Leaf Area Index Distribution Research Articles

Developing a two-step algorithm to estimate the leaf area index of forests with complex structures based on CHRIS/PROBA data

Vegetation is a key factor affecting soil erosion, which depends not only on the horizontal structure of vegetation, but also on its vertical structure. The leaf area index (LAI) of plants, as an important structural parameter of the terrestrial ecosystem, can reflect horizontal vegetation coverage, vertical structure of vegetation, litter thickness and underground biomass. All these are the principal aspects of vegetation affecting soil erosion. The multi-angle remote sensing data can provide the stereoscopic and directional information of the surface target, which is helpful to extract the vertical structure parameters of vegetation effectively. Therefore, in order to improve the estimation accuracy of soil erosion, a two-step method was developed in this study to estimate the forest LAI under different vertical structures using the PROSAIL model and multi-angle CHRIS/PROBA data based on the field data. A total of 70 quadrats (10 m × 10 m sized) were deployed in Mount Zijin with undisturbed natural vegetation, considering the vegetation types and accessibility in the middle of August 2017. The pixels were first classified into two categories depending on complexity of vertical structure; then, a specific algorithm was applied to the different classes. For Class 1, the single angle data of 55° had the highest accuracy, indicating that, for forests with a simple vertical structure, the single angle data was sufficient for LAI estimation. For Class 2, the multi-angle data combination of 0°, 55° and −55° had the highest accuracy, indicating that, for forests with complex vertical structure, the multi-angle data can improve the accuracy of LAI estimation. The mean absolute percentage error (MAPE) and the root mean square error (RMSE) were applied to evaluate the accuracy of the LAI estimation algorithm. The results indicated that the two-step method can significantly improve the LAI estimation accuracy of forests with complex vertical structure, with a decrease in MAPE and RMSE of 19.52% and 23.08%, respectively, compared to the traditional empirical algorithm. The developed LAI estimation algorithm can be widely applied to forests with different vertical structures. Lastly, the two-step method was applied to the CHRIS/PROBA image acquired on September 7th, 2015 for mapping the spatial distribution of forest LAI in the western part of Mount Zijin. The result showed that the northern part and the southern part had higher LAI than the middle part of Mount Zijin.

Seasonal and drought-related changes in leaf area profiles depend on height and light environment in an Amazon forest.

Seasonal dynamics in the vertical distribution of leaf area index (LAI) may impact the seasonality of forest productivity in Amazonian forests. However, until recently, fine-scale observations critical to revealing ecological mechanisms underlying these changes have been lacking. To investigate fine-scale variation in leaf area with seasonality and drought we conducted monthly ground-based LiDAR surveys over 4yr at an Amazon forest site. We analysed temporal changes in vertically structured LAI along axes of both canopy height and light environments. Upper canopy LAI increased during the dry season, whereas lower canopy LAI decreased. The low canopy decrease was driven by highly illuminated leaves of smaller trees in gaps. By contrast, understory LAI increased concurrently with the upper canopy. Hence, tree phenological strategies were stratified by height and light environments. Trends were amplified during a 2015-2016 severe El Niño drought. Leaf area low in the canopy exhibited behaviour consistent with water limitation. Leaf loss from short trees in high light during drought may be associated with strategies to tolerate limited access to deep soil water and stressful leaf environments. Vertically and environmentally structured phenological processes suggest a critical role of canopy structural heterogeneity in seasonal changes in Amazon ecosystem function.

Forest Leaf Area Index Inversion Based on Landsat OLI Data in the Shangri-La City

Leaf Area Index (LAI) is an important index that reflects the growth status of forest vegetation and land surface processes. It is of important practical significance to quantitatively and accurately estimate Leaf Area Index. We used the Landsat-8 operational land imager single-band images, and 15 vegetation indices that were extracted from the multi-band were combined with the LAI data measured from the CI-110 canopy digital imager to establish the LAI estimation model. Through the leave-one-out cross-validation method, the accuracy of various model estimation results was verified and compared, and the optimal estimation model was obtained to generate the LAI distribution map of Shangri-La City. The results show that: (1) the multivariable model method is better than the single-variable model method when estimating LAI, and its determination coefficient is the highest (R2 = 0.7903). (2) The full-sample dataset is divided into Alpine Pine forest, Oak forest, Spruce–fir forest, and Yunnan Pine forest for analysis. The coefficient of determination of the model simulation is improved to varying degrees, and the highest R2 increased by 0.1652, 0.1040, 0.1264, and 0.0079, respectively, over the full-sample. The corresponding best models are LAI–DVI (Difference Vegetation Index), LAI–NNIR (normalized near-infrared), LAI–NMDI (Normalized Multi-band Drought Index), and LAI–RVI (Ratio Vegetation Index). (3) The LAI values in Shangri-La City ranged from 0.9654 to 5.5145 and are mainly concentrated in high vegetation coverage areas; and the higher the vegetation coverage level, the higher the LAI value.

