LU501987B1 - A new method to evaluate topographic correction performance using three types of topographic shadow for land-cover monitoring in rugged mountains - Google Patents

Abstract

This invention provides a method to evaluate topographic correction performance using three types of topographic shadow for land-cover monitoring in rugged mountains, which comprises the following steps: data preparation, shadow extraction, sample division, self-shadow calculation, cast shadow selection, gray shadow selection and result output. Results visualization used the box plot, rose map, scatter plots and histogram. The evaluation method of the invention improves the types of topographic shadow and quantitative analysis of topographic correction. It is significant to understand the topographic effect in rugged mountains and to evaluate topographic correction for land-cover monitoring in rugged mountains.

Description

Description LUS01987 À new method to evaluate topographic correction performance using three types of topographic shadow for land-Cover Monitoring in rugged mountains Technical Neid The invention belongs to a technical field of a guantitative method to evaluate topographic correction performance, particularly relates to an evaluation method using three types of topographic shadow.

Background The existing quantitative evaluation for topographie correction mainly included two types of comparison, One compared the samples between sunny slope and shady slope, the other compared the sunny slope, seif-shadow and cast shadow. These two types mairiy consider of direct solar radiation, namely, the small incident angle of the sun (ugh Humination of sunny slope) and the large incident angle of the sun which causes io lack of direct solar radiation (low illumination of shady siope). These methods ignored the transitional state betwesn them, Le, the gray shadow. Gray shadow is a slopes with the incident angie of the sun close fo but less than 90 degrees. Theoretically, gray shadow receives direct solar radiation, but the Hlumination is low, and it has the same or similar spectral reflectance characteristics as shady slope (self-shadow and cast shadow). Obviously, it is not scientific in the quantitative evaluation of topographic correction {to ignore gray shadow, which influences the understanding of topographie effect in rugged rrountains and the accurate evaluation of topographie correction.

Summary The purpose of the invention is io provide an evaluation method for topographic correction using three types of topographic shadow, which includes the following steps: data preparation, shadow extraction, sample division, seif-shadow calculation, cast shadow selection, gray shadow selection and result output. Results visualization used the box plot, rose map, scatter plots and histogram. The evaluation method of the invention improves the quantitative analysis level of topographic shadow, and provides reliable and specific visuatization. it is significant to understand the topographic effect in rugged mountains and to accurately evaluate topographic correction for land-cover monitoring in rugged rrouniaing,

This invention provides the technical solutions as followings: LUS01987 A new method to evalugte topographic correction performance using three types of topographic shadow for land-cover monitoring in rugged mountains is characterized in comprising the following siens: 51, data preparation obtain optical remote sensing images and digital elevation model {DEM S2, shadow extraction: classify the optical remote sensing image into shadow area and sunny area, and extract shadows.

S3, sample division. dividing samples as four types of sunny slope, self-shadow, cast shadow and gray shadow according to the schematic diagram of the spectral characteristics of rugged mountain. Considering Tobiers first law of geography, every set of evaluation samples (Le, the sunny area, seif-shadow, cast shadow and gray shadow) was selected from the closely located pixels of homogenous vegetation cover.

Sd, self-shadow calculation. extracting seif-shadow sample by the following formula: co = tr tana * cos(T — (w — B)) < tany self 1, tan »* cos(m —(w — B)) > tany ’ where Sserr is the self-shadow, 0 is the slope angle, w is the solar azimuth angle, ß is the aspect angle and y is the solar elevation angle. The values of o and 8 were computed from the DEM data, and the values of y and w were taken directly from the header files of image.

S5, cast shadow selection: along the incident direction of the sun, selecting the cast shadow in the shadow area next to the self-shadow. The cast shadow is usually a flat or shady hillside with the slope towards the sun.

58, gray shadow selection: towards the incident direction of the sun, selecting the gray shadow in the shadow area next to the self-shadow, and meeting the following conditions: cos 7 <cosÿ and cos /> 0; cos {=C0Scecosf-+sincesin Pecos($ — ol; B= = y wherein, (is the solar incident angle, 5 is the solar zenith angle.

57, result output: the correction result is evaluated using the box plot, scatter plots between image parameter and the cos /, rose map of image parameter ranging along topographic aspect, and relative error between image parameter of sunny area and thar of 987 shadow area.

Further, in ST, the slope and aspect were calculated from the DEM.

