scholarly journals Coupling Fine-Scale Root and Canopy Structure Using Ground-Based Remote Sensing

2016 ◽  
Author(s):  
Brady S. Hardiman ◽  
Christopher M. Gough ◽  
John R. Butnor ◽  
Gil Bohrer ◽  
Matteo Detto ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Scales ◽  
Canopy Structure ◽  
Structural Data ◽  
Ecosystem Functions ◽  
Stand Age ◽  
Ecosystem Structure ◽  
Root Mass ◽  
Root Structure ◽  
Area Index

Ecosystem physical structure, defined by the quantity and spatial distribution of biomass, influences a range of ecosystem functions. Remote sensing tools permit the non-destructive characterization of canopy and root features, potentially providing opportunities to link above- and belowground structure at fine spatial resolution in functionally meaningful ways. To test this possibility, we employed ground-based portable canopy lidar (PCL) and ground penetrating radar (GPR) along co-located transects in forested sites spanning multiple stages of ecosystem development and, consequently, of structural complexity. We examined canopy and root structural data for coherence at multiple spatial scales ≤ 10 m within each site using wavelet analysis. Forest sites varied substantially in vertical canopy and root structure, with leaf area index and root mass more evenly distributed by height and depth, respectively, as forests aged. In all sites, above- and belowground structure, characterized as mean maximum canopy height and root mass, exhibited significant coherence at a scale of 3.5-4 meters, and results suggest that the scale of coherence may increase with stand age. Our findings demonstrate that canopy and root structure are linked at characteristic spatial scales, which provides the basis to optimize scales of observation. Our study highlights the potential, and limitations, for fusing lidar and radar technologies to quantitatively couple above- and belowground ecosystem structure.

scholarly journals Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence

2019 ◽  
pp. 201900278 ◽  
Author(s):  
Troy S. Magney ◽  
David R. Bowling ◽  
Barry A. Logan ◽  
Katja Grossmann ◽  
Jochen Stutz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Spatial Scales ◽  
Gross Primary Production ◽  
Canopy Structure ◽  
Operating Efficiency ◽  
Conifer Forest ◽  
Spatiotemporal Resolution ◽  
Evergreen Forests ◽  
Spectrometer System

Northern hemisphere evergreen forests assimilate a significant fraction of global atmospheric CO2 but monitoring large-scale changes in gross primary production (GPP) in these systems is challenging. Recent advances in remote sensing allow the detection of solar-induced chlorophyll fluorescence (SIF) emission from vegetation, which has been empirically linked to GPP at large spatial scales. This is particularly important in evergreen forests, where traditional remote-sensing techniques and terrestrial biosphere models fail to reproduce the seasonality of GPP. Here, we examined the mechanistic relationship between SIF retrieved from a canopy spectrometer system and GPP at a winter-dormant conifer forest, which has little seasonal variation in canopy structure, needle chlorophyll content, and absorbed light. Both SIF and GPP track each other in a consistent, dynamic fashion in response to environmental conditions. SIF and GPP are well correlated (R2 = 0.62–0.92) with an invariant slope over hourly to weekly timescales. Large seasonal variations in SIF yield capture changes in photoprotective pigments and photosystem II operating efficiency associated with winter acclimation, highlighting its unique ability to precisely track the seasonality of photosynthesis. Our results underscore the potential of new satellite-based SIF products (TROPOMI, OCO-2) as proxies for the timing and magnitude of GPP in evergreen forests at an unprecedented spatiotemporal resolution.

scholarly journals Spatial Variation in Canopy Structure across Forest Landscapes

2018 ◽  
Author(s):  
Brady S. Hardiman ◽  
Elizabeth A. LaRue ◽  
Jeff W. Atkins ◽  
Robert T. Fahey ◽  
Franklin W. Wagner ◽  
...  
Keyword(s):  
Temperate Forest ◽  
Forest Canopy ◽  
Spatial Scales ◽  
Canopy Structure ◽  
Ecosystem Functions ◽  
Lidar System ◽  
Stable Point ◽  
Eastern Usa ◽  
Forested Landscapes ◽  
Patch Scale

