Investigating the spectral and ecological characteristics of grassland communities across an ecological gradient of the Inner Mongolian grasslands with in situ hyperspectral data

Grassland degradation is serious in the Mongolian plateau, especially in Inner Mongolia, China. Accurate monitoring of grassland types and qualities is increasingly important for the purposes of grassland conservation and restoration. Using in situ hyperspectral reflectance data and ground-based ecological measurements, we explored the potential for large-scale monitoring grassland communities using imaging spectroradiometers. We compared the spectral reflectance of the major types of grasslands and field plots with/without livestock grazing. We also did statistical analysis about the relationship between hyperspectral indices and aboveground biomass (AGB) of the surveyed grassland communities. The results showed that: (1) the dominant plant species varied across meadow, typical, and desert steppe, and they also varied between fenced and grazed plots; (2) in situ hyperspectral data are useful for differentiating grassland communities of meadow, typical, and desert steppe and grassland communities with and without livestock grazing; and (3) the prediction accuracies of vegetation indices for AGB decreased from desert to typical and meadow steppe, and the results were contrary for the prediction accuracies of red edge inflection point (REIP). REIP may not be suitable for estimating AGB of the low-density grassland communities. The above results implied that care must be taken while using statistical models to link spectral and ecological measurements in large geographical scales since there is lack of portability over different types of grassland communities. This study provides foundations for future large-scale efforts of monitoring grassland communities in Inner Mongolia using imaging spectroradiometers.     

Evaluation of hyperspectral indices for LAI estimation and discrimination of potato crop under different irrigation treatments

In this study, various hyperspectral indices were evaluated for estimation of leaf area index (LAI) and crop discrimination under different irrigation treatments. The study was conducted for potato crop using the spectral reflectance values measured by a hand‐held spectro‐radiometer. Three categories of hyperspectral indices, such as ratio/difference indices, multivariate indices and derivative based indices were computed. It was found that, among various band combinations for NDVI (normalized difference vegetation index) and SAVI (soil adjusted vegetation index), the band combination of the 780～680, produced highest correlation coefficient with LAI. Among all the forms of LAI and VI empirical relationships, the power and exponential equations had highest R ² and F values. Analysis of variance showed that, hyperspectral indices were found to be more efficient than the LAI to detect the differences among crops under different irrigation treatments. The discriminant analysis produced a set of five most optimum bands to discriminate the crops under three irrigation treatments.     

Detection of stressed oil palms from an airborne sensor using optimized spectral indices

Existing vegetation indices and red-edge techniques have been widely used for the assessment of vegetation status and vegetation health from remote-sensing instruments. This study proposed and applied optimized Airborne Imaging Spectrometer for Applications (AISA) airborne hyperspectral indices in assessing and mapping stressed oil palm trees. Six vegetation indices, four red-edge techniques, a standard supervised classifier and three optimized AISA spectral indices were compared in mapping diseased oil palms using AISA airborne hyperspectral imagery. The optimized AISA spectral indices algorithms used newly defined reflectance values at wavelength locations of 734 nm (near-infrared (NIR)) and 616 nm (red). The selection of these two bands was based on laboratory statistical analysis using field spectroradiometer reflectance data. These two bands were then applied to the AISA airborne hyperspectral imagery using the three optimized algorithms for AISA data. The newly formulated AISA hyperspectral indices were D2 = R ₆₁₆/R ₇₃₄, normalized difference vegetation index a (NDVIa)?=?(R ₇₃₄ – R ₆₁₆)/(R ₇₃₄?+?R ₆₁₆) and transformed vegetation index a (TVIa)?=?((NDVIa?+?0.5)/(abs (NDVIa?+?0.5))?×?[abs (NDVIa?+?0.5)]^1/2. The classification results from the optimized AISA hyperspectral indices were compared with the other techniques and the optimized AISA spectral indices obtained the highest overall accuracy. D2 and NDVIa obtained 86% of overall accuracy followed by TVIa with 84% of overall accuracy.     

