Influence of woody elements of a Norway spruce canopy on nadir reflectance simulated by the DART model at very high spatial resolution

A detailed sensitivity analysis investigating the effect of woody elements introduced into the Discrete Anisotropic Radiative Transfer (DART) model on the nadir bidirectional reflectance factor (BRF) for a simulated Norway spruce canopy was performed at a very high spatial resolution (modelling resolution 0.2 m, output pixel size 0.4 m). We used such a high resolution to be able to parameterize DART in an appropriate way and subsequently to gain detailed understanding of the influence of woody elements contributing to the radiative transfer within heterogeneous canopies. Three scenarios were studied by modelling the Norway spruce canopy as being composed of i) leaves, ii) leaves, trunks and first order branches, and finally iii) leaves, trunks, first order branches and small woody twigs simulated using mixed cells (i.e. cells approximated as composition of leaves and/or twigs turbid medium, and large woody constituents). The simulation of each scenario was performed for 10 different canopy closures (CC = 50-95%, in steps of 5%), 25 leaf area index (LAI = 3.0-15.0 m² m^− 2, in steps of 0.5 m² m^− 2), and in four spectral bands (centred at 559, 671, 727, and 783 nm, with a FWHM of 10 nm). The influence of woody elements was evaluated separately for both, sunlit and shaded parts of the simulated forest canopy, respectively. The DART results were verified by quantifying the simulated nadir BRF of each scenario with measured Airborne Imaging Spectroradiometer (AISA) Eagle data (pixel size of 0.4 m). These imaging spectrometer data were acquired over the same Norway spruce stand that was used to parameterise the DART model.The Norway spruce canopy modelled using the DART model consisted of foliage as well as foliage including robust woody constituents (i.e. trunks and branches). All results showed similar nadir BRF for the simulated wavelengths. The incorporation of small woody parts in DART caused the canopy reflectance to decrease about 4% in the near-infrared (NIR), 2% in the red edge (RE) and less than 1% in the green band. The canopy BRF of the red band increased by about 2%. Subsequently, the sensitivity on accounting for woody elements for two spectral vegetation indices, the normalized difference vegetation index (NDVI) and the angular vegetation index (AVI), was evaluated. Finally, we conclude on the importance of including woody elements in radiative transfer based approaches and discuss the applicability of the vegetation indices as well as the physically based inversion approaches to retrieve the forest canopy LAI at very high spatial resolution.     

Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction

We evaluate the potential of deriving fractional cover (fCover) and leaf area index (LAI) from discrete return, small footprint airborne laser scanning (ALS) data. fCover was computed as the fraction of laser vegetation hits over the number of total laser echoes per unit area. Analogous to the concept of contact frequency, an effective LAI proxy was estimated by a fraction of first and last echo types inside the canopy. Validation was carried out using 83 hemispherical photographs georeferenced to centimeter accuracy by differential GPS, for which the respective gap fractions were computed over a range of zenith angles using the Gap Light Analyzer (GLA). LAI was computed by GLA from gap fraction estimations at zenith angles of 0-60°. For ALS data, different data trap sizes were used to compute fCover and LAI proxy, the range of radii was 2-25 m. For fCover, a data trap size of 2 m radius was used, whereas for LAI a radius of 15 m provided best results. fCover was estimated both from first and last echo data, with first echo data overestimating field fCover and last echo data underestimating field fCover. A multiple regression of fCover derived from both echo types with field fCover showed no increase of R² compared to the regression of first echo data, and thus, we only used first echo data for fCover estimation. R² for the fCover regression was 0.73, with an RMSE of 0.18. For the ALS LAI proxy, R² was lower, at 0.69, while the RMSE was 0.01. For LAI larger radii (∼ 15 m ) provided best results for our canopy types, which is due to the importance of a larger range of zenith angles (0-60°) in LAI estimation from hemispherical photographs. Based on the regression results, maps of fCover and LAI were computed for our study area and compared qualitatively to equivalent maps based on imaging spectrometry, revealing similar spatial patterns and ranges of values.     

Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling

Methods for using airborne laser scanning (also called airborne LIDAR) to retrieve forest parameters that are critical for fire behavior modeling are presented. A model for the automatic extraction of forest information is demonstrated to provide spatial coverage of the study area, making it possible to produce 3-D inputs to improve fire behavior models.The Toposys I airborne laser system recorded the last return of each footprint (0.30-0.38 m) over a 2000 m by 190 m flight line. Raw data were transformed into height above the surface, eliminating the effect of terrain on vegetation height and allowing separation of ground surface and crown heights. Data were defined as ground elevation if heights were less than 0.6 m. A cluster analysis was used to discriminate crown base height, allowing identification of both tree and understory canopy heights. Tree height was defined as the 99 percentile of the tree crown height group, while crown base height was the 1 percentile of the tree crown height group. Tree cover (TC) was estimated from the fraction of total tree laser hits relative to the total number of laser hits. Surface canopy (SC) height was computed as the 99 percentile of the surface canopy group. Surface canopy cover is equal to the fraction of total surface canopy hits relative to the total number of hits, once the canopy height profile (CHP) was corrected. Crown bulk density (CBD) was obtained from foliage biomass (FB) estimate and crown volume (CV), using an empirical equation for foliage biomass. Crown volume was estimated as the crown area times the crown height after a correction for mean canopy cover.     

Characterizing forest ecological structure using pulse types and heights of airborne laser scanning

Characterizing forest structure is an important part of any comprehensive biodiversity assessment. However, current methods for measuring structural complexity require a laborious process that involves many logistically expensive point based measurements. An automated or semi-automated method would be ideal. In this study, the utility of airborne laser scanning (LiDAR; Light Detection and Ranging) for characterizing the ecological structure of a forest landscape is examined. The innovation of this paper is to use different laser pulse return properties from a full waveform LiDAR to characterize forest ecological structure. First the LiDAR dataset is stratified into four vertical layers: ground, low vegetation (0-1 m from the ground), medium vegetation (1-5 m from the ground) and high vegetation (> 5 m). Subsequently the “Type” of LiDAR return is analysed: Type 1 (singular returns); Type 2 (first of many returns); Type 3 (intermediate returns); and Type 4 (last of many returns). A forest characterization scheme derived from LiDAR point clouds is proposed. A validation of the scheme is then presented using a network of field sites that recorded commonly used metrics of biodiversity. The proposed forest characterization categories allow for quantification of gaps (above bare ground, low vegetation and medium vegetation), canopy cover and its vertical density as well as the presence of various canopy strata (low, medium and high). Regression analysis showed that LiDAR derived variables were good predictors of field recorded variables (R² = 0.82, P < 0.05 between LiDAR derived presence of low vegetation and field derived LAI for low vegetation). The proposed scheme clearly shows the potential of full waveform LiDAR to provide information on the complexity of habitat structure.     

Evaluation of the MODIS LAI algorithm at a coniferous forest site in Finland

Leaf area index (LAI) collected in a needle-leaf forest site near Ruokolahti, Finland, during a field campaign in June 14-21, 2000, was used to validate Moderate Resolution Imaging Spectroradiometer (MODIS) LAI algorithm. The field LAI data was first related to 30-m resolution Enhanced Thermal Mapper Plus (ETM+) images using empirical methods to create a high-resolution LAI map. The analysis of empirical approaches indicates that preliminary segmentation of the image followed by empirical modeling with the resulting patches, was an effective approach to developing an LAI validation surface. Comparison of the aggregated high-resolution LAI map and corresponding MODIS LAI retrievals suggests satisfactory behavior of the MODIS LAI algorithm although variation in MODIS LAI product is higher than expected. The MODIS algorithm, adjusted to high resolution, generally overestimates the LAI due to the influence of the understory vegetation. This indicates the need for improvements in the algorithm. An improved correlation between field measurements and the reduced simple ratio (RSR) suggests that the shortwave infrared (SWIR) band may provide valuable information for needle-leaf forests.     

Shrub detection using disparate airborne laser scanning acquisitions over varied forest cover types

We explore the possibility of extending the national forest inventory-based point data of understory presence using region-wide, disparate lidar data for the southeastern USA. For this, we developed a simple inferential model that helps to understand the basic underlying relationships and associations between lidar predictor metrics and forest understory shrub presence over a wide range of forest types and topographic conditions. The model (a least absolute shrinkage and selection operator-based logistic regression model) had fair predictive performance (accuracy = 62%, kappa = 0.23). Hence, we were able to propose a set of biophysically meaningful predictor variables that represent understory (4), canopy (3), topographic conditions (1), and sensor characteristics (1). The single most important predictor variable was the understory layer canopy density, which is the ratio of lidar returns in the understory to those near the ground. Hence, we demonstrate that the interplay of several factors affects understory vegetation condition. Overall, our work highlights the potential value of using lidar to characterize understory conditions.     

