scholarly journals Evaluation and Intercomparison of Topographic Correction Methods Based on Landsat Images and Simulated Data

2021 ◽  
Vol 13 (20) ◽  
pp. 4120
Author(s):  
Yichuan Ma ◽  
Tao He ◽  
Ainong Li ◽  
Sike Li
Keyword(s):  
Simulated Data ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Topographic Effects ◽  
Landsat Images ◽  
Topographic Correction ◽  
Surface Cover ◽  
Multi Criteria Evaluation ◽  
Pros And Cons ◽  
Simulated Images

Topographic effects in medium and high spatial resolution remote sensing images greatly limit the application of quantitative parameter retrieval and analysis in mountainous areas. Many topographic correction methods have been proposed to reduce such effects. Comparative analyses on topographic correction algorithms have been carried out, some of which drew different or even contradictory conclusions. Performances of these algorithms over different terrain and surface cover conditions remain largely unknown. In this paper, we intercompared ten widely used topographic correction algorithms by adopting multi-criteria evaluation methods using Landsat images under various terrain and surface cover conditions as well as images simulated by a 3D radiative transfer model. Based on comprehensive analysis, we found that the Teillet regression-based models had the overall best performance in terms of topographic effects’ reduction and overcorrection; however, correction bias may be introduced by Teillet regression models when surface reflectance in the uncorrected images do not follow a normal distribution. We recommend including more simulated images for a more in-depth evaluation. We also recommend that the pros and cons of topographic correction methods reported in this paper should be carefully considered for surface parameters retrieval and applications in mountain regions.

scholarly journals Does topographic normalization of landsat images improve fractional tree cover mapping in tropical mountains?

2015 ◽  
Vol XL-7/W3 ◽  
pp. 261-267 ◽  
Author(s):  
H. Adhikari ◽  
J. Heiskanen ◽  
E. E Maeda ◽  
P. K. E. Pellikka
Keyword(s):  
Predictive Models ◽  
Vegetation Index ◽  
Incidence Angle ◽  
Vegetation Indices ◽  
Airborne Lidar ◽  
Tree Cover ◽  
Topographic Effects ◽  
Landsat Images ◽  
Topographic Correction ◽  
Tropical Mountains

Fractional tree cover (Fcover) is an important biophysical variable for measuring forest degradation and characterizing land cover. Recently, atmospherically corrected Landsat data have become available, providing opportunities for high-resolution mapping of forest attributes at global-scale. However, topographic correction is a pre-processing step that remains to be addressed. While several methods have been introduced for topographic correction, it is uncertain whether Fcover models based on vegetation indices are sensitive to topographic effects. Our objective was to assess the effect of topographic correction on the accuracy of Fcover modelling. The study area was located in the Eastern Arc Mountains of Kenya. We used C-correction as a digital elevation model (DEM) based correction method. We examined if predictive models based on normalized difference vegetation index (NDVI), reduced simple ratio (RSR) and tasseled cap indices (Brightness, Greenness and Wetness) are improved if using topographically corrected data. Furthermore, we evaluated how the results depend on the DEM by correcting images using available global DEM (ASTER GDEM, SRTM) and a regional DEM. Reference Fcover was obtained from wall-to-wall airborne LiDAR data. Landsat images corresponding to minimum and maximum sun elevation were analyzed. We observed that topographic correction could only improve models based on Brightness and had very small effect on the other models. Cosine of the solar incidence angle (<i>cos i</i>) derived from SRTM DEM showed stronger relationship with spectral bands than other DEMs. In conclusion, our results suggest that, in tropical mountains, predictive models based on common vegetation indices are not sensitive to topographic effects.

