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Ronghai Hu
College of Resources and Environment, University of Chinese Academy of Sciences, No.19(A) Yuquan Road, Shijingshan District, Beijing 100049, China

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Journal article
Published: 12 June 2021 in Geoderma
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Despite the importance of microorganisms in soil nitrogen (N) cycling, studies on spatial patterns of microbial N-cycling gene abundances in temperate grasslands are still lacking, whose productivity is limited by N. Here, we investigated N functional genes in soil microorganisms from 60 sites in temperate grasslands across 1661 km in Inner Mongolia, China. Abundances of all N functional genes and 16S rDNA tended to decrease from northeast to southwest, consistent with the changing tend of precipitation but contrary to that of temperature. Non-linear saturation curves dominated patterns for abundances of most N functional genes and 16S rDNA along precipitation as they increased with the rising precipitation when <288–343 mm, but remained stable after these breaking points. Interestingly, these breaking points were clearly related to certain soil types, e.g. nifH did not change with precipitation in Chernozems and Gleysols, but increased with it in other soil types. Non-linear saturation patterns were also observed for most N functional gene and 16S rDNA abundances with temperature and soil total organic carbon. In contrast, no consistent spatial pattern for ratios of N functional genes: 16S rDNA was observed, which varied greatly by different N functional genes. Moreover, decay relationships were discovered for the abundance-based matrix of N functional genes over geographic distance. From the temperate meadows to typical steppes and to temperate desert steppes, the influence of relatively long-term environmental variables increased, but that of short-term ones decreased. Overall, we revealed non-linear patterns of N functional gene abundances along precipitation with a clear relationship with soil type, and discovered that attributions of geographic distance, plant community, historical-contingency and contemporary-disturbance to N functional gene community similarity decay were ecosystem–dependent.

ACS Style

Shutong Zhou; Kai Xue; Biao Zhang; Li Tang; Zhe Pang; Fang Wang; Rongxiao Che; Qinwei Ran; Anquan Xia; Kui Wang; Linfeng Li; Junfu Dong; Jianqing Du; Ronghai Hu; Yanbin Hao; Xiaoyong Cui; Yanfen Wang. Spatial patterns of microbial nitrogen-cycling gene abundances along a precipitation gradient in various temperate grasslands at a regional scale. Geoderma 2021, 404, 115236 .

AMA Style

Shutong Zhou, Kai Xue, Biao Zhang, Li Tang, Zhe Pang, Fang Wang, Rongxiao Che, Qinwei Ran, Anquan Xia, Kui Wang, Linfeng Li, Junfu Dong, Jianqing Du, Ronghai Hu, Yanbin Hao, Xiaoyong Cui, Yanfen Wang. Spatial patterns of microbial nitrogen-cycling gene abundances along a precipitation gradient in various temperate grasslands at a regional scale. Geoderma. 2021; 404 ():115236.

Chicago/Turabian Style

Shutong Zhou; Kai Xue; Biao Zhang; Li Tang; Zhe Pang; Fang Wang; Rongxiao Che; Qinwei Ran; Anquan Xia; Kui Wang; Linfeng Li; Junfu Dong; Jianqing Du; Ronghai Hu; Yanbin Hao; Xiaoyong Cui; Yanfen Wang. 2021. "Spatial patterns of microbial nitrogen-cycling gene abundances along a precipitation gradient in various temperate grasslands at a regional scale." Geoderma 404, no. : 115236.

Journal article
Published: 07 June 2021 in ISPRS International Journal of Geo-Information
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The spatiotemporal variation characteristics of the Normalized Difference Vegetation Index (NDVI) and its climate response patterns are of significance in deepening our understanding of regional vegetation and climate change. The response of vegetation to climate factors varies spatially and may have lag periods. In this paper, we studied the spatiotemporal responses of vegetation to climatic factors on an ecosystem-dependent scale using GIMMS NDVI3g data and climatic parameters. Pure pixels with a single vegetation type were firstly extracted to reduce the influence of mixed vegetation types. Then, a lag correlation analysis was used to explore the lag effects of climatic parameters affecting NDVI. Finally, the stepwise regression method was adopted to calculate the regression equation for NDVI and meteorological data with the consideration of effect lag times. The results show that precipitation has significant lag effects on vegetation. Temperature is the main climatic factor that affects most vegetation types at the start of growing season. At the end of growing season, the temperate desert, temperate steppe, and temperate desert steppe are greatly affected by precipitation. Moreover, the alpine steppe, alpine desert, alpine meadow, and alpine sparse vegetation are greatly affected by temperature. The needleleaf forest, subalpine scrub, and broadleaf evergreen forest are sensitive to sunshine percentage during almost the whole growing season. These findings could contribute to a better understanding of the drivers and mechanisms of vegetation degradation on the Tibetan Plateau.

