scholarly journals EXPLOITING SENTINEL-1 SAR TIME SERIES TO DETECT GRASSLANDS IN THE NORTHERN BRAZILIAN AMAZON

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 259-265
Author(s):  
A. F. Carneiro ◽  
W. V. Oliveira ◽  
S. J. S. Sant'Anna ◽  
J. Doblas ◽  
D. V. Vaz
Keyword(s):  
Time Series ◽  
Cloud Computing ◽  
Remote Sensing Data ◽  
Brazilian Amazon ◽  
Google Earth ◽  
Data Availability ◽  
Kappa Index ◽  
Computing Platform ◽  
Cloud Computing Platform ◽  
Sar Imagery

Abstract. Recent advances in cloud-computing technologies and remote sensing data availability foster the development of studies based on the analysis of optical and SAR imagery time series. In this paper, we assess the potential of Sentinel-1 imagery time series for grassland detection in the northern Brazilian Amazon. We used the Google Earth Engine cloud-computing platform as an alternative to obtain and analyse Sentinel-1 imagery, acquired from 2017 to 2018 over the region of Mojuí dos Campos/PA, Brazil. We extracted several temporal metrics from the imagery time series and used the Random Forest algorithm to perform the classification. In addition, we analysed the time series considering different channels, including the VV and VH polarizations, both separately and in combination, and the CR, RGI and NL indices. We could efficiently discriminate areas of grasslands from forest and agricultural crops using either VH time features or features extracted from the combination of both VV and VH polarizations. The classification map that resulted from the combination of VV and VH data presented the highest accuracy, with an overall accuracy of 95.33% and a 0.93 kappa index. Despite simple, the approach adopted in this paper showed potential to differ grasslands from areas of agriculture and forest in the northern Brazilian Amazon.

scholarly journals Land Cover Classification Based on Cloud Computing Platform

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Nghia Viet Nguyen ◽  
Thu Hoai Thi Trinh ◽  
Hoa Thi Pham ◽  
Trang Thu Thi Tran ◽  
Lan Thi Pham ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Land Cover ◽  
Spatial Data ◽  
Google Earth ◽  
Computer Hardware ◽  
Commercial Software ◽  
Computing Platform ◽  
Land Cover Data ◽  
Cloud Computing Platform ◽  
Google Earth Engine

Land cover is a critical factor for climate change and hydrological models. The extraction of land cover data from remote sensing images has been carried out by specialized commercial software. However, the limitations of computer hardware and algorithms of the commercial software are costly and make it take a lot of time, patience, and skills to do the classification. The cloud computing platform Google Earth Engine brought a breakthrough in 2010 for analyzing and processing spatial data. This study applied Object-based Random Forest classification in the Google Earth Engine platform to produce land cover data in 2010 in the Vu Gia - Thu Bon river basin. The classification results showed 7 categories of land cover consisting of plantation forest, natural forest, paddy field, urban residence, rural residence, bare land, and water surface, with an overall accuracy of 73.9% and kappa of 0.70.

scholarly journals Identification of Short-Rotation Eucalyptus Plantation at Large Scale Using Multi-Satellite Imageries and Cloud Computing Platform

2020 ◽  
Vol 12 (13) ◽  
pp. 2153 ◽  
Author(s):  
Xinping Deng ◽  
Shanxin Guo ◽  
Luyi Sun ◽  
Jinsong Chen
Keyword(s):  
Time Series ◽  
Cloud Computing ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Testing ◽  
Computing Platform ◽  
Short Rotation ◽  
Red Edge ◽  
Eucalyptus Plantations ◽  
Cloud Computing Platform ◽  
Sentinel 2

