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The in situ measurement of the leaf area index (LAI) from gap fraction is often affected by terrain slope. Path length correction (PLC) is commonly used to mitigate the topographic effect on the LAI measurements. However, the terrain-induced uncertainty and the accuracy improvement of the PLC for LAI measurements have not been systematically analyzed, hindering the establishment of an appropriate protocol for LAI measurements over mountainous regions. In this article, the above knowledge gap was filled using a computer simulation framework, which enables the estimated LAI before and after PLC to be benchmarked against the known and precise model truth. The simulation was achieved by using CANOPIX software and a dedicatedly designed ray-tracing method for continuous and discrete canopies, respectively. Simulations show that the slope distorts the angular pattern of the gap fraction, i.e., increasing the gap fraction in the down-slope direction and reducing it in the up-slope direction. The horizontally equivalent hemispheric gap fraction from the PLC can reconstruct the azimuthally symmetric angular pattern of the real horizontal surface. The azimuthally averaged gap fraction for sloping terrain can both be underestimated or overestimated depending on the LAI and can be successfully corrected through PLC. The topography-induced uncertainty in LAI measurements is found to be ~14.3% and >20% for continuous and discrete canopies, respectively. This uncertainty can be, respectively, reduced to ~1.8% and <7.3% after PLC, meeting the up-to-date uncertainty threshold of 15% established by the Global Climate Observing System (GCOS). Closer analysis shows that the topographic effect is influenced by fractional crown cover, and the largest uncertainty which corresponds to extensively clumping canopy can reach nearly up to 50%. The accuracy of the estimated LAI after PLC safely meets the GCOS uncertainty threshold even for this extreme case. This study demonstrates the necessity of a topographic correction for LAI measurements and the applicability of PLC for reconstructing the horizontally equivalent gap fraction and improving the LAI measurements over sloping terrains. The results of this article throw light on the design of a protocol for LAI measurements over mountainous regions.
Gaofei Yin; Biao Cao; Jing Li; Weiliang Fan; Yelu Zeng; Baodong Xu; Wei Zhao. Path Length Correction for Improving Leaf Area Index Measurements Over Sloping Terrains: A Deep Analysis Through Computer Simulation. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 4573 -4589.
AMA StyleGaofei Yin, Biao Cao, Jing Li, Weiliang Fan, Yelu Zeng, Baodong Xu, Wei Zhao. Path Length Correction for Improving Leaf Area Index Measurements Over Sloping Terrains: A Deep Analysis Through Computer Simulation. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (7):4573-4589.
Chicago/Turabian StyleGaofei Yin; Biao Cao; Jing Li; Weiliang Fan; Yelu Zeng; Baodong Xu; Wei Zhao. 2020. "Path Length Correction for Improving Leaf Area Index Measurements Over Sloping Terrains: A Deep Analysis Through Computer Simulation." IEEE Transactions on Geoscience and Remote Sensing 58, no. 7: 4573-4589.
The availability of global high-resolution land cover maps provides promising a priori knowledge for characterizing subpixel heterogeneity and improving predictions of directional reflectance of coarse-resolution pixels. Due to mutual shadowing and sheltering effects between the adjacent forest and cropland patches, the spectral nonlinear mixing of patchy ecotones is significant, especially when the sun illuminates the ecotone from the forest side with high solar zenith angle. The spectral linear mixture (SLM) approach leads to overestimation of the bidirectional reflectance factor (BRF) in the red band in the principal plane (PP), with a maximum absolute error (MAE) of 0.0063 and a maximum relative error (MRE) of 52.5%, and to underestimation in the near-infrared band in PP with an MAE of 0.0940 and an MRE of 14.5%. In a scenario with randomly distributed boundary orientations, the overestimation of SLM increases with the degree of fragmentation and the view zenith angle. We propose a Radiative Transfer model for patchy ECotones (RTEC). which improves R² from 0.61 to 0.94 in the red band of Landsat-8 directional reflectance at the validation site. The RTEC model provides an efficient and analytical approach for directional reflectance predictions over heterogeneous patchy landscapes at coarse resolution and will be used for biophysical parameter retrievals [e.g., the leaf area index (LAI)] in future applications.
Yelu Zeng; Jing Li; Qinhuo Liu; Alfredo R. Huete; Baodong Xu; Gaofei Yin; Weiliang Fan; Yixuan Ouyang; Kai Yan; Dalei Hao; Min Chen. A Radiative Transfer Model for Patchy Landscapes Based on Stochastic Radiative Transfer Theory. IEEE Transactions on Geoscience and Remote Sensing 2019, 58, 2571 -2589.
AMA StyleYelu Zeng, Jing Li, Qinhuo Liu, Alfredo R. Huete, Baodong Xu, Gaofei Yin, Weiliang Fan, Yixuan Ouyang, Kai Yan, Dalei Hao, Min Chen. A Radiative Transfer Model for Patchy Landscapes Based on Stochastic Radiative Transfer Theory. IEEE Transactions on Geoscience and Remote Sensing. 2019; 58 (4):2571-2589.
Chicago/Turabian StyleYelu Zeng; Jing Li; Qinhuo Liu; Alfredo R. Huete; Baodong Xu; Gaofei Yin; Weiliang Fan; Yixuan Ouyang; Kai Yan; Dalei Hao; Min Chen. 2019. "A Radiative Transfer Model for Patchy Landscapes Based on Stochastic Radiative Transfer Theory." IEEE Transactions on Geoscience and Remote Sensing 58, no. 4: 2571-2589.
