Forest Leaf Area Index Research Articles

Estimation of Forest Leaf Area Index Using Height and Canopy Cover Information Extracted From Unmanned Aerial Vehicle Stereo Imagery

Leaf area index (LAI) is an important forest structural parameter in the process of photosynthesis. Most studies have investigated the estimation of forest LAI using the spectral information of optical remote sensing images or the height information from LiDAR data. This paper explored the estimation of forest LAI using canopy cover and forest height information extracted from stereo imagery acquired by cameras onboard an unmanned aerial vehicle (UAV). UAV remote sensing has gradually become practical in recent years. Two alternative methods were proposed to extract forest height information. The height indices method extracted forest height indices within each forest plot based on the vertical histogram of the canopy height model derived from stereo imagery, while the segmentation method characterized forest plots using tree numbers and average tree heights based on the individual tree segmentation. The results showed that canopy cover and forest height are complementary in the estimation of forest LAI no matter what method was used. The combined use of canopy cover and forest height information extracted by the segmentation method had a better estimation accuracy of forest LAI with R 2 = 0.833 and RMSE = 0.288. This paper demonstrated a new approach to predict forest LAI using UAV optical images.

Foreword to the Special Issue on The Recent Progress in Quantitative Land Remote Sensing: Modeling and Estimation

This foreword starts with a brief historical overview of quantitative land remote sensing modeling and estimation from roughly 1970 to the present. The papers of this special issue are mainly derived from the Third National Forum on Quantitative Remote Sensing was held at Beijing Normal University held 14–15 July 2017. There were 296 meeting participants from 65 research institutes and universities in China, and almost all aspects of quantitative land remote sensing were discussed. There were 36 submissions, but only 13 papers were selected covering the following topics: modeling the effects of surface topography on directional shortwave reflection and longwave emission, radiometric calibration, and atmospheric correction, inversion algorithm for estimating downward longwave radiation, surface net radiation, forest leaf area index (LAI), winter wheat LAI and CWC, the Global LAnd Surface Satellite (GLASS) land surface temperature (LST) and fraction of vegetation cover (FVC) products, and evaluation and validation of satellite products of incident shortwave radiation, fractional snow cover (FSC), and cloud cover and cloud optical depth. Brief summaries of these papers are provided.

Influences of stand, soil and space factors on spatial heterogeneity of leaf area index in a spruce-fir valley forest in Xiao Hinggan Ling, China

Influences of stand, soil and space factors on spatial heterogeneity of leaf area index in a spruce-fir valley forest in Xiao Hinggan Ling, China

Retrieving leaf area index in discontinuous forest using ICESat/GLAS full-waveform data based on gap fraction model

Leaf area index (LAI) is an important vegetation structure parameter in terrestrial ecosystem modeling. Although the spaceborne Geoscience Laser Altimeter System (GLAS) on board the Ice, Cloud and land Elevation Satellite (ICESat) has been proved to have potential for deriving forest LAI, previous methods were only applicable to estimate the effective LAI. In this study, a physical method based on the gap fraction model was proposed to retrieve the LAI correcting the between-crown clumping in discontinuous forest using GLAS full-waveform data. Landsat TM imagery was utilized as auxiliary data for providing crown cover information within the footprint. Using the gap probability from GLAS data and the crown coverage fraction from Landsat imagery, the method corrects the between-crown clumping, which has been proved to contribute most to the total clumping effect, and accurately estimates the LAI in discontinuous forest (R2 = 0.83, RMSE = 0.39, n = 47). Additionally, the forest LAI underestimation caused by between-crown clumping was analyzed in practice and theory. Results show that the between-crown clumping has a nonnegligible influence on forest LAI estimation in cases that the forest area within the footprint is close to the nonforest area, and the average LAI of individual tree is high. This study may shed some light on the development of clumping effect and quantitative LAI inversion models.

