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Guangjian Yan
State Key Laboratory of Remote Sensing Science Faculty of Geographical Science Beijing Normal University Beijing China

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Journal article
Published: 02 June 2021 in Journal of Geophysical Research: Atmospheres
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Terrain reflected solar radiation in snow-covered mountains is nonnegligible in investigations of the energy budget. However, it has so far not been investigated thoroughly, especially with regard to the influence of snow cover. Several parameterization approaches have been raised but not yet evaluated in a more uniform and quantitative manner. Based on the three-dimensional (3-D) ray-tracing simulation, we explored the temporal and spatial characteristics of the terrain reflected radiation in 15 domains on the southeastern Tibetan Plateau, and comprehensively evaluated different parameterization approaches. The results indicate that the ratio of reflected radiation to total daily radiation ranges from 0.25% to 10.85% at the scale of 5 × 5 km2 in a winter clear day, and it is 57% higher at noon in spring and autumn due to the higher snow cover fraction. Snow cover not only enhances the magnitude of reflected radiation by increasing surface albedo but also changes the spatial distribution pattern of radiation in partial snow-covered mountains, causing more snow-reflected radiation to be received by the surrounding surfaces. Three forms of terrain configuration factors in common parameterization approaches were evaluated by the ray-tracing model. The complementary of sky view factor shows good consistency with ray-tracing model at domain-averaged scales, with a root mean square error (RMSE) of 2.55 (14%) W/m2, while the other two both underestimate the radiation. The parameterization approach involving the multi-reflection shows better performance with the normalized RMSE decreasing by 5%. However, the uncertainty of it increases with the surface spatial heterogeneity caused by the partial snow cover, especially at high resolution.

ACS Style

Qing Chu; Guangjian Yan; Jianbo Qi; Xihan Mu; Linyuan Li; Yiyi Tong; Yingji Zhou; Yanan Liu; Donghui Xie; Martin Wild. Quantitative Analysis of Terrain Reflected Solar Radiation in Snow‐Covered Mountains: A Case Study in Southeastern Tibetan Plateau. Journal of Geophysical Research: Atmospheres 2021, 126, 1 .

AMA Style

Qing Chu, Guangjian Yan, Jianbo Qi, Xihan Mu, Linyuan Li, Yiyi Tong, Yingji Zhou, Yanan Liu, Donghui Xie, Martin Wild. Quantitative Analysis of Terrain Reflected Solar Radiation in Snow‐Covered Mountains: A Case Study in Southeastern Tibetan Plateau. Journal of Geophysical Research: Atmospheres. 2021; 126 (11):1.

Chicago/Turabian Style

Qing Chu; Guangjian Yan; Jianbo Qi; Xihan Mu; Linyuan Li; Yiyi Tong; Yingji Zhou; Yanan Liu; Donghui Xie; Martin Wild. 2021. "Quantitative Analysis of Terrain Reflected Solar Radiation in Snow‐Covered Mountains: A Case Study in Southeastern Tibetan Plateau." Journal of Geophysical Research: Atmospheres 126, no. 11: 1.

Journal article
Published: 12 April 2021 in Journal of Remote Sensing
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Both leaf inclination angle distribution (LAD) and leaf area index (LAI) dominate optical remote sensing signals. The G-function, which is a function of LAD and remote sensing geometry, is often set to 0.5 in the LAI retrieval of coniferous canopies even though this assumption is only valid for spherical LAD. Large uncertainties are thus introduced. However, because numerous tiny leaves grow on conifers, it is nearly impossible to quantitatively evaluate such uncertainties in LAI retrieval. In this study, we proposed a method to characterize the possible change of G-function of coniferous canopies as well as its effect on LAI retrieval. Specifically, a Multi-Directional Imager (MDI) was developed to capture stereo images of the branches, and the needles were reconstructed. The accuracy of the inclination angles calculated from the reconstructed needles was high. Moreover, we analyzed whether a spherical distribution is a valid assumption for coniferous canopies by calculating the possible range of the G-function from the measured LADs of branches of Larch and Spruce and the true G-functions of other species from some existing inventory data and three-dimensional (3D) tree models. Results show that the constant G assumption introduces large errors in LAI retrieval, which could be as large as 53% in the zenithal viewing direction used by spaceborne LiDAR. As a result, accurate LAD estimation is recommended. In the absence of such data, our results show that a viewing zenith angle between 45 and 65 degrees is a good choice, at which the errors of LAI retrieval caused by the spherical assumption will be less than 10% for coniferous canopies.

ACS Style

Guangjian Yan; Hailan Jiang; Jinghui Luo; Xihan Mu; Fan Li; Jianbo Qi; Ronghai Hu; Donghui Xie; Guoqing Zhou. Quantitative Evaluation of Leaf Inclination Angle Distribution on Leaf Area Index Retrieval of Coniferous Canopies. Journal of Remote Sensing 2021, 2021, 1 -15.

AMA Style

Guangjian Yan, Hailan Jiang, Jinghui Luo, Xihan Mu, Fan Li, Jianbo Qi, Ronghai Hu, Donghui Xie, Guoqing Zhou. Quantitative Evaluation of Leaf Inclination Angle Distribution on Leaf Area Index Retrieval of Coniferous Canopies. Journal of Remote Sensing. 2021; 2021 ():1-15.

Chicago/Turabian Style

Guangjian Yan; Hailan Jiang; Jinghui Luo; Xihan Mu; Fan Li; Jianbo Qi; Ronghai Hu; Donghui Xie; Guoqing Zhou. 2021. "Quantitative Evaluation of Leaf Inclination Angle Distribution on Leaf Area Index Retrieval of Coniferous Canopies." Journal of Remote Sensing 2021, no. : 1-15.

