101
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Discrimination of Canopy Structural Types in the Sierra Nevada Mountains in Central California. REMOTE SENSING 2019. [DOI: 10.3390/rs11091100] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Accurate information about ecosystem structure and biogeochemical properties is essential to providing better estimates ecosystem functioning. Airborne LiDAR (light detection and ranging) is the most accurate way to retrieve canopy structure. However, accurately obtaining both biogeochemical traits and structure parameters requires concurrent measurements from imaging spectrometers and LiDARs. Our main objective was to evaluate the use of imaging spectroscopy (IS) to provide vegetation structural information. We developed models to estimate structural variables (i.e., biomass, height, vegetation heterogeneity and clumping) using IS data with a random forests model from three forest ecosystems (i.e., an oak-pine low elevation savanna, a mixed conifer/broadleaf mid-elevation forest, and a high-elevation montane conifer forest) in the Sierra Nevada Mountains, California. We developed and tested general models to estimate the four structural variables with accuracies greater than 75%, for the structurally and ecologically different forest sites, demonstrating their applicability to a diverse range of forest ecosystems. The model R2 for each structural variable was least in the conifer/broadleaf forest than either the low elevation savanna or the montane conifer forest. We then used the structural variables we derived to discriminate site-specific, ecologically meaningful descriptions of canopy structural types (CST). Our CST results demonstrate how IS data can be used to create comprehensive and easily interpretable maps of forest structural types that capture their major structural features and trends across different vegetation types in the Sierra Nevada Mountains. The mixed conifer/broadleaf forest and montane conifer forest had the most complex structures, containing six and five CSTs respectively. The identification of CSTs within a site allowed us to better identify the main drivers of structural variability in each ecosystem. CSTs in open savanna were driven mainly by differences in vegetation cover; in the mid-elevation mixed forest, by the combination of biomass and canopy height; and in the montane conifer forest, by vegetation heterogeneity and clumping.
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102
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Kattenborn T, Schmidtlein S. Radiative transfer modelling reveals why canopy reflectance follows function. Sci Rep 2019; 9:6541. [PMID: 31024052 PMCID: PMC6484002 DOI: 10.1038/s41598-019-43011-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 04/15/2019] [Indexed: 11/25/2022] Open
Abstract
Optical remote sensing is potentially highly informative to track Earth's plant functional diversity. Yet, causal explanations of how and why plant functioning is expressed in canopy reflectance remain limited. Variation in canopy reflectance can be described by radiative transfer models (here PROSAIL) that incorporate plant traits affecting light transmission in canopies. To establish causal links between canopy reflectance and plant functioning, we investigate how two plant functional schemes, i.e. the Leaf Economic Spectrum (LES) and CSR plant strategies, are related to traits with relevance to reflectance. These traits indeed related to both functional schemes, whereas only traits describing leaf properties correlated with the LES. In contrast, traits related to canopy structure showed no correlation to the LES, but to CSR strategies, as the latter integrates both plant economics and size traits, rather than solely leaf economics. Multiple optically relevant traits featured comparable or higher correspondence to the CSR space than those traits originally used to allocate CSR scores. This evidences that plant functions and strategies are directly expressed in reflectance and entails that canopy 'reflectance follows function'. This opens up new possibilities to understand differences in plant functioning and to harness optical remote sensing data for monitoring Earth´s functional diversity.
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Affiliation(s)
- Teja Kattenborn
- Institute of Geography and Geoecology (IFGG), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131, Karlsruhe, Germany.
| | - Sebastian Schmidtlein
- Institute of Geography and Geoecology (IFGG), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131, Karlsruhe, Germany
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103
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A Random Forest Machine Learning Approach for the Retrieval of Leaf Chlorophyll Content in Wheat. REMOTE SENSING 2019. [DOI: 10.3390/rs11080920] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Developing rapid and non-destructive methods for chlorophyll estimation over large spatial areas is a topic of much interest, as it would provide an indirect measure of plant photosynthetic response, be useful in monitoring soil nitrogen content, and offer the capacity to assess vegetation structural and functional dynamics. Traditional methods of direct tissue analysis or the use of handheld meters, are not able to capture chlorophyll variability at anything beyond point scales, so are not particularly useful for informing decisions on plant health and status at the field scale. Examining the spectral response of plants via remote sensing has shown much promise as a means to capture variations in vegetation properties, while offering a non-destructive and scalable approach to monitoring. However, determining the optimum combination of spectra or spectral indices to inform plant response remains an active area of investigation. Here, we explore the use of a machine learning approach to enhance the estimation of leaf chlorophyll (Chlt), defined as the sum of chlorophyll a and b, from spectral reflectance data. Using an ASD FieldSpec 4 Hi-Res spectroradiometer, 2700 individual leaf hyperspectral reflectance measurements were acquired from wheat plants grown across a gradient of soil salinity and nutrient levels in a greenhouse experiment. The extractable Chlt was determined from laboratory analysis of 270 collocated samples, each composed of three leaf discs. A random forest regression algorithm was trained against these data, with input predictors based upon (1) reflectance values from 2102 bands across the 400–2500 nm spectral range; and (2) 45 established vegetation indices. As a benchmark, a standard univariate regression analysis was performed to model the relationship between measured Chlt and the selected vegetation indices. Results show that the root mean square error (RMSE) was significantly reduced when using the machine learning approach compared to standard linear regression. When exploiting the entire spectral range of individual bands as input variables, the random forest estimated Chlt with an RMSE of 5.49 µg·cm−2 and an R2 of 0.89. Model accuracy was improved when using vegetation indices as input variables, producing an RMSE ranging from 3.62 to 3.91 µg·cm−2, depending on the particular combination of indices selected. In further analysis, input predictors were ranked according to their importance level, and a step-wise reduction in the number of input features (from 45 down to 7) was performed. Implementing this resulted in no significant effect on the RMSE, and showed that much the same prediction accuracy could be obtained by a smaller subset of indices. Importantly, the random forest regression approach identified many important variables that were not good predictors according to their linear regression statistics. Overall, the research illustrates the promise in using established vegetation indices as input variables in a machine learning approach for the enhanced estimation of Chlt from hyperspectral data.
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104
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Analysis of Sentinel-2 and RapidEye for Retrieval of Leaf Area Index in a Saltmarsh Using a Radiative Transfer Model. REMOTE SENSING 2019. [DOI: 10.3390/rs11060671] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The Sentinel satellite fleet of the Copernicus Programme offers new potential to map and monitor plant traits at fine spatial and temporal resolutions. Among these traits, leaf area index (LAI) is a crucial indicator of vegetation growth and an essential variable in biodiversity studies. Numerous studies have shown that the radiative transfer approach has been a successful method to retrieve LAI from remote-sensing data. However, the suitability and adaptability of this approach largely depend on the type of remote-sensing data, vegetation cover and the ecosystem studied. Saltmarshes are important wetland ecosystems threatened by sea level rise among other human- and animal-induced changes. Therefore, monitoring their vegetation status is crucial for their conservation, yet few LAI assessments exist for these ecosystems. In this study, the retrieval of LAI in a saltmarsh ecosystem is examined using Sentinel-2 and RapidEye data through inversion of the PROSAIL radiative transfer model. Field measurements of LAI and some other plant traits were obtained during two succeeding field campaigns in July 2015 and 2016 on the saltmarsh of Schiermonnikoog, a barrier island of the Netherlands. RapidEye (2015) and Sentinel-2 (2016) data were acquired concurrent to the time of the field campaigns. The broadly employed PROSAIL model was inverted using two look-up tables (LUTs) generated in the spectral band’s settings of the two sensors and in which each contained 500,000 records. Different solutions from the LUTs, as well as, different Sentinel-2 spectral subsets were considered to examine the LAI retrieval. Our results showed that generally the LAI retrieved from Sentinel-2 had higher accuracy compared to RapidEye-retrieved LAI. Utilising the mean of the first 10 best solutions from the LUTs resulted in higher R2 (0.51 and 0.59) and lower normalised root means square error (NRMSE) (0.24 and 0.16) for both RapidEye and Sentinel-2 data respectively. Among different Sentinel-2 spectral subsets, the one comprised of the four near-infrared (NIR) and shortwave infrared (SWIR) spectral bands resulted in higher estimation accuracy (R2 = 0.44, NRMSE = 0.21) in comparison to using other studied spectral subsets. The results demonstrated the feasibility of broadband multispectral sensors, particularly Sentinel-2 for retrieval of LAI in the saltmarsh ecosystem via inversion of PROSAIL. Our results highlight the importance of proper parameterisation of radiative transfer models and capacity of Sentinel-2 spectral range and resolution, with impending high-quality global observation aptitude, for retrieval of plant traits at a global scale.
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105
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Assessing Across-Scale Optical Diversity and Productivity Relationships in Grasslands of the Italian Alps. REMOTE SENSING 2019. [DOI: 10.3390/rs11060614] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The linearity and scale-dependency of ecosystem biodiversity and productivity relationships (BPRs) have been under intense debate. In a changing climate, monitoring BPRs within and across different ecosystem types is crucial, and novel remote sensing tools such as the Sentinel-2 (S2) may be adopted to retrieve ecosystem diversity information and to investigate optical diversity and productivity patterns. But are the S2 spectral and spatial resolutions suitable to detect relationships between optical diversity and productivity? In this study, we implemented an integrated analysis of spatial patterns of grassland productivity and optical diversity using optical remote sensing and Eddy Covariance data. Across-scale optical diversity and ecosystem productivity patterns were analyzed for different grassland associations with a wide range of productivity. Using airborne optical data to simulate S2, we provided empirical evidence that the best optical proxies of ecosystem productivity were linearly correlated with optical diversity. Correlation analysis at increasing pixel sizes proved an evident scale-dependency of the relationships between optical diversity and productivity. The results indicate the strong potential of S2 for future large-scale assessment of across-ecosystem dynamics at upper levels of observation.