BP neural networks and random forest models to detect damage by Dendrolimus punctatus Walker

The construction of a pest detection algorithm is an important step to couple “ground-space” characteristics, which is also the basis for rapid and accurate monitoring and detection of pest damage. In four experimental areas in Sanming City, Jiangle County, Sha County and Yanping District in Fujian Province, sample data on pest damage in 182 sets of Dendrolimus punctatus were collected. The data were randomly divided into a training set and testing set, and five duplicate tests and one eliminating-indicator test were done. Based on the characterization analysis of the host for D. punctatus damage, seven characteristic indicators of ground and remote sensing including leaf area index, standard error of leaf area index (SEL) of pine forest, normalized difference vegetation index (NDVI), wetness from tasseled cap transformation (WET), green band (B2), red band (B3), near-infrared band (B4) of remote sensing image are obtained to construct BP neural networks and random forest models of pest levels. The detection results of these two algorithms were comprehensively compared from the aspects of detection precision, kappa coefficient, receiver operating characteristic curve, and a paired t test. The results showed that the seven indicators all were responsive to pest damage, and NDVI was relatively weak; the average pest damage detection precision of six tests by BP neural networks was 77.29%, the kappa coefficient was 0.6869 and after the RF algorithm, the respective values were 79.30% and 0.7151, showing that the latter is more optimized, but there was no significant difference (p > 0.05); the detection precision, kappa coefficient and AUC of the RF algorithm was higher than the BP neural networks for three pest levels (no damage, moderate damage and severe damage). The detection precision and AUC of BP neural networks were a little higher for mild damage, but the difference was not significant (p > 0.05) except for the kappa coefficient for the no damage level (p < 0.05). An “over-fitting” phenomenon tends to occur in BP neural networks, while RF method is more robust, providing a detection effect that is better than the BP neural networks. Thus, the application of the random forest algorithm for pest damage and multilevel dispersed variables is thus feasible and suggests that attention to the proportionality of sample data from various categories is needed when collecting data.

Estimation of forest leaf area index using terrestrial laser scanning data and path length distribution model in open-canopy forests

Terrestrial Laser Scanning (TLS) is an active technology that can acquire the finest characteristics of canopy structure and plays an increasing role in estimating Leaf Area Index (LAI) in forest canopies. However, 3D information is not directly used in conventional TLS-based methods using the gap fraction theory. In addition, quantifying clumping effect within canopies is still a difficult task. In this paper, we presented a method to reduce clumping effect and estimate LAI using TLS data. Our recently proposed path length distribution model was applied to TLS data. Instead of converting 3D points to 2D image, the path length distribution can be extracted using the TLS-recorded 3D data and the crown models built with the alpha shapes algorithm. Two simulated scenes and one actual forest plot were utilized for validation. The results of the proposed method agree well with both the true LAI (in the simulated scenes) and the extracted PAI by the digital hemispherical photography (in the actual plot). This LAI estimation method using TLS and the path length distribution model provides a novel way for ground-based LAI measurements and shows its great potential.

Feasibility of using consumer-grade unmanned aerial vehicles to estimate leaf area index in Mangrove forest

ABSTRACTAs unmanned aerial vehicles (UAVs) become more popular, many studies investigate vegetation based on commercial UAV data. Although compared to satellite data, commercial UAV data can have flexible revisit frequencies, the possibility of using an even cheaper data source, consumer UAVs (red, green, and blue (RGB) only), to study vegetation remains unknown. The purpose of most frequent uses of consumer UAVs is recreation. This paper tests the feasibility of using consumer UAVs for mangrove research and proposed a method for mapping leaf area index (LAI) of mangrove. A commercial UAV image is also used for comparison. RGB-based vegetation indices like Excess Green Vegetation Index (ExG), Negative Excess Red Vegetation Index (NegExR), Green Leaf Index (GLI) and Normalized Green-red Difference Index (NGRDI) were used to build regression models against field measured LAI. The results showed that it was feasible to use consumer UAV data for mapping mangrove forest LAI, and the NegExR achieved the highest coefficient of determination (R2) in predicting LAI among all the indices. This paper showed that researchers who are neither familiar with aerial photogrammetry nor have access to commercial UAV data could perform high spatial resolution vegetation studies at a low cost.