Evaluation of the MODIS LAI product using independent lidar-derived LAI: A case study in mixed conifer forest

This study presents an alternative assessment of the MODIS LAI product for a 58,000 ha evergreen needleleaf forest located in the western Rocky Mountain range in northern Idaho by using lidar data to model (R² = 0.86, RMSE = 0.76) and map LAI at higher resolution across a large number of MODIS pixels in their entirety. Moderate resolution (30 m) lidar-based LAI estimates were aggregated to the resolution of the 1-km MODIS LAI product and compared to temporally-coincident MODIS retrievals. Differences in the MODIS and lidar-derived values of LAI were grouped and analyzed by several different factors, including MODIS retrieval algorithm, sun/sensor geometry, and sub-pixel heterogeneity in both vegetation and terrain characteristics. Of particular interest is the disparity in the results when MODIS LAI was analyzed according to algorithm retrieval class. We observed relatively good agreement between lidar-derived and MODIS LAI values for pixels retrieved with the main RT algorithm without saturation for LAI LAI ≤ 4. Moreover, for the entire range of LAI values, considerable overestimation of LAI (relative to lidar-derived LAI) occurred when either the main RT with saturation or back-up algorithm retrievals were used to populate the composite product regardless of sub-pixel vegetation structural complexity or sun/sensor geometry. These results are significant because algorithm retrievals based on the main radiative transfer algorithm with or without saturation are characterized as suitable for validation and subsequent ecosystem modeling, yet the magnitude of difference appears to be specific to retrieval quality class and vegetation structural characteristics.     

Detection of initial damage in Norway spruce canopies using hyperspectral airborne data

Current broadband sensors are not capable of separating the initial stages of forest damage. The current investigation evaluates the potential of hyperspectral data for detecting the initial stages of forest damage at the canopy level in the Norway spruce (Picea abies (L.) Karst) forests of Czech Republic. Hyperspectral canopy reflectance imagery and foliar samples were acquired contemporaneously for 23 study sites in August 1998. The sites were selected along an air pollution gradient to represent the full range of damage conditions in even-aged spruce forests. The changes in canopy and foliar reflectance, chemistry and pigments associated with forest damage were established. The potential of a large number of spectral indices to identify initial forest damage was determined. Canopy hyperspectral data were able to separate healthy from initially damaged canopies, and therefore provided an improved capability for assessment of forest physiology as compared to broadband systems. The 673-724 nm region exhibited maximum sensitivity to initial damage. The nine spectral indices having the highest potential as indicators of the initial damage included: three simple band ratios, two derivative indices, two modelled red-edge parameters and two normalized bands. The sensitivity of these indices to damage was explained primarily by their relationship to foliar structural chemical compounds, which differed significantly by damage class.     

Estimating field-plot data of forest stands using airborne laser scanning and SPOT HRG data

Airborne laser scanner systems provide detailed forest information that can be used for important improvements in forest management decisions. Planning systems under development use plot-survey data to represent forest stands in large forest holdings which enables new flexible methods to model the forest and optimize selection of silvicultural treatments. In Sweden today, only averages of forest stand variables are used, and the survey methods used do not provide plot-survey data for all stands in large forest holdings. This is a task possibly solved using airborne laser scanner data. Various measures can be derived from laser data, each describing different forest variables, such as tree height distribution, vegetation density and vertical tree crown structure. Here, imputation of field plot (10 m radius) data using measures derived from airborne laser scanner data (TopEye) and optical image data (SPOT 5 HRG satellite sensor) were evaluated as a method to provide data for new long-term management planning systems. In addition to commonly applied measures, the semivariogram of laser measurements was evaluated as a new measure to extract spatial characteristics of the forest. The study used data from 870, 10 m radius field plots (0 to 812 m³ ha^− 1) surveyed for a 1200 ha large forest estate in the south of Sweden. At the best, combining measures derived from laser scanner data and SPOT 5 data, stand mean volume was estimated with a root mean square error (RMSE) of 20% of the sample mean and stem density with 22% RMSE. Bias of stem density estimates was 5%, and stand stem volume 4%. Although these accuracies are sufficient for operational application, estimates of tree species proportions and within-stand variation were clearly not.     