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106
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El-Hendawy S, Al-Suhaibani N, Elsayed S, Refay Y, Alotaibi M, Dewir YH, Hassan W, Schmidhalter U. Combining biophysical parameters, spectral indices and multivariate hyperspectral models for estimating yield and water productivity of spring wheat across different agronomic practices. PLoS One 2019; 14:e0212294. [PMID: 30840631 PMCID: PMC6402754 DOI: 10.1371/journal.pone.0212294] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 01/30/2019] [Indexed: 11/18/2022] Open
Abstract
Manipulating plant densities under different irrigation rates can have a significant impact on grain yield and water use efficiency by exerting positive or negative effects on ET. Whereas traditional spectral reflectance indices (SRIs) have been used to assess biophysical parameters and yield, the potential of multivariate models has little been investigated to estimate these parameters under multiple agronomic practices. Therefore, both simple indices and multivariate models (partial least square regression (PLSR) and support vector machines (SVR)) obtained from hyperspectral reflectance data were compared for their applicability for assessing the biophysical parameters in a field experiment involving different combinations of three irrigation rates (1.00, 0.75, and 0.50 ET) and five plant densities (D1: 150, D2: 250, D3: 350, D4: 450, and D5: 550 seeds m-2) in order to improve productivity and water use efficiency of wheat. Results show that the highest values for green leaf area, aboveground biomass, and grain yield were obtained from the combination of D3 or D4 with 1.00 ET, while the combination of 0.75 ET and D3 was the best treatment for achieving the highest values for water use efficiency. Wheat yield response factor (ky) was acceptable when the 0.75 ET was combined with D2, D3, or D4 or when the 0.50 ET was combined with D2 or D3, as the ky values of these combinations were less than or around one. The production function indicated that about 75% grain yield variation could be attributed to the variation in seasonal ET. Results also show that the performance of the SRIs fluctuated when regressions were analyzed for each irrigation rate or plant density specifically, or when the data of all irrigation rates or plant densities were combined. Most of the SRIs failed to assess biophysical parameters under specific irrigation rates and some specific plant densities, but performance improved substantially for combined data of irrigation rates and some specific plant densities. PLSR and SVR produced more accurate estimations of biophysical parameters than SRIs under specific irrigation rates and plant densities. In conclusion, hyperspectral data are useful for predicting and monitoring yield and water productivity of spring wheat across multiple agronomic practices.
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Affiliation(s)
- Salah El-Hendawy
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
- Department of Agronomy, Faculty of Agriculture, Suez Canal University, Ismailia, Egypt
- * E-mail:
| | - Nasser Al-Suhaibani
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Salah Elsayed
- Evaluation of Natural Resources Department, Environmental Studies and Research Institute, Sadat City University, Menoufia, Egypt
| | - Yahya Refay
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Majed Alotaibi
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Yaser Hassan Dewir
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
- Department of Horticulture, Faculty of Agriculture, Kafrelsheikh University, Kafr El Sheikh, Egypt
| | - Wael Hassan
- Department of Biology, College of Science and Humanities at Quwayiah, Shaqra University, Riyadh, Saudi Arabia
- Department of Agricultural Botany, Faculty of Agriculture, Suez Canal University, Ismailia, Egypt
| | - Urs Schmidhalter
- Chair of Plant Nutrition, Department of Plant Sciences, Technical University of Munich, Freising-Weihenstephan, Freising, Germany
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107
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Spectral Heterogeneity Predicts Local-Scale Gamma and Beta Diversity of Mesic Grasslands. REMOTE SENSING 2019. [DOI: 10.3390/rs11040458] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Plant species diversity is an important metric of ecosystem functioning, but field assessments of diversity are constrained in number and spatial extent by labor and other expenses. We tested the utility of using spatial heterogeneity in the remotely-sensed reflectance spectrum of grassland canopies to model both spatial turnover in species composition and abundances (β diversity) and species diversity at aggregate spatial scales (γ diversity). Shannon indices of γ and β diversity were calculated from field measurements of the number and relative abundances of plant species at each of two spatial grains (0.45 m2 and 35.2 m2) in mesic grasslands in central Texas, USA. Spectral signatures of reflected radiation at each grain were measured from ground-level or an unmanned aerial vehicle (UAV). Partial least squares regression (PLSR) models explained 59–85% of variance in γ diversity and 68–79% of variance in β diversity using spatial heterogeneity in canopy optical properties. Variation in both γ and β diversity were associated most strongly with heterogeneity in reflectance in blue (350–370 nm), red (660–770 nm), and near infrared (810–1050 nm) wavebands. Modeled diversity was more sensitive by a factor of three to a given level of spectral heterogeneity when derived from data collected at the small than larger spatial grain. As estimated from calibrated PLSR models, β diversity was greater, but γ diversity was smaller for restored grassland on a lowland clay than upland silty clay soil. Both γ and β diversity of grassland can be modeled by using spatial heterogeneity in vegetation optical properties provided that the grain of reflectance measurements is conserved.
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108
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Pasqualotto N, Delegido J, Van Wittenberghe S, Rinaldi M, Moreno J. Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI). SENSORS (BASEL, SWITZERLAND) 2019; 19:E904. [PMID: 30795571 PMCID: PMC6412664 DOI: 10.3390/s19040904] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 02/05/2019] [Accepted: 02/18/2019] [Indexed: 12/04/2022]
Abstract
The spatial quantification of green leaf area index (LAIgreen), the total green photosynthetically active leaf area per ground area, is a crucial biophysical variable for agroecosystem monitoring. The Sentinel-2 mission is with (1) a temporal resolution lower than a week, (2) a spatial resolution of up to 10 m, and (3) narrow bands in the red and red-edge region, a highly promising mission for agricultural monitoring. The aim of this work is to define an easy implementable LAIgreen index for the Sentinel-2 mission. Two large and independent multi-crop datasets of in situ collected LAIgreen measurements were used. Commonly used LAIgreen indices applied on the Sentinel-2 10 m × 10 m pixel resulted in a validation R² lower than 0.6. By calculating all Sentinel-2 band combinations to identify high correlation and physical basis with LAIgreen, the new Sentinel-2 LAIgreen Index (SeLI) was defined. SeLI is a normalized index that uses the 705 nm and 865 nm centered bands, exploiting the red-edge region for low-saturating absorption sensitivity to photosynthetic vegetation. A R² of 0.708 (root mean squared error (RMSE) = 0.67) and a R² of 0.732 (RMSE = 0.69) were obtained with a linear fitting for the calibration and validation datasets, respectively, outperforming established indices. Sentinel-2 LAIgreen maps are presented.
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Affiliation(s)
- Nieves Pasqualotto
- Image Processing Laboratory (IPL), University of Valencia, 46980 Valencia, Spain.
| | - Jesús Delegido
- Image Processing Laboratory (IPL), University of Valencia, 46980 Valencia, Spain.
| | | | - Michele Rinaldi
- Council for Agricultural Research and Economics-Research Centre for Cereal and Industrial Crops, S.S. 673 km 25, 200, 71122 Foggia, Italy.
| | - José Moreno
- Image Processing Laboratory (IPL), University of Valencia, 46980 Valencia, Spain.
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109
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Improving Forest Aboveground Biomass (AGB) Estimation by Incorporating Crown Density and Using Landsat 8 OLI Images of a Subtropical Forest in Western Hunan in Central China. FORESTS 2019. [DOI: 10.3390/f10020104] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Forest aboveground biomass (AGB) estimation modeling based on remote sensing is an important method for large-scale biomass estimation; the accuracy of the estimation models has been a topic of broad and current interest. In this study, we used permanent sample plot data and Landsat 8 Operational Land Imager (OLI) images of western Hunan. Remote-sensing-based models were developed for different vegetation types, and different crown density classes were incorporated. The linear model, linear dummy variable model, and linear mixed-effects model were used to determine the most effective and accurate method for remote-sensing-based AGB estimation. The results show that the adjusted coefficient of determination (R2adj) and root mean square error (RMSE) of the linear dummy model and linear mixed-effects model were significantly better than those of the linear model; the R2adj increased more than 0.16 and the RMSE decreased more than 2.12 for each vegetation type, and the F-test also showed significant differences between the linear model and linear dummy variable model and between the linear model and linear mixed-effects model. The accuracies of the AGB estimations of the linear dummy variable model and the linear mixed-effects model were significantly better than those of linear model in the thin and dense crown density classes. There were no significant differences in the AGB estimation performance between the linear dummy variable model and linear mixed-effects model; these two models were more flexible and more suitable than the linear model for remote-sensing-based AGB estimation. The results of this study provide a new approach for solving the low-accuracy estimations of linear models.
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110
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Djanaguiraman M, Schapaugh W, Fritschi F, Nguyen H, Prasad PVV. Reproductive success of soybean (Glycine max L. Merril) cultivars and exotic lines under high daytime temperature. PLANT, CELL & ENVIRONMENT 2019; 42:321-336. [PMID: 30095867 DOI: 10.1111/pce.13421] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Revised: 06/26/2018] [Accepted: 06/27/2018] [Indexed: 05/23/2023]
Abstract
The objectives were to (a) quantify the effects of high daytime temperature (HDT) from gametogenesis to full bloom on photosynthesis and pod set in soybean (Glycine max L. Merril) genotypes and (b) assess the relationships among photosynthesis, cardinal temperatures for pollen germination, in vitro pollen germination percentage, canopy reflectance, and pod-set percentage. Three field experiments were conducted, and Experiment I had HDT between gametogenesis and full bloom (36.5°C to 38.6°C) compared with Experiments II and III (29.5°C to 31.6°C; optimum temperature). HDT decreased photosynthesis (22%) and pod-set percent (11%) compared with Experiment III. Cultivars had higher photosynthesis and pod-set percent than plant introduction (PI) lines. The cultivars (i.e., IA3023 and KS4694) and PI lines (i.e., PI393540 and PI588026A) were HDT tolerant and susceptible, respectively. The decreased pod-set percentage in susceptible genotypes (PI lines) was associated with pollen characteristics. Significant positive (r2 ≥ 0.67) association between photosynthesis, cardinal temperatures for pollen germination (Topt and Tmax ) with pod-set percentage was observed. However, a negative (r2 ≥ -0.43) association between photosynthesis and pod set with canopy reflectance at visible spectrum was observed. In vitro pollen germination and canopy reflectance at visible spectrum can be used as a high-throughput phenotypic tool for breeding HDT-tolerant genotypes.