Retrieval of leaf area index of Phyllostachys praecox forest based on MODIS reflectance time series data.

Based on the MODIS surface reflectance data, five vegetation indices, including norma-lized difference vegetation index (NDVI), simple ratio index (SR), Gitelson green index (GI), enhanced vegetation index (EVI) and soil adjusted vegetation index (SAVI) were constructed as remote sensing variables, coupled with the seven original spectral reflectance bands of MODIS. Stepwise regression and correlation analysis were used to select the variables, and the stepwise regression and Back Propagation (BP) neural network models were constructed based on the measured LAI to retrieve the LAI time series data of Phyllostachys praecox (Lei bamboo) forest during the period from January 2014 to March 2017. The retrieval results were compared with MOD15A2 LAI products during the same period. The results showed that SR was the single variable selected for the stepwise regression model. The correlations of LAI with bands b1, b2, b3, b7 and five vegetation indices were significant, which could be used as input variables of BP neural network model. There was a significant correlation between the LAI estimated from BP neural network and measured LAI, with the R2 of 0.71, RMSE of 0.34, and RMSEr of 13.6%. R2 was increased by 10.9%, RMSE decreased by 5.6%, and RMSEr decreased by 12.3% compared with LAI estimated from stepwise regression method. R2 was increased by 54.5%, RMSE decreased by 79.3%, and RMSEr decreased by 79.1% compared with MODIS LAI. The LAI of Lei bamboo forest could be accurately retrieved using BP neural network method based on MODIS reflectance time series data, which would be a feasible method for rapid monitoring of LAI in Lei bamboo forest.

Remote Sensing of Leaf Area Index from LiDAR Height Percentile Metrics and Comparison with MODIS Product in a Selectively Logged Tropical Forest Area in Eastern Amazonia

Leaf area index (LAI) is an important parameter to describe the capacity of forests to intercept light and thus affects the microclimate and photosynthetic capacity of canopies. In general, tropical forests have a higher leaf area index and it is a challenge to estimate LAI in a forest with a very dense canopy. In this study, it is assumed that the traditional Light Detection and Ranging (LiDAR)-derived fractional vegetation cover (fCover) has weak relationship with leaf area index in a dense forest. We propose a partial least squares (PLS) regression model using the height percentile metrics derived from airborne LiDAR data to estimate the LAI of a dense forest. Ground inventory and airborne LiDAR data collected in a selectively logged tropical forest area in Eastern Amazonia are used to map LAI from the plot level to the landscape scale. The results indicate that the fCover, derived from the first return or the last return, has no significant correlations with the ground-based LAI. The PLS model evaluated by the leave-one-out validation shows that the estimated LAI is significantly correlated with the ground-based LAI with an R2 of 0.58 and a root mean square error (RMSE) of 1.13. A data comparison indicates that the Moderate Resolution Imaging Spectrometer (MODIS) LAI underestimates the landscape-level LAI by about 22%. The MODIS quality control data show that in the selected tile, the cloud state is not the primary factor affecting the MODIS LAI performance; rather, the LAI from the main radiative transfer (RT) algorithm contributes much to the underestimation of the LAI in the tropical forest. In addition, the results show that the LiDAR-based LAI has a better response to the logging activities than the MODIS-based LAI, and that the leaf area reduction caused by logging is about 13%. In contrast, the MODIS-based LAI exhibits no apparent spatial correlation with the LiDAR-based LAI. It is suggested that the main algorithm of MODIS should be improved with regard to tropical forests. The significance of this study is the proposal of a framework to produce ground-based LAI using forest inventory data and determine the plot-level LAI at the airborne and satellite scale using LiDAR data.