Spatial variations in soil-water carrying capacity of three typical revegetation species on the Loess Plateau, China

Re-vegetation is a necessary control measure of soil erosion in the Loess Plateau. However, excessive re-vegetation can aggravate soil water shortage, which can in turn threaten the health and services of restored ecosystems. An optimal plant cover or biomass (i.e., soil-water carrying capacity for vegetation, SWCCV) is important for regional water balance, soil protection and vegetation sustainability. The objective of this study was to determine the spatial distribution of SWCCV for three non-native tree (Robinia pseudoacaia), shrub (Caragana korshinskii) and grass (Medicago sativa) species used in the re-vegetation of the Loess Plateau. The dynamics of actual evapotranspiration (AET), net primary productivity (NPP) and leaf area index (LAI) were simulated using a modified Biome-BGC (Bio-Geochemical Cycles) model. Soil and physiological parameters required by the model were validated using field-observed AET for the three plant species at six sites in the study area. The validated model was used to simulate the dynamics of AET, NPP and LAI for the three plant species at 243 representative sites in the study area for the period 1961–2014. The results show that spatial distributions of mean AET, NPP and LAI generally increased from northwest to southeast, much the same as mean annual precipitation (MAP) gradient. In terms of maximum LAI, the ranges of optimal plant cover were 1.1–3.5 for R. pseudoacaia, 1.0–2.4 for C. korshinskii and 0.7–3.0 for M. sativa. The corresponding SWCCV, expressed as NPP were 202.4–616.5, 83.7–201.7 and 56.3–253.0 g C m−2 yr−1. MAP, mean annual temperature, soil texture and elevation were the main variables driving SWCCV under the plant species; explaining over 86% of the spatial variations in mean NPP in the study area. Further re-vegetation therefore needs careful reconsideration under the prevailing climatic, soil and topographic conditions. The results of the study provide a re-vegetation threshold to guide future re-vegetation activities and to ensure a sustainable eco-hydrological environment in the Loess Plateau.

Effects of Forest Canopy Vertical Stratification on the Estimation of Gross Primary Production by Remote Sensing

Gross primary production (GPP) in forests is the most important carbon flux in terrestrial ecosystems. Forest ecosystems with high leaf area index (LAI) values have diverse species or complex forest structures with vertical stratifications that influence the carbon–water–energy cycles. In this study, we used three light use efficiency (LUE) GPP models and site-level experiment data to analyze the effects of the vertical stratification of dense forest vegetation on the estimates of remotely sensed GPP during the growing season of two forest sites in East Asia: Dinghushan (DHS) and Tomakomai (TMK). The results showed that different controlling environmental factors of the vertical layers, such as temperature and vapor pressure deficit (VPD), produce different responses for the same LUE value in the different sub-ecosystems (defined as the tree, shrub, and grass layers), which influences the GPP estimation. Air temperature and VPD play important roles in the effects of vertical stratification on the GPP estimates in dense forests, which led to differences in GPP uncertainties from −50% to 30% because of the distinct temperature responses in TMK. The unequal vertical LAI distributions in the different sub-ecosystems led to GPP variations of 1–2 gC/m2/day with uncertainties of approximately −30% to 20% because sub-ecosystems have unique absorbed fractions of photosynthetically active radiation (APAR) and LUE. A comparison with the flux tower-based GPP data indicated that the GPP estimations from the LUE and APAR values from separate vertical layers exhibited better model performance than those calculated using the single-layer method, with 10% less bias in DHS and more than 70% less bias in TMK. The precision of the estimated GPP in regions with thick understory vegetation could be effectively improved by considering the vertical variations in environmental parameters and the LAI values of different sub-ecosystems as separate factors when calculating the GPP of different components. Our results provide useful insight that can be used to improve the accuracy of remote sensing GPP estimations by considering vertical stratification parameters along with the LAI of sub-ecosystems in dense forests.