The highly accurate multiresolution leaf area index (LAI) is an important parameter for carbon cycle simulation for bamboo forests at different scales. However, current LAI products have discontinuous resolution with 1 km mostly, that makes it difficult to accurately quantify the spatiotemporal evolution of carbon cycle at different resolutions. Thus, this study used MODIS LAI product (MOD15A2) and MODIS reflectance data (MOD09Q1) of Moso bamboo forest (MBF) from 2015, and it adopted a hierarchical Bayesian network (HBN) algorithm coupled with a dynamic LAI model and the PROSAIL model to obtain high-precision LAI data at multiresolution (i.e., 1000, 500, and 250 m). The results showed the LAIs assimilated using the HBN at the three resolutions corresponded with the actual growth trend of the MBF and correlated significantly with the observed LAI with a determination coefficient (R2) value of > 0.80. The highest-precision assimilated LAI was obtained at 1000-m resolution with R2 values of 0.91. The LAI assimilated using the HBN algorithm achieved better accuracy than the MODIS LAI with increases in the R2 value of 2.7 times and decreases in the root mean square error of 87.8%. Therefore, the HBN algorithm applied in this study can effectively obtain highly accurate multiresolution LAI time series data for bamboo forest.
Luqi Xing; Xuejian Li; Huaqiang Du; Guomo Zhou; Fangjie Mao; Tengyan Liu; Junlong Zheng; Luofan Dong; Meng Zhang; Ning Han; Xiaojun Xu; Weiliang Fan; Di’En Zhu. Assimilating Multiresolution Leaf Area Index of Moso Bamboo Forest from MODIS Time Series Data Based on a Hierarchical Bayesian Network Algorithm. Remote Sensing 2018, 11, 56 .
AMA StyleLuqi Xing, Xuejian Li, Huaqiang Du, Guomo Zhou, Fangjie Mao, Tengyan Liu, Junlong Zheng, Luofan Dong, Meng Zhang, Ning Han, Xiaojun Xu, Weiliang Fan, Di’En Zhu. Assimilating Multiresolution Leaf Area Index of Moso Bamboo Forest from MODIS Time Series Data Based on a Hierarchical Bayesian Network Algorithm. Remote Sensing. 2018; 11 (1):56.
Chicago/Turabian StyleLuqi Xing; Xuejian Li; Huaqiang Du; Guomo Zhou; Fangjie Mao; Tengyan Liu; Junlong Zheng; Luofan Dong; Meng Zhang; Ning Han; Xiaojun Xu; Weiliang Fan; Di’En Zhu. 2018. "Assimilating Multiresolution Leaf Area Index of Moso Bamboo Forest from MODIS Time Series Data Based on a Hierarchical Bayesian Network Algorithm." Remote Sensing 11, no. 1: 56.
Rugged terrain distorts optical remote sensing signals, and land-cover classification and biophysical parameter retrieval over mountainous regions must account for topographic effects. Therefore, topographic correction is a prerequisite for many remote sensing applications. In this study, we proposed a semi-physically based and simple topographic correction method for vegetation canopies based on path length correction (PLC). The PLC method was derived from the solution to the classic radiative transfer equation, and the influence of terrain on the radiative transfer process within the canopy is explicitly considered, making PLC physically sound. The radiative transfer equation was simplified to make PLC mathematically simple. Near-nadir observations derived from a Landsat 8 Operational Land Imager (OLI) image covering a mountainous region and wide field-of-view observations derived from simulation using a canopy reflectance model were combined to test the PLC correction method. Multi-criteria were used to provide objective evaluation results. The performances were compared to that of five other methods: CC, SCS + C, and SE, which are empirical parameter-based methods, and SCS and D-S, which are semi-physical methods without empirical parameter. All the six methods could significantly reduce the topographic effects. However, SCS showed obvious overcorrection for near-nadir observations. The correction results from D-S showed an obvious positive bias. For near-nadir observations, the performance of PLC was comparable to the well-validated parameter-based methods. For wide field-of-view observations, PLC obviously outperformed all other methods. Because of the physical soundness and mathematical simplicity, PLC provides an efficient approach to correct the terrain-induced canopy BRDF distortion and will facilitate the exploitation of multi-angular information for biophysical parameter retrieval over mountainous regions.
Gaofei Yin; Ainong Li; Shengbiao Wu; Weiliang Fan; Yelu Zeng; Kai Yan; Baodong Xu; Jing Li; Qinhuo Liu. PLC: A simple and semi-physical topographic correction method for vegetation canopies based on path length correction. Remote Sensing of Environment 2018, 215, 184 -198.
AMA StyleGaofei Yin, Ainong Li, Shengbiao Wu, Weiliang Fan, Yelu Zeng, Kai Yan, Baodong Xu, Jing Li, Qinhuo Liu. PLC: A simple and semi-physical topographic correction method for vegetation canopies based on path length correction. Remote Sensing of Environment. 2018; 215 ():184-198.
Chicago/Turabian StyleGaofei Yin; Ainong Li; Shengbiao Wu; Weiliang Fan; Yelu Zeng; Kai Yan; Baodong Xu; Jing Li; Qinhuo Liu. 2018. "PLC: A simple and semi-physical topographic correction method for vegetation canopies based on path length correction." Remote Sensing of Environment 215, no. : 184-198.
Topographic correction methods rarely consider the canopy parameter effects directly and explicitly for sloping canopies. In order to address this problem, the topographic correction method MFM-GOST2 was developed by implementing the second version of the Geometric-Optical model for Sloping Terrains (the GOST2 model) in the multiple forward mode (MFM) inversion framework. First, a look up table (LUT) was constructed by multiple forward modeling of the GOST2 model; second, the radiance of a remotely sensed image and its corresponding topographic data were used for searching potential canopy parameter combinations from the LUT; and third, the corrected radiance was determined by averaging potential radiances of horizontal canopies from the LUT according to the canopy parameter combinations. The MFM-GOST2 and twelve generally used topographic correction methods were evaluated via a case study by visual analysis, linear relationship analysis, and the rose diagram analysis. The result showed that the MFM-GOST2 method successfully removed most of the topographic effects of a subset image of the Landsat-8 image in a case study. The case study also illustrates that the rose diagram analysis is a good way to evaluate topographic corrections, but the linear relationship analysis cannot be used independently for the evaluations because the decorrelation is not a sufficient condition to determine a successful topographic correction.