Further, in 32, using the spectral characteristics of the image, a method from the supervised, unsupervised, machine learning or deep learning is adopted to classify remote sensing image into shadow area and sunny ares, Further, in 53, the number of samples in the selected area is greater than 50 groups.

Further, in 34, the slope and aspect maps are generated by the DEM data.

Further, in 87, the image parameters include band reflectance, radiance and digital number (DN), and vegetation indices (VI, leal area index {LAD fractional vegetation coverage (FVC) net primary productivity (NPP) and chlorophyll content.

Compared with the existing arts, this invention has the following beneficial effects

1. improve the types and quantitative analysis of topographic shadow in the evaluation of topographic correction for land-cover monitoring in rugged Mourntairs.

à. The quantitative evaluation results are reliable and specific, Brief Description of the Figures Next the present invention will be described in further detail with the attached figures and specific embodiments: Fig. 1 is a schematic diagram of the spectrai characteristics of rugged mountain in an embodiment of the present rwvention.

Fig. 2 is an overall flow of an embodiment of the present invention.

Fig.3 18 à topographic correction result of remote sensing images surface reflectance according to an embodiment of the present invention, (a) surface reflectance before topographic correction, {5} SCS+C corrected results, (c) 88 correcied results.

Fig. 4 is a box plot of the blue band surface reflectance of remote sensing image according to an embodiment of the present invention (Not TC represents surface refleciance before topographic correction, SCS+C represents SCS+C corrected result, 55 fepresents 55 corrected result} Fig. 5 is a rose map of the blue band surface reflectance of remote sensing image according to an embodiment of the present invention. {{a) surface refleciance before topographie correction, (b) SCS+E corrected result, and (¢} SS corrected result).

Fig. © is 3 scatter plot of the blue band surface reflectance and cos / according 15201987 embodiment of the present invention. ({a) surface reflectance before topographic correction, (b) SGS+C corrected result, and (c} 58 corrected result).

Fig. 7 is a histogram of the shadow relative error of the blue band according to the smbodiment of the present invention (Not TC represents surface refleclance before topographic correction, SCS+C represents SCS+C corrected result, SS represents SS corrected result} Description of the present invention in order to make the features and advantages of this patent easy to understand, the following specific example is described in detail as follows: it should be noted that the following detailed description is exemplary and intended to provide further explanation for this application, Unless otherwise specified, all technical and scientific terms used herein have the same meanings as commonly understood by ordinary people in the technical feld to which this application belongs. It should be noted that the terminology used here is only for describing specific embodiments, and is not intended to mit the exemplary embodiments according to this application. As used herein, unless the context clearly indicates otherwise, the singulier form is also intended to include the plural form. in addition, it should be understood when the terms “comprising” and/or “comprising” are used in this specification, they indicate the presence of features, steps, operations, devices, components and/or combinations thereof As shown in Fig. 2, the present invention includes the following steps: S1, Data preparation download the Landsat 8 OL image (Faiht19/Row042} in Fuzhou, Fujian Province {acquired on December 11th, 2019 the spatial resolution of 30 meiters, the solar elevation angle of 36.98° and the solar azimuth angle of 155.56°) The ASTER GDEM V2 with spatial resolution of 30m shows that the elevation is ranging from 0 to 1823 meter and a slope of 075°, S2, shadow extraction: based on the spectral characteristics of the image bands, the optical remote sensing image is classified into mountain shadow area and sunny area using the Random Forest (RF) method.

$3, Sample division according to the speciral characteristics of rugged mourtan 987 features as shown in Fig 1, four types of samples were divided: sunny slope, self-shadow, cast shadow and gray shadow. Considering Tobler's first law of geography, every set of evaluation samples (Le, the sunny area, self-shadow, cast shadow and gray shadow) was selected from the closely located pixels of homogenous vegetation cover, and totally 213 groups were selected.

SA, Self-shadow calculation: extracting self-shadow samples by the following formuler co = tr tana * cos(T — (w — B)) < tany self 1, tan »* cos(m —(w — B)) > tany ’ where Sserr is the self-shadow, 0 is the slope angle, w is the solar azimuth angle, ß is the aspect angle and y is the solar elevation angle. The values of o and 8 were computed from the DEM data, and the values of y and w were taken directly from the header files of image.

55, Cast shadow selection: along the incident direction of the sun, selecting the cast shadow in the shadow area next to the seif-shadow. The cast shadow is usually a flat or shady hillside with the slope towards the sun (= and F in Figure 1}.

