LAND USE LAND COVER CHANGE MAPPING FROM SENTINEL 1B < 2A IMAGERY USING RANDOM FOREST ALGORITHM IN CÔTE D’IVOIRE

Abstract

Monitoring crop condition, soil properties, and mapping tillage activities can be used to assess land use, forecast crops, monitor seasonal changes, and contribute to the implementation of sustainable development policy. Agricultural maps can provide independent and objective estimates of the extent of crops in a given area or growing season, which can be used to support efforts to ensure food security in vulnerable areas. Satellite data can help detect and classify different types of soil. The evolution of satellite remote sensing technologies has transformed techniques for monitoring the Earth’s surface over the last several decades. The European Space Agency (ESA) and the European Union (EU) created the Copernicus program, which resulted in the European satellites Sentinel-1B (S1B) and Sentinel-2A (S2A), which allow the collection of multi-temporal, spatial, and highly repeatable data, providing an excellent opportunity for the study of land use, land cover, and change. The goal of this study is to map the land cover of Côte d’Ivoire’s West Central Soubre area (5°47′1′′ North, 6°35′38′′ West) between 2014 and 2020. The method is based on a combination of S1B and S2A imagery data, as well as three types of predictors: the biophysical indices Normalized Difference Vegetation Index “(NDVI)”, Modified Normalized Difference Water Index “(MNDWI)”, Normalized Difference Urbanization Index “(NDBI)”, and Normalized Difference Water Index “(NDWI)”, as well as spectral bands (B1, B11, B2, B3, B4, B6, B7, B8) and polarization coefficients VV. For the period 2014–2020, six land classifications have been established: Thick_Forest, Clear_Drill, Urban, Water, Palm_Oil, Bareland, and Cacao_Land. The Random Forest (RF) algorithm with 60 numberOfTrees was the primary categorization approach used in the Google Earth Engine (GEE) platform. The results show that the RF classification performed well, with outOfBagErrorEstimates of 0.0314 and 0.0498 for 2014 and 2020, respectively. The classification accuracy values for the kappa coefficients were above 95%: 96.42% in 2014 and 95.28% in 2020, with an overall accuracy of 96.97% in 2014 and 96 % in 2020. Furthermore, the User Accuracy (UA) and Producer Accuracy (PA) values for the classes were frequently above 80%, with the exception of the Bareland class in 2020, which achieved 79.20%. The backscatter coefficients of the S1B polarization variables had higher GINI significance in 2014: VH (70.80) compared to VH (50.37) in 2020; and VV (57.11) in 2014 compared to VV (46.17) in 2020. Polarization coefficients had higher values than the other spectral and biophysical variables of the three predictor variables. During the study period, the Thick_Forest (35.90% ± 1.17), Palm_Oil (57.59% ± 1.48), and Water (5.90% ± 0.47) classes experienced a regression in area, while the Clear_Drill (16.96% ± 0.80), Urban (2.32% ± 0.29), Bareland (83.54% ± 1.79), and Cacao_Land (35.14% ± 1.16) classes experienced an increase. The approach used is regarded as excellent based on the results obtained.