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.     

Seasonal reflectance trends of hemiboreal birch forests

MODIS, AVHRR and SPOT VEGETATION satellite images have recently been used to track coarse scale seasonal vegetation dynamics of boreal and temperate forests. However, the understanding of driving factors of reflectance seasonality at forest stand level is still in its infancy, and has only preliminarily been linked to, for example, forest structure or site fertility. We present results from a study on the seasonal reflectance trends of 145 hemiboreal birch stands in Estonia from budburst to initial senescence. A time series comprising 32 high resolution Landsat ETM+, TM and SPOT HRVIR, HRV images from April to September was assembled for analyzing empirical reflectance courses of birch stands. The most noteworthy seasonal reflectance dynamics were observed in the red and NIR channels, changes in the green and SWIR spectral channels were relatively small. The most stable period in stand reflectance in all the spectral channels occurred in midsummer i.e. when stand leaf area index (LAI) reached its highest level and changes in solar angle were the smallest. A twenty-day difference was observed between the reflectance development of birch stands growing on infertile and fertile sites. Next, to provide an explanation for the observed reflectance changes, we simulated the mean seasonal reflectance trajectories of the study stands at 10 day intervals for the same period using a radiative transfer model (FRT). Simulated seasonal reflectance courses for the different site fertility classes followed the general pattern of the measured courses. Simulation results indicated that the main driving factors for reflectance seasonality for all the site fertility classes in the red and green bands were stand LAI and leaf chlorophyll content, in the NIR band stand LAI, and in the SWIR band LAI and general water content. Finally, we discuss current limitations related to applying forest radiative transfer models in investigating the driving factors of seasonal reflectance changes in the boreal zone.     

Impact of understory vegetation on forest canopy reflectance and remotely sensed LAI estimates

Forest leaf area index (LAI), is an important variable in carbon balance models. However, understory vegetation is a recognized problem that limits the accuracy of satellite-estimated forest LAI. A canopy reflectance model was used to investigate the impact of the understory vegetation on LAI estimated from reflectance values estimated from satellite sensor data. Reflectance spectra were produced by the model using detailed field data as input, i.e. forest LAI, tree structural parameters, and the composition, distribution and reflectance of the forest floor. Common deciduous and coniferous forest types in southern Sweden were investigated. A negative linear relationship (r² = 0.6) was observed between field estimated LAI and the degree of understory vegetation, and the results indicated better agreement when coniferous and deciduous stands were analysed separately. The simulated spectra verified that the impact of the understory on the reflected signal from the top of the canopy is important; the reflectance values varying by up to ± 18% in the red and up to ± 10% in the near infra-red region of the spectra due to the understory. In order to predict the variation in LAI due to the understory vegetation, model inversions were performed where the input spectra were changed between the minimum, average and maximum reflectance values obtained from the forward runs. The resulting variation in LAI was found to be 1.6 units on average. The LAI of the understory could be predicted indirectly from simple stand data on forest characteristics, i.e. from allometric estimates, as an initial step in the process of estimating LAI. It is suggested here that compensation for the effect of the understory would improve the accuracy in the estimates of canopy LAI considerably.     

玉米叶面积指数与高光谱植被指数关系研究

探讨以不同的植被指数建立的高光谱模型对玉米叶面积指数LAI的反演精度。实测不同水肥耦合作用下，玉米冠层的高光谱反射率与叶面积指数（Leaf Area Index）数据，采用高光谱红光波段（631~760 nm）与近红外波段（760~1 074 nm）逐波段构建NDVI、RVI、DVI、TSAVI、PVI植被指数，分别找出与LAI具有最佳相关性波段组合的植被指数，建立玉米LAI估算模型。结果显示，与LAI具有佳相关性的波段组合分别是NDVI（R760，R990）、RVI（R760，R1001）、DVI（R677，R1070）、TSAVI（R 760，R 975）、PVI（R658，R966），它们反演玉米LAI的确定性系数分别：R²>0.72、R²>0.74、R²＝0.95、R²>0.79、R²>0.95。结果表明，在玉米的整个生长季的47个样本中，通过PVI和DVI方式建立的遥感估算模型能够较为准确地估算玉米LAI，TSAVI次之，NDVI、RVI稍差。     

The potential for integrating Sentinel 2 MSI with SPOT 5 HRG and Landsat 8 OLI imagery for monitoring semi-arid savannah woody cover

