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Yonghua Qu
Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

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Review
Published: 19 June 2021 in Remote Sensing
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The leaf area index (LAI) is an essential input parameter for quantitatively studying the energy and mass balance in soil-vegetation-atmosphere transfer systems. As an active remote sensing technology, light detection and ranging (LiDAR) provides a new method to describe forest canopy LAI. This paper reviewed the primary LAI retrieval methods using point cloud data (PCD) obtained by discrete airborne LiDAR scanner (DALS), its validation scheme, and its limitations. There are two types of LAI retrieval methods based on DALS PCD, i.e., the empirical regression and the gap fraction (GF) model. In the empirical model, tree height-related variables, LiDAR penetration indexes (LPIs), and canopy cover are the most widely used proxy variables. The height-related proxies are used most frequently; however, the LPIs proved the most efficient proxy. The GF model based on the Beer-Lambert law has been proven useful to estimate LAI; however, the suitability of LPIs is site-, tree species-, and LiDAR system-dependent. In the local validation in previous studies, poor scalability of both empirical and GF models in time, space, and across different DALS systems was observed, which means that field measurements are still needed to calibrate both types of models. The method to correct the impact from the clumping effect and woody material using DALS PCD and the saturation effect for both empirical and GF models still needs further exploration. Of most importance, further work is desired to emphasize assessing the transferability of published methods to new geographic contexts, different DALS sensors, and survey characteristics, based on figuring out the influence of each factor on the LAI retrieval process using DALS PCD. In addition, from a methodological perspective, taking advantage of DALS PCD in characterizing the 3D structure of the canopy, making full use of the ability of machine learning methods in the fusion of multisource data, developing a spatiotemporal scalable model of canopy structure parameters including LAI, and using multisource and heterogeneous data are promising areas of research.

ACS Style

Luo Tian; Yonghua Qu; Jianbo Qi. Estimation of Forest LAI Using Discrete Airborne LiDAR: A Review. Remote Sensing 2021, 13, 2408 .

AMA Style

Luo Tian, Yonghua Qu, Jianbo Qi. Estimation of Forest LAI Using Discrete Airborne LiDAR: A Review. Remote Sensing. 2021; 13 (12):2408.

Chicago/Turabian Style

Luo Tian; Yonghua Qu; Jianbo Qi. 2021. "Estimation of Forest LAI Using Discrete Airborne LiDAR: A Review." Remote Sensing 13, no. 12: 2408.

Journal article
Published: 03 December 2020 in Computers and Electronics in Agriculture
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Accurate, efficient, timely, and affordable measurements of crop structural parameters such as leaf area index (LAI) and mean tilt angle (MTA) are needed for crop growth modeling and precision field management. In this paper, a novel method was proposed to simultaneously measure corn (Zea mays L.) LAI and MTA using a low-cost indirect approach. The proposed method is based on multi-directional fractions of sunlit and shaded leaf and soil components obtained from nadir-viewing field photos captured under different solar angles. LAI and MTA were retrieved using a look-up-table (LUT) established using a Pov-Ray based geometrical canopy model. The method was validated using LAI-2200C field-measured data. Results showed that the estimated LAI were consistent with the LAI-2200C measurements, with a mean absolute error (MAE) of 0.11, relative MAE (RMAE) of 5%, and coefficient of determination (R2) of 0.89. The estimated average MTA was also close to that measured using the LAI-2200C, with the MAE of 5.9°, RMAE of 11% and R2 of 0.40. The proposed method provides accurate and efficient measurements of corn crop structural parameters. Thus, it is recommended as an affordable and effective field data collection method.

ACS Style

Yonghua Qu; Zebin Gao; Jiali Shang; Jiangui Liu; Raffaele Casa. Simultaneous measurements of corn leaf area index and mean tilt angle from multi-directional sunlit and shaded fractions using downward-looking photography. Computers and Electronics in Agriculture 2020, 180, 105881 .

AMA Style

Yonghua Qu, Zebin Gao, Jiali Shang, Jiangui Liu, Raffaele Casa. Simultaneous measurements of corn leaf area index and mean tilt angle from multi-directional sunlit and shaded fractions using downward-looking photography. Computers and Electronics in Agriculture. 2020; 180 ():105881.

Chicago/Turabian Style

Yonghua Qu; Zebin Gao; Jiali Shang; Jiangui Liu; Raffaele Casa. 2020. "Simultaneous measurements of corn leaf area index and mean tilt angle from multi-directional sunlit and shaded fractions using downward-looking photography." Computers and Electronics in Agriculture 180, no. : 105881.