Modelling above-ground biomass based on vegetation indexes: a modified approach for biomass estimation in semi-arid grasslands

Remote sensing could be the most effective means for scaling up grassland above-ground biomass (AGB) from the sample scale to the regional scale. Remote sensing approaches using statistical models based on vegetation indexes (VIs) are frequently used because of their simplicity and reliability. And many researchers have already proven the method is scientific, feasible, and can bring relatively better effects in practice. However, the only deficiency of the method has been criticized because of the uncertainties introduced by saturation of spectral reflectance at high-density vegetation levels and the soil surface at low-density vegetation levels. Therefore, in this study, we aimed to improve grassland AGB estimates by using modified VIs (MVIs) to minimize the influence of the soil background. The field study was conducted in the Chen Barag Banner, the Ewenkizu Banner, and the Xin Barag Left Banner in the Hulun Buir Grassland, Inner Mongolia, northern China. Field plots were photographed and AGB samples were collected during field sampling. Remote sensing data were obtained from MOD09A1 (TERRA satellite). Four MVIs were first calculated based on the corresponding VI: the Ratio Vegetation Index (RVI), the Normalized Differential Vegetation Index (NDVI), the Difference Vegetation Index (DVI), and the Modified Soil-Adjusted Vegetation Index (MSAVI), by improving estimates of vegetation cover (VC). Then, MVIs, i.e., MRVI, MDVI, MNDVI, and MMSAVI, were regressed with the sample-scale AGB using an exponential function, a linear function, a logarithmic function, and a power function. When the accuracy of the models was tested by comparing root mean square error (RMSE), relative error (RE), and coefficient of determination (R²), the results demonstrated that MVI-AGB models performed better than the VI-AGB models. The logarithmic MNDVI-AGB model was the best of the regression functions. This model gave the best estimates of AGB from remote sensing data, compared with the values measured in field analyses. Our proposed method provides a new way to estimate regional grassland AGB and will be useful to analyze ecosystem responses under climate change.     

Narrow band vegetation indices overcome the saturation problem in biomass estimation

Remotely sensed vegetation indices such as NDVI, computed using the red and near infrared bands have been used to estimate pasture biomass. These indices are of limited value since they saturate in dense vegetation. In this study, we evaluated the potential of narrow band vegetation indices for characterizing the biomass of Cenchrus ciliaris grass measured at high canopy density. Three indices were tested: Modified Normalized Difference Vegetation Index (MNDVI), Simple Ratio (SR) and Transformed Vegetation Index (TVI) involving all possible two band combinations between 350?nm and 2500?nm. In addition, we evaluated the potential of the red edge position in estimating biomass at full canopy cover. Results indicated that the standard NDVI involving a strong chlorophyll absorption band in the red region and a near infrared band performed poorly in estimating biomass (R ²=0.26). The MNDVIs involving a combination of narrow bands in the shorter wavelengths of the red edge (700–750?nm) and longer wavelengths of the red edge (750–780?nm), yielded higher correlations with biomass (mean R ²=0.77 for the highest 20 narrow band NDVIs). When the three vegetation indices were compared, SR yielded the highest correlation coefficients with biomass as compared to narrow band NDVI and TVI (average R ²=0.80, 0.77 and 0.77 for the first 20 ranked SR, NDVI and TVI respectively). The red edge position yielded comparable results to the narrow band vegetation indices involving the red edge bands. These results indicate that at high canopy density, pasture biomass may be more accurately estimated by vegetation indices based on wavelengths located in the red edge than the standard NDVI.     

Estimating above-ground net primary productivity of the tallgrass prairie ecosystem of the Central Great Plains using AVHRR NDVI

