Topographic Correction Methods Research Articles

Integrating physical model and image simulations to correct topographic effects on surface reflectance

Topography complicates the illumination distribution over rugged terrains and hinders the applications of surface reflectance data over mountainous areas. Topographic correction is an essential process to remove the topographic effects in surface reflectance data. This study proposed a Physical model and image Simulation-based topographic Correction method (PSC) for atmospherically corrected surface reflectance images. In contrast with traditional methods, the proposed approach explicitly estimates the illumination distribution over rugged terrains based on image information and then corrects the topographic effects following physical laws. The proposed method was validated and compared with existing well-performed topographic correction methods, including path length correction (PLC), sun-canopy-sensor correction with c factor (SCSC), C correction (CC), and statistical empirical correction (SE). Simulated and satellite data obtained at different times and geolocations are used for evaluation and comparison. The results demonstrated that most existing methods face challenges in removing the biases induced by topography in surface reflectance images. The PLC method failed to obtain reasonable results for faint illumination conditions when the sun zenith angle is high. SE showed relatively poor performance in terms of physical consistency and outlier percentage. Besides, all the traditional methods failed in the correction of cast shadow regions. Comparably, our method consistently demonstrated superior performance in physical consistency, cast shadow correction, and outlier percentage across various regions, encompassing different sun zenith angles and illumination conditions. The proposed method offers an effective and physically consistent approach for the topographic correction of surface reflectance images, facilitating multi-temporal applications over mountainous areas.

Topographic effects amplify forest disturbances detected by yearly wide-time-window Landsat time series

ABSTRACT Widespread topographic effects in remote sensing images of mountainous regions with rugged terrain severely hinder satellite-based global land surface monitoring and change detection. However, topographic effects in Landsat time series (LTSs) disturbance detection remain highly debated and unquantified. In this study, we proposed a novel postprocessing approach to quantify the impact of topographic effects on long time-series (1989 − 2020) forest disturbance detection, taking the Hengduan Mountains Region (HDMR) of southwest China as an example. This approach applied a pixel-by-pixel simulation based on a semiempirical sun-canopy-sensor with a C-correction (SCS+C) model to identify and remove topography-induced disturbances from the LandTrendr-detected forest disturbances. Filtering of the detected forest disturbances with different patch sizes was conducted, and its effectiveness in removing topography-induced disturbances was quantitatively evaluated. The results showed that 10.43% of the total area of LandTrendr-detected forest disturbances was topography-induced. Removing topography-induced disturbances increased the traditional and area-adjusted overall accuracies (OAs) of the forest disturbance map by 3.50% and 0.62%. Topography-induced disturbances occurred mainly on northwest-facing (300°−360°) slopes with a gradient of 30°−55° and higher latitude and between the day of year (DOY) pairs (such as DOYs 90, 330, and 360) with a considerable disparity in solar azimuth and zenith angle. The pixel-by-pixel simulation approach outperformed spatial noise filtering with a minimum mapping unit (MMU), which considerably decreased the area percentage of topography-induced disturbances at the expense of decreasing the producer’s accuracy (PA) in detecting disturbed forests. This study highlighted the importance of removing topographic effects from the detected forest disturbances and shed new light on the promising potential of the postprocessing topographic correction method to replace the preprocessing topographic correction of the LTSs to map forest disturbances in mountainous regions globally.

A neural network topographic correction method for the Lunar Reconnaissance Orbiter Diviner Christiansen feature map

