Remote Sensing Techniques ◽  
Space Dynamics

The unprecedented rate of urban growth in developing countries causes various problems such as deficiency in public infrastructure services, lack of green spaces and inadequate service provisions. This study applies GIS tools and remote sensing techniques to assess the effects of urban development on urban green space in Ethiopia’s capital. Spatial and non-spatial datasets were collected from different organizations and processed using GIS tools and remote sensing techniques for land use/ land cover classification and analysis. The analysis demonstrated shrinking of urban green spaces- plantations, forestland, grassland and cultivated land (at annual rates of 5.9%, 3.3%, 5.4% and 3.7 % respectively) by 82.1%, 62.1%, 78.8 and 65.8 % respectively during the past three decades (1986-2015) whereas built-up and transport areas increased at annual rate of 5.7% and 1.3% and consumed 419% and 47% of the city’s total area respectively.

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