ACS Style

Shuohao Cai; Xiaoning Song; Ronghai Hu; Da Guo. Ecosystem-Dependent Responses of Vegetation Coverage on the Tibetan Plateau to Climate Factors and Their Lag Periods. ISPRS International Journal of Geo-Information 2021, 10, 394 .

AMA Style

Shuohao Cai, Xiaoning Song, Ronghai Hu, Da Guo. Ecosystem-Dependent Responses of Vegetation Coverage on the Tibetan Plateau to Climate Factors and Their Lag Periods. ISPRS International Journal of Geo-Information. 2021; 10 (6):394.

Chicago/Turabian Style

Shuohao Cai; Xiaoning Song; Ronghai Hu; Da Guo. 2021. "Ecosystem-Dependent Responses of Vegetation Coverage on the Tibetan Plateau to Climate Factors and Their Lag Periods." ISPRS International Journal of Geo-Information 10, no. 6: 394.

Preprint content
Published: 14 May 2021
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Though being fundamental to global diversity distribution, little is known about the geographic pattern of soil microorganisms across different biomes on a large scale. Here, we investigated soil prokaryotic communities from Chinese northern grasslands on a scale up to 4,000 km in both alpine and temperate biomes. Surprisingly, prokaryotic similarities increased with geographic distance after tipping points of 1,760 - 1,920 km, overturning the well-accepted distance-decay relationship and generating a significant U-shape pattern. Such U-shape pattern was likely due to decreased disparities in environmental heterogeneity along with geographic distance when across biomes, as homogeneous environmental selection dominated prokaryotic assembly based on βNTI analysis. Consistently, short-term environmental heterogeneity also followed the U-shape pattern spatially, mainly attributed to dissolved nutrients. In sum, these results demonstrate that homogeneous environmental selection via dissolved nutrients overwhelmed the “distance” effect when across biomes, subverting the previously well-accepted geographic pattern for microbes on a large scale.

ACS Style

Biao Zhang; Kai Xue; Shutong Zhou; Kui Wang; Wenjing Liu; Cong Xu; Lizhen Cui; Linfeng Li; Qinwei Ran; Ronghai Hu; Yanbin Hao; Xiaoyong Cui; Yanfen Wang. Homogeneous environmental selection overturns distance-decay relationship of soil prokaryotic community. 2021, 1 .

AMA Style

Biao Zhang, Kai Xue, Shutong Zhou, Kui Wang, Wenjing Liu, Cong Xu, Lizhen Cui, Linfeng Li, Qinwei Ran, Ronghai Hu, Yanbin Hao, Xiaoyong Cui, Yanfen Wang. Homogeneous environmental selection overturns distance-decay relationship of soil prokaryotic community. . 2021; ():1.

Chicago/Turabian Style

Biao Zhang; Kai Xue; Shutong Zhou; Kui Wang; Wenjing Liu; Cong Xu; Lizhen Cui; Linfeng Li; Qinwei Ran; Ronghai Hu; Yanbin Hao; Xiaoyong Cui; Yanfen Wang. 2021. "Homogeneous environmental selection overturns distance-decay relationship of soil prokaryotic community." , no. : 1.

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

ACS Style

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

AMA Style

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

Chicago/Turabian Style

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

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

ACS Style

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

AMA Style

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

Chicago/Turabian Style

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

Journal article
Published: 27 March 2021 in Remote Sensing
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Grassland remote sensing (GRS) is an important research topic that applies remote sensing technology to grassland ecosystems, reflects the number of grassland resources and grassland health promptly, and provides inversion information used in sustainable development management. A scientometrics analysis based on Science Citation Index-Expanded (SCI-E) was performed to understand the research trends and areas of focus in GRS research studies. A total of 2692 papers related to GRS research studies and 82,208 references published from 1980 to 2020 were selected as the research objects. A comprehensive overview of the field based on the annual documents, research areas, institutions, influential journals, core authors, and temporal trends in keywords were presented in this study. The results showed that the annual number of documents increased exponentially, and more than 100 papers were published each year since 2010. Remote sensing, environmental sciences, and ecology were the most popular Web of Science research areas. The journal Remote Sensing was one of the most popular for researchers to publish documents and shows high development and publishing potential in GRS research studies. The institution with the greatest research documents and most citations was the Chinese Academy of Sciences. Guo X.L., Hill M.J., and Zhang L. were the most productive authors across the 40-year study period in terms of the number of articles published. Seven clusters of research areas were identified that generated contributions to this topic by keyword co-occurrence analysis. We also detected 17 main future directions of GRS research studies by document co-citation analysis. Emerging or underutilized methodologies and technologies, such as unmanned aerial systems (UASs), cloud computing, and deep learning, will continue to further enhance GRS research in the process of achieving sustainable development goals. These results can help related researchers better understand the past and future of GRS research studies.