Estimation of leaf area index from high resolution ZY-3 satellite imagery in a catchment dominated by Larix principis-rupprechtii, northern China

Leaf area index (LAI) is a key factor that determines a forest ecosystem’s net primary production and energy exchange between the atmosphere and land surfaces. LAI can be measured in many ways, but there has been little research to compare LAI estimated by different methods. In this study, we compared the LAI results from two different approaches, i.e., the dimidiate pixel model (DPM) and an empirical statistic model (ESM) using ZY-3 high-accuracy satellite images validated by field data. We explored the relationship of LAI of Larix principis-rupprechtii Mayr plantations with topographic conditions. The results show that DPM improves the simulation of LAI (r = 0.86, RMSE = 0.57) compared with ESM (r = 0.62, RMSE = 0.79). We further concluded that elevation and slope significantly affect the distribution of LAI. The maximum peak of LAI appeared at an aspect of east and southeast at an elevation of 1700–2000 m. Our results suggest that ZY-3 can satisfy the needs of quantitative monitoring of leaf area indices in small-scale catchment areas. DPM provides a simple and accurate method to obtain forest vegetation parameters in the case of non-ground measurement points.

Spatial distribution of LAI and its relationship with throughfall kinetic energy of common tree species in a Chinese subtropical forest plantation

The hilly red soil region in southern China is still facing serious soil erosion, even after long-term afforestation projects. This might result from structural shortcomings of the tree species chosen for afforestation. Within the Biodiversity and Ecosystem Functioning China project (BEF China), we used point cloud data from terrestrial laser scanners (TLS) and splash cups to analyze spatial leaf area index (LAI) and to predict the potential of splash erosion in subtropical forests. High LAI of Lithocarpus glaber and Schima superba was measured mainly at the middle and lower parts of the trees while for Sapindus saponaria it was found at the upper parts. LAI was decreasing from the tree stems to the edges of the canopy. Lognormal and exponential linear models were suitable to describe the vertical and horizontal LAI distribution of selected tree species, respectively. Sapindus saponaria generally had the highest values of throughfall kinetic energy (TKE) among the analyzed tree species and measured rainfall events. In the radial direction, higher LAI tended to produce lower TKE, whereas in the vertical direction, higher skewness of LAI distribution had higher TKE. LAI and its spatial distribution both were important for TKE. These findings can help to understand mechanisms of splash erosion in forest plantations related to unsuitable spatial LAI of tree species planted. It might further improve our knowledge how tree diversity may influence splash erosion by enriching the canopy layers in an early successional stage of subtropical forest plantations.

Parameterization of the vertical distribution of leaf area index (LAI) in rice (Oryza sativa L.) using a plant canopy analyzer

Monitoring the vertical distribution of leaf area index (LAI) is an effective method for evaluating canopy photosynthesis and biomass productivity. In this study, we proposed a novel method to characterize LAI vertical distribution non-destructively by utilizing LAI-2200 plant canopy analyzer, followed by the application of statistical moment equations. Field experiments were conducted with 5 rice cultivars under 2 fertilizer treatments in 2013 and with 3 rice cultivars under 3 plant density treatments in 2014. LAI readings obtained by a plant canopy analyzer for non-destructive stratified measurements were relatively consistent with LAI estimations using the stratified clipping method for every cultivar and treatment. The parameters calculated using the statistical moment equations numerically showed the changes in LAI vertical distribution with plant growth up to the heading stage. The differences in the parameters also quantified the effect of cultivar, fertilizer, and plant density treatments. These results suggest that the non-destructive stratified measurements and the statistical moments evaluated in this study provide quantitative, reliable information on the dynamics of LAI vertical distribution. The method is expected to be utilized by researchers in various research fields sharing common interests.

