Statistics of TanDEM-X DSM, coherence and backscatter for the characterization of tropical forest structural configuration

Grandi, Elsa Carla De; Mitchard, Edward T. A.; Woodhouse, Iain; Verhegghen, Astrid; Muirhead, Fiona

doi:10.1109/igarss.2015.7326141

Cited by 1 publication

(1 citation statement)

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“…Some of the methods of image processing use time series analysis using fine spatial resolution imagery [10,11], topological data extraction by spectral analysis derived from Synthetic Aperture Radar (SAR) [12,13], photogrammetry or Light Detection and Ranging (LiDAR) processing to analyse forest canopy by collecting 3D data [14]. Additionally, image processing may use a supervised classification approach, which includes automated extraction of the training samples and reference maps [15], regression tree analysis for canopy height modelling [16], or statistical methods to analyse forest degradation [17].…”

Section: Introductionmentioning

confidence: 99%

R Libraries for Remote Sensing Data Classification by K-Means Clustering and NDVI Computation in Congo River Basin, DRC

Lemenkova

Debeir

2022

Applied Sciences

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In this paper, an image analysis framework is formulated for Landsat-8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) scenes using the R programming language. The libraries of R are shown to be effective in remote sensing data processing tasks, such as classification using k-means clustering and computing the Normalized Difference Vegetation Index (NDVI). The data are processed using an integration of the RStoolbox, terra, raster, rgdal and auxiliary packages of R. The proposed approach to image processing using R is designed to exploit the parameters of image bands as cues to detect land cover types and vegetation parameters corresponding to the spectral reflectance of the objects represented on the Earth’s surface. Our method is effective at processing the time series of the images taken at various periods to monitor the landscape dynamics in the middle part of the Congo River basin, Democratic Republic of the Congo (DRC). Whereas previous approaches primarily used Geographic Information System (GIS) software, we proposed to explicitly use the scripting methods for satellite image analysis by applying the extended functionality of R. The application of scripts for geospatial data is an effective and robust method compared with the traditional approaches due to its high automation and machine-based graphical processing. The algorithms of the R libraries are adjusted to spatial operations, such as projections and transformations, object topology, classification and map algebra. The data include Landsat-8 OLI-TIRS covering the three regions along the Congo river, Bumba, Basoko and Kisangani, for the years 2013, 2015 and 2022. We also validate the performance of graphical data handling for cartographic visualization using R libraries for visualising changes in land cover types by k-means clustering and calculation of the NDVI for vegetation analysis.

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