Weiliang Fan; Jing Li; Qinhuo Liu; Qian Zhang; Gaifei Yin; Ainong Li; Yelu Zeng; Baodong Xu; Xiaojun Xu; Guomo Zhou; Huaqiang Du. Topographic Correction of Forest Image Data Based on the Canopy Reflectance Model for Sloping Terrains in Multiple Forward Mode. Remote Sensing 2018, 10, 717 .
AMA StyleWeiliang Fan, Jing Li, Qinhuo Liu, Qian Zhang, Gaifei Yin, Ainong Li, Yelu Zeng, Baodong Xu, Xiaojun Xu, Guomo Zhou, Huaqiang Du. Topographic Correction of Forest Image Data Based on the Canopy Reflectance Model for Sloping Terrains in Multiple Forward Mode. Remote Sensing. 2018; 10 (5):717.
Chicago/Turabian StyleWeiliang Fan; Jing Li; Qinhuo Liu; Qian Zhang; Gaifei Yin; Ainong Li; Yelu Zeng; Baodong Xu; Xiaojun Xu; Guomo Zhou; Huaqiang Du. 2018. "Topographic Correction of Forest Image Data Based on the Canopy Reflectance Model for Sloping Terrains in Multiple Forward Mode." Remote Sensing 10, no. 5: 717.
Baodong Xu; Jing Li; Taejin Park; Qinhuo Liu; Yelu Zeng; Gaofei Yin; Jing Zhao; Weiliang Fan; Le Yang; Yuri Knyazikhin; Ranga Myneni. An integrated method for validating long-term leaf area index products using global networks of site-based measurements. Remote Sensing of Environment 2018, 209, 134 -151.
AMA StyleBaodong Xu, Jing Li, Taejin Park, Qinhuo Liu, Yelu Zeng, Gaofei Yin, Jing Zhao, Weiliang Fan, Le Yang, Yuri Knyazikhin, Ranga Myneni. An integrated method for validating long-term leaf area index products using global networks of site-based measurements. Remote Sensing of Environment. 2018; 209 ():134-151.
Chicago/Turabian StyleBaodong Xu; Jing Li; Taejin Park; Qinhuo Liu; Yelu Zeng; Gaofei Yin; Jing Zhao; Weiliang Fan; Le Yang; Yuri Knyazikhin; Ranga Myneni. 2018. "An integrated method for validating long-term leaf area index products using global networks of site-based measurements." Remote Sensing of Environment 209, no. : 134-151.
Understanding the terrestrial carbon and water cycles is crucial for mitigation and adaptation for climate change. Leaf area index (LAI) is a key biophysical parameter in process-based ecosystem models for simulating gross primary productivity (GPP) and evapotranspiration (ET). The uncertainty in satellite-derived LAI products and their effects on the simulation of carbon and water fluxes at regional scales remain unclear. We evaluated three satellite-derived LAI products - MODIS (MCD15), GLASS, and Four-Scale Geometric Optical Model based LAI (FSGOM) over the period 2003–2012 using fine-resolution (30 m) LAI data and field LAI measurements. GLASS had higher accuracy than FSGOM and MCD15 for forests, while FSGOM had higher accuracy than MCD15 and GLASS for grasslands. The three LAI products differed in magnitude, spatial patterns, and trends in LAI. We then examined the resulting discrepancies in simulated annual GPP and ET over China using a process-based, diagnostic terrestrial biosphere model. Mean annual total GPP for China's terrestrial ecosystems based on GLASS (6.32 Pg C yr− 1) and FSGOM (6.15 Pg C yr− 1) was 22.5% and 19.2% higher than that based on MCD15 (5.16 Pg C yr− 1), respectively. Annual GPP based on GLASS and MCD15 increased over larger fractions of China's vegetated area (15.9% and 17.3%, respectively) than that based on FSGOM (12.6%). National annual ET based on GLASS (379.9 mm yr− 1) and FSGOM (374.4 mm yr− 1) was 7.9% and 6.3% higher than that based on MCD15 (352.1 mm yr− 1), respectively. Simulated ET increased in larger fractions of the vegetated area for GLASS (5.7%) and MCD15 (5.8%) than for FSGOM (3.9%). Our study shows that there were large discrepancies in LAI among satellite-derived LAI products and the biases of the LAI products could lead to substantial uncertainties in simulated carbon and water fluxes.
Yibo Liu; Jingfeng Xiao; Weimin Ju; Gaolong Zhu; Xiaocui Wu; Weiliang Fan; Dengqiu Li; Yanlian Zhou. Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes. Remote Sensing of Environment 2018, 206, 174 -188.
AMA StyleYibo Liu, Jingfeng Xiao, Weimin Ju, Gaolong Zhu, Xiaocui Wu, Weiliang Fan, Dengqiu Li, Yanlian Zhou. Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes. Remote Sensing of Environment. 2018; 206 ():174-188.
Chicago/Turabian StyleYibo Liu; Jingfeng Xiao; Weimin Ju; Gaolong Zhu; Xiaocui Wu; Weiliang Fan; Dengqiu Li; Yanlian Zhou. 2018. "Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes." Remote Sensing of Environment 206, no. : 174-188.
