Deforestation is the purpose of converting forest into land and reforestation compared to deforestation is very low.That’s why closely and accurately deforestation monitoring using Sentinel-1 and Sentinel-2 satellite...Deforestation is the purpose of converting forest into land and reforestation compared to deforestation is very low.That’s why closely and accurately deforestation monitoring using Sentinel-1 and Sentinel-2 satellite images for better vision is required.This paper proposes an effective image fusion technique that combines S-1/2 data to improve the deforested areas.Based on review,Optical and SAR image fusion produces high-resolution images for better de-forestation monitoring.To enhance the S-1/2 images,preprocessing is needed as per requirements and then,collocation between the two different types of images to mitigate the image registration problem,and after that,apply an image fu-sion machine learning approach,PCA-Wavelet.As per analysis,PCA helps to maintain spatial resolution,and Wavelet helps to preserve spectral resolution,gives better-fused images compared to other techniques.As per results,2019 S-2 pre-22 processed collocated image enhances 42.2508 km deforested area,S-1 preprocessed collocated image enhances 23.7918 km^(2) deforested area,and after fusion of the 2019 S-1/2 images,it enhances 16.5335 km deforested area.Similarly,the 20232 S-2 preprocessed collocated image enhances 49.2216 km deforested area,S-1 preprocessed collocated image enhances 2223.8459 km deforested area after fusion of the 2023 S-1/2 images,enhancing 35.9185 km deforested area.These im-provements show that combining data sources gives a clearer and more reliable picture of forest loss over time.The overall paper objective is to apply effective techniques for image fusion of Brazil’s Amazon Forest and analyze the difference between collocated image pixels and fused image pixels for accurate analysis of deforested area.展开更多
Soil salinization is one of the most important causes of land degradation and desertification,especially in arid and semi-arid areas.The dynamic monitoring of soil salinization is of great significance to land managem...Soil salinization is one of the most important causes of land degradation and desertification,especially in arid and semi-arid areas.The dynamic monitoring of soil salinization is of great significance to land management,agricultural activities,water quality,and sustainable development.The remote sensing images taken by the synthetic aperture radar(SAR)Sentinel-1 and the multispectral satellite Sentinel-2 with high resolution and short revisit period have the potential to monitor the spatial distribution of soil attribute information on a large area;however,there are limited studies on the combination of Sentinel-1 and Sentinel-2 for digital mapping of soil salinization.Therefore,in this study,we used topography indices derived from digital elevation model(DEM),SAR indices generated by Sentinel-1,and vegetation indices generated by Sentinel-2 to map soil salinization in the Ogan-Kuqa River Oasis located in the central and northern Tarim Basin in Xinjiang of China,and evaluated the potential of multi-source sensors to predict soil salinity.Using the soil electrical conductivity(EC)values of 70 ground sampling sites as the target variable and the optimal environmental factors as the predictive variable,we constructed three soil salinity inversion models based on classification and regression tree(CART),random forest(RF),and extreme gradient boosting(XGBoost).Then,we evaluated the prediction ability of different models through the five-fold cross validation.The prediction accuracy of XGBoost model is better than those of CART and RF,and soil salinity predicted by the three models has similar spatial distribution characteristics.Compared with the combination of topography indices and vegetation indices,the addition of SAR indices effectively improves the prediction accuracy of the model.In general,the method of soil salinity prediction based on multi-source sensor combination is better than that based on a single sensor.In addition,SAR indices,vegetation indices,and topography indices are all effective variables for soil salinity prediction.Weighted Difference Vegetation Index(WDVI)is designated as the most important variable in these variables,followed by DEM.The results showed that the high-resolution radar Sentinel-1 and multispectral Sentinel-2 have the potential to develop soil salinity prediction model.展开更多
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
文摘Deforestation is the purpose of converting forest into land and reforestation compared to deforestation is very low.That’s why closely and accurately deforestation monitoring using Sentinel-1 and Sentinel-2 satellite images for better vision is required.This paper proposes an effective image fusion technique that combines S-1/2 data to improve the deforested areas.Based on review,Optical and SAR image fusion produces high-resolution images for better de-forestation monitoring.To enhance the S-1/2 images,preprocessing is needed as per requirements and then,collocation between the two different types of images to mitigate the image registration problem,and after that,apply an image fu-sion machine learning approach,PCA-Wavelet.As per analysis,PCA helps to maintain spatial resolution,and Wavelet helps to preserve spectral resolution,gives better-fused images compared to other techniques.As per results,2019 S-2 pre-22 processed collocated image enhances 42.2508 km deforested area,S-1 preprocessed collocated image enhances 23.7918 km^(2) deforested area,and after fusion of the 2019 S-1/2 images,it enhances 16.5335 km deforested area.Similarly,the 20232 S-2 preprocessed collocated image enhances 49.2216 km deforested area,S-1 preprocessed collocated image enhances 2223.8459 km deforested area after fusion of the 2023 S-1/2 images,enhancing 35.9185 km deforested area.These im-provements show that combining data sources gives a clearer and more reliable picture of forest loss over time.The overall paper objective is to apply effective techniques for image fusion of Brazil’s Amazon Forest and analyze the difference between collocated image pixels and fused image pixels for accurate analysis of deforested area.
基金This work was financially supported by the National Natural Science Foundation of China(41771470)the China Postdoctoral Science Foundation(2020M672776).
文摘Soil salinization is one of the most important causes of land degradation and desertification,especially in arid and semi-arid areas.The dynamic monitoring of soil salinization is of great significance to land management,agricultural activities,water quality,and sustainable development.The remote sensing images taken by the synthetic aperture radar(SAR)Sentinel-1 and the multispectral satellite Sentinel-2 with high resolution and short revisit period have the potential to monitor the spatial distribution of soil attribute information on a large area;however,there are limited studies on the combination of Sentinel-1 and Sentinel-2 for digital mapping of soil salinization.Therefore,in this study,we used topography indices derived from digital elevation model(DEM),SAR indices generated by Sentinel-1,and vegetation indices generated by Sentinel-2 to map soil salinization in the Ogan-Kuqa River Oasis located in the central and northern Tarim Basin in Xinjiang of China,and evaluated the potential of multi-source sensors to predict soil salinity.Using the soil electrical conductivity(EC)values of 70 ground sampling sites as the target variable and the optimal environmental factors as the predictive variable,we constructed three soil salinity inversion models based on classification and regression tree(CART),random forest(RF),and extreme gradient boosting(XGBoost).Then,we evaluated the prediction ability of different models through the five-fold cross validation.The prediction accuracy of XGBoost model is better than those of CART and RF,and soil salinity predicted by the three models has similar spatial distribution characteristics.Compared with the combination of topography indices and vegetation indices,the addition of SAR indices effectively improves the prediction accuracy of the model.In general,the method of soil salinity prediction based on multi-source sensor combination is better than that based on a single sensor.In addition,SAR indices,vegetation indices,and topography indices are all effective variables for soil salinity prediction.Weighted Difference Vegetation Index(WDVI)is designated as the most important variable in these variables,followed by DEM.The results showed that the high-resolution radar Sentinel-1 and multispectral Sentinel-2 have the potential to develop soil salinity prediction model.
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>
