Abstract
The processing tool TREX, standing for ‘Tool for Raster data EXploration’ is presented and evaluated in the Biebrza wetlands in northeastern Poland. TREX was designed for the automatization of processing satellite data from the Proba-V satellite into maps of NDVI or LAI in any defined by the user projection, spatial resolution, or extent. The open source and access concept of TREX encourages the potential community of users to collaborate, develop, and integrate the tool with other satellite imagery and models. TREX reprojects, shifts, and resamples original data obtained from the Proba-V satellite to deliver reliable maps of NDVI and LAI. Validation of TREX in Biebrza wetlands resulted in correlations between 0.79 and 0.92 for NDVI data (measured with ASD Field Spec 4) and 0.92 for LAI data (measured with LiCOR—LAI-2000 Plant Canopy Analyzer).
Highlights
Launched in 2013, the Belgian satellite Proba-V provides free, global, and nearly daily observation coverage at various spatial resolutions (100 m, 300 m, or 1 km) [1] This satellite sensor is capable of registering reflectance spectra in four bands that enables deriving one of the most popular remote sensing indices: the Normalized Differential Vegetation Index (NDVI)
We present the TREX to public domain for carrying out research without involving advanced costly software, and we provide a demonstration and evaluation of its application to provide NDVI and Leaf area index (LAI) maps using Proba-V images in natural wetlands belonging to the Biebrza National Park in northeastern part of Poland
This work demonstrates successful application of TREX in estimating NDVI (Pearson correlation between 0.79 and 0.92 with data measured with ASD—field spec) and LAI of Biebrza wetlands
Summary
Launched in 2013, the Belgian satellite Proba-V provides free, global, and nearly daily observation coverage at various spatial resolutions (100 m, 300 m, or 1 km) [1] This satellite sensor is capable of registering reflectance spectra in four bands (green, red and NIR, SWIR) that enables deriving one of the most popular remote sensing indices: the Normalized Differential Vegetation Index (NDVI). Timeseries of NDVI show changes in healthy green vegetation cover, allowing applications varying from environmental [2] and agricultural [3,4] to healthcare [5] or natural disasters monitoring [6]. Leaf area index (LAI), defined as a total one-sided green leaf canopy area per ground unit, is derivable from various remote sensing indices [7] including NDVI [8]. Dynamics of growing or degrading vegetation are often unpredictable difficult to properly represent in simulations without integrating this information from other sources like remote sensing
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