Changes In Leaf Area Index Research Articles

Hyperspectral Estimation of Winter Wheat Leaf Area Index Based on Continuous Wavelet Transform and Fractional Order Differentiation.

Leaf area index (LAI) is highly related to crop growth, and the traditional LAI measurement methods are field destructive and unable to be acquired by large-scale, continuous, and real-time means. In this study, fractional order differential and continuous wavelet transform were used to process the canopy hyperspectral reflectance data of winter wheat, the fractional order differential spectral bands and wavelet energy coefficients with more sensitive to LAI changes were screened by correlation analysis, and the optimal subset regression and support vector machine were used to construct the LAI estimation models for different growth stages. The precision evaluation results showed that the LAI estimation models constructed by using wavelet energy coefficients combined with a support vector machine at the jointing stage, fractional order differential combined with support vector machine at the booting stage, and wavelet energy coefficients combined with optimal subset regression at the flowering and filling stages had the best prediction performance. Among these, both flowering and filling stages could be used as the best growth stages for LAI estimation with modeling and validation R2 of 0.87 and 0.71, 0.84 and 0.77, respectively. This study can provide technical reference for LAI estimation of crops based on remote sensing technology.

Identifying the main drivers of the seasonal decline of near-infrared reflectance of a temperate deciduous forest

The physical mechanisms behind correlations of earth observations and remote sensing products are of vital importance. The so-called ’near-infrared reflectance of vegetation’ (NIRV) and gross primary production (GPP) show high correlations among different ecosystems and temporal scales but the underlying relationship is still poorly understood. NIRV is defined as the product of normalized difference vegetation index (NDVI) and near-infrared (NIR) canopy reflectance (RNIR). We examined this relationship in the case of a temperate deciduous forest in Germany. GPP, RNIR and NIRV all exhibited a strong rise during leaf development in spring and a continual decline after the maximum in early summer. The decline of NIRV in late summer was mainly driven by the decline of RNIR, since NDVI remained saturated.Here we tested the RNIR decline attributions to changes in leaf area index, leaf optical properties, canopy structure, sun-sensor geometry, or understory vegetation by measuring seasonal variations of those factors of the temperate deciduous forest. Leaf area was nearly constant between May and mid September, leaf albedo decreased slightly, leaf angles increased over time towards more vertical leaves, and understory reflectance decreased considerably.We simulated the seasonal RNIR decline of the forest using the radiative transfer model FRT and quantified the sensitivity of the decline to variations in the measured parameters. FRT captured well the observed seasonal RNIR decline by Sentinel 2 using the measured optical and structural properties. Decreasing understory reflectance alone explained 43% of the simulated RNIR decrease, while leaf angle variations explained 31%, the solar zenith angle (SZA) 21%, leaf albedo 7%, and LAI 0%. The effect size of the SZA depended on the viewing angle and would hence be different for different satellites and for local instruments. The results may help to better understand and help to track seasonal changes in forest structure and leaf optical properties using remote sensing techniques. They also suggest that the proposed link between the seasonal evolution of GPP and NIRV may be weaker than expected.

Revisiting dry season vegetation dynamics in the Amazon rainforest using different satellite vegetation datasets

There has been a debate regarding whether the Amazon rainforest is greening during the dry season. This is partially because of the great uncertainty associated with the ability of different vegetation indices to accurately assess tropical vegetation status. This paper, revisit this issue by comprehensively examining the seasonal variations in vegetation recorded in various satellite-based vegetation datasets, namely, the leaf area index (LAI), contiguous solar-induced fluorescence (CSIF), enhanced vegetation index (EVI), and vegetation optical depth (VOD). All four of these vegetation datasets show an increase in vegetation during the dry season in most parts of the Amazon; however, the vegetation changes are not only spatially variable, but also differ among the datasets. This may be attributable in part to the different physical characteristics captured by each of the datasets. For example, the seasonal maximum value occurs first in the LAI, followed by the CSIF, EVI, and VOD, in that order. The seasonal cycle of the LAI agrees reasonably well with in-situ observations of leaf flush and leaf fall. As new leaf production offsets senescence and abscission, the dry-season vegetation increases in most parts of the Amazon rainforest. Partial correlation analysis was used to further investigate the potential climatic cues (i.e., precipitation, temperature and radiation) associated with the seasonal changes recorded in the vegetation data. We found that precipitation and radiation were the dominant potential cues for seasonal VOD (48%) and LAI (59%) changes, respectively. However, CSIF appears to be associated more closely with temperature and precipitation, with significant correlations observed across ∼x223C 37% of the Amazon rainforest area for both with CSIF. Finally, variations in the EVI showed similar sensitivity to all three climatic variables considered. The findings presented here will greatly improve our understanding of vegetation dynamics and the carbon cycle in the Amazon rainforest ecosystem.

