Spatial heterogeneity of leaf area index in a temperate old-growth forest: Spatial autocorrelation dominates over biotic and abiotic factors

2018 ◽  
Vol 634 ◽  
pp. 287-295 ◽  
Author(s):  
Zhili Liu ◽  
Feng Jiang ◽  
Yu Zhu ◽  
Fengri Li ◽  
Guangze Jin
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Autocorrelation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Abiotic Factors ◽  
Old Growth ◽  
Area Index ◽  
Biotic And Abiotic Factors ◽  
Old Growth Forest

scholarly journals Rule-based analysis of throughfall kinetic energy to evaluate biotic and abiotic factor thresholds to mitigate erosive power

2016 ◽  
Vol 40 (3) ◽  
pp. 431-449 ◽  
Author(s):  
Philipp Goebes ◽  
Karsten Schmidt ◽  
Werner Härdtle ◽  
Steffen Seitz ◽  
Felix Stumpf ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Kinetic Energy ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Abiotic Factors ◽  
Tree Height ◽  
Abiotic Factor ◽  
Area Index ◽  
Biotic And Abiotic Factors ◽  
Optimal Set

Below vegetation, throughfall kinetic energy (TKE) is an important factor to express the potential of rainfall to detach soil particles and thus for predicting soil erosion rates. TKE is affected by many biotic (e.g. tree height, leaf area index) and abiotic (e.g. throughfall amount) factors because of changes in rain drop size and velocity. However, studies modelling TKE with a high number of those factors are lacking. This study presents a new approach to model TKE. We used 20 biotic and abiotic factors to evaluate thresholds of those factors that can mitigate TKE and thus decrease soil erosion. Using these thresholds, an optimal set of biotic and abiotic factors was identified to minimize TKE. The model approach combined recursive feature elimination, random forest (RF) variable importance and classification and regression trees (CARTs). TKE was determined using 1405 splash cup measurements during five rainfall events in a subtropical Chinese tree plantation with five-year-old trees in 2013. Our results showed that leaf area, tree height, leaf area index and crown area are the most prominent vegetation traits to model TKE. To reduce TKE, the optimal set of biotic and abiotic factors was a leaf area lower than 6700 mm2, a tree height lower than 290 cm combined with a crown base height lower than 60 cm, a leaf area index smaller than 1, more than 47 branches per tree and using single tree species neighbourhoods. Rainfall characteristics, such as amount and duration, further classified high or low TKE. These findings are important for the establishment of forest plantations that aim to minimize soil erosion in young succession stages using TKE modelling.

Comparison of Methods of Estimating Leaf-Area Index In Old-Growth Douglas-Fir

Ecology ◽  
1986 ◽  
Vol 67 (4) ◽  
pp. 975-979 ◽  
Author(s):  
J. D. Marshall ◽  
R. H. Waring
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Douglas Fir ◽  
Comparison Of Methods ◽  
Old Growth ◽  
Area Index

scholarly journals Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations

2020 ◽  
Vol 12 (11) ◽  
pp. 1843 ◽  
Author(s):  
Andrew Revill ◽  
Anna Florence ◽  
Alasdair MacArthur ◽  
Stephen Hoad ◽  
Robert Rees ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Stages ◽  
Two Stage ◽  
Area Index ◽  
Field Surveys ◽  
Red Edge ◽  
Calibration Approach ◽  
Sentinel 2

Leaf area index (LAI) estimates can inform decision-making in crop management. The European Space Agency’s Sentinel-2 satellite, with observations in the red-edge spectral region, can monitor crops globally at sub-field spatial resolutions (10–20 m). However, satellite LAI estimates require calibration with ground measurements. Calibration is challenged by spatial heterogeneity and scale mismatches between field and satellite measurements. Unmanned Aerial Vehicles (UAVs), generating high-resolution (cm-scale) LAI estimates, provide intermediary observations that we use here to characterise uncertainty and reduce spatial scaling discrepancies between Sentinel-2 observations and field surveys. We use a novel UAV multispectral sensor that matches Sentinel-2 spectral bands, flown in conjunction with LAI ground measurements. UAV and field surveys were conducted on multiple dates—coinciding with different wheat growth stages—that corresponded to Sentinel-2 overpasses. We compared chlorophyll red-edge index (CIred-edge) maps, derived from the Sentinel-2 and UAV platforms. We used Gaussian processes regression machine learning to calibrate a UAV model for LAI, based on ground data. Using the UAV LAI, we evaluated a two-stage calibration approach for generating robust LAI estimates from Sentinel-2. The agreement between Sentinel-2 and UAV CIred-edge values increased with growth stage—R2 ranged from 0.32 (stem elongation) to 0.75 (milk development). The CIred-edge variance between the two platforms was more comparable later in the growing season due to a more homogeneous and closed wheat canopy. The single-stage Sentinel-2 LAI calibration (i.e., direct calibration from ground measurements) performed poorly (mean R2 = 0.29, mean NRMSE = 17%) when compared to the two-stage calibration using the UAV data (mean R2 = 0.88, mean NRMSE = 8%). The two-stage approach reduced both errors and biases by >50%. By upscaling ground measurements and providing more representative model training samples, UAV observations provide an effective and viable means of enhancing Sentinel-2 wheat LAI retrievals. We anticipate that our UAV calibration approach to resolving spatial heterogeneity would enhance the retrieval accuracy of LAI and additional biophysical variables for other arable crop types and a broader range of vegetation cover types.