Forest canopy structure (CS) controls many ecosystem functions and is highly variable across landscapes, but the magnitude and scale of this variation is not well understood. We used a portable canopy lidar system to characterize variation in five categories of CS along N = 3 transects (140–800 m long) at each of six forested landscapes within the eastern USA. The cumulative coefficient of variation was calculated for subsegments of each transect to determine the point of stability for individual CS metrics. We then quantified the scale at which CS is autocorrelated using Moran’s I in an Incremental Autocorrelation analysis. All CS metrics reached stable values within 300 m but varied substantially within and among forested landscapes. A stable point of 300 m for CS metrics corresponds with the spatial extent that many ecosystem functions are measured and modeled. Additionally, CS metrics were spatially autocorrelated at 40 to 88 m, suggesting that patch scale disturbance or environmental factors drive these patterns. Our study shows CS is heterogeneous across temperate forest landscapes at the scale of 10’s of meters, requiring a resolution of this size for upscaling CS with remote sensing to large spatial scales.

Satellite-based monitoring of biodiversity

2019 ◽  
pp. 20-36
Author(s):  
Nathalie Pettorelli
Keyword(s):  
Remote Sensing ◽  
Genetic Structure ◽  
Satellite Remote Sensing ◽  
Spatial Scales ◽  
Species Traits ◽  
Ecosystem Functions ◽  
Multiple Spatial Scales ◽  
Satellite Information ◽  
Monitoring Of Biodiversity ◽  
Composition Structure

This chapter explores how satellite information can help track biodiversity at multiple spatial scales and resolutions. To attempt to capture the various organisational dimensions of biodiversity, the chapter focuses on six classes of variables: genetic structure and composition, species populations, species traits, ecosystem extent and structure, ecosystem composition, and ecosystem functions. From helping map species and ecosystem distribution to supporting the monitoring of ecosystem composition, structure, and functioning, this contribution provides a series of examples that demonstrate how satellite remote sensing can open multiple opportunities for various stakeholders to improve on the management of wildlife. The chapter concludes with a discussion on limitations and possible ways forward.

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>

Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through lidar remote sensing: A case study in Sierra National Forest, CA

2014 ◽  
Vol 143 ◽  
pp. 131-141 ◽  
Author(s):  
Hao Tang ◽  
Matthew Brolly ◽  
Feng Zhao ◽  
Alan H. Strahler ◽  
Crystal L. Schaaf ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Spatial Scales ◽  
National Forest ◽  
Area Index ◽  
Multiple Spatial Scales

scholarly journals Co-varying effects of vegetation structure and terrain attributes are responsible for soil respiration spatial patterns in a sandy forest–steppe transition zone

Web Ecology ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 95-107
Author(s):  
Gabriella Süle ◽  
Szilvia Fóti ◽  
László Körmöczi ◽  
Dóra Petrás ◽  
Levente Kardos ◽  
...  
Keyword(s):  
Soil Respiration ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Structure ◽  
Canopy Structure ◽  
Spatiotemporal Variability ◽  
Ecosystem Functions ◽  
Forest Steppe ◽  
Area Index ◽  
Terrain Features

Abstract. Forest–steppe habitats in central Hungary have contrasting canopy structure with strong influence on the spatiotemporal variability of ecosystem functions. Canopy differences also co-vary with terrain feature effects, hampering the detection of key drivers of carbon cycling in this threatened habitat. We carried out seasonal measurements of ecosystem functions (soil respiration and leaf area index), microclimate and soil variables as well as terrain features along transects for 3 years in poplar groves and the surrounding grasslands. We found that the terrain features and the canopy differences co-varyingly affected the abiotic and biotic factors of this habitat. Topography had an effect on the spatial distribution of soil organic carbon content. Canopy structure had a strong modifying effect through allocation patterns and microclimatic conditions, both affecting soil respiration rates. Due to the vegetation structure difference between the groves and grasslands, spatial functional diversity was observed. We found notably different conditions under the groves with high soil respiration, soil water content and leaf area index; in contrast, on the grasslands (especially in E–SE–S directions from the trees) soil temperature and vapor pressure deficit showed high values. Processes of aridification due to climate change threaten these habitats and may cause reduction in the amount and extent of forest patches and decrease in landscape diversity. Owing to habitat loss, reduction in carbon stock may occur, which in turn has a significant impact on the local and global carbon cycles.

