Remote Sensing Techniques

Gandhi, Sujay; Sarkar, B.C.

doi:10.1016/b978-0-12-805329-4.00011-9

Cited by 8 publications

(1 citation statement)

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“…Such case studies assume that evaluating land cover changes using time series analysis of satellite images is possible by a comparison of images taken with repetitive time gap of years during the same months. This is based on finding the differences in spectral reflectance of land cover types in various bands of a satellite image for the same spatial extent and processing images using various classification techniques (Gandhi and Sarkar, 2016;Merry et al, 2023;Shahi et al, 2023). Since spatial object depicted on the image scenes has different brightness reflected in distinct colours of pixels, a proper combination of spectral band followed by image classification enables to highlight target features that categorise land surface objects.…”

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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Section: Introductionmentioning

confidence: 99%