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

Leaf area index (LAI) controls many eco-physiological processes and can be widely used to scale-up leaf processes to ecosystem, landscape and regional levels. However, the macro-scale spatial heterogeneity of LAI and its controlling factors are not fully understood. We estimated annual maximum LAI using an LAI-2200 plant canopy analyzer in a 9-ha, old-growth, mixed broadleaved-Korean pine (Pinus koraiensis) forest in China. We analyzed the spatial heterogeneity of LAI and mapped its distribution using geostatistical methods; then, through variance partitioning, we examined the influences of several biotic factors, abiotic factors and spatial autocorrelation on the LAI distribution. Variance partitioning showed that these factors altogether explained 59% of the variation in the distribution of LAI. Compared to biotic and abiotic factors, spatial autocorrelation controlled more spatial heterogeneity of LAI by explaining 51.4% of the total variation in LAI. For biotic and abiotic factors, the mean diameter at breast height (DBH) of large trees (DBH > 10 cm), elevation, soil temperature and soil mass moisture content significantly affected the LAI distribution (P < 0.01). Notably, spatial autocorrelation unexpectedly explained the most variation in the LAI values, and it also varies with cardinal direction and is a key descriptor of LAI spatial variability. These results suggest that the influence of spatial autocorrelation on LAI distribution should attract more attention and that both the relative importance of and interactions among different determining factors is helpful for better understanding the mechanistic determinants of LAI distributions in temperate mixed forests.

Leaf area index in a forested mountain catchment

Leaf area index (LAI) belongs among the catchment characteristics widely used in hydrological models but still associated with great uncertainties. In a mountain forest catchment, the leaf area affects retention and evapotranspiration loss, and it could be significantly modified by forestry practices. In this study, LAI in mature stands of Norway spruce (Picea abies) and European beech (Fagus sylvatica) was analysed in headwater catchments of the Jizera Mountains (Czech Republic) between 2012 and 2016. A comparison evaluation of LAI in harvested site with dominant herbaceous vegetation was taken into account by applying direct ground investigation what was compared with hemispherical canopy photography (Gap light analyser GLA-V2) and satellite remote sensing (Sentinel-2 mission). While the direct ground measurement includes only the foliage (leaves or needles), the Gap light analysis is affected by trunks and branches, and the remote sensing techniques by herbaceous understory. The results of the Gap light analyser underestimated the ground based LAI values by 52–76 per cent, and satellite interpretations by 29–73 per cent. The remote sensing is capable to provide effective information on the distribution of LAI within the time and space. However, in a catchment scale, the satellite detection underestimated average LAI values approx. by 42–62 per cent. Changes in the observed rainfall interception reflected well the LAI variation.

An improved parameterization of the allocation of assimilated carbon to plant parts in vegetation dynamics for Noah‐MP

AbstractIn the land surface models predicting vegetation growth and decay, representation of the seasonality of land surface energy and mass fluxes largely depends on how to describe the vegetation dynamics. In this study, we developed a new parameterization scheme to characterize allocation of the assimilated carbon to plant parts, including leaves and fine roots. The amount of carbon allocation in this scheme depends on the climatological net primary production (NPP) of the plants. The newly developed scheme is implemented in the augmented Noah land surface model with multiple parameterization options (Noah‐MP) along with other biophysical processes related to variations in photosynthetic capacity. The scheme and the augmented biophysical processes are evaluated against tower measurements of vegetation from four forest sites in various regions—two for the deciduous broadleaf and two for the needleleaf evergreen forest. Results from the augmented Noah‐MP showed good agreement with the observations and demonstrated improvements in representing the seasonality of leaf area index (LAI), gross primary production (GPP), ecosystem respiration (ER), and latent heat flux. In particular, significant improvements are found in simulating amplitudes and phase shift timing in the LAI seasonal cycle, and the amount of GPP and ER in the growing season. Furthermore, the augmented Noah‐MP performed reasonably well in simulating the spatial distributions of LAI, GPP, and NPP in East Asia, consistent with the satellite observations.