Topographic correction for lunar optical and thermal infrared remote sensing images is a challenging task. Although geometrical, photometrical or albedo-dependent correction methods have been used in an attempt to eliminate topographic effects in the initial lunar reflectance and emissivity images, these methods did not perform well on lunar steep slopes, large crater interiors or walls. In this paper, a novel neural network topographic correction (NNTC) model that was trained based on the relationships between flat (< 0.1° slope angle) and rugged pixels with similar Kaguya FeO abundance and optical maturity parameters (OMATs) (relative ratio < 0.1%) was applied to the Lunar Reconnaissance Orbiter (LRO) Diviner standard Christiansen feature (CF) image. The topographic correction accuracy is 0.03 μm, and no outliers are produced. Visual comparisons with the previous topographic corrected reflectance and emissivity images indicate that the topographic effects on various slope and aspect pixels were effectively corrected. The excavated anorthosite materials in the highland crater (such as the Jackson, Giordano Bruno and Tycho) interiors, surroundings, ejecta, and ray deposits have very similar NNTC CF values. The crater structures in maria or cryptomaria are identified more clearly. In addition, the NNTC method has the potential to be a universal topographic correction method for lunar optical and thermal infrared images, and it provides reliable data sources and a new method for space weathering correction.

Fusion of LiDAR and Multispectral Data for Aboveground Biomass Estimation in Mountain Grassland

Grassland aboveground biomass (AGB) is an important indicator for studying the change in grassland ecological quality and carbon cycle. The rapid development of high-resolution remote sensing and unmanned aerial vehicles (UAV) provides a new opportunity for accurate estimation of grassland AGB on the plot scale. In this study, the mountain grassland was taken as the research object. Using UAV Light Detection and Ranging (LiDAR) data and multispectral satellite images, the influence of topographic correction methods on AGB estimation was compared and a series of LiDAR metrics and vegetation indices were extracted. On this basis, a comprehensive indicator, the vegetation index-height-intensity model (VHI), was proposed to estimate AGB quickly. The results show that: (1) Among the four topographic correction methods, the Teillet regression has the best effect, and can effectively improve the accuracy of AGB estimation in mountain grassland. The correlation between corrected ratio vegetation index and AGB was the highest (correlation coefficient: 0.682). (2) Among the height and intensity metrics, median height and max intensity yielded the higher accuracy in estimating AGB, with Root Mean Square Error (RMSE) of 322 g/m2 and 333 g/m2, respectively. (3) The VHI integrated spectrum and LiDAR information, and its accuracy for AGB estimation for mountain grassland, was obviously better than other indicators, with an RMSE of 272 g/m2. We also found that the accuracy of VHI in univariate models was comparable to that of complex multivariate models such as stepwise regression, support vector machine, and random forest. This study provides a new approach for estimating grassland AGB with multi-source data. As a simple and effective indicator, VHI has shown strong application potential for grassland AGB estimating in mountainous areas, and can be further applied to grassland carbon cycle research and fine management.

A Synthetic Angle Normalization Model of Vegetation Canopy Reflectance for Geostationary Satellite Remote Sensing Data

High-frequency imaging characteristics allow a geostationary satellite (GSS) to capture the diurnal variation in vegetation canopy reflectance spectra, which is of very important practical significance for monitoring vegetation via remote sensing (RS). However, the observation angle and solar angle of high-frequency GSS RS data usually differ, and the differences in bidirectional reflectance from the reflectance spectra of the vegetation canopy are significant, which makes it necessary to normalize angles for GSS RS data. The BRDF (Bidirectional Reflectance Distribution Function) prototype library is effective for the angle normalization of RS data. However, its spatiotemporal applicability and error propagation are currently unclear. To resolve this problem, we herein propose a synthetic angle normalization model (SANM) for RS vegetation canopy reflectance; this model exploits the GSS imaging characteristics, whereby each pixel has a fixed observation angle. The established model references a topographic correction method for vegetation canopies based on path-length correction, solar zenith angle normalization, and the Minnaert model. It also considers the characteristics of diurnal variations in vegetation canopy reflectance spectra by setting the time window. Experiments were carried out on the eight Geostationary Ocean Color Imager (GOCI) images obtained on 22 April 2015 to validate the performance of the proposed SANM. The results show that SANM significantly improves the phase-to-phase correlation of the GOCI band reflectance in the morning time window and retains the instability of vegetation canopy spectra in the noon time window. The SANM provides a preliminary solution for normalizing the angles for the GSS RS data and makes the quantitative comparison of spatiotemporal RS data possible.