Multitemporal archived imagery enables the monitoring of savannah woody cover, for ecological purposes. Compatibility in multitemporal, multiple sensor image data would facilitate the monitoring. The decommissioning of SPOT 5 (Système Pour l’Observation de la Terre 5) left a void in multispectral imagery at the 10 m spatial resolution of its high-resolution geometric (HRG) sensor. The subsequent launch of Sentinel 2 presented an opportunity for data continuity to monitor the savannah woody cover, using equivalent 10 m resolution multispectral instrument (MSI) bands. This study examined the integration potential of Sentinel 2 MSI with the longer archive HRG and Landsat 8 (Land Satellite 8) Operational Land Imager (OLI) imagery, in assessing savannah woody cover. Images of three semi-arid savannah sites acquired on same season dates that excluded herbaceous vegetation from the spectral signature were used: November 2014 (HRG) and December 2015 (MSI, OLI). Using equivalent green (G), red (R), and near infrared (NIR) bands at 10 m (MSI, HRG) and 30 m (OLI) resolution, the woody cover was mapped through subpixel classification. The mapped woody cover was compared for statistical differences using χ² analysis at 10 m resolution (MSI, HRG) and at a degradation of the MSI and HRG images to the 30 m OLI pixel size. Conversion to top-of-atmosphere reflectance values facilitated inter-sensor correlation of G, R, and NIR reflectance for field sampling sites where woody cover was quantified. Inter-sensor regression functions in G, R, and NIR band MSI and HRG images were developed. The 10 m resolution classifications of woody cover were not statistically different. Due to spatial resolution similarity, SPOT 5 HRG multispectral imagery was established as suitable for integration with equivalent band MSI imagery in mapping the woody cover in a multitemporal analysis. For dense woody cover, Landsat 8 OLI imagery was more suitable for integration with MSI than HRG images due to higher radiometric sensitivity, which can permit monitoring physiology-related woody reflectance.     

Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data

Biomass and leaf area index (LAI) are important variables in many ecological and environmental applications. In this study, the suitability of visible to shortwave infrared advanced spaceborne thermal emission and reflection radiometer (ASTER) data for estimating aboveground tree and LAI in the treeline mountain birch forests was tested in northernmost Finland. The biomass and LAI of the 128 plots were surveyed, and the empirical relationships between forest variables and ASTER data were studied using correlation analysis and linear and non‐linear regression analysis. The studied spectral features also included several spectral vegetation indices (SVI) and canonical correlation analysis (CCA) transformed reflectances. The results indicate significant relationships between the biomass, LAI and ASTER data. The variables were predicted most accurately by CCA transformed reflectances, the approach corresponding to the multiple regression analysis. The lowest RMSEs were 3.45 t ha^?1 (41.0%) and 0.28 m²m^?2 (37.0%) for biomass and LAI respectively. The red band was the band with the strongest correlation against the biomass and LAI. SR and NDVI were the SVIs with the strongest linear and non‐linear relationships. Although the best models explained about 85% of the variation in biomass and LAI, the undergrowth vegetation and background reflectance are likely to affect the observed relationships.     

Assessment of MODIS sun-sensor geometry variations effect on observed NDVI using MSG SEVIRI geostationary data