ACS Style

Tong Li; Lizhen Cui; Zhihong Xu; Ronghai Hu; Pawan Joshi; Xiufang Song; Li Tang; Anquan Xia; Yanfen Wang; Da Guo; Jiapei Zhu; Yanbin Hao; Lan Song; Xiaoyong Cui. Quantitative Analysis of the Research Trends and Areas in Grassland Remote Sensing: A Scientometrics Analysis of Web of Science from 1980 to 2020. Remote Sensing 2021, 13, 1279 .

AMA Style

Tong Li, Lizhen Cui, Zhihong Xu, Ronghai Hu, Pawan Joshi, Xiufang Song, Li Tang, Anquan Xia, Yanfen Wang, Da Guo, Jiapei Zhu, Yanbin Hao, Lan Song, Xiaoyong Cui. Quantitative Analysis of the Research Trends and Areas in Grassland Remote Sensing: A Scientometrics Analysis of Web of Science from 1980 to 2020. Remote Sensing. 2021; 13 (7):1279.

Chicago/Turabian Style

Tong Li; Lizhen Cui; Zhihong Xu; Ronghai Hu; Pawan Joshi; Xiufang Song; Li Tang; Anquan Xia; Yanfen Wang; Da Guo; Jiapei Zhu; Yanbin Hao; Lan Song; Xiaoyong Cui. 2021. "Quantitative Analysis of the Research Trends and Areas in Grassland Remote Sensing: A Scientometrics Analysis of Web of Science from 1980 to 2020." Remote Sensing 13, no. 7: 1279.

Journal article
Published: 19 March 2021 in Remote Sensing
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The Hindu Kush Himalayan (HKH) region is one of the most ecologically vulnerable regions in the world. Several studies have been conducted on the dynamic changes of grassland in the HKH region, but few have considered grassland net ecosystem productivity (NEP). In this study, we quantitatively analyzed the temporal and spatial changes of NEP magnitude and the influence of climate factors on the HKH region from 2001 to 2018. The NEP magnitude was obtained by calculating the difference between the net primary production (NPP) estimated by the Carnegie–Ames Stanford Approach (CASA) model and the heterotrophic respiration (Rh) estimated by the geostatistical model. The results showed that the grassland ecosystem in the HKH region exhibited weak net carbon uptake with NEP values of 42.03 gC∙m−2∙yr−1, and the total net carbon sequestration was 0.077 Pg C. The distribution of NEP gradually increased from west to east, and in the Qinghai–Tibet Plateau, it gradually increased from northwest to southeast. The grassland carbon sources and sinks differed at different altitudes. The grassland was a carbon sink at 3000–5000 m, while grasslands below 3000 m and above 5000 m were carbon sources. Grassland NEP exhibited the strongest correlation with precipitation, and it had a lagging effect on precipitation. The correlation between NEP and the precipitation of the previous year was stronger than that of the current year. NEP was negatively correlated with temperature but not with solar radiation. The study of the temporal and spatial dynamics of NEP in the HKH region can provide a theoretical basis to help herders balance grazing and forage.

ACS Style

Da Guo; Xiaoning Song; Ronghai Hu; Xinming Zhu; Yazhen Jiang; Shuohao Cai; Yanan Zhang; Xiaoyong Cui. Large-Scale Analysis of the Spatiotemporal Changes of Net Ecosystem Production in Hindu Kush Himalayan Region. Remote Sensing 2021, 13, 1180 .

AMA Style

Da Guo, Xiaoning Song, Ronghai Hu, Xinming Zhu, Yazhen Jiang, Shuohao Cai, Yanan Zhang, Xiaoyong Cui. Large-Scale Analysis of the Spatiotemporal Changes of Net Ecosystem Production in Hindu Kush Himalayan Region. Remote Sensing. 2021; 13 (6):1180.

Chicago/Turabian Style

Da Guo; Xiaoning Song; Ronghai Hu; Xinming Zhu; Yazhen Jiang; Shuohao Cai; Yanan Zhang; Xiaoyong Cui. 2021. "Large-Scale Analysis of the Spatiotemporal Changes of Net Ecosystem Production in Hindu Kush Himalayan Region." Remote Sensing 13, no. 6: 1180.

