scholarly journals From Geometric-Optical Remote Sensing Modeling to Quantitative Remote Sensing Science—In Memory of Academician Xiaowen Li

2018 ◽  
Vol 10 (11) ◽  
pp. 1764 ◽  
Author(s):  
Qinhuo Liu ◽  
Guangjian Yan ◽  
Ziti Jiao ◽  
Qing Xiao ◽  
Jianguang Wen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Thermal Emission ◽  
Scale Effects ◽  
Remote Sensing Data ◽  
Geometric Optic ◽  
Optical Remote Sensing ◽  
Research Activities ◽  
Quantitative Remote Sensing ◽  
Vegetation Canopies ◽  
High Regard

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.

scholarly journals Fault-Based Geological Lineaments Extraction Using Remote Sensing and GIS—A Review

Geosciences ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 183
Author(s):  
Hemayatullah Ahmadi ◽  
Emrah Pekkan
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Thermal Emission ◽  
Active Faults ◽  
Remote Sensing Data ◽  
Shuttle Radar Topography Mission ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Remotely Sensed Data ◽  
Aperture Radar

Geological lineaments are the earth’s linear features indicating significant tectonic units in the crust associated with the formation of minerals, active faults, groundwater controls, earthquakes, and geomorphology. This study aims to provide a systematic review of the state-of-the-art remote sensing techniques and data sets employed for geological lineament analysis. The critical challenges of this approach and the diverse data verification and validation techniques will be presented. Thus, this review spanned academic articles published since 1975, including expert reports and theses. Landsat series, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Sentinel 2 are the prevalent optical remote sensing data widely used for lineament detection. Moreover, Shuttle Radar Topography Mission (SRTM) derived Digital Elevation Model (DEM), Synthetic-aperture radar (SAR), Interferometric synthetic aperture radar (InSAR), and Sentinel 1 are the typical radar remotely sensed data which are widely used for the detection of geological lineaments. The geological lineaments acquired via GIS techniques are not consistent even though a variety of manual, semi-automated, and automated techniques are applied. Therefore, a single method may not provide an accurate lineament distribution and may include artifacts requiring integration of multiple algorithms, e.g., manual and automated algorithms.

scholarly journals An Improved Segmentation Method for Automatic Mapping of Cone Karst from Remote Sensing Data Based on DeepLab V3+ Model

2021 ◽  
Vol 13 (3) ◽  
pp. 441
Author(s):  
Han Fu ◽  
Bihong Fu ◽  
Pilong Shi
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Guizhou Province ◽  
Training Dataset ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Karst Landscape ◽  
Heritage Site ◽  
Training Samples ◽  
Input Module

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.

scholarly journals Rapid Evaluation and Validation Method of Above Ground Forest Biomass Estimation Using Optical Remote Sensing in Tundi Reserved Forest Area, India

2021 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Praveen Kumar ◽  
Akhouri P. Krishna ◽  
Thorkild M. Rasmussen ◽  
Mahendra K. Pal
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Remote Sensing Data ◽  
Test Site ◽  
Forest Area ◽  
Relational Model ◽  
Biomass Estimation ◽  
Optical Remote Sensing ◽  
Ground Sample ◽  
Sensing Data

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.

scholarly journals Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2313
Author(s):  
Elena Prudnikova ◽  
Igor Savin
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Reflectance ◽  
Remote Sensing Data ◽  
Soil Surface ◽  
Optical Remote Sensing ◽  
Test Field ◽  
Sensing Data ◽  
Sentinel 2

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.

scholarly journals Mapping Paleohydrologic Features in the Arid Areas of Saudi Arabia Using Remote-Sensing Data

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 417 ◽  
Author(s):  
Mohamed Abdelkareem ◽  
Fathy Abdalla ◽  
Samar Y. Mohamed ◽  
Farouk El-Baz
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Volcanic Activity ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Alos Palsar ◽  
Sensing Data ◽  
The Past ◽  
Aeolian Deposits

At present, the Arabian Peninsula is one of the driest regions on Earth; however, this area experienced heavy rainfall in the past thousand years. During this period, catchments received substantial amounts of surface water and sustained vast networks of streams and paleolakes, which are currently inactive. The Advanced Land Observing Satellite (ALOS) Phased Array Type L-band Synthetic Aperture Radar (PALSAR) data reveal paleohydrologic features buried under shallow aeolian deposits in many areas of the ad-Dawasir, Sahba, Rimah/Batin, and as-Sirhan wadis. Optical remote-sensing data support that the middle of the trans-peninsula Wadi Rimah/Batin, which extends for ~1200 km from the Arabian Shield to Kuwait and covers ~200,000 km2, is dammed by linear sand dunes formed by changes in climate conditions. Integrating Landsat 8 Operational Land Imager (OLI), Geo-Eye, Shuttle Radar Topography Mission (SRTM) digital elevation model, and ALOS/PALSAR data allowed for the characterization of paleodrainage reversals and diversions shaped by structural and volcanic activity. Evidence of streams abruptly shifting from one catchment to another is preserved in Wadi ad-Dawasir along the fault trace. Volcanic activity in the past few thousand years in northern Saudi Arabia has also changed the slope of the land and reversed drainage systems. Relics of earlier drainage directions are well maintained as paleoslopes and wide upstream patterns. This study found that paleohydrologic activity in Saudi Arabia is impacted by changes in climate and by structural and volcanic activity, resulting in changes to stream direction and activity. Overall, the integration of radar and optical remote-sensing data is significant for deciphering past hydrologic activity and for predicting potential water resource areas.

Spatiotemporal monitoring of surface soil moisture using optical remote sensing data: a case study in a semi-arid area

2018 ◽  
Vol 65 (3) ◽  
pp. 481-499 ◽  
Author(s):  
Rida Khellouk ◽  
Ahmed Barakat ◽  
Abdelghani Boudhar ◽  
Rachid Hadria ◽  
Hayat Lionboui ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Arid Area ◽  
Optical Remote Sensing ◽  
Surface Soil Moisture ◽  
Sensing Data ◽  
Semi Arid
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