scholarly journals Development of synthetic GOES-R ABI aerosol products

2014 ◽  
Vol 7 (9) ◽  
pp. 10131-10157 ◽  
Author(s):  
R. M. Hoff ◽  
S. Kondragunta ◽  
P. Ciren ◽  
C. Xu ◽  
H. Zhang ◽  
...  
Keyword(s):  
Simulation Experiment ◽  
Simulated Data ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
User Group ◽  
Display System ◽  
State And Local ◽  
Systems Simulation ◽  
The Web ◽  
Observing Systems

Abstract. An Observing Systems Simulation Experiment (OSSE) for GOES-R Advanced Baseline Imager (ABI) aerosol products has been carried out. The generation of simulated data involves prediction of aerosol chemical composition fields at one-hour resolution and 12 km × 12 km spacing. These data are then fed to a radiative transfer model to simulate the on-orbit radiances that the GOES-R ABI will see in six channels. This allows the ABI aerosol algorithm to be tested to produce products that will be available after launch. In cooperation with a user group of 40+ state and local air quality forecasters, the system has been tested in real-time experiments where the results mimic what the forecasters will see after 2016 when GOES-R launches. Feedback from this group has allowed refinement of the web display system for the ABI aerosol products and has creatively called for new products that were not envisaged by the satellite team.

scholarly journals Can statistics of turbulent tracer dispersion be inferred from camera observations of SO<sub>2</sub> in the ultraviolet?

2019 ◽  
Author(s):  
Arve Kylling ◽  
Hamidreza Ardeshiri ◽  
Massimo Cassiani ◽  
Anna Solvejg Dinger ◽  
Soon-Young Park ◽  
...  
Keyword(s):  
Turbulent Dispersion ◽  
Relative Dispersion ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Eddy Simulation ◽  
Tracer Dispersion ◽  
Large Eddy ◽  
Sensitivity Studies ◽  
Mean Differences ◽  
Simulated Images

Abstract. Turbulence is one of the unsolved problems of physics. Atmospheric turbulence and in particular its effect on tracer dispersion may be measured by cameras sensitive to the absorption of ultraviolet (UV) sun-light by sulfur dioxide (SO2), a gas that can be considered a passive tracer over short transport distances. We present a method to simulate UV camera measurements of SO2 with a 3D Monte Carlo radiative transfer model which takes input from a large eddy simulation (LES) of a SO2 plume released from a point source. From the simulated images the apparent absorbance and various plume density statistics (centerline position, meandering, absolute and relative dispersion, skewness, and fractal dimension) were calculated. These were compared with corresponding quantities obtained directly from the LES. Mean differences of centerline position, absolute and relative dispersion, and skewness between the simulated images and the LES were found to be smaller than a quarter of one camera pixel, with standard deviations between 1/2 and 3/2 camera pixel. Furthermore sensitivity studies were made to quantify how changes in solar azimuth and zenith angles, aerosol loading (background and in plume), and surface albedo impact the UV camera image plume statistics. Changing the values of these parameters within realistic limits have negligible effect on the centerline position, meandering, absolute and relative dispersions, and skewness of the SO2 plume. Thus, we demonstrate that UV camera images of SO2 plumes may be used to derive plume statistics of relevance for the study of atmospheric turbulent dispersion.

scholarly journals A Variational Method to Retrieve the Extinction Profile in Liquid Clouds Using Multiple-Field-of-View Lidar

2012 ◽  
Vol 51 (2) ◽  
pp. 350-365 ◽  
Author(s):  
Nicola L. Pounder ◽  
Robin J. Hogan ◽  
Tamás Várnai ◽  
Alessandro Battaglia ◽  
Robert F. Cahalan
Keyword(s):  
Optical Depth ◽  
Vertical Profile ◽  
Global Radiation ◽  
Simulated Data ◽  
Field Of View ◽  
Radiation Budget ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Optimization Schemes ◽  
Multiple Field

