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学者姓名：邹杰
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Abstract ：
The clumping effect is the main issue causing the heterogeneity in vegetation canopies and the underestimation of leaf area index (LAI) obtained using indirect measurement methods. Significant efforts have been exerted to correct for the clumping effect and derive the true LAI. Recent research has shown that the fractal dimension (FD) is directly related to the clumping effect of foliage, yet practical methods are needed to calculate field estimates. Considering that widely used LAI applications such as digital hemispherical photography (DHP), tracing radiation and architecture of canopies (TRAC), and digital cover photography (DCP) estimate LAI with one-dimensional (1D) gap probability and gap size data, we propose a method to correct for the clumping effect using 1D FD. Resulting formulae describing the relationship between LAI, CI, and 1D FD were based on the box-counting method (BCM) and a binomial distribution model. Sixty-four simulated scenes including four RAdiation transfer Model Intercomparison (RAMI) actual canopies and field measurements from nine plots (four orchard plots and five coniferous forest plots) were used to validate the novel method. Results showed good agreement with reference LAI values for simulated scenes (R2 = 0.96 and RMSE = 0.35). The 1DFD method generated higher LAI estimates compared with the LAI measured using TRAC at the four orchard plots especially at high canopy closure, while its results were more consistent with LAI obtained by litter collection than those of comparable methods at coniferous forest plots (bias from-13.5% to 9.9% for DCP images, from-3.0% to 19.7% for DHP images, and from-3.8% to 17.0% for TRAC transects). Our validation efforts indicate that the method proposed herein corrects for the clumping effect of vegetated canopies more effectively with DCP images, DHP images, and TRAC measurement when compared with traditional indirect optical methods. The 1DFD method is expected to improve indirect measurement accuracy of LAI.
Keyword ：
Clumping effect Clumping effect Clumping index (CI) Clumping index (CI) Fractal dimension (FD) Fractal dimension (FD) Leaf area index (LAI) Leaf area index (LAI)
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| GB/T 7714 | Lai, Yongkang , Mu, Xihan , Li, Weihua et al. Correcting for the clumping effect in leaf area index calculations using one-dimensional fractal dimension [J]. | REMOTE SENSING OF ENVIRONMENT , 2022 , 281 . |
| MLA | Lai, Yongkang et al. "Correcting for the clumping effect in leaf area index calculations using one-dimensional fractal dimension" . | REMOTE SENSING OF ENVIRONMENT 281 (2022) . |
| APA | Lai, Yongkang , Mu, Xihan , Li, Weihua , Zou, Jie , Bian, Yuequn , Zhou, Kun et al. Correcting for the clumping effect in leaf area index calculations using one-dimensional fractal dimension . | REMOTE SENSING OF ENVIRONMENT , 2022 , 281 . |
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The leaf inclination angle distribution function is a key determinant that influences radiation penetration through forest canopies. In this study, the needle and shoot inclination angle distributions of five contrasting Larix principis-rupprechtii plots were obtained via the frequently used leveled digital camera photography method. We also developed a quasi-automatic method to derive the needle inclination angles based on photographs obtained using the leveled digital camera photography method and further verified using manual measurements. Then, the variations of shoot and needle inclination angle distributions due to height levels, plots, and observation years were investigated. The results showed that the developed quasi-automatic method is effective in deriving needle inclination angles. The shoot and needle inclination angle distributions at the whole-canopy scale tended to be planophile and exhibited minor variations with plots and observation years. The small variations in the needle inclination angle distributions with height level in the five plots might be caused by contrasting light conditions at different height levels. The whole-canopy and height level needle projection functions also tended to be planophile, and minor needle projection function variations with plots and observation years were observed. We attempted to derive the shoot projection functions of the five plots by using a simple and applicable method and further evaluated the performance of the new method.
