The small size of agricultural plots is the main difficulty for crops mapping with remote sensing data in the Sahelian region of Africa. The study aims to combine Sentinel-1 (radar) and Sentinel-2 (Optic) data to disc...The small size of agricultural plots is the main difficulty for crops mapping with remote sensing data in the Sahelian region of Africa. The study aims to combine Sentinel-1 (radar) and Sentinel-2 (Optic) data to discriminate millet, maize and peanut crops. Training plots were used in order to analyse temporal variation of the three crops’ signals. T<span style="font-family:Verdana;">he NDVI (Normalized Difference Vegetation Index) was able to differentiate crops only at the end of the rainy season (October). </span><span style="font-family:Verdana;">The optical data as well as the radar ones could not easily discriminate the three crops during the growing season, because in that period vegetation cover is low, and soil contribution to the signals (due to roughness and moisture) was more important than that of real vegetation. However, the ratio of VH/VV (VH: incident signal in vertical polarization and reflected signal in horizontal polarization;VV: incident signal in vertical polarization and reflected signal in horizontal polarization) gave a difference between millet and the two other crops at the beginning cultural season (July 11). Difference appears from the second third of September when the harvest of cereals crops (millet and maize) began. From middle of October, the peanut signal dropped sharply thus facilitating the differentiation of peanut from the two other crops. This analysis led to the identification of data that have could be used to discriminate these crops (useful data). Classification of the combined useful data gave an overall high accuracy of 82%, with 96%, 61% and 65% for peanut, maize and millet, respectively. The non-agricultural areas (water, natural vegetation, habit, bare soil) were well classified with an accuracy greater than 90%.</span>展开更多
Urban areas house vegetation cover in several forms, fulfilling several ecological functions like thermal regulator, biodiversity, air quality, etc. However, their extent is often not very well known, especially in Af...Urban areas house vegetation cover in several forms, fulfilling several ecological functions like thermal regulator, biodiversity, air quality, etc. However, their extent is often not very well known, especially in African cities, making it sometimes difficult to assess their real impact on the urban ecosystem functioning. This work aims to analyse the capacity of satellite sensors for mapping vegetation and wetlands in urban areas. The data produced by the MSI sensors of Sentinel 2 and OLI of Landsat-8 are used to identify and map the vegetation cover in the Dakar region through a supervised classification with the Support Vector Machine (SVM) algorithm. The results show that it is sometimes not very easy to analyse urban vegetation with high spatial resolution images (HRS) resulting from the configuration of the vegetation in an urban environment, sometimes characterized by isolated trees or small green spaces. This explains why Sentinel-2 data which spatial resolution of 10 meters gives a better result compared to Landsat-8 data which is 30 meters. However, a good rendering is noted for the vegetation around the wetlands area for the two sensors resulting from the high density and the size of the vegetated perimeters in this part of the capital. Overall, there is an underestimation of urban vegetation cover, particularly for Landsat-8. The use of very high spatial resolution images could be necessary to better assess the potential of satellite data for monitoring urban vegetation in Sahelian context.展开更多
文摘The small size of agricultural plots is the main difficulty for crops mapping with remote sensing data in the Sahelian region of Africa. The study aims to combine Sentinel-1 (radar) and Sentinel-2 (Optic) data to discriminate millet, maize and peanut crops. Training plots were used in order to analyse temporal variation of the three crops’ signals. T<span style="font-family:Verdana;">he NDVI (Normalized Difference Vegetation Index) was able to differentiate crops only at the end of the rainy season (October). </span><span style="font-family:Verdana;">The optical data as well as the radar ones could not easily discriminate the three crops during the growing season, because in that period vegetation cover is low, and soil contribution to the signals (due to roughness and moisture) was more important than that of real vegetation. However, the ratio of VH/VV (VH: incident signal in vertical polarization and reflected signal in horizontal polarization;VV: incident signal in vertical polarization and reflected signal in horizontal polarization) gave a difference between millet and the two other crops at the beginning cultural season (July 11). Difference appears from the second third of September when the harvest of cereals crops (millet and maize) began. From middle of October, the peanut signal dropped sharply thus facilitating the differentiation of peanut from the two other crops. This analysis led to the identification of data that have could be used to discriminate these crops (useful data). Classification of the combined useful data gave an overall high accuracy of 82%, with 96%, 61% and 65% for peanut, maize and millet, respectively. The non-agricultural areas (water, natural vegetation, habit, bare soil) were well classified with an accuracy greater than 90%.</span>
文摘Urban areas house vegetation cover in several forms, fulfilling several ecological functions like thermal regulator, biodiversity, air quality, etc. However, their extent is often not very well known, especially in African cities, making it sometimes difficult to assess their real impact on the urban ecosystem functioning. This work aims to analyse the capacity of satellite sensors for mapping vegetation and wetlands in urban areas. The data produced by the MSI sensors of Sentinel 2 and OLI of Landsat-8 are used to identify and map the vegetation cover in the Dakar region through a supervised classification with the Support Vector Machine (SVM) algorithm. The results show that it is sometimes not very easy to analyse urban vegetation with high spatial resolution images (HRS) resulting from the configuration of the vegetation in an urban environment, sometimes characterized by isolated trees or small green spaces. This explains why Sentinel-2 data which spatial resolution of 10 meters gives a better result compared to Landsat-8 data which is 30 meters. However, a good rendering is noted for the vegetation around the wetlands area for the two sensors resulting from the high density and the size of the vegetated perimeters in this part of the capital. Overall, there is an underestimation of urban vegetation cover, particularly for Landsat-8. The use of very high spatial resolution images could be necessary to better assess the potential of satellite data for monitoring urban vegetation in Sahelian context.
