Abstract
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.
Highlights
In the field of remote sensing, the 21st century is referred to as “the era of big data”, in which a large amount of () freely available data is accessible
The correction itself was done for each image separately
The step, final three-month time series were created for six case studies and statistics before and afterand the step, three-month time series were created for six case studies and statistics before correction were compared
Summary
In the field of remote sensing, the 21st century is referred to as “the era of big data”, in which a large amount of () freely available data is accessible. When processing a large amount of data, especially for time series analysis, it is productive to use tools that can process them quickly and effectively. There may be time and performance issues and limitations using traditional desktop solutions, where it is necessary to download and preprocess data before the necessary analyses can be performed on them. The so-called cloud platforms have been developed. They store images and data archives, but they bring the computing technology needed for data processing [1,2]. In forest monitoring, multispectral optical satellite data have proven to be a very effective data source. Optical data have certain shortcomings, especially regarding the presence of clouds. Electromagnetic waves in the microwave spectrum can penetrate clouds, fog and light rain and are not dependent on sunlight
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