Journal article
Published: 01 April 2021 in IEEE Transactions on Geoscience and Remote Sensing
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To improve our capacity to map long-term vegetation dynamics in heterogeneous landscapes, this study proposed a new prior knowledge-based spatiotemporal enhancement method, namely, PK-STEM, to fuse MODIS and Landsat FPAR products following the remote sensing trend surface framework. PK-STEM uses historical Landsat FPAR images as prior knowledge and fuses them with new satellite-derived FPAR data. PK-STEM can work in three modes: 1) using only MODIS data; 2) using only Landsat data; and 3) using both MODIS and Landsat data. This study retrieved FPAR from Landsat images using a scaling-based method and tested the performance of PK-STEM in a regional application. For the entire year of 2012, we compared the performance of PK-STEM in different modes and with that of two typical spatiotemporal fusion methods, the enhanced spatial and temporal adaptive reflectance model (ESTARFM) and unmixing-based linear mixing growth model (LMGM). Then, a long time series FPAR data set at 30-m resolution and eight-day intervals was generated for 13 years (2000-2012). Our results show that PK-STEM in mode III is the most robust and accurate (root mean squared error (RMSE) = 0.062; mean R = 0.851) among the three modes and more accurate than ESTARFM (mean RMSE = 0.065; mean R = 0.776) and LMGM (mean RMSE = 0.074; mean R = 0.734). For the 12 years (2000-2011), PK-STEM also achieves high accuracies with mean RMSE = 0.066 and R = 0.938. PK-STEM is very flexible with a continual update mechanism and is efficient for long time series applications.

ACS Style

Yiting Wang; Guangjian Yan; Donghui Xie; Ronghai Hu; Hu Zhang. Generating Long Time Series of High Spatiotemporal Resolution FPAR Images in the Remote Sensing Trend Surface Framework. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -15.

AMA Style

Yiting Wang, Guangjian Yan, Donghui Xie, Ronghai Hu, Hu Zhang. Generating Long Time Series of High Spatiotemporal Resolution FPAR Images in the Remote Sensing Trend Surface Framework. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-15.

Chicago/Turabian Style

Yiting Wang; Guangjian Yan; Donghui Xie; Ronghai Hu; Hu Zhang. 2021. "Generating Long Time Series of High Spatiotemporal Resolution FPAR Images in the Remote Sensing Trend Surface Framework." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-15.

Journal article
Published: 22 March 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Bidirectional reflectance distribution function (BRDF) models are used to correct surface bidirectional effects and estimate land surface albedo. Many operational BRDF/albedo algorithms adopt a Roujean linear kernel-driven BRDF (RLKB) model because of its simple form and good performance in fitting multidirectional surface reflectance values. However, this model does not explicitly consider topographic effects, resulting in errors when applied over rugged terrain. To address this issue, we proposed a hybrid algorithm suitable for both flat and rugged terrain, called topographical kernel-driven (Topo-KD). First, we constructed a linear kernel-driven BRDF model considering terrain (LKB_T) which describes the topographic effects with a mountain radiative transfer (MRT) model. Then, the Topo-KD algorithm adaptively selects the most suitable model (RLKB or LKB_T) according to the terrain conditions and fitting residuals. The performances of Topo-KD and RLKB using the RossThick-LiSparseReciprocal (RTLSR) kernel are compared using simulated data sets and moderate-resolution imaging spectroradiometer (MODIS) observations. The results show that the BRDF of the pixel is affected by topography. But the RTLSR model does not specifically account for it, resulting in larger biases over rugged terrain than the Topo-KD algorithm in both the red and near-infrared (NIR) bands. The experiment using MODIS data sets demonstrates that the Topo-KD algorithm reduces fitting residuals in the red and NIR bands by 21.5% and 27.4% compared with the RTLSR model. These results indicate that the Topo-KD algorithm can be a better choice for retrieving land surface parameters and describing the radiative transfer process in mountainous areas.

ACS Style

Kai Yan; Hanliang Li; Wanjuan Song; Yiyi Tong; Dalei Hao; Yelu Zeng; Xihan Mu; Guangjian Yan; Yuan Fang; Ranga B. Myneni; Crystal Schaaf. Extending a Linear Kernel-Driven BRDF Model to Realistically Simulate Reflectance Anisotropy Over Rugged Terrain. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -16.

AMA Style

Kai Yan, Hanliang Li, Wanjuan Song, Yiyi Tong, Dalei Hao, Yelu Zeng, Xihan Mu, Guangjian Yan, Yuan Fang, Ranga B. Myneni, Crystal Schaaf. Extending a Linear Kernel-Driven BRDF Model to Realistically Simulate Reflectance Anisotropy Over Rugged Terrain. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-16.

Chicago/Turabian Style

Kai Yan; Hanliang Li; Wanjuan Song; Yiyi Tong; Dalei Hao; Yelu Zeng; Xihan Mu; Guangjian Yan; Yuan Fang; Ranga B. Myneni; Crystal Schaaf. 2021. "Extending a Linear Kernel-Driven BRDF Model to Realistically Simulate Reflectance Anisotropy Over Rugged Terrain." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-16.

Review
Published: 18 March 2021 in Journal of Remote Sensing
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The MODIS LAI/FPAR products have been widely used in various fields since their first public release in 2000. This review intends to summarize the history, development trends, scientific collaborations, disciplines involved, and research hotspots of these products. Its aim is to intrigue researchers and stimulate new research direction. Based on literature data from the Web of Science (WOS) and associated funding information, we conducted a bibliometric visualization review of the MODIS LAI/FPAR products from 1995 to 2020 using bibliometric and social network analysis (SNA) methods. We drew the following conclusions: (1) research based on the MODIS LAI/FPAR shows an upward trend with a multiyear average growth rate of 24.9% in the number of publications. (2) Researchers from China and the USA are the backbone of this research area, among which the Chinese Academy of Sciences (CAS) is the core research institution. (3) Research based on the MODIS LAI/FPAR covers a wide range of disciplines but mainly focus on environmental science and ecology. (4) Ecology, crop production estimation, algorithm improvement, and validation are the hotspots of these studies. (5) Broadening the research field, improving the algorithms, and overcoming existing difficulties in heterogeneous surface, scale effects, and complex terrains will be the trend of future research. Our work provides a clear view of the development of the MODIS LAI/FPAR products and valuable information for scholars to broaden their research fields.

ACS Style

Kai Yan; Dongxiao Zou; Guangjian Yan; Hongliang Fang; Marie Weiss; Miina Rautiainen; Yuri Knyazikhin; Ranga B. Myneni. A Bibliometric Visualization Review of the MODIS LAI/FPAR Products from 1995 to 2020. Journal of Remote Sensing 2021, 2021, 1 -20.

AMA Style

Kai Yan, Dongxiao Zou, Guangjian Yan, Hongliang Fang, Marie Weiss, Miina Rautiainen, Yuri Knyazikhin, Ranga B. Myneni. A Bibliometric Visualization Review of the MODIS LAI/FPAR Products from 1995 to 2020. Journal of Remote Sensing. 2021; 2021 ():1-20.

