1,326 research outputs found
Effects of tree trunks on estimation of clumping index and LAI from HemiView and terrestrial LiDAR
Estimating clumping indices is important for determining the leaf area index (LAI) of forest canopies. The spatial distribution of the clumping index is vital for LAI estimation. However, the neglect of woody tissue can result in biased clumping index estimates when indirectly deriving them from the gap probability and LAI observations. It is difficult to effectively and automatically extract woody tissue from digital hemispherical photos. In this study, a method for the automatic detection of trunks from Terrestrial Laser Scanning (TLS) data was used. Between-crown and within-crown gaps from TLS data were separated to calculate the clumping index. Subsequently, we analyzed the gap probability, clumping index, and LAI estimates based on TLS and HemiView data in consideration of woody tissue (trunks). Although the clumping index estimated from TLS had better agreement (R-2 = 0.761) than that from HemiView, the change of angular distribution of the clumping index affected by the trunks from TLS data was more obvious than with the HemiView data. Finally, the exclusion of the trunks led to a reduction in the average LAI by similar to 19.6% and 8.9%, respectively, for the two methods. These results also showed that the detection of woody tissue was more helpful for the estimation of clumping index distribution. Moreover, the angular distribution of the clumping index is more important for the LAI estimate than the average clumping index value. We concluded that woody tissue should be detected for the clumping index estimate from TLS data, and 3D information could be used for estimating the angular distribution of the clumping index, which is essential for highly accurate LAI field measurements
Terrestrial laser scanning for vegetation analyses with a special focus on savannas
Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome
Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors
The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels
Advances in measuring forest structure by terrestrial laser scanning with the Dual Wavelength ECHIDNA® LIDAR (DWEL)
Leaves in forests assimilate carbon from the atmosphere and woody components store the net production of that assimilation. Separate structure measurements of leaves and woody components advance the monitoring and modeling of forest ecosystem functions. This dissertation provides a method to determine, for the first time, the 3-D spatial arrangement and the amount of leafy and woody materials separately in a forest by classification of lidar returns from a new, innovative, lidar scanner, the Dual-Wavelength Echidna® Lidar (DWEL). The DWEL uses two lasers pulsing simultaneously and coaxially at near-infrared (1064 nm) and shortwave-infrared (1548 nm) wavelengths to locate scattering targets in 3-D space, associated with their reflectance at the two wavelengths. The instrument produces 3-D bispectral "clouds" of scattering points that reveal new details of forest structure and open doors to three-dimensional mapping of biophysical and biochemical properties of forests.
The three parts of this dissertation concern calibration of bispectral lidar returns; retrieval of height profiles of leafy and woody materials within a forest canopy; and virtual reconstruction of forest trees from multiple scans to estimate their aboveground woody biomass. The test area was a midlatitude forest stand within the Harvard Forest, Petersham, Massachusetts, scanned at five locations in a 1-ha site in leaf-off and leaf-on conditions in 2014. The model for radiometric calibration assigned accurate values of spectral apparent reflectance, a range-independent and instrument-independent property, to scattering points derived from the scans. The classification of leafy and woody points, using both spectral and spatial context information, achieved an overall accuracy of 79±1% and 75±2% for leaf-off and leaf-on scans, respectively. Between-scan variation in leaf profiles was larger than wood profiles in leaf-off seasons but relatively similar to wood profiles in leaf-on seasons, reflecting the changing spatial heterogeneity within the stand over seasons. A 3-D structure-fitting algorithm estimated wood volume by modeling stems and branches from point clouds of five individual trees with cylinders. The algorithm showed the least variance for leaf-off, woody-points-only data, validating the value of separating leafy and woody points to the direct biomass estimates through the structure modeling of individual trees
Remote Sensing of Savannas and Woodlands
Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome
Remote Sensing of Biophysical Parameters
Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)
Towards a methodical framework for comprehensively assessing forest multifunctionality
Funded by Deutsche Forschungsgemeinschaft. Grant Number: DFG FOR 891/1-3 National Natural Science Foundation of China. Grant Numbers: 30710103907, 30930005, 31170457, 31210103910 Swiss National Science Foundation (SNSF) Sino-German Centre for Research Promotion in Beijing. Grant Number: GZ 986Peer reviewedPublisher PD
Uncertainty in parameterizing floodplain forest friction for natural flood management, using remote sensing
One potential Natural Flood Management (NFM) option is floodplain reforestation or manage existing riparian forests, with a view to increasing flow resistance and attenuate flood hydrographs. However, the effectiveness of floodplain forests as resistance agents, during different magnitude overbank floods, has yet to be appropriately parameterized for hydraulic models. Remote sensing offers high-resolution datasets capable of characterizing vegetation structure from a variety of platforms, but they contain uncertainty. For the first time, we demonstrate uncertainty propagation in remote sensing derivations of complex vegetation structure through roughness prediction and floodplain flow for extreme flows and different forest types (young and old Poplar plantations, young and old Pine plantations, and an unmanaged riparian forest). The lowest uncertainties resulted from terrestrial and airborne lidar, where airborne lidar is currently best at defining canopy leaf area, but more research is needed to determine wood area. Mean literature uncertainties in stem density, trunk diameter, wood, and leaf area indices (20, 10, 30, 20%, respectively) resulted in a combined Manning’s n uncertainty from 11–13% to 11–17% at 2 m to 8 m flow depths. This equates to 7–8% roughness uncertainty per 10% combined forest structure uncertainty. Individually, stem density and trunk diameter uncertainties resulted in the largest Manning’s n uncertainty at all flow depths, especially for flow though Pine plantations. For deeper flows, leaf and woody areas become much more important, especially for unmanaged riparian forests with low canopy morphology. Forest structure errors propagated to flow depth demonstrate that even small flows can change by a decimeter, while deeper flows can change by 40 cm or more. For flow depth, errors in canopy structure are deemed more severe in flows depths beyond 4–6 m. This study highlights the need for lower uncertainty in all forest structure components using remote sensing, to improve roughness parameterization and flood modeling for NFM
- …