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Coastal mangrove forests provide important ecosystem goods and services, including carbon sequestration, biodiversity conservation, and hazard mitigation. However, they are being destroyed at an alarming rate by human activities. To characterize mangrove forest changes, evaluate their impacts, and support relevant protection and restoration decision making, accurate and up-to-date mangrove extent mapping at large spatial scales is essential. Available large-scale mangrove extent data products use a single machine learning method commonly with 30 m Landsat imagery, and significant inconsistencies remain among these data products. With huge amounts of satellite data involved and the heterogeneity of land surface characteristics across large geographic areas, finding the most suitable method for large-scale high-resolution mangrove mapping is a challenge. The objective of this study is to evaluate the performance of a machine learning ensemble for mangrove forest mapping at 20 m spatial resolution across West Africa using Sentinel-2 (optical) and Sentinel-1 (radar) imagery. The machine learning ensemble integrates three commonly used machine learning methods in land cover and land use mapping, including Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network (NN). The cloud-based big geospatial data processing platform Google Earth Engine (GEE) was used for pre-processing Sentinel-2 and Sentinel-1 data. Extensive validation has demonstrated that the machine learning ensemble can generate mangrove extent maps at high accuracies for all study regions in West Africa (92%–99% Producer’s Accuracy, 98%–100% User’s Accuracy, 95%–99% Overall Accuracy). This is the first-time that mangrove extent has been mapped at a 20 m spatial resolution across West Africa. The machine learning ensemble has the potential to be applied to other regions of the world and is therefore capable of producing high-resolution mangrove extent maps at global scales periodically.

A novel method for calculating optimum incidence angle for the TanDEM-X system using any available digital elevation model (DEM) for the given area is proposed in this study. This method includes the plotting of slopes and aspect of the test area in a statistical way and applying mathematical approach using acquisition geometry in ascending and descending pass TanDEM-X data to optimize the incidence angle for obtaining precise DEM. Furthermore, the TanDEM-X raw DEMs in ascending and descending pass over Mumbai, India are combined using a simple weighted fusion algorithm and the quality of fused DEM thus generated is enhanced. The method adopted for fusion is just an experimental study. The problem of optimum weight selection for fusion has been addressed using height error map and a robust layover shadow mask calculated in "Integrated TanDEM-X Processor" (ITP) during TanDEM-X DEM generation. The height error map is calculated from the interferometric coherence with geometrical considerations and the robust layover and shadow map is calculated using TanDEM-X DEM and the corresponding slant range. Results show a significant reduction in the number of invalid pixels after fusion. In the fused DEM, invalids are only 2.14%, while ascending and descending pass DEMs have 5.02% and 6.34%, respectively. Statistical analysis shows a slight improvement in standard deviation of the error in fused DEM by 8% in urban area and about 5% for the whole scene. Only slight improvement in accuracy of fused DEM can be attributed to the coarse resolution of the SRTM-X DEM used as reference.Index Terms-Fusion, layover, optimum incidence angle, TanDEM-X digital elevation model (DEM), weightage.

ABSTRACT:TanDEM-X mission has been acquiring InSAR data to produce high resolution global DEM with greater vertical accuracy since 2010. In this study, TanDEM-X CoSSC data were processed to produce DEMs at 6 m spatial resolution for two test areas of India. The generated DEMs were compared with DEMs available from airborne LiDAR, photogrammetry, SRTM and ICESat elevation point data. The first test site is in Bihar state of India with almost flat terrain and sparse vegetation cover and the second test site is around Godavari river in Andhra Pradesh (A.P.) state of India with flat to moderate hilly terrain. The quality of the DEMs in these two test sites has been specified in terms of most widely used accuracy measures viz. mean, standard deviation, skew and RMSE. The TanDEM-X DEM over Bihar test area gives 5.0 m RMSE by taking airborne LiDAR data as reference. With ICESat elevation data available at 9000 point locations, RMSE of 5.9 m is obtained. Similarly, TanDEM-X DEM for Godavari area was compared with high resolution aerial photogrammetric DEM and SRTM DEM and found RMSE of 5.3 m and 7.5 m respectively. When compared with ICESat elevation data at several point location and also the same point locations of photogrammetric DEM and SRTM, the RMS errors are 4.1 m, 3.5 m and 4.3 m respectively. DEMs were also compared for open-pit coal mining area where elevation changes from -147 m to 189 m. X-and Y-profiles of all DEMs were also compared to see their trend and differences.

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