Large-scale leaf area index inversion algorithms from high-resolution airborne imagery

Large-scale leaf area index (LAI) inversion algorithms were developed to determine the LAI of a forest located in Gatineau Park, Canada, using high-resolution colour and colour infrared (CIR) digital airborne imagery. The algorithms are parameter-independent and developed based on the principles of optical field instruments for gap fraction measurements. Cloud-free colour and CIR images were acquired on 21 August 2007 with 35 and 60 cm nominal ground pixel size, respectively. Normalized Difference Vegetation Index (NDVI), maximum likelihood and object-oriented classifications, and principal component analysis (PCA) methods were applied to calculate the mono-directional gap fraction. Subsequently, LAI was derived from inversion and compared with ground measurements made in 54 plots of 20 by 20 m using hemispherical photography between 10 and 20 August 2007. There was high inter-correlation (the Pearson correlation coefficient, R > 0.5, p < 0.01) among LAI values inverted using the classifications and PCA methods, but neither were highly correlated with LAI inverted from the NDVI method. LAI inverted from the NDVI-based gap fraction significantly correlated with ground-measured LAI (R?=?0.63, root mean square error (RMSE) = 0.52), while LAI inverted from the classification and PCA-derived gap fraction showed poor correlation with ground-measured LAI. Consequently, the NDVI method was used to invert LAI for the whole study area and produce a 20‐m resolution LAI map.     

Estimating the leaf area index in Indian tropical forests using Landsat-8 OLI data

Leaf area index (LAI) is a key vegetation biophysical parameter and is extensively used in modelling of phenology, primary production, light interception, evapotranspiration, carbon, and nitrogen dynamics. In the present study, we attempt to spatially characterize LAI for natural forests of Western Ghats India, using ground based and Landsat-8 Operational Land Imager (OLI) sensor satellite data. For this, 41 ground-based LAI measurements were carried out across a gradient of tropical forest types, viz. dry, moist, and evergreen forests using LAI-2200 plant canopy analyser, during the month of March 2015. Initially, measured LAI values were regressed with 15 spectral variables, including nine spectral vegetation indices (SVIs) and six Landsat-8 surface reflectance (ρ) variables using univariate correlation analysis. Results showed that the red (ρ_red), near-infrared (ρ_NIR), shortwave infrared (ρ_SWIR1, ρ_SWIR2) reflectance bands (R² > 0.6), and all SVIs (R² > 0.7) except simple ratio (SR) have the highest and second highest coefficient of determination with ground-measured LAI. In the second step, to select significant (high R², low root mean square error (RMSE), and p-level < 0.05) SVIs to determine the best representative model, stepwise multiple linear regression (SMLR) was implemented. The results indicate that the SMLR model predicted LAI with better coefficient of determination (R² = 0.83, RMSE = 0.78) using normalized difference vegetation index, enhanced vegetation index, and soil-adjusted vegetation index variables compared to the univariate approach. The predicted SMLR model was used to estimate a spatial map of LAI. It is desirable to evaluate the stability and potentiality of regional LAI models in natural forest ecosystems against the operationally accepted Moderate Resolution Imaging Spectroradiometer (MODIS) global LAI product. To do this, the Landsat-8 pixel-based LAI map was resampled to 1 km resolution and compared with the MODIS derived LAI map. Results suggested that Landsat-8 OLI-based VIs provide significant LAI maps at moderate resolution (30 m) as well as coarse resolution (1 km) for regional climate models.     

Leaf area index mapping in northern Canada

Leaf area index (LAI) is an important structural vegetation parameter that is commonly derived from remotely sensed data. It has been used as a reliable indicator for vegetation's cover, status, health and productivity. In the past two decades, various Canada-wide LAI maps have been generated by the Canada Centre for Remote Sensing (CCRS). These products have been produced using a variety of very coarse satellite data such as those from SPOT VGT and NOAA AVHRR satellite data. However, in these LAI products, the mapping of the Canadian northern vegetation has not been performed with field LAI measurements due in large part to scarce in situ measurements over northern biomes. The coarse resolution maps have been extensively used in Canada, but finer resolution LAI maps are needed over the northern Canadian ecozones, in particular for studying caribou habitats and feeding grounds.