Above-ground net primary productivity (ANPP) is indicative of an ecosystem's ability to capture solar energy and convert it to organic carbon (or biomass), which may be used by consumers or decomposers, or stored in the form of living and nonliving organic matter. Annual and interannual variation in ANPP is often linked to climate dynamics and anthropogenic influences, such as fertilization, irrigation, above-ground biomass harvest, and so on. The Central Great Plains grasslands occupy over 1.5 million km² and are a primary resource for livestock production in North America. The tallgrass prairies are the most productive grasslands in this region, and the Flint Hills of North America represent the largest contiguous area of unploughed tallgrass prairie (1.6 million ha). Measurements of ANPP are of critical importance to the proper management and understanding of climatic and anthropogenic influences on tallgrass prairie. Yet, accurate, detailed, and systematic measurements of ANPP over large geographic regions do not exist for this ecosystem. For these reasons, this study was conducted to investigate the use of the normalized difference vegetation index (NDVI) to model ANPP of the tallgrass prairie. Many studies have established a positive relationship between the NDVI and ANPP, but the strength of this relationship is influenced by vegetation types and can vary significantly from year to year depending on land use and climatic conditions. The goal of this study was to develop a robust model using the Advanced Very High Resolution Radiometer (AVHRR) biweekly NDVI values to predict tallgrass ANPP. This study was conducted using ANPP measurements from a watershed within the Konza Prairie Biological Station (KPBS) as the primary study area, with additional measurements from the Rannells Flint Hills Prairie Preserve (RFHPP) and biennial ANPP measurements by Kansas State University (KSU) students from tallgrass areas near Manhattan, Kansas. Data from the primary study site covered the period of 1989–2005. The optimal period for estimating ANPP using AVHRR NDVI composite data sets was found to be late July. The Tallgrass ANPP Model (TAM) explained 54% (coefficient of determination, R ² = 0.54, p < 0.001) of the year-to-year variation in ANPP. The creation of 1.0 km × 1.0 km resolution ANPP maps for a four-county (～7000 ha) area for years 1989–2007 showed considerable variation in annual and interannual ANPP spatial patterns, suggesting complex interactions among factors influencing ANPP spatially and temporally.     

Using hyperspectral vegetation indices to estimate the fraction of photosynthetically active radiation absorbed by corn canopies

The fraction of photosynthetically active radiation (FPAR) absorbed by vegetation – a key parameter in crop biomass and yields as well as net primary productivity models – is critical to guiding crop management activities. However, accurate and reliable estimation of FPAR is often hindered by a paucity of good field-based spectral data, especially for corn crops. Here, we investigate the relationships between the FPAR of corn (Zea mays L.) canopies and vegetation indices (VIs) derived from concurrent in situ hyperspectral measurements in order to develop accurate FPAR estimates. FPAR is most strongly (positively) correlated to the green normalized difference vegetation index (GNDVI) and the scaled normalized difference vegetation index (NDVI*). Both GNDVI and NDVI* increase with FPAR, but GNDVI values stagnate as FPAR values increase beyond 0.75, as previously reported according to the saturation of VIs – such as NDVI – in high biomass areas, which is a major limitation of FPAR-VI models. However, NDVI* shows a declining trend when FPAR values are greater than 0.75. This peculiar VI–FPAR relationship is used to create a piecewise FPAR regression model – the regressor variable is GNDVI for FPAR values less than 0.75, and NDVI* for FPAR values greater than 0.75. Our analysis of model performance shows that the estimation accuracy is higher, by as much as 14%, compared with FPAR prediction models using a single VI. In conclusion, this study highlights the feasibility of utilizing VIs (GNDVI and NDVI*) derived from ground-based spectral data to estimate corn canopy FPAR, using an FPAR estimation model that overcomes limitations imposed by VI saturation at high FPAR values (i.e. in dense vegetation).     

Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in bahia grass (Paspalum notatum Flugge.)