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

ACS Style

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

AMA Style

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

Chicago/Turabian Style

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

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

ACS Style

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

AMA Style

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

Chicago/Turabian Style

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

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

ACS Style

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

AMA Style

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

Chicago/Turabian Style

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

Journal article
Published: 18 May 2019 in Remote Sensing
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The alpine grassland on the Qinghai-Tibet Plateau covers an area of about 1/3 of China’s total grassland area and plays a crucial role in regulating grassland ecological functions. Both environmental changes and irrational use of the grassland can result in severe grassland degradation in some areas of the Qinghai-Tibet Plateau. However, the magnitude and patterns of the physical and anthropogenic factors in driving grassland variation over northern Tibet remain debatable, and the interactive influences among those factors are still unclear. In this study, we employed a geographical detector model to quantify the primary and interactive impacts of both the physical factors (precipitation, temperature, sunshine duration, soil type, elevation, slope, and aspect) and the anthropogenic factors (population density, road density, residential density, grazing density, per capita GDP, and land use type) on vegetation variation from 2000 to 2015 in northern Tibet. Our results show that the vegetation index in northern Tibet significantly decreased from 2000 to 2015. Overall, the stability of vegetation types was sorted as follows: the alpine scrub > the alpine steppe > the alpine meadow. The physical factors, rather than the anthropogenic factors, have been the primary driving factors for vegetation dynamics in northern Tibet. Specifically, meteorological factors best explained the alpine meadow and alpine steppe variation. Precipitation was the key factor that influenced the alpine meadow variation, whereas temperature was the key factor that contributed to the alpine steppe variation. The anthropogenic factors, such as population density, grazing density and per capita GDP, influenced the alpine scrub variation most. The influence of population density is highly similar to that of grazing density, which may provide convenient access to simplify the study of the anthropogenic activities in the Tibet plateau. The interactions between the driving factors had larger effects on vegetation than any single factor. In the alpine meadow, the interaction between precipitation and temperature can explain 44.6% of the vegetation variation. In the alpine scrub, the interaction between temperature and GDP was the highest, accounting for 27.5% of vegetation variation. For the alpine steppe, the interaction between soil type and population density can explain 29.4% of the vegetation variation. The highest value of vegetation degradation occurred in the range of 448–469 mm rainfall in the alpine meadow, 0.61–1.23 people/km2 in the alpine scrub and –0.83–0.15 °C in the alpine steppe, respectively. These findings could contribute to a better understanding of degradation prevention and sustainable development of the alpine grassland ecosystem in northern Tibet.

ACS Style

Qinwei Ran; Yanbin Hao; Anquan Xia; Wenjun Liu; Ronghai Hu; Xiaoyong Cui; Kai Xue; Xiaoning Song; Cong Xu; Boyang Ding; Yanfen Wang. Quantitative Assessment of the Impact of Physical and Anthropogenic Factors on Vegetation Spatial-Temporal Variation in Northern Tibet. Remote Sensing 2019, 11, 1183 .

AMA Style

Qinwei Ran, Yanbin Hao, Anquan Xia, Wenjun Liu, Ronghai Hu, Xiaoyong Cui, Kai Xue, Xiaoning Song, Cong Xu, Boyang Ding, Yanfen Wang. Quantitative Assessment of the Impact of Physical and Anthropogenic Factors on Vegetation Spatial-Temporal Variation in Northern Tibet. Remote Sensing. 2019; 11 (10):1183.

Chicago/Turabian Style

Qinwei Ran; Yanbin Hao; Anquan Xia; Wenjun Liu; Ronghai Hu; Xiaoyong Cui; Kai Xue; Xiaoning Song; Cong Xu; Boyang Ding; Yanfen Wang. 2019. "Quantitative Assessment of the Impact of Physical and Anthropogenic Factors on Vegetation Spatial-Temporal Variation in Northern Tibet." Remote Sensing 11, no. 10: 1183.

Journal article
Published: 18 March 2019 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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The spatial scale variability of surface shortwave radiation (SSR) budget is dominated by clouds. Therefore, a prerequisite step for accurate estimation of the instantaneous SSR components under all-sky conditions, especially partly cloudy conditions, is to determine a proper spatial scale. In this study, a modified one-dimensional (1-D) radiative transfer (RT) model is developed to study the effect of cloud cover on SSR field, in which cloud fraction (CF) is introduced to improve the classic 1-D plane-parallel RT equations and a global sensitivity analysis (GSA) is performed to quantitatively understand the effect of CF on SSR under both optically thick and optically thin cloudy conditions. An artificial neural network approach is then employed to generate multiple SSR components based on extensive RT simulations using the modified 1-D RT model and the GSA results. The optimal spatial scale is quantitatively determined through eight cloudy scenarios over the Tibetan Plateau, with different amounts and spatial distribution patterns of clouds. The GSA results show that the top three vital parameters for the modified 1-D RT model are solar zenith angle, CF, and land surface albedo. The optimal spatial scale for applying the modified 1-D RT model is about 20 km, which is surprisingly consistent with some theoretically and technically complicated simulation studies. The finding of the current study adds new evidence to the growing body of knowledge about the spatial scale consideration for estimating all-sky instantaneous SSR with 1-D RT theory.