Automatic detection of harvested trees and determination of forest growth using airborne laser scanning

This paper demonstrates the applicability of small footprint, high sampling density airborne laser scanners for boreal forest change detection, i.e. the estimation of forest growth and monitoring of harvested trees. Two laser acquisitions were carried out on a test site using a Toposys-1 laser scanner. Three-dimensional canopy height models were calculated for both data sets using raster-based algorithms. Object-oriented algorithms were developed for detecting harvested and fallen trees, and for measuring forest growth at plot and stand levels. Out of 83 field-checked harvested trees, 61 could be automatically and correctly detected. All mature harvested trees were detected; it was mainly the smaller trees that were not. Forest growth was demonstrated at plot and stand levels using an object-oriented tree-to-tree matching algorithm and statistical analysis. The precision of the estimated growth, based on field checking or statistical analysis, was about 5 cm at stand level and about 10–15 cm at plot level. The authors expect that the methods may be feasible in large area forest inventories that make use of permanent sample plots. Together with methods for detecting individual sample trees, the methods described may be used to replace a large number of permanent plots with laser scanning techniques.     

Determining forest canopy characteristics using airborne laser data

A pulsed laser system was flown over a forested area in Pennsylvania which exhibited a wide range of canopy closure conditions. The lasing system acts as the ultraviolet light equivalent of radar, sensing not only the distance to the top of the forest canopy, but also the range to the forest floor. The data were analyzed to determine which components of the laser data could explain the variability in crown closure along the flight transect. Results indicated that canopy closure was most strongly related to the penetration capability of the laser pulse. Pulses were attenuated more quickly in a dense canopy. Hence the inability to find a strong ground return in the laser data after initially sensing the top of the canopy connoted dense canopy cover. Photogrammetrically acquired tree heights were compared to laser estimates; average heights differed by less than 1 m. The results indicated that the laser system may be used to remotely sense the vertical forest canopy profile. Elements of this profile are linearly related to crown closure and may be used to assess tree height.     

Bryophyte cover estimation in a boreal black spruce forest using airborne lidar and multispectral sensors

Bryophytes are the dominant ground cover vegetation layer in many boreal forests and in some of these forests the net primary production of bryophytes exceeds the overstory. Therefore it is necessary to quantify their spatial coverage and species composition in boreal forests to improve boreal forest carbon budget estimates. We present results from a small exploratory test using airborne lidar and multispectral remote sensing data to estimate the percentage of ground cover for mosses in a boreal black spruce forest in Manitoba, Canada. Multiple linear regression was used to fit models that combined spectral reflectance data from CASI and indices computed from the SLICER canopy height profile. Three models explained 63-79% of the measured variation of feathermoss cover while three models explained 69-92% of the measured variation of sphagnum cover. Root mean square errors ranged from 3-15% when predicting feathermoss, sphagnum, and total moss ground cover. The results from this case study warrant further testing for a wider range of boreal forest types and geographic regions.     

Discrimination of vegetation strata in a multi-layered Mediterranean forest ecosystem using height and intensity information derived from airborne laser scanning

Height and intensity information derived from Airborne Laser Scanning (ALS) was used to obtain a quantitative vertical stratification of vegetation in a multi-layered Mediterranean ecosystem. A new methodology for the separation of different vegetation strata was implemented using supervised classification of a two-dimensional feature space spanned by ALS return height (terrain corrected) and intensity. The classification was carried out using Gaussian mixture models tuned on a control plot. The approach was validated using extensive field measurements from treated plots, ranging from single vegetation strata to a more complex multi-layered ecosystem. Plot-level canopy profiles derived from ALS and from a geometric reconstruction based on field measurements were in very good agreement, with correlation coefficients ranging from 0.73 (for complex, 3-layered) to 0.96 (simple, single-layered). In addition, it was possible to derive plot-level information on layer height, vertical extent and coverage with absolute accuracies of some decimetres (simple plots) to a meter (complex plots) for both height and vertical extent and about 10 to 15% for layer coverage. The approach was then used to derive maps of the layer height, vertical extent and percentage of ground cover for a larger area, and classification accuracy was evaluated on a per-pixel basis. The method performed best for single-layered plots or dominant layers on multi-layered plots, obtaining an overall accuracy of 80 to 90%. For subdominant layers in the more complex plots, accuracies obtained were as low as 48%.Our results demonstrate the possibility of deriving qualitative (presence and absence of specific vegetation layers) and quantitative, physical data (height, vertical extent and ground cover) describing the vertical structure of complex multi-layered forest ecosystems using ALS-based height and intensity information.     

