Modeling spatial distribution of carbon sequestration, CO2 absorption, and O2 production in an urban area: integrating ground-based data, remote sensing technique, and GWR model

2021 ◽  
Author(s):  
Loghman Khodakarami ◽  
Saeid Pourmanafi ◽  
Alireza Soffianian ◽  
Ali Lotfi
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Carbon Sequestration ◽  
Urban Area ◽  
Co2 Absorption ◽  
Remote Sensing Technique ◽  
Gwr Model ◽  
O2 Production

Analysis of Ecosystem Carbon Sequestration Change in Guangxi, China from 2000 to 2010 Based on Remote Sensing Data

2014 ◽  
Vol 962-965 ◽  
pp. 1377-1380
Author(s):  
Rong Yu ◽  
Wu Sheng Xiang ◽  
Huan Mei Yao ◽  
Xiu Qin Bu ◽  
Yong Yu Pan
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Carbon Sequestration ◽  
Global Climate ◽  
Terrestrial Ecosystem ◽  
Carbon Stocks ◽  
Guangxi Province ◽  
Mountain Areas ◽  
Fixed Amount ◽  
Ecosystem Carbon

The global change and terrestrial ecosystem (GCTE) is an important research issue in research of global climate change , and terrestrial Ecosystem carbon sequestration is one of the main content of the study. In this paper, ecosystem carbon sequestration capacity calculation and trend analysis of Guangxi Province were made based on the data of remote sensing inversion, for the study of the main ecosystem carbon fixed quantity of spatial distribution and change of Guangxi province over the past 10 years (2000-2010). The results indicated that the ecosystem carbon fixed amount of Guangxi region present high on all sides and low in the center , the spatial distribution of carbon is mainly depends on the spatial distribution of vegetation ecosystem, as the areas of artificial vegetation carbon stocks are generally lower than that of carbon stocks in mountain areas. The amount of carbon fixed showed a general trend of increase from 2000 to 2010.

scholarly journals Spatial distribution of empty fruit bunch production as potential electric resource using remote sensing technique

2019 ◽  
Vol 158 ◽  
pp. 3565-3571
Author(s):  
Anggoro Cahyo Fitrianto ◽  
Arif Darmawan ◽  
Koji Tokimatsu ◽  
Kunio Yoshikawa
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Empty Fruit Bunch ◽  
Remote Sensing Technique

scholarly journals A conceptual vulnerability and risk framework as outline to identify capabilities of remote sensing

2008 ◽  
Vol 8 (3) ◽  
pp. 409-420 ◽  
Author(s):  
H. Taubenböck ◽  
J. Post ◽  
A. Roth ◽  
K. Zosseder ◽  
G. Strunz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Urban Area ◽  
Natural Hazards ◽  
Social Economic ◽  
Population Distribution ◽  
Inductive Method ◽  
Small Scale ◽  
Conceptual Approach ◽  
Risk Framework

Abstract. This study aims at creating a holistic conceptual approach systematizing the interrelation of (natural) hazards, vulnerability and risk. A general hierarchical risk meta-framework presents potentially affected components of a given system, such as its physical, demographic, social, economic, political or ecological spheres, depending on the particular hazard. Based on this general meta-framework, measurable indicators are specified for the system "urban area" as an example. This framework is used as an outline to identify the capabilities of remote sensing to contribute to the assessment of risk. Various indicators contributing to the outline utilizing diverse remote sensing data and methods are presented. Examples such as built-up density, main infrastructure or population distribution identify the capabilities of remote sensing within the holistic perspective of the framework. It is shown how indexing enables a multilayer analysis of the complex and small-scale urban landscape to take different types of spatial indicators into account to simulate concurrence. The result is an assessment of the spatial distribution of risks within an urban area in the case of an earthquake and its secondary threats, using an inductive method. The results show the principal capabilities of remote sensing to contribute to the identification of physical and demographic aspects of vulnerability, as well as provide indicators for the spatial distribution of natural hazards. Aspects of social, economic or political indicators represent limitations of remote sensing for an assessment complying with the holistic risk framework.

A new remote sensing-based carbon sequestration potential index (CSPI): A tool to support land carbon management

2021 ◽  
Vol 494 ◽  
pp. 119343
Author(s):  
Adrián Pascual ◽  
Christian P. Giardina ◽  
Paul C. Selmants ◽  
Leah J. Laramee ◽  
Gregory P. Asner
Keyword(s):  
Remote Sensing ◽  
Carbon Sequestration ◽  
Carbon Management ◽  
Carbon Sequestration Potential ◽  
Potential Index ◽  
Sequestration Potential

scholarly journals Semantic segmentation of PolSAR image data using advanced deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rajat Garg ◽  
Anil Kumar ◽  
Nikunj Bansal ◽  
Manish Prateek ◽  
Shashi Kumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Urban Area ◽  
Urban Areas ◽  
Learning Algorithms ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

Sign in / Sign up

Export Citation Format

Share Document