Spatiotemporal analyses of urban vegetation structural attributes using multitemporal Landsat TM data and field measurements

We conducted spatiotemporal analyses of urban vegetation structural attributes using multitemporal Landsat TM data and field measurements. We showed that multitemporal TM data has the potential of rapidly estimating urban vegetation structural attributes including LAI, CC , and BA at an urban landscape level. Urban vegetation structural properties/attributes are closely linked to their ecological functions and thus directly affect urban ecosystem process such as energy, water, and gas exchange. Understanding spatiotemporal dynamics of urban vegetation structures is important for sustaining urban ecosystem service and improving the urban environment. The purposes of this study were to evaluate the potential of estimating urban vegetation structural attributes from multitemporal Landsat TM imagery and to analyze spatiotemporal changes of the urban structural attributes. We first collected three scenes of TM images acquired in 1997, 2004, and 2010 and conducted a field survey to collect urban vegetation structural data (including crown closure (CC), tree height (H), leaf area index (LAI), basal area (BA), stem density (SD), diameter at breast height (DBH), etc.). We then calculated and normalized NDVI maps from the multitemporal TM images. Finally, spatiotemporal urban vegetation structural maps were created using NDVI-based urban vegetation structure predictive models. The results show that NDVI can be used as a predictor for some selected urban vegetation structural attributes (i.e., CC, LAI, and BA), but not for the other attributes (i.e., H, SD, and DBH) that are well predicted by NDVI in natural vegetation. The results also indicate that urban vegetation structural attributes (i.e., CC, LAI, and BA) in the study area decreased sharply from 1997 to 2004 but increased slightly from 2004 to 2010. The CC, LAI, and BA class distributions were all skewed toward low values in 1997 and 2004. Moreover, LAI, CC, and BA of urban vegetation all present a decreasing trend from suburban areas to urban central areas. The experimental results demonstrate that Landsat TM imagery could provide a fast and cost-effective method to obtain a spatiotemporal 30-m resolution urban vegetation structural dataset (including CC, LAI, and BA).

Combined effects of climate and land management on watershed vegetation dynamics in an arid environment

Leaf area index (LAI) is a key parameter to characterize vegetation dynamics and ecosystem structure that determines the ecosystem functions and services such as clean water supply and carbon sequestration in a watershed. However, linking LAI dynamics and environmental controls (i.e., coupling biosphere, atmosphere, and anthroposphere) remains challenging and such type of studies have rarely been done at a watershed scale due to data availability. The present study examined the spatial and temporal variations of LAI for five ecosystem types within a watershed with a complex topography in the Upper Heihe River Basin, a major inland river in the arid and semi-arid western China. We integrated remote sensing-based GLASS (Global Land Surface Satellite) LAI products, interpolated climate data, watershed characteristics, and land management records for the period of 2001–2012. We determined the relationships among LAI, topography, air temperature and precipitation, and grazing history by five ecosystem types using several advanced statistical methods. We show that long-term mean LAI distribution had an obvious vertical pattern as controlled by precipitation and temperature in a hilly watershed. Overall, watershed-wide mean LAI had an increasing trend overtime for all ecosystem types during 2001–2012, presumably as a result of global warming and a wetting climate. However, the fluctuations of observed LAI at a pixel scale (1km) varied greatly across the watershed. We classified the vegetation changes within the watershed as ‘Improved’, ‘Stabilized’, and ‘Degraded’ according their respective LAI changes. We found that climate was not the only driver for temporal vegetation changes for all land cover types. Grazing partially contributed to the decline of LAI in some areas and masked the positive climate warming effects in other areas. Extreme weathers such as cold spells and droughts could substantially affect inter-annual variability of LAI dynamics. We concluded that temporal and spatial LAI dynamics were rather complex and were affected by both climate variations and human disturbances in the study basin. Future monitoring studies should focus on the functional interactions among vegetation dynamics, climate variations, land management, and human disturbances.

Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA).