Landscape modelling using integrated airborne multi-spectral and laser scanning data

By integrating multi-spectral and elevation data from airborne sensors (CASI and ALTM) and adopting a parcel-based approach, a progression is achieved from land-cover classification to landscape modelling. This work involved data integration, per-parcel classification, knowledge-based correction and the derivation of landscape objects. For a 1 km 2 study area, a 14 land-cover class vector dataset was generated in which the parcels relate to landscape objects and contain information on their structure and 'terrain' context. At a 1 m spatial resolution, the correspondence between land-cover mapped using the airborne sensor data and identified by Countryside Survey 2000 field surveyors was 88%.     

Geo-referencing forest field plots by co-registration of terrestrial and airborne laser scanning data

Remote sensing plays an important role within the field of forest inventory. Airborne laser scanning (ALS) has become an effective tool for acquiring forest inventory data. In most ALS-based forest inventories, accurately positioned field plots are used in the process of relating ALS data to field-observed biophysical properties. The geo-referencing of these field plots is typically carried out by means of differential global navigation satellite systems (dGNSS), and often relies on logging times of 15–20 min to ensure adequate accuracy under different forest conditions. Terrestrial laser scanning (TLS) has been proposed as a possible tool for collection of field data in forest inventories and can facilitate rapid acquisition of these data. In the present study, a novel method for co-registration of TLS and ALS data by posterior analysis of remote-sensing data – rather than using dGNSS – was proposed and then tested on 71 plots in a boreal forest. The method relies on an initial position obtained with a recreational-grade GPS receiver, in addition to analysis of the ALS and TLS data. First, individual tree positions were derived from the remote-sensing data. A search algorithm was then used to find the best match for the TLS-derived trees among the ALS-derived trees within a search area, defined relative to the initial position. The accuracy of co-registration was assessed by comparison with an accurately measured reference position. With a search radius of 25 m and using low-density ALS data (0.7 points m^?2), 82% and 51% of the TLS scans were co-registered with positional errors within 1 m and 0.5 m, respectively. By using ALS data of medium density (7.5 points m^?2), 87% and 78% of the scans were co-registered with errors within 1 m and 0.5 m of the reference position, respectively. These results are promising and the method can facilitate rapid acquisition and geo-referencing of field data. Robust methods to identify and handle erroneous matches are, however, required before it is suitable for operational use.     

Airborne laser scanning for riverbank erosion assessment

Worldwide, rivers and streams are negatively impacted by sedimentation. However, there are few broad scale techniques for quantifying the sources of sediment, i.e. upland vs. river bank erosion. This research was designed to evaluate the use of airborne LIDAR for characterizing sediment and phosphorus contributions from river bank erosion. The evaluation was done on the main stem of the Blue Earth River in southern Minnesota. Detailed topographic data were collected on an annual basis in April 2001 and 2002 over a 56 km length of the river with a helicopter mounted Topeye laser system. The raw database included X, Y, Z coordinates of laser returns sampled from the river valley with a density of 1-3.3 elevations per m². Uniform 1 m bare earth digital elevation models were constructed by stripping vegetation laser returns and interpolation. The two models were differenced to determine volume change over time, which was then converted to mass wasting by multiplying volume change with bulk density. Mass wasting rates were further converted to sediment load based on percentage of transportable material in the bank strata. The average difference between LIDAR measured elevations and RTK GPS surveyed elevations on 5 highway bridge surfaces was 2.5 and 8.8 cm for the 2001 and 2002 scans, respectively. The elevation errors were quasi-normally distributed with standard deviation of 6.7 and 6.1 cm for 2001 and 2002, respectively. No elevation or planimetric corrections were made to the laser data before calculating mass wasting rates because it was not possible to determine the source of error or if it was uniform within and between scans. The mass wasting estimate from the LIDAR surveys varied from 23% to 56% of the sediment mass transported past the downstream gauging station depending on the range of textural material that was entrained once in the river. These estimates are in the range of values reported in the literature. Total P contribution due to bank erosion from the river reach was estimated to be 201 t/yr.     