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Affiliation(s)
- Maduraimuthu Djanaguiraman
- Department of Agronomy, Kansas State University, Manhattan, Kansas
- Department of Crop Physiology, Tamil Nadu Agricultural University, Coimbatore, India
| | | | - Felix Fritschi
- Division of Plant Sciences, University of Missouri, Columbia, Missouri
| | - Henry Nguyen
- Division of Plant Sciences, University of Missouri, Columbia, Missouri
| | - P V Vara Prasad
- Department of Agronomy, Kansas State University, Manhattan, Kansas
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111
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Verrelst J, Malenovský Z, Van der Tol C, Camps-Valls G, Gastellu-Etchegorry JP, Lewis P, North P, Moreno J. Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods. SURVEYS IN GEOPHYSICS 2019; 40:589-629. [PMID: 36081834 PMCID: PMC7613341 DOI: 10.1007/s10712-018-9478-y] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given.
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Affiliation(s)
- Jochem Verrelst
- Image Processing Laboratory (IPL), Parc Científic, Universitat de València, Paterna, València 46980, Spain
| | - Zbyněk Malenovský
- Surveying and Spatial Sciences Group, School of Technology, Environments and Design, University of Tasmania, Private Bag 76, Hobart, TAS 7001, Australia
- Remote Sensing Department, Global Change Research Institute CAS, Bělidla 986/4a, 60300 Brno, Czech Republic
- USRA/GESTAR, Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, 8800 Greenbelt Rd, Greenbelt, MD 20771, USA
| | - Christiaan Van der Tol
- Department of Water Resources, Faculty ITC, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
| | - Gustau Camps-Valls
- Image Processing Laboratory (IPL), Parc Científic, Universitat de València, Paterna, València 46980, Spain
| | | | - Philip Lewis
- Department of Geography, University College London, Pearson Building, Gower Street, WC1E 6BT London, UK
- National Centre for Earth Observation, Department of Physics and Astronomy, The University of Leicester, Michael Atiyah Building, LE1 7RH Leicester, UK
| | - Peter North
- Department of Geography, Swansea University, Swansea SA2 8PP, UK
| | - Jose Moreno
- Image Processing Laboratory (IPL), Parc Científic, Universitat de València, Paterna, València 46980, Spain
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112
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Asbjornsen H, Campbell JL, Jennings KA, Vadeboncoeur MA, McIntire C, Templer PH, Phillips RP, Bauerle TL, Dietze MC, Frey SD, Groffman PM, Guerrieri R, Hanson PJ, Kelsey EP, Knapp AK, McDowell NG, Meir P, Novick KA, Ollinger SV, Pockman WT, Schaberg PG, Wullschleger SD, Smith MD, Rustad LE. Guidelines and considerations for designing field experiments simulating precipitation extremes in forest ecosystems. Methods Ecol Evol 2018. [DOI: 10.1111/2041-210x.13094] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Heidi Asbjornsen
- Department of Natural Resources and the EnvironmentUniversity of New Hampshire Durham New Hampshire
- Earth Systems Research CenterInstitute for Earth, Oceans, and SpaceUniversity of New Hampshire Durham New Hampshire
| | - John L. Campbell
- Northern Research StationUSDA Forest Service Durham New Hampshire
| | - Katie A. Jennings
- Department of Natural Resources and the EnvironmentUniversity of New Hampshire Durham New Hampshire
- Earth Systems Research CenterInstitute for Earth, Oceans, and SpaceUniversity of New Hampshire Durham New Hampshire
| | - Matthew A. Vadeboncoeur
- Earth Systems Research CenterInstitute for Earth, Oceans, and SpaceUniversity of New Hampshire Durham New Hampshire
| | - Cameron McIntire
- Department of Natural Resources and the EnvironmentUniversity of New Hampshire Durham New Hampshire
| | | | | | - Taryn L. Bauerle
- School of Integrative Plant ScienceCornell University Ithaca New York
| | - Michael C. Dietze
- Department of Earth and EnvironmentBoston University Boston Massachusetts
| | - Serita D. Frey
- Department of Natural Resources and the EnvironmentUniversity of New Hampshire Durham New Hampshire
| | - Peter M. Groffman
- Department of Earth and Environmental SciencesAdvanced Science Research Center at the Graduate Center of the City University of New York and Brooklyn College New York New York
| | - Rosella Guerrieri
- Centre for Ecological Research and Forestry Applications (CREAF)Universidad Autonoma de Barcelona Barcelona Spain
| | - Paul J. Hanson
- Environmental Sciences DivisionOak Ridge National Laboratory Oak Ridge Tennessee
| | - Eric P. Kelsey
- Department of Atmospheric Science and ChemistryPlymouth State University Plymouth New Hampshire
- Mount Washington Observatory North Conway New Hampshire
| | - Alan K. Knapp
- Department of Biology and Graduate Degree Program in EcologyColorado State University Fort Collins Colorado
| | | | - Patrick Meir
- Research School of BiologyAustralian National University Canberra ACT Australia
- School of GeosciencesUniversity of Edinburgh Edinburgh UK
| | - Kimberly A. Novick
- School of Public and Environmental AffairsIndiana University Bloomington Indiana
| | - Scott V. Ollinger
- Department of Natural Resources and the EnvironmentUniversity of New Hampshire Durham New Hampshire
| | - Will T. Pockman
- Department of BiologyUniversity of New Mexico Albuquerque New Mexico
| | | | - Stan D. Wullschleger
- Environmental Sciences DivisionOak Ridge National Laboratory Oak Ridge Tennessee
| | - Melinda D. Smith
- Department of Biology and Graduate Degree Program in EcologyColorado State University Fort Collins Colorado
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113
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Anderson CB. The CCB-ID approach to tree species mapping with airborne imaging spectroscopy. PeerJ 2018; 6:e5666. [PMID: 30324011 PMCID: PMC6181071 DOI: 10.7717/peerj.5666] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 08/29/2018] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Biogeographers assess how species distributions and abundances affect the structure, function, and composition of ecosystems. Yet we face a major challenge: it is difficult to precisely map species across landscapes. Novel Earth observations could overcome this challenge for vegetation mapping. Airborne imaging spectrometers measure plant functional traits at high resolution, and these measurements can be used to identify tree species. In this paper, I describe a trait-based approach to species identification with imaging spectroscopy, the Center for Conservation Biology species identification (CCB-ID) method, which was developed as part of an ecological data science evaluation competition. METHODS These methods were developed using airborne imaging spectroscopy data from the National Ecological Observatory Network (NEON). CCB-ID classified tree species using trait-based reflectance variation and decision tree-based machine learning models, approximating a morphological trait and dichotomous key method inspired by botanical classification. First, outliers were removed using a spectral variance threshold. The remaining samples were transformed using principal components analysis (PCA) and resampled to reduce common species biases. Gradient boosting and random forest classifiers were trained using the transformed and resampled feature data. Prediction probabilities were calibrated using sigmoid regression, and sample-scale predictions were averaged to the crown scale. RESULTS CCB-ID received a rank-1 accuracy score of 0.919, and a cross-entropy cost score of 0.447 on the competition test data. Accuracy and specificity scores were high for all species, but precision and recall scores varied for rare species. PCA transformation improved accuracy scores compared to models trained using reflectance data, but outlier removal and data resampling exacerbated class imbalance problems. DISCUSSION CCB-ID accurately classified tree species using NEON data, reporting the best scores among participants. However, it failed to overcome several species mapping challenges like precisely identifying rare species. Key takeaways include (1) selecting models using metrics beyond accuracy (e.g., recall) could improve rare species predictions, (2) within-genus trait variation may drive spectral separability, precluding efforts to distinguish between functionally convergent species, (3) outlier removal and data resampling can exacerbate class imbalance problems, and should be carefully implemented, (4) PCA transformation greatly improved model results, and (5) targeted feature selection could further improve species classification models. CCB-ID is open source, designed for use with NEON data, and available to support species mapping efforts.
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Affiliation(s)
- Christopher B. Anderson
- Department of Biology, Stanford University, Stanford, CA, USA
- Center for Conservation Biology, Stanford University, Stanford, CA, USA
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114
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Löfgren O, Prentice HC, Moeckel T, Schmid BC, Hall K. Landscape history confounds the ability of the
NDVI
to detect fine‐scale variation in grassland communities. Methods Ecol Evol 2018. [DOI: 10.1111/2041-210x.13036] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Oskar Löfgren
- Department of Physical Geography and Ecosystem ScienceLund University Lund Sweden
- Department of BiologyLund University Sölvegatan Lund Sweden
| | | | - Thomas Moeckel
- Grassland Science and Renewable Plant ResourcesUniversität Kassel Witzenhausen Germany
| | | | - Karin Hall
- Department of Physical Geography and Ecosystem ScienceLund University Lund Sweden
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115
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Landscape variation in canopy nitrogen and carbon assimilation in a temperate mixed forest. Oecologia 2018; 188:595-606. [PMID: 30003370 DOI: 10.1007/s00442-018-4223-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Accepted: 07/09/2018] [Indexed: 10/28/2022]
Abstract
Canopy nitrogen (N) is a key factor regulating carbon cycling in forest ecosystems through linkages among foliar N and photosynthesis, decomposition, and N cycling. This analysis examined landscape variation in canopy nitrogen and carbon assimilation in a temperate mixed forest surrounding Harvard Forest in central Massachusetts, USA by integration of canopy nitrogen mapping with ecosystem modeling, and spatial data from soils, stand characteristics and disturbance history. Canopy %N was mapped using high spectral resolution remote sensing from NASA's AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) instrument and linked to an ecosystem model, PnET-II, to estimate gross primary productivity (GPP). Predicted GPP was validated with estimates derived from eddy covariance towers. Estimated canopy %N ranged from 0.5 to 2.9% with a mean of 1.75% across the study region. Predicted GPP ranged from 797 to 1622 g C m-2 year-1 with a mean of 1324 g C m-2 year-1. The prediction that spatial patterns in forest growth are associated with spatial patterns in estimated canopy %N was supported by a strong, positive relationship between field-measured canopy %N and aboveground net primary production. Estimated canopy %N and GPP were related to forest composition, land-use history, and soil drainage. At the landscape scale, PnET-II GPP was compared with predicted GPP from the BigFoot project and from NASA's MODIS (Moderate Resolution Imaging Spectroradiometer) data products. Estimated canopy %N explained much of the difference between MODIS GPP and PnET-II GPP, suggesting that global MODIS GPP estimates may be improved if broad-scale estimates of foliar N were available.