ACS Style

Ling Chen; Guangjian Yan; Tianxing Wang; Huazhong Ren; Ronghai Hu; Shengbo Chen; Hongmin Zhou. Spatial Scale Consideration for Estimating All-Sky Surface Shortwave Radiation With a Modified 1-D Radiative Transfer Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2019, 12, 821 -835.

AMA Style

Ling Chen, Guangjian Yan, Tianxing Wang, Huazhong Ren, Ronghai Hu, Shengbo Chen, Hongmin Zhou. Spatial Scale Consideration for Estimating All-Sky Surface Shortwave Radiation With a Modified 1-D Radiative Transfer Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2019; 12 (3):821-835.

Chicago/Turabian Style

Ling Chen; Guangjian Yan; Tianxing Wang; Huazhong Ren; Ronghai Hu; Shengbo Chen; Hongmin Zhou. 2019. "Spatial Scale Consideration for Estimating All-Sky Surface Shortwave Radiation With a Modified 1-D Radiative Transfer Model." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, no. 3: 821-835.

Review
Published: 06 December 2018 in Agricultural and Forest Meteorology
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Leaf area index (LAI) is a key parameter of vegetation structure in the fields of agriculture, forestry, and ecology. Optical indirect methods based on the Beer-Lambert law are widely adopted in numerous fields given their high efficiency and feasibility for LAI estimation. These methods have undergone considerable progress in the past decades, thereby making them operational in ground-based LAI measurement and even in airborne estimation. However, several challenges remain, given the requirement of increasing accuracy and new applications. Clumping effect correction attained significant progress for continuous canopies with non-randomly disturbed leaves while non-continuous canopies are rarely studied. Convenient and operational measurement of leaf angle distribution and woody components is lacked. Accurate and comprehensive validations are still very difficult due to the limitations of direct measurement. The introduction of active laser scanning technology is a driving force for addressing several challenges, but its three-dimensional information has not been fully explored and utilized. In order to update the general knowledge and identify the possible error source, this study comprehensively reviews the temporal development, theoretical framework, and issues of indirect LAI measurement, followed by current methods, instruments, and platforms. Latest methods and instruments are introduced and compared to traditional ones. Current challenges, recent advances, and future perspectives are discussed to provide recommendations for further research.

ACS Style

Guangjian Yan; Ronghai Hu; Jinghui Luo; Marie Weiss; Hailan Jiang; Xihan Mu; Donghui Xie; Wuming Zhang. Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives. Agricultural and Forest Meteorology 2018, 265, 390 -411.

AMA Style

Guangjian Yan, Ronghai Hu, Jinghui Luo, Marie Weiss, Hailan Jiang, Xihan Mu, Donghui Xie, Wuming Zhang. Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives. Agricultural and Forest Meteorology. 2018; 265 ():390-411.

Chicago/Turabian Style

Guangjian Yan; Ronghai Hu; Jinghui Luo; Marie Weiss; Hailan Jiang; Xihan Mu; Donghui Xie; Wuming Zhang. 2018. "Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives." Agricultural and Forest Meteorology 265, no. : 390-411.

Journal article
Published: 14 September 2018 in Agricultural and Forest Meteorology
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Terrestrial Laser Scanning (TLS) is an active technology that can acquire the finest characteristics of canopy structure and plays an increasing role in estimating Leaf Area Index (LAI) in forest canopies. However, 3D information is not directly used in conventional TLS-based methods using the gap fraction theory. In addition, quantifying clumping effect within canopies is still a difficult task. In this paper, we presented a method to reduce clumping effect and estimate LAI using TLS data. Our recently proposed path length distribution model was applied to TLS data. Instead of converting 3D points to 2D image, the path length distribution can be extracted using the TLS-recorded 3D data and the crown models built with the alpha shapes algorithm. Two simulated scenes and one actual forest plot were utilized for validation. The results of the proposed method agree well with both the true LAI (in the simulated scenes) and the extracted PAI by the digital hemispherical photography (in the actual plot). This LAI estimation method using TLS and the path length distribution model provides a novel way for ground-based LAI measurements and shows its great potential.