Bahia grass (Paspalum notatum Flugge.) plants were grown in silica sand and irrigated daily with one of five levels of Zn (0, 0.5, 25, 50, or 100 mg l⁻¹) to determine the effects of the heavy metal on the growth and development of plant canopies. Healthy and stressed plants were measured with two hyperspectral imagers, laser-induced fluorescence spectroscopy (LIFS), and laser-induced fluorescence imaging (LIFI) systems in order to determine if the four handheld remote sensing instruments were equally capable of detecting plant stress and measuring canopy chlorophyll levels in bahia grass. Symptoms of bahia grass plants grown at deficient (0 mg l⁻¹) or toxic (25, 50, or 100 mg l⁻¹) concentrations of Zn were dominated by leaf chlorosis and plant stunting. Leaf fresh weight, leaf dry weight, CO₂ assimilation, total chlorophyll, and leaf thickness followed (+) quadratic models in which control plants (0.5 mg l⁻¹ Zn) exhibited higher responses than plants grown at either deficient or toxic levels of Zn. Normalized difference vegetation index [NDVI=(NIR−Red)/(NIR+Red)] and ratio vegetation index [RVI=R₇₅₀/R₇₀₀, in which R denotes reflectance] values were calculated for calibrated digital images from both hyperspectral imagers. The NDVI and RVI values from both hyperspectral imagers were fit best by (+) quadratic models when treatments were constrained between 0 and 100 mg l⁻¹ Zn, but were fit best by linear regression models with (−) slopes when treatments were constrained between 0.5 and 100 mg l⁻¹ Zn. Furthermore, both NDVI and RVI algorithms were effective in predicting the concentrations of chlorophyll in canopies of bahia grass grown at the various levels of Zn. In contrast, red/far-red (R/FR) fluorescence ratios estimated from leaf fluorescence values measured with the LIFS and LIFI instruments were fit best by (−) quadratic models when treatments were constrained between 0 and 100 mg l⁻¹ Zn, but were fit best by linear regression models with (+) slopes when treatments were constrained between 0.5 and 100 mg l⁻¹ Zn. A series of regression analyses were conducted among plant biometric, biochemical, and leaf anatomical parameters (treated as independent variables) and the remote sensing algorithms, NDVI, RVI, blue/green (BL/GR), and R/FR (treated as dependant variables). In general, residuals were significantly higher for NDVI and RVI models compared to the BL/GR and R/FR models indicating that the NDVI and RVI algorithms were able to measure total chlorophyll and plant biomass more accurately than the BL/GR and R/FR algorithms. However, unique capabilities of LIFS and LIFI instruments continue to argue for the development of laser-induced fluorescence remote sensing technologies.     

Close-range remote sensing of aquatic macrophyte vegetation cover

This study used ground-based hyperspectral radiometry to examine variations in visible and near-infrared spectral reflectance of spatterdock (Nuphar polysepalum Engelm.) as a function of vegetation cover. Sites were sampled in Swan Lake in Grand Teton National Park, Wyoming, using a 512-band spectroradiometer to measure reflectance over the range 326.5-1055.3nm (visible-nearinfrared) and simultaneous estimates of spatterdock cover. Linear correlations between spatterdock cover and spectral reflectance were statistically significant at the 0.05 significance level in two specific ranges of the spectrum: 518-607 nm; and 697-900nm. Predictability of spatterdock cover using spectral variables was best using an NDVI transformation of the data in a non-linear equation (r ² = 0.95).     

Estimation of foliar pigment concentration in floating macrophytes using hyperspectral vegetation indices

Foliar pigment concentrations of chlorophylls and cartenoids are important indicators of plant physiological status, photosynthesis rate, and net primary productivity. Although the utility of hyperspectral derived vegetation indices for estimating foliar pigment concentration has been documented for many vegetation types, floating macrophytes have not been assessed despite their ecological importance. This study surveyed 39 wetland species (12 floating macrophytes (FM), 8 grasses/sedges/rushes (GSR), and 19 herbs/wildflowers (HWF)) to determine whether foliar pigment concentrations could be estimated from hyperspectral reflectance. Hyperspectral reflectance of samples was recorded using an ASD FieldSpec3 Max portable spectroradiometer with the plant probe attachment or via a typical laboratory set-up. A semi-empirical relationship was established using either a linear, second-degree polynomial or logarithmic function between 13 candidate vegetation indices and chl-a, chl-b, Car, and chl-a + b pigment concentrations. Vegetation indices R-M, CI-Red, and MTCI were strongly correlated with foliar pigment concentrations using a linear fitting function. Chl-a + b and chl-b concentrations for all samples were reasonably estimated by the R-M index (R² = 0.66 and 0.64), although Chl-a and Car concentration estimates using CI-Red were weaker (R² = 0.63 and 0.51). Regression results indicate that pooled samples to estimate individual foliar pigments were less correlated than when each type of vegetation type was treated separately. For instance, chl-a + b was best estimated by CI-Red for FM (R² = 0.80), MTCI for HWF (R² = 0.77), and R-M for GSR (R² = 0.67). Although floating macrophytes feature unique adaptions to their aquatic environment, their foliar pigment concentrations and spectral signatures were comparable to other wetland vegetation types. Overall, vegetation indices that exploit the red-edge region were a reasonable compromise, having good explanatory power for estimation of foliar pigments across the sampled wetland vegetation types and with CI-Red the best suited index for floating macrophytes.     