In this study, a new LAI algorithm was developed with particular emphasis over northern Canada using a much finer resolution of remotely sensed data and in situ measurements collected over a wide range of northern arctic vegetation. A statistical relationship was developed between the in situ LAI measurements collected over vegetation plots in northern Canada and their corresponding pixel spectral information from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. Furthermore, all Landsat TM and ETM+ data have been pre-normalized to NOAA AVHRR and SPOT VGT data from the growing season of 2005 to reduce any seasonal or temporal variations. Various spectral vegetation indices developed from the Landsat TM and ETM?+?data were analysed in this study. The reduced simple ratio index (RSR) was found to be the most robust and an accurate estimator of LAI for northern arctic vegetation. An exponential relationship developed using the Theil–Sen regression technique showed an R ² of 0.51 between field LAI measurement and the RSR. The developed statistical relationship was applied to a pre-existing Landsat TM 250 m resolution mosaic for northern Canada to produce the final LAI map for northern Canada ecological zones. Furthermore, the 250 m resolution LAI estimates, per ecological zone, were almost generally lower than those of the CCRS Canada-wide VGT LAI maps for the same ecozones. Validation of the map with LAI field data from the 2008 season, not used in the derivation of the algorithm, shows strong agreement between the in situ LAI measurement values and the map-estimated LAI values.     

Leaf area index estimation of bamboo forest in Fujian province based on IRS P6 LISS 3 imagery

Leaf area index (LAI) is an important surface biophysical parameter as an input to many process-oriented ecosystem models. Much work has been reported in the literature on LAI estimation in boreal forests using remotely sensed imagery. However, few if any explicit LAI retrieval studies on bamboo forests in Asian subtropical monsoon-climate regions based on remote sensing technology have been performed. Our goal is to carry out a comparative study on the LAI estimation methods of bamboo forest in Fujian province, China, based on IRS P6 LISS 3 imagery. Both the traditional empirical–statistical approach and the newly proposed normalized distance (ND) method were employed in this study, and a total of 18 modelling parameters were regressed against ground-based LAI measurements. The results show that simple ratio (SR) is the best predictor for LAI estimation in this study area, with the highest R ² (coefficient of determination) value of 0.68; modified simple ratio (MSR) and normalized difference vegetation index (NDVI) ranked second and third, respectively. The good performance of these three vegetation indices (VIs) can be explained by the ratioing principle. The overall good modelling performance of the ND method in our study area also indicates it is a promising method.     

Validation of LAI and assessment of winter wheat status using spectral data and vegetation indices from SPOT VEGETATION and simulated PROBA-V images

Due to the information gap between the VEGETATION sensors and Sentinel-3 mission, the Belgian state decided to build a small satellite, Project for Onboard Autonomy-Vegetation (PROBA-V), to ensure the continuity of the data record for vegetation studies. In this study, simulated PROBA-V data generated by the Landsat Thematic Mapper (TM) were used to evaluate the potential of this mission to assess winter wheat status. The root mean square error (RMSE) of PROBA-V's leaf area index (LAI), which was generated using the exponential method and the interpolation method, is 0.33 and 0.96 for March 2011 and 1.40 and 3.33 for May 2011, respectively. Système Pour l'Observation de la Terre (SPOT) VEGETATION's LAI does not show a significant relationship with the reference LAI values except for the LAI values during the stem elongation 100% phenological stage generated using the exponential method (correlation coefficient, r = 0.91; p = 0.01). For the tillering and stem elongation 100% phenological stages, linear regression models for the fraction of absorbed photosynthetically active radiation (FAPAR) with PROBA-V's normalized difference vegetation index (NDVI) were developed (coefficient of determination, R ², of 0.94 and 0.88). Exponential models for LAI (R ² of 0.91 and 0.93) and fresh weight of above-ground biomass (AGBf) (R ² of 0.90 and 0.93) with PROBA-V's near-infrared (NIR) and visible and near-infrared bands (VNIR B2) were developed accordingly. The assessment of winter wheat status showed that the highest and the lowest values of PROBA-V's simulated data (SD), i.e. NDVI, normalized difference water index (NDWI), and LAI of Field 2 and Field 4, correspond to the ground-measured biometric parameters.     