ACS Style

Yiming Chen; Wuming Zhang; Ronghai Hu; Jianbo Qi; Jie Shao; Dan Li; Peng Wan; Chen Qiao; Aojie Shen; Guangjian Yan. Estimation of forest leaf area index using terrestrial laser scanning data and path length distribution model in open-canopy forests. Agricultural and Forest Meteorology 2018, 263, 323 -333.

AMA Style

Yiming Chen, Wuming Zhang, Ronghai Hu, Jianbo Qi, Jie Shao, Dan Li, Peng Wan, Chen Qiao, Aojie Shen, Guangjian Yan. Estimation of forest leaf area index using terrestrial laser scanning data and path length distribution model in open-canopy forests. Agricultural and Forest Meteorology. 2018; 263 ():323-333.

Chicago/Turabian Style

Yiming Chen; Wuming Zhang; Ronghai Hu; Jianbo Qi; Jie Shao; Dan Li; Peng Wan; Chen Qiao; Aojie Shen; Guangjian Yan. 2018. "Estimation of forest leaf area index using terrestrial laser scanning data and path length distribution model in open-canopy forests." Agricultural and Forest Meteorology 263, no. : 323-333.

Journal article
Published: 14 August 2018 in ISPRS Journal of Photogrammetry and Remote Sensing
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Urban leaf area measurement is crucial to properly determining the effect of urban trees on micro-climate regulation, heat island effect, building cooling, air quality improvement, and ozone formation. Previous works on the leaf area measurement have mainly focused on the stand level, although the presence of individual trees is more common than forests in urban areas. The only feasible ways for an operational non-destructive leaf area measurement, namely, optical indirect methods, are mostly limited in urban areas because light path is constantly intercepted by surrounding buildings or other objects. A terrestrial laser scanner (TLS), which can extract an individual tree by using its unique distance information, provides a possibility for indirectly measuring the leaf area index (LAI) in urban areas. However, indirect LAI measurement theory, which uses the cosine of an observation zenith angle for path-length correction, is incompatible for an individual tree because the representative projected area of LAI changes while the observation zenith angle changes, thus making the results incomparable and ambiguous. Therefore, we modified a path length distribution model for the leaf area measurement of an individual tree by replacing the traditional cosine path length correction for a continuous canopy with real path length distribution. We reconstructed the tree crown envelope from a TLS point cloud and calculated a real path length distribution through laser pulse-envelope intersections. Consequently, leaf area density was separated from the path length distribution model for leaf area calculation. Comparisons with reference measurement for an individual tree showed that the TLS-derived leaf area using the path length distribution is insensitive to the scanning resolution and agrees well with an allometric measurement with an overestimation from 5 m2 to 18 m2 (3–10%, respectively). Results from different stations are globally consistent, and using a weighted mean for different stations by sample numbers further improves the universality and efficiency of the proposed method. Further automation of the proposed method can facilitate a rapid and operational leaf area extraction of an individual tree for urban climate modeling.

ACS Style

Ronghai Hu; Elena Bournez; Shiyu Cheng; Hailan Jiang; Françoise Nerry; Tania Landes; Marc Saudreau; Pierre Kastendeuch; Georges Najjar; Jérôme Colin; Guangjian Yan. Estimating the leaf area of an individual tree in urban areas using terrestrial laser scanner and path length distribution model. ISPRS Journal of Photogrammetry and Remote Sensing 2018, 144, 357 -368.

AMA Style

Ronghai Hu, Elena Bournez, Shiyu Cheng, Hailan Jiang, Françoise Nerry, Tania Landes, Marc Saudreau, Pierre Kastendeuch, Georges Najjar, Jérôme Colin, Guangjian Yan. Estimating the leaf area of an individual tree in urban areas using terrestrial laser scanner and path length distribution model. ISPRS Journal of Photogrammetry and Remote Sensing. 2018; 144 ():357-368.

Chicago/Turabian Style

Ronghai Hu; Elena Bournez; Shiyu Cheng; Hailan Jiang; Françoise Nerry; Tania Landes; Marc Saudreau; Pierre Kastendeuch; Georges Najjar; Jérôme Colin; Guangjian Yan. 2018. "Estimating the leaf area of an individual tree in urban areas using terrestrial laser scanner and path length distribution model." ISPRS Journal of Photogrammetry and Remote Sensing 144, no. : 357-368.