The quality of Earth observation (EO) based vegetation monitoring has improved during recent years, which can be attributed to the enhanced sensor design of new satellites such as MODIS (Moderate Resolution Imaging Spectroradiometer) on Terra and Aqua. It is however expected that sun-sensor geometry variations will have a more visible impact on the Normalized Difference Vegetation Index (NDVI) from MODIS compared to earlier data sources, since noise related to atmosphere and sensor calibration is substantially reduced in the MODIS data stream. For this reason, the effect of varying MODIS viewing geometry on red, near-infrared (NIR) and NDVI needs to be quantified. Data from the geostationary MSG (Meteosat Second Generation) SEVIRI (Spinning Enhanced Visible and Infrared Imager) sensor is well suited for this purpose due to the fixed position of the sensor, the spectral resolution, including a red and NIR band, and the high temporal resolution (15 min) of data, enabling MSG data to be used as a reference for estimating MODIS surface reflectance and NDVI variations caused by varying sun-sensor geometry. The study was performed on data covering West Africa for periods of lowest possible cloud cover for three consecutive years (2004–2006). An analysis covering the entire range of NDVI revealed day-to-day variations in observed MODIS NDVI of 50–60% for medium dense vegetation (NDVI ≈ 0.5) caused by variations in MODIS view zenith angles (VZAs) between nadir and the high forward-scatter view direction. Statistical analysis on red, NIR and NDVI from MODIS and MSG SEVIRI for three transects (characterized by different vegetation densities) showed that both MODIS red and NIR reflectances are highly dependant on MODIS VZA and relative azimuth angle (RAA), due to the anisotropic behaviour of red and NIR reflectances. The anisotropic reflectance in the red and NIR band was to some degree minimized by the ratioing properties of NDVI. The minimization by the NDVI normalization is very dependent on the vegetation density however, since the degree of anisotropy in red and NIR reflectances depends on the amount of vegetation present. MODIS VZA and RAA effects on NDVI were highest for medium dense vegetation (NDVI ≈ 0.5–0.6). The VZA and RAA effects were less for sparsely vegetated areas (NDVI ≈ 0.3–0.35) and the smallest effect on NDVI was found for dense vegetation (NDVI ≈ 0.7). These results have implications for the end users' interpretation of NDVI, and challenge the expediency of the MODIS NDVI compositing technique, which should be refined to distinguish between forward- and backward-scatter viewing direction by taking RAA into account.     

Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data

The potential of canopy reflectance modelling to retrieve simultaneously several structural variables in managed Norway spruce stands was investigated using the “Invertible Forest Reflectance Model”, INFORM. INFORM is an innovative extension of the FLIM model, with crown transparency, infinite crown reflectance and understory reflectance simulated using physically based sub-models (SAILH, LIBERTY and PROSPECT). The INFORM model was inverted with hyperspectral airborne HyMap data using a neural network approach. INFORM based estimates of forest structural variables were produced using site-specific ranges of stand structural variables. A relatively simple three layer feed-forward backpropagation neural network with two input neurons, one neuron in the hidden layer and three output neurons was employed to map leaf area index (LAI), crown coverage and stem density.To identify the optimum 2-band spectral subset to be used in the inversion process, all 2-band combinations of the HyMap dataset were systematically evaluated for model inversion. Field measurements of structural variables from 39 forest stands were used to validate the maps produced from HyMap imagery. Using two HyMap wavebands at 837 nm and 1148 nm the obtained accuracy of the LAI map amounts to an rmse of 0.58 (relative rmse = 18% of mean, R² = 0.73). With HyMap data resampled to Landsat TM spectral bands and using two “optimum” bands at 840 nm and 1650 nm, rmse was 0.66 and relative rmse 21%. In contrast to approaches based on empirical relations between spectral vegetation indices and structural variables, the main advantage of the inversion approach is that it does not require previous calibration.     

Seasonal reflectance dynamics of common understory types in a northern European boreal forest

The influence of the seasonal cycle of boreal forest understory has been noticed in global remote sensing of vegetation, especially in remote sensing of biophysical properties (e.g. leaf area index) of the tree-layer in a forest. A general problem in the validation of operationally produced global biophysical vegetation products is the lack of ground reference data on the seasonal variability of different land surface types. Currently, little is known about the spectral properties of the understory layers of boreal forests, and even less is known about the seasonal dynamics of the spectra. In this paper, we report seasonal trajectories of understory reflectance spectra measured in a European boreal forest. Four study sites representing different forest fertility site types were selected from central Finland. The understory composition was recorded and its spectra measured with an ASD FieldSpec Hand-Held UV/VNIR Spectroradiometer ten times during the growing period (from May to September) in 2010. Our results show that the spectral differences between and within understory types are the largest at the peak of the growing season in early July whereas in the beginning and end of the growing season (i.e. early May and late September, respectively) the differences between the understory types are marginal. In general, the fertile sites had the brightest NIR spectra throughout the growing season whereas infertile types appeared darker in NIR. Our results also indicated that a mismatch in the seasonal development of understory and tree layers does not occur in boreal forests: the understory and tree layer vegetation develop at a similar pace in the spring (i.e. there are no or only few spring ephemerals present), and the forests with the strongest seasonal dynamics in tree canopy structure (LAI) have also the strongest dynamics in understory spectra.     