Inverting a forest canopy reflectance model to retrieve the overstorey and understorey leaf area index for forest stands

In this article, the Kuusk–Nilson forest reflectance and transmittance (FRT) model was inverted to retrieve the overstorey and understorey leaf area index (OU-LAI) of forest stands in the Longmenhe forest nature reserve in China. Data from detailed sample sites were collected in 30 forest stands representing the typical vegetation community in the study area. An uncertainty and sensitivity matrix (USM) was used to analyse the sensitivity of the FRT model parameters based on these data. The results indicated that overstorey LAI strongly influenced stand reflectance, whereas understorey LAI had a much lower impact. To predict OU-LAI in forest stands, FRT model inversion is carried out by minimizing a merit function that provides a measure of the difference between the reflectance simulated by the FRT model and the reflectance originating from optimal band selection of Hyperion data. Various combinations of Hyperion bands were tested to evaluate the most effective wavelengths for the inversion of OU-LAI. The best estimates from 17 Hyperion bands (5 VIS, 8 NIR, 4 SWIR) by the FRT model inversion showed an R ²?=?0.41 and RMSE/mean?=?0.21 for overstorey LAI and R ²?=?0.49 and RMSE/mean?=?0.91 for understorey LAI. Advantages and disadvantages of FRT inversion for retrieval OU-LAI combined with Hyperion data are discussed.     

Validation of MERIS LAI and FAPAR products for winter wheat-sown test fields in North-East Bulgaria

Progress in deriving land surface biophysical parameters in a spatially explicit manner using remotely sensed data has greatly enhanced our ability to model ecosystem processes and monitor crop development. A multitude of satellite sensors and algorithms have been used to generate ready-to-use maps of various biophysical parameters. Validation of these products for different vegetation types is needed to assess their reliability and consistency. While most of the current satellite biophysical products have spatial resolution of one kilometre, a recent effort utilizing data from the Medium Resolution Imaging Spectrometer (MERIS) provided leaf area index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and other canopy parameters in a resolution as fine as 300 m over the European continent. This resolution would be more appropriate for application at the regional scale, particularly for crop monitoring. This higher-resolution MERIS product has been evaluated in a limited number of studies to date. This article aims to validate LAI and FAPAR from the MERIS 10-day composite BioPar BP-10 product over winter wheat fields in northeast Bulgaria. The ground measurements of LAI and FAPAR were up-scaled and 30 m resolution reference raster layers were created using empirical relationships with Landsat TM (RMSE = 0.06 and RMSE = 0.22 for FAPAR and LAI, respectively). MERIS FAPAR and LAI were found to have significant correlation with FAPAR and LAI from the reference raster layers (R² = 0.84 and R² = 0.78, respectively). When MERIS Green LAI was calculated (incorporating the fraction of vegetation and brown vegetation cover from the BioPar BP-10 product), better correspondence with LAI values from the reference raster layer was achieved, with RMSE and bias reduced by 30–35%. The results from this study confirm the findings of previous validations showing that MERIS Green LAI tends to overestimate LAI values lower than 1. As a conclusion of the study, the BioPar BP-10 product was found to provide reliable estimates of FAPAR and acceptably accurate estimates of LAI for winter wheat crops in North-East Bulgaria.     

Mapping daily leaf area index at 30 m resolution over a meadow steppe area by fusing Landsat,Sentinel-2A and MODIS data