Journal article
Published: 11 October 2020 in Remote Sensing
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Accurate and continuous monitoring of leaf area index (LAI), a widelyused vegetation structural parameter, is crucial to characterize crop growth conditions and forecast crop yield. Meanwhile, advancements in collecting field LAI measurements have provided strong support for validating remotesensingderived LAI. This paper evaluates the performance of LAI retrieval from multisource, remotely sensed data through comparisons with continuous field LAI measurements. Firstly, field LAI was measured continuously over periods of time in 2018 and 2019 using LAINet, a continuous LAI measurement system deployed using wireless sensor network (WSN) technology, over an agricultural region located at the Heihe watershed at northwestern China. Then, cloudfree images from optical satellite sensors, including Landsat 7 the Enhanced Thematic Mapper Plus (ETM+), Landsat 8 the Operational Land Imager (OLI), and Sentinel2A/B Multispectral Instrument (MSI), were collected to derive LAI through inversion of the PROSAIL radiation transfer model using a lookuptable (LUT) approach. Finally, field LAI data were used to validate the multi-temporal LAI retrieved from remotesensing data acquired by different satellite sensors. The results indicate that good accuracy was obtained using different inversion strategies for each sensor, while Green Chlorophyll Index (CIgreen) and a combination of three red-edge bands perform better for Landsat 7/8 and Sentinel2 LAI inversion, respectively. Furthermore, the estimated LAI has good consistency with in situ measurements at vegetative stage (coefficient of determination R2 = 0.74, and root mean square error RMSE = 0.53 m2 m−2). At the reproductive stage, a significant underestimation was found (R2 = 0.41, and 0.89 m2 m−2 in terms of RMSE). This study suggests that timeseries LAI can be retrieved from multisource satellite data through model inversion, and the LAINet instrument could be used as a lowcost tool to provide continuous field LAI measurements to support LAI retrieval.

ACS Style

Lihong Yu; Jiali Shang; Zhiqiang Cheng; Zebin Gao; Zixin Wang; Luo Tian; Dantong Wang; Tao Che; Rui Jin; Jiangui Liu; Taifeng Dong; Yonghua Qu. Assessment of Cornfield LAI Retrieved from Multi-Source Satellite Data Using Continuous Field LAI Measurements Based on a Wireless Sensor Network. Remote Sensing 2020, 12, 3304 .

AMA Style

Lihong Yu, Jiali Shang, Zhiqiang Cheng, Zebin Gao, Zixin Wang, Luo Tian, Dantong Wang, Tao Che, Rui Jin, Jiangui Liu, Taifeng Dong, Yonghua Qu. Assessment of Cornfield LAI Retrieved from Multi-Source Satellite Data Using Continuous Field LAI Measurements Based on a Wireless Sensor Network. Remote Sensing. 2020; 12 (20):3304.

Chicago/Turabian Style

Lihong Yu; Jiali Shang; Zhiqiang Cheng; Zebin Gao; Zixin Wang; Luo Tian; Dantong Wang; Tao Che; Rui Jin; Jiangui Liu; Taifeng Dong; Yonghua Qu. 2020. "Assessment of Cornfield LAI Retrieved from Multi-Source Satellite Data Using Continuous Field LAI Measurements Based on a Wireless Sensor Network." Remote Sensing 12, no. 20: 3304.

Journal article
Published: 08 January 2020 in Remote Sensing
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The leaf area index (LAI) is a crucial structural parameter of forest canopies. Light Detection and Ranging (LiDAR) provides an alternative to passive optical sensors in the estimation of LAI from remotely sensed data. However, LiDAR-based LAI estimation typically relies on empirical models, and such methods can only be applied when the field-based LAI data are available. Compared with an empirical model, a physically-based model—e.g., the Beer–Lambert law based light extinction model—is more attractive due to its independent dataset with training. However, two challenges are encountered when applying the physically-based model to estimate LAI from discrete LiDAR data: i.e., deriving the gap fraction and the extinction coefficient from the LiDAR data. We solved the first problem by integrating LiDAR and hyperspectral data to transfer the LiDAR penetration ratio to the forest gap fraction. For the second problem, the extinction coefficient was estimated from tiled (1 km × 1 km) LiDAR data by nonlinearly optimizing the cost function of the angular LiDAR gap fraction and simulated gap fraction from the Beer–Lambert law model. A validation against LAI-2000 measurements showed that the estimates were significantly correlated to the reference LAI with an R2 of 0.66, a root mean square error (RMSE) of 0.60 and a relative RMSE of 0.15. We conclude that forest LAI can be directly estimated by the nonlinear optimization method utilizing the Beer–Lambert model and a spectrally corrected LiDAR penetration ratio. The significance of the proposed method is that it can produce reliable remotely sensed forest LAI from discrete LiDAR and spectral data when field-measured LAI are unavailable.

ACS Style

Yonghua Qu; Ahmed Shaker; Lauri Korhonen; Carlos Alberto Silva; Kun Jia; Luo Tian; Jinling Song. Direct Estimation of Forest Leaf Area Index based on Spectrally Corrected Airborne LiDAR Pulse Penetration Ratio. Remote Sensing 2020, 12, 217 .

AMA Style

Yonghua Qu, Ahmed Shaker, Lauri Korhonen, Carlos Alberto Silva, Kun Jia, Luo Tian, Jinling Song. Direct Estimation of Forest Leaf Area Index based on Spectrally Corrected Airborne LiDAR Pulse Penetration Ratio. Remote Sensing. 2020; 12 (2):217.

Chicago/Turabian Style

Yonghua Qu; Ahmed Shaker; Lauri Korhonen; Carlos Alberto Silva; Kun Jia; Luo Tian; Jinling Song. 2020. "Direct Estimation of Forest Leaf Area Index based on Spectrally Corrected Airborne LiDAR Pulse Penetration Ratio." Remote Sensing 12, no. 2: 217.