$6, Gray shadow selection. towards the incident direction of the sun, selecting the gray shadow in the shadow area next to the self-shadow, and meeting the following conditions: cos /< cos and cos /> 0; cos =coscecosÿ-+sinossin Jecos(f — a); §==-y, wherein, à is the solar incident angle, & is the solar zenith angle.

37, result output the correction result is evaluated using the box plot, scatter plots between image parameter and the cos /, rose map of image parameter ranging along topographic aspect, and relative error between image parameter of sunny area and that of shadow area wherein, the image parameters include but are not limited to the image parameters of band reflectance, radiance and DN, and VI, LAL FYC, NPP and chiorophyll content.

Fig, 5 is an image of red-green-blue (RGB) composite, and other embodiments of visugiization are shown in Figs 4-7 and Table 1.

Table 1

Linear regression of blue band surface reflectance {p) vs. cos à and determination coefficient (7) 901987 root mean square error (RMSE)

onan caracion | SCSI unis | SS once ents

| p=0.0153 cos / + 0.0075, | 5=0.0037 cos i + 0.0138, | p=0.0015 os i + 0.0179,

| r*=0.50, RMSE=0.0053 r*=0.04, RMSE=0.0063 | r*=0.02, RMSE=0.0035 8

The above examples show the advantages that the quantitative evaluation results of the schemes provided by this embodiment are reliable and specific,

This patent is not limited to the above-mentioned best embodiment.

Other methods of topographic correction using three types of shadow under the inspiration of this patent, and ali the relative changes and modifications made based on the patent invention belong to the scope of this patent.

Claims (6)

Claims LU501987

1. À new method to evaluate topographie correction performance using three types of topographic shadow for land-cover monitoring in rugged mountains is characterized in comprising the following steps. ST, data preparation: obtain optical remote sensing images and DEM, SZ, shadow extraction: classify the optical remote sensing image into shadow area and sunny area; 53, sample division: dividing samples as four types of sunny slope, saif-shadow, cast shadow and gray shadow according to the schematic diagram of the spectral characteristics of rugged mountain, considering Toblers first law of geography, every set of evaluation samples (Le, the sunny area, self-shadow, cast shadow and gray shadow) was selected from the closely located pixels of homogenous vegatation cover, 54, seif-shadow calculation: extracting seif-shadow sample by the following formula! co = tr tana * cos(T — (w — B)) < tany self 1, tan »* cos(m —(w — B)) > tany ’ where Sserr is the self-shadow, 0 is the slope angle, w is the solar azimuth angle, ß is the aspect angle and y is the solar elevation angle; the values of o and 6 were computed from the DEM data, and the values of y and w were taken directly from the header files of image; 85, cast shadow selection: along the incident direction of the sun, selecting the cast shadow in the shadow area next to the self-shadow, the cast shadow is usually a flat or shady hillside with the slope lowards the sun, 56, gray shadow selection towards the incident direction of the sun, selecting the gray shadow in the shadow area next to the self-shadow, and mesting the following conditions: cos / <cosf and cos /> Ù, cos j=cosmrecostfsineesinécos(f — a) go = >, wherein, / is tha solar incident angle, 6 is the solar zenith angle, 57, result output: the correction result is evaluated using the box plot, scatter plots between image parameter and the cos /, rose map of image parameter ranging along topographic aspect, and relative error between image parameter of sunny area and that of shadow area.

2. A new method to evaluate topographic correction performance using three types 241987 topographic shadow for land-cover monitoring in rugged mountaing, according to claim 1, is characterized in that in ST, the slope and aspect were calculated from the DEM.

3. A new method to evaluate topographie correction performance using three types of topographic shadow for land-cover monitoring in rugged mountains, according to claim 1, is characterized in that in 82, according to the spectral characteristics of the image, one classification method from the supervised, unsupervised, machine learning or deep learning is adopted to classify remote sensing image into shadow area and sunny area.

4, À new method to evaluate topographic correction performance using ihres types of topographic shadow for land-cover monitoring in rugged mountains, according to claim 1, is characterized in that in 83, the number of samples in the selecied area is greater than 60 Groups.

5. A new method to evaluate topographic correction performance using three types of topographic shadow for land-cover monitoring in rugged mountains, according to claim 1, is characterized in that in 84, the slopes and aspect maps are generated by DEM data, &. À new method to evaluate topographic correction performance using three types of topographic shadow for land-cover monitoring in ruggsd mountaing, according to claim 1, is characterized in that in S7, the image parameters include band reflectance, radiance and DN, and VE LAL FVC, NPP and chiorophyll content