| 1. |
- Cai, Zhanzhang, et al.
(författare)
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Modelling Daily Gross Primary Productivity with Sentinel-2 Data in the Nordic Region-Comparison with Data from MODIS
- 2021
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Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 13:3
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Tidskriftsartikel (refereegranskat)abstract
- The high-resolution Sentinel-2 data potentially enable the estimation of gross primary productivity (GPP) at finer spatial resolution by better capturing the spatial variation in a heterogeneous landscapes. This study investigates the potential of 10 m resolution reflectance from the Sentinel-2 Multispectral Instrument to improve the accuracy of GPP estimation across Nordic vegetation types, compared with the 250 m and 500 m resolution reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS). We applied linear regression models with inputs of two-band enhanced vegetation index (EVI2) derived from Sentinel-2 and MODIS reflectance, respectively, together with various environmental drivers to estimate daily GPP at eight Nordic eddy covariance (EC) flux tower sites. Compared with the GPP from EC measurements, the accuracies of modelled GPP were generally high (R-2 = 0.84 for Sentinel-2; R-2 = 0.83 for MODIS), and the differences between Sentinel-2 and MODIS were minimal. This demonstrates the general consistency in GPP estimates based on the two satellite sensor systems at the Nordic regional scale. On the other hand, the model accuracy did not improve by using the higher spatial-resolution Sentinel-2 data. More analyses of different model formulations, more tests of remotely sensed indices and biophysical parameters, and analyses across a wider range of geographical locations and times will be required to achieve improved GPP estimations from Sentinel-2 satellite data.
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| 2. |
- Eklundh, Lars, et al.
(författare)
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High resolution mapping of vegetation dynamics from Sentinel-2
- 2012
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Ingår i: Proceedings of 1st Sentinel-2 Preparatory Symposium. - 0379-6566. - 9789290922711 ; 707 SP
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Konferensbidrag (refereegranskat)abstract
- The aim of this work is to develop and test a method for generation of information on vegetation dynamics from high-spatial resolution data, such as Sentinel-2. In order to accomplish this, Sentinel-2 data were simulated from existing SPOT HRG and HRVIR scenes over Sweden. We used TIMESAT, a well-tested computer package for generating smooth seasonal profiles and generation of seasonality parameters, like start and end, length, amplitude, integrated values, seasonal maximum, derivatives, etc. The processing works on a pixel-by-pixel basis and is resistant to clouds and noise. Data gaps are handled, and quality information can be included to increase the fidelity of the fits. The pilot study demonstrated that TIMESAT was successful in fitting smooth model functions to the data, and generating seasonality parameters for the test area at 10 × 10 m resolution. We conclude that TIMESAT will be useful for generating vegetation dynamics data from high-spatial resolution data such as Sentinel-2. The smooth seasonal profiles will be extremely useful for driving high-resolution biophysical vegetation models, and the seasonality parameters will be excellent for change detection, and for studying trends in vegetation productivity and seasonality.
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| 3. |
- Jamali, Sadegh, et al.
(författare)
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Detecting changes in vegetation trends using time series segmentation
- 2015
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 156:January, s. 182-195
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Tidskriftsartikel (refereegranskat)abstract
- Although satellite-based sensors have made vegetation data series available for several decades, the detection of vegetation trend and change is not yet straightforward. This is partly due to the scarcity of available change detection algorithms suitable for identifying and characterizing both abrupt and non-abrupt changes, without sacrificing accuracy or computational speed. We propose a user-friendly program for analysing vegetation time series, with two main application domains: generalising vegetation trends to main features, and characterizing vegetation trend changes. This program, Detecting Breakpoints and Estimating Segments in Trend (DBEST) uses a novel segmentation algorithm which simplifies the trend into linear segments using one of three user-defined parameters: a generalisation-threshold parameter δ, the m largest changes, or a threshold β for the magnitude of changes of interest for detection. The outputs of DBEST are the simplified trend, the change type (abrupt or non-abrupt), and estimates for the characteristics (time and magnitude) of the change. DBEST was tested and evaluated using simulated Normalized Difference Vegetation Index (NDVI) data at two sites, which included different types of changes. Evaluation results demonstrate that DBEST quickly and robustly detects both abrupt and non-abrupt changes, and accurately estimates change time and magnitude. DBEST was also tested using data from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI image time series for Iraq for the period 1982–2006, and was able to detect and quantify major change over the area. This showed that DBEST is able to detect and characterize changes over large areas. We conclude that DBEST is a fast, accurate and flexible tool for trend detection, and is applicable to global change studies using time series of remotely sensed data sets.
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