Journal article
Published: 30 November 2019 in ISPRS International Journal of Geo-Information
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Leaf area index (LAI) is one of the most important canopy structure parameters utilized in process-based models of climate, hydrology, and biogeochemistry. In order to determine the reliability and applicability of satellite LAI products, it is critical to validate satellite LAI products. Due to surface heterogeneity and scale effects, it is difficult to validate the accuracy of LAI products. In order to improve the spatio-temporal accuracy of satellite LAI products, we propose a new multi-scale LAI product validation method based on a crop growth cycle. In this method, we used the PROSAIL model to derive Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LAI data and Gaofen-1 (GF-1) for the study area. The Empirical Bayes Kriging (EBK) interpolation method was used to perform a spatial multi-scale transformation of Moderate Resolution Imaging Spectroradiometer (MODIS) LAI products, GF-1 LAI data, and ASTER LAI data. Finally, MODIS LAI satellite products were compared with field measured LAI data, GF-1 LAI data, and ASTER LAI data during the growing season of crop field. This study was conducted in the agricultural oasis area of the middle reaches of the Heihe River Basin in northwestern China and the Conghua District of Guangzhou in Guangdong Province. The results suggest that the validation accuracy of the multi-scale MODIS LAI products validated by ASTER LAI data were higher than those of the GF-1 LAI data and the reference field measured LAI data, showing a R2 of 0.758 and relative mean square error (RRMSE) of 28.73% for 15 m ASTER LAI and a R2 of 0.703 and RRMSE of 30.80% for 500 m ASTER LAI, which imply that the 15 m MODIS LAI product generated by the EBK method was more accurate than the 500 m and 8 m products. This study provides a new validation method for satellite remotely sensed products.

ACS Style

Ting Wang; Yonghua Qu; Ziqing Xia; Yiping Peng; Zhenhua Liu. Multi-Scale Validation of MODIS LAI Products Based on Crop Growth Period. ISPRS International Journal of Geo-Information 2019, 8, 547 .

AMA Style

Ting Wang, Yonghua Qu, Ziqing Xia, Yiping Peng, Zhenhua Liu. Multi-Scale Validation of MODIS LAI Products Based on Crop Growth Period. ISPRS International Journal of Geo-Information. 2019; 8 (12):547.

Chicago/Turabian Style

Ting Wang; Yonghua Qu; Ziqing Xia; Yiping Peng; Zhenhua Liu. 2019. "Multi-Scale Validation of MODIS LAI Products Based on Crop Growth Period." ISPRS International Journal of Geo-Information 8, no. 12: 547.

Technical note
Published: 20 June 2019 in Remote Sensing
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Net primary productivity (NPP) is a key vegetation parameter and ecological indicator for tracking natural environmental change. High-quality Moderate Resolution Imaging Spectroradiometer Net primary productivity (MODIS-NPP) products are critical for assuring the scientific rigor of NPP analyses. However, obtaining high-quality MODIS-NPP products consistently is challenged by factors such as cloud contamination, heavy aerosol pollution, and atmospheric variability. This paper proposes a method combining the discrete wavelet transform (DWT) with an extended Kalman filter (EKF) for generating high-quality MODIS-NPP data. In this method, the DWT is used to remove noise in the original MODIS-NPP data, and the EKF is applied to the de-noised images. The de-noised images are modeled as a triply modulated cosine function that predicts the NPP data values when excessive cloudiness is present. This study was conducted in South China. By comparing measured NPP data to original MODIS-NPP and NPP estimates derived from combining the DWT and EKF, we found that the accuracy of the NPP estimates was significantly improved. The MODIS-NPP estimates had a mean relative error (RE) of 13.96% and relative root mean square error (rRMSE) of 15.67%, while the original MODIS-NPP had a mean RE of 23.58% and an rRMSE of 24.98%. The method combining DWT and EKF provides a feasible approach for generating new, high-quality NPP data in the absence of high-quality original MODIS-NPP data.

ACS Style

Zhenhua Liu; Ting Wang; Yonghua Qu; Huiming Liu; Xiaofang Wu; Ya Wen. Prediction of High-Quality MODIS-NPP Product Data. Remote Sensing 2019, 11, 1458 .

AMA Style

Zhenhua Liu, Ting Wang, Yonghua Qu, Huiming Liu, Xiaofang Wu, Ya Wen. Prediction of High-Quality MODIS-NPP Product Data. Remote Sensing. 2019; 11 (12):1458.

Chicago/Turabian Style

Zhenhua Liu; Ting Wang; Yonghua Qu; Huiming Liu; Xiaofang Wu; Ya Wen. 2019. "Prediction of High-Quality MODIS-NPP Product Data." Remote Sensing 11, no. 12: 1458.