Keyword ：
coniferous forest coniferous forest G function G function Larix Larix leveled digital photography leveled digital photography needle inclination angle distribution needle inclination angle distribution shoot inclination angle distribution shoot inclination angle distribution
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| GB/T 7714 | Zou, Jie , Zhong, Peihong , Hou, Wei et al. Estimating Needle and Shoot Inclination Angle Distributions and Projection Functions in Five Larix principis-rupprechtii Plots via Leveled Digital Camera Photography [J]. | FORESTS , 2021 , 12 (1) . |
| MLA | Zou, Jie et al. "Estimating Needle and Shoot Inclination Angle Distributions and Projection Functions in Five Larix principis-rupprechtii Plots via Leveled Digital Camera Photography" . | FORESTS 12 . 1 (2021) . |
| APA | Zou, Jie , Zhong, Peihong , Hou, Wei , Zuo, Yong , Leng, Peng . Estimating Needle and Shoot Inclination Angle Distributions and Projection Functions in Five Larix principis-rupprechtii Plots via Leveled Digital Camera Photography . | FORESTS , 2021 , 12 (1) . |
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Measurements of leaf area index (LAI) are important for modeling microclimate in vegetation research. Among the instruments for measuring the LAI, smartphone cameras are becoming an attractive alternative to special LAI instruments. However, the narrow full field of view (FOV) of the common smartphones offer only an effective viewing zenith angle (VZA) of less than 35 degrees when the camera is pointing straight up. To overcome this limitation, we propose a method to estimate LAI from an inclined smartphone camera that can enlarge the range of the sensor's effective VZA. With the directional gap fractions extracted from the images taken by the inclined smartphone camera, a curve matching algorithm is used to iteratively search for the simulated G functions, i.e. functions of mean tilt angle (MTA) and VZA. The MTAs corresponding to the matched G functions are selected as ancillary parameters to help calculate the LAI. The proposed method is validated using data collected over crops and trees by a LAI-2200 instrument and a Huawei Honor 7 smartphone. The results reveal that an inclination angle of 30 degrees from zenith is superior to other angles of 0, 45 and 60 degrees. A good agreement between the LAI measurements from the proposed method and those from the LAI-2200 supports the accurate estimation of MTAs. The success of the MTA estimates and thus LAI measurements is attributed to the enlarged VZA ranging from 4 degrees to 60 degrees, and this VZA is comparable with that of the LAI-2200 instrument. The attraction of the proposed method is that it does not rely on the empiric al G value or MTA, providing an affordable alternative to traditional commercial instruments. Future efforts can be directed to automatically capture images when the smartphone is inclined to the desired angle.
Keyword ：
Directional gap fraction Directional gap fraction Inclined photography Inclined photography Leaf area index Leaf area index Mean tilt angle Mean tilt angle Smartphone camera Smartphone camera
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| GB/T 7714 | Qu, Yonghua , Wang, Zixin , Shang, Jiali et al. Estimation of leaf area index using inclined smartphone camera [J]. | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2021 , 191 . |
| MLA | Qu, Yonghua et al. "Estimation of leaf area index using inclined smartphone camera" . | COMPUTERS AND ELECTRONICS IN AGRICULTURE 191 (2021) . |
| APA | Qu, Yonghua , Wang, Zixin , Shang, Jiali , Liu, Jiangui , Zou, Jie . Estimation of leaf area index using inclined smartphone camera . | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2021 , 191 . |
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马缨丹(Lantana camara)是全球最具破坏力的100种入侵物种之一,目前已对我国南方部分地区生物多样性造成严重危害。为分析马缨丹在我国的潜在适生区,以全国479个马缨丹分布记录点、生物气候变量和地形因子为基础,采用最大熵(maximum entropy,Maxent)模型和地理信息系统(GIS)相结合进行预测。结果表明:(1)马缨丹适生区以秦岭-淮河线为界,主要分布在我国南方地区,广东、广西、香港、福建、海南和云南西南部属于马缨丹高度适生区,其面积占全国陆地面积的8.6%;浙江、江西、湖南、贵州、云南、四川和重庆等部分地区属于中度适生区,其面积占比为10.1%;低适生区和非适生区面积...
