scholarly journals CLOUD-DESKTOP REMOTE SENSING DATA MANAGEMENT TO ENSURE TIME SERIES ANALYSIS, INTEGRATION OF QGIS AND GOOGLE EARTH ENGINE

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 553-557
Author(s):  
E. Panidi ◽  
I. Rykin ◽  
P. Kikin ◽  
A. Kolesnikov
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Data Storage ◽  
Remote Sensing Data ◽  
Growing Season ◽  
Data Representation ◽  
Google Earth ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Google Earth Engine

Abstract. Our context research is conducted to investigate the possibility of common application of the remote sensing and ground-based monitoring data to detection and observation of the dynamics and change in climate and vegetation cover parameters. We applied the analysis of the annual graphs of Normalized Difference Water Index to estimate the length and time frames of growing seasons. Basing on previously gained results, we concluded that we can use the Index-based monitoring of growing season parameters as a relevant technique. We are working on automation of computations that can be applied to processing satellite imagery, computing Normalized Difference Water Index time series (in the forms of maps and annual graphs), and estimation of growing season parameters. As currently used data amounts are big (or up-to-big) geospatial data, we use the Google Earth Engine platform to process initial datasets. Our currently described experimental work incorporates investigation of the possibilities for integration of cloud computing data storage and processing with client-side data representation in universal desktop GISs. To ensure our study needs we developed a prototype of a QGIS plugin capable to run processing in GEE and represent results in QGIS.

Fusion of Sentinel-1 radar and Sentinel-2 MSI imagery for water extraction in Tibetan plateau

2020 ◽  
Author(s):  
Dan Li ◽  
Baosheng Wu ◽  
Bowei Chen ◽  
Yanjun Wang ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radar Data ◽  
Google Earth ◽  
Water Extraction ◽  
Tibet Plateau ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Google Earth Engine ◽  
Optical Imagery ◽  
Sentinel 2

<p><strong>Abstract:</strong> Water plays a vital role in plants, animals and human survival, as well as water resources planning and protection. The spatial and temporal changes of rivers have a profound impact on climate change and the scientific protection of the regional ecological environment in Qingzang-Tibet plateau. Due to the influence of snow and cloud cover, optical remote sensing images in this region have less effective coverage. Many researches in the past mainly faced the challenge of misclassification caused by shadows from cloud and mountain. In this study, we proposed a method to improve the extraction of rivers by reducing the effect of shadows by fusing Sentinel-1 radar data and Sentinel-2 optical imagery. For the optical imagery, water indices including MNDWI (Modified Normalized Difference Water Index) and RNDWI (Revised Normalized Difference Water Index) and morphological operations were used to extract the river coverage. In addition, radar data is used to extract water in areas where there is no optical image coverage or where optical images are misclassified by using a combination of both the histogram and Otsu threshold methods. The GEE (Google Earth Engine) platform is used to implement the analysis using two classification datasets at a regional level. Relevant results from Sentinel-1 and Sentinel-2 data showed that the RNDWI has a more accurate water extraction results in this region. We further compared the final river width results with the manually measured samples from Google Earth and situ data of hydrological stations for accuracy assessment. The R<sup>2 </sup>value is 0.90, and the standard deviation is 18.663m. The river width can be estimated well by this method, which can provide basic data for the study of water in depopulated zone.</p><p><strong>Keywords: </strong>Remote sensing, shadow removal, water extraction, water index, Otsu threshold, Google Earth Engine</p>

scholarly journals Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

2021 ◽  
Vol 13 (8) ◽  
pp. 1433
Author(s):  
Shobitha Shetty ◽  
Prasun Kumar Gupta ◽  
Mariana Belgiu ◽  
S. K. Srivastav
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Sampling ◽  
Sampling Design ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Multi Temporal ◽  
Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

scholarly journals Agricultural Expansion in Mato Grosso from 1986–2000: A Bayesian Time Series Approach to Tracking Past Land Cover Change

2020 ◽  
Vol 12 (4) ◽  
pp. 688 ◽  
Author(s):  
Jacky Lee ◽  
Jeffrey A. Cardille ◽  
Michael T. Coe
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Spatial Resolution ◽  
Processing Speed ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Wide Audience ◽  
Data Set ◽  
Mato Grosso