Chicago/Turabian Style

Kai Yan; Dongxiao Zou; Guangjian Yan; Hongliang Fang; Marie Weiss; Miina Rautiainen; Yuri Knyazikhin; Ranga B. Myneni. 2021. "A Bibliometric Visualization Review of the MODIS LAI/FPAR Products from 1995 to 2020." Journal of Remote Sensing 2021, no. : 1-20.

Journal article
Published: 18 March 2021 in Remote Sensing
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Leaf angle distribution (LAD) is an important attribute of forest canopy architecture and affects the solar radiation regime within the canopy. Terrestrial laser scanning (TLS) has been increasingly used in LAD estimation. The point clouds data suffer from the occlusion effect, which leads to incomplete scanning and depends on measurement strategies such as the number of scans and scanner location. Evaluating these factors is important to understand how to improve LAD, which is still lacking. Here, we introduce an easy way of estimating the LAD using open source software. Importantly, the influence of the occlusion effect on the LAD was evaluated by combining the proposed complete point clouds (CPCs) with the simulated data of 3D tree models of Aspen, Pin Oak and White Oak. We analyzed the effects of the point density, the number of scans and the scanner height on the LAD and G-function. Results show that: (1) the CPC can be used to evaluate the TLS-based normal vector reconstruction accuracy without an occlusion effect; (2) the accuracy is slightly affected by the normal vector reconstruction method and is greatly affected by the point density and the occlusion effect. The higher the point density (with a number of points per unit leaf area of 0.2 cm−2 to 27 cm−2 tested), the better the result is; (3) the performance is more sensitive to the scanner location than the number of scans. Increasing the scanner height improves LAD estimation, which has not been seriously considered in previous studies. It is worth noting that relatively tall trees suffer from a more severe occlusion effect, which deserves further attention in further study.

ACS Style

Hailan Jiang; Ronghai Hu; Guangjian Yan; Shiyu Cheng; Fan Li; Jianbo Qi; Linyuan Li; Donghui Xie; Xihan Mu. Influencing Factors in Estimation of Leaf Angle Distribution of an Individual Tree from Terrestrial Laser Scanning Data. Remote Sensing 2021, 13, 1159 .

AMA Style

Hailan Jiang, Ronghai Hu, Guangjian Yan, Shiyu Cheng, Fan Li, Jianbo Qi, Linyuan Li, Donghui Xie, Xihan Mu. Influencing Factors in Estimation of Leaf Angle Distribution of an Individual Tree from Terrestrial Laser Scanning Data. Remote Sensing. 2021; 13 (6):1159.

Chicago/Turabian Style

Hailan Jiang; Ronghai Hu; Guangjian Yan; Shiyu Cheng; Fan Li; Jianbo Qi; Linyuan Li; Donghui Xie; Xihan Mu. 2021. "Influencing Factors in Estimation of Leaf Angle Distribution of an Individual Tree from Terrestrial Laser Scanning Data." Remote Sensing 13, no. 6: 1159.

Journal article
Published: 20 January 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Remote sensing estimation based on the dimidiate pixel model (DPM) using vegetation indices (VIs) is a common approach for mapping fractional vegetation cover (FVC). The major drawback of DPM is that it does not consider real endmember conditions and multiple scattering between soil and vegetation. An analysis of FVC uncertainties caused by these model deficiencies is still lacking. Here, we first calculated the FVC theoretical uncertainty caused by reflectance uncertainties based on the law of prapagation of uncertainty (LPU). Then, we tested the performance of DPM using six VIs over 3-D forest scenes. We simulated both Aqua-MODIS and Landsat-OLI surface reflectance (SR) at their corresponding spatial resolutions and spectral response functions (SRFs) using a well-validated 3-D radiative transfer (RT) model which helps to separate the model and input uncertainties. We found that ratio vegetation index (RVI)- and enhanced vegetation index (EVI)-based models were most affected by sensors, followed by the normalized difference vegetation index (NDVI)-, enhanced vegetation index 2 (EVI2)-, renormalized difference vegetation index (RDVI)-, and difference vegetation index (DVI)-based models. Without considering SR uncertainties, the DVI-based model performed best (FVC absolute difference < 0.1); however, the commonly used NDVI model reached a maximum difference of 0.35. At the same time, input uncertainty increased the uncertainty of FVC retrieval. We noticed that the increase of solar zenith angle (SZA) resulted in a clear increase of retrieved FVC under the uniform distribution, which can be explained by the increased shadow proportion. Besides, model accuracy was dominated by the purity of soil (vegetation) endmember in low (high) vegetation cover area. This study provides a reference for the selection of the optimal VI for FVC retrieval based on the DPM.

ACS Style

Kai Yan; Si Gao; Haojing Chi; Jianbo Qi; Wanjuan Song; Yiyi Tong; Xihan Mu; Guangjian Yan. Evaluation of the Vegetation-Index-Based Dimidiate Pixel Model for Fractional Vegetation Cover Estimation. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -14.

AMA Style

Kai Yan, Si Gao, Haojing Chi, Jianbo Qi, Wanjuan Song, Yiyi Tong, Xihan Mu, Guangjian Yan. Evaluation of the Vegetation-Index-Based Dimidiate Pixel Model for Fractional Vegetation Cover Estimation. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-14.

Chicago/Turabian Style

Kai Yan; Si Gao; Haojing Chi; Jianbo Qi; Wanjuan Song; Yiyi Tong; Xihan Mu; Guangjian Yan. 2021. "Evaluation of the Vegetation-Index-Based Dimidiate Pixel Model for Fractional Vegetation Cover Estimation." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-14.