A new approach to predict normalized difference vegetation index using time-delay neural network in the arid and semi-arid grassland

ABSTRACT

Normalized difference vegetation index (NDVI) has been used to conduct important research on plant growth and vegetation productivity. In this paper, a new approach to predict NDVI based on precipitation in the grass-growing season for the arid and semi-arid grassland is proposed using time-delay neural network (TDNN). To intuitively know the ability of TDNN to learn the relationship between NDVI and precipitation and to predict NDVI, the performance of the TDNN model is compared with back propagation neural network (BPNN) trained with the same data. The results indicate that TDNN works well to predict precipitation. Moreover, the relationship between precipitation and NDVI can be predicted accurately by the proposed TDNN model. The results show the goodness-of-fit between the observed NDVI and predicted NDVI measured by the determination coefficient of R² being 0.999 from the TDNN model, with the mean absolute percentage error, mean absolute error, and root-mean-square error to be 0.23%, 0.20, and 0.19, respectively. The analysis shows that the proposed method can result in an accurate NDVI prediction. Thus, the algorithm is applied to predict the NDVI during the grass-growing season for the validation of the approach. This validation results suggest the potential application of this approach for reduction of overgrazing pressure and vegetation restoration in the arid and semi-arid grassland.     

Estimating aboveground biomass of grassland having a high canopy cover: an exploratory analysis of in situ hyperspectral data

To improve the estimation of aboveground biomass of grassland having a high canopy cover based on remotely sensed data, we measured in situ hyperspectral reflectance and the aboveground green biomass of 42 quadrats in an alpine meadow ecosystem on the Qinghai–Tibetan Plateau. We examined the relationship between aboveground green biomass and the spectral features of original reflectance, first-order derivative reflectance (FDR), and band-depth indices by partial least squares (PLS) regression, as well as the relationship between the aboveground biomass and narrow-band vegetation indices by linear and nonlinear regression analyses. The major findings are as follows. (1) The effective portions of spectra for estimating aboveground biomass of a high-cover meadow were within the red-edge and near infrared (NIR) regions. (2) The band-depth ratio (BDR) feature, using NIR region bands (760–950 nm) in combination with the red-edge bands, yields the best predictive accuracy (RMSE?=?40.0 g m^?2) for estimating biomass among all the spectral features used as independent variables in the partial least squares regression method. (3) The ratio vegetation index (RVI2) and the normalized difference vegetation index (NDVI2) proposed by Mutanga and Skidmore (Mutanga, O. and Skidmore, A.K., 2004a Mutanga, O. and Skidmore, A. K. 2004a. Narrow band vegetation indices solve the saturation problem in biomass estimation. International Journal of Remote Sensing, 25: 1–16.  [Google Scholar], Narrow band vegetation indices solve the saturation problem in biomass estimation. International Journal of Remote Sensing, 25, pp. 1–6) are better correlated to the aboveground biomass than other VIs (R ²?=?0.27 for NDVI2 and 0.26 for RVI2), while RDVI, TVI and MTV1 predicted biomass with higher accuracy (RMSE?=?37.2 g m^?2, 39.9 g m^?2 and 39.8 g m^?2, respectively). Although all of the models developed in this study are probably acceptable, the models developed in this study still have low accuracy, indicating the urgent need for further efforts.     

Early detection of the invasive alien plant Solidago altissima in moist tall grassland using hyperspectral imagery

We assessed aerial hyperspectral imagery with high spatial (1.5 m) and spectral (8.9 nm) resolutions for detecting and mapping the early invasion by Solidago altissima of understory vegetation in moist tall grassland. Generalized linear models (GLMs) were constructed to predict S. altissima occurrence using 1.5 m pixels from hyperspectral data collected during the spring when understory vegetation was directly observable from above. A data set of presence–absence derived from percentage cover data was used for the analyses. The values of the area under the receiver operating characteristic (ROC) curve (AUC) ranged from 0.77–0.87 in the validation data set. Three minimum noise fraction (MNF) bands differentiated S. altissima in the best-performing model (selected based on Akaike's information criterion) for the occurrence of S. altissima. The results suggest that the aerial hyperspectral images obtained during spring before the seasonal development of the grass canopy are useful for the early detection and mapping ofS. altissima invading moist tall grassland.     