Book chapter
Published: 26 February 2019 in River Basin Management
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Leaf area index (LAI) is a primary parameter for vegetation structure and is one of the important products from remote sensing data. Ground-based LAI estimation of LAI is the important activity for global satellite product validation. In this chapter, the main methods on LAI measurement were reviewed, and the emphasis was put on the new advanced method on LAI measurement. We present two new instruments (LAINet and LAISmart) designed by Beijing Normal University which use either modern communication network or mobile computing platform to obtain LAI with high efficiency and low cost. LAINet is an instrument constructed on the base of wireless sensor network, and the principle of LAINet is capturing sunlight transmittance using a series of wireless sensors in different sun zenith angles. And LAI is estimated from the sensed transmittances. Borrowing the wireless communication technique, the measured data can be transferred to remote computer server, thus, LAINet can reduce the cost of field data collection. LAISmart is a mobile application deployed on the smartphone, and the LAI is calculated by the classification of captured image. By integration of capturing images and real time computing of smartphone, LAISmart provide automatic measurement method compared with the traditional digital hemispherical photography method. In the end of this chapter, the prospect of the methods on the LAI ground-based measurement is summarized, and it is pointed out that the integration of passive and active optical signal to produce low cost and light weight and thus affordable and portable device may be a promising tendency.

ACS Style

Yonghua Qu. Leaf Area Index: Advances in Ground-Based Measurement. River Basin Management 2019, 359 -378.

AMA Style

Yonghua Qu. Leaf Area Index: Advances in Ground-Based Measurement. River Basin Management. 2019; ():359-378.

Chicago/Turabian Style

Yonghua Qu. 2019. "Leaf Area Index: Advances in Ground-Based Measurement." River Basin Management , no. : 359-378.

Journal article
Published: 24 January 2019 in Remote Sensing
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Many applications, including crop growth and yield monitoring, require accurate long-term time series of leaf area index (LAI) at high spatiotemporal resolution with a quantification of the associated uncertainties. We propose an LAI retrieval approach based on a combination of the LAINet observation system, the Consistent Adjustment of the Climatology to Actual Observations (CACAO) method, and Gaussian process regression (GPR). First, the LAINet wireless sensor network provides temporally continuous field measurements of LAI. Then, the CACAO approach generates synchronous reflectance data at high spatiotemporal resolution (30-m and 8-day) from the fusion of multitemporal MODIS and high spatial resolution Landsat satellite imagery. Finally, the GPR machine learning regression algorithm retrieves the LAI maps and their associated uncertainties. A case study in a cropland site in China showed that the accuracy of LAI retrievals is 0.36 (12.7%) in terms of root mean square error and R2 = 0.88 correlation with ground measurements as evaluated over the entire growing season. This paper demonstrates the potential of the joint use of newly developed software and hardware technologies in deriving concomitant LAI and uncertainty maps with high spatiotemporal resolution. It will contribute to precision agriculture, as well as to the retrieval and validation of LAI products.

ACS Style

Gaofei Yin; Aleixandre Verger; Yonghua Qu; Wei Zhao; Baodong Xu; Yelu Zeng; Ke Liu; Jing Li; Qinhuo Liu. Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion. Remote Sensing 2019, 11, 244 .

AMA Style

Gaofei Yin, Aleixandre Verger, Yonghua Qu, Wei Zhao, Baodong Xu, Yelu Zeng, Ke Liu, Jing Li, Qinhuo Liu. Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion. Remote Sensing. 2019; 11 (3):244.

Chicago/Turabian Style

Gaofei Yin; Aleixandre Verger; Yonghua Qu; Wei Zhao; Baodong Xu; Yelu Zeng; Ke Liu; Jing Li; Qinhuo Liu. 2019. "Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion." Remote Sensing 11, no. 3: 244.

Journal article
Published: 17 June 2018 in Remote Sensing
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Leaf area index (LAI) is an important parameter to describe the capacity of forests to intercept light and thus affects the microclimate and photosynthetic capacity of canopies. In general, tropical forests have a higher leaf area index and it is a challenge to estimate LAI in a forest with a very dense canopy. In this study, it is assumed that the traditional Light Detection and Ranging (LiDAR)-derived fractional vegetation cover (fCover) has weak relationship with leaf area index in a dense forest. We propose a partial least squares (PLS) regression model using the height percentile metrics derived from airborne LiDAR data to estimate the LAI of a dense forest. Ground inventory and airborne LiDAR data collected in a selectively logged tropical forest area in Eastern Amazonia are used to map LAI from the plot level to the landscape scale. The results indicate that the fCover, derived from the first return or the last return, has no significant correlations with the ground-based LAI. The PLS model evaluated by the leave-one-out validation shows that the estimated LAI is significantly correlated with the ground-based LAI with an R2 of 0.58 and a root mean square error (RMSE) of 1.13. A data comparison indicates that the Moderate Resolution Imaging Spectrometer (MODIS) LAI underestimates the landscape-level LAI by about 22%. The MODIS quality control data show that in the selected tile, the cloud state is not the primary factor affecting the MODIS LAI performance; rather, the LAI from the main radiative transfer (RT) algorithm contributes much to the underestimation of the LAI in the tropical forest. In addition, the results show that the LiDAR-based LAI has a better response to the logging activities than the MODIS-based LAI, and that the leaf area reduction caused by logging is about 13%. In contrast, the MODIS-based LAI exhibits no apparent spatial correlation with the LiDAR-based LAI. It is suggested that the main algorithm of MODIS should be improved with regard to tropical forests. The significance of this study is the proposal of a framework to produce ground-based LAI using forest inventory data and determine the plot-level LAI at the airborne and satellite scale using LiDAR data.