Journal article
Published: 14 January 2021 in Remote Sensing
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Despite its wide applications, the spatiotemporal fusion of coarse- and fine-resolution satellite images is limited primarily to the availability of clear-sky fine-resolution images, which are commonly scarce due to unfavorable weather, and such a limitation might cause errors in spatiotemporal fusion. Thus, the effective use of limited fine-resolution images, while critical, remains challenging. To address this issue, in this paper we propose a new phenological similarity strategy (PSS) to select the optimal combination of image pairs for a prediction date. The PSS considers the temporal proximity and phenological similarity between the base and prediction images and computes a weight for identifying the optimal combination of image pairs. Using the PSS, we further evaluate the influence of input data on the fusion accuracy by varying the number and temporal distribution of input images. The results show that the PSS (mean R = 0.827 and 0.760) outperforms the nearest date (mean R = 0.786 and 0.742) and highest correlation (mean R = 0.821 and 0.727) strategies in both the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the linear mixing growth model (LMGM), respectively, for fusing Landsat 8 OLI and MODIS NDVI datasets. Furthermore, base images adequately covering different growth stages yield better predictability than simply increasing the number of base images.

ACS Style

Yiting Wang; Donghui Xie; Yinggang Zhan; Huan Li; Guangjian Yan; Yuanyuan Chen. Assessing the Accuracy of Landsat-MODIS NDVI Fusion with Limited Input Data: A Strategy for Base Data Selection. Remote Sensing 2021, 13, 266 .

AMA Style

Yiting Wang, Donghui Xie, Yinggang Zhan, Huan Li, Guangjian Yan, Yuanyuan Chen. Assessing the Accuracy of Landsat-MODIS NDVI Fusion with Limited Input Data: A Strategy for Base Data Selection. Remote Sensing. 2021; 13 (2):266.

Chicago/Turabian Style

Yiting Wang; Donghui Xie; Yinggang Zhan; Huan Li; Guangjian Yan; Yuanyuan Chen. 2021. "Assessing the Accuracy of Landsat-MODIS NDVI Fusion with Limited Input Data: A Strategy for Base Data Selection." Remote Sensing 13, no. 2: 266.

Journal article
Published: 10 September 2020 in IEEE Transactions on Geoscience and Remote Sensing
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Occlusion effect, an inherent problem of terrestrial laser scanning (TLS) measurements, limits the potential of TLS data in tree attribute estimation. Multiple scans seek to mitigate this effect to provide enhanced scan completeness. However, the numbers and locations of the scans (i.e., the scan design) are usually determined via a subjective assessment of the tree density, spatial patterns of trees, and attributes to be derived. These could cause suboptimal scan completeness and limit tree attribute estimation. This study proposed an iterative-mode scan design to minimize the occlusion effect. First, we introduced a PoTo index based on visibility analysis to evaluate how many trees can be scanned from a location and to select effective candidates for the optimal TLS location. Second, we introduced a cumulative degree of ring closure (CDRC) to quantify the scan completeness for each candidate and determine the optimal TLS location. The TLS data sets of virtual forests with field-measured and synthetic plot parameter settings were simulated according to iterative- and regular-mode designs by using a Heidelberg light detection and ranging (LiDAR) Operations Simulator (HELIOS). The results demonstrated that an iterative-mode design can improve the scan completeness of trees compared to the regular-mode design. The tree attribute (diameter at breast height (DBH), tree height, stem curve, and crown volume) estimates of the iterative-mode design were less erroneous than those of the regular-mode design (e.g., the root-mean-square error (RMSE) could decrease the stem curve estimation by 38% and the crown volume estimation by 15%). This study suggests that the iterative-mode design can obtain an improved quality of the TLS data, especially for dense stands.

ACS Style

Linyuan Li; Xihan Mu; Maxime Soma; Peng Wan; Jianbo Qi; Ronghai Hu; Wuming Zhang; Yiyi Tong; Guangjian Yan. An Iterative-Mode Scan Design of Terrestrial Laser Scanning in Forests for Minimizing Occlusion Effects. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 3547 -3566.

AMA Style

Linyuan Li, Xihan Mu, Maxime Soma, Peng Wan, Jianbo Qi, Ronghai Hu, Wuming Zhang, Yiyi Tong, Guangjian Yan. An Iterative-Mode Scan Design of Terrestrial Laser Scanning in Forests for Minimizing Occlusion Effects. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (4):3547-3566.

Chicago/Turabian Style

Linyuan Li; Xihan Mu; Maxime Soma; Peng Wan; Jianbo Qi; Ronghai Hu; Wuming Zhang; Yiyi Tong; Guangjian Yan. 2020. "An Iterative-Mode Scan Design of Terrestrial Laser Scanning in Forests for Minimizing Occlusion Effects." IEEE Transactions on Geoscience and Remote Sensing 59, no. 4: 3547-3566.

Journal article
Published: 18 June 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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As a primary nighttime light (NTL) data source, the day/night band (DNB) sensor of the visible infrared imaging radiometer suite (VIIRS) is used in a wide range of studies. However, this signal is influenced by the atmosphere and may cause uncertainty while monitoring ground NTL. Given the lack of the quantitative analysis of the atmospheric effect on NTL, this study analyzes the relationship between VIIRS DNB NTL radiance and aerosol optical depth (AOD) in the urban areas of Beijing and three other Chinese cities of different urbanization levels. Results suggest a significantly negative relationship between the NTL radiance and AOD. The linear and log-linear models generate similar coefficients of determination ( R 2 ) for the AOD and NTL data, which vary for different urban centers. In Beijing, where the aerosol robotic network observations are available, R 2 reached 0.655 between the monthly NTL radiance and AOD. A slight decrease of R 2 occurred while using the Himawari AOD. The relationship between the NTL radiance and AOD varies among the cities. The NTL radiance may decrease by approximately 10 nW.cm −2 sr −1 when daily AOD increases one unit. For Beijing, this decrease may be above 15 nW.cm −2 sr −1 , which is comparable with the threshold used to extract urban areas. These findings underscore the importance of AOD in the application of NTL data that are potentially useful in the reconstruction of stable time series VIIRS DNB images by removing the aerosol effects.

ACS Style

Xuejun Wang; Xihan Mu; Guangjian Yan. Quantitative Analysis of Aerosol Influence on Suomi-NPP VIIRS Nighttime Light in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 3557 -3568.

AMA Style

Xuejun Wang, Xihan Mu, Guangjian Yan. Quantitative Analysis of Aerosol Influence on Suomi-NPP VIIRS Nighttime Light in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 ():3557-3568.

Chicago/Turabian Style

Xuejun Wang; Xihan Mu; Guangjian Yan. 2020. "Quantitative Analysis of Aerosol Influence on Suomi-NPP VIIRS Nighttime Light in China." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. : 3557-3568.