NDVI responses to the forest canopy and floor from spring to summer observed by airborne spectrometer in eastern Siberia

A popular method of satellite-based monitoring of the photosynthetic potential of vegetation is to calculate the normalised difference vegetation index (NDVI) from measurements of the red (RED) and near-infrared (NIR) bands. Enormous amounts of vegetation information have been obtained over continental to global areas based on NDVI derived from NOAA-AVHRR, Terra/Aqua-MODIS, and SPOT-VEGETATION satellite observations. In eastern Siberia, where sparse boreal forests are dominant, the lack of landscape-scale canopy-reflectance observations impedes interpretation of how NDVI seasonality is controlled by the forest canopy and floor status. We discuss the NDVI of the canopy and floor separately based on airborne spectral reflectance measurements and simultaneous airborne land surface images acquired around Yakutsk, Siberia, using a hedgehopping aircraft from spring to summer 2000. The aerial land surface images (4402 scenes) were visually classified into four types according to the forest condition: no-green canopy and snow floor (Type 1), green canopy and snow floor (Type 2), no-green canopy and no-snow floor (Type 3), and green canopy and no-snow floor (Type 4). The spectral reflectance from 350 to 1200 nm was then calculated for these four types. Type 1 had almost no difference in reflectance between the RED and NIR bands, and the resultant NDVI was slightly negative (− 0.03). Although Type 2 showed a significant difference between the two bands because of canopy greenness, the resultant NDVI was rather low (0.17) because of high reflection from the snow cover on the floor. In Type 3, the significant difference between the two bands was mainly caused by the greenness of the floor, and the NDVI was relatively high (0.45). The NDVI for Type 4 was the highest (0.75) among the four types. The contributions of reflectance from the forest canopy and floor to the total reflectance were tested with a forest radiative transfer model. The reflectance difference between NIR and RED bands (NIR − RED) of Type 4 (15.6%) was approximately double the differences of Type 2 (7.0%) and of Type 3 (7.9%), suggesting half-and-half contributions of forest canopy greenness and floor greenness to the total greenness. The result also suggested that the satellite-derived NDVI in the larch forest around Yakutsk reaches 85% of the maximum NDVI owing to the forest floor greenness, and only the other 15% of the increase in NDVI should be attributed to the canopy foliation. These results quantitatively reveal that the NDVI depends considerably on forest floor greenness and snow cover in addition to canopy greenness in the case of relatively sparse forest in Siberia.     

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.     

森林叶面积指数遥感反演模型构建及区域估算

基于eCognition面向对象分类算法及校正后的TM遥感影像,获取研究区2010年土地利用/覆被数据。同时在ArcGIS平台下,提取遥感影像6个波段反射率及RVI、NDVI、SLAVI、EVI、VII、MSR、NDVIc、BI、GVI和WI等10个植被指数,并辅助于DEM、ASPECT、SLOPE等地形信息,在与植物冠层分析仪(TRAC)实测各森林类型叶面积指数相关性分析的基础上,研究表明:相对多元线性回归方法,偏最小二乘法能够更好地把握各森林类型LAI动态变化,而后结合研究区森林覆被信息进行区域估算。     

Thinning-caused change in reflectance of ground vegetation in boreal forest

Ground reflectances were measured in the blue, green, red and near infrared (NIR) regions of the spectrum in a set of recently thinned pine- and spruce-dominated stands near Umea, Sweden. Compared with the untouched reference stands, the change in ground reflectance of the thinned stands was approximately linearly related to the thinning grade and to the coverage of the cutting waste left on the ground. Typically, thinning resulted in a reflectance increase in the red and decrease in the NIR band. The major effects of the presence of cutting waste on the ground reflectance can be simulated following a rather simple theoretical analysis. It appeared to be more difficult to quantitatively describe the effects on reflectance caused by the successional changes in the ground and field layer vegetation.     

Multi-angular reflectance properties of a hemiboreal forest: An analysis using CHRIS PROBA data