ACS Style

Yonghua Qu; Ahmed Shaker; Carlos Alberto Silva; Carine Klauberg; Ekena Rangel Pinagé. Remote Sensing of Leaf Area Index from LiDAR Height Percentile Metrics and Comparison with MODIS Product in a Selectively Logged Tropical Forest Area in Eastern Amazonia. Remote Sensing 2018, 10, 970 .

AMA Style

Yonghua Qu, Ahmed Shaker, Carlos Alberto Silva, Carine Klauberg, Ekena Rangel Pinagé. Remote Sensing of Leaf Area Index from LiDAR Height Percentile Metrics and Comparison with MODIS Product in a Selectively Logged Tropical Forest Area in Eastern Amazonia. Remote Sensing. 2018; 10 (6):970.

Chicago/Turabian Style

Yonghua Qu; Ahmed Shaker; Carlos Alberto Silva; Carine Klauberg; Ekena Rangel Pinagé. 2018. "Remote Sensing of Leaf Area Index from LiDAR Height Percentile Metrics and Comparison with MODIS Product in a Selectively Logged Tropical Forest Area in Eastern Amazonia." Remote Sensing 10, no. 6: 970.

Journal article
Published: 30 May 2018 in Remote Sensing of Environment
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Clumping index (CI) is a measure of foliage aggregation relative to a random distribution of leaves in space. The CI can help with estimating fractions of sunlit and shaded leaves for a given leaf area index (LAI) value. Both the CI and LAI can be obtained from global Earth Observation data from sensors such as the Moderate Resolution Imaging Spectrometer (MODIS). Here, the synergy between a MODIS-based CI and a MODIS LAI product is examined using the theory of spectral invariants, also referred to as photon recollision probability (‘p-theory’), along with raw LAI-2000/2200 Plant Canopy Analyzer data from 75 sites distributed across a range of plant functional types. The p-theory describes the probability (p-value) that a photon, having intercepted an element in the canopy, will recollide with another canopy element rather than escape the canopy. We show that empirically-based CI maps can be integrated with the MODIS LAI product. Our results indicate that it is feasible to derive approximate p-values for any location solely from Earth Observation data. This approximation is relevant for future applications of the photon recollision probability concept for global and local monitoring of vegetation using Earth Observation data.

ACS Style

Jan Pisek; Henning Buddenbaum; Fernando Camacho; Joachim Hill; Jennifer Jensen; Holger Lange; Zhili Liu; Arndt Piayda; Yonghua Qu; Olivier Roupsard; Shawn Serbin; Svein Solberg; Oliver Sonnentag; Anne Thimonier; Francesco Vuolo. Data synergy between leaf area index and clumping index Earth Observation products using photon recollision probability theory. Remote Sensing of Environment 2018, 215, 1 -6.

AMA Style

Jan Pisek, Henning Buddenbaum, Fernando Camacho, Joachim Hill, Jennifer Jensen, Holger Lange, Zhili Liu, Arndt Piayda, Yonghua Qu, Olivier Roupsard, Shawn Serbin, Svein Solberg, Oliver Sonnentag, Anne Thimonier, Francesco Vuolo. Data synergy between leaf area index and clumping index Earth Observation products using photon recollision probability theory. Remote Sensing of Environment. 2018; 215 ():1-6.

Chicago/Turabian Style

Jan Pisek; Henning Buddenbaum; Fernando Camacho; Joachim Hill; Jennifer Jensen; Holger Lange; Zhili Liu; Arndt Piayda; Yonghua Qu; Olivier Roupsard; Shawn Serbin; Svein Solberg; Oliver Sonnentag; Anne Thimonier; Francesco Vuolo. 2018. "Data synergy between leaf area index and clumping index Earth Observation products using photon recollision probability theory." Remote Sensing of Environment 215, no. : 1-6.

Journal article
Published: 19 June 2017 in Forests
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Forest leaf area index (LAI) is a key characteristic affecting a field canopy microclimate. In addition to traditional professional measuring instruments, smartphone-based methods have been used to measure forest LAI. However, when smartphone methods were used to measure conifer forest LAI, very different performances were obtained depending on whether the smartphone was held at the zenith angle or at a 57.5° angle. To further validate the potential of smartphone sensors for measuring conifer LAI and to find the limits of this method, this paper reports the results of a comparison of two smartphone methods with an LAI-2000 instrument. It is shown that the method with the smartphone oriented vertically upwards always produced better consistency in magnitude with LAI-2000. The bias of the LAI between the smartphone method and the LAI-2000 instrument was explained with regards to four aspects that can affect LAI: gap fraction; leaf projection ratio; sensor field of view (FOV); and viewing zenith angle (VZA). It was concluded that large FOV and large VZA cause the 57.5° method to overestimate the gap fraction and hence underestimate conifer LAI. For the vertically upward method, the bias caused by the overestimated gap fraction is compensated for by an underestimated leaf projection ratio.

ACS Style

Yonghua Qu; Jian Wang; Jinling Song; Jindi Wang. Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone-Based Method. Forests 2017, 8, 217 .