ABSTRACT

The leaf area index (LAI) is a key vegetation canopy structure parameter and is closely associated with vegetation photosynthesis, transpiration, and energy balance. Developing a landscape-scale LAI dataset with a high temporal resolution (daily) is essential for capturing rapidly changing vegetation structure at field scales and supporting regional biophysical modeling efforts. In this study, two daily 30 m LAI time series from 2014 to 2016 over a meadow steppe site in northern China were generated using a spatial and temporal adaptive re?ectance fusion model (STARFM) combined with an LAI retrieval radiative transfer model (PROSAIL). Gap-filled Landsat 7, Landsat 8 and Sentinel-2A surface reflectance (SR) images were used to generate fine-resolution LAI maps with the PROSAIL look-up table method. Two daily 500 m moderate-resolution imaging spectroradiometer (MODIS) LAI product-the existing MCD15A3H LAI product and one was generated from the MCD43A4 SR product and the PROSAIL model, were used to provide temporally continuous LAI variations. The STARFM model was then used to fuse the fine-resolution LAI maps with the two 500 m LAI products separately to generate two daily 30 m LAI time series. Both results were assessed for three types of pasture (mowed pasture, grazing pasture, and fenced pasture) using ground measurements from 2014–2015. The results showed that the PROSAIL-generated LAI maps all exhibited a high accuracy, and the root mean squared errors (RMSEs) for the Landsat 7 LAI and Landsat 8 LAI compared to the ground-measured LAI were 0.33 and 0.28 respectively. The Landsat LAI maps also showed good agreement and similar spatial patterns with the Sentinel-2A LAI with mean differences between ± 0.5. The MCD43A4_PROSPECT LAI product exhibited similar seasonal variability to the ground measurements and to the Landsat and Sentinel-2A LAIs, and these data are also smoother and contain fewer noisy points than the gap-filled MCD15A3H LAI product. Compared to the ground measurements, the daily 30 m LAI time series fused from the fine-resolution LAI maps and PROSPECT generated MODIS LAI product demonstrated better performance with an RMSE of 0.44 and a mean absolute error (MAE) of 0.34, which is an improvement from the LAI time series fused from the fine-resolution LAI maps and the existing MCD15A3H LAI product (RMSE of 0.56 and MAE of 0.42). The latter dataset also exhibited abnormal temporal fluctuations, which may have been caused by the interpolation method. The results also demonstrated the very good performance of the STARFM model in grazing and mowed pasture with homogeneous surfaces compared to fenced pasture with smaller patch sizes. The Sentinel-2A data offers increased landscape vegetation observation frequency and provides temporal information about canopy changes that occur between Landsat overpass dates. The scheme developed in this study can be used as a reference for regional vegetation dynamic studies and can be applied to larger areas to improve grassland modeling efforts.     

Accuracy assessment of fraction of vegetation cover and leaf area index estimates from pragmatic methods in a cropland area

The fraction of vegetation cover (FVC) and the leaf area index (LAI) are important parameters for many agronomic, ecological and meteorological applications. Several in‐situ and remote sensing techniques for estimating FVC and LAI have been developed in recent years. In this paper, the uncertainty of in‐situ FVC and LAI measurements was evaluated by comparing estimates from LAI‐2000 and digital hemispherical photography (DHP). The accuracy achieved with a spectral mixture analysis algorithm and two vegetation indices‐based methods was assessed using atmospherically corrected Landsat Thematic Mapper (TM) data over the Barrax cropland area where the European Space Agency (ESA) SENtinel‐2 and FLuorescence EXperiment (SEN2FLEX) field campaign was carried out in July 2005. The results indicate that LAI‐2000 and DHP performances are comparable, with uncertainties of 5% for FVC and 15% for effective LAI. The selected remote sensing methods are shown to be consistent, with a notable overall accuracy (root mean square error, RMSE) of 0.07 (10% in relative terms) for FVC and 0.8 (30%) for LAI. Similar bounds were found on upscaling in‐situ measurements with empirical transfer functions (TFs). These results suggest that the pragmatic methods considered applied at high resolution with minimum calibration data could be useful for mapping FVC and LAI in the study area, reducing in‐situ labour‐intensive characterization necessities for validation studies.     

A modified vegetation backscattering model for leaf area index retrieval from SAR time series

In this study, a semi-empirical modified vegetation backscattering model was developed to retrieve leaf area index (LAI) based on multi-temporal Radarsat-2 data and ground observations collected in China. This model combined the contribution of the vegetation and bare soil at the pixel level by adding vegetation coverage and the influence of bare soil on the total backscatter coefficients. Then, a lookup table algorithm was applied to calculate the value of vegetation water content and retrieve the LAI based on the linear relationship between the vegetation water content and LAI. The results indicated that the modified model was effective in evaluating and reproducing the total backscatter coefficients. Meanwhile, the LAI retrieval was well conducted with coefficient of determination (R²) and root mean square error (RMSE) of 89% and 0.19 m² m^?2, respectively. Additionally, this method offers insight into the required application accuracy of LAI retrieval in the agricultural regions.     

Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model

Leaf area index (LAI) is an important structural property of vegetation canopy and is also one of the basic quantities driving the algorithms used in regional and global biogeochemical, ecological and meteorological applications. LAI can be estimated from remotely sensed data through the vegetation indices (VI) and the inversion of a canopy radiative transfer (RT) model. In recent years, applications of the genetic algorithms (GA) to a variety of optimization problems in remote sensing have been successfully demonstrated. In this study, we estimated LAI by integrating a canopy RT model and the GA optimization technique. This method was used to retrieve LAI from field measured reflectance as well as from atmospherically corrected Landsat ETM+ data. Four different ETM+ band combinations were tested to evaluate their effectiveness. The impacts of using the number of the genes were also examined. The results were very promising compared with field measured LAI data, and the best results were obtained with three genes in which the R² is 0.776 and the root-mean-square error (RMSE) 1.064.     

Estimation of forest leaf area index from SPOT imagery using NDVI distribution over forest stands

Leaf area index (LAI) is a key parameter of atmosphere–vegetation exchanges, affecting the net ecosystem exchange and the productivity. At regional or continental scales, LAI can be estimated by remotely‐sensed spectral vegetation indices (SVI). Nevertheless, relationships between LAI and SVI show saturation for LAI values greater than 3–5. This is one of the principal limitations of remote sensing of LAI in forest canopies. In this article, a new approach is developed to determine LAI from the spatial variability of radiometric data. To test this method, in situ measurements for LAI of 40 stands, with three dominant species (European beech, oak and Scots pine) were available over 5 years in the Fontainebleau forest near Paris. If all years and all species are pooled, a good linear relationship without saturation is founded between average stand LAI measurements and a model combining the logarithm of the standard deviation and the skewness of the normalized difference vegetation index (NDVI) (R ² = 0.73 rmse = 1.08). We demonstrate that this relation can be slightly improved by using different linear models for each year and each species (R ² = 0.82 rmse = 0.86), but the standard deviation is less sensitive to the species and the year effects than the mean NDVI and is therefore a performing index.     

Discrete return lidar-based prediction of leaf area index in two conifer forests

Leaf area index (LAI) is a key forest structural characteristic that serves as a primary control for exchanges of mass and energy within a vegetated ecosystem. Most previous attempts to estimate LAI from remotely sensed data have relied on empirical relationships between field-measured observations and various spectral vegetation indices (SVIs) derived from optical imagery or the inversion of canopy radiative transfer models. However, as biomass within an ecosystem increases, accurate LAI estimates are difficult to quantify. Here we use lidar data in conjunction with SPOT5-derived spectral vegetation indices (SVIs) to examine the extent to which integration of both lidar and spectral datasets can estimate specific LAI quantities over a broad range of conifer forest stands in the northern Rocky Mountains. Our results show that SPOT5-derived SVIs performed poorly across our study areas, explaining less than 50% of variation in observed LAI, while lidar-only models account for a significant amount of variation across the two study areas located in northern Idaho; the St. Joe Woodlands (R² = 0.86; RMSE = 0.76) and the Nez Perce Reservation (R² = 0.69; RMSE = 0.61). Further, we found that LAI models derived from lidar metrics were only incrementally improved with the inclusion of SPOT 5-derived SVIs; increases in R² ranged from 0.02–0.04, though model RMSE values decreased for most models (0–11.76% decrease). Significant lidar-only models tended to utilize a common set of predictor variables such as canopy percentile heights and percentile height differences, percent canopy cover metrics, and covariates that described lidar height distributional parameters. All integrated lidar-SPOT 5 models included textural measures of the visible wavelengths (e.g. green and red reflectance). Due to the limited amount of LAI model improvement when adding SPOT 5 metrics to lidar data, we conclude that lidar data alone can provide superior estimates of LAI for our study areas.     

