Evaluation on the two filling functions for the recovery of forest information in mountainous shadows on Landsat ETM + Image

Shahtahmassebi, Amir Reza; Wang, Ke; Shen, Zhangquan; Deng, Jinsong; Zhu, Wenjuan; Han, Ning; Lin, Fengfang; Moore, Nathan

doi:10.1007/s11629-011-2051-5

Cited by 9 publications

(8 citation statements)

References 13 publications

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“…Shadow detection using a histogram thresholding analysis with an NIR band has been explored in several studies on forest areas following urban and agricultural areas [29,31,45,51]. One of the disadvantages of this technique is that it can often be difficult to distinguish shadow areas and other dark surfaces such as water bodies [45]; however, this limitation did not apply to our small study areas where no water features such as river, lake, or ocean were found.…”

Section: Shadow Removalmentioning

confidence: 98%

Estimating the Threshold of Detection on Tree Crown Defoliation Using Vegetation Indices from UAS Multispectral Imagery

Otsu

Pla

Duane

et al. 2019

Drones

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Periodical outbreaks of Thaumetopoea pityocampa feeding on pine needles may pose a threat to Mediterranean coniferous forests by causing severe tree defoliation, growth reduction, and eventually mortality. To cost–effectively monitor the temporal and spatial damages in pine–oak mixed stands using unmanned aerial systems (UASs) for multispectral imagery, we aimed at developing a simple thresholding classification tool for forest practitioners as an alternative method to complex classifiers such as Random Forest. The UAS flights were performed during winter 2017–2018 over four study areas in Catalonia, northeastern Spain. To detect defoliation and further distinguish pine species, we conducted nested histogram thresholding analyses with four UAS-derived vegetation indices (VIs) and evaluated classification accuracy. The normalized difference vegetation index (NDVI) and NDVI red edge performed the best for detecting defoliation with an overall accuracy of 95% in the total study area. For discriminating pine species, accuracy results of 93–96% were only achievable with green NDVI in the partial study area, where the Random Forest classification combined for defoliation and tree species resulted in 91–93%. Finally, we achieved to estimate the average thresholds of VIs for detecting defoliation over the total area, which may be applicable across similar Mediterranean pine stands for monitoring regional forest health on a large scale.

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Section: Shadow Removalmentioning

confidence: 98%

Estimating the Threshold of Detection on Tree Crown Defoliation Using Vegetation Indices from UAS Multispectral Imagery

Otsu

Pla

Duane

et al. 2019

Drones

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“…A shadow correction algorithm is relied a two-step process: detecting the location of shadow and de-shadowing. However, only a few pa-pers have described this topic in detail (Riano et al, 2003;Mather, 2004;Dare, 2005;Jensen, 2007;Ren et al, 2009;Shahtahmassebi et al, 2011). In particular, the investigations on the sequence of shadow correction (detection and de-shadowing) with respect to topographic shadow, urban shadow, cloud shadow, and composite shadow are missing.…”

Section: Introductionmentioning

confidence: 99%

“…First, although the reflectance recorded in the shadow regions is weak, there is still useful information in these areas which makes shadow restoration possible (Chen et al, 2007;Wang et al, 2008). Second, it may use surrounding information of shadowy areas (spatial information) in order to fill shadowy regions (Rossi et al, 1994;Addink and Stein, 1999;Kouchi and Yamazaki, 2007;Shahtahmassebi et al, 2011;Nole et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Review of shadow detection and de-shadowing methods in remote sensing

et al. 2013

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Shadow is one of the major problems in remotely sensed imagery which hampers the accuracy of information extraction and change detection. In these images, shadow is generally produced by different objects, namely, cloud, mountain and urban materials. The shadow correction process consists of two steps: detection and de-shadowing. This paper reviews a range of techniques for both steps, focusing on urban regions (urban shadows), mountainous areas (topographic shadow), cloud shadows and composite shadows. Several issues including the problems and the advantages of those algorithms are discussed. In recent years, thresholding and recovery techniques have become important for shadow detection and de-shadowing, respectively. Research on shadow correction is still an important topic, particularly for urban regions (in high spatial resolution data) and mountainous forest (in high and medium spatial resolution data). Moreover, new algorithms are needed for shadow correction, especially given the advent of new satellite images.

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“…cover identification results [5], [6]. Thus, the topographic normalization process is required to improve vegetation cover identification in mountainous area with high topographic variations [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Band-Based Best Model Selection for Topographic Normalization of Normalized Difference Vegetation Index Map

Park

Jung

2020

IEEE Access

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Topographic effect in remote sensing images is severe in high mountainous areas. Efficiently to reduce the effects, several topographic normalization models have been proposed. Since the performance of the models is largely dependent on the spectral band and land surface type, the best performance model can vary from image to image in an area as well as from band to band in an image. The normalized difference vegetation index (NDVI) map has been widely used for the vegetation monitoring and assessment. An efficient reduction of the topographic effect in the NDVI map must be required for the spatial analysis of the vegetation monitoring and assessment. In this paper, we propose an efficient method to select the best topographic normalization model in each band to reduce the topographic effect of NDVI maps. The histogram structural similarity (HSSIM) index was used for the model selection because the index allows to select the best model in each band of an image. Five topographic normalization models were used for the test, which include the sun-canopy-sensor (SCS), statistical-empirical, C-correction, Minnaert, and Minnaert + SCS. The performance of the proposed method was validated by using two different season Landsat-8 OLI images including the forest area of northern Malaysia. The standard deviations of the two NDVI maps generated from the test images were reduced by about 53.1% and 28.6% after correction in profile analysis. The coefficient of determination (R 2) between the two different NDVI maps increased from 0.626 to 0.759. It indicates that the proposed method effectively reduced the topographic effect of the NDVI maps. This result implies that the proposed method can work well in the topographic normalization. Furthermore, the proposed method would be successfully applied to index maps including the normalized difference snow index (NDSI), normalized difference water index (NDWI), etc. INDEX TERMS Histogram structural similarity index, land cover identification, normalized difference vegetation index (NDVI), performance assessment, topographic normalization models.

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Evaluation on the two filling functions for the recovery of forest information in mountainous shadows on Landsat ETM + Image

Cited by 9 publications

References 13 publications

Estimating the Threshold of Detection on Tree Crown Defoliation Using Vegetation Indices from UAS Multispectral Imagery

Estimating the Threshold of Detection on Tree Crown Defoliation Using Vegetation Indices from UAS Multispectral Imagery

Review of shadow detection and de-shadowing methods in remote sensing

Band-Based Best Model Selection for Topographic Normalization of Normalized Difference Vegetation Index Map

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