Landsat 5 has produced imagery for decades that can now be viewed and manipulated in Google Earth Engine, but a general, automated way of producing a coherent time series from these images—particularly over cloudy areas in the distant past—is elusive. Here, we create a land use and land cover (LULC) time series for part of tropical Mato Grosso, Brazil, using the Bayesian Updating of Land Cover: Unsupervised (BULC-U) technique. The algorithm built backward in time from the GlobCover 2009 data set, a multi-category global LULC data set at 300 m resolution for the year 2009, combining it with Landsat time series imagery to create a land cover time series for the period 1986–2000. Despite the substantial LULC differences between the 1990s and 2009 in this area, much of the landscape remained the same: we asked whether we could harness those similarities and differences to recreate an accurate version of the earlier LULC. The GlobCover basis and the Landsat-5 images shared neither a common spatial resolution nor time frame, But BULC-U successfully combined the labels from the coarser classification with the spatial detail of Landsat. The result was an accurate fine-scale time series that quantified the expansion of deforestation in the study area, which more than doubled in size during this time. Earth Engine directly enabled the fusion of these different data sets held in its catalog: its flexible treatment of spatial resolution, rapid prototyping, and overall processing speed permitted the development and testing of this study. Many would-be users of remote sensing data are currently limited by the need to have highly specialized knowledge to create classifications of older data. The approach shown here presents fewer obstacles to participation and allows a wide audience to create their own time series of past decades. By leveraging both the varied data catalog and the processing speed of Earth Engine, this research can contribute to the rapid advances underway in multi-temporal image classification techniques. Given Earth Engine’s power and deep catalog, this research further opens up remote sensing to a rapidly growing community of researchers and managers who need to understand the long-term dynamics of terrestrial systems.

scholarly journals AUTOMATED GIS-BASED TECHNIQUE FOR EVALUATION OF INDIRECT GROWING SEASON ESTIMATIONS

2019 ◽  
Vol XLII-4/W14 ◽  
pp. 209-211
Author(s):  
I. Rykin ◽  
E. Panidi ◽  
V. Tsepelev
Keyword(s):  
Meteorological Data ◽  
Growing Season ◽  
Google Earth ◽  
Water Quantity ◽  
Special Significance ◽  
Water Index ◽  
Atmospheric Scattering ◽  
The Past ◽  
Normalized Difference Water Index ◽  
Automated Technique

<p><strong>Abstract.</strong> This article is based on NDWI (Normalized Difference Water Index) which is automatically computed from the daily MODIS data. The main purpose of the article is to tell how the evaluation of NDWI-based growing season estimations can be automated. The NDWI is used as an indicator of liquid water quantity in vegetation, which is less sensitive to atmospheric scattering effect then the famous growing index (NDVI). The NDWI is computed using cloud-based platform (Google Earth Engine was applied) and compared with the daily meteorological data. The available meteorological data is collected for the past 130 years and NDWI data is collecting for the past 20 years. An automated technique has been probated on the example of republic of Komi, as it has a different climate forming factors. This approach can be used to evaluate growing season estimations for other territories that contain vegetation. Due to the accumulated amount of data, the study is relevant and has a special significance for areas with sparse hydrometeorological network.</p>

scholarly journals SCIDB BASED FRAMEWORK FOR STORAGE AND ANALYSIS OF REMOTE SENSING BIG DATA

2019 ◽  
Vol XLII-5/W3 ◽  
pp. 43-47
Author(s):  
A. Joshi ◽  
E. Pebesma ◽  
R. Henriques ◽  
M. Appel
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Big Data ◽  
Time Series Analysis ◽  
Data Storage ◽  
Satellite Imagery ◽  
Distributed Storage ◽  
Remote Sensing Data ◽  
Observation Data ◽  
Series Analysis

Abstract. Earth observation data of large part of the world is available at different temporal, spectral and spatial resolution. These data can be termed as big data as they fulfil the criteria of 3 Vs of big data: Volume, Velocity and Variety. The size of image in archives are multiple petabyte size, the size is growing continuously and the data have varied resolution and usages. These big data have variety of applications including climate change study, forestry application, agricultural application and urban planning. However, these big data also possess challenge of data storage, management and high computational requirement for processing. The solution to this computational and data management requirements is database system with distributed storage and parallel computation.In this study SciDB, an array-based database is used to store, manage and process multitemporal satellite imagery. The major aim of this study is to develop SciDB based scalable solution to store and perform time series analysis on multi-temporal satellite imagery. Total 148 scene of landsat image of 10 years period between 2006 and 2016 were stored as SciDB array. The data was then retrieved, processed and visualized. This study provides solution for storage of big RS data and also provides workflow for time series analysis of remote sensing data no matter how large is the size.