基于GF-1 WFV影像和机器学习算法的玉米叶面积指数估算

目的 叶面积指数（LAI）是重要的植被生物理化参数,对农作物长势和产量预测具有重要研究意义。基于物理模型和经验模型的LAI估算方法被认为是当前最常用的方法,但两种方法的估算效率和精度有限。近年来,机器学习算法在遥感监测领域广泛应用,算法具有描述非线性数据拟合、融合更多辅助信息的能力,为了评价机器学习算法在玉米LAI遥感估算中的适用性,本文分析比较了随机森林和BP神经网络算法估算玉米LAI的能力,并与传统经验模型进行了比较。方法 以河北省怀来县东花园镇为研究区,基于野外实测玉米LAI数据,结合同时期国产高分卫星（GF1-WFV影像）,首先分析了8种植被指数与LAI的相关性,进而采用保留交叉验证的方式将所有样本数据分为两部分,65%的数据作为模型训练集,35%作为验证集,重复随机分为3组,构建以8种植被指数为自变量,对应LAI值为因变量的RF模型、BP神经网络模型及传统经验模型。采用决定系数R²和均方根误差（RMSE）作为模型评价指标。结果 8种植被指数与LAI的相关性分析表明所有样本数据中,实测LAI值与各植被指数均在（P<0.01）水平下极显著相关,且相关系数均高于0.5;将3组不同样本数据在随机森林、BP神经网络算法中多次训练,并基于验证数据集进行估算精度检验,经验模型采用训练数据集建模,验证数据集检验,结果表明,RF模型表现出了较强的预测能力,LAI预测值与实测值R²分别为0.681、0.757、0.701,均高于BP模型（0.504、0.589、0.605）和经验模型（0.492、0.557、0.531）,对应RMSE分别为0.264、0.292、0.259;均低于BP模型（0.284、0.410、0.283）和经验模型（0.541、0.398、0.306）。结论 研究表明,RF算法能更好地进行玉米LAI遥感估算,为快速准确进行农作物LAI遥感监测提供了技术参考。     

Model development and comparison to predict softwood and hardwood per cent cover using high and medium spatial resolution imagery

The recent availability of high spatial resolution multispectral scanners provides an opportunity to adapt existing methods and test models to derive spatially explicit forest type and per cent cover information at the Landsat pixel level. A regression modelling methodology was applied for scaling‐up high resolution (IKONOS) to medium spatial resolution satellite imagery (Landsat) to predict softwood and hardwood forest type and density (per cent cover) in a northern Maine study area. Regression relationships (63 different models) were developed and compared. The model variables included vegetation indices and several date (season) combinations of Landsat Enhanced Thematic Mapper Plus (ETM+) imagery (August, September, October and May).

A model incorporating all variables from four dates of Landsat ETM+ imagery produced the highest coefficient of variation in predicting both softwood (0.655) and hardwood cover (0.66). The addition of vegetation indices with the six ETM+ reflected bands did not significantly improve or detract from the regression relationships for any of the multi‐date or single date models examined. A two‐date combination of October and May variables provided an acceptable (and arguably more cost‐effective) model as the adjusted R ² value was 0.645 for softwood and 0.649 for hardwood. A significant result was that all single‐date models produced inferior results with a sharp drop in adjusted R ², compared with the multi‐date seasonal models. This research has demonstrated that the regression models including multi‐date variables produce good results and can provide spatially explicit forest type and stand structure data that has been difficult or infeasible to obtain from medium spatial resolution imagery using traditional classification methods.     

Monitoring boreal forest leaf area index across a Siberian burn chronosequence: a MODIS validation study

Landscapes containing differing amounts of ecological disturbance provide an excellent opportunity to validate and better understand the emerging Moderate Resolution Imaging Spectrometer (MODIS) vegetation products. Four sites, including 1‐year post‐fire coniferous, 13‐year post‐fire deciduous, 24‐year post‐fire deciduous, and >100 year old post‐fire coniferous forests, were selected to serve as a post‐fire chronosequence in the central Siberian region of Krasnoyarsk (57.3°N, 91.6°E) with which to study the MODIS leaf area index (LAI) and vegetation index (VI) products. The collection 4 MODIS LAI product correctly represented the summer site phenologies, but significantly underestimated the LAI value of the >100 year old coniferous forest during the November to April time period. Landsat 7‐derived enhanced vegetation index (EVI) performed better than normalized difference vegetation index (NDVI) to separate the deciduous and conifer forests, and both indices contained significant correlation with field‐derived LAI values at coniferous forest sites (r ² = 0.61 and r ² = 0.69, respectively). The reduced simple ratio (RSR) markedly improved LAI prediction from satellite measurements (r ² = 0.89) relative to NDVI and EVI. LAI estimates derived from ETM+ images were scaled up to evaluate the 1 km resolution MODIS LAI product; from this analysis MODIS LAI overestimated values in the low LAI deciduous forests (where LAI<5) and underestimated values in the high LAI conifer forests (where LAI>6). Our results indicate that further research on the MODIS LAI product is warranted to better understand and improve remote LAI quantification in disturbed forest landscapes over the course of the year.     