Effects of environmental conditions and leaf area index changes on seasonal variations in carbon fluxes over a wheat-maize cropland rotation.

Environmental conditions (EV) changes not only affect temporal variations in carbon fluxes directly, but affect them indirectly by impacting plant biotic traits. Investigating the extent of the effects of EV and biotic changes can help deepen our understanding of ecosystem carbon cycling. Therefore, we partitioned and quantified the contributions of EV and biotic changes' effects on seasonal variations in carbon fluxes (net ecosystem carbon exchange (NEE), and its components, i.e., gross ecosystem carbon exchange (GEE) and ecosystem respiration (RE)) in a (winter) wheat-(summer) maize rotation ecosystem from 2010 to 2012. A path analysis accompanied by Granger causality tests (GCTs), which filtered out several variables that were not causal for dependent variables, was used to calculate their respective contributions by integrating path coefficients. The seasonal variations in NEE, RE, and GEE were significantly and jointly affected by EV and the leaf area index (LAI) with R2 values ranging from 0.63 to 0.94 after the GCT. The path analysis indicated that the seasonal variations of carbon fluxes were dominated by the effects of EV changes (induced from varying EV for different fluxes, crops, and years), which contributed 60.7% (mean of two years), 64.5%, and 58.2% to wheat NEE, RE, and GEE, respectively, and 62.5%, 82.3%, and 58.1% to maize NEE, RE, and GEE, respectively. Overall, our study provided a new basis that future climatic changes may have important impacts on carbon exchanges in this rotation cropland.

Inconsistent changes in NPP and LAI determined from the parabolic LAI versus NPP relationship

Rapidly altering vegetation dynamics, is an aspect of global environmental change which has consequences for ecosystem structure, function, and the provision of ecosystem services. However, the responses of vegetation greenness and vegetation productivity to environmental changes may be inconsistent. Currently, we do not know how these inconsistent trends of greenness and productivity vary across the globe, nor the main causes of that variability. We used leaf area index (LAI) and net primary productivity (NPP) to represent vegetation greenness and productivity, respectively. The results showed that about 42% of the global vegetated area showed inconsistent trends of LAI and NPP from 2000 to 2015. These inconsistent trends were mainly found in humid areas. A parabolic relationship was found between the NPP and LAI of global vegetation, with a turning point in LAI of global vegetation of 4.5 ± 0.4 m2 m−2. The ratio of NPP to LAI (NPP/LAI), net primary production per unit leaf area, decreases as the LAI increases. The variability of NPP/LAI is significantly correlated with the consistency of NPP and LAI changes in different land cover types. NPP/LAI is lower in tree-dominated biomes with higher canopies (such as forests), and higher in areas dominated by short vegetation (such as shrubland and grassland). Drylands have higher NPP/LAI than humid regions which are perhaps due to high rates of net photosynthesis and narrow leaves. The results of this study not only clearly portray the relationship between vegetation greenness and productivity but also help us to understand the impact of vegetation dynamics on global carbon cycling in a changing environment.