Journal article
Published: 26 March 2018 in IEEE Transactions on Geoscience and Remote Sensing
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The airborne laser scanner (ALS) provides great potential for mapping the leaf area index (LAI) at the landscape scale using grid cell statistics, while its application is restricted by the lack of clumping information, which has been an unsolved issue highlighted for a long time. ALS generally provides an effective LAI because its footprint is too large to capture small gaps to apply traditional ground-based clumping correction methods. Here, we present a grid cell method based on path length distribution model to calculate the clumping-corrected LAI using ALS data without the requirement of additional field measurements. We separated the within- and between-crown areas to consider between-crown clumping, and used the path length distribution as estimated by local canopy height distribution to consider 3-D foliage profile and within-crown clumping. The path length distribution model takes advantage of the 3-D information rather than the gap size distribution, thus avoiding the limitation of large ALS footprint. With the 0.4-m-footprint ALS data, the results are generally promising and a multilevel clumping analysis is consistent with landscape flown. The ALS LAIs of different resolutions are consistent, with a difference of less than 5% from 5- to 250-m resolutions. Due to its consistency and simple configuration, the method provides an opportunity to map the clumping-corrected LAI operationally and strengthens the ability of airborne lidar to monitor vegetation change and validate the satellite product. This grid cell method based on path length distribution is worth further testing and application using more recent laser technology.

ACS Style

Ronghai Hu; Guangjian Yan; Francoise Nerry; Yunshu Liu; Yumeng Jiang; Shuren Wang; Yiming Chen; Xihan Mu; Wuming Zhang; Donghui Xie. Using Airborne Laser Scanner and Path Length Distribution Model to Quantify Clumping Effect and Estimate Leaf Area Index. IEEE Transactions on Geoscience and Remote Sensing 2018, 56, 3196 -3209.

AMA Style

Ronghai Hu, Guangjian Yan, Francoise Nerry, Yunshu Liu, Yumeng Jiang, Shuren Wang, Yiming Chen, Xihan Mu, Wuming Zhang, Donghui Xie. Using Airborne Laser Scanner and Path Length Distribution Model to Quantify Clumping Effect and Estimate Leaf Area Index. IEEE Transactions on Geoscience and Remote Sensing. 2018; 56 (6):3196-3209.

Chicago/Turabian Style

Ronghai Hu; Guangjian Yan; Francoise Nerry; Yunshu Liu; Yumeng Jiang; Shuren Wang; Yiming Chen; Xihan Mu; Wuming Zhang; Donghui Xie. 2018. "Using Airborne Laser Scanner and Path Length Distribution Model to Quantify Clumping Effect and Estimate Leaf Area Index." IEEE Transactions on Geoscience and Remote Sensing 56, no. 6: 3196-3209.

Journal article
Published: 01 November 2017 in Agricultural and Forest Meteorology
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ACS Style

Xihan Mu; Ronghai Hu; Yelu Zeng; Tim R. McVicar; Huazhong Ren; Wanjuan Song; Yuanyuan Wang; Raffaele Casa; Jianbo Qi; Donghui Xie; Guangjian Yan. Estimating structural parameters of agricultural crops from ground-based multi-angular digital images with a fractional model of sun and shade components. Agricultural and Forest Meteorology 2017, 246, 162 -177.

AMA Style

Xihan Mu, Ronghai Hu, Yelu Zeng, Tim R. McVicar, Huazhong Ren, Wanjuan Song, Yuanyuan Wang, Raffaele Casa, Jianbo Qi, Donghui Xie, Guangjian Yan. Estimating structural parameters of agricultural crops from ground-based multi-angular digital images with a fractional model of sun and shade components. Agricultural and Forest Meteorology. 2017; 246 ():162-177.

Chicago/Turabian Style

Xihan Mu; Ronghai Hu; Yelu Zeng; Tim R. McVicar; Huazhong Ren; Wanjuan Song; Yuanyuan Wang; Raffaele Casa; Jianbo Qi; Donghui Xie; Guangjian Yan. 2017. "Estimating structural parameters of agricultural crops from ground-based multi-angular digital images with a fractional model of sun and shade components." Agricultural and Forest Meteorology 246, no. : 162-177.

Journal article
Published: 01 July 2017 in Journal of Applied Remote Sensing
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Terrestrial laser scanners (TLS) have demonstrated great potential in estimating structural attributes of forest canopy, such as leaf area index (LAI). However, the inversion accuracy of LAI is highly dependent on the measurement configuration of TLS and spatial characteristics of the scanned tree. Therefore, a modified gap fraction model integrating the path length distribution is developed to improve the accuracy of retrieved single-tree leaf area (LA) by considering the shape of a single-tree crown. The sensitivity of TLS measurement configurations on the accuracy of the retrieved LA is also discussed by using the modified gap fraction model based on several groups of simulated and field-measured point clouds. We conclude that (1) the modified gap fraction model has the potential to retrieve LA of an individual tree and (2) scanning distance has the enhanced impact on the accuracy of the retrieved LA than scanning step. A small scanning step for broadleaf trees reduces the scanning time, the storage volume, and postprocessing work in the condition of ensuring the accuracy of the retrieved LA. This work can benefit the design of an optimal survey configuration for the field campaign.