Decreasing the error in the measurement of the ecosystem effective leaf area index of a Pinus massoniana forest

Decreasing the forest ecosystem leaf-area index error (LAIe) helps accurately estimate the growth and light energy utilization of aboveground foliage. Analyzing light transmission in forest ecosystems can effectively determine LAIe. The LAI-2200 plant canopy analyzer (PCA) is used extensively for rapid field-effective LAI (LAIe) measurements and primarily to measure forest canopy LAIe values. However, sometimes this parameter must also be measured in forests with small clearings. In this study, we used the LAI-2200 PCA to obtain one A-value and four B-values each for the canopy, herbaceous layer, and forest ecosystem LAIe. Field measurements showed that the three LAIe types were obviously different. In certain quadrats, the average herbaceous layer (Dicranopteris dichotoma Bernh.) LAIe apparently exceeded that of the Pinus massoniana forest ecosystem. The sources of this error were measuring and recording A-value readings for small canopies and underestimating the ecosystem LAIe. We obtained similar coefficients of determination for both the pre-recomputation and post-recomputation of the canopy and forest ecosystem LAIe (R2 ≥ 0.96 and R2 ≥ 0.99, respectively); thus, the error was decreased. Measuring field LAIe with the LAI-2200 PCA and recomputation should compensate for LAIe underestimation in complex forest ecosystems.

Overstorey evapotranspiration in a seasonally dry Mediterranean eucalypt forest: Response to groundwater and mining

AbstractGroundwater levels in the northern jarrah forest have declined at rates up to 0.5 m year−1 owing to increased aridity in south‐western Australia in the last 40 years. The forest has also been mined and rehabilitated resulting in significant areas of postmining forest. We tested the impact of declining groundwater levels and mining on evapotranspiration by jarrah forest overstorey. We hypothesized that trees in jarrah forest are facultative phreatophytes (will use groundwater where available but are not reliant on it) and water use per unit overstorey leaf area index (Los) of postmining forest is the same as that of postharvest forest. We measured sapflow at 7 sites in the northern jarrah forest and measured rainfall interception by the canopy at 9 sites. Stemflow was measured at 3 sites. Shallow depth to groundwater was associated with a larger ratio of transpiration per unit leaf area (Eos/Los), but there was little difference in Eos/Los between postmining and postharvest jarrah forest. Eos/Los ranged from 250 ‐ 340 mm year−1 (m2 m−2)−1 at sites where depth to groundwater was &gt;15 m but was up to 400–500 mm year−1 (m2 m−2)−1 at some sites with shallow groundwater. Based on relationships between transpiration, rainfall interception, and Los, it is possible to estimate overstorey evapotranspiration in jarrah forest from Los, especially if spatial layers are available for depth to groundwater. We conclude that jarrah forest is conservative in its water use and likely to be resilient to a drying climate. Management implications for the northern jarrah forest are briefly discussed.

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.

Estimation of forest LAI by inverting canopy reflectance models and multi-angle imagery

Computer simulation models have seldom been applied for estimating the structural and biophysical variables of forest canopy. In this study, an approach for the estimation of leaf area index (LAI) using the information contained in hyperspectral, multi-angle images and the inversion of a computer simulation model are explored. For this purpose, L-systems combined with forest growth model ZELIG were applied to render 3-D forest architectural scenarios. The Radiosity-graphics combined model (RGM) was used to estimate forest LAI from the Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/PROBA) data. LAI inversion was performed using the look-up table (LUT) method. The estimated LAI was evaluated against in situ LAI measurement and compared against the LAI predictions from CHRIS data obtained using the Li-Strahler geometric-optical canopy reflectance model (GOMS). The results indicated that the method used in this study can be efficient strategy to estimate LAI by RGM model inversion.

Determining the K coefficient to leaf area index estimations in a tropical dry forest.

