scholarly journals Monitoring the Vegetation Dynamics in the Dongting Lake Wetland from 2000 to 2019 Using the BEAST Algorithm Based on Dense Landsat Time Series

2020 ◽  
Vol 10 (12) ◽  
pp. 4209
Author(s):  
Yaotong Cai ◽  
Shutong Liu ◽  
Hui Lin
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Vegetation Dynamics ◽  
Dongting Lake ◽  
Wetland Vegetation ◽  
Dynamic Monitoring ◽  
Interval Length ◽  
Absolute Difference ◽  
Vegetation Changes ◽  
Temporal Validation

The dynamic monitoring and analysis of wetland vegetation play important roles in revealing the change, restoration and reconstruction of the ecosystem environment. The increasing availability of high spatial-temporal resolution remote sensing data provides an unprecedented opportunity for wetland dynamic monitoring and change detection. Using the reconstructed dense monthly Landsat time series, this study focuses on the continuous monitoring of vegetation dynamics in Dongting Lake wetland, south China, in the last two decades (2000–2019) by using the Bayesian estimator of abrupt change, seasonal change, and trend (BEAST) method. Firstly, the flexible spatiotemporal data fusion (FSDAF) model is applied to blend Landsat and moderate-resolution imaging spectroradiometer (MODIS) images on the basis of the input image pair selection strategy named “cross-fusion” to generate the monthly time-series normalized difference vegetation index (NDVI) with the spatial resolution of 30 m. Then, the abrupt changes, trend, and seasonality of the vegetation in the study area as well as the uncertainties of change detection are estimated by the BEAST method. Results show that there is a close relationship between the ground true data and the estimated changepoints. A high overall accuracy (OA) of 87.37% and Kappa coefficient of 0.85 were achieved by the proposed framework. Additionally, the temporal validation got the interval intersection of 86.57% and the absolute difference of mean interval length of 6.8 days. All of the results demonstrate that the vegetation changes in the Dongting Lake wetland varied spatially and temporally in the last two decades, because of extreme weathers and anthropogenic factors. The presented approach can accurately identify the vegetation changes and time of disturbance in both the spatial and temporal domains, and also can retrieve the evolution process of wetland vegetation under the influence of climate changes and human activities. Therefore, it can be used to reveal potential causes of the degradation and recovery of wetland vegetation in subtropical areas.

C-band microwave sensors reflect the spring water uptake of temperate deciduous broadleaf trees

2020 ◽  
Author(s):  
Isabella Pfeil ◽  
Wolfgang Wagner ◽  
Mariette Vreugdenhil ◽  
Matthias Forkel ◽  
Wouter Dorigo
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Water Uptake ◽  
North American ◽  
Vegetation Dynamics ◽  
Spring Water ◽  
Absolute Difference ◽  
Deciduous Trees ◽  
Vegetation Water ◽  
Microwave Sensors

<p>Observations from the C-band scatterometers ERS ESCAT and Metop ASCAT have been used to monitor vegetation dynamics predominantly in agricultural areas and grasslands (<em>Schroeder et al., 2016, Vreugdenhil et al., 2016, Vreugdenhil et al., 2017</em>). In particular, the slope  between the measured radar backscatter and the incidence angle of the observations has been found to reflect structural changes in the vegetation (e.g., size and orientation of stems and leaves) and vegetation water content, as well as deficits therein, as for example during an extensive drought period in North American grasslands (<em>Steele-Dunne 2019</em>).</p><p>Often, a peak in the slope time series is observed during spring. This peak occurs predominantly in regions covered by deciduous broadleaf forests (DBF), and recurs in most years around the beginning of April. We carried out a detailed study of the causes of such spring peaks over Austria by comparing the timing of the peaks to phenology observations of leaf emergence, leaf area index and temperature conditions. The comparison showed a good agreement between the timing of the ASCAT spring peaks and the reference datasets, even in regions with low coverage of DBF, with a median absolute difference between the peak in ASCAT and the reference datasets of less than 14 days for grid cells with at least 10% DBF (<em>Pfeil et al., in prep.</em>).</p><p>In this presentation, we assess if similar spring peaks occur in passive microwave satellite observations. Therefore we investigate the spring behavior of vegetation optical depth (VOD) time series from the radiometers AMSR-E and AMSR2 over DBF and find similar peaks, which are less pronounced but occur very close in time to the ASCAT peaks. It can thus be said that the spring peak is not a sensor-dependent phenomenon, but reflects the sensitivity of C-band microwave sensors to leaf development in deciduous trees. In summary, the results of the study suggest that spring water uptake in deciduous trees manifests in active and passive C-band microwave observations, as it causes increased scattering from the bare twigs and branches, followed by an attenuation of the twigs- and branches scattering by the emerging leaves.</p><p> </p><p><strong>References</strong></p><ul><li>Schroeder, R., McDonald, K. C., Azarderakhsh, M., & Zimmermann, R. (2016). ASCAT MetOp-A diurnal backscatter observations of recent vegetation drought patterns over the contiguous US: An assessment of spatial extent and relationship with precipitation and crop yield. Remote sensing of environment, 177, 153-159.</li> <li>Steele-Dunne, S. C., Hahn, S., Wagner, W., & Vreugdenhil, M. (2019). Investigating vegetation water dynamics and drought using Metop ASCAT over the North American Grasslands. Remote Sensing of Environment, 224, 219-235.</li> <li>Vreugdenhil, M., Dorigo, W. A., Wagner, W., De Jeu, R. A., Hahn, S., & Van Marle, M. J. (2016). Analyzing the vegetation parameterization in the TU-Wien ASCAT soil moisture retrieval. IEEE Transactions on Geoscience and Remote Sensing, 54(6), 3513-3531.</li> <li>Vreugdenhil, M., Hahn, S., Melzer, T., Bauer-Marschallinger, B., Reimer, C., Dorigo, W. A., & Wagner, W. (2017). Assessing vegetation dynamics over mainland Australia with metop ASCAT. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(5), 2240-2248.</li> </ul>

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.

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

2020 ◽  
Vol 19 ◽  
pp. 100347 ◽  
Author(s):  
Asmaa Nasser Mohamed Eid ◽  
C.O. Olatubara ◽  
T.A. Ewemoje ◽  
Mohamed Talaat El-Hennawy ◽  
Haitham Farouk
Keyword(s):  
Time Series ◽  
Change Detection

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

2015 ◽  
Vol 107 ◽  
pp. 64-76 ◽  
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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