The wedge-pentahedra method (WPM): Topographic reduction of local terrain in the context of solar system surface gravimetry and robotic exploration

In classical gravimetry, different corrections are applied, e.g. to correct for the measurement elevation above a reference plane and the gravitational attraction of the material lying between the measurement point and reference plane. Additionally, and especially in non-flat regions, a correction for the topography is generally needed. While this contribution is relatively small on spherical celestial objects, it can be more important for irregularly shaped bodies, such as small bodies or some natural satellites. With the surface gravity being much smaller, the relative importance of the topographic correction increases, while the approximation errors of the surface will become larger. In this work, the novel Wedge-Pentahedra Method (WPM) for topographic correction for (near-) surface gravimetric measurements and simulations is presented that allows precise topographic corrections for asteroids and natural satellites. For a first study, the WPM is applied to the Martian moon Phobos. Taking an exemplary surface location, a high-resolution artificial terrain is added to the surrounding, and the gravitational influence of this topography compared to the original surface is assessed. It is found that the influence of topography on the surface gravity of a small body such as Phobos can be in the order of a few percent, making it an important correction not only for surface gravity science, but likewise for landing and surface operations, to best ensure the mission success. Therefore, the here presented WPM opens a manifold of possible future applications in the context of Solar System exploration, regarding both space science and space technology.

DSRC: An Improved Topographic Correction Method for Optical Remote-Sensing Observations Based on Surface Downwelling Shortwave Radiation

The complex terrain in mountainous areas distorts solar illumination, which brings a strong topographic effect on optical remote-sensing observations. Although many efforts have been done to correct this effect via normalizing solar illumination induced differences, there are still high uncertainty, especially for poor illuminated surfaces. In this study, a downwelling shortwave radiation (DSR)-based correction (DSRC) method was proposed. The topographic effects were accounted by normalizing DSR differences at different topographic conditions, and a stratified correction strategy was applied by separating the image into different groups according to normalized difference vegetation index (NDVI) to consider the spectral differences of different land-cover types. The DSRC method was applied to nine Landsat 8 scenes with high-resolution DSR data acquired by downscaling the Meteosat Second Generation (MSG) DSR product. The performance analysis indicates that the correlation coefficient between the corrected surface reflectance and illumination conditions notably decreased. Compared with SCS + C, empirical rotation, Statistical-Empirical, and Modified Minnaert methods, the DSRC method well retains inherent spectral pattern and provides good advantages in normalizing the aspect differences of surface reflectance. Furthermore, the comparison of NDVI values before and after correction indicated that DSRC preserved the original values and successfully corrected the overestimated NDVI values of poor illuminated surfaces. The corrected NDVI time series provide more reasonable cycle of the phenology of vegetated surfaces than the original series. In summary, the DSRC method has a strong potential for reducing topographic effects that currently limit the applications of remotely sensed data in mountainous areas.

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

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.

A practical topographic correction method for improving Moderate Resolution Imaging Spectroradiometer gross primary productivity estimation over mountainous areas

Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) has been served as an effective tool to assess the terrestrial carbon budget for the entire globe since 2000. However, the current MODIS GPP product neglects the surface heterogeneity in the modeling process and is always generated at 500 m or 1 km resolution, which could cause errors to these estimates over mountainous areas. In this work, the MODIS GPP model (MOD17) was applied to obtain 1 km GPP estimates at eleven mountainous sites. Then, a topographic correction method based on three indexes associated with the spatial heterogeneity of received radiation (TCIAPAR), temperature (TCITMIN), and water (TCIVPD) stresses was developed to reduce GPP errors in these MOD17-simulated estimates. Results showed that a closer relationship between tower-based GPP and MOD17-simulated GPP was achieved after applying the proposed topographic correction method, with the determination coefficient (R2) increased from 0.61 to 0.74 and root mean square error (RMSE) reduced from 24.24 to 14.56 gC m−2 8d−1 at all the eleven mountainous sites. As for the effectiveness of each topographic correction index, an obvious improvement of MOD17-simulated GPP was observed after TCIAPAR correction (increasing R2 by 0.09 and decreasing RMSE by 8.75 gC m−2 8d−1), TCITMIN correction (increasing R2 by 0.05 and decreasing RMSE by 7.80 gC m−2 8d−1), and TCIVPD correction (increasing R2 by 0.06 and decreasing RMSE by 7.89 gC m−2 8d−1), indicating that the spatial heterogeneity information of radiation, temperature, and water within coarse pixels is necessary for improving the MODIS GPP over mountainous areas. It is notable that the combination of the TCIAPAR, TCITMIN, and TCIVPD corrections was found to have the largest improvement for MOD17-simulated GPP (increasing R2 by 0.13 and decreasing RMSE by 9.68 gC m−2 8d−1), indicating that the combined consideration of topographic factors in the correction process might achieve a larger improvement. This study highlights the feasibility of incorporating surface topographic characteristics into current coarse resolution GPP products in obtaining large-scale mountain GPP estimates.

About the Transferability of Topographic Correction Methods From Spaceborne to Airborne Optical Data

In rugged terrain, topography substantially influences the illumination and observation geometry, and thus, the bidirectional reflectance distribution function (BRDF) of a surface. While this problem has been known and investigated for spaceborne optical data since the 1980s, it has led to several well-known topographic correction methods. To date, the methods developed for spaceborne data were equivalently applied to airborne data with distinctly higher spatial resolution, illumination/observation angle configurations and finally (instantaneous) field of view (FOV). On the one hand, this article evaluates, whether such a transfer of methods from spaceborne to airborne acquisitions is reasonable. On the other hand, a new Lambertian/statistical-empirical correction method is introduced. While in the spaceborne case the Modified Minnaert (MM) and the Statistical-Empirical (SE) methods performed best, MM led to the statistically and visually best compromise for the airborne data. Our results suggest further that with a higher spatial resolution various effects come into play (FOV widening; changing the fraction of geometric, volumetric and isotropic scattering, etc.), compromising previously successful methods, such as the SE method.

Evaluation of Topographic Correction Effects for Landsat-5 Thematic Mapper Images with Complex Lighting Conditions

A number of methods are available for topographic correction of remote sensing data, and various approaches have been proposed for the evaluation of topographic correction effects. However, few studies have evaluated the topographic correction effects for images obtained under all possible sun elevation conditions. This study employed three correction methods, namely, Statistical-Empirical (SE), C-correction (C), and the Sun-Canopy-Sensor + C-correction (SCS + C) methods. These were used for topographic correction of 11 images obtained by the Landsat-5 Thematic Mapper sensor from June to December. An evaluation of the effectiveness of these three methods was carried out based on five evaluation indices, with the goal of investigating the variation in the topographic correction effect with changes in lighting conditions and the adaptability of topographic correction methods to those conditions. The results showed that the SE method effectively reduced the dependence of surface reflectance on the terrain when the solar elevation was greater than 46°, yielding a very stable surface reflectance and a low proportion of outliers. At sun elevation below 46°, the SCS + C method has good correction effect. After topographic correction using these three methods, the correction effect was compromised as the lighting conditions weakened. Within areas where the topographic and geographical conditions are similar to that of this study, the correction effect can be divided into three levels based on the solar elevation while obtaining the images, i.e., > 60°, 50–60° and < 50°. Caution should be taken when conducting quantitative research on images obtained when the solar elevation is < 50°.