Bamboo forest has great potential in climate change mitigation. However, the spatiotemporal pattern of carbon storage of global bamboo forest is still cannot be accurately estimated, because the lack of an accurate global bamboo forest distribution information. In this paper, the global bamboo forest distribution was mapped with the following steps. To begin with, training samples were obtained based on investigation data, statistic data, and literature data. Then, a decision tree was constructed for mapping the global bamboo forest distribution by integrating Landsat 8 OLI and MODIS data. Finally, the global bamboo forest area was estimated using a pixel unmixing algorithm. The constructed decision tree succeeds in extracting global bamboo forest based on remote sensing data, where the overall accuracy of classification was 78.81%. The estimated total global bamboo forest area was 30538.35 × 10 3 ha, with a low root-mean-square error of 611.1 × 10 3 ha. The estimated bamboo forest area of each province in China and each country were high consistent with the National Forest Inventory in China and Food and Agriculture Organization of the United Nations statistic results (average R 2 > 0.9), respectively. Therefore, the global bamboo forest map yielded a satisfactory accuracy in both classification and area estimation, and could provide accurate and significant support for global bamboo forest resource management and carbon cycle research.
Huaqiang Du; Fangjie Mao; Xuejian Li; Guomo Zhou; Xiaojun Xu; Ning Han; Shaobo Sun; Guolong Gao; Lu Cui; Yangguang Li; Dien Zhu; Yuli Liu; Liang Chen; Weiliang Fan; Pingheng Li; Yongjun Shi; Yufeng Zhou. Mapping Global Bamboo Forest Distribution Using Multisource Remote Sensing Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2018, 11, 1458 -1471.
AMA StyleHuaqiang Du, Fangjie Mao, Xuejian Li, Guomo Zhou, Xiaojun Xu, Ning Han, Shaobo Sun, Guolong Gao, Lu Cui, Yangguang Li, Dien Zhu, Yuli Liu, Liang Chen, Weiliang Fan, Pingheng Li, Yongjun Shi, Yufeng Zhou. Mapping Global Bamboo Forest Distribution Using Multisource Remote Sensing Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2018; 11 (5):1458-1471.
Chicago/Turabian StyleHuaqiang Du; Fangjie Mao; Xuejian Li; Guomo Zhou; Xiaojun Xu; Ning Han; Shaobo Sun; Guolong Gao; Lu Cui; Yangguang Li; Dien Zhu; Yuli Liu; Liang Chen; Weiliang Fan; Pingheng Li; Yongjun Shi; Yufeng Zhou. 2018. "Mapping Global Bamboo Forest Distribution Using Multisource Remote Sensing Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, no. 5: 1458-1471.
Accurate information on the temporal and spatial distributions of solar radiation is very important in many scientific fields. In this study, instantaneous solar irradiances on a horizontal surface at 10:30 and 13:30 local time (LT) were calculated from Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric data products with relatively high spatial resolution using a solar radiation model. These solar irradiances were combined to derive half-hourly averages of solar irradiance (HASI) and daily global solar radiation (GSR) on a horizontal surface using linear interpolation, piecewise linear regression, and quadratic polynomial regression. Compared with field observations, the HASI were estimated accurately when the total cloud fraction (TCF) was <0.6. The accuracy of the estimates of the HASI was determined mainly by the quality of TCF. For TCF values 0.6. Overall, the daily GSR estimated in this study was better than that estimated by the Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis of NASA. The daily GSR estimated in this study was underestimated, whereas it was overestimated by MERRA. The combination of the daily GSR estimates of this study and MERRA offers a simple and feasible technique for reducing uncertainty in daily GSR estimates.
Xiaojun Xu; Huaqiang Du; Guomo Zhou; Fangjie Mao; Pingheng Li; Weiliang Fan; Dien Zhu. A method for daily global solar radiation estimation from two instantaneous values using MODIS atmospheric products. Energy 2016, 111, 117 -125.
AMA StyleXiaojun Xu, Huaqiang Du, Guomo Zhou, Fangjie Mao, Pingheng Li, Weiliang Fan, Dien Zhu. A method for daily global solar radiation estimation from two instantaneous values using MODIS atmospheric products. Energy. 2016; 111 ():117-125.
Chicago/Turabian StyleXiaojun Xu; Huaqiang Du; Guomo Zhou; Fangjie Mao; Pingheng Li; Weiliang Fan; Dien Zhu. 2016. "A method for daily global solar radiation estimation from two instantaneous values using MODIS atmospheric products." Energy 111, no. : 117-125.
Current bidirectional reflectance distribution function (BRDF) inversions using ordinary least squares (OLS) criterion can be easily contaminated by observations with residual cloud and undetected high aerosols, which leads to abrupt fluctuations in the normalized difference vegetation index (NDVI) time series. The OLS criterion assumes the noise has Gaussian distribution, which is often violated due to positive noise biases caused by clouds and high aerosols. A changing-weight iterative BRDF/NDVI inversion algorithm (CWI) based on a posteriori variance estimation of observation errors is presented to explicitly consider the asymmetrically distributed noise and observations with unequal accuracy in the BRDF retrieval. CWI employs a posteriori variance estimation and an NDVI-based indicator to iteratively adjust the weight of each observation according to its noise level. The validation results suggest CWI performs better than the Li-Gao and OLS approaches. The rmse was reduced from 0.074 to 0.028, and the relative error decreased from 13.4% to 3.8% at the U.S. Department of Agriculture Beltsville Agricultural Research Center site. Similarly, at the Harvard Forest site, the rmse was reduced from 0.086 to 0.031, and the relative error decreased from 9.5% to 2.7%. The average noise and relative noise of the CWI NDVI time series over ten EOS Land Validation Core Sites from 2003-2009 was smaller (0.028, 3.7%) than those of MOD13A2 (0.041, 5.2%), MYD13A2 (0.039, 4.9%) and MCD43B4 (0.030, 4.4%). The results demonstrate the robustness of the CWI approach in suppressing the influence of contaminated observations in BRDF retrievals by producing results that are less affected by undetected clouds and high aerosols.