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116
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Assessing Ecosystem Isoprene Emissions by Hyperspectral Remote Sensing. REMOTE SENSING 2018. [DOI: 10.3390/rs10071086] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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117
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DuBois S, Desai AR, Singh A, Serbin SP, Goulden ML, Baldocchi DD, Ma S, Oechel WC, Wharton S, Kruger EL, Townsend PA. Using imaging spectroscopy to detect variation in terrestrial ecosystem productivity across a water-stressed landscape. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2018; 28:1313-1324. [PMID: 29694698 DOI: 10.1002/eap.1733] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 01/30/2018] [Accepted: 03/14/2018] [Indexed: 06/08/2023]
Abstract
A central challenge to understanding how climate anomalies, such as drought and heatwaves, impact the terrestrial carbon cycle, is quantification and scaling of spatial and temporal variation in ecosystem gross primary productivity (GPP). Existing empirical and model-based satellite broadband spectra-based products have been shown to miss critical variation in GPP. Here, we evaluate the potential of high spectral resolution (10 nm) shortwave (400-2,500 nm) imagery to better detect spatial and temporal variations in GPP across a range of ecosystems, including forests, grassland-savannas, wetlands, and shrublands in a water-stressed region. Estimates of GPP from eddy covariance observations were compared against airborne hyperspectral imagery, collected across California during the 2013-2014 HyspIRI airborne preparatory campaign. Observations from 19 flux towers across 23 flight campaigns (102 total image-flux tower pairs) showed GPP to be strongly correlated to a suite of spectral wavelengths and band ratios associated with foliar physiology and chemistry. A partial least squares regression (PLSR) modeling approach was then used to predict GPP with higher validation accuracy (adjusted R2 = 0.71) and low bias (0.04) compared to existing broadband approaches (e.g., adjusted R2 = 0.68 and bias = -5.71 with the Sims et al. model). Significant wavelengths contributing to the PLSR include those previously shown to coincide with Rubisco (wavelengths 1,680, 1,740, and 2,290 nm) and Vcmax (wavelengths 1,680, 1,722, 1,732, 1,760, and 2,300 nm). These results provide strong evidence that advances in satellite spectral resolution offer significant promise for improved satellite-based monitoring of GPP variability across a diverse range of terrestrial ecosystems.
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Affiliation(s)
- Sean DuBois
- ICF, Fairfax, Virginia, 22031, USA
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
- Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
| | - Ankur R Desai
- Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
| | - Aditya Singh
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
- Department of Agricultural and Biological Engineering, University of Florida, Gainesville, Florida, 32611, USA
| | - Shawn P Serbin
- Environment and Climate Sciences Department, Brookhaven National Laboratory, Upton, New York, 11973, USA
| | - Michael L Goulden
- Department of Earth System Science, University of California-Irvine, Irvine, California, 92697, USA
| | - Dennis D Baldocchi
- Department of Environmental Science, Policy, and Management, University of California-Berkeley, Berkeley, California, 94720, USA
| | - Siyan Ma
- Department of Environmental Science, Policy, and Management, University of California-Berkeley, Berkeley, California, 94720, USA
| | - Walter C Oechel
- Department of Biology, San Diego State University, San Diego, California, 92182, USA
- Department of Geography, University of Exeter, Exeter, EX4 4QD, UK
| | - Sonia Wharton
- Atmospheric, Earth and Energy Division, Lawrence Livermore National Laboratory, Livermore, California, 94550, USA
| | - Eric L Kruger
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
| | - Philip A Townsend
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
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118
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Padilla FM, Gallardo M, Peña-Fleitas MT, de Souza R, Thompson RB. Proximal Optical Sensors for Nitrogen Management of Vegetable Crops: A Review. SENSORS (BASEL, SWITZERLAND) 2018; 18:E2083. [PMID: 29958482 PMCID: PMC6069161 DOI: 10.3390/s18072083] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 06/05/2018] [Accepted: 06/27/2018] [Indexed: 11/04/2022]
Abstract
Optimal nitrogen (N) management is essential for profitable vegetable crop production and to minimize N losses to the environment that are a consequence of an excessive N supply. Proximal optical sensors placed in contact with or close to the crop can provide a rapid assessment of a crop N status. Three types of proximal optical sensors (chlorophyll meters, canopy reflectance sensors, and fluorescence-based flavonols meters) for monitoring the crop N status of vegetable crops are reviewed, addressing practical caveats and sampling considerations and evaluating the practical use of these sensors for crop N management. Research over recent decades has shown strong relationships between optical sensor measurements, and different measures of crop N status and of yield of vegetable species. However, the availability of both: (a) Sufficiency values to assess crop N status and (b) algorithms to translate sensor measurements into N fertilizer recommendations are limited for vegetable crops. Optical sensors have potential for N management of vegetable crops. However, research should go beyond merely diagnosing crop N status. Research should now focus on the determination of practical fertilization recommendations. It is envisaged that the increasing environmental and societal pressure on sustainable crop N management will stimulate progress in this area.
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Affiliation(s)
- Francisco M Padilla
- Department of Agronomy, University of Almeria, Carretera de Sacramento s/n, 04120 La Cañada de San Urbano, Almería, Spain.
- CIAIMBITAL Research Centre for Mediterranean Intensive Agrosystems and Agrifood Biotechnology, University of Almeria, 04120 La Cañada de San Urbano, Almería, Spain.
| | - Marisa Gallardo
- Department of Agronomy, University of Almeria, Carretera de Sacramento s/n, 04120 La Cañada de San Urbano, Almería, Spain.
- CIAIMBITAL Research Centre for Mediterranean Intensive Agrosystems and Agrifood Biotechnology, University of Almeria, 04120 La Cañada de San Urbano, Almería, Spain.
| | - M Teresa Peña-Fleitas
- Department of Agronomy, University of Almeria, Carretera de Sacramento s/n, 04120 La Cañada de San Urbano, Almería, Spain.
| | - Romina de Souza
- Department of Agronomy, University of Almeria, Carretera de Sacramento s/n, 04120 La Cañada de San Urbano, Almería, Spain.
| | - Rodney B Thompson
- Department of Agronomy, University of Almeria, Carretera de Sacramento s/n, 04120 La Cañada de San Urbano, Almería, Spain.
- CIAIMBITAL Research Centre for Mediterranean Intensive Agrosystems and Agrifood Biotechnology, University of Almeria, 04120 La Cañada de San Urbano, Almería, Spain.
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119
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Vergara-Díaz O, Chairi F, Vicente R, Fernandez-Gallego JA, Nieto-Taladriz MT, Aparicio N, Kefauver SC, Araus JL. Leaf dorsoventrality as a paramount factor determining spectral performance in field-grown wheat under contrasting water regimes. JOURNAL OF EXPERIMENTAL BOTANY 2018; 69:3081-3094. [PMID: 29617831 PMCID: PMC5972577 DOI: 10.1093/jxb/ery109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 03/13/2018] [Indexed: 05/31/2023]
Abstract
The effects of leaf dorsoventrality and its interaction with environmentally induced changes in the leaf spectral response are still poorly understood, particularly for isobilateral leaves. We investigated the spectral performance of 24 genotypes of field-grown durum wheat at two locations under both rainfed and irrigated conditions. Flag leaf reflectance spectra in the VIS-NIR-SWIR (visible-near-infrared-short-wave infrared) regions were recorded in the adaxial and abaxial leaf sides and at the canopy level, while traits providing information on water status and grain yield were evaluated. Moreover, leaf anatomical parameters were measured in a subset of five genotypes. The spectral traits studied were more affected by the leaf side than by the water regime. Leaf dorsoventral differences suggested higher accessory pigment content in the abaxial leaf side, while water regime differences were related to increased chlorophyll, nitrogen, and water contents in the leaves in the irrigated treatment. These variations were associated with anatomical changes. Additionally, leaf dorsoventral differences were less in the rainfed treatment, suggesting the existence of leaf-side-specific responses at the anatomical and biochemical level. Finally, the accuracy in yield prediction was enhanced when abaxial leaf spectra were employed. We concluded that the importance of dorsoventrality in spectral traits is paramount, even in isobilateral leaves.