Keyword ：
刀切法 刀切法 地理信息系统 地理信息系统 最大熵模型 最大熵模型 潜在分布 潜在分布
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| GB/T 7714 | 张华纬 , 赵健 , 阎波杰 et al. 基于Maxent模型和GIS的马缨丹在中国的适生区预测 [J]. | 生态与农村环境学报 , 2020 , 36 (11) : 1420-1427 . |
| MLA | 张华纬 et al. "基于Maxent模型和GIS的马缨丹在中国的适生区预测" . | 生态与农村环境学报 36 . 11 (2020) : 1420-1427 . |
| APA | 张华纬 , 赵健 , 阎波杰 , 邹杰 , 李志鹏 . 基于Maxent模型和GIS的马缨丹在中国的适生区预测 . | 生态与农村环境学报 , 2020 , 36 (11) , 1420-1427 . |
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Optical methods are frequently used as a routine method to obtain the elementary sampling unit (ESU) leaf area index (LAI) of forests. However, few studies have attempted to evaluate whether the ESU LAI obtained from optical methods matches the accuracy required by the LAI map product validation community. In this study, four commonly used optical methods, including digital hemispherical photography (DHP), digital cover photography (DCP), tracing radiation of canopy and architecture (TRAC) and multispectral canopy imager (MCI), were adopted to estimate the ESU (25 m x 25 m) LAI of five Larix principis-rupprechtii forests with contrasting structural characteristics. The impacts of three factors, namely, inversion model, canopy element or woody components clumping index (Omega(e) or Omega(w)) algorithm, and the woody components correction method, on the ESU LAI estimation of the four optical methods were analyzed. Then, the LAI derived from the four optical methods was evaluated using the LAI obtained from litter collection measurements. Results show that the performance of the four optical methods in estimating the ESU LAI of the five forests was largely affected by the three factors. The accuracy of the LAI obtained from the DHP and MCI strongly relied on the inversion model, the Omega(e) or Omega(w) algorithm, and the woody components correction method adopted in the estimation. Then the best Omega(e) or Omega(w) algorithm, inversion model and woody components correction method to be used to obtain the ESU LAI of L. principis-rupprechtii forests with the smallest root mean square error (RMSE) and mean absolute error (MAE) were identified. Amongst the three typical woody components correction methods evaluated in this study, the woody-to-total area ratio obtained from the destructive measurements is the most effective method for DHP to derive the ESU LAI with the smallest RMSE and MAE. In contrast, using the woody area index obtained from the leaf-off DHP or DCP images as the woody components correction method would result in a large LAI underestimation. TRAC and MCI outperformed DHP and DCP in the ESU LAI estimation of the five forests, with the smallest RMSE and MAE. All the optical methods, except DCP, are qualified to obtain the ESU LAI of L. principis-rupprechtii forests with an MAE of <20% that is required by the global climate observation system. None of the optical methods, except TRAC, show the potential to obtain the ESU LAI of L. principis-rupprechtii forests with an MAE of <5%.
Keyword ：
clumping effects clumping effects elementary sampling unit elementary sampling unit inversion model inversion model Larix-dominated forest plots Larix-dominated forest plots leaf area index leaf area index optical method optical method woody components correction method woody components correction method
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| GB/T 7714 | Zou, Jie , Zuo, Yong , Zhong, Peihong et al. Performance of Four Optical Methods in Estimating Leaf Area Index at Elementary Sampling Unit of Larix principis-rupprechtii Forests [J]. | FORESTS , 2020 , 11 (1) . |
| MLA | Zou, Jie et al. "Performance of Four Optical Methods in Estimating Leaf Area Index at Elementary Sampling Unit of Larix principis-rupprechtii Forests" . | FORESTS 11 . 1 (2020) . |
| APA | Zou, Jie , Zuo, Yong , Zhong, Peihong , Hou, Wei , Leng, Peng , Chen, Bin . Performance of Four Optical Methods in Estimating Leaf Area Index at Elementary Sampling Unit of Larix principis-rupprechtii Forests . | FORESTS , 2020 , 11 (1) . |
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Background: Digital hemispherical photography (DHP) is widely used to estimate the leaf area index (LAI) of forest plots due to its advantages of high efficiency and low cost. A crucial step in the LAI estimation of forest plots via DHP is choosing a sampling scheme. However, various sampling schemes involving DHP have been used for the LAI estimation of forest plots. To date, the impact of sampling schemes on LAI estimation from DHP has not been comprehensively investigated. Methods: In this study, 13 commonly used sampling schemes which belong to five sampling types (i.e. dispersed, square, cross, transect and circle) were adopted in the LAI estimation of fiveLarix principis-rupprechtiiplots (25 m x 25 m). An additional sampling scheme (with a sample size of 89) was generated on