Assessment of Land Degradation Status and Its Impact in Arid and Semi-Arid Areas by Correlating Spectral and Principal Component Analysis Neo-Bands

Alfred Homère Ngandam Mfondoum, Joachim Etouna, Buji Kindess Nongsi, Fabrice Armel Mvogo Moto, Florine Gustave Noulaquape Deussieu, (doi: 10.23953/cloud.ijarsg.77)


This paper aimed to assess the status of land degradation in arid and semi-arid areas based on a correlation analysis between spectral and statistical neo-bands. The methodology uses vegetation and soil spectral indices as the second Modified Soil Adjusted Vegetation Index (MSAVI2), Normalized Difference Bare Soil Index (NDBSI), Texture Index (NDTeI), Crust Index (CI), Top Soil Grain Size Index (GSI), Normalized Difference Sand Dune Index (NDSDI) and the first Specific Principal Component of the red, near infrared, shortwave infrared bands stacking (SPC1R-NIR-SWIR1-SWIR2). The vegetation is considered here as the main object of soil sub-surface. Thus after all the spectral and the statistic neo-bands are performed on Landsat8 OLI sensor image, a linear regression is generated to assess their correlation with MSAVI2. Based on the visual interpretation and the regression curves the results show that the determination coefficient R2 and the P values all significant as less than 0.0001. Each neo-band is weighted with its R2 to improve its contribution to the model and the synthesis image obtained enhances the land degradation sensing in six classes; these are respectively named as ‘‘severe’’ (3139 km2), ‘‘high’’ (6763 km2), ‘‘moderate’’ (8341 km2), ‘‘low’’ (7454 km2), ‘‘very low’’ (6947 km2) and ‘‘close to nil’’ (5437 km2). This last image is summed with population layer to produce a decision map helpful for further government decision. At the end the degradation image has given interesting results for the detection of land degradation comparatively to derivation and comparison of individual indices.


Correlation Analysis; Decision Map; Linear Regression; Specific Principal Component; Spectral Indices

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