The ratio of leaf area to ground area (leaf area index, LAI) is an important state variable in ecosystem studies since it influences fluxes of matter and energy between the land surface and the atmosphere. As a basis for generating temporally continuous and spatially distributed datasets of LAI, the current study contributes an analysis of its spatial variability and spatial structure. Soil-vegetation-atmosphere fluxes of water, carbon and energy are nonlinearly related to LAI. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation (150 m resolution) with field measurements and a detailed land use map. Test area is the arable land in the fertile loess plain of the Rur catchment on the Germany-Belgium-Netherlands border. LAI from remote sensing and simulation compares well with field measurements. Based on the simulation results, we describe characteristic crop-specific temporal patterns of LAI spatial variability. By means of these patterns, we explain the complex multimodal frequency distributions of LAI in the remote sensing data. In the test area, variability between agricultural fields is higher than within fields. Therefore, spatial resolutions less than the 5 m of the remote sensing scenes are sufficient to infer LAI spatial variability. Frequency distributions from the simulation agree better with the multimodal distributions from remote sensing than normal distributions do. The spatial structure of LAI in the test area is dominated by a short distance referring to field sizes. Longer distances that refer to soil and weather can only be derived from remote sensing data. Therefore, simulations alone are not sufficient to characterize LAI spatial structure. It can be concluded that a comprehensive picture of LAI spatial heterogeneity and its temporal course can contribute to the development of an approach to create spatially distributed and temporally continuous datasets of LAI.

Generating pseudo large footprint waveforms from small footprint full-waveform airborne LiDAR data for the layered retrieval of LAI in orchards.

Leaf area index (LAI) is a key parameter for the study of biogeochemical cycles in ecosystems. Remote sensing techniques have been widely used to estimate LAIs in a wide range of vegetation types. However, limited by the sensor detection capability, considerable fewer studies investigated the layered estimation of LAIs in the vertical direction, which can significantly affect the precision evaluation of vegetation biophysical and biochemical processes. This study tried to generate a kind of pseudo large footprint waveform from the small footprint full-waveform airborne LiDAR data by an aggregation approach. The layered distribution of canopy heights and LAIs were successfully retrieved based on the large footprint waveform data in an agricultural landscape of orchards with typical multi-layer vegetation covers. The Gaussian fitting was conducted on the normalized large footprint waveforms to identify the vertical positions for different vegetation layers. Then, the gap theory was applied to retrieve the layered LAIs. Statistically significant simple linear regression models were fitted between the LiDAR-retrieved and field-observed values for the canopy heights and LAIs in different layers. Satisfactory results were obtained with a root mean square error of 0.36 m for the overstorey canopy height (R2 = 0.82), 0.29 m for the understory canopy height (R2 = 0.76), 0.28 for overstorey LAI (R2 = 0.75), 0.40 for understory LAI (R2 = 0.64), and 0.38 for total LAI (R2 = 0.69), respectively. To conclude, estimating the layered LAIs in the multi-layer agriculture orchards from the pseudo large footprint waveforms is feasible and the estimation errors are acceptable, which will provide some new ideas and methods for the quantitative remote sensing with vegetation.

Mapping tree distribution and LAI in peatlands using field methods and imaging spectroscopy: a case study for the Haaksbergerveen, the Netherlands

Leaf area index (LAI) is among the vegetation parameters that play an important role in climate, hydrological and ecological studies, and is used for assessing growth and expansion of vegetation. The main objective of this study was to develop a methodology to map the LAI distribution of birch trees (Betula pendula) in peatland ecosystems using field-based instruments and airborne-based remote-sensing techniques. The developed mapping method was validated using field-based LAI measurements using the LAI-2000 instrument. First vegetation indices, including simple ratio (SR), normalized difference vegetation index (NDVI), and reduced simple ratio (RSR), were derived from HyMap data and related to ground-based measurements of LAI. LAI related better with RSR (R2 = 0.68), followed by NDVI (R2 = 0.63) and SR (R2 = 0.58), respectively. Areas with birch were identified using Spectral Angle Mapper (SAM) to classify the image into 11 end members of dominant species including bare soil and open water. Next, the relationship between LAI and RSR was applied to areas with birch, yielding a birch LAI map. Comparison of the map of the birch trees and field-based LAI data was done using linear regression, yielding an R2 = 0.38 and an RMSE = 0.25, which is fairly accurate for a structurally highly diverse field situation. The method may prove an invaluable tool to monitor tree encroachment and assess tree LAI in these remote and poorly accessible areas.