AMA Style

Yonghua Qu, Jian Wang, Jinling Song, Jindi Wang. Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone-Based Method. Forests. 2017; 8 (6):217.

Chicago/Turabian Style

Yonghua Qu; Jian Wang; Jinling Song; Jindi Wang. 2017. "Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone-Based Method." Forests 8, no. 6: 217.

Preprint
Published: 17 January 2017
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Plant leaf area index (LAI) is a key characteristic affecting field canopy microclimate. In addition to traditional professional measuring instruments, smartphone camera sensors have been used to measure plant LAI. However, when smartphone methods were used to measure conifer forest LAI, very different performances were obtained depending on whether the smartphone was held at the zenith angle or at a 57.5° angle. To validate further the potential of smartphone sensors for measuring conifer LAI and to find the limits of this method, this paper reports the results of a comparison of two smartphone methods with an LAI-2000 instrument. It is shown that both methods can be used to reveal the conifer leaf-growing trajectory. However, the method with the phone oriented vertically upwards always produced better consistency in magnitude with LAI-2000. The bias of the LAI between the smartphone method and the LAI-2000 instrument was explained with regard to four aspects that can affect LAI: gap fraction, leaf projection ratio, sensor field of view (FOV), and viewing zenith angle (VZA). It was concluded that large FOV and large VZA cause the 57.5° method to overestimate the gap fraction and hence underestimate conifer LAI, especially when tree height is greater than 2.0 m. For the vertically upward method, the bias caused by the overestimated gap fraction is compensated for by an underestimated leaf projection ratio.

ACS Style

Yonghua Qu; Jian Wang; Jinling Song; Jindi Wang. Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone Camera Sensors. 2017, 1 .

AMA Style

Yonghua Qu, Jian Wang, Jinling Song, Jindi Wang. Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone Camera Sensors. . 2017; ():1.

Chicago/Turabian Style

Yonghua Qu; Jian Wang; Jinling Song; Jindi Wang. 2017. "Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone Camera Sensors." , no. : 1.

Journal article
Published: 27 December 2016 in Remote Sensing
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High-resolution leaf area index (LAI) maps from remote sensing data largely depend on empirical models, which link field LAI measurements to the vegetation index. The existing empirical methods often require the field measurements to be sufficient for constructing a reliable model. However, in many regions of the world, there are limited field measurements available. This paper presents a prior knowledge-based (PKB) method to derivate LAI with limited field measurements, in an effort to improve the accuracy of empirical model. Based on the assumption that the experimental sites with the same vegetation type can be represented by similar models, a priori knowledge for crops was extracted from the published models in various cropland sites. The knowledge, composed of an initial guess of each model parameter with the associated uncertainty, was then combined with the local field measurements to determine a semi-empirical model using the Bayesian inversion method. The proposed method was evaluated at a cropland site in the Huailai region of Hebei Province, China. Compared with the regression method, the proposed PKB method can effectively improve the accuracy of empirical model and LAI estimation, when the field measurements were limited. The results demonstrate that a priori knowledge extracted from the universal sites can provide important auxiliary information to improve the representativeness of the empirical model in a given study area.

ACS Style

Yuechan Shi; Jindi Wang; Jian Wang; Yonghua Qu. A Prior Knowledge-Based Method to Derivate High-Resolution Leaf Area Index Maps with Limited Field Measurements. Remote Sensing 2016, 9, 13 .

AMA Style

Yuechan Shi, Jindi Wang, Jian Wang, Yonghua Qu. A Prior Knowledge-Based Method to Derivate High-Resolution Leaf Area Index Maps with Limited Field Measurements. Remote Sensing. 2016; 9 (1):13.

Chicago/Turabian Style

Yuechan Shi; Jindi Wang; Jian Wang; Yonghua Qu. 2016. "A Prior Knowledge-Based Method to Derivate High-Resolution Leaf Area Index Maps with Limited Field Measurements." Remote Sensing 9, no. 1: 13.

Journal article
Published: 30 September 2015 in Remote Sensing
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Upscaling in situ leaf area index (LAI) measurements to the footprint scale is important for the validation of medium resolution remote sensing products. However, surface heterogeneity and temporal variation of vegetation make this difficult. In this study, a two-step upscaling algorithm was developed to obtain the representative ground truth of LAI time series in heterogeneous surfaces based on in situ LAI data measured by the wireless sensor network (WSN) observation system. Since heterogeneity within a site usually arises from the mixture of vegetation and non-vegetation surfaces, the spatial heterogeneity of vegetation and land cover types were separately considered. Representative LAI time series of vegetation surfaces were obtained by upscaling in situ measurements using an optimal weighted combination method, incorporating the expectation maximum (EM) algorithm to derive the weights. The ground truth of LAI over the whole site could then be determined using area weighted combination of representative LAIs of different land cover types. The algorithm was evaluated using a dataset collected in Heihe Watershed Allied Telemetry Experimental Research (HiWater) experiment. The proposed algorithm can effectively obtain the representative ground truth of LAI time series in heterogeneous cropland areas. Using the normal method of an average LAI measurement to represent the heterogeneous surface produced a root mean square error (RMSE) of 0.69, whereas the proposed algorithm provided RMSE = 0.032 using 23 sampling points. The proposed ground truth derived method was implemented to validate four major LAI products.