Leaf Area Index derivation from hyperspectral vegetation indicesand the red edge position

The aim of this study was to compare the performance of various narrowband vegetation indices in estimating Leaf Area Index (LAI) of structurally different plant species having different soil backgrounds and leaf optical properties. The study uses a dataset collected during a controlled laboratory experiment. Leaf area indices were destructively acquired for four species with different leaf size and shape. Six widely used vegetation indices were investigated. Narrowband vegetation indices involved all possible two band combinations which were used for calculating RVI, NDVI, PVI, TSAVI and SAVI2. The red edge inflection point (REIP) was computed using three different techniques. Linear regression models as well as an exponential model were used to establish relationships. REIP determined using any of the three methods was generally not sensitive to variations in LAI (R ² < 0.1). However, LAI was estimated with reasonable accuracy from red/near-infrared based narrowband indices. We observed a significant relationship between LAI and SAVI2 (R ² = 0.77, RMSE = 0.59 (cross validated)). Our results confirmed that bands from the SWIR region contain relevant information for LAI estimation. The study verified that within the range of LAI studied (0.3 ≤ LAI ≤ 6.1), linear relationships exist between LAI and the selected narrowband indices.     

Modelling light use efficiency using vegetation index and land surface temperature from MODIS in Harvard Forest

Light use efficiency (LUE) is of great importance for carbon cycle and climate change research. This study presents a new LUE model incorporation of vegetation indices (VIs) and land surface temperature (LST) derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) in Harvard Forest. Three indices, including the normalized difference vegetation index (NDVI), the two-band enhanced vegetation index (EVI2) and the soil-adjusted vegetation index (SAVI), were selected as indicators of forest canopy greenness. A single VI provided moderate estimates of LUE with coefficients of determination (R ²) 0.6219, 0.7094 and 0.7502 for NDVI, EVI2 and SAVI, respectively. Our results demonstrated that canopy LUE was related both to the canopy photosynthesis efficiency and air temperature (R ²?=?0.5634). Therefore, the MODIS LST product was incorporated as a surrogate for monitoring of environmental stresses as the observed relationship between LST and both air temperature (R ²?=?0.8828) and vapour pressure deficit (VPD) (R ²?=?0.6887). The new model in terms of (VI)?×?(Scaled (LST)) provided improved estimates of LUE estimation with R ² of 0.7349, 0.7561 and 0.7879 for NDVI, EVI2 and SAVI, respectively. The results will be useful for the development of future LUE models based entirely on remote-sensing observations.     

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.     

Aboveground biomass estimation in intervened and non-intervened Nothofagus pumilio forests using remotely sensed data

Forest inventory data can be used along with remotely sensed data to estimate biomass and carbon stocks over large and inaccessible forested areas. In this study, the relationship between satellite-derived multispectral data and forest variables from intervened and non-intervened Nothofagus pumilio forest stands located in the Magellan region of Chile was examined, in order to quantify the over bark volume (OBV) and aboveground tree biomass (AGTB). Four vegetation parameters – the green normalised difference vegetation index (GNDVI), normalised difference vegetation index (NDVI), simple ratio (SR) and vegetation cover fraction (VCF) – were retrieved from an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image of the study area. The results indicate that only the VCF presents significant differences among intervened and non-intervened stands. The best OBV and AGTB models (R² = 0.58) were found using the SR index and the VCF as predictors. This result could be transferred to estimate biomass and volume in other Nothofagus pumilio forests with similar conditions. Moreover, it can be used to assess temporal carbon changes.     

Mapping of continuous floristic gradients in grasslands using hyperspectral imagery

Transitions between plant species assemblages are often continuous with the form of the transition dependent on the ‘slope’ of environmental gradients and on the style of self-organization in vegetation. Image segmentation can present misleading or even erroneous results if applied to continuous spatial changes in vegetation. Even methods that allow for multiple-class memberships of pixels presuppose the existence of ideal types of species assemblages that constitute mixtures—an assumption that does not fit the case of continua where any section of a gradient is as ‘pure’ as any other section like in modulations of grassland species composition.Thus, we attempted to spatially model floristic gradients in Bavarian meadows by extrapolating axes of an unconstrained ordination of species data. The models were based on high-resolution hyperspectral airborne imagery. We further modelled the distribution of plant functional response types (Ellenberg indicator values) and the cover values of selected species. The models were made with partial least squares (PLS) regression analyses. The realistic utility of the regression models was evaluated by full leave-one-out cross-validation.The modelled floristic gradients showed a considerable agreement with ground-based observations of floristic gradients (R²=0.71 and 0.66 for the first two axes of ordination). Apart from mapping the most important continuous floristic differences, we mapped gradients in the appearance of plant functional response groups as represented by averaged Ellenberg indicator values for soil pH (R²=0.76), water supply (R²=0.66) and nutrient supply (R²=0.75), while models for the cover of single species were weak.Compared to many other vegetation attributes, plant species composition is difficult to detect with remote sensing techniques. This is partly caused by a lack of compatibility between methods of vegetation ecology and remote sensing. We believe that the present study has the potential to increase compatibility as neither spectral nor vegetation information gets lost by a classifying step.     