Leaf Area Index Variations in Ecoregions of Ardabil Province, Iran

The leaf area index (LAI) is an important vegetation biophysical index that provides broad information on the dynamic behavior of an ecosystem’s productivity and related climate, topography, and edaphic impacts. The spatiotemporal changes of LAI were assessed throughout Ardabil Province—a host of relevant plant communities within the critical ecoregion of a semi-arid climate. In a comparative study, novel data from Google Earth Engine (GEE) was tested against traditional ENVI measures to provide LAI estimations. Moreover, it is of important practical significance for institutional networks to quantitatively and accurately estimate LAI, at large areas in a short time, and using appropriate baseline vegetation indices. Therefore, LAI was characterized for ecoregions of Ardabil Province using remote sensing indices extracted from Landsat 8 Operational Land Imager (OLI), including the Enhanced Vegetation Index calculated in GEE (EVIG) and ENVI5.3 software (EVIE), as well as the Normalized Difference Vegetation Index estimated in ENVI5.3 software (NDVIE). Moreover, a new field measurement method, i.e., the LaiPen LP 100 portable device (LP 100), was used to evaluate the accuracy of the derived indices. Accordingly, the LAI was measured in June and July 2020, in 822 ground points distributed in 16 different ecoregions-sub ecoregions having various plant functional types (PFTs) of the shrub, bush, and tree. The analyses revealed heterogeneous spatial and temporal variability in vegetation indices and LAIs within and between ecoregions. The mean (standard deviation) value of EVIG, EVIE, and NDVIE at a province scale yielded 1.1 (0.41), 2.20 (0.78), and 3.00 (1.01), respectively in June, and 0.67 (0.37), 0.80 (0.63), and 1.88 (1.23), respectively, in July. The highest mean values of EVIG-LAI, EVIE-LAI, and NDVIE-LAI in June are found in Meshginshahr (1.40), Meshginshahr (2.80), and Hir (4.33) ecoregions and in July are found in Andabil ecoregion respectively with values of 1.23, 1.5, and 3.64. The lowest mean values of EVIG-LAI, EVIE-LAI, and NDVIE-LAI in June were observed for Kowsar (0.67), Meshginshahr (1.8), and Neur (2.70) ecoregions, and in July, the Bilesavar ecoregion, respectively, with values of 0.31, 0.31, and 0.81. High correlation and determination coefficients (r > 0.83 and R2 > 0.68) between LP 100 and remote sensing derived LAI were observed in all three PFTs (except for NDVIE-LAI in June with r = 0.56 and R2 = 0.31). On average, all three examined LAI measures tended to underestimate compared to LP 100-LAI (r > 0.42). The findings of the present study could be promising for effective monitoring and proper management of vegetation and land use in the Ardabil Province and other similar areas.

Structural and photosynthetic dynamics mediate the response of SIF to water stress in a potato crop

Solar-induced Fluorescence (SIF) has an advantage over greenness-based Vegetation Indices in detecting drought. This advantage is the mechanistic coupling between SIF and Gross Primary Productivity (GPP). Under water stress, SIF tends to decrease with photosynthesis, due to an increase in non-photochemical quenching (NPQ), resulting in rapid and/or sustained reductions in the fluorescence quantum efficiency (ΦF). Water stress also affects vegetation structure via highly dynamic changes in leaf angular distributions (LAD) or slower changes in leaf area index (LAI). Critically, these responses are entangled in space and time and their relative contribution to SIF, or to the coupling between SIF and GPP, is unclear. In this study, we quantify the relative effect of structural and photosynthetic dynamics on the diurnal and spatial variation of canopy SIF in a potato crop in response to a replicated paired-plot water stress experiment. We measured SIF using two platforms: a hydraulic lift and an Unmanned Aerial Vehicle (UAV) to capture temporal and spatial variation, respectively. LAD parameters were estimated from point clouds and photographic data and used to assess structural dynamics. Leaf ΦF estimated from PAM fluorescence measurements were used to represent variations in photosynthetic regulation. We also measured foliar pigments, operating quantum yield of photosystem II (PSII), photosynthetic gas exchange, stomatal conductance and LAI. We used a radiative transfer model (SCOPE) to provide a means of decoupling structural and photosynthetic factors across the diurnal and spatial domains. The results demonstrate that diurnal variation in SIF is driven by photosynthetic and structural dynamics. The influence of ΦF was prominent in the diurnal SIF response to water stress, with reduced fluorescence efficiencies in stressed plants. Structural factors dominated the spatial response of SIF to water stress over and above ΦF. The results showed that the relationship between SIF and GPP is maintained in response to water stress where adjustments in NPQ and leaf angle co-operate to enhance the correlation between SIF and GPP. This study points to the complexity of interpreting and modelling the spatiotemporal connection between SIF and GPP which requires simultaneous knowledge of vegetation structural and photosynthetic dynamics.