Forest types differ in their hyperspectral anisotropy patterns mainly due to species-specific geometrical structure, spatial arrangement of canopies and subsequent shadow patterns. This paper examines the multi-angular, hyperspectral reflectance properties of typical hemiboreal forests during summer time using three simultaneous CHRIS PROBA (mode 3) scenes and stand inventory data from the Järvselja Training and Experimental Forestry District in southeastern Estonia. We investigated the magnitude and reasons for the differences in the anisotropy patterns of deciduous and coniferous stands at three backward viewing angles. A forest reflectance model (FRT) was used as a tool to provide a theoretical basis to the discussion, and to estimate the directional contribution of scattering from crowns and ground to total stand reflectance for the two forest types. The FRT model simulated successfully the HDRF (hemispherical–directional reflectance factor) curves of the study stands to match those obtained from the CHRIS image, yet it produced a smaller and less wavelength-dependent angular reflectance effect than was observed in the satellite image. The main results of this study provide new information for separating the spectral contribution of the forest floor (or understory layer) from the tree canopy layer: (1) the red edge domain was identified to have the largest contribution from forest understory, and (2) the more oblique the viewing angle, the smaller the contribution from the understory. In addition, coniferous stands were observed to have a specific angular effect at the red and red edge domain, possibly as a result of the hierarchical structure and arrangement of coniferous canopies.     

Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of Eucalyptus plantations

The leaf area index (LAI) of fast-growing Eucalyptus plantations is highly dynamic both seasonally and inter-annually, and is spatially variable depending on pedo-climatic conditions. LAI is very important in determining the carbon and water balance of a stand, but is difficult to measure during a complete stand rotation and at large scales. Remote-sensing methods allowing the retrieval of LAI time series with accuracy and precision are therefore necessary. Here, we tested two methods for LAI estimation from MODIS 250m resolution red and near-infrared (NIR) reflectance time series. The first method involved the inversion of a coupled model of leaf reflectance and transmittance (PROSPECT4), soil reflectance (SOILSPECT) and canopy radiative transfer (4SAIL2). Model parameters other than the LAI were either fixed to measured constant values, or allowed to vary seasonally and/or with stand age according to trends observed in field measurements. The LAI was assumed to vary throughout the rotation following a series of alternately increasing and decreasing sigmoid curves. The parameters of each sigmoid curve that allowed the best fit of simulated canopy reflectance to MODIS red and NIR reflectance data were obtained by minimization techniques. The second method was based on a linear relationship between the LAI and values of the GEneralized Soil Adjusted Vegetation Index (GESAVI), which was calibrated using destructive LAI measurements made at two seasons, on Eucalyptus stands of different ages and productivity levels. The ability of each approach to reproduce field-measured LAI values was assessed, and uncertainty on results and parameter sensitivities were examined. Both methods offered a good fit between measured and estimated LAI (R² = 0.80 and R² = 0.62 for model inversion and GESAVI-based methods, respectively), but the GESAVI-based method overestimated the LAI at young ages.     

波段位置和宽度对河口湿地4种植被NDVI的影响

研究不同波段位置和宽度对植被NDVI的影响,对于进一步认识NDVI指数具有重要的意义。采用ASD(Analytical Spectral Devices)地物光谱仪测定闽江河口互花米草(Spartina alterniflora)、秋茄(Kandelia candel)、芦苇(Phragmites australis)和短叶茳芏(Cyperus malaccensis)冠层光谱,利用ViewSpecPro和Oragin8.0软件对光谱数据进行分析和处理,探讨不同波段位置和波段宽度对河口湿地4种植被NDVI的影响。结果表明:①当红光波段固定,近红外波段以50 nm宽度移动时,4种湿地植被NDVI随近红外波段中心位置增加而迅速增加,之后趋于平稳,在925~1 050 nm出现一个小的谷值,互花米草和短叶茳芏的谷值要比其他两种植物更为明显;不同波段宽度影响表现为:除红边与970 nm附近区域对NDVI的影响较显著外,其他波段影响不显著;②当近红外波段固定,红光波段以10 nm宽度移动时,4种湿地植被NDVI随红光波段中心位置移动先略有增加或变化不大,然后迅速降低;不同波段宽度影响表现为:在650~700 nm波段宽度越宽,NDVI值越小,600~650 nm范围内波段宽度对NDVI的影响不大;③4种湿地植被红光波段宽度对NDVI的影响要大于近红外波段。     

Application of photon recollision probability in coniferous canopy reflectance simulations