the basis of all the sample points of the 13 sampling schemes. Three typical inversion models and four canopy element clumping index (Omega(e)) algorithms were involved in the LAI estimation. The impacts of the sampling schemes on four variables, including gap fraction, Omega(e), effective plant area index (PAI(e)) and LAI estimation from DHP were analysed. The LAI estimates obtained with different sampling schemes were then compared with those obtained from litter collection measurements. Results: Large differences were observed for all four variable estimates (i.e. gap fraction, Omega(e), PAI(e)and LAI) under different sampling schemes. The differences in impact of sampling schemes on LAI estimation were not obvious for the three inversion models, if the four Omega(e) algorithms, except for the traditional gap-size analysis algorithm were adopted in the estimation. The accuracy of LAI estimation was not always improved with an increase in sample size. Moreover, results indicated that with the appropriate inversion model, Omega(e) algorithm and sampling scheme, the maximum estimation error of DHP-estimated LAI at elementary sampling unit can be less than 20%, which is required by the global climate observing system, except in forest plots with extremely large LAI values (similar to > 6.0). However, obtaining an LAI from DHP with an estimation error lower than 5% is impossible regardless of which combination of inversion model, Omega(e) algorithm and sampling scheme is used. Conclusion: The LAI estimation of L.principis-rupprechtii forests from DHP was largely affected by the sampling schemes adopted in the estimation. Thus, the sampling scheme should be seriously considered in the LAI estimation. One square and two transect sampling schemes (with sample sizes ranging from 3 to 9) were recommended to be used to estimate the LAI ofL.principis-rupprechtiiforests with the smallest mean relative error (MRE). By contrast, three cross and one dispersed sampling schemes were identified to provide LAI estimates with relatively large MREs.
Keyword ：
Clumping index Clumping index Digital hemispherical photography Digital hemispherical photography Elementary sampling unit Elementary sampling unit Forest Forest Larix Larix Leaf area index Leaf area index Sampling scheme Sampling scheme
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| GB/T 7714 | Zou, Jie , Hou, Wei , Chen, Ling et al. Evaluating the impact of sampling schemes on leaf area index measurements from digital hemispherical photography in Larix principis-rupprechtii forest plots [J]. | FOREST ECOSYSTEMS , 2020 , 7 (1) . |
| MLA | Zou, Jie et al. "Evaluating the impact of sampling schemes on leaf area index measurements from digital hemispherical photography in Larix principis-rupprechtii forest plots" . | FOREST ECOSYSTEMS 7 . 1 (2020) . |
| APA | Zou, Jie , Hou, Wei , Chen, Ling , Wang, Qianfeng , Zhong, Peihong , Zuo, Yong et al. Evaluating the impact of sampling schemes on leaf area index measurements from digital hemispherical photography in Larix principis-rupprechtii forest plots . | FOREST ECOSYSTEMS , 2020 , 7 (1) . |
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Remote sensing spatiotemporal fusion models blend multi-source images of different spatial resolutions to create synthetic images with high resolution and frequency, contributing to time series research where high quality observations are not available with sufficient frequency. However, existing models are vulnerable to spatial heterogeneity and land cover changes, which are frequent in human-dominated regions. To obtain quality time series of satellite images in a human-dominated region, this study developed the Modified Flexible Spatial-temporal Data Fusion (MFSDAF) approach based on the Flexible Spatial-temporal Data Fusion (FSDAF) model by using the enhanced linear regression (ELR). Multiple experiments of various land cover change scenarios were conducted based on both actual and simulated satellite images, respectively. The proposed MFSDAF model was validated by using the correlation coefficient (r), relative root mean square error (RRMSE), and structural similarity (SSIM), and was then compared with the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and FSDAF models. Results show that in the presence of significant land cover change, MFSDAF showed a maximum increase in r, RRMSE, and SSIM of 0.0313, 0.0109 and 0.049, respectively, compared to FSDAF, while ESTARFM performed best with less temporal difference of in the input images. In conditions of stable landscape changes, the three performance statistics indicated a small advantage of MFSDAF over FSDAF, but were 0.0286, 0.0102, 0.0317 higher than for ESTARFM, respectively. MFSDAF showed greater accuracy of capturing subtle changes and created high-precision images from both actual and simulated satellite images.