scholarly journals Spatial and seasonal variations of leaf area index (LAI) in subtropical secondary forests related to floristic composition and stand characters

2016 ◽  
Author(s):  
Wenjuan Zhu ◽  
Wenhua Xiang ◽  
Qiong Pan ◽  
Yelin Zeng ◽  
Shuai Ouyang ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Seasonal Variations ◽  
Basal Area ◽  
Floristic Composition ◽  
Controlling Factors ◽  
Forest Types ◽  
Area Index ◽  
Subtropical Forests

Abstract. Leaf area index (LAI) is an important parameter related to carbon, water and energy exchange between canopy and atmosphere, and is widely applied in the process models to simulate production and hydrological cycle in forest ecosystems. However, fine-scale spatial heterogeneity of LAI and its controlling factors have not been fully understood in Chinese subtropical forests. We used hemispherical photography to measure LAI values in three subtropical forests (i.e. Pinus massoniana – Lithocarpus glaber coniferous and evergreen broadleaved mixed forests, Choerospondias axillaris deciduous broadleaved forests, and L. glaber – Cyclobalanopsis glauca evergreen broadleaved forests) during period from April, 2014 to January, 2015. Spatial heterogeneity of LAI and its controlling factors were analysed by using geostatistics method the generalised additive models (GAMs), respectively. Our results showed that LAI values differed greatly in the three forests and their seasonal variations were consistent with plant phenology. LAI values exhibited strong spatial autocorrelation for three forests measured in January and for the L. glaber – C. glauca forest in April, July and October. Obvious patch distribution pattern of LAI values occurred in three forests during the non-growing period and this pattern gradually dwindled in the growing season. Stand basal area, crown coverage, crown width, proportion of deciduous species on basal area basis and forest types affected the spatial variations in LAI values in January, while species richness, crown coverage, stem number and forest types affected the spatial variations in LAI values in July. Floristic composition, spatial heterogeneity and seasonal variations should be considered for sampling strategy in indirect LAI measurement and application of LAI to simulate functional processes in subtropical forests.

scholarly journals Environmental control on water vapour and energy exchanges over grasslands in semiarid region of Brazil

2015 ◽  
Vol 19 (1) ◽  
pp. 3-8 ◽  
Author(s):  
Patrícia S. de S. Gondim ◽  
José R. de S. Lima ◽  
Antonio C. D. Antonino ◽  
Claude Hammecker ◽  
Renan A. B. da Silva ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Soil Water ◽  
Leaf Area ◽  
Water Vapour ◽  
Environmental Control ◽  
Abiotic Factors ◽  
Diurnal Variations ◽  
Energy Partitioning ◽  
Area Index ◽  
Seasonal And Diurnal Variations

A micrometeorological experiment was conducted over grasslands in a semi-arid region of north-eastern Brazil (São João, Pernambuco) from January to December 2011, using the Bowen ratio energy balance method, to improve the current understanding of energy partitioning and water vapour exchange over this ecosystem in this region. The objectives of the present study were to quantify the seasonal and diurnal variations in energy and water vapour exchanges over grasslands and understand the biotic and abiotic factors controlling the energy partitioning of this ecosystem. In the dry period, the low stored soil water limited the grass production and leaf area index, and as a consequence of these conditions, most of the annual net radiation (58%) was consumed in sensible heat flux. During the course of the study the evaporative fraction was linearly related to the leaf area index. The total annual evapotranspiration and its daily maximum were 543.8 mm and 3.14 mm d-1. The seasonal and diurnal variations in energy partitioning and evapotranspiration were controlled by soil water availability and leaf area index.

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