Mapping pond aquaculture for the entire coastal zone of Asia using high resolution Sentinel-1 and Sentinel-2 data 

2021 ◽  
Author(s):  
Marco Ottinger ◽  
Felix Bachofer ◽  
Juliane Huth
Keyword(s):  
Coastal Zone ◽  
Time Series Data ◽  
Earth Observation ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
World Population ◽  
Total Production ◽  
Pond Aquaculture ◽  
Continental Scale ◽  
Sentinel 2

<p>Asia is the world’s most important region for aquaculture and generates almost 90 percent of the total production. The farming of fish and shrimp in land-based aquaculture systems expanded mainly along the shorelines of South Asia, Southeast Asia, and East Asia, and is a primary protein source for millions of people. The production of fish and shrimp in freshwater and brackish water ponds in coastal regions of Asia has increased rapidly since the 1990s due to the rising demand for protein-rich foods from a growing (world) population. The aquaculture sector generates income, employment and contributes to food security, has become a billion-dollar industry with high socio-economic value, but has also led to severe environmental degradation. In this regard, geospatial information on aquaculture can support the management of this growing food sector for the sustainable development of coastal ecosystems, resources and human health.</p><p>With free and open access to the rapidly growing volume of data from the European Sentinel satellites as well as using machine learning algorithms and cloud computing services, we extracted coastal aquaculture at a continental-scale. We present a multi-sensor approach which utilizes Earth Observation time series data for the mapping of pond aquaculture within the entire Asian coastal zone, defined as a buffer of 200km from the coastline. In this research, we developed an object-based framework to detect and extract aquaculture at single pond level based on temporal features derived from high spatial resolution SAR and optical satellite acquired from the Sentinel-1 and Sentinel-2 satellites. In a second step, we performed spatial and statistical data analyses of the Earth observation derived aquaculture dataset to investigate spatial distribution and to identify production hotspots in various administrative units at regional, national, and sub-national scale.</p>

scholarly journals Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series

2021 ◽  
Vol 14 (1) ◽  
pp. 153
Author(s):  
Marco Ottinger ◽  
Felix Bachofer ◽  
Juliane Huth ◽  
Claudia Kuenzer
Keyword(s):  
Time Series ◽  
Coastal Zone ◽  
Earth Observation ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
World Population ◽  
Total Production ◽  
Pond Aquaculture ◽  
Continental Scale ◽  
Sentinel 2

Asia dominates the world’s aquaculture sector, generating almost 90 percent of its total annual global production. Fish, shrimp, and mollusks are mainly farmed in land-based pond aquaculture systems and serve as a primary protein source for millions of people. The total production and area occupied for pond aquaculture has expanded rapidly in coastal regions in Asia since the early 1990s. The growth of aquaculture was mainly boosted by an increasing demand for fish and seafood from a growing world population. The aquaculture sector generates income and employment, contributes to food security, and has become a billion-dollar industry with high socio-economic value, but has also led to severe environmental degradation. In this regard, geospatial information on aquaculture can support the management of this growing food sector for the sustainable development of coastal ecosystems, resources, and human health. With free and open access to the rapidly growing volume of data from the Copernicus Sentinel missions as well as machine learning algorithms and cloud computing services, we extracted coastal aquaculture at a continental scale. We present a multi-sensor approach that utilizes Earth observation time series data for the mapping of pond aquaculture within the entire Asian coastal zone, defined as the onshore area up to 200 km from the coastline. In this research, we developed an object-based framework to detect and extract aquaculture at a single-pond level based on temporal features derived from high-spatial-resolution SAR and optical satellite data acquired from the Sentinel-1 and Sentinel-2 satellites. In a second step, we performed spatial and statistical data analyses of the Earth-observation-derived aquaculture dataset to investigate spatial distribution and identify production hotspots at various administrative units at regional, national, and sub-national scale.

The identification of meaningful variables from Sentinel-2 time series data for effective tree species classification

2021 ◽  
Author(s):  
Tiziana L. Koch ◽  
Marius Rüetschi ◽  
Lars T. Waser
Keyword(s):  
Time Series ◽  
Tree Species ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Calculated Data ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Species Classification ◽  
Swiss Canton ◽  
Sentinel 2

<p>Sentinel-2 time series provide large amounts of data and information which can be easily used to classify tree species with machine learning algorithms. In addition to the original Sentinel-2 bands, further data such as indices, phenological metrics or even synthetical images can be derived. While tree species classifications highly benefit from such additional data resulting in improved prediction accuracy, severe drawbacks have to be considered - For large data sets, large storage is needed, the computation time expands and a linkage to ecological or phenological reasons behind the usage of these variables can hardly be drawn. Therefore, the implemented variables should be limited to the ones, which are meaningful and on the same time providing the best prediction accuracy. To identify meaningful variables from original Sentinel-2 images and the additionally calculated data first we used basic correlation analyses and subsequently feature selection methods in combination with the commonly used Random Forest algorithm. We classified the most common forest tree species in the Swiss canton of Grisons, which is mountainous and characterized by diverse landscapes. The presented approach will lead to higher efficiency for classifying tree species and additionally provides potential conclusions regarding ecological patterns beyond the distinction of tree species by remote sensing data. Moreover, the proposed approach can also be used to improve classifications or predictions of other outcome variables for vegetated areas with Sentinel-2.</p>

scholarly journals Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

2020 ◽  
Vol 12 (11) ◽  
pp. 1876 ◽  
Author(s):  
Katsuto Shimizu ◽  
Tetsuji Ota ◽  
Nobuya Mizoue ◽  
Hideki Saito
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Forest Monitoring ◽  
Series Data ◽  
Landsat 8 ◽  
Multi Temporal ◽  
Sentinel 2