scholarly journals The NDVI algorithm utilization on the google earth engine platform to monitor changes in forest density in mining area

2021 ◽  
Vol 886 (1) ◽  
pp. 012100
Author(s):  
Munajat Nursaputra ◽  
Siti Halimah Larekeng ◽  
Nasri ◽  
Andi Siady Hamzah
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Large Scale ◽  
Vegetation Index ◽  
Forest Degradation ◽  
Google Earth ◽  
Aerial Images ◽  
Forest Monitoring ◽  
Forest Density ◽  
Google Earth Engine

Abstract Periodic forest monitoring needs to be done to avoid forest degradation. In general, forest monitoring can be conducted manually (field surveys) or using technological innovations such as remote sensing data derived from aerial images (drone results) or cloud computing-based image processing. Currently, remote sensing technology provides large-scale forest monitoring using multispectral sensors and various vegetation index processing algorithms. This study aimed to evaluate the use of the Google Earth Engine (GEE) platform, a geospatial dataset platform, in the Vale Indonesia mining concession area to improve accountable forest monitoring. This platform integrates a set of programming methods with a publicly accessible time-series database of satellite imaging services. The method used is NDVI processing on Landsat multispectral images in time series format, which allows for the description of changes in forest density levels over time. The results of this NDVI study conducted on the GEE platform have the potential to be used as a tool and additional supporting data for monitoring forest conditions and improvement in mining regions.

scholarly journals Analysis of riverbank changes in Ho Chi Minh city in the period 1989 – 2015

2019 ◽  
Vol 2 (2) ◽  
pp. 80-88
Author(s):  
Thu Trang Hoang ◽  
Khoi Nguyen Dao ◽  
Loi Thi Pham ◽  
Hong Van Nguyen
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Ho Chi Minh City ◽  
Landsat 8 ◽  
River Bank ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Multi Temporal ◽  
Erosion And Accretion ◽  
Landsat Satellite

The objective of this study was to analyze the changes of riverbanks in Ho Chi Minh City for the period 1989-2015 using remote sensing and GIS. Combination of Modified Normalized Difference Water Index (MNDWI) and thresholding method was used to extract the river bank based on the multi-temporal Landsat satellite images, including 12 Landsat 4-5 (TM) images and 2 Landsat 8 images in the period 1989-2015. Then, DSAS tool was used to calculate the change rates of river bank. The results showed that, the processes of erosion and accretion intertwined but most of the main riverbanks had erosion trend in the period 1989-2015. Specifically, the Long Tau River, Sai Gon River, Soai Rap River had erosion trends with a rate of about 10.44 m/year. The accretion process mainly occurred in Can Gio area, such as Dong Tranh river and Soai Rap river with a rate of 8.34 m/year. Evaluating the riverbank changes using multi-temporal remote sensing data may contribute an important reference to managing and protecting the riverbanks.

scholarly journals Monitoring Urban Expansion Using Remote-Sensing Data Aided by Google Earth Engine

2021 ◽  
Vol 3 (1) ◽  
pp. 1-8
Author(s):  
Majid Aghlmand ◽  
Gordana Kaplan
Keyword(s):  
Remote Sensing ◽  
Accuracy Assessment ◽  
Urban Expansion ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Support Vector ◽  
Sensing Data ◽  
Google Earth Engine ◽  
Landsat Satellite ◽  
The City

Urbanizationis accompanied by rapid social and economic development, while the process of urbanization causes the degradation of the natural ecology. Direct loss in vegetation biomass from areas with a high probability of urban expansion can contribute to the total emissions from tropical deforestation and land-use change. Monitoring of urban expansion is essential for more efficient urban planning, protecting the ecosystem and the environment. In this paper, we use remote sensing data aided by Google Earth Engine (GEE) to evaluate the urban expansion of the city of Isfahan in the last thirty years. Thus, in this paper we use Landsat satellite images from 1986 and 2019, integrated into GEE, implementing Support vector machine (SVM) classification method. The accuracy assessment for the classified images showed high accuracy (95-96%), while the results showed a significant increase in the urban area of the city of Isfahan, occupying more than 70% of the study area. For future studies, we recommend a more detailed investigation about the city expansion and the negative impacts that may occur due to urban expansion.

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