AbstractLiquid clouds play a profound role in the global radiation budget, but it is difficult to retrieve their vertical profile remotely. Ordinary narrow-field-of-view (FOV) lidars receive a strong return from such clouds, but the information is limited to the first few optical depths. Wide-angle multiple-FOV lidars can isolate radiation that is scattered multiple times before returning to the instrument, often penetrating much deeper into the cloud than does the single-scattered signal. These returns potentially contain information on the vertical profile of the extinction coefficient but are challenging to interpret because of the lack of a fast radiative transfer model for simulating them. This paper describes a variational algorithm that incorporates a fast forward model that is based on the time-dependent two-stream approximation, and its adjoint. Application of the algorithm to simulated data from a hypothetical airborne three-FOV lidar with a maximum footprint width of 600 m suggests that this approach should be able to retrieve the extinction structure down to an optical depth of around 6 and a total optical depth up to at least 35, depending on the maximum lidar FOV. The convergence behavior of Gauss–Newton and quasi-Newton optimization schemes are compared. Results are then presented from an application of the algorithm to observations of stratocumulus by the eight-FOV airborne Cloud Thickness from Off-Beam Lidar Returns (THOR) lidar. It is demonstrated how the averaging kernel can be used to diagnose the effective vertical resolution of the retrieved profile and, therefore, the depth to which information on the vertical structure can be recovered. This work enables more rigorous exploitation of returns from spaceborne lidar and radar that are subject to multiple scattering than was previously possible.

scholarly journals Can statistics of turbulent tracer dispersion be inferred from camera observations of SO<sub>2</sub> in the ultraviolet? A modelling study

2020 ◽  
Vol 13 (6) ◽  
pp. 3303-3318
Author(s):  
Arve Kylling ◽  
Hamidreza Ardeshiri ◽  
Massimo Cassiani ◽  
Anna Solvejg Dinger ◽  
Soon-Young Park ◽  
...  
Keyword(s):  
Turbulent Dispersion ◽  
Relative Dispersion ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Line Position ◽  
Eddy Simulation ◽  
Centre Line ◽  
Tracer Dispersion ◽  
Mean Differences ◽  
Simulated Images

Abstract. Atmospheric turbulence and in particular its effect on tracer dispersion may be measured by cameras sensitive to the absorption of ultraviolet (UV) sunlight by sulfur dioxide (SO2), a gas that can be considered a passive tracer over short transport distances. We present a method to simulate UV camera measurements of SO2 with a 3D Monte Carlo radiative transfer model which takes input from a large eddy simulation (LES) of a SO2 plume released from a point source. From the simulated images the apparent absorbance and various plume density statistics (centre-line position, meandering, absolute and relative dispersion, and skewness) were calculated. These were compared with corresponding quantities obtained directly from the LES. Mean differences of centre-line position, absolute and relative dispersions, and skewness between the simulated images and the LES were generally found to be smaller than or about the voxel resolution of the LES. Furthermore, sensitivity studies were made to quantify how changes in solar azimuth and zenith angles, aerosol loading (background and in plume), and surface albedo impact the UV camera image plume statistics. Changing the values of these parameters within realistic limits has negligible effects on the centre-line position, meandering, absolute and relative dispersions, and skewness of the SO2 plume. Thus, we demonstrate that UV camera images of SO2 plumes may be used to derive plume statistics of relevance for the study of atmospheric turbulent dispersion.

scholarly journals CANOPY MODELING OF AQUATIC VEGETATION: CONSTRUCTION OF SUBMERGED VEGETATION INDEX

2018 ◽  
Vol XLII-3 ◽  
pp. 1253-1258
Author(s):  
Z. Ma ◽  
G. Zhou
Keyword(s):  
Vegetation Index ◽  
Aquatic Vegetation ◽  
Spectral Characteristics ◽  
Vegetation Indices ◽  
Simulated Data ◽  
Radiative Transfer Model ◽  
Future Research ◽  
Transfer Model ◽  
Submerged Vegetation ◽  
Terrestrial Vegetation