Estimation of forest Leaf Area Index using remote sensing and GIS data for modelling net primary production

Ecosystem models can be used to estimate potential net primary production (pNPP) using GIS data, and remote sensing input of actual forest leaf area to such models can provide estimates of current actual net primary production (aNPP) . Comparisons of pNPP and aNPP for a given site or regional landscape can be used to identify forest stands for different management treatments, and may provide new information on wildlife habitat, forest diversity and growth characteristics. Leaf area estimates may be obtained from satellite imagery through correlation with physiologically-based vegetation indices such as the Normalized Difference Vegetation Index (NDVI). However, in areas with high Leaf Area Index (LAI), vegetation indices usually saturate at leaf areas greater than about 4. In predominantly deciduous (hardwood) and mixedwood stands remote sensing estimates may be influenced by understory and other factors. We examined digital Landsat TM imagery and GIS data in the Fundy Model Forest of southeastern New Brunswick to determine relations to forest leaf area index within different stand structures or covertypes. The image data were stratified using GIS covertype information prior to development of LAI predictive equations using spectral reflectance, and the prediction of LAI from Landsat TM imagery was improved with reference to estimates of stem density which are standard forest inventory information contained in GIS databases. Actual stand LAI was compared to assumed maximum LAI values for several species and sites using an ecosystem process model (BIOME-BGC) which relies on climate, soils and topographic information also obtained from the GIS. Subsequent comparison of pNPP and aNPP revealed that even disturbed sites in this environment can reach close to maximum site potential. Specific sites with suboptimal species composition were identified. A future refinement of this approach is to classify the imagery independently of the GIS, which assumes a homogeneous covertype for each polygon in the system, and thus improve still further the aNPP estimates through higher covertype and LAI estimation accuracy.     

Evaluating image-based estimates of leaf area index in boreal conifer stands over a range of scales using high-resolution CASI imagery

Leaf area index (LAI) is an important surface biophysical parameter as a measure of vegetation cover, vegetation productivity, and as an input to ecosystem process models. Recently, a number of coarse-scale (1-km) LAI maps have been generated over large regions including the Canadian boreal forest. This study focuses on the production of fine-scale (≤30-m) LAI maps using the forest light interaction model-clustering (FLIM-CLUS) algorithm over selected boreal conifer stands and the subsequent comparison of the fine-scale maps to coarse-scale LAI maps synthesized from Landsat TM imagery. The fine-scale estimates are validated using surface LAI measurements to give relative root mean square errors of under 7% for jack pine sites and under 14% for black spruce sites. In contrast, finer scale site mean LAI ranges between 49% and 86% of the mean of surface estimates covering only part of the sites and 54% to 110% of coarse-scale site mean LAI. Correlations between fine-scale and coarse-scale estimates range from near 0.5 for 30-m coarse-scale images to under 0.3 to 1-km coarse-scale images but increase to near 0.90 after imposing fine-scale zero LAI areas in coarse-scale estimates. The increase suggests that coarse-scale image-based LAI estimates require consideration of sub-pixel open areas. Both FLIM-CLUS and coarse-scale site mean LAI are substantially lower than surface estimates over northern sites. The assumption of spatially random residuals in regression-based estimates of LAI may not be valid and may therefore add to local bias errors in estimating LAI remotely. Differences between fine-scale airborne LAI maps and 30-m-scale Landsat TM LAI maps suggests that, for sparse boreal conifer stands, LAI maps produced from Landsat TM alone may not always be sufficient for validation of coarser scale LAI maps. In addition, previous studies may have used biased LAI estimates over the study site. Fine-scale spatial LAI maps offer one means of assessing and correcting for effects of sub-pixel open area patches and for characterising the spatial pattern of residuals in coarse-scale LAI estimates in comparison to the true distribution of LAI on the surface.     