Journal article
Published: 16 June 2020 in IEEE Transactions on Geoscience and Remote Sensing
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The 3-D information collected from sample plots is significant for forest inventories. Terrestrial laser scanning (TLS) has been demonstrated to be an effective device in data acquisition of forest plots. Although TLS is able to achieve precise measurements, multiple scans are usually necessary to collect more detailed data, which generally requires more time in scan preparation and field data acquisition. In contrast, mobile laser scanning (MLS) is being increasingly utilized in mapping due to its mobility. However, the geometrical peculiarity of forests introduces challenges. In this article, a test backpack-based MLS system, i.e., backpack laser scanning (BLS), is designed for forest plot mapping without a global navigation satellite system/inertial measurement unit (GNSS-IMU) system. To achieve accurate matching, this article proposes to combine the line and point features for calculating transformation, in which the line feature is derived from trunk skeletons. Then, a scan-to-map matching strategy is proposed for correcting positional drift. Finally, this article evaluates the effectiveness and the mapping accuracy of the proposed method in forest sample plots. The experimental results indicate that the proposed method achieves accurate forest plot mapping using the BLS; meanwhile, compared to the existing methods, the proposed method utilizes the geometric attributes of the trees and reaches a lower mapping error, in which the mean errors and the root square mean errors for the horizontal/vertical direction in plots are less than 3 cm.

ACS Style

Jie Shao; Wuming Zhang; Nicolas Mellado; Shuangna Jin; Shangshu Cai; Lei Luo; Lingbo Yang; Guangjian Yan; Guoqing Zhou. Single Scanner BLS System for Forest Plot Mapping. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 1675 -1685.

AMA Style

Jie Shao, Wuming Zhang, Nicolas Mellado, Shuangna Jin, Shangshu Cai, Lei Luo, Lingbo Yang, Guangjian Yan, Guoqing Zhou. Single Scanner BLS System for Forest Plot Mapping. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (2):1675-1685.

Chicago/Turabian Style

Jie Shao; Wuming Zhang; Nicolas Mellado; Shuangna Jin; Shangshu Cai; Lei Luo; Lingbo Yang; Guangjian Yan; Guoqing Zhou. 2020. "Single Scanner BLS System for Forest Plot Mapping." IEEE Transactions on Geoscience and Remote Sensing 59, no. 2: 1675-1685.

Journal article
Published: 27 May 2020 in IEEE Transactions on Geoscience and Remote Sensing
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ACS Style

Guangjian Yan; Qing Chu; Yiyi Tong; Xihan Mu; Jianbo Qi; Yingji Zhou; Yanan Liu; Tianxing Wang; Donghui Xie; Wuming Zhang; Kai Yan; Shengbo Chen; Hongmin Zhou. An Operational Method for Validating the Downward Shortwave Radiation Over Rugged Terrains. IEEE Transactions on Geoscience and Remote Sensing 2020, 1 -18.

AMA Style

Guangjian Yan, Qing Chu, Yiyi Tong, Xihan Mu, Jianbo Qi, Yingji Zhou, Yanan Liu, Tianxing Wang, Donghui Xie, Wuming Zhang, Kai Yan, Shengbo Chen, Hongmin Zhou. An Operational Method for Validating the Downward Shortwave Radiation Over Rugged Terrains. IEEE Transactions on Geoscience and Remote Sensing. 2020; ():1-18.

Chicago/Turabian Style

Guangjian Yan; Qing Chu; Yiyi Tong; Xihan Mu; Jianbo Qi; Yingji Zhou; Yanan Liu; Tianxing Wang; Donghui Xie; Wuming Zhang; Kai Yan; Shengbo Chen; Hongmin Zhou. 2020. "An Operational Method for Validating the Downward Shortwave Radiation Over Rugged Terrains." IEEE Transactions on Geoscience and Remote Sensing , no. : 1-18.

Journal article
Published: 25 March 2020 in ISPRS Journal of Photogrammetry and Remote Sensing
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Precise structural information collected from plots is significant in the management of and decision-making regarding forest resources. Currently, laser scanning is widely used in forestry inventories to acquire three-dimensional (3D) structural information. There are three main data-acquisition modes in ground-based forest measurements: single-scan terrestrial laser scanning (TLS), multi-scan TLS and multi-single-scan TLS. Nevertheless, each of these modes causes specific difficulties for forest measurements. Due to occlusion effects, the single-scan TLS mode provides scans for only one side of the tree. The multi-scan TLS mode overcomes occlusion problems, however, at the cost of longer acquisition times, more human labor and more effort in data preprocessing. The multi-single-scan TLS mode decreases the workload and occlusion effects but lacks the complete 3D reconstruction of forests. These problems in TLS methods are largely avoided with mobile laser scanning (MLS); however, the geometrical peculiarity of forests (e.g., similarity between tree shapes, placements, and occlusion) complicates the motion estimation and reduces mapping accuracy. Therefore, this paper proposes a novel method combining single-scan TLS and MLS for forest 3D data acquisition. We use single-scan TLS data as a reference, onto which we register MLS point clouds, so they fill in the omission of the single-scan TLS data. To register MLS point clouds on the reference, we extract virtual feature points that are sampling the centerlines of tree stems and propose a new optimization-based registration framework. In contrast to previous MLS-based studies, the proposed method sufficiently exploits the natural geometric characteristics of trees. We demonstrate the effectiveness, robustness, and accuracy of the proposed method on three datasets, from which we extract structural information. The experimental results show that the omission of tree stem data caused by one scan can be compensated for by the MLS data, and the time of the field measurement is much less than that of the multi-scan TLS mode. In addition, single-scan TLS data provide strong global constraints for MLS-based forest mapping, which allows low mapping errors to be achieved, e.g., less than 2.0 cm mean errors in both the horizontal and vertical directions.

ACS Style

Jie Shao; Wuming Zhang; Nicolas Mellado; Nan Wang; Shuangna Jin; Shangshu Cai; Lei Luo; Thibault Lejemble; Guangjian Yan. SLAM-aided forest plot mapping combining terrestrial and mobile laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing 2020, 163, 214 -230.

AMA Style

Jie Shao, Wuming Zhang, Nicolas Mellado, Nan Wang, Shuangna Jin, Shangshu Cai, Lei Luo, Thibault Lejemble, Guangjian Yan. SLAM-aided forest plot mapping combining terrestrial and mobile laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing. 2020; 163 ():214-230.

