Evaluation of understory LAI estimation methodologies over forest ecosystem ICOS sites across Europe

2020 ◽  
Author(s):  
Jan-Peter George ◽  
Jan Pisek ◽  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Forest Ecosystem ◽  
Remote Sensing Data ◽  
Ecosystem Functions ◽  
Area Index ◽  
Fractional Cover ◽  
Semi Empirical

<p>Leaf area index (i.e. one-half the total green leaf area per unit of horizontal ground surface area) is a crucial parameter in carbon balancing and modeling. Forest overstory and understory layers differ in carbon and water cycle regimes and phenology, as well as in ecosystem functions. Separate retrievals of leaf area index (LAI) for these two layers would help to improve modeling forest biogeochemical cycles, evaluating forest ecosystem functions and also remote sensing of forest canopies by inversion of canopy reflectance models. The aim of this study is to compare currently available understory LAI assessment methodologies over a diverse set of greenhouse gas measurement sites distributed along a wide latitudinal and elevational gradient across Europe. This will help to quantify  the fraction of the canopy LAI which is represented by understory, since this is still the major source of uncertainty in global LAI products derived from remote sensing data. For this, we took ground photos as well as in-situ reflectance measurements of the understory vegetation at 30 ICOS (Integration Carbon Observation System) sites distributed across 10 countries in Europe. The data were analyzed by means of three conceptually different methods for LAI estimation and comprised purely empirical (fractional cover), semi-empirical (in-situ NDVI linked to the radiative transfer model FLiES), and purely deterministic (Four-scale geometrical optical model) approaches. Finally, our results are compared with global forest understory LAI maps derived from remote sensing data at 1 km resolution (Liu et al. 2017). While we found some agreement among the three methods (e.g. Pearson-correlation between empirical and semi-empirical = 0.63), we also identified sources that are particularly prone to error inclusion such as inaccurate assessment of fractional cover from ground photos. Relationships between understory LAI and long-term climate variables were weak and suggested that understory LAI at the ICOS sites is probably more strongly determined by microclimatic conditions.</p><p><strong>Liu Y. et al. (2017):</strong> Separating overstory and understory leaf area indices for global needleleaf and deciduous broadleaf forests by fusion of MODIS and MISR data. Biogeosciences 14: 1093-1110.</p>

scholarly journals Estimation of Leaf Area Index Using Remote Sensing Data.

1999 ◽  
Vol 12 (3) ◽  
pp. 210-220 ◽  
Author(s):  
Takashi ISHII ◽  
Makoto NASHIMOTO ◽  
Hisashi SHIMOGAKI
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Remote Sensing Data ◽  
Area Index ◽  
Sensing Data

scholarly journals Remote sensing of macrophyte morphological traits: Implications for the management of shallow lakes

2017 ◽  
Vol 76 (s1) ◽  
Author(s):  
Paolo Villa ◽  
Monica Pinardi ◽  
Viktor R. Tóth ◽  
Peter D. Hunter ◽  
Rossano Bolpagni ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Shallow Lake ◽  
Morphological Traits ◽  
Spectral Response ◽  
Freshwater Ecosystems ◽  
Area Index ◽  
Fractional Cover ◽  
Spectral Models

Macrophytes are important elements of freshwater ecosystems, fulfilling a pivotal role in biogeochemical cycles. The synoptic capabilities provided by remote sensing make it a powerful tool for monitoring aquatic vegetation characteristics and the functional status of shallow lake systems in which they occur. The latest generation of airborne and spaceborne imaging sensors can be effectively exploited for mapping morphologically – and physiologically – relevant vegetation features based on their canopy spectral response. The objectives of this study were to calibrate semi-empirical models for mapping macrophyte morphological traits (i.e., fractional cover, leaf area index and above-water biomass) from hyperspectral data, and to investigate the capabilities of remote sensing in supporting macrophyte monitoring and management. We calibrated spectral models using in situ reflectance and morphological trait measures and applied them to airborne hyperspectral imaging data, acquired over two shallow European water bodies (Lake Hídvégi, in Hungary, and Mantua lakes system, in Italy) in two key phenological phases. Maps of morphological traits were produced covering a broad range of aquatic plant types (submerged, floating, and emergent), common to temperate and continental regions, with an error level of 5.4% for fractional cover, 0.10 m2 m-2 for leaf area index, and 0.06 kg m-2 for above-water biomass. Based on these maps, we discuss how remote sensing could support monitoring strategies and shallow lake management with reference to our two case studies: i.e., by providing insight into spatial and species-wise variability, by assessing nutrient uptake by aquatic plants, and by identifying hotspot areas where invasive species could become a threat to ecosystem functioning and service provision.

Assessment of different methods for estimating forest leaf area index from remote sensing data

2014 ◽  
Vol 34 (16) ◽  
Author(s):  
王修信 WANG Xiuxin ◽  
孙涛 SUN Tao ◽  
朱启疆 ZHU Qijiang ◽  
刘馨 LIU Xin ◽  
高凤飞 GAO Fengfei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Remote Sensing Data ◽  
Area Index ◽  
Sensing Data

scholarly journals Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation

2020 ◽  
Author(s):  
Francesco Novelli ◽  
Heide Spiegel ◽  
Taru Sandén ◽  
Francesco Vuolo
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Field Data ◽  
Remote Sensing Data ◽  
Crop Growth ◽  
Crop Growth Model ◽  
Area Index ◽  
Sentinel 2

<p>The work is based on a previously published study with the aim to further analyse the results obtained. Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate (EPIC) model to estimate crop yield. The experiment was implemented for a winter wheat crop during two growing seasons (2016 and 2017) under four different fertilization management strategies. A number of field measurements were conducted spanning from LAI to biomass and crop yields.<br>LAI showed a good correlation between the Sentinel-2 estimates and the ground measurements using non-destructive method. Better RMSE and RRMSE were obtained in 2017 compared to 2016 (RMSE = 0.44 vs. 0.46) (RRMSE = 17% vs. 19%). In 2016 year, a slightly lower R<sup>2</sup> value was found compared to 2017 (R<sup>2</sup> = 0.72 vs. 0.89). A correlating fit between satellite LAI curves and EPIC modelled LAI curves was also observed. The work shows that the assimilation of remote sensing data into the crop growth model can help to overtake some structural problems of the model.  The assimilation framework has to be tested under different environmental conditions before being applied on a larger scale with limited field data.</p>

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