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Affiliation(s)
- Omar Vergara-Díaz
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Diagonal, Barcelona, Spain
| | - Fadia Chairi
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Diagonal, Barcelona, Spain
| | - Rubén Vicente
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Diagonal, Barcelona, Spain
| | - Jose A Fernandez-Gallego
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Diagonal, Barcelona, Spain
| | | | - Nieves Aparicio
- Technological and Agricultural Institute of Castilla y León (ITACyL), Valladolid, Spain
| | - Shawn C Kefauver
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Diagonal, Barcelona, Spain
| | - José Luis Araus
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Diagonal, Barcelona, Spain
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120
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Random Forest Variable Importance Spectral Indices Scheme for Burnt Forest Recovery Monitoring—Multilevel RF-VIMP. REMOTE SENSING 2018. [DOI: 10.3390/rs10060807] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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121
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Plant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem function. Nat Ecol Evol 2018; 2:976-982. [DOI: 10.1038/s41559-018-0551-1] [Citation(s) in RCA: 138] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 04/03/2018] [Indexed: 11/09/2022]
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122
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Pasqualotto N, Delegido J, Van Wittenberghe S, Verrelst J, Rivera JP, Moreno J. Retrieval of canopy water content of different crop types with two new hyperspectral indices: Water Absorption Area Index and Depth Water Index. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION : ITC JOURNAL 2018; 67:69-78. [PMID: 36082024 PMCID: PMC7613340 DOI: 10.1016/j.jag.2018.01.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Crop canopy water content (CWC) is an essential indicator of the crop's physiological state. While a diverse range of vegetation indices have earlier been developed for the remote estimation of CWC, most of them are defined for specific crop types and areas, making them less universally applicable. We propose two new water content indices applicable to a wide variety of crop types, allowing to derive CWC maps at a large spatial scale. These indices were developed based on PROSAIL simulations and then optimized with an experimental dataset (SPARC03; Barrax, Spain). This dataset consists of water content and other biophysical variables for five common crop types (lucerne, corn, potato, sugar beet and onion) and corresponding top-of-canopy (TOC) reflectance spectra acquired by the hyperspectral HyMap airborne sensor. First, commonly used water content index formulations were analysed and validated for the variety of crops, overall resulting in a R2 lower than 0.6. In an attempt to move towards more generically applicable indices, the two new CWC indices exploit the principal water absorption features in the near-infrared by using multiple bands sensitive to water content. We propose the Water Absorption Area Index (WAAI) as the difference between the area under the null water content of TOC reflectance (reference line) simulated with PROSAIL and the area under measured TOC reflectance between 911 and 1271 nm. We also propose the Depth Water Index (DWI), a simplified four-band index based on the spectral depths produced by the water absorption at 970 and 1200 nm and two reference bands. Both the WAAI and DWI outperform established indices in predicting CWC when applied to heterogeneous croplands, with a R2 of 0.8 and 0.7, respectively, using an exponential fit. However, these indices did not perform well for species with a low fractional vegetation cover (< 30%). HyMap CWC maps calculated with both indices are shown for the Barrax region. The results confirmed the potential of using generically applicable indices for calculating CWC over a great variety of crops.
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Affiliation(s)
- Nieves Pasqualotto
- Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, 46980, Paterna, Valencia, Spain
- Corresponding author. (N. Pasqualotto)
| | - Jesús Delegido
- Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, 46980, Paterna, Valencia, Spain
| | - Shari Van Wittenberghe
- Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, 46980, Paterna, Valencia, Spain
| | - Jochem Verrelst
- Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, 46980, Paterna, Valencia, Spain
| | - Juan Pablo Rivera
- CONACYT-UAN, Secretariat of Research and Postgraduate, C/3, 63173, Tepic, Mexico
| | - José Moreno
- Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, 46980, Paterna, Valencia, Spain
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123
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Wu J, Kobayashi H, Stark SC, Meng R, Guan K, Tran NN, Gao S, Yang W, Restrepo-Coupe N, Miura T, Oliviera RC, Rogers A, Dye DG, Nelson BW, Serbin SP, Huete AR, Saleska SR. Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest. THE NEW PHYTOLOGIST 2018; 217:1507-1520. [PMID: 29274288 DOI: 10.1111/nph.14939] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Accepted: 11/06/2017] [Indexed: 06/07/2023]
Abstract
Satellite observations of Amazon forests show seasonal and interannual variations, but the underlying biological processes remain debated. Here we combined radiative transfer models (RTMs) with field observations of Amazon forest leaf and canopy characteristics to test three hypotheses for satellite-observed canopy reflectance seasonality: seasonal changes in leaf area index, in canopy-surface leafless crown fraction and/or in leaf demography. Canopy RTMs (PROSAIL and FLiES), driven by these three factors combined, simulated satellite-observed seasonal patterns well, explaining c. 70% of the variability in a key reflectance-based vegetation index (MAIAC EVI, which removes artifacts that would otherwise arise from clouds/aerosols and sun-sensor geometry). Leaf area index, leafless crown fraction and leaf demography independently accounted for 1, 33 and 66% of FLiES-simulated EVI seasonality, respectively. These factors also strongly influenced modeled near-infrared (NIR) reflectance, explaining why both modeled and observed EVI, which is especially sensitive to NIR, captures canopy seasonal dynamics well. Our improved analysis of canopy-scale biophysics rules out satellite artifacts as significant causes of satellite-observed seasonal patterns at this site, implying that aggregated phenology explains the larger scale remotely observed patterns. This work significantly reconciles current controversies about satellite-detected Amazon phenology, and improves our use of satellite observations to study climate-phenology relationships in the tropics.
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Affiliation(s)
- Jin Wu
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, 85721, USA
- Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - Hideki Kobayashi
- Department of Environmental Geochemical Cycle Research, Japan Agency for Marine-Earth Science and Technology, Yokohama, Kanagawa Prefecture, 236-0001, Japan
| | - Scott C Stark
- Department of Forestry, Michigan State University, East Lansing, MI, 48824, USA
| | - Ran Meng
- Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - Kaiyu Guan
- Department of Natural Resources and Environmental Sciences, National Center for Supercomputing Applications, University of Illinois at Urbana Champaign, Urbana, IL, 61801, USA
| | - Ngoc Nguyen Tran
- Climate Change Cluster, University of Technology Sydney, Ultimo, NSW, 2007, Australia
| | - Sicong Gao
- Climate Change Cluster, University of Technology Sydney, Ultimo, NSW, 2007, Australia
| | - Wei Yang
- Center for Environmental Remote Sensing, Chiba University, Chiba-shi, Chiba, 263-8522, Japan
| | - Natalia Restrepo-Coupe
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, 85721, USA
| | - Tomoaki Miura
- Department of Natural Resources and Environmental Management, University of Havaii, Honolulu, HI, 96822, USA
| | | | - Alistair Rogers
- Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - Dennis G Dye
- School of Earth Sciences and Environmental Sustainability, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - Bruce W Nelson
- Environmental Dynamics Department, Brazil's National Institute for Amazon Research (INPA), Manaus, AM, 69067-375, Brazil
| | - Shawn P Serbin
- Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - Alfredo R Huete
- Climate Change Cluster, University of Technology Sydney, Ultimo, NSW, 2007, Australia
| | - Scott R Saleska
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, 85721, USA
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124
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Ewald M, Skowronek S, Aerts R, Dolos K, Lenoir J, Nicolas M, Warrie J, Hattab T, Feilhauer H, Honnay O, Garzón-López CX, Decocq G, Van De Kerchove R, Somers B, Rocchini D, Schmidtlein S. Analyzing remotely sensed structural and chemical canopy traits of a forest invaded by Prunus serotina over multiple spatial scales. Biol Invasions 2018. [DOI: 10.1007/s10530-018-1700-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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125
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Wang R, Gamon JA, Cavender-Bares J, Townsend PA, Zygielbaum AI. The spatial sensitivity of the spectral diversity-biodiversity relationship: an experimental test in a prairie grassland. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2018; 28:541-556. [PMID: 29266500 DOI: 10.1002/eap.1669] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Revised: 05/12/2017] [Accepted: 05/26/2017] [Indexed: 06/07/2023]
Abstract
Remote sensing has been used to detect plant biodiversity in a range of ecosystems based on the varying spectral properties of different species or functional groups. However, the most appropriate spatial resolution necessary to detect diversity remains unclear. At coarse resolution, differences among spectral patterns may be too weak to detect. In contrast, at fine resolution, redundant information may be introduced. To explore the effect of spatial resolution, we studied the scale dependence of spectral diversity in a prairie ecosystem experiment at Cedar Creek Ecosystem Science Reserve, Minnesota, USA. Our study involved a scaling exercise comparing synthetic pixels resampled from high-resolution images within manipulated diversity treatments. Hyperspectral data were collected using several instruments on both ground and airborne platforms. We used the coefficient of variation (CV) of spectral reflectance in space as the indicator of spectral diversity and then compared CV at different scales ranging from 1 mm2 to 1 m2 to conventional biodiversity metrics, including species richness, Shannon's index, Simpson's index, phylogenetic species variation, and phylogenetic species evenness. In this study, higher species richness plots generally had higher CV. CV showed higher correlations with Shannon's index and Simpson's index than did species richness alone, indicating evenness contributed to the spectral diversity. Correlations with species richness and Simpson's index were generally higher than with phylogenetic species variation and evenness measured at comparable spatial scales, indicating weaker relationships between spectral diversity and phylogenetic diversity metrics than with species diversity metrics. High resolution imaging spectrometer data (1 mm2 pixels) showed the highest sensitivity to diversity level. With decreasing spatial resolution, the difference in CV between diversity levels decreased and greatly reduced the optical detectability of biodiversity. The optimal pixel size for distinguishing α diversity in these prairie plots appeared to be around 1 mm to 10 cm, a spatial scale similar to the size of an individual herbaceous plant. These results indicate a strong scale-dependence of the spectral diversity-biodiversity relationships, with spectral diversity best able to detect a combination of species richness and evenness, and more weakly detecting phylogenetic diversity. These findings can be used to guide airborne studies of biodiversity and develop more effective large-scale biodiversity sampling methods.