Chicago/Turabian Style

Jie Shao; Wuming Zhang; Nicolas Mellado; Nan Wang; Shuangna Jin; Shangshu Cai; Lei Luo; Thibault Lejemble; Guangjian Yan. 2020. "SLAM-aided forest plot mapping combining terrestrial and mobile laser scanning." ISPRS Journal of Photogrammetry and Remote Sensing 163, no. : 214-230.

Journal article
Published: 19 March 2020 in IEEE Transactions on Geoscience and Remote Sensing
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Accurate estimation of the fine-resolution fraction of absorbed photosynthetically active radiation (FPAR) across broad spatial extents and long time periods requires efficient and applicable methods. The existing methods can hardly provide a balance between accuracy, simplicity, and transferability through space and time. Within the remote-sensing trend-surface conceptual framework, this article proposes a scaling-based method to efficiently retrieve FPAR from fine-resolution satellite data using coarse-resolution FPAR products as a reference. The method was particularly developed and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) FPAR product and Landsat imagery. First, necessary prior knowledge related to FPAR retrieval and scaling theories was used to explicitly linearize the complex relationship between MODIS FPAR and Landsat surface reflectance. Second, the explicit linear model for FPAR estimation was trained through one-pair image learning for each date to estimate FPAR from Landsat imagery in real time. Both homogeneous and heterogeneous cases were considered. The method was validated at ten selected worldwide sites from the Validation of Land European Remote Sensing Instruments (VALERI) program and derived an overall root mean squared error (RMSE) of 0.133. A long time series of FPAR data set at the 30-m resolution was generated at the regional scale (approximately 2000 km²) for 13 years (2000-2012). The results were accurate (RMSE = 0.072) and MODIS-consistent, which were significantly better than those of the normalized difference vegetation index (NDVI) downscaling-based and regression tree methods. The scaling-based method provides accurate, MODIS-consistent and spatially consistent FPAR estimates in real time, is highly transferrable through space and time, and allows for future extension of FPAR estimates to the era of the Landsat series satellites.

ACS Style

Yiting Wang; Guangjian Yan; Ronghai Hu; Donghui Xie; Wei Chen. A Scaling-Based Method for the Rapid Retrieval of FPAR From Fine-Resolution Satellite Data in the Remote-Sensing Trend-Surface Framework. IEEE Transactions on Geoscience and Remote Sensing 2020, 1 -14.

AMA Style

Yiting Wang, Guangjian Yan, Ronghai Hu, Donghui Xie, Wei Chen. A Scaling-Based Method for the Rapid Retrieval of FPAR From Fine-Resolution Satellite Data in the Remote-Sensing Trend-Surface Framework. IEEE Transactions on Geoscience and Remote Sensing. 2020; (99):1-14.

Chicago/Turabian Style

Yiting Wang; Guangjian Yan; Ronghai Hu; Donghui Xie; Wei Chen. 2020. "A Scaling-Based Method for the Rapid Retrieval of FPAR From Fine-Resolution Satellite Data in the Remote-Sensing Trend-Surface Framework." IEEE Transactions on Geoscience and Remote Sensing , no. 99: 1-14.

Journal article
Published: 25 February 2020 in IEEE Transactions on Geoscience and Remote Sensing
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Soil-Adjusted Vegetation Index (SAVI) is found to be undesirable to estimate Leaf Area Index (LAI) with heterogeneous canopy structure in low vegetation cover. In this article, three new vegetation indices (VIs), such as Normalized Hotspot-Signature Vegetation Index 2 (NHVI2), Hotspot-Signature Soil-Adjusted Vegetation Index (HSVI), and Hotspot-Signature 2-Band Enhanced Vegetation Index (HEVI2), are proposed for a better quantitative estimation of LAI and soil-noise resistance than with SAVI. To obtain these new indices, the angular index called Normalized Difference between Hotspot and Darkspot (NDHD) is introduced which represents the distribution of foliage in vegetation canopy. The validity of new VIs is statistically verified using simulated data and field measurements. The Discrete Anisotropic Radiative Transfer (DART) model is used to simulate both the homogeneous and heterogeneous canopy for analyzing vegetation isolines behaviors, soil-noise resistance, and LAI estimation. In situ measurements of LAI and bidirectional reflectance factor from the Boreal Ecosystem-Atmosphere Study (BOREAS) are also used to test the robustness of the new VIs for the estimation of LAI. By considering the distribution of the foliage, the accuracy of LAI estimation of SAVI for heterogeneous canopy improved almost 16% using exponential regression analysis. With the improvement of multiangular remote-sensing and Bidirectional Reflectance Distribution Function (BRDF) models in the future, hotspot-signature VIs have the potential to provide a more accurate LAI estimation for heterogeneous canopy in strong soil-noise interference area.

ACS Style

Zhijun Zhen; Shengbo Chen; Wenhan Qin; Guangjian Yan; Jean-Philippe Gastellu-Etchegorry; Lisai Cao; Mike Murefu; Jian Li; Bingbing Han. Potentials and Limits of Vegetation Indices With BRDF Signatures for Soil-Noise Resistance and Estimation of Leaf Area Index. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 5092 -5108.

AMA Style

Zhijun Zhen, Shengbo Chen, Wenhan Qin, Guangjian Yan, Jean-Philippe Gastellu-Etchegorry, Lisai Cao, Mike Murefu, Jian Li, Bingbing Han. Potentials and Limits of Vegetation Indices With BRDF Signatures for Soil-Noise Resistance and Estimation of Leaf Area Index. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (7):5092-5108.

Chicago/Turabian Style

Zhijun Zhen; Shengbo Chen; Wenhan Qin; Guangjian Yan; Jean-Philippe Gastellu-Etchegorry; Lisai Cao; Mike Murefu; Jian Li; Bingbing Han. 2020. "Potentials and Limits of Vegetation Indices With BRDF Signatures for Soil-Noise Resistance and Estimation of Leaf Area Index." IEEE Transactions on Geoscience and Remote Sensing 58, no. 7: 5092-5108.