Vegetation indices are useful tools to remotely estimate several important parameters related to ecosystem functioning. However, improving and validating estimations for a wide range of vegetation types are necessary. In this study, we provide a methodology for the estimation of the leaf area index (LAI) in a tropical dry forest (TDF) using the light diffusion through the canopy as a function of the successional stage. For this purpose, we estimated the K coefficient, a parameter that relates the normalized difference vegetation index (NDVI) to LAI, based on photosynthetically active radiation (PAR) and solar radiation. The study was conducted in the Mata Seca State Park, in southeastern Brazil, from 2012 to 2013. We defined four successional stages (very early, early, intermediate, and late) and established one optical phenology tower at one plot of 20 × 20m per stage. Towers measured the incoming and reflected solar radiation and PAR for NDVI calculation. For each plot, we established 24 points for LAI sampling through hemispherical photographs. Because leaf cover is highly seasonal in TDFs, we determined ΔK (leaf growth phase) and Kmax (leaf maturity phase). We detected a strong correlation between NDVI and LAI, which is necessary for a reliable determination of the K coefficient. Both NDVI and LAI varied significantly between successional stages, indicating sensitivity to structural changes in forest regeneration. Furthermore, the K values differed between successional stages and correlated significantly with other environmental variables such as air temperature and humidity, fraction of absorbed PAR, and soil moisture. Thus, we established a model based on spectral properties of the vegetation coupled with biophysical characteristics in a TDF that makes possible to estimate LAI from NDVI values. The application of the K coefficient can improve remote estimations of forest primary productivity and gases and energy exchanges between vegetation and atmosphere. This model can be applied to distinguish different successional stages of TDFs, supporting environmental monitoring and conservation policies towards this biome.

Remote estimation of canopy leaf area index and chlorophyll content in Moso bamboo (Phyllostachys edulis (Carrière) J. Houz.) forest using MODIS reflectance data

We estimated the leaf area index (LAI) and canopy chlorophyll content (CC) of Moso bamboo forest by using statistical models based on MODIS data and field measurements. Results showed that the statistical model driven by MODIS data has the potential to accurately estimate LAI and CC, while the structure of the calibration models varied between on- and off-years because of the different leaf change and bamboo shoot production characteristics between these types of years. LAI and CC (gram per square meter of ground area) are important parameters for determining carbon exchange between Moso bamboo forest (Phyllostachys edulis (Carriere) J. Houz.) and the atmosphere. This study evaluated the ability of a statistical model driven by MODIS data to accurately estimate the LAI and CC in Moso bamboo forest, and differences in the LAI and CC between on-years (years with great shoot production) and off-years (years with less shoot production) were analyzed. The LAI and CC measurements were collected in Anji County, Zhejiang Province, China. Indicators of LAI and CC were calculated from MODIS data. Then, a regression analysis was used to build relationships between the LAI and CC and various indicators on the basis of leaf change and bamboo shoot production characteristics of Moso bamboo forest. LAI and CC were accurately estimated by using the regression analysis driven by MODIS-derived indicators with a relative root mean squared error (RMSEr) of 9.04 and 13.1%, respectively. The structure of the calibration models varied between on- and off-years. Long-term time series analysis from 2000 to 2015 showed that LAI and CC differed largely between on- and off-years. This study demonstrates that LAI and CC of Moso bamboo forest can be estimated accurately by using a statistical model driven by MODIS-derived indicators, but attention should be paid to differences in the calibration models between on- and off-years.

Effects of Different LiDAR Intensity Normalization Methods on Scotch Pine Forest Leaf Area Index Estimation

Effects of Different LiDAR Intensity Normalization Methods on Scotch Pine Forest Leaf Area Index Estimation

Estimation of Leaf Area Index in a Mountain Forest of Central Japan with a 30-m Spatial Resolution Based on Landsat Operational Land Imager Imagery: An Application of a Simple Model for Seasonal Monitoring