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Affiliation(s)
- Ran Wang
- Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, Alberta, T6G 2E3, Canada
| | - John A Gamon
- Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, Alberta, T6G 2E3, Canada
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, T6G 2E9, Canada
- School of Natural Resources, University of Nebraska, Lincoln, Nebraska, 68583, USA
| | - Jeannine Cavender-Bares
- Department of Ecology, Evolution and Behavior, University of Minnesota, Saint Paul, Minnesota, 55108, USA
| | - Philip A Townsend
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
| | - Arthur I Zygielbaum
- School of Natural Resources, University of Nebraska, Lincoln, Nebraska, 68583, USA
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126
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Impacts of Leaf Age on Canopy Spectral Signature Variation in Evergreen Chinese Fir Forests. REMOTE SENSING 2018. [DOI: 10.3390/rs10020262] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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127
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Hakkenberg CR, Zhu K, Peet RK, Song C. Mapping multi-scale vascular plant richness in a forest landscape with integrated LiDAR and hyperspectral remote-sensing. Ecology 2018; 99:474-487. [DOI: 10.1002/ecy.2109] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 10/25/2017] [Accepted: 11/20/2017] [Indexed: 11/12/2022]
Affiliation(s)
- C. R. Hakkenberg
- Curriculum for the Environment and Ecology; University of North Carolina at Chapel Hill; Chapel Hill North Carolina 27599 USA
| | - K. Zhu
- Department of Environmental Studies; University of California at Santa Cruz; Santa Cruz California 95064 USA
| | - R. K. Peet
- Curriculum for the Environment and Ecology; University of North Carolina at Chapel Hill; Chapel Hill North Carolina 27599 USA
- Department of Biology; University of North Carolina at Chapel Hill; Chapel Hill North Carolina 27599 USA
| | - C. Song
- Curriculum for the Environment and Ecology; University of North Carolina at Chapel Hill; Chapel Hill North Carolina 27599 USA
- Department of Geography; University of North Carolina at Chapel Hill; Chapel Hill North Carolina 27599 USA
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128
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An Approach for Foliar Trait Retrieval from Airborne Imaging Spectroscopy of Tropical Forests. REMOTE SENSING 2018. [DOI: 10.3390/rs10020199] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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129
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Nestola E, Scartazza A, Di Baccio D, Castagna A, Ranieri A, Cammarano M, Mazzenga F, Matteucci G, Calfapietra C. Are optical indices good proxies of seasonal changes in carbon fluxes and stress-related physiological status in a beech forest? THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 612:1030-1041. [PMID: 28892844 DOI: 10.1016/j.scitotenv.2017.08.167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2017] [Revised: 08/15/2017] [Accepted: 08/15/2017] [Indexed: 06/07/2023]
Abstract
This study investigates the functionality of a Mediterranean-mountain beech forest in Central Italy using simultaneous determinations of optical measurements, carbon (C) fluxes, leaf eco-physiological and biochemical traits during two growing seasons (2014-2015). Meteorological variables showed significant differences between the two growing seasons, highlighting a heat stress coupled with a reduced water availability in mid-summer 2015. As a result, a different C sink capacity of the forest was observed between the two years of study, due to the differences in stressful conditions and the related plant physiological status. Spectral indices related to vegetation (VIs, classified in structural, chlorophyll and carotenoid indices) were computed at top canopy level and used to track CO2 fluxes and physiological changes. Optical indices related to structure (EVI 2, RDVI, DVI and MCARI 1) were found to better track Net Ecosystem Exchange (NEE) variations for 2014, while indices related to chlorophylls (SR red edge, CL red edge, MTCI and DR) provided better results for 2015. This suggests that when environmental conditions are not limiting for forest sink capacity, structural parameters are more strictly connected to C uptake, while under stress conditions indices related to functional features (e.g., chlorophyll content) become more relevant. Chlorophyll indices calculated with red edge bands (SR red edge, NDVI red edge, DR, CL red edge) resulted to be highly correlated with leaf nitrogen content (R2>0.70), while weaker, although significant, correlations were found with chlorophyll content. Carotenoid indices (PRI and PSRI) were strongly correlated with both chlorophylls and carotenoids content, suggesting that these indices are good proxies of the shifting pigment composition related to changes in soil moisture, heat stress and senescence. Our work suggests the importance of integrating different methods as a successful approach to understand how changing climatic conditions in the Mediterranean mountain region will impact on forest conditions and functionality.
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Affiliation(s)
- E Nestola
- Institute of Agroenvironmental and Forest Biology, National Research Council of Italy (CNR), Via Marconi 2, 05010 Porano, TR, Italy; Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Via S. Camillo de Lellis snc, 01100 Viterbo, Italy.
| | - A Scartazza
- Institute of Agroenvironmental and Forest Biology, National Research Council of Italy (CNR), Via Marconi 2, 05010 Porano, TR, Italy; Institute of Agroenvironmental and Forest Biology, National Research Council of Italy (CNR), Via Salaria km 29,300, 00016, Monterotondo Scalo, Roma, RM, Italy.
| | - D Di Baccio
- Institute of Agroenvironmental and Forest Biology, National Research Council of Italy (CNR), Via Salaria km 29,300, 00016, Monterotondo Scalo, Roma, RM, Italy
| | - A Castagna
- Department of Agriculture, Food and Environment, University of Pisa, via del Borghetto 80, 56124 Pisa, Italy
| | - A Ranieri
- Department of Agriculture, Food and Environment, University of Pisa, via del Borghetto 80, 56124 Pisa, Italy
| | - M Cammarano
- Institute of Agroenvironmental and Forest Biology, National Research Council of Italy (CNR), Via Salaria km 29,300, 00016, Monterotondo Scalo, Roma, RM, Italy
| | - F Mazzenga
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Via S. Camillo de Lellis snc, 01100 Viterbo, Italy; Institute of Agroenvironmental and Forest Biology, National Research Council of Italy (CNR), Via Salaria km 29,300, 00016, Monterotondo Scalo, Roma, RM, Italy
| | - G Matteucci
- Institute for Agriculture and Forestry Systems in the Mediterranean, National Research Council of Italy (CNR), Via Patacca, 85 I-80056 Ercolano, NA, Italy
| | - C Calfapietra
- Institute of Agroenvironmental and Forest Biology, National Research Council of Italy (CNR), Via Marconi 2, 05010 Porano, TR, Italy; Czechglobe, Global Change Research Institute, Academy of Sciences of the Czech Republic, Brno, Czech Republic
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130
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Deacon NJ, Grossman JJ, Schweiger AK, Armour I, Cavender-Bares J. Genetic, morphological, and spectral characterization of relictual Niobrara River hybrid aspens ( Populus × smithii). AMERICAN JOURNAL OF BOTANY 2017; 104:1878-1890. [PMID: 29247028 DOI: 10.3732/ajb.1700268] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Accepted: 11/06/2017] [Indexed: 05/26/2023]
Abstract
PREMISE OF THE STUDY Aspen groves along the Niobrara River in Nebraska have long been a biogeographic curiosity due to morphological differences from nearby remnant Populus tremuloides populations. Pleistocene hybridization between P. tremuloides and P. grandidentata has been proposed, but the nearest P. grandidentata populations are currently several hundred kilometers east. We tested the hybrid-origin hypothesis using genetic data and characterized putative hybrids phenotypically. METHODS We compared nuclear microsatellite loci and chloroplast sequences of Niobrara River aspens to their putative parental species. Parental species and putative hybrids were also grown in a common garden for phenotypic comparison. On the common garden plants, we measured leaf morphological traits and leaf-level spectral reflectance profiles, from which chemical traits were derived. KEY RESULTS The genetic composition of the three unique Niobrara aspen genotypes is consistent with the hybridization hypothesis and with maternal chloroplast inheritance from P. grandidentata. Leaf margin dentition and abaxial pubescence differentiated taxa, with the hybrids showing intermediate values. Spectral profiles allowed statistical separation of taxa in short-wave infrared wavelengths, with hybrids showing intermediate values, indicating that traits associated with internal structure of leaves and water absorption may vary among taxa. However, reflectance values in the visible region did not differentiate taxa, indicating that traits related to pigments are not differentiated. CONCLUSIONS Both genetic and phenotypic results support the hypothesis of a hybrid origin for these genetically unique aspens. However, low genetic diversity and ongoing ecological and climatic threats to the hybrid taxon present a challenge for conservation of these relictual boreal communities.
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Affiliation(s)
- Nicholas John Deacon
- Ecology, Evolution and Behavior, University of Minnesota, 140 Gortner Laboratory, 1479 Gortner Avenue, St. Paul, MN 55108 USA
- Plant Biology, University of Minnesota, 140 Gortner Laboratory, 1479 Gortner Avenue, St. Paul, MN 55108 USA
| | - Jake Joseph Grossman
- Ecology, Evolution and Behavior, University of Minnesota, 140 Gortner Laboratory, 1479 Gortner Avenue, St. Paul, MN 55108 USA
| | - Anna Katharina Schweiger
- Ecology, Evolution and Behavior, University of Minnesota, 140 Gortner Laboratory, 1479 Gortner Avenue, St. Paul, MN 55108 USA
| | | | - Jeannine Cavender-Bares
- Ecology, Evolution and Behavior, University of Minnesota, 140 Gortner Laboratory, 1479 Gortner Avenue, St. Paul, MN 55108 USA
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131
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Poblete T, Ortega-Farías S, Moreno MA, Bardeen M. Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV). SENSORS 2017; 17:s17112488. [PMID: 29084169 PMCID: PMC5713508 DOI: 10.3390/s17112488] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Revised: 10/23/2017] [Accepted: 10/24/2017] [Indexed: 11/25/2022]
Abstract
Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential (Ψstem). However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on unmanned aerial vehicle (UAV) is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI) that use information between 500–800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN) models derived from multispectral images to predict the Ψstem spatial variability of a drip-irrigated Carménère vineyard in Talca, Maule Region, Chile. The coefficient of determination (R2) obtained between ANN outputs and ground-truth measurements of Ψstem were between 0.56–0.87, with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model could estimate Ψstem with a mean absolute error (MAE) of 0.1 MPa, root mean square error (RMSE) of 0.12 MPa, and relative error (RE) of −9.1%. For the validation of the CMI, the MAE, RMSE and RE values were between 0.26–0.27 MPa, 0.32–0.34 MPa and −24.2–25.6%, respectively.
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Affiliation(s)
- Tomas Poblete
- Centro de Investigación y Transferencia en Riego y Agroclimatología (CITRA), Universidad de Talca, Casilla 747, Talca 3460000, Chile.
| | - Samuel Ortega-Farías
- Centro de Investigación y Transferencia en Riego y Agroclimatología (CITRA), Universidad de Talca, Casilla 747, Talca 3460000, Chile.
- Research program on Adaptation of Agriculture to Climate Change (A2C2), Universidad de Talca, Casilla 747, Talca 3460000, Chile.
| | - Miguel Angel Moreno
- Regional Centre of Water Research, University of Castilla-La Mancha, Campus Universitario s/n, 02071 Albacete, Spain.
| | - Matthew Bardeen
- Research program on Adaptation of Agriculture to Climate Change (A2C2), Universidad de Talca, Casilla 747, Talca 3460000, Chile.
- Facultad de Ingeniería, Universidad de Talca, Curicó 3340000, Chile.
