Monitoring forest changes in Borneo on a yearly basis by an object-based change detection algorithm using SPOT-VEGETATION time series

2012 ◽  
Vol 33 (15) ◽  
pp. 4673-4699 ◽  
Author(s):  
Sophie Bontemps ◽  
Andreas Langner ◽  
Pierre Defourny
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Detection Algorithm ◽  
Object Based

scholarly journals Object-Based Change Detection in the Cerrado Biome Using Landsat Time Series

2019 ◽  
Vol 11 (5) ◽  
pp. 570 ◽  
Author(s):  
Inacio Bueno ◽  
Fausto Acerbi Júnior ◽  
Eduarda Silveira ◽  
José Mello ◽  
Luís Carvalho ◽  
...  
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Seasonal Changes ◽  
Vegetation Indices ◽  
Detection Methods ◽  
Object Based Image Analysis ◽  
Spectral Bands ◽  
Object Based ◽  
Shortwave Infrared ◽  
Spectral Channels

Change detection methods are often incapable of accurately detecting changes within time series that are heavily influenced by seasonal variations. Techniques for de-seasoning time series or methods that apply the spatial context have been used to improve the results of change detection. However, few studies have explored Landsat’s shortwave infrared channel (SWIR 2) to discriminate between seasonal changes and land use/land cover changes (LULCC). Here, we explored the effectiveness of Operational Land Imager (OLI) spectral bands and vegetation indices for detecting deforestation in highly seasonal areas of Brazilian savannas. We adopted object-based image analysis (OBIA), applying a multidate segmentation to an OLI time series to generate input data for discrimination of deforestation from seasonal changes using the Random Forest (RF) algorithm. We found adequate separability between deforested objects and seasonal changes using SWIR 2. Using spectral indices computed from SWIR 2, the RF algorithm generated a change map with an overall accuracy of 88.3%. For deforestation, the producer’s accuracy was 88.0% and the user’s accuracy was 84.6%. The SWIR 2 channel as well as the mid-infrared burn index presented the highest importance among spectral variables computed by the RF average impurity decrease measure. Our results give support to further change detection studies regarding to suitable spectral channels and provided a useful foundation for savanna change detection using an object-based method applied to Landsat time series.

scholarly journals Accounting for seasonality in a soil moisture change detection algorithm for ASAR Wide Swath time series

2011 ◽  
Vol 8 (6) ◽  
pp. 10333-10367
Author(s):  
J. Van doninck ◽  
J. Peters ◽  
H. Lievens ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Change Detection ◽  
Incidence Angle ◽  
Arable Land ◽  
Correlation Coefficients ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Second Phase ◽  
Wide Swath

Abstract. A change detection algorithm is applied on a three year time series of ASAR Wide Swath images in VV polarization over Calabria, Italy, in order to derive information on temporal soil moisture dynamics. The algorithm, adapted from an algorithm originally developed for ERS Scatterometer, was validated using a simple hydrological model incorporating meteorological and pedological data. Strong positive correlations between modelled soil moisture and ASAR soil moisture were observed over arable land, while the correlation became much weaker over more vegetated areas. In a second phase, an attempt was made to incorporate seasonality in the different model parameters. It was observed that seasonally changing vegetation and soil moisture mainly affected the multitemporal incidence angle normalization. When applying a seasonal angular normalization, correlation coefficients between modelled soil moisture and retrieved soil moisture increased overall. Attempts to account for seasonality in the other model parameters did not result in an improved performance.

scholarly journals Accounting for seasonality in a soil moisture change detection algorithm for ASAR Wide Swath time series

2012 ◽  
Vol 16 (3) ◽  
pp. 773-786 ◽  
Author(s):  
J. Van doninck ◽  
J. Peters ◽  
H. Lievens ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Change Detection ◽  
Incidence Angle ◽  
Arable Land ◽  
Correlation Coefficients ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Second Phase ◽  
Wide Swath