A new method to identify short-rotation eucalyptus plantations by exploring both the changing pattern of vegetation indices due to tree crop rotation and spectral characteristics of eucalyptus in the red-edge region is presented. It can be adopted to produce eucalyptus maps of high spatial resolution (30 m) at large scales, with the use of open remote sensing images from Landsat 8 Operational Land Imager (OLI), MODerate resolution Imaging Spectroradiometer (MODIS), and Sentinel-2 MultiSpectral Instrument (MSI), as well as a free cloud computing platform, Google Earth Engine (GEE). The method is composed of three main steps. First, a time series of Enhanced Vegetation Index (EVI) is constructed from Landsat data for each pixel, and a statistical hypothesis testing is followed to determine whether the pixel belongs to a tree plantation or not based on the idea that tree crops should be harvested in a specific period. Then, a broadleaf/needleleaf classification is applied to distinguish eucalyptus from coniferous trees such as pine and fir using the red-edge bands of Sentinel-2 data. Refinements based on superpixel are performed at last to remove the salt-and-pepper effects resulted from per-pixel detection. The proposed method allows gaps in the time series that are very common in tropical and subtropical regions by employing time series segmentation and statistical hypothesis testing, and could capture forest disturbances such as conversion of natural forest or agricultural lands to eucalyptus plantations emerged in recent years by using a short observing time. The experiment in Guangxi province of China demonstrated that the method had an overall accuracy of 87.97%, with producer’s accuracy of 63.85% and user’s accuracy of 66.89% for eucalyptus plantations.

scholarly journals WATER QUALITY MONITORING OVER FINGER LAKES REGION USING SENTINEL-2 IMAGERY ON GOOGLE EARTH ENGINE CLOUD COMPUTING PLATFORM

2021 ◽  
Vol V-3-2021 ◽  
pp. 279-283
Author(s):  
R. M. Khan ◽  
B. Salehi ◽  
M. Mahdianpari ◽  
F. Mohammadimanesh
Keyword(s):  
Water Quality ◽  
Cloud Computing ◽  
Agricultural Runoff ◽  
Google Earth ◽  
Computing Platform ◽  
Finger Lakes ◽  
Cloud Computing Platform ◽  
Google Earth Engine ◽  
Sentinel 2 ◽  
Finger Lakes Region

Abstract. Surface water quality is degrading continuously both due to natural and anthropogenic causes. There are several indicators of water quality, among which sediment loading is mainly determined by turbidity. Normalized Difference Water Index (NDWI) is one indirect measure of sediments present in water. This study focuses on detecting and monitoring sediments through NDWI over the Finger Lakes region, New York. Time series analysis is performed using Sentinel 2 imagery on the Google Earth Engine (GEE) platform. Finger Lakes region holds high socio-economic value because of tourism, water-based recreation, industry, and agriculture sector. The deteriorating water quality within the Finger Lake region has been reported based on ground sampling techniques. This study takes advantage of a cloud computing platform and medium resolution atmospherically corrected satellite imagery to detect and analyse water quality through sediment detection. In addition, precipitation data is used to understand the underlying cause of sediment increase. The results demonstrate the amount of sediments is greater in the early spring and summer months compared to other seasons. This can be due to the agricultural runoff from the nearing areas as a result of high precipitation. The results confirm the necessity for monitoring the quality of these lakes and understanding the underlying causes, which are beneficial for all the stakeholders to devise appropriate policies and strategies for timely preservation of the water quality.

Study on Secure Dynamic Covering Algorithm for E-Logistics Information in a Cloud Computing Platform

2021 ◽  
pp. 1097-1111
Author(s):  
Yan He
Keyword(s):  
Time Series ◽  
Cloud Computing ◽  
Clustering Algorithm ◽  
Experimental Results ◽  
Distribution Model ◽  
Information Flows ◽  
Data Network ◽  
Computing Platform ◽  
Cloud Computing Platform ◽  
Logistics Information

For traditional coverage method, local adaptive weighted search is adopted for subspace reconstruction of e-logistics information, which requires significant iterative computations and leads to large coverage errors and unsatisfactory results. This paper proposes a secure dynamic covering algorithm for e-logistics information based on the basis of directional clustering for the envelope of optimization solution vectors. A data network distribution model of e-logistics information on a cloud computing platform is constructed to extract features of e-logistics information and to construct time series of information flows. The directional clustering algorithm for the envelope of optimization solution vectors is introduced to schedule features of e-logistics information. The experimental results show that the proposed algorithm has higher coverage rate, smaller error, and increases performance of e-logistics information transmission and higher application value on a cloud computing platform.

Sign in / Sign up

Export Citation Format

Share Document