Yelu Zeng; Jing Li; Qinhuo Liu; Alfredo R. Huete; Baodong Xu; Gaofei Yin; Jing Zhao; Le Yang; Weiliang Fan; Shengbiao Wu; Kai Yan. An Iterative BRDF/NDVI Inversion Algorithm Based on A Posteriori Variance Estimation of Observation Errors. IEEE Transactions on Geoscience and Remote Sensing 2016, 54, 6481 -6496.
AMA StyleYelu Zeng, Jing Li, Qinhuo Liu, Alfredo R. Huete, Baodong Xu, Gaofei Yin, Jing Zhao, Le Yang, Weiliang Fan, Shengbiao Wu, Kai Yan. An Iterative BRDF/NDVI Inversion Algorithm Based on A Posteriori Variance Estimation of Observation Errors. IEEE Transactions on Geoscience and Remote Sensing. 2016; 54 (11):6481-6496.
Chicago/Turabian StyleYelu Zeng; Jing Li; Qinhuo Liu; Alfredo R. Huete; Baodong Xu; Gaofei Yin; Jing Zhao; Le Yang; Weiliang Fan; Shengbiao Wu; Kai Yan. 2016. "An Iterative BRDF/NDVI Inversion Algorithm Based on A Posteriori Variance Estimation of Observation Errors." IEEE Transactions on Geoscience and Remote Sensing 54, no. 11: 6481-6496.
Jun Geng; Jing-Ming Chen; Li-Li Tu; Qing-Jiu Tian; Lei Wang; Ran-Ran Yang; Yan-Jun Yang; Yan Huang; Wei-Liang Fan; Chun-Guang Lv; Guang Zheng. Influence of the exclusion distance among trees on gap fraction and foliage clumping index of forest plantations. Trees 2016, 30, 1683 -1693.
AMA StyleJun Geng, Jing-Ming Chen, Li-Li Tu, Qing-Jiu Tian, Lei Wang, Ran-Ran Yang, Yan-Jun Yang, Yan Huang, Wei-Liang Fan, Chun-Guang Lv, Guang Zheng. Influence of the exclusion distance among trees on gap fraction and foliage clumping index of forest plantations. Trees. 2016; 30 (5):1683-1693.
Chicago/Turabian StyleJun Geng; Jing-Ming Chen; Li-Li Tu; Qing-Jiu Tian; Lei Wang; Ran-Ran Yang; Yan-Jun Yang; Yan Huang; Wei-Liang Fan; Chun-Guang Lv; Guang Zheng. 2016. "Influence of the exclusion distance among trees on gap fraction and foliage clumping index of forest plantations." Trees 30, no. 5: 1683-1693.
Landscape heterogeneity is a common natural phenomenon but is seldom considered in current radiative transfer (RT) models for predicting the surface reflectance. This paper developed an analytical RT model for heterogeneous Agro-Forestry scenarios (RTAF) by dividing the scenario into nonboundary regions (NRs) and boundary regions (BRs). The scattering contribution of the NRs can be estimated from the scattering-by-arbitrarily-inclined-leaves-with-the-hot-spot-effect model as homogeneous canopies, whereas that of the BRs is calculated based on the bidirectional gap probability by considering the interactions and mutual shadowing effects among different patches. The multiangular airborne observations and discrete-anisotropic-RT model simulations were used to validate and evaluate the RTAF model over an agro-forestry scenario in the Heihe River Basin, China. The results suggest that the RTAF model can accurately simulate the hemispherical-directional reflectance factors (HDRFs) of the heterogeneous scenarios in the red and near-infrared (NIR) bands. The boundary effect can significantly influence the angular distribution of the HDRFs and consequently enlarge the HDRF variations between the backward and forward directions. Compared with the widely used dominant cover type (DCT) and spectral linear mixture (SLM) models, the RTAF model reduced the maximum relative error from 25.7% (SLM) and 23.0% (DCT) to 9.8% in the red band and from 19.6% (DCT) and 13.7% (SLM) to 8.7% in the NIR band. The RTAF model provides a promising way to improve the retrieval of biophysical parameters (e.g., leaf area index) from remote sensing data over heterogeneous agro-forestry scenarios.
Yelu Zeng; Jing Li; Qinhuo Liu; Alfredo Huete; Gaofei Yin; Baodong Xu; Weiliang Fan; Jing Zhao; Kai Yan; Xihan Mu. A Radiative Transfer Model for Heterogeneous Agro-Forestry Scenarios. IEEE Transactions on Geoscience and Remote Sensing 2016, 54, 4613 -4628.
AMA StyleYelu Zeng, Jing Li, Qinhuo Liu, Alfredo Huete, Gaofei Yin, Baodong Xu, Weiliang Fan, Jing Zhao, Kai Yan, Xihan Mu. A Radiative Transfer Model for Heterogeneous Agro-Forestry Scenarios. IEEE Transactions on Geoscience and Remote Sensing. 2016; 54 (8):4613-4628.
Chicago/Turabian StyleYelu Zeng; Jing Li; Qinhuo Liu; Alfredo Huete; Gaofei Yin; Baodong Xu; Weiliang Fan; Jing Zhao; Kai Yan; Xihan Mu. 2016. "A Radiative Transfer Model for Heterogeneous Agro-Forestry Scenarios." IEEE Transactions on Geoscience and Remote Sensing 54, no. 8: 4613-4628.