Abstract. A change detection algorithm is applied on a three year time series of ASAR Wide Swath images in VV polarization over Calabria, Italy, in order to derive information on temporal soil moisture dynamics. The algorithm, adapted from an algorithm originally developed for ERS scatterometer, was validated using a simple hydrological model incorporating meteorological and pedological data. Strong positive correlations between modelled soil moisture and ASAR soil moisture were observed over arable land, while the correlation became much weaker over more vegetated areas. In a second phase, an attempt was made to incorporate seasonality in the different model parameters. It was observed that seasonally changing surface properties mainly affected the multitemporal incidence angle normalization. When applying a seasonal angular normalization, correlation coefficients between modelled soil moisture and retrieved soil moisture increased overall. Attempts to account for seasonality in the other model parameters did not result in an improved performance.

scholarly journals DERIVING EXCLUSION MAPS FROM C-BAND SAR TIME-SERIES: AN ADDITIONAL INFORMATION LAYER FOR SAR-BASED FLOOD EXTENT MAPPING

2020 ◽  
Vol V-1-2020 ◽  
pp. 395-400
Author(s):  
J. Zhao ◽  
M. Chini ◽  
R. Pelich ◽  
P. Matgen ◽  
R. Hostache ◽  
...  
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Urban Areas ◽  
Flood Management ◽  
Mapping Algorithms ◽  
Additional Information ◽  
Object Based ◽  
Information Layer ◽  
Intensity Images ◽  
Time Series Analyses

Abstract. Change detection has been widely used in many flood-mapping algorithms using pairs of Synthetic Aperture Radar (SAR) intensity images as floodwater often leads to a substantial decrease of backscatter. However, limitations still exist in many areas, such as shadow, layover, urban areas and densely vegetated areas, where the SAR backscatter is not sufficiently impacted by floodwater-related surface changes. This study focuses on these so-called exclusion areas, i.e. areas where SAR does not allow detecting water based on change detection. Our approach considers both pixel-based time series analyses and object-based spatial analyses using 20m Sentinel-1 Interferometric Wide Swath data, including 922 Sentinel-1 tiles covering the River Severn basin (UK) and the Lake Maggiore area (Italy). The results show that our exclusion map presents a good agreement (∼63%) with reference data derived from different data sources and indicate that it may complement SAR-derived flood extent maps. Allowing to accurately identify potential misclassifications in flood extent mapping, our exclusion map provides valuable information for flood management and, in particular, flood forecasting and prediction.

scholarly journals BFAST Lite: A Lightweight Break Detection Method for Time Series Analysis

2021 ◽  
Vol 13 (16) ◽  
pp. 3308 ◽  
Author(s):  
Dainius Masiliūnas ◽  
Nandin-Erdene Tsendbazar ◽  
Martin Herold ◽  
Jan Verbesselt
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Missing Values ◽  
Confusion Matrix ◽  
Detection Algorithm ◽  
Information Criteria ◽  
Modis Imagery ◽  
Similar Accuracy

BFAST Lite is a newly proposed unsupervised time series change detection algorithm that is derived from the original BFAST (Breaks for Additive Season and Trend) algorithm, focusing on improvements to speed and flexibility. The goal of the BFAST Lite algorithm is to aid the upscaling of BFAST for global land cover change detection. In this paper, we introduce and describe the algorithm and then compare its accuracy, speed and features with other algorithms in the BFAST family: BFAST and BFAST Monitor. We tested the three algorithms on an eleven-year-long time series of MODIS imagery, using a global reference dataset with over 30,000 point locations of land cover change to validate the results. We set the parameters of all algorithms to comparable values and analysed the algorithm accuracy over a range of time series ordered by the certainty of that the input time series has at least one abrupt break. To compare the algorithm accuracy, we analysed the time difference between the detected breaks and the reference data to obtain a confusion matrix and derived statistics from it. Lastly, we compared the processing speed of the algorithms using both the original R code as well as an optimised C++ implementation for each algorithm. The results showed that BFAST Lite has similar accuracy to BFAST but is significantly faster, more flexible and can handle missing values. Its ability to use alternative information criteria to select the number of breaks resulted in the best balance between the user’s and producer’s accuracy of detected changes of all the tested algorithms. Therefore, BFAST Lite is a useful addition to the BFAST family of unsupervised time series break detection algorithms, which can be used as an aid in narrowing down areas with changes for updating land cover maps, detecting disturbances or estimating magnitudes and rates of change over large areas.

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