QUANTITATIVE VEGETATION MAPPING OF URBAN AREA USING HIGH-RESOLUTION MULTISPECTRAL SATELLITE IMAGERY

Maintenance of green spaces within the city is very important under current ecological conditions. In the present context, assessment of green spaces held by visually determining indicators plantations clarifying its instrumental measurements. The accuracy of this method strongly depends on the qualifications and professional experience of the employee conducting an assessment. High spatial and temporal resolution satellite systems provide information for operational monitoring of environmental conditions in the city and promote management decision making. Normalized Difference Vegetation Index (NDVI) – index of plant “greenness” or photosynthetic activity – is widely used for vegetation studying. However, a more important phytometric parameter of vegetation is Leaf Area Index (LAI). Actually LAI is defined as the one sided green leaves area per unit of ground area in broadleaf canopies, or as the projected needle leaves area per unit of ground area in needle canopies. Multispectral imagery processing to evaluate the NDVI and LAI provides informative and reliable indicators of vegetation quantity. High-resolution image from Pleiades-1A satellite was used in the study. Complete geoinformation technology for integrated quantitative assessment of vegetation in urbanized territory provides a distribution of LAI within the study area. The first group of operations was used for preprocessing of multispectral satellite image. Output of these operations is a scene surface reflectance. The second group of operations comprises the steps of LAI calculations based on vegetation mask and LAI versus NDVI regression dependence. Final group of operations provides visualization and interpretation of the results. The methodology for calculating the vegetation area within the land registry boundaries gives biased values of green mass and its status. At the beginning of the growing season the regulatory area of green zone is much greater than the actual amount of vegetation. It is possible to determine more accurately the boundaries and the total area of vegetation cover using the NDVI. Also the described LAI-based geoinformation technology provides tuning out the current phase of the phenological cycle. As a result, more accurate estimation of vegetation amount at moment and its changes over time are available too. Further studies aimed at assessing not only the quantity but also the quality of vegetation. The increase in number of ground control points and number of in-situ measurements are planned to clarify the regression confidence. There will also be studying the state of plant communities at different phases of the phenological cycle. High-resolution image from Pleiades-1A satellite was kindly provided by TVIS-INFO LLC (TVIS) Company, which is one of the leading geospatial and GIS solutions provider in Ukraine.

Space-time LAI variability in Northern Puglia (Italy) from SPOT VGT data.

The vegetation space–time variability during 1999–2010 in the North of the Apulian region (Southern Italy) was analysed using SPOT VEGETATION (VGT) sensor data. Three bands of VEGETATION (RED, NIR and SWIR) were used to implement the vegetation index named reduced simple ratio (RSR) to derive leaf area index (LAI). The monthly average LAI is an indicator of biomass and canopy cover, while the difference between the annual maximum and minimum LAI is an indicator of annual leaf turnover. The space–time distribution of LAI at the catchment scale was analysed over the examined period to detect the consistency of vegetation dynamics in the study area. A diffuse increase of LAI was observed in the examined years that cannot be directly explained only in terms of increasing water availability. Thus, in order to explain such a general behaviour in terms of climatic factors, the analysis was performed upon stratification of land cover classes, focusing on the most widespread species: forest and wheat. An interesting ascending–descending behaviour was observed in the relationship between inter-annual increments of maximum LAI and rainfall, and in particular, a strong negative correlation was found when the rainfall amount in January and February exceeded a critical threshold of about 100 mm.

Variabilidade do índice de área foliar em campos naturais e floresta de transição na região Sul do Amazonas

O Indice de Area Foliar (IAF) e determinante nos processos de trocas de massa e energia nos ecossistemas, sendo fundamental para o entendimento das mudancas no clima regional e na dinâmica do ecossistema. O objetivo deste trabalho foi analisar, por meio da geoestatistica, a variabilidade e dependencia espacial dos valores de indice de area foliar (IAF) em areas naturais de campo 'baixo', campo 'alto' e floresta de transicao no Sul do Amazonas. Os valores de IAF foram determinados numa malha regular com 33 pontos para cada area estudada, e a aquisicao dos dados foi feita pelo analisador de dossel LAI-2000, que permite se calcular a estrutura da vegetacao com base na radiacao que penetra na copa das arvores. Os valores medios do IAF variaram de acordo com a area estudada, com maiores valores na floresta (4,42 m²m-2), seguida pelo campo alto (2,03 m²m-2) e campo baixo (1,72 m²m-2). Em todas as areas os valores de IAF mostraram dependencia espacial com o uso da krigagem entre as amostras e apresentam distribuicao espacial diferenciada em cada area estudada: o grau de dependencia espacial foi moderado na floresta de transicao e fraco para os campos naturais, sendo que no campo alto a distância limite em que os pontos estao correlacionados e de 35,3 m. No mapa de krigagem foi possivel observar manchas com valores altos e baixos distribuidos de forma diferenciada entre as areas estudadas.