Using wavelet analysis of hyperspectral remote-sensing data to estimate canopy chlorophyll content of winter wheat under stripe rust stress

Chlorophyll content can be used as an indicator to monitor crop diseases. In this article, an experiment on winter wheat stressed by stripe rust was carried out. The canopy reflectance spectra were collected when visible symptoms of stripe rust in wheat leaves were seen, and canopy chlorophyll content was measured simultaneously in laboratory. Continuous wavelet transform (CWT) was applied to process the smoothed spectral and derivative spectral data of winter wheat, and the wavelet coefficient features obtained by CWT were regarded as the independent variable to establish estimation models of chlorophyll content. The hyperspectral vegetation indices were also regarded as the independent variable to build estimation models. Then, two types of models above-mentioned were compared to ascertain which type of model is better. The cross-validation method was used to determine the model accuracies. The results indicated that the estimation model of chlorophyll content, which is a multivariate linear model constructed using wavelet coefficient features extracted by Mexican Hat wavelet function processing the smoothed spectrum (WSMH1 and WSMH2), is the best model. It has the highest estimation accuracy with modelled coefficient of determination (R²) of 0.905, validated R² of 0.913, and root mean square error (RMSE) of 0.288 mg fg^?1. The univariate linear model built by wavelet coefficient feature of WSMH1 is secondary and the modelled R² is 0.797, validated R² is 0.795, and RMSE is 0.397 mg fg^?1. Both estimation models are better than those of all hyperspectral vegetation indices. The research shows that the feature information of canopy chlorophyll content of winter wheat can be captured by wavelet coefficient features which are extracted by the method of CWT processing canopy reflectance spectrum data. Therefore, it could provide theoretical support on detecting diseases of crop by remote sensing quantitatively estimating chlorophyll content.     

Using MODIS time series data to estimate aboveground biomass and its spatio-temporal variation in Inner Mongolia’s grassland between 2001 and 2011

It is critical to understanding grassland biomass and its dynamics to study regional carbon cycles and the sustainable use of grassland resources. In this study, we estimated aboveground biomass (AGB) and its spatio-temporal pattern for Inner Mongolia’s grassland between 2001 and 2011 using field samples, Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index (MODIS-NDVI) time series data, and statistical models based on the relationship between NDVI and AGB. We also explored possible relationships between the spatio-temporal pattern of AGB and climatic factors. The following results were obtained: (1) AGB averaged 19.1 Tg C (1 Tg = 10¹² g) over a total area of 66.01 × 10⁴ km² between 2001 and 2011 and experienced a general fluctuation (coefficient of variation = 9.43%), with no significant trend over time (R² = 0.05, p > 0.05). (2) The mean AGB density was 28.9 g C m^?2 over the whole study area during the 11 year period, and it decreased from the northeastern part of the grassland to the southwestern part, exhibiting large spatial heterogeneity. (3) The AGB variation over the 11 year period was closely coupled with the pattern of precipitation from January to July, but we did not find a significant relationship between AGB and the corresponding temperature changes. Precipitation was also an important factor in the spatial pattern of AGB over the study area (R² = 0.41, p < 0.001), while temperature seemed to be a minor factor (R² = 0.14, p < 0.001). A moisture index that combined the effects of precipitation and temperature explained more variation in AGB than did precipitation alone (R² = 0.45, p < 0.001). Our findings suggest that establishing separate statistical models for different vegetation conditions may reduce the uncertainty of AGB estimation on a large spatial scale. This study provides support for grassland administration for livestock production and the assessment of carbon storage in Inner Mongolia.