Patch-Based Forest Change Detection from Landsat Time Series

The increasing availability of dense time series of earth observation data has incited a growing interest in time series analysis for vegetation monitoring and change detection. Vegetation monitoring algorithms need to deal with several time series characteristics such as seasonality, irregular sampling intervals, and signal artefacts. While common algorithms based on deterministic harmonic regression models account for intra-annual seasonality, inter-annual variations of the seasonal pattern related to shifts in vegetation phenology due to different temperature and rainfall are usually not accounted for. We propose a transition to stochastic modelling and present a near real-time change detection method that combines a structural time series model with the Kalman filter. The model continuously adapts to new observations and allows to better separate phenology-related deviations from vegetation anomalies or land cover changes. The method is tested in a forest change detection application aiming at the assessment of damages caused by storm events and insect calamities. Forest changes are detected based on the cumulative sum control chart (CUSUM) which is used to decide if new observations deviate from model-based forecasts. The performance is evaluated in two test sites, one in Malawi (dry tropical forest) and one in Austria (temperate deciduous, coniferous and mixed forests) based on Sentinel-2 time series. Both forest areas are characterized by a distinct, but temporally varying leaf-off season. The presented change detection method shows overall accuracies above 99%, users’ accuracies of 76.8% to 88.6%, and producers’ accuracies of 68.2% to 80.4% for the forest change stratum (minimum mapping unit: 0.1 ha). Results are based on visually interpreted points derived by stratified random sampling. A further analysis revealed that increasing the time series density by merging data from two Sentinel-2 orbits yields better forest change detection accuracies in comparison to using data from one orbit only. The resulting increase in users’ accuracy amounts to 7.6%. The presented method is capable of near real-time processing and could be used for a variety of automated forest monitoring applications.

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Time-series tropical forest change detection: a visual and quantitative approach

10.1117/12.256076 ◽

1996 ◽

Cited By ~ 1

Author(s):

Steven A. Sader ◽

Thomas Sever ◽

James C. Smoot

Keyword(s):

Time Series ◽

Change Detection ◽

Tropical Forest ◽

Quantitative Approach ◽

Forest Change

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

Remote Sensing ◽

10.3390/rs13152869 ◽

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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Inland wetland time-series digital change detection based on SAVI and NDWI indecies: Wadi El-Rayan lakes, Egypt

Remote Sensing Applications Society and Environment ◽

10.1016/j.rsase.2020.100347 ◽

2020 ◽

Vol 19 ◽

pp. 100347 ◽

Cited By ~ 1

Author(s):

Asmaa Nasser Mohamed Eid ◽

C.O. Olatubara ◽

T.A. Ewemoje ◽

Mohamed Talaat El-Hennawy ◽

Haitham Farouk

Keyword(s):

Time Series ◽

Change Detection

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Change detection matrix for multitemporal filtering and change analysis of SAR and PolSAR image time series

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2015.02.008 ◽

2015 ◽

Vol 107 ◽

pp. 64-76 ◽

Cited By ~ 24

Author(s):

Thu Trang Lê ◽

Abdourrahmane M. Atto ◽

Emmanuel Trouvé ◽

Akhmad Solikhin ◽

Virginie Pinel

Keyword(s):

Time Series ◽

Change Detection ◽

Change Analysis

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Distance metric-based forest cover change detection using MODIS time series

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2014.01.004 ◽

2014 ◽

Vol 29 ◽

pp. 78-92 ◽

Cited By ~ 19

Author(s):

Xiaoman Huang ◽

Mark A. Friedl

Keyword(s):

Time Series ◽

Change Detection ◽

Forest Cover ◽

Distance Metric ◽

Forest Cover Change

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Structural Change Detection of Time Series Using Sequential Probability Ratio Test

10.1109/icbake.2009.56 ◽

2009 ◽

Author(s):

Katsunori Takeda ◽

Tetsuo Hattori ◽

Izumi Tetsuya ◽

Hiromichi Kawano

Keyword(s):

Time Series ◽

Structural Change ◽

Change Detection ◽

Sequential Probability Ratio Test ◽

Ratio Test ◽

Probability Ratio ◽

Sequential Probability

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A Recursive Algorithm for Adaptive Estimation and Parameter Change Detection of Time Series Models

Journal of the Operational Research Society ◽

10.2307/2582287 ◽

1986 ◽

Vol 37 (10) ◽

pp. 987

Author(s):

Tep Sastri

Keyword(s):

Time Series ◽

Change Detection ◽

Adaptive Estimation ◽

Recursive Algorithm ◽

Time Series Models ◽

Parameter Change

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Land Cover-Specific Local Incidence Angle Correction: A Method for Time-Series Analysis of Forest Ecosystems

Remote Sensing ◽

10.3390/rs13091743 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1743

Author(s):

Daniel Paluba ◽

Josef Laštovička ◽

Antonios Mouratidis ◽

Přemysl Štych

Keyword(s):

Time Series ◽

Land Cover ◽

Forest Type ◽

Incidence Angle ◽

Correction Method ◽

Google Earth ◽

Forest Change ◽

Corine Land Cover ◽

Angle Correction ◽

Wide Range

This study deals with a local incidence angle correction method, i.e., the land cover-specific local incidence angle correction (LC-SLIAC), based on the linear relationship between the backscatter values and the local incidence angle (LIA) for a given land cover type in the monitored area. Using the combination of CORINE Land Cover and Hansen et al.’s Global Forest Change databases, a wide range of different LIAs for a specific forest type can be generated for each scene. The algorithm was developed and tested in the cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, Shuttle Radar Topography Mission (SRTM) digital elevation model, and CORINE Land Cover and Hansen et al.’s Global Forest Change databases. The developed method was created primarily for time-series analyses of forests in mountainous areas. LC-SLIAC was tested in 16 study areas over several protected areas in Central Europe. The results after correction by LC-SLIAC showed a reduction of variance and range of backscatter values. Statistically significant reduction in variance (of more than 40%) was achieved in areas with LIA range >50° and LIA interquartile range (IQR) >12°, while in areas with low LIA range and LIA IQR, the decrease in variance was very low and statistically not significant. Six case studies with different LIA ranges were further analyzed in pre- and post-correction time series. Time-series after the correction showed a reduced fluctuation of backscatter values caused by different LIAs in each acquisition path. This reduction was statistically significant (with up to 95% reduction of variance) in areas with a difference in LIA greater than or equal to 27°. LC-SLIAC is freely available on GitHub and GEE, making the method accessible to the wide remote sensing community.

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