scholarly journals A New Classification Algorithm integrating Multi-temporal Data.

1992 ◽  
Vol 31 (4) ◽  
pp. 45-54
Author(s):  
Sunpyo HONG ◽  
Kiyonari FUKUE ◽  
Haruhisa SHIMODA ◽  
Toshibumi SAKATA
Keyword(s):  
Classification Algorithm ◽  
Temporal Data ◽  
New Classification ◽  
Multi Temporal

Land use change around hydroelectric dams using Landsat multi-temporal data: A challenge for a sustainable environment in Mexico

2021 ◽  
pp. 1-14
Author(s):  
Zuleyma Zarco-González ◽  
Octavio Monroy-Vilchis ◽  
Xanat Antonio-Némiga ◽  
Angel Rolando Endara-Agramont
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Temporal Data ◽  
Hydroelectric Dams ◽  
Sustainable Environment ◽  
Multi Temporal

scholarly journals Mapping Smallholder Maize Farms Using Multi-Temporal Sentinel-1 Data in Support of the Sustainable Development Goals

2021 ◽  
Vol 13 (9) ◽  
pp. 1666
Author(s):  
Zinhle Mashaba-Munghemezulu ◽  
George Johannes Chirima ◽  
Cilence Munghemezulu
Keyword(s):  
Sustainable Development ◽  
Food Insecurity ◽  
Sustainable Development Goals ◽  
Smallholder Farmers ◽  
Support Vector ◽  
Temporal Data ◽  
The Sustainable Development ◽  
Multi Temporal ◽  
Production Area ◽  
Development Goals

Reducing food insecurity in developing countries is one of the crucial targets of the Sustainable Development Goals (SDGs). Smallholder farmers play a crucial role in combating food insecurity. However, local planning agencies and governments do not have adequate spatial information on smallholder farmers, and this affects the monitoring of the SDGs. This study utilized Sentinel-1 multi-temporal data to develop a framework for mapping smallholder maize farms and to estimate maize production area as a parameter for supporting the SDGs. We used Principal Component Analysis (PCA) to pixel fuse the multi-temporal data to only three components for each polarization (vertical transmit and vertical receive (VV), vertical transmit and horizontal receive (VH), and VV/VH), which explained more than 70% of the information. The Support Vector Machine (SVM) and Extreme Gradient Boosting (Xgboost) algorithms were used at model-level feature fusion to classify the data. The results show that the adopted strategy of two-stage image fusion was sufficient to map the distribution and estimate production areas for smallholder farms. An overall accuracy of more than 90% for both SVM and Xgboost algorithms was achieved. There was a 3% difference in production area estimation observed between the two algorithms. This framework can be used to generate spatial agricultural information in areas where agricultural survey data are limited and for areas that are affected by cloud coverage. We recommend the use of Sentinel-1 multi-temporal data in conjunction with machine learning algorithms to map smallholder maize farms to support the SDGs.

Use of Sentinel-1 Dual Polarization Multi-Temporal Data with Gray Level Co-Occurrence Matrix Textural Parameters for Building Damage Assessment

2021 ◽  
Vol 31 (2) ◽  
pp. 240-250
Author(s):  
Asset Akhmadiya ◽  
Nabi Nabiyev ◽  
Khuralay Moldamurat ◽  
Kanagat Dyussekeyev ◽  
Sabyrzhan Atanov
Keyword(s):  
Damage Assessment ◽  
Building Damage ◽  
Gray Level ◽  
Temporal Data ◽  
Dual Polarization ◽  
Multi Temporal ◽  
Textural Parameters ◽  
Occurrence Matrix

scholarly journals Raising awareness of the cultural, architectural, and perceptive values of historic gardens and related landscapes: panoramic cones and multi-temporal data

2020 ◽  
Author(s):  
Alberta Cazzani ◽  
Carlotta Maria Zerbi ◽  
Raffaella Brumana ◽  
Anna Lobovikov-Katz
Keyword(s):  
Open Data ◽  
Historical Maps ◽  
Temporal Data ◽  
Educational Values ◽  
Policy Makers ◽  
Cadastral Maps ◽  
Raising Awareness ◽  
Multi Temporal ◽  
History Of ◽  
Historic Maps

AbstractHistoric gardens and their related landscapes are often experienced only for their social, aesthetic, and environmental resources, yet their cultural, architectural, and perceptive significance is often ignored. The paper demonstrates how historic and educational values of historic gardens and related landscapes can be revealed by combining historic maps, reading perspective cones, and also applying advanced digital and educational methods and techniques. Historical maps, especially military and cadastral maps, associated with historical iconography, can provide us with a lot of information to study historical gardens and also to define conservation and valorization plans that are related to the history of the site: geomatics tools to georeference and co-relate metric and non-metric historical maps provide growing useful outputs, that can be deployed through the use of Virtual Hubs, boosting the availability of content and the accessibility of open data for policy makers, experts, and non-expert members. Moreover, they can also support heritage education programs providing the opportunity to allow to understand the wealth of sites now simplified, in their system, with different functions and with a transformed context. The study of historic gardens involves the analysis of the landscape in its dynamism and complexity, defines tools that make users more aware of the richness of our heritage.

A New Classification Algorithm and a New Oversampling Method of Mapping Common Data Elements to the BRIDG Model

2021 ◽  
Author(s):  
Shengyu Li ◽  
Yulong Huang ◽  
Mohan Vamsi Kasukurthi ◽  
Jiajie Yang ◽  
Dongqi Li ◽  
...  
Keyword(s):  
Classification Algorithm ◽  
New Classification ◽  
Common Data Elements ◽  
Data Elements

Quantifying Urban Sprawl With Spatial Autocorrelation Techniques Using Multi-Temporal Satellite Data

2019 ◽  
pp. 1624-1644
Author(s):  
Gabriele Nolè ◽  
Rosa Lasaponara ◽  
Antonio Lanorte ◽  
Beniamino Murgante
Keyword(s):  
Remote Sensing ◽  
Spatial Autocorrelation ◽  
Urban Areas ◽  
Urban Expansion ◽  
Data Sets ◽  
Temporal Data ◽  
Landsat Images ◽  
Urban Settlements ◽  
Multi Temporal ◽  
Soil Consumption

This study deals with the use of satellite TM multi-temporal data coupled with statistical analyses to quantitatively estimate urban expansion and soil consumption for small towns in southern Italy. The investigated area is close to Bari and was selected because highly representative for Italian urban areas. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and geospatial data analysis to reveal spatial patterns. Analyses have been carried out using global and local spatial autocorrelation, applied to multi-date NASA Landsat images acquired in 1999 and 2009 and available free of charge. Moreover, in this paper each step of data processing has been carried out using free or open source software tools, such as, operating system (Linux Ubuntu), GIS software (GRASS GIS and Quantum GIS) and software for statistical analysis of data (R). This aspect is very important, since it puts no limits and allows everybody to carry out spatial analyses on remote sensing data. This approach can be very useful to assess and map land cover change and soil degradation, even for small urbanized areas, as in the case of Italy, where recently an increasing number of devastating flash floods have been recorded. These events have been mainly linked to urban expansion and soil consumption and have caused loss of human lives along with enormous damages to urban settlements, bridges, roads, agricultural activities, etc. In these cases, remote sensing can provide reliable operational low cost tools to assess, quantify and map risk areas.

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