Cross-ECV consistency at global scale: LAI and FAPAR changes

A framework is proposed for assessing the physical consistency between two terrestrial Essential Climate Variables (ECVs) products retrieved from Earth Observation at global scale. The methodology assessed the level of agreement between the temporal variations of Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). The simultaneous changes were classified according to their sign, magnitude and level of confidence, whereby the respective products uncertainties were taken into consideration. A set of proposed agreement metrics were used to identify temporal and spatial biases of non-coherency, non-significance, sensitivity and the overall level of agreement of the temporal changes between two ECVs. We applied the methodology using the Joint Research Center (JRC) Two-stream Inversion Package (TIP) products at 1 km, those provided by the Copernicus Global Land Service (CGLS) based on the SPOT/VGT and Proba-V at 1 km, and the MODIS MCD15A3 at 500 m. In addition, the same analysis was applied with aggregated products at a larger scale over Southern Africa. We found that the CGLS LAI and FAPAR products lacked consistency in their spatial and temporal changes and were severely affected by trends. The MCD15A3 products were characterized by the highest number of non-coherent changes between the two ECVs but temporal inconsistencies were mainly located over the eastern hemisphere. The JRC-TIP products were highly consistent. The results showed the advantages of physically-based retrieval algorithms, in both JRC-TIP and MODIS products, and indicated also that, except for MODIS over forests, aggregated products using an uncertainty-based weighted average led to higher agreement between the ECVs changes.

Modeling the impact of spatiotemporal vegetation dynamics on groundwater recharge

Climate change affects the growth of vegetation and its physiological states such as leaf area index (LAI), which in turn can affect groundwater recharge because of changes in evapotranspiration (ET). Presently, most recharge modeling studies over-simplify transient vegetation conditions and the potential corresponding impact on recharge by using climatological values of vegetation parameters such as LAI. Our study uses the Community Land Model (CLMv4.5) to investigate the sensitivity of recharge to interannual varying vegetation in Minnesota (USA) across different climate, hydrogeology, and ecoregions at a 25 km spatial resolution and for the period of 2000–2015. The Ensemble Kalman Filter (EnKF) was used to calibrate soil and runoff parameters to statewide water table depth observations. Results of the study indicate that although year-to-year varying vegetation does not affect long-term climatological recharge estimates, it can drive disproportionately large variability in annual and seasonal recharge. Comparing simulations with dynamic and climatological vegetation inputs, the average magnitude difference (root mean square difference, RMSD) for recharge was 21.1% in response to only a 4% difference in LAI inputs. Regression analysis revealed that the combination of local hydrogeology and vegetation-type affects the magnitude of recharge response to LAI and ET changes. We also found cross-ecoregion dominance of temperature rather than precipitation controlling LAI anomalies and resulting recharge variability, with springtime temperature being the primary factor because of its impact on leaf-out conditions. Drier western Minnesota showed higher relative LAI differences as well as higher spring time and relative annual recharge compared to the wetter eastern part of the state, indicating higher vulnerability of the water-limited region to changing vegetation and climatic conditions. Our study shows that models can underestimate or overestimate annual and seasonal recharge if vegetation dynamics are neglected, demonstrating the need to incorporate transient vegetation conditions when assessing the impact of future climate change on recharge.

Winter wheat yield estimation based on assimilated Sentinel-2 images with the CERES-Wheat model

Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale. To verify this method, we applied the ensemble Kalman filter (EnKF) to assimilate the leaf area index (LAI) derived from Sentinel-2 data and simulated by the CERES-Wheat model. From this, we obtained the assimilated daily LAI during the growth stage of winter wheat across three counties located in the southeast of the Loess Plateau in China: Xiangfen, Xinjiang, and Wenxi. We assigned LAI weights at different growth stages by comparing the improved analytic hierarchy method, the entropy method, and the normalized combination weighting method, and constructed a yield estimation model with the measurements to accurately estimate the yield of winter wheat. We found that the changes of assimilated LAI during the growth stage of winter wheat strongly agreed with the simulated LAI. With the correction of the derived LAI from the Sentinel-2 images, the LAI from the green-up stage to the heading–filling stage was enhanced, while the LAI decrease from the milking stage was slowed down, which was more in line with the actual changes of LAI for winter wheat. We also compared the simulated and derived LAI and found the assimilated LAI had reduced the root mean square error (RMSE) by 0.43 and 0.29 m2 m−2, respectively, based on the measured LAI. The assimilation improved the estimation accuracy of the LAI time series. The highest determination coefficient (R2) was 0.8627 and the lowest RMSE was 472.92 kg ha−1 in the regression of the yields estimated by the normalized weighted assimilated LAI method and measurements. The relative error of the estimated yield of winter wheat in the study counties was less than 1%, suggesting that Sentinel-2 data with high spatial-temporal resolution can be assimilated with the CERES-Wheat model to obtain more accurate regional yield estimates.