Leaf area and clumping indices for a boreal mixed-wood forest: lidar,hyperspectral, and Landsat models

The leaf area index (LAI) and the clumping index (CI) provide valuable insight into the spatial patterns of forest canopies, the canopy light regime and forest productivity. This study examines the spatial patterns of LAI and CI in a boreal mixed-wood forest, using extensive field measurements and remote sensing analysis. The objectives of this study are to: (1) examine the utility of airborne lidar (light detection and ranging) and hyperspectral data to model LAI and clumping indices; (2) compare these results to those found from commonly used Landsat vegetation indices (i.e. the normalized difference vegetation index (NDVI) and the simple ratio (SR)); (3) determine whether the fusion of lidar data with Landsat and/or hyperspectral data will improve the ability to model clumping and LAI; and (4) assess the relationships between clumping, LAI and canopy biochemistry.

Regression models to predict CI were much stronger than those for LAI at the site. Lidar was the single best predictor of CI (r ² > 0.8). Landsat NDVI and SR also had a moderately strong predictive performance for CI (r ² > 0.68 with simple linear and non-linear regression forms), suggesting that canopy clumping can be predicted operationally from satellite platforms, at least in boreal mixed-wood environments. Foliar biochemistry, specifically canopy chlorophyll, carotenoids, magnesium, phosphorus and nitrogen, was strongly related to the clumping index. Combined, these results suggest that Landsat models of clumping could provide insight into the spatial distribution of foliar biochemistry, and thereby photosynthetic capacity, for boreal mixed-wood canopies. LAI models were weak (r ² < 0.4) unless separate models were used for deciduous and coniferous plots. Coniferous LAI was easier to model than deciduous LAI (r ² > 0.8 for several indices). Deciduous models of LAI were weaker for all remote sensing indices (r ² < 0.67). There was a strong, linear relationship between foliar biochemistry and LAI for the deciduous plots. Overall, our results suggest that broadband satellite indices have strong predictive performance for clumping, but that airborne hyperspectral or lidar data are required to develop strong models of LAI at this boreal mixed-wood site.     

Retrieval of crop chlorophyll content and leaf area index from decompressed hyperspectral data: the effects of data compression

The objective of this study is to evaluate whether the retrieval of the leaf chlorophyll content and leaf area index (LAI) for precision agriculture application from hyperspectral data is significantly affected by data compression. This analysis was carried out using the hyperspectral data sets acquired by Compact Airborne Spectrographic Imager (CASI) over corn fields at L'Acadie experimental farm (Agriculture and Agri-Food Canada) during the summer of 2000 and over corn, soybean and wheat fields at the former Greenbelt farm (Agriculture and Agri-Food Canada) in three intensive field campaigns during the summer of 2001. Leaf chlorophyll content and LAI were retrieved from the original data and the reconstructed data compressed/decompressed by the compression algorithm called Successive approximation multi-stage vector quantization (SAMVQ) at compression ratios of 20:1, 30:1, and 50:1. The retrieved products were evaluated against the ground-truth.In the retrieval of leaf chlorophyll content (the first data set), the spatial patterns were examined in all of the images created from the original and reconstructed data and were proven to be visually unchanged, as expected. The data measures R², absolute RMSE, and relative RMSE between the leaf chlorophyll content derived from the original and reconstructed data cubes, and the laboratory-measured values were calculated as well. The results show the retrieval accuracy of crop chlorophyll content is not significantly affected by SAMVQ at the compression ratios of 20:1, 30:1, and 50:1, relative to the observed uncertainties in ground truth values. In the retrieval of LAI (the second data set), qualitative and quantitative analyses were performed. The results show that the spatial and temporal patterns of the LAI images are not significantly affected by SAMVQ and the retrieval accuracies measured by the R², absolute RMSE, and relative RMSE between the ground-measured LAI and the estimated LAI are not significantly affected by the data compression either.