Light use efficiency (LUE) models are widely used to estimate gross primary productivity (GPP), a dominant component of the terrestrial carbon cycle. Their outputs are very sensitive to LUE. Proper determination of this parameter is a prerequisite for LUE models to simulate GPP at regional and global scales. This study was devoted to investigating the ability of the photochemical reflectance index (PRI) to track LUE variations for a sub-tropical planted coniferous forest in southern China using tower-based PRI and GPP measurements over the period from day 101 to 275 in 2013. Both half-hourly PRI and LUE exhibited detectable diurnal and seasonal variations, and decreased with increases of vapor pressure deficit (VPD), air temperature (Ta), and photosynthetically active radiation (PAR). Generally, PRI is able to capture diurnal and seasonal changes in LUE. However, correlations of PRI with LUE varied dramatically throughout the growing season. The correlation was the strongest (R2 = 0.6427, p < 0.001) in July and the poorest in May. Over the entire growing season, PRI relates better to LUE under clear or partially cloudy skies (clearness index, CI > 0.3) with moderate to high VPD (>20 hPa) and high temperatures (>31 C). Overall, we found that PRI is most sensitive to variations in LUE under stressed conditions, and the sensitivity decreases as the growing conditions become favorable when atmosphere water vapor, temperature and soil moisture are near the optimum conditions.
Qian Zhang; Weimin Ju; Jing M. Chen; Huimin Wang; Fengting Yang; Weiliang Fan; Qing Huang; Ting Zheng; Yongkang Feng; Yanlian Zhou; Mingzhu He; Feng Qiu; Xiaojie Wang; Jun Wang; Fangmin Zhang; Shuren Chou. Ability of the Photochemical Reflectance Index to Track Light Use Efficiency for a Sub-Tropical Planted Coniferous Forest. Remote Sensing 2015, 7, 16938 -16962.
AMA StyleQian Zhang, Weimin Ju, Jing M. Chen, Huimin Wang, Fengting Yang, Weiliang Fan, Qing Huang, Ting Zheng, Yongkang Feng, Yanlian Zhou, Mingzhu He, Feng Qiu, Xiaojie Wang, Jun Wang, Fangmin Zhang, Shuren Chou. Ability of the Photochemical Reflectance Index to Track Light Use Efficiency for a Sub-Tropical Planted Coniferous Forest. Remote Sensing. 2015; 7 (12):16938-16962.
Chicago/Turabian StyleQian Zhang; Weimin Ju; Jing M. Chen; Huimin Wang; Fengting Yang; Weiliang Fan; Qing Huang; Ting Zheng; Yongkang Feng; Yanlian Zhou; Mingzhu He; Feng Qiu; Xiaojie Wang; Jun Wang; Fangmin Zhang; Shuren Chou. 2015. "Ability of the Photochemical Reflectance Index to Track Light Use Efficiency for a Sub-Tropical Planted Coniferous Forest." Remote Sensing 7, no. 12: 16938-16962.
The development of near-surface remote sensing requires the accurate extraction of leaf area index (LAI) from networked digital cameras under all illumination conditions. The widely used directional gap fraction model is more suitable for overcast conditions due to the difficulty to discriminate the shaded foliage from the shadowed parts of images acquired on sunny days. In this study, a new LAI extraction method by the sunlit foliage component from downward-looking digital photography under clear-sky conditions is proposed. In this method, the sunlit foliage component was extracted by an automated image classification algorithm named LAB2, the clumping index was estimated by a path length distribution-based method, the LAD and G function were quantified by leveled digital images and, eventually, the LAI was obtained by introducing a geometric-optical (GO) model which can quantify the sunlit foliage proportion. The proposed method was evaluated at the YJP site, Canada, by the 3D realistic structural scene constructed based on the field measurements. Results suggest that the LAB2 algorithm makes it possible for the automated image processing and the accurate sunlit foliage extraction with the minimum overall accuracy of 91.4%. The widely-used finite-length method tends to underestimate the clumping index, while the path length distribution-based method can reduce the relative error (RE) from 7.8% to 6.6%. Using the directional gap fraction model under sunny conditions can lead to an underestimation of LAI by (1.61; 55.9%), which was significantly outside the accuracy requirement (0.5; 20%) by the Global Climate Observation System (GCOS). The proposed LAI extraction method has an RMSE of 0.35 and an RE of 11.4% under sunny conditions, which can meet the accuracy requirement of the GCOS. This method relaxes the required diffuse illumination conditions for the digital photography, and can be applied to extract LAI from downward-looking webcam images, which is expected for the regional to continental scale monitoring of vegetation dynamics and validation of satellite remote sensing products.
Yelu Zeng; Jing Li; Qinhuo Liu; Ronghai Hu; Xihan Mu; Weiliang Fan; Baodong Xu; Gaofei Yin; Shengbiao Wu. Extracting Leaf Area Index by Sunlit Foliage Component from Downward-Looking Digital Photography under Clear-Sky Conditions. Remote Sensing 2015, 7, 13410 -13435.
AMA StyleYelu Zeng, Jing Li, Qinhuo Liu, Ronghai Hu, Xihan Mu, Weiliang Fan, Baodong Xu, Gaofei Yin, Shengbiao Wu. Extracting Leaf Area Index by Sunlit Foliage Component from Downward-Looking Digital Photography under Clear-Sky Conditions. Remote Sensing. 2015; 7 (10):13410-13435.
Chicago/Turabian StyleYelu Zeng; Jing Li; Qinhuo Liu; Ronghai Hu; Xihan Mu; Weiliang Fan; Baodong Xu; Gaofei Yin; Shengbiao Wu. 2015. "Extracting Leaf Area Index by Sunlit Foliage Component from Downward-Looking Digital Photography under Clear-Sky Conditions." Remote Sensing 7, no. 10: 13410-13435.