Climate Change Impacts on the Future of Forests in Great Britain

Forests provide important ecosystem services but are being affected by climate change, not only changes in temperature and precipitation but potentially also directly through the plant-physiological effects of increases in atmospheric CO2. We applied a tree-species-based dynamic model (LPJ-GUESS) at a high 5-km spatial resolution to project climate and CO2 impacts on tree species and thus forests in Great Britain. Climatic inputs consisted of a novel large climate scenario ensemble derived from a regional climate model (RCM) under an RCP 8.5 emission scenario. The climate change impacts were assessed using leaf area index (LAI) and net primary productivity (NPP) for the 2030s and the 2080s compared to baseline (1975–2004). The potential CO2 effects, which are highly uncertain, were examined using a constant CO2 level scenario for comparison. Also, a climate vulnerability index was developed to assess the potential drought impact on modeled tree species. In spite of substantial future reductions in rainfall, the mean projected LAI and NPP generally showed an increase over Britain, with a larger increment in Scotland, northwest England, and west Wales. The CO2 increase led to higher projected LAI and NPP, especially in northern Britain, but with little effect on overall geographical patterns. However, without accounting for plant-physiological effects of elevated CO2, NPP in Southern and Central Britain and easternmost parts of Wales showed a decrease relative to 2011, implying less ecosystem service provisioning, e.g., in terms of timber yields and carbon storage. The projected change of LAI and NPP varied from 5 to 100% of the mean change, due to the uncertainty arising from natural weather-induced variability, with Southeast England being most sensitive to this. It was also the most susceptible to climate change and drought, with reduced suitability for broad-leaved trees such as beech, small-leaved lime, and hornbeam. These could lead to important changes in woodland composition across Great Britain.

Estimating global aerodynamic parameters in 1982–2017 using remote-sensing data and a turbulent transfer model

The bulk surface properties, including canopy height (h), zero-plane displacement height (d), roughness length (z0), and aerodynamic resistance (rb and rd), are crucial biophysical parameters that influence momentum, energy and mass exchanges at the land-atmosphere interface. The variabilities of these parameters are important for understanding possible impacts of the ecosystem on climate change, yet they have not been systematically evaluated due to the lack of large-scale, long-term observations. Here we provide global estimates of these bulk aerodynamic parameters, including d, z0, rb, and rd, for the period 1982–2017 based on remote-sensed leaf area index (LAI), h, and plant functional type dependent canopy morphological characteristics. The global h estimate is acquired from LAI using a semi-empirical relation in which the coefficients have been optimized based on the canopy height product from the Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat). Two LAI products, Global Land Surface Satellite (GLASS) and Global Inventory Modeling and Mapping Studies (GIMMS), are used to calculate canopy height and parameters separately. The products derived from the above two LAI datasets agree very well (spatial correlation coefficient, SCC = 0.99, relative root-mean-square-error, rRMSE = 8.28% for h, SCC = 0.99, rRMSE = 12.15% for d, and SCC = 0.98, rRMSE = 13.78% for z0). Verification of the products against in-situ canopy height records from the FLUXNET and eddy-covariance (EC)-based d and z0 estimates shows that the estimates can reproduce the annual means and seasonal variations of the bulk surface properties. We found significant positive trends in global h (0.04–0.05% year−1), d (0.07% year−1) and z0 (0.07% year−1) associated with the Earth greening, but they are different from the trend in LAI (0.14–0.25% year−1). The differences between the trends in LAI and the trends in derived parameters are also found in different latitudes. For instance, in the Northern Hemisphere Polar region, the GLASS LAI increases by 0.48% year−1, leading to positive trends of 0.03% year−1 in h, 0.18% in d, and 0.20% year−1 in z0. The above results highlight the importance of comprehensively considering changes in LAI, canopy height, and aerodynamic parameters when evaluating the effects of ecosystem change on climate. This long-term dataset can be used in climate models to assess the response of bulk aerodynamic parameters to climate changes and the impact of vegetation dynamics on regional and global climate.