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132
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El-Hendawy S, Al-Suhaibani N, Hassan W, Tahir M, Schmidhalter U. Hyperspectral reflectance sensing to assess the growth and photosynthetic properties of wheat cultivars exposed to different irrigation rates in an irrigated arid region. PLoS One 2017; 12:e0183262. [PMID: 28829809 PMCID: PMC5567659 DOI: 10.1371/journal.pone.0183262] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Accepted: 08/01/2017] [Indexed: 11/18/2022] Open
Abstract
Simultaneous indirect assessment of multiple and diverse plant parameters in an exact and expeditious manner is becoming imperative in irrigated arid regions, with a view toward creating drought-tolerant genotypes or for the management of precision irrigation. This study aimed to evaluate whether spectral reflectance indices (SRIs) in three parts of the electromagnetic spectrum ((visible-infrared (VIS), near-infrared (NIR)), and shortwave-infrared (SWIR)) could be used to track changes in morphophysiological parameters of wheat cultivars exposed to 1.00, 0.75, and 0.50 of the estimated evapotranspiration (ETc). Significant differences were found in the parameters of growth and photosynthetic efficiency, and canopy spectral reflectance among the three cultivars subjected to different irrigation rates. All parameters were highly and significantly correlated with each other particularly under the 0.50 ETc treatment. The VIS/VIS- and NIR/VIS-based indices were sufficient and suitable for assessing the growth and photosynthetic properties of wheat cultivars similar to those indices based on NIR/NIR, SWIR/NIR, or SWIR/SWIR. Almost all tested SRIs proved to assess growth and photosynthetic parameters, including transpiration rate, more efficiently when regressions were analyzed for each water irrigation rate individually. This study, the type of which has rarely been conducted in irrigated arid regions, indicates that spectral reflectance data can be used as a rapid and non-destructive alternative method for assessment of the growth and photosynthetic efficiency of wheat under a range of water irrigation rates.
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Affiliation(s)
- Salah El-Hendawy
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
- Department of Agronomy, Faculty of Agriculture, Suez Canal University, Ismailia, Egypt
| | - Nasser Al-Suhaibani
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Wael Hassan
- Department of Agricultural Botany, Faculty of Agriculture, Suez Canal University, Ismailia, Egypt
- Department of Biology, Quwayiyah College of Science and Humanities, Shaqra University, Shaqra, Saudi Arabia
| | - Mohammad Tahir
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Urs Schmidhalter
- Chair of Plant Nutrition, Department of Plant Sciences, Technical University of Munich, Freising-Weihenstephan, Germany
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133
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Predicting Vascular Plant Diversity in Anthropogenic Peatlands: Comparison of Modeling Methods with Free Satellite Data. REMOTE SENSING 2017. [DOI: 10.3390/rs9070681] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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134
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Hyperspectral Monitoring of Green Roof Vegetation Health State in Sub-Mediterranean Climate: Preliminary Results. SENSORS 2017; 17:s17040662. [PMID: 28333081 PMCID: PMC5419775 DOI: 10.3390/s17040662] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Revised: 03/06/2017] [Accepted: 03/20/2017] [Indexed: 12/04/2022]
Abstract
In urban and industrial environments, the constant increase of impermeable surfaces has produced drastic changes in the natural hydrological cycle. Decreasing green areas not only produce negative effects from a hydrological-hydraulic perspective, but also from an energy point of view, modifying the urban microclimate and generating, as shown in the literature, heat islands in our cities. In this context, green infrastructures may represent an environmental compensation action that can be used to re-equilibrate the hydrological and energy balance and reduce the impact of pollutant load on receiving water bodies. To ensure that a green infrastructure will work properly, vegetated areas have to be continuously monitored to verify their health state. This paper presents a ground spectroscopy monitoring survey of a green roof installed at the University of Calabria fulfilled via the acquisition and analysis of hyperspectral data. This study is part of a larger research project financed by European Structural funds aimed at understanding the influence of green roofs on rainwater management and energy consumption for air conditioning in the Mediterranean area. Reflectance values were acquired with a field-portable spectroradiometer that operates in the range of wavelengths 350–2500 nm. The survey was carried out during the time period November 2014–June 2015 and data were acquired weekly. Climatic, thermo-physical, hydrological and hydraulic quantities were acquired as well and related to spectral data. Broadband and narrowband spectral indices, related to chlorophyll content and to chlorophyll–carotenoid ratio, were computed. The two narrowband indices NDVI705 and SIPI turned out to be the most representative indices to detect the plant health status.
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135
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Badgley G, Field CB, Berry JA. Canopy near-infrared reflectance and terrestrial photosynthesis. SCIENCE ADVANCES 2017; 3:e1602244. [PMID: 28345046 PMCID: PMC5362170 DOI: 10.1126/sciadv.1602244] [Citation(s) in RCA: 138] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 02/09/2017] [Indexed: 05/19/2023]
Abstract
Global estimates of terrestrial gross primary production (GPP) remain highly uncertain, despite decades of satellite measurements and intensive in situ monitoring. We report a new approach for quantifying the near-infrared reflectance of terrestrial vegetation (NIRV). NIRV provides a foundation for a new approach to estimate GPP that consistently untangles the confounding effects of background brightness, leaf area, and the distribution of photosynthetic capacity with depth in canopies using existing moderate spatial and spectral resolution satellite sensors. NIRV is strongly correlated with solar-induced chlorophyll fluorescence, a direct index of photons intercepted by chlorophyll, and with site-level and globally gridded estimates of GPP. NIRV makes it possible to use existing and future reflectance data as a starting point for accurately estimating GPP.
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Affiliation(s)
- Grayson Badgley
- Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA
- Department of Earth System Science, Stanford University, Stanford, CA 94305, USA
- Corresponding author.
| | - Christopher B. Field
- Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA
- Department of Earth System Science, Stanford University, Stanford, CA 94305, USA
| | - Joseph A. Berry
- Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA
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136
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Windthrow Detection in European Forests with Very High-Resolution Optical Data. FORESTS 2017. [DOI: 10.3390/f8010021] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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137
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Cochavi A, Rapaport T, Gendler T, Karnieli A, Eizenberg H, Rachmilevitch S, Ephrath JE. Recognition of Orobanche cumana Below-Ground Parasitism Through Physiological and Hyper Spectral Measurements in Sunflower ( Helianthus annuus L.). FRONTIERS IN PLANT SCIENCE 2017. [PMID: 28638389 PMCID: PMC5461261 DOI: 10.3389/fpls.2017.00909] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Broomrape (Orobanche and Phelipanche spp.) parasitism is a severe problem in many crops worldwide, including in the Mediterranean basin. Most of the damage occurs during the sub-soil developmental stage of the parasite, by the time the parasite emerges from the ground, damage to the crop has already been done. One feasible method for sensing early, below-ground parasitism is through physiological measurements, which provide preliminary indications of slight changes in plant vitality and productivity. However, a complete physiological field survey is slow, costly and requires skilled manpower. In recent decades, visible to-shortwave infrared (VIS-SWIR) hyperspectral tools have exhibited great potential for faster, cheaper, simpler and non-destructive tracking of physiological changes. The advantage of VIS-SWIR is even greater when narrow-band signatures are analyzed with an advanced statistical technique, like a partial least squares regression (PLS-R). The technique can pinpoint the most physiologically sensitive wavebands across an entire spectrum, even in the presence of high levels of noise and collinearity. The current study evaluated a method for early detection of Orobanche cumana parasitism in sunflower that combines plant physiology, hyperspectral readings and PLS-R. Seeds of susceptible and resistant O. cumana sunflower varieties were planted in infested (15 mg kg-1 seeds) and non-infested soil. The plants were examined weekly to detect any physiological or structural changes; the examinations were accompanied by hyperspectral readings. During the early stage of the parasitism, significant differences between infected and non-infected sunflower plants were found in the reflectance of near and shortwave infrared areas. Physiological measurements revealed no differences between treatments until O. cumana inflorescences emerged. However, levels of several macro- and microelements tended to decrease during the early stage of O. cumana parasitism. Analysis of leaf cross-sections revealed differences in range and in mesophyll structure as a result of different levels of nutrients in sunflower plants, manifesting the presence of O. cumana infections. The findings of an advanced PLS-R analysis emphasized the correlation between specific reflectance changes in the SWIR range and levels of various nutrients in sunflower plants. This work demonstrates potential for the early detection of O. cumana parasitism on sunflower roots using hyperspectral tools.
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Affiliation(s)
- Amnon Cochavi
- The French Associates Institute for Agriculture and Biotechnology of Drylands, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the NegevBeer-Sheva, Israel
| | - Tal Rapaport
- The Remote Sensing Laboratory, The Swiss Institute for Dryland Environmental & Energy Research, The Jacob Blaustein Institutes for Desert Research, Ben Gurion University of the NegevBeer-Sheva, Israel
| | - Tania Gendler
- The French Associates Institute for Agriculture and Biotechnology of Drylands, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the NegevBeer-Sheva, Israel
| | - Arnon Karnieli
- The Remote Sensing Laboratory, The Swiss Institute for Dryland Environmental & Energy Research, The Jacob Blaustein Institutes for Desert Research, Ben Gurion University of the NegevBeer-Sheva, Israel
| | - Hanan Eizenberg
- Department of Plant Pathology and Weed Research, Newe Ya’ar Research Center, Agricultural Research Organization, Volcani CenterRamat Yishay, Israel
| | - Shimon Rachmilevitch
- The French Associates Institute for Agriculture and Biotechnology of Drylands, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the NegevBeer-Sheva, Israel
| | - Jhonathan E. Ephrath
- The French Associates Institute for Agriculture and Biotechnology of Drylands, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the NegevBeer-Sheva, Israel
- *Correspondence: Jhonathan E. Ephrath,
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138
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Skowronek S, Asner GP, Feilhauer H. Performance of one-class classifiers for invasive species mapping using airborne imaging spectroscopy. ECOL INFORM 2017. [DOI: 10.1016/j.ecoinf.2016.11.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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139
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Vegetation Dynamics in the Upper Guinean Forest Region of West Africa from 2001 to 2015. REMOTE SENSING 2016. [DOI: 10.3390/rs9010005] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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140
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Keenan TF, Niinemets Ü. Global leaf trait estimates biased due to plasticity in the shade. NATURE PLANTS 2016; 3:16201. [PMID: 27991884 DOI: 10.1038/nplants.2016.201] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2016] [Accepted: 11/21/2016] [Indexed: 05/05/2023]
Abstract
The study of leaf functional trait relationships, the so-called leaf economics spectrum1,2, is based on the assumption of high-light conditions (as experienced by sunlit leaves). Owing to the exponential decrease of light availability through canopies, however, the vast majority of the world's vegetation exists in at least partial shade. Plant functional traits vary in direct dependence of light availability3, with different traits varying to different degrees, sometimes in conflict with expectations from the economic spectrum3. This means that the derived trait relationships of the global leaf economic spectrum are probably dependent on the extent to which observed data in existing large-scale plant databases represent high-light conditions. Here, using an extensive worldwide database of within-canopy gradients of key physiological, structural and chemical traits3, along with three different global trait databases4,5, we show that: (1) accounting for light-driven trait plasticity can reveal novel trait relationships, particularly for highly plastic traits (for example, the relationship between net assimilation rate per area (Aa) and leaf mass per area (LMA)); and (2) a large proportion of leaf traits in current global plant databases reported as measured in full sun were probably measured in the shade. The results show that even though the majority of leaves exist in the shade, along with a large proportion of observations, our current understanding is too focused on conditions in the sun.