云冷杉针阔混交林叶面积指数的空间异质性

PDF HTML阅读 XML下载 导出引用 引用提醒 云冷杉针阔混交林叶面积指数的空间异质性 DOI: 10.5846/stxb201403300593 作者: 作者单位: 中国林业科学研究院资源信息研究所,中国林业科学研究院资源信息研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 国家"十二五"科技支撑计划课题(2012BAD22B02) Spatial heterogeneity of leaf area index of mixed spruce-fir-deciduous stands in northeast China Author: Affiliation: Institute of Forest Resource Information Techniques，Chinese Academy of Forestry,Institute of Forest Resource Information Techniques，Chinese Academy of Forestry Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:叶面积指数(Leaf area index, LAI)是森林生态过程的关键参数和描述森林冠层结构的重要指标。用半球摄影技术对吉林省汪清林业局金沟岭林场的10块1 hm2云冷杉针阔混交林的LAI进行测定,采用地统计学的半变异函数和普通克里格法对研究区的LAI的空间异质性进行了分析。结果表明:10块样地的10 m×10 m小样方内以及样地间的LAI离散程度较小,但分布有一定的规律。LAI的空间相关性存在不确定性,可由线性、孔状、高斯3种模型有效的描述,空间相关性存在的尺度范围变异大,中等和强空间相关性的影响范围在15-155m之间,10块样地LAI的空间相关性的影响范围平均为65.637m。普通克里格插值结果显示,LAI的空间分布呈明显的条带状和斑块状的梯度变化。LAI与林分平均胸径、林分平均高和林分平均冠长呈显著负相关,与林分平均冠幅、林分密度以及树种个数呈显著正相关。研究结果可为不同尺度云冷杉针阔混交林LAI的估计提供依据。 Abstract:Leaf area index (LAI) is defined as half the total leaf area per unit horizontal ground surface area. LAI is a significant attribute of forest ecosystem that controls, in part, physiological processes such as photosynthesis, transpiration, and leaf maintenance respiration, as well as physical processes including canopy water interception, evaporation, and light attenuation. There are several procedures to estimate LAI, both direct and indirect. However, the direct methods are slow and labor intensive. Although remote sensing potentially offers the opportunity to access features of ecological phenomena on a large scale, the scaling effect in retrieving LAI is a significant problem because of the heterogeneity in pixels, which can be well described using a semivariogram. In this study, we monitored LAI of ten mixed spruce-fir-deciduous stands in Wangqing Forestry Bureau, Jilin Province, by using a 10 m × 10 m grid in 1 hm2 plots and digital hemispherical photography (DPH), the most commonly implemented method in forest research. In total, we obtained 1000 LAI values in these plots. The spatial heterogeneity of the LAI was explored using geostatistical analysis. We found that the average LAI in mixed spruce-fir-deciduous stands was 2.410, ranging from 1.265 to 4.029. LAI had low variability within and among plots, with the coefficient of variation ranging from 9.981% to 19.440%. Linear, hole, and Gaussian models were found to best describe the spatial autocorrelation of LAI. The coefficients of determination of these models were between 0.643 and 0.880. The LAIs had strong, moderate and weak spatial dependence according to their structure ratios. The ranges of these semivariogram models varied greatly, from 15 to 155 m, with an average of 65.637 m. One reason for the large range was the large homogeneous areas at the scale that were longer than the lags for which the semivariograms were calculated. Ordinary kriging was used to analyze the spatial distribution of LAI and showed apparent belt- and spot-shaped massive gradient changes. The relationship between LAI and stand variables was examined. LAI exhibited a significantly negative relationship with average diameter at breast height, average height, and average crown length, but showed a significantly positive relationship with average crown width, stand density, and tree species richness. LAI was closely related to tree species, and plots with few coniferous species had low values. Some unknown factors also contributed to the pattern of LAI in these plots. The results of this study provide a reference for estimating LAI at stand and large scales. 参考文献 相似文献 引证文献