Climate Change in China Affects Runoff and Terrestrial Ecosystem Water Retention More Than Changes in Leaf Area Index and Land Use/Cover Over the Period 1982–2015

AbstractClimate, land use/cover, and leaf area index (LAI) have changed notably in China since 1980, and these have had major impacts on river runoff and the terrestrial ecosystem water retention (TEWR) service in China. However, the mechanisms underlying the spatiotemporal changes of runoff and TEWR are not understood well. Here, meteorological, LAI, and land use/cover data were applied in a hydrological model (variable infiltration capacity) to quantify their contributions to runoff and TEWR changes during the 1980s, the 1990s, the 2000s, and 1982–2015, respectively. The results show that climate change, particularly precipitation change, was the dominant factor controlling runoff and TEWR changes. Between 1982 and 2015, climate change increased the runoff in large areas in northwestern and southwestern China and the Hai River (HR) basin and improved the TEWR in large areas in northwestern and northeastern China and the Huai River (HuR) basin. However, the magnitudes and signs of the climate change impact varied in different regions and time periods. The LAI increased extensively, which led to decreased runoff and increased TEWR in most of China from 1982 to 2015, especially in the Yellow River basin, HuR basin, and HR basin. The impacts of land use/cover change on runoff and TEWR were in general less than those of climate and LAI changes, nevertheless, they were important in some local areas. This study improves the understanding of the spatiotemporal patterns and the underlying mechanisms of runoff and TEWR changes across China between 1982 and 2015.

Early‐ And late‐waterlogging differentially affect the yield of wheat, barley, oilseed rape and field pea through changes in leaf area index, radiation interception and radiation use efficiency

AbstractWaterlogging is a severe stress that constrains crop production. We studied ecophysiological traits related to waterlogging tolerance in wheat, barley oilseed rape and field pea. Outdoor experiments using canopies of wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), oilseed rape (Brassica napus L.) and field pea (Pisum sativum L.) were subjected to a 14‐day‐waterlogging at early (vegetative) or late (reproductive) stages and a non‐waterlogged treatment (as control). Leaf area index (LAI), radiation interception, radiation use efficiency (RUE) and aerial dry matter were monitored along the life‐cycle; quantifying yield and its components at maturity. Wheat was able to withstand waterlogging, with similar yields than controls. Barley showed no differences in yield at early stage, but late‐waterlogging resulted in 64%–66% lower yield owing to fewer spikes/m2 and poor recovery of radiation interception and RUE. Oilseed rape showed yield penalization due to waterlogging, but responses differed from 17%–30% to 42%–69% for early and late stages, respectively; due to the limited ability to restore radiation interception and RUE in late stage; and the additional losses in seed number and weight. Field pea was unable to restore LAI, radiation interception, or RUE and suffered substantial yield reductions (85%–100%). Wheat could be cultivated in areas prone to transient waterlogging (i.e., early or late stages) of up to 2 weeks; barley and rapeseed are recommended only with waterlogging occurring in early stages and field pea is not suitable for areas facing waterlogging.

Forage accumulation and radiation use of alfalfa under deficit irrigation

AbstractThe supply of high‐quality forage is fundamental to the development of ruminant animal production in drought areas where irrigation provides strong support to the forage production. The objective was to explore the response of alfalfa (Medicago sativa L.) growth and radiation use to deficit irrigation in a 2‐yr field study. Two deficit‐irrigation treatments and a full‐irrigation control (CK) were established under flood irrigation. Growth‐related indicators and radiation use were measured regularly. Results showed that deficit irrigation reduced forage accumulation, leaf area index (LAI), plant relative water content (RWC), and radiation use efficiency (RUE) of alfalfa. Compared with CK, slight deficit irrigation (SD) and moderate deficit irrigation (MD) decreased forage accumulation rate by 20 and 30%, respectively. Logistic model as a function of growing degree days (GDD) fitted well LAI change (R2 > .9). Deficit irrigation reduced the average rate of quick growth of LAI from 5 to 36%. Water deficit did not significantly change the GDD corresponding to the rapid growth phase but reduced the growth rate. Under deficit irrigation, decreased LAI did not always lead to significant cumulative intercepted photosynthetically active radiation (CIPAR) decrease, but resulted in significantly lowered RUE. In conclusion, the decrease of forage accumulation of alfalfa under deficit irrigation would be caused mainly by weakened photosynthetic ability (representing RUE).

