scholarly journals In situ data supporting remote sensing estimation of spruce forest parameters at the ecosystem station Bílý Kříž

Beskydy ◽  
2017 ◽  
Vol 10 (1-2) ◽  
pp. 75-86 ◽  
Author(s):  
Lucie Homolová ◽  
Růžena Janoutová ◽  
Petr Lukeš ◽  
Jan Hanuš ◽  
Jan Novotný ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Spatial Scales ◽  
Carotenoid Content ◽  
Area Index ◽  
Needle Age ◽  
Quantitative Parameters ◽  
In Situ Data

Remote sensing offers an effective way of mapping vegetation parameters in a spatially continuous manner, at larger spatial scales and repeatedly in time compared to traditional in situ mapping approaches that are typically accurate, but limited to a few distributed location and few repetitions. In case of forest ecosystems, remote sensing allow to assess quantitative parameters or indicators related to forest health status such as leaf area index, leaf pigment content, chlorophyll fluorescence, etc. Development, calibration and validation of remote sensing-based methods, however, still rely on supportive in situ data. The aim of this contribution is to introduce the individual in situ components in the framework for the retrieval of forest quantitative parameters from airborne imaging spectroscopy data. All measurements were acquired during an extensive in situ/flight campaign that took place at the Norway spruce dominated study site Bílý Kříž (Moravian-Silesian Beskydy Mts., Czech Republic) during August 2016. In addition to airborne remote sensing data acquisition, the in situ activities included terrestrial laser scanning for tree 3D modelling, measurements of needle biochemical and optical properties, leaf area index measurements and spectral measurements of various natural and artificial surfaces. Leaf pigments varied between 25.2 and 49.1 µg cm-2 for chlorophyll a+b content, 4.9 – 10.6 µg cm-2 for carotenoid content depending on needle age and its adaptation to sun illumination, whereas ratio between the two pigments was stable around 4.6 – 5. 3. Specific leaf area of spruce needles varied between 49.3 and 105.8 cm2 g-1, being the highest for the shade adapted needles of the current year. Leaf area index of spruce stands of various age and densities varied between 5.3 and 9. 3.

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 Comparison of Leaf Area Index, Surface Temperature, and Actual Evapotranspiration Estimated Using the METRIC Model and In Situ Measurements

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1857 ◽  
Author(s):  
Arturo Reyes-González ◽  
Jeppe Kjaersgaard ◽  
Todd Trooien ◽  
David G. Reta-Sánchez ◽  
Juan I. Sánchez-Duarte ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Leaf Area Index ◽  
Leaf Area ◽  
In Situ Measurements ◽  
Actual Evapotranspiration ◽  
Landsat 8 ◽  
Area Index ◽  
Metric Model

The verification of remotely sensed estimates of surface variables is essential for any remote sensing study. The objective of this study was to compare leaf area index (LAI), surface temperature (Ts), and actual evapotranspiration (ETa), estimated using the remote sensing-based METRIC model and in situ measurements collected at the satellite overpass time. The study was carried out at a commercial corn field in eastern South Dakota. Six clear-sky images from Landsat 7 and Landsat 8 (Path 29, Row 29) were processed and used for the assessment. LAI and Ts were measured in situ, and ETa was estimated using an atmometer and independent crop coefficients. The results revealed good agreement between the variables measured in situ and estimated by the METRIC model. LAI showed r2 = 0.76, and RMSE = 0.59 m2 m−2, the Ts comparison had an agreement of r2 = 0.87 and RMSE 1.24 °C, and ETa presented r2 = 0.89 and RMSE = 0.71 mm day−1.

scholarly journals Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales

2020 ◽  
Vol 12 (17) ◽  
pp. 2764
Author(s):  
John S. Iiames ◽  
Ellen Cooter ◽  
Andrew N. Pilant ◽  
Yang Shao
Keyword(s):  
Air Quality ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Dynamic Range ◽  
Spatial Scales ◽  
Atmospheric Modeling ◽  
Area Index ◽  
Quality Models ◽  
Modis Lai

Modeled leaf area index (LAI) in conjunction with satellite-derived LAI data streams may be used to support various regional and local scale air quality models for retrospective and future meteorological assessments. The Environmental Policy Integrated Climate (EPIC) model holds promise for providing LAI within a dynamic range for input into climate and air quality models, improving on current LAI distribution assumptions typical within atmospheric modeling. To assess the potential use of EPIC LAI, we first evaluated the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product collections 5 and 6 (i.e., Mc5, Mc6) with in situ LAI estimates upscaled at four 1.0 km resolution research sites distributed over the Albemarle-Pamlico Basin in North Carolina and Virginia, USA. We then compared the EPIC modeled 12.0 km resolution LAI to aggregated MODIS LAI (Mc5, Mc6) over a 3 × 3 grid (or 36 km × 36 km) centered over the same four research sites. Upscaled in situ LAI comparison with MODIS LAI showed improvement with the newer collection where the Mc5 overestimate of +2.22 LAI was reduced to +0.97 LAI with the Mc6. On three of the four sites, the EPIC/MODIS LAI comparison at 12.0 km resolution grid showed similar weighted mean LAI differences (LAI 1.29–1.34), with both Mc5 and Mc6 exceeding EPIC LAI across most dates. For all four research sites, both MODIS collections showed a positive bias when compared to EPIC LAI, with Mc6 (LAI = 0.40) aligning closer to EPIC than the Mc5 (LAI = 0.61) counterpart. Despite modest differences between both MODIS collections and EPIC LAI, the overestimation trend suggests the potential for EPIC to be used for future meteorological alternative management applications on a regional or national scale.

