Using multi-remote sensing data to assess Phragmites invasion of the Detroit river international wildlife refuge

2016 ◽  
Vol 13 (1) ◽  
pp. 44-52
Author(s):  
Xinxia Liu ◽  
Anbing Zhang ◽  
Hefeng Wang ◽  
Haixin Liu
Keyword(s):  
Time Series ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Landsat Images ◽  
Validation Data ◽  
Detroit River ◽  
Content Type ◽  
Wildlife Refuge ◽  
Integrated Time Series ◽  
Digital Orthophoto

Purpose This paper aims to develope an integrated image processing method to investigate the spatiotemporal dynamics of Phragmites invasion in the Detroit River International Wildlife Refuge on the basis of publically available sources. Design/methodology/approach This new approach integrates the standard time-series analysis of Landsat images with USDA National Agriculture Imagery Program (NAIP) imagery and USGS Digital Orthophoto Quarter Quads (DOQQ) datasets, which are either classified or manually interpreted with the aid of ground control points. Three different types of spatiotemporal dimensions are designed to test this integrated time-series image analysis method: the selected sites and selected time-points with high spatial resolution and sufficient validation data points, the intermediate time-series with continued yearly images and periodical validation data, and the long time-series with periodical images without enough validation data. The support vector machine (SVM) method was used to classify the Landast TM sequence images to detect the Phragmites invasion. Findings The habitat map produced by NAIP images and field collection data shows that the total Phragmites area of DRIWR in 2010 is 4221.87 acres without treatment areas and similar with the removed non-vegetation method. It is confirmed that the pre-classification method can obtain more accurate results. Originality value The test results show that the Landsat-5 data can be used for long-term environmental management and monitoring of Phragmites invasion and can achieve rehabilitation of invasion areas.

scholarly journals Statistical Stability and Spatial Instability in Mapping Forest Tree Species by Comparing 9 Years of Satellite Image Time Series

2019 ◽  
Vol 11 (21) ◽  
pp. 2512 ◽  
Author(s):  
Nicolas Karasiak ◽  
Jean-François Dejoux ◽  
Mathieu Fauvel ◽  
Jérôme Willm ◽  
Claude Monteil ◽  
...  
Keyword(s):  
Time Series ◽  
Tree Species ◽  
Cross Validation ◽  
Satellite Image ◽  
Support Vector ◽  
Validation Data ◽  
Spatial Instability ◽  
Classification Errors ◽  
Leave One Out ◽  
The Impact

Mapping forest composition using multiseasonal optical time series remains a challenge. Highly contrasted results are reported from one study to another suggesting that drivers of classification errors are still under-explored. We evaluated the performances of single-year Formosat-2 time series to discriminate tree species in temperate forests in France and investigated how predictions vary statistically and spatially across multiple years. Our objective was to better estimate the impact of spatial autocorrelation in the validation data on measurement accuracy and to understand which drivers in the time series are responsible for classification errors. The experiments were based on 10 Formosat-2 image time series irregularly acquired during the seasonal vegetation cycle from 2006 to 2014. Due to lot of clouds in the year 2006, an alternative 2006 time series using only cloud-free images has been added. Thirteen tree species were classified in each single-year dataset based on the Support Vector Machine (SVM) algorithm. The performances were assessed using a spatial leave-one-out cross validation (SLOO-CV) strategy, thereby guaranteeing full independence of the validation samples, and compared with standard non-spatial leave-one-out cross-validation (LOO-CV). The results show relatively close statistical performances from one year to the next despite the differences between the annual time series. Good agreements between years were observed in monospecific tree plantations of broadleaf species versus high disparity in other forests composed of different species. A strong positive bias in the accuracy assessment (up to 0.4 of Overall Accuracy (OA)) was also found when spatial dependence in the validation data was not removed. Using the SLOO-CV approach, the average OA values per year ranged from 0.48 for 2006 to 0.60 for 2013, which satisfactorily represents the spatial instability of species prediction between years.

An optimized hybrid model based on artificial intelligence for grape price forecasting

2019 ◽  
Vol 121 (12) ◽  
pp. 3247-3265
Author(s):  
Xiaoquan Chu ◽  
Yue Li ◽  
Dong Tian ◽  
Jianying Feng ◽  
Weisong Mu
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Hybrid Model ◽  
Adaptive Strategy ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Price Forecasting ◽  
Content Type ◽  
Model Based ◽  
Statistical Measures

Purpose The purpose of this paper is to propose an optimized hybrid model based on artificial intelligence methods, use the method of time series forecasting, to deal with the price prediction issue of China’s table grape. Design/methodology/approach The approaches follows the framework of “decomposition and ensemble,” using ensemble empirical mode decomposition (EEMD) to optimize the conventional price forecasting methods, and, integrating the multiple linear regression and support vector machine to build a hybrid model which could be applied in solving price series predicting problems. Findings The proposed EEMD-ADD optimized hybrid model is validated to be considered satisfactory in a case of China’ grape price forecasting in terms of its statistical measures and prediction performance. Practical implications This study would resolve the difficulties in grape price forecasting and provides an adaptive strategy for other agricultural economic predicting problems as well. Originality/value The paper fills the vacancy of concerning researches, proposes an optimized hybrid model integrating both classical econometric and artificial intelligence models to forecast price using time series method.