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

scholarly journals Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building

2021 ◽  
Vol 13 (3) ◽  
pp. 67
Author(s):  
Eric Hitimana ◽  
Gaurav Bajpai ◽  
Richard Musabe ◽  
Louis Sibomana ◽  
Jayavel Kayalvizhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Support Vector ◽  
Human Beings ◽  
Feed Forward Network

Many countries worldwide face challenges in controlling building incidence prevention measures for fire disasters. The most critical issues are the localization, identification, detection of the room occupant. Internet of Things (IoT) along with machine learning proved the increase of the smartness of the building by providing real-time data acquisition using sensors and actuators for prediction mechanisms. This paper proposes the implementation of an IoT framework to capture indoor environmental parameters for occupancy multivariate time-series data. The application of the Long Short Term Memory (LSTM) Deep Learning algorithm is used to infer the knowledge of the presence of human beings. An experiment is conducted in an office room using multivariate time-series as predictors in the regression forecasting problem. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. The information collected was applied to the LSTM algorithm and compared with other machine learning algorithms. The compared algorithms are Support Vector Machine, Naïve Bayes Network, and Multilayer Perceptron Feed-Forward Network. The outcomes based on the parametric calibrations demonstrate that LSTM performs better in the context of the proposed application.

Prediction and Analysis of Gold Prices using Ensemble Machine Learning Algorithms

2021 ◽  
Vol 9 (VI) ◽  
pp. 4367-4374
Author(s):  
Gudipally Chandrashakar
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Gold Price ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Gradient Boosting ◽  
Support Vector ◽  
Average Value ◽  
Ensemble Machine Learning

In this article, we used historical time series data up to the current day gold price. In this study of predicting gold price, we consider few correlating factors like silver price, copper price, standard, and poor’s 500 value, dollar-rupee exchange rate, Dow Jones Industrial Average Value. Considering the prices of every correlating factor and gold price data where dates ranging from 2008 January to 2021 February. Few algorithms of machine learning are used to analyze the time-series data are Random Forest Regression, Support Vector Regressor, Linear Regressor, ExtraTrees Regressor and Gradient boosting Regression. While seeing the results the Extra Tree Regressor algorithm gives the predicted value of gold prices more accurately.

scholarly journals Risk Monitoring and Quantitative Results of Various Attributes of Machine Learning Algorithms with a Time Series Data

2019 ◽  
Vol 8 (11) ◽  
pp. 4018-4022
Keyword(s):  
Machine Learning ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Machine Learning Algorithm ◽  
Risk Modelling ◽  
Risk Monitoring ◽  
Quantitative Results

The aim of this research is to do risk modelling after analysis of twitter posts based on certain sentiment analysis. In this research we analyze posts of several users or a particular user to check whether they can be cause of concern to the society or not. Every sentiment like happy, sad, anger and other emotions are going to provide scaling of severity in the conclusion of final table on which machine learning algorithm is applied. The data which is put under the machine learning algorithms are been monitored over a period of time and it is related to a particular topic in an area

scholarly journals Growth in Vegetable Production and its Decomposition in Karnataka

2021 ◽  
Vol 66 (1) ◽  
Author(s):  
Afrin Zainab Bi
Keyword(s):  
Time Series Data ◽  
Leafy Vegetables ◽  
Series Data ◽  
Vegetable Production ◽  
Annual Growth Rate ◽  
Total Production ◽  
Great Opportunity ◽  
Nutritional Security ◽  
Cole Crops ◽  
Indian Agriculture

Vegetables are important constituents of Indian agriculture and nutritional security. Along with health benefits, vegetables help in flourishing countries economy, as it provides a great opportunity for income and employment generation for the farming sector. The study has an objective to understand the extent of growth each vegetable experiencing and to derive the major factor for the growth in Karnataka, utilizing time-series data. The total area showed an increasing trend over the period with about 40 % increase in a span of two decades. However, figures for increased production were more appealing than its area, as it has shown 60 % increase. Total production of vegetables in Karnataka has increased from 42 lakh tonnes in 1998-99 to 68 lakh tonnes in 2018-19, with an annual growth rate of 3.9 %. The highest growth in production was observed in onion (7.5% annually) followed by tomato and cole crops. The area effect was the most responsible factor for increasing production of tomato, onion, guards, cole crops and other vegetables group. Thus, in effect for overall vegetables, it is 66 % of the total increased production effect. However, for potato and leafy vegetables, only yield effect was found to be positively contributing to the production.

scholarly journals Comparative Performance of Machine Learning Algorithms for Cryptocurrency Forecasting

2018 ◽  
Vol 11 (3) ◽  
pp. 1121 ◽  
Author(s):  
Nor Azizah Hitam ◽  
Amelia Ritahani Ismail
Keyword(s):  
Machine Learning ◽  
Time Series Data ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Support Vector ◽  
Small Range ◽  
Accuracy Rate ◽  
Comparative Performance ◽  
Vector Machines

Machine Learning is part of Artificial Intelligence that has the ability to make future forecastings based on the previous experience. Methods has been proposed to construct models including machine learning algorithms such as Neural Networks (NN), Support Vector Machines (SVM) and Deep Learning. This paper presents a comparative performance of Machine Learning algorithms for cryptocurrency forecasting. Specifically, this paper concentrates on forecasting of time series data. SVM has several advantages over the other models in forecasting, and previous research revealed that SVM provides a result that is almost or close to actual result yet also improve the accuracy of the result itself. However, recent research has showed that due to small range of samples and data manipulation by inadequate evidence and professional analyzers, overall status and accuracy rate of the forecasting needs to be improved in further studies. Thus, advanced research on the accuracy rate of the forecasted price has to be done.

Sign in / Sign up

Export Citation Format

Share Document