An accurate estimation of the leaf area index (LAI) by satellite remote sensing is essential for studying the spatial variation of ecosystem structure. The goal of this study was to estimate the spatial variation of LAI over a forested catchment in a mountainous landscape (ca. 60 km2) in central Japan. We used a simple model to estimate LAI using spectral reflectance by adapting the Monsi-Saeki light attenuation theory for satellite remote sensing. First, we applied the model to Landsat Operational Land Imager (OLI) imagery to estimate the spatial variation of LAI in spring and summer. Second, we validated the model’s performance with in situ LAI estimates at four study plots that included deciduous broadleaf, deciduous coniferous, and evergreen coniferous forest types. Pre-processing of the Landsat OLI imagery, including atmospheric correction by elevation-dependent dark object subtraction and Minnaert topographic correction, together with application of the simple model, enabled a satisfactory 30-m spatial resolution estimation of forest LAI with a maximum of 5.5 ± 0.2 for deciduous broadleaf and 5.3 ± 0.2―for evergreen coniferous forest areas. The LAI variation in May (spring) suggested an altitudinal gradient in the degree of leaf expansion, whereas the LAI variation in August (mid-summer) suggested an altitudinal gradient of yearly maximum forest foliage density. This study demonstrated the importance of an accurate estimation of fine-resolution spatial LAI variations for ecological studies in mountainous landscapes, which are characterized by complex terrain and high vegetative heterogeneity.

Оцінка індексу площі листкової поверхні деревостанів сосни звичайної в умовах Північного Степу України

Оцінка індексу площі листкової поверхні деревостанів сосни звичайної в умовах Північного Степу України

A Comparison of Simulated and Field-Derived Leaf Area Index (LAI) and Canopy Height Values from Four Forest Complexes in the Southeastern USA.

Vegetative leaf area is a critical input to models that simulate human and ecosystem exposure to atmospheric pollutants. Leaf area index (LAI) can be measured in the field or numerically simulated, but all contain some inherent uncertainty that is passed to the exposure assessments that use them. LAI estimates for minimally managed or natural forest stands can be particularly difficult to develop as a result of interspecies competition, age and spatial distribution. Satellite-based LAI estimates hold promise for retrospective analyses, but we must continue to rely on numerical models for alternative management analysis. Our objective for this study is to calculate and validate LAI estimates generated from the USDA Environmental Policy Impact Climate (EPIC) model (a widely used, field-scale, biogeochemical model) on four forest complexes spanning three physiographic provinces in Virginia and North Carolina. Measurements of forest composition (species and number), LAI, tree diameter, basal area, and canopy height were recorded at each site during the 2002 field season. Calibrated EPIC results show stand-level temporally resolved LAI estimates with R2 values ranging from 0.69 to 0.96, and stand maximum height estimates within 20% of observation. This relatively high level of performance is attributable to EPIC’s approach to the characterization of forest stand biogeochemical budgets, stand history, interspecies competition and species-specific response to local weather conditions. We close by illustrating the extension of this site-level approach to scales that could support regional air quality model simulations.

Comparative Performances of Airborne LiDAR Height and Intensity Data for Leaf Area Index Estimation

Leaf area index (LAI) estimation based on remote sensing data has often relied on the use of spectral vegetation indices from optical data. However, it is difficult to accurately estimate LAI due to saturation of spectral signals. Light detection and ranging (LiDAR) systems have emerged as promising technologies for overcoming the saturation problem, and an increasing number of studies have been conducted on LAI estimation using LiDAR data. In this study, we compared the performance of LAI estimation using LiDAR height and intensity data, and explored the potential for estimating forest LAI using combined LiDAR height and intensity data. LAI estimation models were established using LiDAR height, intensity, and a combination of LiDAR height and intensity metrics based on a random forest regression algorithm. Our results show that the laser intercept index derived from LiDAR height or intensity data was the most important predictor for LAI. Field measurements of LAI at 64 sites were used to assess the power of various LiDAR metrics in predicting LAI. The results show that both LiDAR height and intensity metrics alone could reliably estimate forest LAI. However, compared to LiDAR intensity metrics [ $R^{{\rm 2}}\,= \,0.610$ with root mean squared error (RMSE) of 0.664], LiDAR height metrics had a better predictive power ( $R^{{\rm 2}}\,= \,0.765$ with RMSE of 0.562). Moreover, the combined LiDAR height and intensity metrics resulted in the highest LAI estimation accuracy ( $R^{{\rm 2}}\,= \,0.809$ with RMSE of 0.501). Therefore, the combination of LiDAR height and intensity data has a great potential for improving the LAI estimation accuracy.