Keyword ：
enhanced linear regression enhanced linear regression heterogeneous heterogeneous land cover change land cover change MFSDAF MFSDAF time-series time-series
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| GB/T 7714 | Tang, Jia , Zeng, Jingyu , Zhang, Li et al. A modified flexible spatiotemporal data fusion model [J]. | FRONTIERS OF EARTH SCIENCE , 2020 , 14 (3) : 601-614 . |
| MLA | Tang, Jia et al. "A modified flexible spatiotemporal data fusion model" . | FRONTIERS OF EARTH SCIENCE 14 . 3 (2020) : 601-614 . |
| APA | Tang, Jia , Zeng, Jingyu , Zhang, Li , Zhang, Rongrong , Li, Jinghan , Li, Xingrong et al. A modified flexible spatiotemporal data fusion model . | FRONTIERS OF EARTH SCIENCE , 2020 , 14 (3) , 601-614 . |
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Estimates of clumping index (Omega) are required to improve the indirect estimation of leaf area index (L) from optical field-based instruments such as digital hemispherical photography (DHP). A widely used method allows estimation of Omega from DHP using simple gap fraction averaging formulas (LX). This method is simple and effective but has the disadvantage of being sensitive to the spatial scale (i.e., the azimuth segment size in DHP) used for averaging and canopy density. In this study, we propose a new method to estimate Omega ( LXG) based on ordered weighted gap fraction averaging (OWA) formulas, which addresses the disadvantages of LX and also accounts for gap size distribution. The new method was tested in 11 broadleaved forest stands in Italy; Omega estimated from LXG was compared with other commonly used clumping correction methods (LX, CC, and CLX). Results showed that LXG yielded more accurate Omega estimates, which were also more correlated with the values obtained from the gap size distribution methods (CC and CLX) than Omega obtained from LX. Leaf area index estimates, adjusted by LXG, are only 5%-6% lower than direct measurements obtained from litter traps, while other commonly used clumping correction methods yielded more underestimation.
Keyword ：
canopy nonrandomness canopy nonrandomness hemispherical photography hemispherical photography leaf area index leaf area index ordered weighted averaging (OWA) operator ordered weighted averaging (OWA) operator orness orness
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| GB/T 7714 | Chianucci, Francesco , Zou, Jie , Leng, Peng et al. A new method to estimate clumping index integrating gap fraction averaging with the analysis of gap size distribution [J]. | CANADIAN JOURNAL OF FOREST RESEARCH , 2019 , 49 (5) : 471-479 . |
| MLA | Chianucci, Francesco et al. "A new method to estimate clumping index integrating gap fraction averaging with the analysis of gap size distribution" . | CANADIAN JOURNAL OF FOREST RESEARCH 49 . 5 (2019) : 471-479 . |
| APA | Chianucci, Francesco , Zou, Jie , Leng, Peng , Zhuang, Yinguo , Ferrara, Carlotta . A new method to estimate clumping index integrating gap fraction averaging with the analysis of gap size distribution . | CANADIAN JOURNAL OF FOREST RESEARCH , 2019 , 49 (5) , 471-479 . |
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Forest biomass is a key biophysical parameter for climate change, ecological modeling and forest management. Compared with discrete-return LiDAR data, full-waveform LiDAR data can provide more accurate and abundant vertical structure information on vegetation and thus have been increasingly applied to the estimation of forest aboveground biomass (AGB). The main objective of this research is to estimate forest AGB using full waveform airborne LiDAR data. In this study, we constructed voxel-based waveforms (0.5 x 0.5 m) using small footprint full waveform LiDAR data, and then aggregated voxel based waveforms into pseudo large footprint waveforms with a plot size of 20 x 20 m. We extracted a range of waveform metrics from voxel-based waveforms and pseudo-large-footprint waveforms (FWm), respectively, and then calculated the mean of the voxel-based waveform metrics within a plot (FW mu). Based on the Random Forest (RF) regression, the forest biomasses were estimated using two types of waveform metrics: FWm (R-2 = 0.84, RMSE% = 21.4%, bias = -0.11 Mg ha(-1)) and FW mu (R-2 = 0.81, RMSE% = 23.3%, bias = 0.13 Mg ha(-1)). We found that slightly higher biomass estimation accuracy was obtained with FW(m )than with FW mu. In addition, a comparison between the biomasses predicted by the waveform metrics and by the traditional discrete-return metrics (R-2 = 0.80, RMSE% = 23.4%, bias = 0.20 Mg ha(-1)) was performed to explore the potential to improve biomass estimates using the waveform metrics, and the results showed that both waveform metrics and discrete-return metrics could accurately predict forest biomass. However, the biomass estimations from the waveform metrics were more accurate than those from the traditional discrete-return metrics. We concluded that the method proposed in this study has the potential to estimate vegetation structure parameters using full-waveform LiDAR data.