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Affiliation(s)
- Trevor F Keenan
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Ülo Niinemets
- Estonian University of Life Sciences, Kreutzwaldi 1, 51014 Tartu, Estonia
- Estonian Academy of Sciences, Kohtu 6, 10130 Tallinn, Estonia
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141
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Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics. REMOTE SENSING 2016. [DOI: 10.3390/rs8121029] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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142
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Launiainen S, Katul GG, Kolari P, Lindroth A, Lohila A, Aurela M, Varlagin A, Grelle A, Vesala T. Do the energy fluxes and surface conductance of boreal coniferous forests in Europe scale with leaf area? GLOBAL CHANGE BIOLOGY 2016; 22:4096-4113. [PMID: 27614117 DOI: 10.1111/gcb.13497] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2016] [Revised: 06/07/2016] [Accepted: 07/04/2016] [Indexed: 05/14/2023]
Abstract
Earth observing systems are now routinely used to infer leaf area index (LAI) given its significance in spatial aggregation of land surface fluxes. Whether LAI is an appropriate scaling parameter for daytime growing season energy budget, surface conductance (Gs ), water- and light-use efficiency and surface-atmosphere coupling of European boreal coniferous forests was explored using eddy-covariance (EC) energy and CO2 fluxes. The observed scaling relations were then explained using a biophysical multilayer soil-vegetation-atmosphere transfer model as well as by a bulk Gs representation. The LAI variations significantly alter radiation regime, within-canopy microclimate, sink/source distributions of CO2 , H2 O and heat, and forest floor fluxes. The contribution of forest floor to ecosystem-scale energy exchange is shown to decrease asymptotically with increased LAI, as expected. Compared with other energy budget components, dry-canopy evapotranspiration (ET) was reasonably 'conservative' over the studied LAI range 0.5-7.0 m2 m-2 . Both ET and Gs experienced a minimum in the LAI range 1-2 m2 m-2 caused by opposing nonproportional response of stomatally controlled transpiration and 'free' forest floor evaporation to changes in canopy density. The young forests had strongest coupling with the atmosphere while stomatal control of energy partitioning was strongest in relatively sparse (LAI ~2 m2 m-2 ) pine stands growing on mineral soils. The data analysis and model results suggest that LAI may be an effective scaling parameter for net radiation and its partitioning but only in sparse stands (LAI <3 m2 m-2 ). This finding emphasizes the significance of stand-replacing disturbances on the controls of surface energy exchange. In denser forests, any LAI dependency varies with physiological traits such as light-saturated water-use efficiency. The results suggest that incorporating species traits and site conditions are necessary when LAI is used in upscaling energy exchanges of boreal coniferous forests.
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Affiliation(s)
- Samuli Launiainen
- Nature Resources Institute Finland, Environmental Impacts of Production, Jokiniemenkuja 1, Vantaa, Finland
| | - Gabriel G Katul
- Nicholas School of the Environment, Duke University, PO Box 90328, Duke University, Durham, NC 27708-0328, USA
| | - Pasi Kolari
- Department of Physics, University of Helsinki, PO Box 64, 00140 University of Helsinki, Finland
| | - Anders Lindroth
- Department of Earth and Ecosystem Sciences, Lund University, Sölvegatan 12, Lund, 223 62, Sweden
| | - Annalea Lohila
- Finnish Meteorological Institute, P.O. Box 503, FI-00101, Helsinki, Finland
| | - Mika Aurela
- Finnish Meteorological Institute, P.O. Box 503, FI-00101, Helsinki, Finland
| | - Andrej Varlagin
- A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Leninsky pr. 33, Moscow, 119071, Russia
| | - Achim Grelle
- Department of Ecology, Swedish University of Agricultural Sciences, PO Box 7044, Uppsala, 750 07, Sweden
| | - Timo Vesala
- Department of Physics, University of Helsinki, PO Box 64, 00140 University of Helsinki, Finland
- Department of Forest Sciences, University of Helsinki, PO Box 27, 00140, Helsinki, Finland
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143
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144
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Effects of climate and fire on short-term vegetation recovery in the boreal larch forests of Northeastern China. Sci Rep 2016; 6:37572. [PMID: 27857204 PMCID: PMC5114605 DOI: 10.1038/srep37572] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Accepted: 11/01/2016] [Indexed: 11/13/2022] Open
Abstract
Understanding the influence of climate variability and fire characteristics in shaping postfire vegetation recovery will help to predict future ecosystem trajectories in boreal forests. In this study, I asked: (1) which remotely-sensed vegetation index (VI) is a good proxy for vegetation recovery? and (2) what are the relative influences of climate and fire in controlling postfire vegetation recovery in a Siberian larch forest, a globally important but poorly understood ecosystem type? Analysis showed that the shortwave infrared (SWIR) VI is a good indicator of postfire vegetation recovery in boreal larch forests. A boosted regression tree analysis showed that postfire recovery was collectively controlled by processes that controlled seed availability, as well as by site conditions and climate variability. Fire severity and its spatial variability played a dominant role in determining vegetation recovery, indicating seed availability as the primary mechanism affecting postfire forest resilience. Environmental and immediate postfire climatic conditions appear to be less important, but interact strongly with fire severity to influence postfire recovery. If future warming and fire regimes manifest as expected in this region, seed limitation and climate-induced regeneration failure will become more prevalent and severe, which may cause forests to shift to alternative stable states.
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145
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Grassland and Cropland Net Ecosystem Production of the U.S. Great Plains: Regression Tree Model Development and Comparative Analysis. REMOTE SENSING 2016. [DOI: 10.3390/rs8110944] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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146
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Neumann C, Itzerott S, Weiss G, Kleinschmit B, Schmidtlein S. Mapping multiple plant species abundance patterns - A multiobjective optimization procedure for combining reflectance spectroscopy and species ordination. ECOL INFORM 2016. [DOI: 10.1016/j.ecoinf.2016.10.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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147
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148
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Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis. REMOTE SENSING 2016. [DOI: 10.3390/rs8080673] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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149
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Gillison AN, Asner GP, Fernandes ECM, Mafalacusser J, Banze A, Izidine S, da Fonseca AR, Pacate H. Biodiversity and agriculture in dynamic landscapes: Integrating ground and remotely-sensed baseline surveys. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2016; 177:9-19. [PMID: 27064732 DOI: 10.1016/j.jenvman.2016.03.037] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2012] [Revised: 03/22/2016] [Accepted: 03/23/2016] [Indexed: 06/05/2023]
Abstract
Sustainable biodiversity and land management require a cost-effective means of forecasting landscape response to environmental change. Conventional species-based, regional biodiversity assessments are rarely adequate for policy planning and decision making. We show how new ground and remotely-sensed survey methods can be coordinated to help elucidate and predict relationships between biodiversity, land use and soil properties along complex biophysical gradients that typify many similar landscapes worldwide. In the lower Zambezi valley, Mozambique we used environmental, gradient-directed transects (gradsects) to sample vascular plant species, plant functional types, vegetation structure, soil properties and land-use characteristics. Soil fertility indices were derived using novel multidimensional scaling of soil properties. To facilitate spatial analysis, we applied a probabilistic remote sensing approach, analyzing Landsat 7 satellite imagery to map photosynthetically active and inactive vegetation and bare soil along each gradsect. Despite the relatively low sample number, we found highly significant correlations between single and combined sets of specific plant, soil and remotely sensed variables that permitted testable spatial projections of biodiversity and soil fertility across the regional land-use mosaic. This integrative and rapid approach provides a low-cost, high-return and readily transferable methodology that permits the ready identification of testable biodiversity indicators for adaptive management of biodiversity and potential agricultural productivity.
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Affiliation(s)
- Andrew N Gillison
- Center for Biodiversity Management, P.O. Box 120 Yungaburra, Queensland 4884, Australia.
| | - Gregory P Asner
- Department of Global Ecology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305, USA
| | - Erick C M Fernandes
- Agriculture and Rural Development (LCSAR), The World Bank, 1818 H Street, NW Mail Stop 16-603, Washington, DC 20433, USA
| | | | - Aurélio Banze
- National Institute for Agricultural Research (IIAM), No. 2698, Avenida das FPLM, Maputo, Mozambique
| | - Samira Izidine
- National Institute for Agricultural Research (IIAM), No. 2698, Avenida das FPLM, Maputo, Mozambique
| | | | - Hermenegildo Pacate
- DPCA Provincial Directorate of Coordination of Environmental Action (DCPA), Tete, Mozambique
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150
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Spectral Indices Accurately Quantify Changes in Seedling Physiology Following Fire: Towards Mechanistic Assessments of Post-Fire Carbon Cycling. REMOTE SENSING 2016. [DOI: 10.3390/rs8070572] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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