scholarly journals Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

2020 ◽  
Vol 12 (18) ◽  
pp. 2934
Author(s):  
Jin Xu ◽  
Lindi J. Quackenbush ◽  
Timothy A. Volk ◽  
Jungho Im
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Spatial Scales ◽  
Remote Sensing Data ◽  
Quantitative Relationship ◽  
Empirical Models ◽  
Ease Of Use ◽  
Growth Stages ◽  
Area Index

Leaf area index (LAI) is an important vegetation leaf structure parameter in forest and agricultural ecosystems. Remote sensing techniques can provide an effective alternative to field-based observation of LAI. Differences in canopy structure result in different sensor types (active or passive), platforms (terrestrial, airborne, or satellite), and models being appropriate for the LAI estimation of forest and agricultural systems. This study reviews the application of remote sensing-based approaches across different system configurations (passive, active, and multisource sensors on different collection platforms) that are used to estimate forest and crop LAI and explores uncertainty analysis in LAI estimation. A comparison of the difference in LAI estimation for forest and agricultural applications given the different structure of these ecosystems is presented, particularly as this relates to spatial scale. The ease of use of empirical models supports these as the preferred choice for forest and crop LAI estimation. However, performance variation among different empirical models for forest and crop LAI estimation limits the broad application of specific models. The development of models that facilitate the strategic incorporation of local physiology and biochemistry parameters for specific forests and crop growth stages from various temperature zones could improve the accuracy of LAI estimation models and help develop models that can be applied more broadly. In terms of scale issues, both spectral and spatial scales impact the estimation of LAI. Exploration of the quantitative relationship between scales of data from different sensors could help forest and crop managers more appropriately and effectively apply different data sources. Uncertainty coming from various sources results in reduced accuracy in estimating LAI. While Bayesian approaches have proven effective to quantify LAI estimation uncertainty based on the uncertainty of model inputs, there is still a need to quantify uncertainty from remote sensing data source, ground measurements and related environmental factors to mitigate the impacts of model uncertainty and improve LAI estimation.

scholarly journals Remote Sensing to Study Mangrove Fragmentation and Its Impacts on Leaf Area Index and Gross Primary Productivity in the South of Peninsular Malaysia

2021 ◽  
Vol 13 (8) ◽  
pp. 1427
Author(s):  
Kasturi Devi Kanniah ◽  
Chuen Siang Kang ◽  
Sahadev Sharma ◽  
A. Aldrie Amir
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Primary Productivity ◽  
Peninsular Malaysia ◽  
Area Index ◽  
Mangrove Area ◽  
The Mean ◽  
Shape Complexity ◽  
The Impact

Mangrove is classified as an important ecosystem along the shorelines of tropical and subtropical landmasses, which are being degraded at an alarming rate despite numerous international treaties having been agreed. Iskandar Malaysia (IM) is a fast-growing economic region in southern Peninsular Malaysia, where three Ramsar Sites are located. Since the beginning of the 21st century (2000–2019), a total loss of 2907.29 ha of mangrove area has been estimated based on medium-high resolution remote sensing data. This corresponds to an annual loss rate of 1.12%, which is higher than the world mangrove depletion rate. The causes of mangrove loss were identified as land conversion to urban, plantations, and aquaculture activities, where large mangrove areas were shattered into many smaller patches. Fragmentation analysis over the mangrove area shows a reduction in the mean patch size (from 105 ha to 27 ha) and an increase in the number of mangrove patches (130 to 402), edge, and shape complexity, where smaller and isolated mangrove patches were found to be related to the rapid development of IM region. The Moderate Resolution Imaging Spectro-radiometer (MODIS) Leaf Area Index (LAI) and Gross Primary Productivity (GPP) products were used to inspect the impact of fragmentation on the mangrove ecosystem process. The mean LAI and GPP of mangrove areas that had not undergone any land cover changes over the years showed an increase from 3.03 to 3.55 (LAI) and 5.81 g C m−2 to 6.73 g C m−2 (GPP), highlighting the ability of the mangrove forest to assimilate CO2 when it is not disturbed. Similarly, GPP also increased over the gained areas (from 1.88 g C m−2 to 2.78 g C m−2). Meanwhile, areas that lost mangroves, but replaced them with oil palm, had decreased mean LAI from 2.99 to 2.62. In fragmented mangrove patches an increase in GPP was recorded, and this could be due to the smaller patches (<9 ha) and their edge effects where abundance of solar radiation along the edges of the patches may increase productivity. The impact on GPP due to fragmentation is found to rely on the type of land transformation and patch characteristics (size, edge, and shape complexity). The preservation of mangrove forests in a rapidly developing region such as IM is vital to ensure ecosystem, ecology, environment, and biodiversity conservation, in addition to providing economical revenue and supporting human activities.

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