Physically-based approaches for estimating Leaf Area Index (LAI) using remote sensing data rely on radiative transfer (RT) models. Currently, many RT models are freely available, but determining the appropriate RT model for LAI retrieval is still problematic. This study aims to evaluate the necessity of RT model selection for LAI retrieval and to propose a retrieval methodology using different RT models for different vegetation types. Both actual experimental observations and RT model simulations were used to conduct the evaluation. Each of them includes needleleaf forests and croplands, which have contrasting structural attributes. The scattering from arbitrarily inclined leaves (SAIL) model and the four-scale model, which are 1D and 3D RT models, respectively, were used to simulate the synthetic test datasets. The experimental test dataset was established through two field campaigns conducted in the Heihe River Basin. The results show that the realistic representation of canopy structure in RT models is very important for LAI retrieval. If an unsuitable RT model is used, then the root mean squared error (RMSE) will increase from 0.43 to 0.60 in croplands and from 0.52 to 0.63 in forests. In addition, an RT model’s potential to retrieve LAI is limited by the availability of a priori information on RT model parameters. 3D RT models require more a priori information, which makes them have poorer generalization capability than 1D models. Therefore, physically-based retrieval algorithms should embed more than one RT model to account for the availability of a priori information and variations in structural attributes among different vegetation types.
Gaofei Yin; Jing Li; Qinhuo Liu; Weiliang Fan; Baodong Xu; Yelu Zeng; Jing Zhao. Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection. Remote Sensing 2015, 7, 4604 -4625.
AMA StyleGaofei Yin, Jing Li, Qinhuo Liu, Weiliang Fan, Baodong Xu, Yelu Zeng, Jing Zhao. Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection. Remote Sensing. 2015; 7 (4):4604-4625.
Chicago/Turabian StyleGaofei Yin; Jing Li; Qinhuo Liu; Weiliang Fan; Baodong Xu; Yelu Zeng; Jing Zhao. 2015. "Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection." Remote Sensing 7, no. 4: 4604-4625.
Accurately simulating the area ratios of the sunlit and shaded foliage in multiple-view angles presents a challenge in developing a geometric-optical (GO) model. GOST model by Fan et al.[1] proposed a high computationally demanding ray tracing method on this issue. In order to relax the computational restriction, a new hybrid canopy reflectance model GOST2 based on GOST is developed with a “ray tracing + GO” method, which is used for simulating the area ratios of the sunlit and shaded foliage. GOST2 shows the explicitly physical mechanism and has the capability in modeling the area ratios of the sunlit and shaded foliage on slopes. The area ratios of the four scene components of the five GO models, such as GOST2, GOST, the Li-Strahler model, the four-scale model, and Unified, are quantitatively evaluated. The canopy reflectances by the five GO models and the three-dimensional virtual canopy model are validated by the observed reflectance. It indicates that GOST2 is both reliable and computationally undemanding canopy reflectance model.
Weiliang Fan; Jing Li; Qinhuo Liu. GOST2: The Improvement of the Canopy Reflectance Model GOST in Separating the Sunlit and Shaded Leaves. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2015, 8, 1423 -1431.
AMA StyleWeiliang Fan, Jing Li, Qinhuo Liu. GOST2: The Improvement of the Canopy Reflectance Model GOST in Separating the Sunlit and Shaded Leaves. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2015; 8 (4):1423-1431.
Chicago/Turabian StyleWeiliang Fan; Jing Li; Qinhuo Liu. 2015. "GOST2: The Improvement of the Canopy Reflectance Model GOST in Separating the Sunlit and Shaded Leaves." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, no. 4: 1423-1431.
A new geometric optical (GO)-radiative transfer (RT) model with a multiple scattering scheme suitable for sloping forest canopies is developed in this study. It is based on a Geometrical-Optical model for Sloping Terrains and an RT method. This new model overcomes the difficulty to prescribe bidirectional reflectance factors (BRFs) of shaded components (shaded foliage and background) in GO modeling through simulating radiation multiple scattering within a sloping forest. A case study shows that multiply scattered radiation depends on topographic factors and leaf area index. The contributions of the shaded components to stand-level BRF are less than 3% in the red band and can reach up to 40% in the near-infrared (NIR) band. The “multiangle” Moderate Resolution Imaging Spectroradiometer (MODIS) data over sloping pixels are selected to validate the modeled forest BRF. Considering the multiple scattering schemes and topographic factors, the modeled BRF is closer to the MODIS surface reflectance (BRF product) (red band: R 2 = 0.8614, rRMSE = 0.1339; NIR band: R 2 = 0.7573, rRMSE = 0.0850) than the modeled BRF (red band: R 2 = 0.7771, rRMSE=0.1839; NIR band: R 2 =0.5176, rRMSE = 0.1155) without topographic consideration. It is also shown that the MODIS surface reflectance of sloping forests at multiple angles can be simulated well using the newly developed model.
Weiliang Fan; Jing M. Chen; Weimin Ju; Nadine Nesbitt. Hybrid Geometric Optical–Radiative Transfer Model Suitable for Forests on Slopes. IEEE Transactions on Geoscience and Remote Sensing 2014, 52, 5579 -5586.
AMA StyleWeiliang Fan, Jing M. Chen, Weimin Ju, Nadine Nesbitt. Hybrid Geometric Optical–Radiative Transfer Model Suitable for Forests on Slopes. IEEE Transactions on Geoscience and Remote Sensing. 2014; 52 (9):5579-5586.
Chicago/Turabian StyleWeiliang Fan; Jing M. Chen; Weimin Ju; Nadine Nesbitt. 2014. "Hybrid Geometric Optical–Radiative Transfer Model Suitable for Forests on Slopes." IEEE Transactions on Geoscience and Remote Sensing 52, no. 9: 5579-5586.
