Multi-Temporal and Multi-Frequency SAR Analysis for Forest Land Cover Mapping of the Mai-Ndombe District (Democratic Republic of Congo)

Haarpaintner, Jörg; Hindberg, Heidi

doi:10.3390/rs11242999

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…This method presents a machine learning approach for assigning pixels into the defined groups (or coherent clusters) according to their intensity and brightness, as seen in Listing 4. The clustering and classification of data is a commonly accepted practice for land cover mapping aimed at modelling changes in a forest structure by examining land-use categories [2,54,88,101,102]. 35 legend ( " t o p l e f t " , legend=c ( " 1 " , " 2 " , " 3 " , " 4 " , " 5 " , " 6 " , " 7 " , " 8 " , " 9 " , " 10 " ) , f i l l = c o l o r s , t i t l e = " C l a s s e s " , h o r i z = FALSE , bty = " n " , t e x t .…”

Section: K-means Clusteringmentioning

confidence: 99%

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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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Section: K-means Clusteringmentioning

confidence: 99%

“…Typically, methods of forest mapping are based on the use of Landsat imagery [51][52][53], though others utilize SAR [54][55][56][57][58] or Moderate Resolution Imaging Spectroradiometer (MODIS) scenes [59,60] for land cover classification in forest areas.…”

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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“…There are different types of SAR satellites that can be classified using two-wavelength and frequency characteristics: 1) 2.4 and 3.8 cm and frequency between 12.5 and 8 GHz (X band); 2) 3.8-7.5 cm and 8 to 4 GHz (C band); 3) 15-30 cm and 2 to 1 GHz (L band); 4) 30-100 cm/1 to 0.3 GHz (P band) (Haarpaintner and Hindberg, 2019). The transmitted and received waves can be linear or circular.…”

Section: Synthetic Aperture Radar Satellitementioning

confidence: 99%

Green Energy Sources Assessment Using Sentinel-1 Satellite Remote Sensing

et al. 2021

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Due to the growing use of Green Energy Sources (GESs), the activities of mapping, monitoring, measurement, and detection of various GESs have become crucial. Assessing and measuring GESs are very complex since different environmental conditions occur. This importance is even greater when researchers face a shortage of measuring instruments and tools in many parts of the world. GES assessment is a challenging task that requires accurate and continuous measurement methods. Currently, traditional methods are very time-consuming and require spending money and human sources. So, the use of accurate and fast measurement methods and tools assessing measuring GESs potential are seriously recommended, which can greatly help the growth of the use of GESs, especially to cover and focus large areas. Satellite remote sensing is used to observe the environment in many fields and new and fast applications. Satellites remote sensing technologies and techniques for GESs assessing are fast, accurate, and can help to reduce costs and decision-making risks of GESs converters installations projects and provide suitable products to the public end-users. These could also be used to identify regions of interest for energy converter installations and to accurately identify new areas with interesting potentials. In this case, researchers can dramatically reduce the possibility of significant error in assessment methods. There is a lack of in-situ measuring tools mainly due to their high economic costs in the interested areas; an accurate analysis was carried out to assess the GESs energy potential. Since there are only limited options for further expanding the measurement over large areas, the use of satellites makes it easier to overcome in-situ limitations. Actually, to use and develop it as much as possible, a correct interdisciplinary understanding is needed. Satellite remote sensing technology for identifying suitable areas for GESs power plants could be a powerful tool that is constantly increasing in its new and fast applications but requires good planning to apply it in various GESs converters installations projects. In this article, a comprehensive review on wind, wave, biomass, geothermal sources assessment using Sentinel-1 (S-1) Synthetic Aperture Radar (SAR) satellite estimation has been summarized along with the different techniques available to measure GESs using satellites. In the paper, several of the successful estimation techniques were introduced in each section and compared for the understanding of limitations and strengths of different methods of GESs availability evaluation.

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“…Time series analysis of Earth observation data has proven to be effective in the evaluation of landscape changes using several images covering the same area in various consecutive years. For instance, satellite-derived trends are used as monitoring methods in a wide variety of environmental applications: mapping and monitoring wetlands (Wu, 2018), assessment of deforestation and forest degradation (Haarpaintner and Hindberg, 2019;Mashhadi and Alganci, 2022;Masolele et al, 2021;Schneibel et al, 2017), monitoring wetland dynamics (Kovács et al, 2022;Xie et al, 2022), evaluation of vegetation cover fraction and soil depletion (Dube et al, 2017;Gallo et al, 2023), computing vegetation indices (Lemenkova and Debeir, 2022a;Liu et al, 2022;Venter et al, 2020), estimating variations in land surface temperature (Carrillo-Niquete et al, 2022), assessment of spatio-temporal variations in night lights emissions in urban studies (Rehman et al, 2021) and more.…”

Section: Introductionmentioning

confidence: 99%

Time Series Analysis of Landsat Images for Monitoring Flooded Areas in the Inner Niger Delta, Mali

Lemenkova,

Debeir

2023

Artificial Satellites

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This paper presents an R-based approach to mapping dynamics of the flooded areas in the Inner Niger Delta (IND), Mali, using time series analysis of Landsat 8–9 satellite images. As the largest inland wetland in West Africa, the habitats of IND offers high potential for biodiversity of the flood-dependent eco systems. IND is one of the most productive areas in West Africa. Mapping flooded areas based on satellite images enables to provide strategies for land management and rice planting and modelling vegetation types of IND. Our approach is based on using libraries of R programming language for processing six Landsat images, and each image was taken on November from 2013 to 2022. By capturing spatial and temporal structures of the satellite images on 2013, 2015, 2018, 2020, 2021 and 2022, the remote sensing data are combined to yield estimates of landscape dynamics that is temporally coherent, while helping to analyse fluctuations of spatial extent in fluvial wetlands caused by the hydrological processes of seasonal flooding. Further, by allowing packages of R to support image processing, an approach to mapping vegetation by NDVI, SAVI and EVI indices and visualising changes in distribution of different land cover classes over time is realised. In this context, processing Earth observation data by advanced scripting tools of R language provides new insights into complex interlace of climate-hydrological processes and vegetation responses. Our study contributes to the sustainable management of natural resources and improving knowledge on the functioning of IND ecosystems in Mali, West Africa.

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Multi-Temporal and Multi-Frequency SAR Analysis for Forest Land Cover Mapping of the Mai-Ndombe District (Democratic Republic of Congo)

Cited by 6 publications

References 30 publications

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

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

Green Energy Sources Assessment Using Sentinel-1 Satellite Remote Sensing

Time Series Analysis of Landsat Images for Monitoring Flooded Areas in the Inner Niger Delta, Mali

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