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

Abstract

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.

The Spatiotemporal Changing Dynamics of Miombo Deforestation and Illegal Human Activities for Forest Fire in Kundelungu National Park, Democratic Republic of the Congo

“…Given that land-use change due to the repeated intrusion of riparian populations into KNP can occur at different spatio-temporal scales, it is relevant to use remote sensing and geographic information systems (GIS). These tools are known to be effective for monitoring phenomena that can be observed at different spatio-temporal scales, such as deforestation [37]. Applied as a complement, landscape ecology analysis tools allow for a better appreciation of the ecological processes underlying the spatio-temporal dynamics of anthropogenic effects on natural landscapes [38].…”

Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data

“…At the same time, ANN methods and scripting libraries are promising tools for cartographic tasks and image processing for mapping areas of coastal lagoons, which are notable for the high complexity of land cover patterns and the heterogeneity of landscapes [39][40][41][42][43][44]. In this regard, GRASS GIS presents a powerful cartographic toolset that includes diverse modules that can be used for satellite image processing [45].…”

Multispectral Satellite Image Analysis for Computing Vegetation Indices by R in the Khartoum Region of Sudan, Northeast Africa

“…Satellite images can be used to analyse the links between complex hydrological and climate processes and vegetation responses that lead to desertification. For example, Landsat images are known to be a reliable source of data for relatively accurate techniques for classifying time-series and detecting forest and land cover types [10][11][12][13], computing vegetation indices [14][15][16] and specifically desertification [17] to show the advance or retreat of arid areas using the analysis of satellite images. Specifically, for the Sudan and Nile Basin, the Landsat data are a precious source of information due to data scarcity [18] regarding regular measurements of rainfall, streams run-off and weather stations data.…”