Journal article
Published: 16 January 2020 in Remote Sensing
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Vegetation cover estimation for overstory and understory layers provides valuable information for modeling forest carbon and water cycles and refining forest ecosystem function assessment. Although previous studies demonstrated the capability of light detection and ranging (LiDAR) in the three-dimensional (3D) characterization of forest overstory and understory communities, the high cost inhibits its application in frequent and successive survey tasks. Low-cost commercial red–green–blue (RGB) cameras mounted on unmanned aerial vehicles (UAVs), as LiDAR alternatives, provide operational systems for simultaneously quantifying overstory crown cover (OCC) and understory vegetation cover (UVC). We developed an effective method named back-projection of 3D point cloud onto superpixel-segmented image (BAPS) to extract overstory and forest floor pixels using 3D structure-from-motion (SfM) point clouds and two-dimensional (2D) superpixel segmentation. The OCC was estimated from the extracted overstory crown pixels. A reported method, called half-Gaussian fitting (HAGFVC), was used to segement green vegetation and non-vegetation pixels from the extracted forest floor pixels and derive UVC. The UAV-based RGB imagery and field validation data were collected from eight forest plots in Saihanba National Forest Park (SNFP) plantation in northern China. The consistency of the OCC estimates between BAPS and canopy height model (CHM)-based methods (coefficient of determination: 0.7171) demonstrated the capability of the BAPS method in the estimation of OCC. The segmentation of understory vegetation was verified by the supervised classification (SC) method. The validation results showed that the OCC and UVC estimates were in good agreement with reference values, where the root-mean-square error (RMSE) of OCC (unitless) and UVC (unitless) reached 0.0704 and 0.1144, respectively. The low-cost UAV-based observation system and the newly developed method are expected to improve the understanding of ecosystem functioning and facilitate ecological process modeling.

ACS Style

Linyuan Li; Jun Chen; Xihan Mu; Weihua Li; Guangjian Yan; Donghui Xie; Wuming Zhang. Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation. Remote Sensing 2020, 12, 298 .

AMA Style

Linyuan Li, Jun Chen, Xihan Mu, Weihua Li, Guangjian Yan, Donghui Xie, Wuming Zhang. Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation. Remote Sensing. 2020; 12 (2):298.

Chicago/Turabian Style

Linyuan Li; Jun Chen; Xihan Mu; Weihua Li; Guangjian Yan; Donghui Xie; Wuming Zhang. 2020. "Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation." Remote Sensing 12, no. 2: 298.

Journal article
Published: 29 November 2019 in Remote Sensing
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Advanced Very High Resolution Radiometer (AVHRR) sensors provide a valuable data source for generating long-term global land surface temperature (LST). However, changes in the observation time that are caused by satellite orbit drift restrict their wide application. Here, a generalized split-window (GSW) algorithm was implemented to retrieve the LST from the time series AVHRR data. Afterwards, a novel orbit drift correction (ODC) algorithm, which was based on the diurnal temperature cycle (DTC) model and Bayesian optimization algorithm, was also proposed for normalizing the estimated LST to the same local time. This ODC algorithm is pixel-based and it only needs one observation every day. The resulting LSTs from the six-year National Oceanic and Atmospheric Administration (NOAA)-14 satellite data were validated while using Surface Radiation Budget Network (SURFRAD) in-situ measurements. The average accuracies for LST retrieval varied from −0.4 K to 2.0 K over six stations and they also depended on the viewing zenith angle and season. The simulated data illustrate that the proposed ODC method can improve the LST estimate at a similar magnitude to the accuracy of the LST retrieval, i.e., the root-mean-square errors (RMSEs) of the corrected LSTs were 1.3 K, 2.2 K, and 3.1 K for the LST with a retrieval RMSE of 1 K, 2 K, and 3 K, respectively. This method was less sensitive to the fractional vegetation cover (FVC), including the FVC retrieval error, size, and degree of change within a neighboring area, which suggested that it could be easily updated by applying other LST expression models. In addition, ground validation also showed an encouraging correction effect. The RMSE variations of LST estimation that were introduced by ODC were within ±0.5 K, and the correlation coefficients between the corrected LST errors and original LST errors could approach 0.91.

ACS Style

Xiangyang Liu; Bo-Hui Tang; Guangjian Yan; Zhao-Liang Li; Shunlin Liang. Retrieval of Global Orbit Drift Corrected Land Surface Temperature from Long-term AVHRR Data. Remote Sensing 2019, 11, 2843 .

AMA Style

Xiangyang Liu, Bo-Hui Tang, Guangjian Yan, Zhao-Liang Li, Shunlin Liang. Retrieval of Global Orbit Drift Corrected Land Surface Temperature from Long-term AVHRR Data. Remote Sensing. 2019; 11 (23):2843.

Chicago/Turabian Style

Xiangyang Liu; Bo-Hui Tang; Guangjian Yan; Zhao-Liang Li; Shunlin Liang. 2019. "Retrieval of Global Orbit Drift Corrected Land Surface Temperature from Long-term AVHRR Data." Remote Sensing 11, no. 23: 2843.

Journal article
Published: 05 November 2019 in Remote Sensing
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Craters contain important information on geological history and have been widely used for dating absolute age and reconstructing impact history. The impact process results in a lot of ejected fragments and these fragments may form secondary craters. Studies on distinguishing primary craters from secondary craters are helpful in improving the accuracy of crater dating. However, previous studies about distinguishing primary craters from secondary craters were either conducted by manual identification or used approaches mainly concerning crater spatial distribution, which are time-consuming or have low accuracy. This paper presents a machine learning approach to distinguish primary craters from secondary craters. First, samples used for training and testing were identified and unified. The whole dataset contained 1032 primary craters and 4041 secondary craters. Then, considering the differences between primary and secondary craters, features mainly related to crater shape, depth, and density were calculated. Finally, a random forest classifier was trained and tested. This approach showed a favorable performance. The accuracy and F1-score for fivefold cross-validation were 0.939 and 0.839, respectively. The proposed machine learning approach enables an automated method of distinguishing primary craters from secondary craters, which results in better performance.

ACS Style

Qiangyi Liu; Weiming Cheng; Guangjian Yan; Yunliang Zhao; Jianzhong Liu. A Machine Learning Approach to Crater Classification from Topographic Data. Remote Sensing 2019, 11, 2594 .

AMA Style

Qiangyi Liu, Weiming Cheng, Guangjian Yan, Yunliang Zhao, Jianzhong Liu. A Machine Learning Approach to Crater Classification from Topographic Data. Remote Sensing. 2019; 11 (21):2594.