scholarly journals Site Characteristics Mediate the Relationship Between Forest Productivity and Satellite Measured Solar Induced Fluorescence

2021 ◽  
Vol 4 ◽  
Author(s):  
Theresia Yazbeck ◽  
Gil Bohrer ◽  
Pierre Gentine ◽  
Luping Ye ◽  
Nicola Arriga ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Gross Primary Production ◽  
Canopy Structure ◽  
Structural Response ◽  
Local Effect ◽  
High Temporal Resolution ◽  
Canopy Conductance ◽  
Area Index ◽  
The Relationship

Solar-Induced Chlorophyll Fluorescence (SIF) can provide key information about the state of photosynthesis and offers the prospect of defining remote sensing-based estimation of Gross Primary Production (GPP). There is strong theoretical support for the link between SIF and GPP and this relationship has been empirically demonstrated using ground-based, airborne, and satellite-based SIF observations, as well as modeling. However, most evaluations have been based on monthly and annual scales, yet the GPP:SIF relations can be strongly influenced by both vegetation structure and physiology. At the monthly timescales, the structural response often dominates but short-term physiological variations can strongly impact the GPP:SIF relations. Here, we test how well SIF can predict the inter-daily variation of GPP during the growing season and under stress conditions, while taking into account the local effect of sites and abiotic conditions. We compare the accuracy of GPP predictions from SIF at different timescales (half-hourly, daily, and weekly), while evaluating effect of adding environmental variables to the relationship. We utilize observations for years 2018–2019 at 31 mid-latitudes, forested, eddy covariance (EC) flux sites in North America and Europe and use TROPOMI satellite data for SIF. Our results show that SIF is a good predictor of GPP, when accounting for inter-site variation, probably due to differences in canopy structure. Seasonally averaged leaf area index, fraction of absorbed photosynthetically active radiation (fPAR) and canopy conductance provide a predictor to the site-level effect. We show that fPAR is the main factor driving errors in the linear model at high temporal resolution. Adding water stress indicators, namely canopy conductance, to a multi-linear SIF-based GPP model provides the best improvement in the model precision at the three considered timescales, showing the importance of accounting for water stress in GPP predictions, independent of the SIF signal. SIF is a promising predictor for GPP among other remote sensing variables, but more focus should be placed on including canopy structure, and water stress effects in the relationship, especially when considering intra-seasonal, and inter- and intra-daily resolutions.

Canopy light transmittance in a chronosequence of mixed-species deciduous forests

1994 ◽  
Vol 24 (8) ◽  
pp. 1694-1703 ◽  
Author(s):  
Martin J. Brown ◽  
Geoffrey G. Parker
Keyword(s):  
Leaf Area ◽  
Geometric Mean ◽  
Canopy Structure ◽  
Three Dimensional ◽  
Distinct Pattern ◽  
Light Transmittance ◽  
Dimensional Structure ◽  
Stand Age ◽  
Area Index ◽  
Frequency Distributions