Estimation of forest cover change using Sentinel-2 multi-spectral imagery in Georgia (the Caucasus)

Abstract: Our objective was to use Sentinel-2A multispectral data in order to cost-effectively detect change in forest cover in Georgia (the Caucasus). Generalized additive models (GAMs) were used to fit forest cover measures to Sentinel-2A spectral band values modified using different topographic correction methods. Canopy closure (calculated from upward-looking fisheye photographs taken beneath forest canopy) was the best forest cover measure accounted for by the Sentinel-2 spectral data that were topographically corrected using the Minnaert Correction (R2 = 0.882). Spectral bands best explaining canopy closure were Band 3 (Green), Band 8 (NIR) and Band 12 (SWIR). Our model is able to reasonably detect spatial and temporal changes in canopy closure, even in highly rugged terrain and diverse vegetation cover, and it has potential to be improved to the extent that it can be applied by managers of natural resources. Based on free open source applications in combination with cheap gadgets our approach might play an important role in monitoring the forests of countries with low economic indicators.

The Effect of Topographic Correction on Canopy Density Mapping Using Satellite Imagery in Mountainous Area

One of the main factors contributing to radiometric distortion on remote sensing data is topographic effect, but it can be reduced by applying topographic correction. This study identifies the effect of topographic correction on canopy density mapping in Menoreh Mountains, Indonesia. Topographic correction methods examined in this research are C-Correction, Minnaert, and Sun-Canopy-Sensor+C (SCS+C). Canopy density estimation was approached using vegetation indices, i.e. Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI), Aerosol Free Vegetation Index (AFRI) 1.6, and AFRI 2.1 derived from Landsat-8 OLI imagery. We evaluated the performance of topographic correction by visual and statistical analysis prior to comparing the accuracy of canopy density estimation of different vegetation indices and correction methods. The results showed that topographic normalization is able to increase the accuracy of canopy density mapping. The most significant improvement is the model using MSAVI which increases 1.207% using Minnaert method to reach 86.692% accuracy. Meanwhile, NDVI, AFRI 1.6, and AFRI 2.1 have less significant improvement with the maximum increase of 0.241%, 0.057%, and 0.032% using SCS+C method, reaching the accuracy of 88.980%, 83.303%, and 82.308%, respectively. The accuracies were slightly improved since the algorithms have already reduced the effect of topography which are categorized as ratio vegetation indices. SCS+C is the best topographic correction method, because of not only the appropriate assumption of canopy normalization but also its consistency and better accuracy in canopy density estimation among other methods.

Topographic Correction for Landsat 8 OLI Vegetation Reflectances Through Path Length Correction: A Comparison Between Explicit and Implicit Methods

Topographic correction is a prerequisite for generating radiometrically consistent Landsat 8 OLI vegetation reflectances in support of temporally continuous and spatially mosaicked applications. Path length correction (PLC) is a physically solid topographic correction method that avoids the involvement of any empirical parameter and is therefore suitable for reproducing the inherent reflectance of vegetation. This article compared two different implementation pathways of PLC, i.e., the explicit method (EM) and the implicit method (IM), which are based on the numerical inverse and analytical approximation of the PLC model, respectively. The results show that both EM and IM can obviously reduce the topographic effects on Landsat 8 OLI vegetation reflectances. EM performed slightly better than IM in eliminating the correlation between the topographic characteristics and the vegetation reflectances: the coefficient of determination between the green/red/near-infrared (Nir) band reflectance and the local illumination was reduced from 0.257/0.148/0.467 for the uncorrected (UNCORR) case to 0.016/0.004/0.012 and 0.027/0.014/0.094 for the EM and IM corrected results, respectively. The coefficient of variation of the three band reflectances across different aspects was reduced from 16.5%/18.5%/18.7% for the UNCORR case to 3.2%/1.8%/0.9% and 5.3%/7.1%/7.3% for the EM and IM corrected results, respectively. In addition, the intraclass reflectance variability was also reduced after both the EM and IM corrections. Nevertheless, due to the ill-posed nature of the numerical inverse process, EM cannot fully reproduce the inherent vegetation reflectances, and the reflectances after topographic correction overestimated the inherent vegetation values. In contrast, the IM can achieve an appropriate tradeoff between topographic effect elimination and vegetation inherent reflectance preservation. In addition, IM is computationally very efficient compared to EM: using an ordinary laptop, IM can finish the topographic correction for a Landsat OLI image within several seconds, while this would take more than 20 h for EM. This article highlights the potential of using IM for generating radiometrically consistent Landsat 8 OLI vegetation reflectances.