ACS Style

Donghui Xie; Yan Wang; Ronghai Hu; Yiming Chen; Guangjian Yan; Wuming Zhang; Peijuan Wang. Modified gap fraction model of individual trees for estimating leaf area using terrestrial laser scanner. Journal of Applied Remote Sensing 2017, 11, 035012 .

AMA Style

Donghui Xie, Yan Wang, Ronghai Hu, Yiming Chen, Guangjian Yan, Wuming Zhang, Peijuan Wang. Modified gap fraction model of individual trees for estimating leaf area using terrestrial laser scanner. Journal of Applied Remote Sensing. 2017; 11 (3):035012.

Chicago/Turabian Style

Donghui Xie; Yan Wang; Ronghai Hu; Yiming Chen; Guangjian Yan; Wuming Zhang; Peijuan Wang. 2017. "Modified gap fraction model of individual trees for estimating leaf area using terrestrial laser scanner." Journal of Applied Remote Sensing 11, no. 3: 035012.

Conference paper
Published: 03 November 2016 in 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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Spatial scale effects, defined as the phenomenon that the estimates at multiple resolution are inconsistent, have aroused wide concerns in remote sensing studies. But very few studies have paid attention to the effects of scale on phenological studies. This paper investigated the scale effects in estimating phenological transitional dates from remote sensing data. A prior-knowledge vegetation index (VI) time series at 30 m resolution was composed, based on which the time series at 240 m, 480 m and 960 m were derived. The green-up onset and dormancy onset dates were then estimated from the VI time series using a double-logistic plant growth model. The derived estimates at multiple resolutions were compared and the effects of spatial scales were verified. Landscape heterogeneities were found to be related to spatial scale effects. The changes in the estimated green-up onset dates and dormancy onset dates exhibited different patterns with the coarsening of spatial resolution.

ACS Style

Yiting Wang; Donghui Xie; Ronghai Hu; Guangjian Yan. Spatial scale effect on vegetation phenological analysis using remote sensing data. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2016, 1329 -1332.

AMA Style

Yiting Wang, Donghui Xie, Ronghai Hu, Guangjian Yan. Spatial scale effect on vegetation phenological analysis using remote sensing data. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2016; ():1329-1332.

Chicago/Turabian Style

Yiting Wang; Donghui Xie; Ronghai Hu; Guangjian Yan. 2016. "Spatial scale effect on vegetation phenological analysis using remote sensing data." 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , no. : 1329-1332.

Journal article
Published: 10 June 2016 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Spatial heterogeneity within canopies and woody components are two factors that limit the accuracy of indirect leaf area index (LAI) measurements, but they have not been fully considered because of the limitations of commercial instruments. This study combined the path length distribution model and multispectral canopy imager for the first time to improve the accuracy of indirect LAI measurements. Indirect and direct in situ measurements were conducted in broadleaf and coniferous forests. Results show that spatial heterogeneity within canopies underestimates the LAI by 16–25%, whereas woody components overestimate LAI by 14–28% in four forest sites. These two factors exhibit opposing effects, which may be misleading and may thus complicate the quantification and validation of the effect of each factor. Ignoring woody components underestimates the degree of spatial heterogeneity or clumping in forests. Considering both nonrandomness within canopies and woody components is necessary in indirect LAI measurements.

ACS Style

Ronghai Hu; Jinghui Luo; Guangjian Yan; Jie Zou; Xihan Mu. Indirect Measurement of Forest Leaf Area Index Using Path Length Distribution Model and Multispectral Canopy Imager. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2016, 9, 2532 -2539.

AMA Style

Ronghai Hu, Jinghui Luo, Guangjian Yan, Jie Zou, Xihan Mu. Indirect Measurement of Forest Leaf Area Index Using Path Length Distribution Model and Multispectral Canopy Imager. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2016; 9 (6):2532-2539.

Chicago/Turabian Style

Ronghai Hu; Jinghui Luo; Guangjian Yan; Jie Zou; Xihan Mu. 2016. "Indirect Measurement of Forest Leaf Area Index Using Path Length Distribution Model and Multispectral Canopy Imager." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, no. 6: 2532-2539.