The unique spectral characteristics of submerged vegetation in wetlands determine that the conventional terrestrial vegetation index cannot be directly employed to species identification and parameter inversion of submerged vegetation. Based on the Aquatic Vegetation Radiative Transfer model (AVRT), this paper attempts to construct an index suitable for submerged vegetation, the model simulated data and a scene of Sentinel-2A image in Taihu Lake, China are utilized for assessing the performance of the newly constructed indices and the existent vegetation indices. The results show that the angle index composed by 525&amp;thinsp;nm, 555&amp;thinsp;nm and 670&amp;thinsp;nm can resist the effects of water columns and is more sensitive to vegetation parameters such as LAI. Furthermore, it makes a well discrimination between submerged vegetation and water bodies in the satellite data. We hope that the new index will provide a theoretical basis for future research.

scholarly journals A SEMI-EMPIRICAL TOPOGRAPHIC CORRECTION MODEL FOR MULTI-SOURCE SATELLITE IMAGES

2018 ◽  
Vol IV-3 ◽  
pp. 225-232
Author(s):  
Sa Xiao ◽  
Xinpeng Tian ◽  
Qiang Liu ◽  
Jianguang Wen ◽  
Yushuang Ma ◽  
...  
Keyword(s):  
Satellite Images ◽  
Near Infrared ◽  
Satellite Image ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Surface Reflectance ◽  
The Sun ◽  
Topographic Correction ◽  
Correction Model ◽  
Semi Empirical

Topographic correction of surface reflectance in rugged terrain areas is the prerequisite for the quantitative application of remote sensing in mountainous areas. Physics-based radiative transfer model can be applied to correct the topographic effect and accurately retrieve the reflectance of the slope surface from high quality satellite image such as Landsat8 OLI. However, as more and more images data available from various of sensors, some times we can not get the accurate sensor calibration parameters and atmosphere conditions which are needed in the physics-based topographic correction model. This paper proposed a semi-empirical atmosphere and topographic corrction model for muti-source satellite images without accurate calibration parameters.Based on this model we can get the topographic corrected surface reflectance from DN data, and we tested and verified this model with image data from Chinese satellite HJ and GF. The result shows that the correlation factor was reduced almost 85&amp;thinsp;% for near infrared bands and the classification overall accuracy of classification increased 14&amp;thinsp;% after correction for HJ. The reflectance difference of slope face the sun and face away the sun have reduced after correction.

scholarly journals FOREST COVER MAPPING IN ISKANDAR MALAYSIA USING SATELLITE DATA

2016 ◽  
Vol XLII-4/W1 ◽  
pp. 71-75 ◽  
Author(s):  
K. D. Kanniah ◽  
N. E. Mohd Najib ◽  
T. T. Vu
Keyword(s):  
Land Use ◽  
Oil Palm ◽  
Forest Cover ◽  
Soil Surface ◽  
Peninsular Malaysia ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Landsat Images ◽  
The Government ◽  
Landsat Satellite

Malaysia is the third largest country in the world that had lost forest cover. Therefore, timely information on forest cover is required to help the government to ensure that the remaining forest resources are managed in a sustainable manner. This study aims to map and detect changes of forest cover (deforestation and disturbance) in Iskandar Malaysia region in the south of Peninsular Malaysia between years 1990 and 2010 using Landsat satellite images. The Carnegie Landsat Analysis System-Lite (CLASlite) programme was used to classify forest cover using Landsat images. This software is able to mask out clouds, cloud shadows, terrain shadows, and water bodies and atmospherically correct the images using 6S radiative transfer model. An Automated Monte Carlo Unmixing technique embedded in CLASlite was used to unmix each Landsat pixel into fractions of photosynthetic vegetation (PV), non photosynthetic vegetation (NPV) and soil surface (S). Forest and non-forest areas were produced from the fractional cover images using appropriate threshold values of PV, NPV and S. CLASlite software was found to be able to classify forest cover in Iskandar Malaysia with only a difference between 14% (1990) and 5% (2010) compared to the forest land use map produced by the Department of Agriculture, Malaysia. Nevertheless, the CLASlite automated software used in this study was found not to exclude other vegetation types especially rubber and oil palm that has similar reflectance to forest. Currently rubber and oil palm were discriminated from forest manually using land use maps. Therefore, CLASlite algorithm needs further adjustment to exclude these vegetation and classify only forest cover.