scholarly journals Prediction of Urban Area Expansion with Implementation of MLC, SAM and SVMs’ Classifiers Incorporating Artificial Neural Network Using Landsat Data

2021 ◽  
Vol 10 (8) ◽  
pp. 513
Author(s):  
Saeid Zare Naghadehi ◽  
Milad Asadi ◽  
Mohammad Maleki ◽  
Seyed-Mohammad Tavakkoli-Sabour ◽  
John Lodewijk Van Genderen ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Large Area ◽  
Landsat Images ◽  
Spectral Angle Mapper ◽  
Artificial Neural ◽  
Land Cover Maps

A reliable land cover (LC) map is essential for planners, as missing proper land cover maps may deviate a project. This study is focusing on land cover classification and prediction using three well known classifiers and remote sensing data. Maximum Likelihood classifier (MLC), Spectral Angle Mapper (SAM), and Support Vector Machines (SVMs) algorithms are used as the representatives for parametric, non-parametric and subpixel capable methods for change detection and change prediction of Urmia City (Iran) and its suburbs. Landsat images of 2000, 2010, and 2020 have been used to provide land cover information. The results demonstrated 0.93–0.94 overall accuracies for MLC and SVMs’ algorithms, but it was around 0.79 for the SAM algorithm. The MLC performed slightly better than SVMs’ classifier. Cellular Automata Artificial neural network method was used to predict land cover changes. Overall accuracy of MLC was higher than others at about 0.94 accuracy, although, SVMs were slightly more accurate for large area segments. Land cover maps were predicted for 2030, which demonstrate the city’s expansion from 5500 ha in 2000 to more than 9000 ha in 2030.

scholarly journals Lake water volume calculation using time series LANDSAT satellite data: a geospatial analysis of Deepor Beel Lake, Guwahati

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ishita Afreen Ahmed ◽  
Shahfahad Shahfahad ◽  
Mirza Razi Imam Baig ◽  
Swapan Talukdar ◽  
Md Sarfaraz Asgher ◽  
...  
Keyword(s):  
Time Series ◽  
Lake Water ◽  
Water Surface ◽  
Water Resource Management ◽  
Storage Capacity ◽  
Water Volume ◽  
Landsat Images ◽  
Rapid Urbanization ◽  
Volume Calculation ◽  
Content Type

PurposeDeepor Beel is one of the Ramsar Site and a wetland of great biodiversity, situated in the south-western part of Guwahati, Assam. With urban development at its forefront city of Guwahati, Deepor Beel is under constant threat. The study aims to calculate the lake water volume from the water surface area and the underwater terrain data using a triangulated irregular network (TIN) volume model.Design/methodology/approachThe lake water surface boundaries for each year were combined with field-observed water level data to generate a description of the underwater terrain. Time series LANDSAT images of 2001, 2011 and 2019 were used to extract the modified normalized difference water index (MNDWI) in GIS domain.FindingsThe MNDWI was 0.462 in 2001 which reduced to 0.240 in 2019. This shows that the lake water storage capacity shrank in the last 2 decades. This leads to a major problem, i.e. the storage capacity of the lake has been declining gradually from 20.95 million m3 in 2001 to 16.73 million m3 in 2011 and further declined to 15.35 million m3 in 2019. The fast decline in lake water volume is a serious concern in the age of rapid urbanization of big cities like Guwahati.Originality/valueNone of the studies have been done previously to analyze the decline in the volume of Deepor Beel lake. Therefore, this study will provide useful insights in the water resource management and the conservation of Deepor Beel lake.

Making investment decisions in stock markets using a forecasting-Markowitz based decision-making approaches

2019 ◽  
Vol 15 (2) ◽  
pp. 647-659 ◽  
Author(s):  
Zahra Moeini Najafabadi ◽  
Mehdi Bijari ◽  
Mehdi Khashei
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Stock Markets ◽  
Time Series Prediction ◽  
Investment Decisions ◽  
Rate Of Return ◽  
Expected Returns ◽  
Prediction Methods ◽  
Content Type ◽  
Markowitz Model

Purpose This study aims to make investment decisions in stock markets using forecasting-Markowitz based decision-making approaches. Design/methodology/approach The authors’ approach offers the use of time series prediction methods including autoregressive, autoregressive moving average and artificial neural network, rather than calculating the expected rate of return based on distribution. Findings The results show that using time series prediction methods has a significant effect on improving investment decisions and the performance of the investments. Originality/value In this study, in contrast to previous studies, the alteration in the Markowitz model started with the investment expected rate of return. For this purpose, instead of considering the distribution of returns and determining the expected returns, time series prediction methods were used to calculate the future return of each asset. Then, the results of different time series methods replaced the expected returns in the Markowitz model. Finally, the overall performance of the method, as well as the performance of each of the prediction methods used, was examined in relation to nine stock market indices.

scholarly journals A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

2021 ◽  
Vol 13 (15) ◽  
pp. 2869
Author(s):  
MohammadAli Hemati ◽  
Mahdi Hasanlou ◽  
Masoud Mahdianpari ◽  
Fariba Mohammadimanesh
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Policy Development ◽  
Atmospheric Correction ◽  
Open Data ◽  
Detection Methods ◽  
Landsat 8 ◽  
Landsat Images ◽  
Detection Analysis ◽  
The Impact

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

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