Effects of shadowing types on ground-measured visible and near-infrared shadow reflectances

Leblon, Brigitte; Gallant, Lisa; Granberg, Hardy B.

doi:10.1016/s0034-4257(96)00079-x

Cited by 53 publications

(24 citation statements)

References 11 publications

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“…We can note that different land covers have different spectral curves. Visible and near-infrared reflection of shadows are significantly affected by the surface type (Leblon et al, 1996). DN values received from shadowed areas that will be also affected by different land cover as from nonshadow area.…”

Section: Spectral Characteristics Of the Shadow Areamentioning

confidence: 99%

Analyzing Spectral Characteristics of Shadow Area From Ads-40 High Radiometric Resolution Aerial Images

Hsieh

Chen

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VII, WG VII/4KEY WORDS: Shadow, shadow spectra, ADS-40, high radiometric resolution ABSTRACT:The shadows in optical remote sensing images are regarded as image nuisances in numerous applications. The classification and interpretation of shadow area in a remote sensing image are a challenge, because of the reduction or total loss of spectral information in those areas. In recent years, airborne multispectral aerial image devices have been developed 12-bit or higher radiometric resolution data, including Leica ADS-40, Intergraph DMC. The increased radiometric resolution of digital imagery provides more radiometric details of potential use in classification or interpretation of land cover of shadow areas. Therefore, the objectives of this study are to analyze the spectral properties of the land cover in the shadow areas by ADS-40 high radiometric resolution aerial images, and to investigate the spectral and vegetation index differences between the various shadow and non-shadow land covers. According to research findings of spectral analysis of ADS-40 image: (i) The DN values in shadow area are much lower than in nonshadow area; (ii) DN values received from shadowed areas that will also be affected by different land cover, and it shows the possibility of land cover property retrieval as in nonshadow area; (iii) The DN values received from shadowed regions decrease in the visible band from short to long wavelengths due to scattering; (iv) The shadow area NIR of vegetation category also shows a strong reflection; (v) Generally, vegetation indexes (NDVI) still have utility to classify the vegetation and non-vegetation in shadow area. The spectral data of high radiometric resolution images (ADS-40) is potential for the extract land cover information of shadow areas.

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Section: Spectral Characteristics Of the Shadow Areamentioning

confidence: 99%

Analyzing Spectral Characteristics of Shadow Area From Ads-40 High Radiometric Resolution Aerial Images

Hsieh

Chen

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The following values are introduced as Band Shadow Reduction Factors (BSRFs) and are estimated based on the work of Zhang et al (2015) and Leblon et al (1996).…”

Section: Larch Signal Extractionmentioning

confidence: 99%

“…The third method proposed includes the binary shadows, but also a parametrization of terrain shadowing by an inversion of the solar irradiation amount during the satellite overpass. The shadow reflectance in each of the second two approaches can only be estimated and is mainly based on the findings of Zhang et al (2015) and Leblon et al (1996). In default of the "true" and pure larch signal, results are compared to two other studies that also deal with larch reflectance.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Spectral Signal Investigation of a Larch Forest Combining Ground- And Satellite-Based Measurements

Landmann¹,

Rutzinger²,

Bremer³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Collecting comprehensive knowledge about spectral signals in areas composed by complex structured objects is a challenging task in remote sensing. In the case of vegetation, shadow effects on reflectance are especially difficult to determine. This work analyzes a larch forest stand (Larix decidua MILL.) in Pinnis Valley (Tyrol, Austria). The main goal is extracting the larch spectral signal on Landsat 8 (LS8) Operational Land Imager (OLI) images using ground measurements with the Cropscan Multispectral Radiometer with five bands (MSR5) simultaneously to satellite overpasses in summer 2015. First, the relationship between field spectrometer and OLI data on a cultivated grassland area next to the forest stand is investigated. Median ground measurements for each of the grassland parcels serve for calculation of the mean difference between the two sensors. Differences are used as "bias correction" for field spectrometer values. In the main step, spectral unmixing of the OLI images is applied to the larch forest, specifying the larch tree spectral signal based on corrected field spectrometer measurements of the larch understory. In order to determine larch tree and shadow fractions on OLI pixels, a representative 3D tree shape is used to construct a digital forest. Benefits of this approach are the computational savings compared to a radiative transfer modeling. Remaining shortcomings are the limited capability to consider exact tree shapes and nonlinear processes. Different methods to implement shadows are tested and spectral vegetation indices like the Normalized Difference Vegetation Index (NDVI) and Greenness Index (GI) can be computed even without considering shadows.

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“…It can either cause reduction or loss of information in the image. Reduction of information could potentially lead to the corruption of biophysical parameters derived from pixels values, such as vegetation indices (Leblon et al, 1996). Total loss of information could mean that the areas of the image cannot be interpreted, and value-added products, such as digital terrain models, cannot be created (Dare, 2005) Shadowed areas have been traditionally left unclassified or simply classified as shadows (Shackelford and Davis, 2003).…”

Section: Introductionmentioning

confidence: 99%

Object Based Agricultural Land Cover Classification Map of Shadowed Areas From Aerial Image and Lidar Data Using Support Vector Machine

Alberto

Serrano

Damian

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Aerial image and LiDAR data offers a great possibility for agricultural land cover mapping. Unfortunately, these images leads to shadowy pixels. Management of shadowed areas for classification without image enhancement were investigated. Image segmentation approach using three different segmentation scales were used and tested to segment the image for ground features since only the ground features are affected by shadow caused by tall features. The RGB band and intensity were the layers used for the segmentation having an equal weights. A segmentation scale of 25 was found to be the optimal scale that will best fit for the shadowed and non-shadowed area classification. The SVM using Radial Basis Function kernel was then applied to extract classes based on properties extracted from the Lidar data and orthophoto. Training points for different classes including shadowed areas were selected homogeneously from the orthophoto. Separate training points for shadowed areas were made to create additional classes to reduced misclassification. Texture classification and object-oriented classifiers have been examined to reduced heterogeneity problem. The accuracy of the land cover classification using 25 scale segmentation after accounting for the shadow detection and classification was significantly higher compared to higher scale of segmentation.

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Effects of shadowing types on ground-measured visible and near-infrared shadow reflectances

Cited by 53 publications

References 11 publications

Analyzing Spectral Characteristics of Shadow Area From Ads-40 High Radiometric Resolution Aerial Images

Analyzing Spectral Characteristics of Shadow Area From Ads-40 High Radiometric Resolution Aerial Images

Comprehensive Spectral Signal Investigation of a Larch Forest Combining Ground- And Satellite-Based Measurements

Object Based Agricultural Land Cover Classification Map of Shadowed Areas From Aerial Image and Lidar Data Using Support Vector Machine

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