Tree species differentiation using intensity data derived from leaf-on and leaf-off airborne laser scanner data

Tree species identification is important for a variety of natural resource management and monitoring activities including riparian buffer characterization, wildfire risk assessment, biodiversity monitoring, and wildlife habitat assessment. Intensity data recorded for each laser point in a LIDAR system is related to the spectral reflectance of the target material and thus may be useful for differentiating materials and ultimately tree species. The aim of this study is to test if LIDAR intensity data can be used to differentiate tree species. Leaf-off and leaf-on LIDAR data were obtained in the Washington Park Arboretum, Seattle, Washington, USA. Field work was conducted to measure tree locations, tree species and heights, crown base heights, and crown diameters of individual trees for eight broadleaved species and seven coniferous species. LIDAR points from individual trees were identified using the field-measured tree location. Points from adjacent trees within a crown were excluded using a procedure to separate crown overlap. Mean intensity values of laser returns within individual tree crowns were compared between species. We found that the intensity values for different species were related not only to reflective properties of the vegetation, but also to a presence or absence of foliage and the arrangement of foliage and branches within individual tree crowns. The classification results for broadleaved and coniferous species using linear discriminant function with a cross validation suggests that the classification rate was higher using leaf-off data (83.4%) than using leaf-on data (73.1%), with highest (90.6%) when combining these two LIDAR data sets. The result also indicates that different ranges of intensity values between two LIDAR datasets didn't affect the result of discriminant functions. Overall results indicate that some species and species groups can be differentiated using LIDAR intensity data and implies the potential of combining two LIDAR datasets for one study.     

Floodplain roughness parameterization using airborne laser scanning and spectral remote sensing

Floodplain roughness parameterization is one of the key elements of hydrodynamic modeling of river flow, which is directly linked to exceedance levels of the embankments of lowland fluvial areas. The present way of roughness mapping is based on manually delineated floodplain vegetation types, schematized as cylindrical elements of which the height (m) and the vertical density (the projected plant area in the direction of the flow per unit volume, m^− 1) have to be assigned using a lookup table. This paper presents a novel method of automated roughness parameterization. It delivers a spatially distributed roughness parameterization in an entire floodplain by fusion of CASI multispectral data with airborne laser scanning (ALS) data. The method consists of three stages: (1) pre-processing of the raw data, (2) image segmentation of the fused data set and classification into the dominant land cover classes (KHAT = 0.78), (3) determination of hydrodynamic roughness characteristics for each land cover class separately. In stage three, a lookup table provides numerical values that enable roughness calculation for the classes water, sand, paved area, meadows and built-up area. For forest and herbaceous vegetation, ALS data enable spatially detailed analysis of vegetation height and density. The hydrodynamic vegetation density of forest is mapped using a calibrated regression model. Herbaceous vegetation cover is further subdivided in single trees and non-woody vegetation. Single trees were delineated using a novel iterative cluster merging method, and their height is predicted (R² = 0.41, rse = 0.84 m). The vegetation density of single trees was determined in an identical way as for forest. Vegetation height and density of non-woody herbaceous vegetation were also determined using calibrated regression models. A 2D hydrodynamic model was applied with the results of this novel method, and compared with a traditional roughness parameterization approach. The modeling results showed that the new method is well able to provide accurate output data. The new method provides a faster, repeatable, and more accurate way of obtaining floodplain roughness, which enables regular updating of river flow models.     

Modeling MODIS LAI time series using three statistical methods

Leaf Area Index (LAI) is one of the most important variables characterizing land surface vegetation and dynamics. Many satellite data, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), have been used to generate LAI products. It is important to characterize their spatial and temporal variations by developing mathematical models from these products. In this study, we aim to model MODIS LAI time series and further predict its future values by decomposing the LAI time series of each pixel into several components: trend, intra-annual variations, seasonal cycle, and stochastic stationary or irregular parts. Three such models that can characterize the non-stationary time series data and predict the future values are explored, including Dynamic Harmonics Regression (DHR), STL (Seasonal-Trend Decomposition Procedure based on Loess), and Seasonal ARIMA (AutoRegressive Intergrated Moving Average) (SARIMA). The preliminary results using six years (2001-2006) of the MODIS LAI product indicate that all these methods are effective to model LAI time series and predict 2007 LAI values reasonably well. The SARIMA model gives the best prediction, DHR produces the smoothest curve, and STL is more sensitive to noise in the data. These methods work best for land cover types with pronounced seasonal variations.