ESTIMATING THE LIMIT OF BIO-AEROSOL DETECTION WITH PASSIVE INFRARED SPECTROSCOPY

2008 ◽  
Vol 18 (03) ◽  
pp. 701-711
Author(s):  
AGUSTIN IFARRAGUERRI ◽  
AVISHAI BEN-DAVID ◽  
RICHARD G. VANDERBEEK
Keyword(s):  
Turbulence Model ◽  
Hypothesis Test ◽  
Simulated Data ◽  
Roc Curves ◽  
Detection Algorithm ◽  
Radiative Transfer Model ◽  
Spectral Radiance ◽  
Transfer Model ◽  
Infrared Measurements ◽  
Passive Infrared

To investigate the detection limits of biological aerosols using passive infrared measurements, we have developed a computational model that relies on physics-based simulations to generate a statistical sample. The simulation consists of three principal models: an atmospheric turbulence model, a radiative transfer model and a target detection model. The turbulence model is used to generate microscale atmospheric variability. Resulting temperature and density profiles, along with custom aerosol profiles, are used to generate inputs for MODTRAN5, which produces simulated atmospheric spectral radiance. The simulated data is then analyzed by using an optimal detection algorithm and a hypothesis test, resulting in receiver operating characteristic (ROC) curves.

scholarly journals Probability Index of Low Stratus and Fog at Dawn using Dual Geostationary Satellite Observations from COMS and FY-2D near the Korean Peninsula

2019 ◽  
Vol 11 (11) ◽  
pp. 1283
Author(s):  
Jung-Hyun Yang ◽  
Jung-Moon Yoo ◽  
Yong-Sang Choi ◽  
Dong Wu ◽  
Jin-Hee Jeong
Keyword(s):  
Korean Peninsula ◽  
Simulated Data ◽  
Probability Of Detection ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Detection Accuracy ◽  
High Detection Rate ◽  
High Detection ◽  
Meteorological Satellite ◽  
Probability Index

We developed a new remote sensing method for detecting low stratus and fog (LSF) at dawn in terms of probability index (PI) of LSF from simultaneous stereo observations of two geostationary-orbit satellites; the Korean Communication, Ocean, and Meteorological Satellite (COMS; 128.2°E); and the Chinese FengYun satellite (FY-2D; 86.5°E). The algorithm was validated near the Korean Peninsula between the months of April and August from April 2012 to June 2015, by using surface observations at 45 meteorological stations in South Korea. The optical features of LSF were estimated by using satellite retrievals and simulated data from the radiative transfer model. The PI was calculated using the combination of three satellite-observed variables: 1) the reflectance at 0.67 μm (R0.67) from COMS, and 2) the FY-2D R0.67 minus the COMS R0.67 (△R0.67) and 3) the FY-2D-COMS difference in the brightness temperature difference between 3.7 and 11.0 μm (ΔBTD3.7-11). The three variables, adopted from the top three probability of detection (POD) scores for their fog detection thresholds: △R0.67 (0.82) > ΔBTD3.7-11 (0.73) > R0.67 (0.70) > BTD3.7-11 (0.51). The LSF PI for this algorithm was significantly better in the two case studies compared to that using COMS only (i.e., R0.67 or BTD3.7-11), so that this improvement was due to △R0.67 and ΔBTD3.7-11. Overall, PI in the LSF spatial distribution has the merits of a high detection rate, a specific probability display, and a low rate of seasonality and variability in detection accuracy. Therefore, PI would be useful for monitoring LSF in near-real-time, and to further its forecast ability, using next-generation satellites.

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