ACS Style

Yuechan Shi; Jindi Wang; Jun Qin; Yonghua Qu. An Upscaling Algorithm to Obtain the Representative Ground Truth of LAI Time Series in Heterogeneous Land Surface. Remote Sensing 2015, 7, 12887 -12908.

AMA Style

Yuechan Shi, Jindi Wang, Jun Qin, Yonghua Qu. An Upscaling Algorithm to Obtain the Representative Ground Truth of LAI Time Series in Heterogeneous Land Surface. Remote Sensing. 2015; 7 (10):12887-12908.

Chicago/Turabian Style

Yuechan Shi; Jindi Wang; Jun Qin; Yonghua Qu. 2015. "An Upscaling Algorithm to Obtain the Representative Ground Truth of LAI Time Series in Heterogeneous Land Surface." Remote Sensing 7, no. 10: 12887-12908.

Journal article
Published: 26 January 2015 in Remote Sensing
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A sampling strategy to define elementary sampling units (ESUs) for an entire site at the kilometer scale is an important step in the validation process for moderate-resolution leaf area index (LAI) products. Current LAI-sampling strategies are unable to consider the vegetation seasonal changes and are better suited for single-day LAI product validation, whereas the increasingly used wireless sensor network for LAI measurement (LAINet) requires an optimal sampling strategy across both spatial and temporal scales. In this study, we developed an efficient and robust LAI Sampling strategy based on Multi-temporal Prior knowledge (SMP) for long-term, fixed-position LAI observations. The SMP approach employed multi-temporal vegetation index (VI) maps and the vegetation classification map as a priori knowledge. The SMP approach minimized the multi-temporal bias of the VI frequency histogram between the ESUs and the entire site and maximized the nearest-neighbor index to ensure that ESUs were dispersed in the geographical space. The SMP approach was compared with four sampling strategies including random sampling, systematic sampling, sampling based on the land-cover map and a sampling strategy based on vegetation index prior knowledge using the PROSAIL model-based simulation analysis in the Heihe River basin. The results indicate that the ESUs selected using the SMP method spread more evenly in both the multi-temporal feature space and geographical space over the vegetation cycle. By considering the temporal changes in heterogeneity, the average root-mean-square error (RMSE) of the LAI reference maps can be reduced from 0.12 to 0.05, and the relative error can be reduced from 6.1% to 2.2%. The SMP technique was applied to assign the LAINet ESU locations at the Huailai Remote Sensing Experimental Station in Beijing, China, from 4 July to 28 August 2013, to validate three MODIS C5 LAI products. The results suggest that the average R2, RMSE, bias and relative uncertainty for the three MODIS LAI products were 0.60, 0.33, −0.11, and 12.2%, respectively. The MCD15A2 product performed best, exhibiting a RMSE of 0.20, a bias of −0.07 and a relative uncertainty of 7.4%. Future efforts are needed to obtain more long-term validation datasets using the SMP approach on different vegetation types for validating moderate-resolution LAI products in time series.

ACS Style

Yelu Zeng; Jing Li; Qinhuo Liu; Yonghua Qu; Alfredo R. Huete; Baodong Xu; Geofei Yin; Jing Zhao. An Optimal Sampling Design for Observing and Validating Long-Term Leaf Area Index with Temporal Variations in Spatial Heterogeneities. Remote Sensing 2015, 7, 1300 -1319.

AMA Style

Yelu Zeng, Jing Li, Qinhuo Liu, Yonghua Qu, Alfredo R. Huete, Baodong Xu, Geofei Yin, Jing Zhao. An Optimal Sampling Design for Observing and Validating Long-Term Leaf Area Index with Temporal Variations in Spatial Heterogeneities. Remote Sensing. 2015; 7 (2):1300-1319.

Chicago/Turabian Style

Yelu Zeng; Jing Li; Qinhuo Liu; Yonghua Qu; Alfredo R. Huete; Baodong Xu; Geofei Yin; Jing Zhao. 2015. "An Optimal Sampling Design for Observing and Validating Long-Term Leaf Area Index with Temporal Variations in Spatial Heterogeneities." Remote Sensing 7, no. 2: 1300-1319.

Journal article
Published: 24 December 2014 in Remote Sensing
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This paper aims to retrieve temporal high-resolution LAI derived by fusing MOD15 products (1 km resolution), field-measured LAI and ASTER reflectance (15-m resolution). Though the inversion of a physically based canopy reflectance model using high-resolution satellite data can produce high-resolution LAI products, the obstacle to producing temporal products is obvious due to the low temporal resolution of high resolution satellite data. A feasible method is to combine different source data, taking advantage of the spatial and temporal resolution of different sensors. In this paper, a high-resolution LAI retrieval method was implemented using a dynamic Bayesian network (DBN) inversion framework. MODIS LAI data with higher temporal resolution were used to fit the temporal background information, which is then updated by new, higher resolution data, herein ASTER data. The interactions between the different resolution data were analyzed from a Bayesian perspective. The proposed method was evaluated using a dataset collected in the HiWater (Heihe Watershed Allied Telemetry Experimental Research) experiment. The determination coefficient and RMSE between the estimated and measured LAI are 0.80 and 0.43, respectively. The research results suggest that even though the coarse-resolution background information differs from the high-resolution satellite observations, a satisfactory estimation result for the temporal high-resolution LAI can be produced using the accumulated information from both the new observations and background information.