Chicago/Turabian Style

Qiangyi Liu; Weiming Cheng; Guangjian Yan; Yunliang Zhao; Jianzhong Liu. 2019. "A Machine Learning Approach to Crater Classification from Topographic Data." Remote Sensing 11, no. 21: 2594.

Journal article
Published: 01 October 2019 in ISPRS Journal of Photogrammetry and Remote Sensing
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Remote sensing via unmanned aerial vehicles (UAVs) is becoming a very important tool for augmenting traditional spaceborne and airborne remote sensing techniques. Commercial RGB cameras are often the payload on UAVs, because they are inexpensive, easy to operate and require little data processing. RGB images are increasingly being used for mapping of fractional vegetation cover (FVC). However, the presence of significantly mixed pixels in close-range RGB images prevents the accurate estimation of FVC. Even where pixel unmixing is applied, limited quantitative spectral information and colour variability within these images could lead to profound errors and uncertainties. This paper proposes a colour mixture analysis (CMA) method based on the Hue-Saturation-Value (HSV) colour space to alleviate the above-mentioned concerns, thereby improving the accuracy and efficiency of FVC estimation from UAV-captured RGB images. First, the a priori colour information of the pure vegetation and background endmembers are extracted from the Hue channel of the UAV proximal sensing images, obviating ground-based image capture and the attendant cost and inconvenience. Second, the relationship between the probability distribution of mixed pixels and that of the two endmembers is estimated. Finally, we estimate FVC from UAV remote sensing images with a maximum a posteriori parameter (MAP) estimator. Two UAV-captured RGB image datasets and a synthetic RGB image dataset were used to test the new method. CMA was compared with three other FVC estimation algorithms, namely, FCLS, HAGFVC and LAB2. The FVC estimates by CMA were found to be highly accurate, with root mean squared errors (RMSE) of less than 0.007 and mean absolute error (MAE) of less than 0.01 for both field datasets. The accuracy was shown to be superior to that of all three algorithms. A comprehensive analysis of the estimation accuracy under various spatial resolutions and vegetation cover levels was conducted using both field and synthetic datasets. Results show that the CMA method can robustly and accurately estimate FVC across the full range of vegetation coverage and various resolutions. Uncertainty and sensitivity analysis of colour variability due to heterogeneity and shadow were also tested. Overall, CMA was shown to be robust to variation in colour and illumination.

ACS Style

Guangjian Yan; Linyuan Li; André Coy; Xihan Mu; Shengbo Chen; Donghui Xie; Wuming Zhang; Qingfeng Shen; Hongmin Zhou. Improving the estimation of fractional vegetation cover from UAV RGB imagery by colour unmixing. ISPRS Journal of Photogrammetry and Remote Sensing 2019, 158, 23 -34.

AMA Style

Guangjian Yan, Linyuan Li, André Coy, Xihan Mu, Shengbo Chen, Donghui Xie, Wuming Zhang, Qingfeng Shen, Hongmin Zhou. Improving the estimation of fractional vegetation cover from UAV RGB imagery by colour unmixing. ISPRS Journal of Photogrammetry and Remote Sensing. 2019; 158 ():23-34.

Chicago/Turabian Style

Guangjian Yan; Linyuan Li; André Coy; Xihan Mu; Shengbo Chen; Donghui Xie; Wuming Zhang; Qingfeng Shen; Hongmin Zhou. 2019. "Improving the estimation of fractional vegetation cover from UAV RGB imagery by colour unmixing." ISPRS Journal of Photogrammetry and Remote Sensing 158, no. : 23-34.

Journal article
Published: 01 May 2019 in Remote Sensing
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Separating point clouds into ground and non-ground points is a preliminary and essential step in various applications of airborne light detection and ranging (LiDAR) data, and many filtering algorithms have been proposed to automatically filter ground points. Among them, the progressive triangulated irregular network (TIN) densification filtering (PTDF) algorithm is widely employed due to its robustness and effectiveness. However, the performance of this algorithm usually depends on the detailed initial terrain and the cautious tuning of parameters to cope with various terrains. Consequently, many approaches have been proposed to provide as much detailed initial terrain as possible. However, most of them require many user-defined parameters. Moreover, these parameters are difficult to determine for users. Recently, the cloth simulation filtering (CSF) algorithm has gradually drawn attention because its parameters are few and easy-to-set. CSF can obtain a fine initial terrain, which simultaneously provides a good foundation for parameter threshold estimation of progressive TIN densification (PTD). However, it easily causes misclassification when further refining the initial terrain. To achieve the complementary advantages of CSF and PTDF, a novel filtering algorithm that combines cloth simulation (CS) and PTD is proposed in this study. In the proposed algorithm, a high-quality initial provisional digital terrain model (DTM) is obtained by CS, and the parameter thresholds of PTD are estimated from the initial provisional DTM based on statistical analysis theory. Finally, PTD with adaptive parameter thresholds is used to refine the initial provisional DTM. These contributions of the implementation details achieve accuracy enhancement and resilience to parameter tuning. The experimental results indicate that the proposed algorithm improves performance over their direct predecessors. Furthermore, compared with the publicized improved PTDF algorithms, our algorithm is not only superior in accuracy but also practicality. The fact that the proposed algorithm is of high accuracy and easy-to-use is desirable for users.

ACS Style

Shangshu Cai; Wuming Zhang; Xinlian Liang; Peng Wan; Jianbo Qi; Sisi Yu; Guangjian Yan; Jie Shao. Filtering Airborne LiDAR Data Through Complementary Cloth Simulation and Progressive TIN Densification Filters. Remote Sensing 2019, 11, 1037 .

AMA Style

Shangshu Cai, Wuming Zhang, Xinlian Liang, Peng Wan, Jianbo Qi, Sisi Yu, Guangjian Yan, Jie Shao. Filtering Airborne LiDAR Data Through Complementary Cloth Simulation and Progressive TIN Densification Filters. Remote Sensing. 2019; 11 (9):1037.

Chicago/Turabian Style

Shangshu Cai; Wuming Zhang; Xinlian Liang; Peng Wan; Jianbo Qi; Sisi Yu; Guangjian Yan; Jie Shao. 2019. "Filtering Airborne LiDAR Data Through Complementary Cloth Simulation and Progressive TIN Densification Filters." Remote Sensing 11, no. 9: 1037.