Exploring the long-term vegetation dynamics of different ecological zones in the farming-pastoral ecotone in northern China.

The vegetation in the farming-pastoral ecotone in northern China is influenced by both natural and anthropogenic factors and has undergone drastic changes in the past decades. The farming-pastoral ecotone is the transition zone from agriculture to animal husbandry. The ecological environment of this ecotone is complex and fragile. Most researches have primarily focused on the entire farming-pastoral ecotone, seldomly considering the differences between different ecological zones characterized by soil, climate, and biome conditions. Based on the long time series of leaf area index (LAI) data, meteorological data, and land-use dataset, this study analyzed LAI variation trends, the correlations between LAI and climate factors, and the impact of land-use type change on vegetation in the farming-pastoral ecotone in northern China. Moreover, this paper makes a full study of the changes of the whole study area from the perspective of the differences between different ecological zones. The results showed that over 36years, areas with vegetation improvements were considerably larger than those with degradations. However, there were still 49.56% of the total area showing no significant vegetation change. There are differences in vegetation change and response to climate between the forest ecological zones and the grassland ecological zones. The vegetation improvement trends of the forest ecological zones were larger and more sensitive to temperature, while the vegetation improvements of the grassland ecological zones were relatively small, and were more sensitive to precipitation. Human activities promote LAI changes in areas close to the forest ecological zones. The change of land use indicates that the decrease of the overall natural vegetation area has not resulted in decreasing LAI. And there is a growing trend of woodland area in the grassland ecological zones. The study provides a theoretical basis for the management of the environment and vegetation in the farming-pastoral ecotone in northern China.

Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data

An important component in improving the quality of forests is to study the interference intensity of forest fires, in order to describe the intensity of the forest fire and the vegetation recovery, and to improve the monitoring ability of the dynamic change of the forest. Using a forest fire event in Bilahe, Inner Monglia in 2017 as a case study, this study extracted the burned area based on the BAIS2 index of Sentinel-2 data for 2016–2018. The leaf area index (LAI) and fractional vegetation cover (FVC), which are more suitable for monitoring vegetation dynamic changes of a burned area, were calculated by comparing the biophysical and spectral indices. The results showed that patterns of change of LAI and FVC of various land cover types were similar post-fire. The LAI and FVC of forest and grassland were high during the pre-fire and post-fire years. During the fire year, from the fire month (May) through the next 4 months (September), the order of areas of different fire severity in terms of values of LAI and FVC was: low > moderate > high severity. During the post fire year, LAI and FVC increased rapidly in areas of different fire severity, and the ranking of areas of different fire severity in terms of values LAI and FVC was consistent with the trend observed during the pre-fire year. The results of this study can improve the understanding of the mechanisms involved in post-fire vegetation change. By using quantitative inversion, the health trajectory of the ecosystem can be rapidly determined, and therefore this method can play an irreplaceable role in the realization of sustainable development in the study area. Therefore, it is of great scientific significance to quantitatively retrieve vegetation variables by remote sensing.

Canopy recovery four years after logging: a management study in a southern brazilian secondary forest

ABSTRACT Background: Understanding forest dynamics after logging is essential to define forest management cycles and intensities. In secondary forest, especially in the Atlantic Forest Domain, these studies are still scarce. Monitoring of the canopy structure after tree harvesting can be performed by hemispherical photographs, where canopy opening is commonly analyzed. This study evaluated changes in canopy opening four years after tree harvesting in a secondary Atlantic Rainforest in southern Brazil. We used hemispherical photographs to determine the Canopy Openness (CO), Leaf Area Index (LAI), and Diffuse Fraction of Photosynthetically Active Absorbed Radiation (FAPARdif) in eleven permanent plots. Results: We found that harvesting resulted in a momentary increase in canopy opening and light availability in the understory. Four years after harvesting, CO, LAI and FAPARdif recovered or even exceeded the original values of the forest. We observed a significant correlation between CO and number of trees harvested with DBH > 30 cm. Weak correlations were found between these canopy related variables and the logging intensity. Conclusion: In conclusion, we recognized that changes of CO, LAI and FAPARdif after timber harvesting presented short duration. This indicates that the applied logging intensities, 21.8 to 51.1% of the total basal area, did not exceed the resilience of the forest canopy and it’s recovering four years later. However, additional studies should be carried out to observe vegetation dynamics, such as species composition, vertical structure, productivity and community stability, in order to improve management schemes of secondary stands in the Atlantic Forest.