ACS Style

Yonghua Qu; Wenchao Han; Mingguo Ma. Retrieval of a Temporal High-Resolution Leaf Area Index (LAI) by Combining MODIS LAI and ASTER Reflectance Data. Remote Sensing 2014, 7, 195 -210.

AMA Style

Yonghua Qu, Wenchao Han, Mingguo Ma. Retrieval of a Temporal High-Resolution Leaf Area Index (LAI) by Combining MODIS LAI and ASTER Reflectance Data. Remote Sensing. 2014; 7 (1):195-210.

Chicago/Turabian Style

Yonghua Qu; Wenchao Han; Mingguo Ma. 2014. "Retrieval of a Temporal High-Resolution Leaf Area Index (LAI) by Combining MODIS LAI and ASTER Reflectance Data." Remote Sensing 7, no. 1: 195-210.

Journal article
Published: 24 December 2014 in Remote Sensing
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This paper aims to retrieve temporal high-resolution LAI derived by fusing MOD15 products (1 km resolution), field-measured LAI and ASTER reflectance (15-m resolution). Though the inversion of a physically based canopy reflectance model using high-resolution satellite data can produce high-resolution LAI products, the obstacle to producing temporal products is obvious due to the low temporal resolution of high resolution satellite data. A feasible method is to combine different source data, taking advantage of the spatial and temporal resolution of different sensors. In this paper, a high-resolution LAI retrieval method was implemented using a dynamic Bayesian network (DBN) inversion framework. MODIS LAI data with higher temporal resolution were used to fit the temporal background information, which is then updated by new, higher resolution data, herein ASTER data. The interactions between the different resolution data were analyzed from a Bayesian perspective. The proposed method was evaluated using a dataset collected in the HiWater (Heihe Watershed Allied Telemetry Experimental Research) experiment. The determination coefficient and RMSE between the estimated and measured LAI are 0.80 and 0.43, respectively. The research results suggest that even though the coarse-resolution background information differs from the high-resolution satellite observations, a satisfactory estimation result for the temporal high-resolution LAI can be produced using the accumulated information from both the new observations and background information.

ACS Style

Yonghua Qu; Wenchao Han; Mingguo Ma. Retrieval of a Temporal High-Resolution Leaf Area Index (LAI) by Combining MODIS LAI and ASTER Reflectance Data. Remote Sensing 2014, 7, 195 -210.

AMA Style

Yonghua Qu, Wenchao Han, Mingguo Ma. Retrieval of a Temporal High-Resolution Leaf Area Index (LAI) by Combining MODIS LAI and ASTER Reflectance Data. Remote Sensing. 2014; 7 (1):195-210.

Chicago/Turabian Style

Yonghua Qu; Wenchao Han; Mingguo Ma. 2014. "Retrieval of a Temporal High-Resolution Leaf Area Index (LAI) by Combining MODIS LAI and ASTER Reflectance Data." Remote Sensing 7, no. 1: 195-210.

Journal article
Published: 23 May 2014 in Sensors
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The canopy foliage clumping effect is primarily caused by the non-random distribution of canopy foliage. Currently, measurements of clumping index (CI) by handheld instruments is typically time- and labor-intensive. We propose a low-cost and low-power automatic measurement system called Multi-point Linear Array of Optical Sensors (MLAOS), which consists of three above-canopy and nine below-canopy optical sensors that capture plant transmittance at different times of the day. Data communication between the MLAOS node is facilitated by using a ZigBee network, and the data are transmitted from the field MLAOS to a remote data server using the Internet. The choice of the electronic element and design of the MLAOS software is aimed at reducing costs and power consumption. A power consumption test showed that, when a 4000 mAH Li-ion battery is used, a maximum of 8–10 months of work can be achieved. A field experiment on a coniferous forest revealed that the CI of MLAOS may reveal a clumping effect that occurs within the canopy. In further work, measurement of the multi-scale clumping effect can be achieved by utilizing a greater number of MLAOS devices to capture the heterogeneity of the plant canopy.

ACS Style

Yonghua Qu; Lizhe Fu; Wenchao Han; Yeqing Zhu; Jindi Wang. MLAOS: A Multi-Point Linear Array of Optical Sensors for Coniferous Foliage Clumping Index Measurement. Sensors 2014, 14, 9271 -9289.

AMA Style

Yonghua Qu, Lizhe Fu, Wenchao Han, Yeqing Zhu, Jindi Wang. MLAOS: A Multi-Point Linear Array of Optical Sensors for Coniferous Foliage Clumping Index Measurement. Sensors. 2014; 14 (5):9271-9289.

Chicago/Turabian Style

Yonghua Qu; Lizhe Fu; Wenchao Han; Yeqing Zhu; Jindi Wang. 2014. "MLAOS: A Multi-Point Linear Array of Optical Sensors for Coniferous Foliage Clumping Index Measurement." Sensors 14, no. 5: 9271-9289.