We measured the photosynthetically active radiation transmitted through the canopies of 24 Maryland forest stands, each around midday in midsummer. We then compared the observed values of PAR transmittance with stand age and measures of canopy structure. Generally, transmittance was low, with positively skewed frequency distributions. The geometric mean transmittance followed a distinct pattern with stand age. It was lowest (about 1%) in the youngest stands, increased to about 2.5% as forests approached ages of about 50 years, and then declined with age in the oldest sites (65–340 years). Transmittance was not significantly correlated with many simple measures of forest structure, including estimated aboveground biomass and leaf area index. Better predictions of transmittance used information on the vertical arrangement of the canopy, such as leaf area density. The results are contrary to the common assumptions that forests get consistently darker through time, and that transmittance is inversely proportional to the sheer mass or leaf area of the canopy. The Beer–Lambert extinction coefficient, k, changed with stand age and structure and was especially high in very young stands. We suggest that the variation in light transmittance, and k, can be explained by successional changes in the three-dimensional structure of the canopy.

scholarly journals Spatial Variation in Canopy Structure across Forest Landscapes

Forests ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 474 ◽  
Author(s):  
Brady Hardiman ◽  
Elizabeth LaRue ◽  
Jeff Atkins ◽  
Robert Fahey ◽  
Franklin Wagner ◽  
...  
Keyword(s):  
Temperate Forest ◽  
Forest Canopy ◽  
Spatial Scales ◽  
Canopy Structure ◽  
Ecosystem Functions ◽  
Lidar System ◽  
Stable Point ◽  
Eastern Usa ◽  
Forested Landscapes ◽  
Patch Scale

Forest canopy structure (CS) controls many ecosystem functions and is highly variable across landscapes, but the magnitude and scale of this variation is not well understood. We used a portable canopy LiDAR system to characterize variation in five categories of CS along N = 3 transects (140–800 m long) at each of six forested landscapes within the eastern USA. The cumulative coefficient of variation was calculated for subsegments of each transect to determine the point of stability for individual CS metrics. We then quantified the scale at which CS is autocorrelated using Moran’s I in an Incremental Autocorrelation analysis. All CS metrics reached stable values within 300 m but varied substantially within and among forested landscapes. A stable point of 300 m for CS metrics corresponds with the spatial extent that many ecosystem functions are measured and modeled. Additionally, CS metrics were spatially autocorrelated at 40 to 88 m, suggesting that patch scale disturbance or environmental factors drive these patterns. Our study shows CS is heterogeneous across temperate forest landscapes at the scale of 10 s of meters, requiring a resolution of this size for upscaling CS with remote sensing to large spatial scales.

Forest structure estimation and pattern exploration from discrete-return lidar in subalpine forests of the central Rockies

2008 ◽  
Vol 38 (8) ◽  
pp. 2081-2096 ◽  
Author(s):  
K. R. Sherrill ◽  
M. A. Lefsky ◽  
J. B. Bradford ◽  
M. G. Ryan
Keyword(s):  
Leaf Area ◽  
Forest Structure ◽  
Stand Structure ◽  
Forest Biomass ◽  
Canopy Structure ◽  
Primary Source ◽  
Subalpine Forest ◽  
Stand Age ◽  
Area Index ◽  
The North

This study evaluates the relative ability of simple light detection and ranging (lidar) indices (i.e., mean and maximum heights) and statistically derived canonical correlation analysis (CCA) variables attained from discrete-return lidar to estimate forest structure and forest biomass variables for three temperate subalpine forest sites. Both lidar and CCA explanatory variables performed well with lidar models having slightly higher explained variance and lower root mean square error. Adjusted R2 values were 0.93 and 0.93 for mean height, 0.74 and 0.73 for leaf area index, and 0.93 and 0.85 for all carbon in live biomass for the lidar and CCA explanatory regression models, respectively. The CCA results indicate that the primary source of variability in canopy structure is related to forest height, biomass, and total leaf area, and the second most important source of variability is related to the amount of midstory foliage and tree density. When stand age is graphed as a function of individual plot scores for canonicals one and two, there is a clear relationship with stand age and the development of stand structure. Lidar-derived biomass and related estimates developed in this work will be used to parameterize decision-support tools for analysis of carbon cycle impacts as part of the North American Carbon Program.

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