A new semi-physical forest reflectance model, PARAS, is presented in the paper. PARAS is a simple parameterization model for taking into account the effect of within-shoot scattering on coniferous canopy reflectance. Multiple scattering at the small scale represented by a shoot is a conifer-specific characteristic which causes the spectral signature of coniferous forests to differ from that of broadleaved forests. This has for long led to problems in remote sensing of canopy structural variables in coniferous dominated regions. The PARAS model uses a relationship between photon recollision probability and leaf area index (LAI) for simulating forest reflectance. The recollision probability is a measurable, wavelength independent variable which is defined as the probability with which a photon scattered in the canopy interacts with a phytoelement again. In this study, we present application results using PARAS in simulating reflectance of coniferous forests for approximately 800 Scots pine and Norway spruce dominated stands. The results of this study clearly indicate that a major improvement in simulating canopy reflectance in near-infrared (NIR) is achieved by simply accounting for the within-shoot scattering. In other words, the low NIR reflectance observed in coniferous areas is mainly due to within-shoot scattering. In the red wavelength the effect of within-shoot scattering was not pronounced due to the high level of needle absorption in the red range. To conclude the paper, further application possibilities of the presented parameterization model are discussed.     

Land cover classification in SE Asia using near and short wave infrared bands

A land cover classification map is necessary for modelling interactions between the land surface and the atmosphere, monitoring the environment and estimating food production. In order to classify land cover in SE Asia in 2000, Normalized Difference Vegetation Index (NDVI), reflectance of near-infrared (NIR) band, and reflectance of short wave infrared (SWIR) band of Systeme pour l'Observation de la Terre (SPOT) VEGETATION data were used in this study. First, ground data were collected for training data. In addition, supervised classification was performed on twelve months of NDVI data. As a result, some deserts and peripheral sparse vegetative areas were classified into urban, compared with the world atlas. Secondly, the number of months when the reflectance of the SWIR band is higher than that of the NIR band was counted (SWIR>NIR month-count condition) in each pixel, and pixels with counts of 10 were classified as Sparse Herbaceous/Shrub and of 11 or 12 were classified as Bare Areas, respectively. Finally, land cover was classified based on the SWIR>NIR month-count condition combined with NDVI, and it was compared with the existing land cover map. It was found that the SWIR>NIR month-count condition gives a better result for areas of non- or sparsely vegetative classification than when using only NDVI.     

The effect of crown shape on the reflectance of coniferous stands

The Kuusk-Nilson forest reflectance model was used to study the effect of crown shape on the reflectance of Scots pine and Norway spruce stands. In the first part of the study, we examined spruce and pine stands with an age range of 20-100 years and compared their simulated hemispherical-directional reflectance factors (HDRFs) at nadir in red (661 nm), NIR (838 nm) and MIR (1677 nm) when crowns were modeled as ellipsoids or cones. In all the cases, when a stand was modeled with conical crowns, it had a smaller reflectance factor than the same stand with ellipsoidal crowns.To analyze the sensitivity of HDRF on crown shape, in the second part of the study we simulated the angular distributions of HDRF of two pine stands with different leaf area index (LAI) and canopy closure values at 661 nm assuming four different crown shapes (cone, cylinder, ellipsoid, and cylinder bottom, cone top) and separated the components forming the HDRF. Considerable difference in the HDRF between the four crown shapes was observed: The larger the crown volume, the higher the canopy reflectance at similar LAI and canopy closure. A comparison of the two stands revealed that in denser stands (with a higher canopy closure) single scattering from tree crowns was responsible for the difference in HDRF between the different crown shapes, whereas in stands with a smaller canopy closure the single scattering from ground dominated the HDRF. Finally, the role of crown shape for the retrieval of LAI by inversion from remotely sensed data is discussed.     

基于模型模拟的植被NDVI与观测天顶角和LAI的关系

利用PROSPECT和SAIL模型模拟了不同叶绿素含量、不同LAI和不同观测天顶角下的植被冠层反射率,分析了NDVI随LAI、观测天顶角和叶绿素含量的变化规律。结果表明:叶绿素影响冠层反射率主要在可见光波段,冠层反射率随叶绿素含量的增加而下降;冠层反射率随观测天顶角的增加而增加,而LAI较高时,其受观测天顶角的影响较小。观测天顶角相同时,随叶绿素含量的增加NDVI呈上升趋势;叶绿素含量一定时,NDVI随LAI的增加而增加。LAI为1时,在不同叶绿素含量下,随观测天顶角的增加,NDVI呈先下降后上升的趋势,拐点在观测天顶角65°或70°处,而LAI为3、5和7时,NDVI呈现下降趋势。叶绿素含量较高时,NDVI受观测天顶角的影响较小。当LAI较大和叶绿素含量较低时,NDVI随观测天顶角的增加(>70°)下降较快。