Analysis of the ASTER Image Topographic Correction Method in Complex Mountain Areas

The phenomenon of “anisotropic reflection” caused by gradient and aspect changes in complex mountain areas has large disturbance on the spectral information of ground feature in remote sensing images, which will further affect the inversion of surface information based on the spectral characteristics of ground feature. Topographic correction is an effective means to weaken the topographical effect and improve the inversion accuracy of spectral reflectance of remote sensing images. Ten topographic correction methods, such as Teillet regression, VECA, Cosine, Cosine-B, Cosine-C, Cosine-T, C, SCS, SCS+C and Minnaert, were used to conduct topographic correction of ASTER remote sensing images in the complex mountain areas of Qulong, and visual effects, statistical results and spectral changes of ground features were used to quantitatively evaluate the correction results. The results show that the topographic effect in the study area is weakened to different degrees. Teillet-regression correction model has the best correction effect and the topographic effect is weakened to the maximum extent. VECA, cosine-c, and Minnaert calibration models followed. Teillet-regression model can be used as topographic correction model in complex mountain areas.

A Kernel-Driven BRDF Approach to Correct Airborne Hyperspectral Imagery over Forested Areas with Rugged Topography

Airborne hyper-spectral imaging has been proven to be an efficient means to provide new insights for the retrieval of biophysical variables. However, quantitative estimates of unbiased information derived from airborne hyperspectral measurements primarily require a correction of the anisotropic scattering properties of the land surface depicted by the bidirectional reflectance distribution function (BRDF). Hitherto, angular BRDF correction methods rarely combined viewing-illumination geometry and topographic information to achieve a comprehensive understanding and quantification of the BRDF effects. This is in particular the case for forested areas, frequently underlaid by rugged topography. This paper describes a method to correct the BRDF effects of airborne hyperspectral imagery over forested areas overlying rugged topography, referred in the reminder of the paper as rugged topography-BRDF (RT-BRDF) correction. The local viewing and illumination geometry are calculated for each pixel based on the characteristics of the airborne scanner and the local topography, and these two variables are used to adapt the Ross-Thick-Maignan and Li-Transit-Reciprocal kernels in the case of rugged topography. The new BRDF model is fitted to the anisotropy of multi-line airborne hyperspectral data. The number of pixels is set at 35,000 in this study, based on a stratified random sampling method to ensure a comprehensive coverage of the viewing and illumination angles and to minimize the fitting error of the BRDF model for all bands. Based on multi-line airborne hyperspectral data acquired with the Chinese Academy of Forestry’s LiDAR, CCD, and Hyperspectral system (CAF-LiCHy) in the Pu’er region (China), the results applying the RT-BRDF correction are compared with results from current empirical (C, and sun-canopy-sensor (SCS) adds C (SCS+C)) and semi-physical (SCS) topographic correction methods. Both quantitative assessment and visual inspection indicate that RT-BRDF, C, and SCS+C correction methods all reduce the topographic effects. However, the RT-BRDF method appears more efficient in reducing the variability in reflectance of overlapping areas in multiple flight-lines, with the advantage of reducing the BRDF effects caused by the combination of wide field of view (FOV) airborne scanner, rugged topography, and varying solar illumination angle over long flight time. Specifically, the average decrease in coefficient of variation (CV) is 3% and 3.5% for coniferous forest and broadleaved forest, respectively. This improvement is particularly marked in the near infrared (NIR) region (i.e., &gt;750 nm). This finding opens new possible applications of airborne hyperspectral surveys over large areas.