Sampling protocols of specific leaf area for improving accuracy of the estimation of forest leaf area index

Litterfall collection is a non-destructive direct method to estimate forest leaf area index (LAI) and validate indirect LAI products. However, the potential errors associated with the variation in specific leaf area (SLA) are rarely explored. Here, we measured the SLA of leaf litter for each tree species in a temperate deciduous forest using the litterfall collection method from 2012 to 2018, and assessed the spatial and temporal variation in SLA and its consequence on LAI estimates. The results showed that the spatial and temporal variation in SLA for the 10 major species across the seven years ranged from 0.8% to 24.3%, with the variation across the nine permanent plots (20 m × 30 m) being higher than that within plot (five 1-m2 traps), and interannual higher than seasonal. Across the 63 plot-years, the 10 simplified SLA sampling protocols introduced the errors in LAI by −12.4% to 22.2% relative to the reference protocol (sampling leaves from each trap at each collection date for the major species). Applying the SLA obtained from one trap in each plot, one plot, at the leaf fall peak for each year, or for a single year induced the errors in LAI by –3.7% to 2.9%, −6.4% to 14.7%, −9.2% to 4.7%, and −8.1% to 8.6%, respectively. Considering the trade-off between inter-plot and interannual variation, we recommend sampling from one-trap for each plot at the leaf-fall peak for measuring SLA and quantifying the spatial pattern of LAI, and sampling each species in the typical plot for measuring SLA and monitoring the temporal fluctuation in LAI. The tight relationship between the MODIS and ground reference LAI across the seven years (R2 = 0.77) validated the use of MODIS LAI to study the long-term change in forest LAI. These findings help to establish a standardized protocol for long-term accurately measuring forest LAI with the litterfall collection.

Postfire Tree Structure from High-Resolution LiDAR and RBR Sentinel 2A Fire Severity Metrics in a Pinus halepensis-Dominated Burned Stand

Tree and plant structures remaining after fires reflect well their degree of consumption, and are therefore good indicators of fire severity. Satellite optical images are commonly used to estimate fire severity. However, depending on the severity of a fire, these sensors have a limited ability to penetrate the canopy down to the ground. Airborne light detection and ranging (LiDAR) can overcome this limitation. Assessing the differences between areas that have been burned in different fire severities based on satellite images of plant and tree structures remaining after fires is important, given its widespread use to characterize fires and fire impacts (e.g., carbon emissions). Here, we measured the remaining tree structures after a fire in a forest stand burned in SE Spain in the summer of 2017. We used high-resolution LiDAR data, acquired from an unmanned aerial vehicle (UAV) six months after the fire. This information was crossed with fire severity levels based on the relativized burnt ratio (RBR) derived from Sentinel 2A images acquired a few months before and after fire. LiDAR tree structure data derived from vertical canopy profiles (VCPs) were classified into three clusters, using hierarchical principal component analysis (HPCA), followed by a random forest (RF) to select the most important variables in distinguishing the cluster groups. Among these, crown leaf area index (LAI), crown leaf area density (LAD), crown volume, tree height and tree height skewness, among others, were the most significant variables, and reflected well the degree of combustion undergone by the trees based on the response of these variables to variations in fire severity from RBR Sentinel 2A. LiDAR metrics were able to distinguish crown fire from surface fire through changes in the understory LAI and understory and midstory vegetation. The three tree structure clusters were well separated among each other and significantly related with the RBR Sentinel 2A-derived fire severity categories. Unburned and low-severity burned areas were more diverse in tree structures than moderate and high severity burned ones. The LiDAR metrics derived from VCPs demonstrated promising potential for characterizing fine-grained post-fire plant structures and fire damage when crossed with satellite-based fire severity metrics, turning into a promising approach for better characterizing fire impacts at a resolution needed for many ecological processes.