The contribution of acoustic seafloor mapping techniques to outlining coral reef geomorphology: A case study in the Republic of Maldives (Magoodhoo Reef – Maldivian Archipelago)

Author(s):  
Alessandra Savini ◽  
Fabio Marchese ◽  
Luca Fallati ◽  
Sebastian Krastel ◽  
Aaron Micallef ◽  
...  

<p>Optical remote sensing data coupled with a dense network of field surveys have historically played a crucial role in geomorphological mapping of coral reef environments. Recently this field has undergone a major upgrade thanks to the integration of new advanced methods such as LiDAR, AUV-based and close-range digital photogrammetry and acoustic remote sensing techniques, which are able to investigate the deeper components of this complex geomorphic system. The new detailed maps can produce seamless digital elevation model (DEM) of coral reef environments, by integrating the elevation datasets acquired by the combination of the mentioned survey techniques.</p><p>In our work, a harmonised geomorphological map is generated for the Magoodhoo reef, which borders the southwestern discontinuous marginal rim of a subcircular atoll (i.e. Faafu Atoll) of the Maldivian archipelago. In its north-eastern sector the reef consists of a cuspate reef joined to an almost closed ring reef to the south-west, where Magoodhoo Island is located. The map was generated from the analysis of Sentinel data, orthomosaics and 3D optical models generated by the application of SfM techniques to UAV images, as well as bathymetry and backscatter intensity measurements. The latter were collected down to a depth of up to 120 m along the oceanward margin of the atoll's rim, and to a depth of roughly 60 m along the lagoonward margin. Direct observations were also performed using an observational ROV on the forereef and within the lagoon, and video-transects on the reef flat.</p><p>The oceanward margin shows steep terraced slopes that reveal a complex history of late Pleistocene/Holocene sea level oscillations, while the backreef slopes (toward the lagoon) are generally more gentle, although at places can show abrupt escarpments and overhangs. The lagoon submarine landscape is distinctly featured by patch reefs of variable shapes (from circular to sub-elongated) and dimensions (from few meters to 30m high). Their distribution is clearly controlled by the surface circulation pattern, regulated by the pass that borders the reef to the west. Towards the deeper edge of the mapped sector of the lagoon floor, where patch reefs are totally absent, intriguing small-scale depressions have been detected instead. The regular circular and concave shape calls for their interpretation as pockmarks, but their origin is still unknow due to the  lack of core samples and geochemical analysis in the area. New data are actually needed to precisely outline the sedimentary environments that feature Faafu Atoll and its inner lagoon. Nevertheless, the obtained geomorphological map and the mapped landforms shed new light and a more complete understanding on the processes that drive morphological changes of the entire Magoodhoo reef.</p>

1996 ◽  
pp. 51-54 ◽  
Author(s):  
N. V. M. Unni

The recognition of versatile importance of vegetation for the human life resulted in the emergence of vegetation science and many its applications in the modern world. Hence a vegetation map should be versatile enough to provide the basis for these applications. Thus, a vegetation map should contain not only information on vegetation types and their derivatives but also the geospheric and climatic background. While the geospheric information could be obtained, mapped and generalized directly using satellite remote sensing, a computerized Geographic Information System can integrate it with meaningful vegetation information classes for large areas. Such aft approach was developed with respect to mapping forest vegetation in India at. 1 : 100 000 (1983) and is in progress now (forest cover mapping at 1 : 250 000). Several review works reporting the experimental and operational use of satellite remote sensing data in India were published in the last years (Unni, 1991, 1992, 1994).


2021 ◽  
Vol 13 (3) ◽  
pp. 441
Author(s):  
Han Fu ◽  
Bihong Fu ◽  
Pilong Shi

The South China Karst, a United Nations Educational, Scientific and Cultural Organization (UNESCO) natural heritage site, is one of the world’s most spectacular examples of humid tropical to subtropical karst landscapes. The Libo cone karst in the southern Guizhou Province is considered as the world reference site for these types of karst, forming a distinctive and beautiful landscape. Geomorphic information and spatial distribution of cone karst is essential for conservation and management for Libo heritage site. In this study, a deep learning (DL) method based on DeepLab V3+ network was proposed to document the cone karst landscape in Libo by multi-source data, including optical remote sensing images and digital elevation model (DEM) data. The training samples were generated by using Landsat remote sensing images and their combination with satellite derived DEM data. Each group of training dataset contains 898 samples. The input module of DeepLab V3+ network was improved to accept four-channel input data, i.e., combination of Landsat RGB images and DEM data. Our results suggest that the mean intersection over union (MIoU) using the four-channel data as training samples by a new DL-based pixel-level image segmentation approach is the highest, which can reach 95.5%. The proposed method can accomplish automatic extraction of cone karst landscape by self-learning of deep neural network, and therefore it can also provide a powerful and automatic tool for documenting other type of geological landscapes worldwide.


2021 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Praveen Kumar ◽  
Akhouri P. Krishna ◽  
Thorkild M. Rasmussen ◽  
Mahendra K. Pal

Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.


2021 ◽  
Vol 13 (12) ◽  
pp. 2313
Author(s):  
Elena Prudnikova ◽  
Igor Savin

Optical remote sensing only provides information about the very thin surface layer of soil. Rainfall splash alters soil surface properties and its spectral reflectance. We analyzed the impact of rainfall on the success of soil organic matter (SOM) content (% by mass) detection and mapping based on optical remote sensing data. The subject of the study was the arable soils of a test field located in the Tula region (Russia), their spectral reflectance, and Sentinel-2 data. Our research demonstrated that rainfall negatively affects the accuracy of SOM predictions based on Sentinel-2 data. Depending on the average precipitation per day, the R2cv of models varied from 0.67 to 0.72, RMSEcv from 0.64 to 1.1% and RPIQ from 1.4 to 2.3. The incorporation of information on the soil surface state in the model resulted in an increase in accuracy of SOM content detection based on Sentinel-2 data: the R2cv of the models increased up to 0.78 to 0.84, the RMSEcv decreased to 0.61 to 0.71%, and the RPIQ increased to 2.1 to 2.4. Further studies are necessary to identify how the SOM content and composition of the soil surface change under the influence of rainfall for other soils, and to determine the relationships between rainfall-induced SOM changes and soil surface spectral reflectance.


2018 ◽  
Vol 10 (11) ◽  
pp. 1764 ◽  
Author(s):  
Qinhuo Liu ◽  
Guangjian Yan ◽  
Ziti Jiao ◽  
Qing Xiao ◽  
Jianguang Wen ◽  
...  

The academician Xiaowen Li devoted much of his life to pursuing fundamental research in remote sensing. A pioneer in the geometric-optical modeling of vegetation canopies, his work is held in high regard by the international remote sensing community. He codeveloped the Li–Strahler geometric-optic model, and this paper was selected by a member of the International Society for Optical Engineering (SPIE) milestone series. As a chief scientist, Xiaowen Li led a scientific team that made outstanding advances in bidirectional reflectance distribution modeling, directional thermal emission modeling, comprehensive experiments, and the understanding of spatial and temporal scale effects in remote sensing information, and of quantitative inversions utilizing remote sensing data. In addition to his broad research activities, he was noted for his humility and his dedication in making science more accessible for the general public. Here, the life and academic contributions of Xiaowen Li to the field of quantitative remote sensing science are briefly reviewed.


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