Advances in topographic correction methods for optical remote sensing imageries

Advances in topographic correction methods for optical remote sensing imageries

Assessing the spatial, spectral, and temporal consistency of topographically corrected Landsat time series composites across the mountainous forests of Nepal

In mountainous regions slope and aspect result in variations in the illumination condition and the same land cover type can therefore show differences in the reflectance depending on the orientation of the terrain slope towards the sun and the sensor of the satellite. Different topographic illumination correction methods exist and their performance varies depending on e.g. sun zenith and land cover type. Similarly a variety of evaluation criteria exist to assess the performance of the topographic correction methods and each evaluation criteria usually only considers certain aspects of the ability of the topographic correction method in reducing signal-to-noise. In this article we present a novel framework for the evaluation of topographically corrected image composites using cloud computing. We evaluated six topographic correction methods (Bin Tan, C-Correction, Minnaert with slope, Sun-Canopy-Sensor plus C-Correction, Statistical-Empirical, and Variable Empirical Coefficient Algorithm) in forest areas of four Landsat footprints in Nepal for a time series of image composites from 1988 to 2016. Our evaluation shows that the Statistical-Empirical topographic correction method provides the best overall performance, but in some years and footprints also other methods can show the best performance. We discuss these differences and related reasons in detail and give recommendations on the best use and evaluation of topographic correction methods.

Evaluation of topographic correction methods for LULC preparation based on multi-source DEMs and Landsat-8 imagery

Topographic shadows of irregular mountains obstruct the analysis of satellite images in hilly areas. Due to this effect, there is high variability in the reflectance response of similar vegetation types, i.e. sunny areas show more than actual reflectance, whereas shaded areas show less than expected reflectance. In this study, we have evaluated the performance of five topographic correction methods, namely Cosine, C-Huang Wei, semi empirical C, SCS + C and Variable Empirical Coefficient Algorithm (VECA) depending on the solar incidence angle and exitance angle. The two well-known digital elevation models (DEM) i.e. Shuttle Radar Topography Mission (SRTM) and TanDEM-X have been used for the study. The efficiency of the correction methods is assessed on Landsat-8 satellite image using three criteria: visual interpretation, statistical assessment and classification accuracy assessment. As seen from the statistical analysis, VECA and C-correction method provides good correction of topography for both SRTM and TanDEM-X elevation models. We have used support vector machine (SVM) classifier for classification of topographically corrected images. Our results show that VECA and C-correction method increased classification accuracy from 65.60% (for uncorrected image) to 82.40% for SRTM and 64.00% to 80.00% for TanDEM-X respectively. The highest accuracy of classification is obtained using VECA/C method with SRTM DEM. However, the SCS + C-correction method impressively reduced the visual topography effects.

Topographic Correction for the Data of SQUID-based TEM Using a Ground Loop

Superconducting quantum interference device (SQUID) can be used to detect the signal of transient electromagnetic method (TEM) due to its superiority of high sensitivity in the low frequency range. However, the measuring direction of SQUID is hardly consistent with the normal direction of the transmitting coil of a TEM system because of the undulating topography in the field. In this case, the central magnetic field measured by SQUID is only a component of the theoretical central magnetic field. There will be larger errors if we directly use the measured central magnetic field for geological interpretation. To solve this problem, we propose a topographic correction method for the data of SQUID-based TEM using ground loop. The theoretical central magnetic field of the ground loop is calculated after the trapezoidal transmitting current wave is turned off. Then, we use the theoretical central magnetic field of the ground loop as the reference to correct the measured central magnetic field of SQUID-based on the trigonometric function relation between the measuring direction of SQUID and the topographic inclination. The experiment of SQUID-based TEM using a ground loop was carried out in the field. The result shows that at the measurement point with larger topographic inclination, the average absolute error of the measured central magnetic field reduces significantly with the proposed correction method. This method can also be applied to the correction of complex topographic effects when using SQUID to measure three components of magnetic field.