Keyword ：
Biomass Biomass Full-waveform Full-waveform LiDAR LiDAR Random Forest Random Forest Small-footprint Small-footprint Voxel Voxel
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| GB/T 7714 | Luo, Shezhou , Wang, Cheng , Xi, Xiaohuan et al. Estimating forest aboveground biomass using small-footprint full-waveform airborne LiDAR data [J]. | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2019 , 83 . |
| MLA | Luo, Shezhou et al. "Estimating forest aboveground biomass using small-footprint full-waveform airborne LiDAR data" . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 83 (2019) . |
| APA | Luo, Shezhou , Wang, Cheng , Xi, Xiaohuan , Nie, Sheng , Fan, Xieyu , Chen, Hanyue et al. Estimating forest aboveground biomass using small-footprint full-waveform airborne LiDAR data . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2019 , 83 . |
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Optical methods require model inversion to infer plant area index (PAI) and woody area index (WAI) of leaf-on and leaf-off forest canopy from gap fraction or radiation attenuation measurements. Several inversion models have been developed previously, however, a thorough comparison of those inversion models in obtaining the PAI and WAI of leaf-on and leaf-off forest canopy has not been conducted so far. In the present study, an explicit 3D forest scene series with different PAI, WAI, phenological periods, stand density, tree species composition, plant functional types, canopy element clumping index, and woody component clumping index was generated using 50 detailed 3D tree models. The explicit 3D forest scene series was then used to assess the performance of seven commonly used inversion models to estimate the PAI and WAI of the leaf-on and leaf-off forest canopy. The PAI and WAI estimated from the seven inversion models and simulated digital hemispherical photography images were compared with the true PAI and WAI of leaf-on and leaf-off forest scenes. Factors that contributed to the differences between the estimates of the seven inversion models were analyzed. Results show that both the factors of inversion model, canopy element and woody component projection functions, canopy element and woody component estimation algorithms, and segment size are contributed to the differences between the PAI and WAI estimated from the seven inversion models. There is no universally valid combination of inversion model, needle-to-shoot area ratio, canopy element and woody component clumping index estimation algorithm, and segment size that can accurately measure the PAI and WAI of all leaf-on and leaf-off forest canopies. The performance of the combinations of inversion model, needle-to-shoot area ratio, canopy element and woody component clumping index estimation algorithm, and segment size to estimate the PAI and WAI of leaf-on and leaf-off forest canopies is the function of the inversion model as well as the canopy element and woody component clumping index estimation algorithm, segment size, PAI, WAI, tree species composition, and plant functional types. The impact of canopy element and woody component projection function measurements on the PAI and WAI estimation of the leaf-on and leaf-off forest canopy can be reduced to a low level (<4%) by adopting appropriate inversion models.
Keyword ：
canopy element and woody component projection functions canopy element and woody component projection functions clumping effect clumping effect digital hemispherical photography digital hemispherical photography forest canopy forest canopy forest scenes forest scenes inversion model inversion model leaf area index (LAI) leaf area index (LAI) plant area index (PAI) plant area index (PAI) woody area index (WAI) woody area index (WAI)
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| GB/T 7714 | Zou, Jie , Zhuang, Yinguo , Chianucci, Francesco et al. Comparison of Seven Inversion Models for Estimating Plant and Woody Area Indices of Leaf-on and Leaf-off Forest Canopy Using Explicit 3D Forest Scenes [J]. | REMOTE SENSING , 2018 , 10 (8) . |
| MLA | Zou, Jie et al. "Comparison of Seven Inversion Models for Estimating Plant and Woody Area Indices of Leaf-on and Leaf-off Forest Canopy Using Explicit 3D Forest Scenes" . | REMOTE SENSING 10 . 8 (2018) . |
| APA | Zou, Jie , Zhuang, Yinguo , Chianucci, Francesco , Mai, Chunna , Lin, Weimu , Leng, Peng et al. Comparison of Seven Inversion Models for Estimating Plant and Woody Area Indices of Leaf-on and Leaf-off Forest Canopy Using Explicit 3D Forest Scenes . | REMOTE SENSING , 2018 , 10 (8) . |
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