GOST is a geometric-optical (GO) model for sloping terrains developed in this study based on the four-scale GO model, which simulates the bidirectional reflectance distribution function (BRDF) of forest canopies on flat surfaces. The four-scale GO model considers four scales of canopy architecture: tree groups, tree crowns, branches, and shoots. In order to make this model suitable for sloping terrains, the mathematical description for the projection of tree crowns on the ground has been modified to consider the fact that trees grow vertically rather than perpendicularly to sloping grounds. The simulated canopy gap fraction and the area ratios of the four scene components (sunlit foliage, sunlit background, shaded foliage, and shaded background) by GOST compare well with those simulated by 3-D virtual canopy computer modeling techniques for a hypothetical forest. GOST simulations show that the differences in area ratios of the four scene components between flat and sloping terrains can reach up to 50%-60% in the principal plane and about 30% in the perpendicular plane. Two case studies are conducted to compare modeled canopy reflectance with observations. One comparison is made against Landsat-5 Thematic Mapper (TM) reflectance, demonstrating the ability of GOST to model canopy reflectance variations with slope and aspect of the terrain. Another comparison is made against MODIS surface reflectance, showing that GOST with topographic consideration outperforms that without topographic consideration. These comparisons confirm the ability of GOST to model canopy reflectance on sloping terrains over a large range of view angles.
Weiliang Fan; Jing M. Chen; Weimin Ju; Gaolong Zhu. GOST: A Geometric-Optical Model for Sloping Terrains. IEEE Transactions on Geoscience and Remote Sensing 2013, 52, 5469 -5482.
AMA StyleWeiliang Fan, Jing M. Chen, Weimin Ju, Gaolong Zhu. GOST: A Geometric-Optical Model for Sloping Terrains. IEEE Transactions on Geoscience and Remote Sensing. 2013; 52 (9):5469-5482.
Chicago/Turabian StyleWeiliang Fan; Jing M. Chen; Weimin Ju; Gaolong Zhu. 2013. "GOST: A Geometric-Optical Model for Sloping Terrains." IEEE Transactions on Geoscience and Remote Sensing 52, no. 9: 5469-5482.
Vegetation fractional coverage (VFC) is an important vegetation parameter affecting exchanges of carbon, water, energy between the atmosphere and surface. In this study, the applicability of tonal and texture measures calculated using an IKONOS_2 image in retrieving VFC of forests was investigated in the urban area of Nanjing city, China. Four spectral vegetation indices (VI) and six texture measures (TEX) were related to VFCs acquired from in situ measurements. Models for estimating VFC based on VIs or/and TEXs were established and validated for planted low broad-leaf forest plots (PLB), planted mature forest plots (PMF), natural broad-leaf forest plots (NBF), and all forest plots (ALLv), respectively. The results show that high spatial resolution remote sensing data is applicable to estimate VFC in urban areas, and TEXs may act as effective supplements of vegetation indices (VIs) for the retrieval of VFC. VIs are suitable for VFC estimation of mature forests (such as NBF and PMF) with high vegetation density, and TEXs can yield a more accurate estimate for planted forests (such as PLB and PMF) with regular spatial distribution if they are calculated with proper parameters, such as window size. The combination of VIs and TEXs improve the estimation of VFC if forest types are not previously differentiated. The results can be used as a reference for determining effective spectral or texture parameters in VFC estimation under similar environmental conditions according to vegetation maturity and regularity.
Zhujun Gu; Weimin Ju; Lin Li; Dengqiu Li; Yibo Liu; Weiliang Fan. Using vegetation indices and texture measures to estimate vegetation fractional coverage (VFC) of planted and natural forests in Nanjing city, China. Advances in Space Research 2013, 51, 1186 -1194.
AMA StyleZhujun Gu, Weimin Ju, Lin Li, Dengqiu Li, Yibo Liu, Weiliang Fan. Using vegetation indices and texture measures to estimate vegetation fractional coverage (VFC) of planted and natural forests in Nanjing city, China. Advances in Space Research. 2013; 51 (7):1186-1194.
Chicago/Turabian StyleZhujun Gu; Weimin Ju; Lin Li; Dengqiu Li; Yibo Liu; Weiliang Fan. 2013. "Using vegetation indices and texture measures to estimate vegetation fractional coverage (VFC) of planted and natural forests in Nanjing city, China." Advances in Space Research 51, no. 7: 1186-1194.
Reconstruction of the pixel missing regions on remote sensing images is an important research issue in remote sensing image processing. These regions due to clouds and stripes are generally processed using the traditional synthesis and image inpainting techniques. Such processing can also be implemented with multispectral/multitemporal based methods. However, these methods have some limitations in applications, such as the sharpened borders between the filled regions and their neighbors and the requirement for multiple images. A method is developed for reconstructing pixel missing regions using a corresponding recent term image (reference image) acquired from different sensors with different spectral bands. A case study shows that the new method obviously outperforms the replacement method.
Weiliang Fan; Weimin Ju; Zhujun Gu. Method for reconstructing the pixel missing region on remote sensing images. Journal of Applied Remote Sensing 2013, 7, 073536 -073536.
AMA StyleWeiliang Fan, Weimin Ju, Zhujun Gu. Method for reconstructing the pixel missing region on remote sensing images. Journal of Applied Remote Sensing. 2013; 7 (1):073536-073536.
Chicago/Turabian StyleWeiliang Fan; Weimin Ju; Zhujun Gu. 2013. "Method for reconstructing the pixel missing region on remote sensing images." Journal of Applied Remote Sensing 7, no. 1: 073536-073536.