Monitoring post-burn recovery of chaparral vegetation in southern California using multi-temporal satellite data

Monitoring the post-burn recovery condition of chaparral vegetation in southern California is important for managers to determine the appropriate time to conduct controlled burns. Due to the difficulty of monitoring post-fire recovery over large areas and the absence of detailed fire records in many areas, we examined the possibility of using satellite observations to establish the postfire recovery stage of chamise chaparral stands in this region. SPOT XS data collected on three dates between 1986 and 1992 were analysed to determine if temporal changes in a spectral vegetation index tracked the expected post-fire recovery trajectory of the above-ground biomass of chamise chaparral stands of varying post-fire ages. Results of the study indicated that neither the normalized difference vegetation index nor the soil adjusted vegetation index followed the expected post-fire recovery patterns in these stands. These findings are explained by interannual variations in precipitation having a larger than expected effect on the growth of this drought-resistant evergreen community, with changes in green leaf area dominating the temporal variations in the spectral vegetation indices.     

Using multiple image endmember spectral mixture analysis to study chaparral regrowth in southern California

This research tested the ability of a multiple endmember (EM) spectral mixture analysis (SMA) approach, applied to multi-temporal Landsat Thematic Mapper (TM) data, to produce realistic and meaningful EM fractions for the study of post-fire regrowth in a southern California chaparral landscape. Eight different image EMs were used, two types for each EM class of interest (green vegetation (GV), non-photosynthetic vegetation (NPV), soil, and shade); the best EM combination was selected for each pixel. These EM fractions were validated with fractions derived from 1?m Airborne Data Acquisition and Registration multi-spectral image data. The EM fractions from the two datasets were similar (r=0.873, 0.776, 0.790 for GV, NPV, and soil, respectively). Chaparral stands were delineated using vegetation type, fire history and slope aspect GIS layers. Mean EM fractions were calculated for each stand, and analysis of variance was performed to determine if EM fractions were different for stands of different age. Short-term trajectories of individual stands appeared to exhibit trends consistent with trends reported in the literature. However, only the youngest and oldest stands were consistently significantly different.     

Testing the Woodcock-Harward image segmentation algorithm in an area of southern California chaparral and woodland vegetation

Vegetation maps were produced by applying a region-growing segmentation algorithm to Landsat Thematic Mapper (TM) data, and labelling the resulting segments or map polygons by overlay of a per-pixel classification and applying a plurality rule. Thus, each segment was assigned a vegetation class label based on the most frequently occurring pixels in the segment. The segmentation improved overall map accuracies by an average of 10 per cent relative to the underlying per-pixel classification for three subimages within a southern California montane watershed based on a comparison with photointerpreted maps. While it was hypothesized that including transformed slope aspect and image texture as input to the segmentation would improve map accuracy by creating segments corresponding more closely to vegetation stands, our results did not support these hypotheses. Further, performing the segmentation on principal components bands, or a vegetation index, did not improve results over the segmentation based on TM bands 2, 3, and 4.     

Parallel adjustments in vegetation greenness and ecosystem CO2 exchange in response to drought in a Southern California chaparral ecosystem

Some form of the light use efficiency (LUE) model is used in most models of ecosystem carbon exchange based on remote sensing. The strong relationship between the normalized difference vegetation index (NDVI) and light absorbed by green vegetation make models based on LUE attractive in the remote sensing context. However, estimation of LUE has proven problematic since it varies with vegetation type and environmental conditions. Here we propose that LUE may in fact be correlated with vegetation greenness (measured either as NDVI at constant solar elevation angle, or a red edge chlorophyll index), making separate estimates of LUE unnecessary, at least for some vegetation types. To test this, we installed an automated tram system for measurement of spectral reflectance in the footprint of an eddy covariance flux system in the Southern California chaparral. This allowed us to match the spatial and temporal scales of the reflectance and flux measurements and thus to make direct comparisons over time scales ranging from minutes to years. The 3-year period of this study included both “normal” precipitation years and an extreme drought in 2002. In this sparse chaparral vegetation, diurnal and seasonal changes in solar angle resulted in large variation in NDVI independent of the actual quantity of green vegetation. In fact, one would come to entirely different conclusions about seasonal changes in vegetation greenness depending on whether NDVI at noon or NDVI at constant solar elevation angle were used. Although chaparral vegetation is generally considered “evergreen”, we found that the majority of the shrubs were actually semi-deciduous, leading to large seasonal changes in NDVI at constant solar elevation angle. LUE was correlated with both greenness indices at the seasonal timescale across all years. In contrast, the relationship between LUE and PRI was inconsistent. PRI was well correlated with LUE during the “normal” years but this relationship changed dramatically during the extreme drought. Contrary to expectations, none of the spectral reflectance indices showed consistent relationships with CO₂ flux or LUE over the diurnal time-course, possibly because of confounding effects of sun angle and stand structure on reflectance. These results suggest that greenness indices can be used to directly estimate CO₂ exchange at weekly timescales in this chaparral ecosystem, even in the face of changes in LUE. Greenness indices are unlikely to be as good predictors of CO₂ exchange in dense evergreen vegetation as they were in the sparse, semi-deciduous chaparral. However, since relatively few ecosystems are entirely evergreen at large spatial scales or over long time spans due to disturbance, these relationships need to be examined across a wider range of vegetation types.     

Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem

Estuaries are among the most invaded ecosystems on the planet. Such invasions have led in part, to the formation of a massive $1 billion restoration effort in California's Sacramento–San Joaquin River Delta. However, invasions of weeds into riparian, floodplain, and aquatic habitats threaten the success of restoration efforts within the watershed and jeopardize economic activities. The doctrine of early detection and rapid response to invasions has been adopted by land and water resource managers, and remote sensing is the logical tool of choice for identification and detection. However meteorological, physical, and biological heterogeneity in this large system present unique challenges to successfully detecting invasive weeds. We present three hyperspectral case studies which illustrate the challenges, and potential solutions, to mapping invasive weeds in wetland systems: 1) Perennial pepperweed was mapped over one portion of the Delta using a logistic regression model to predict weed occurrence. 2) Water hyacinth and 3) submerged aquatic vegetation (SAV), primarily composed of Brazilian waterweed, were mapped over the entire Delta using a binary decision tree that incorporated spectral mixture analysis (SMA), spectral angle mapping (SAM), band indexes, and continuum removal products. Perennial pepperweed detection was moderately successful; phenological stage influenced detection rates. Water hyacinth was mapped with modest accuracies, and SAV was mapped with high accuracies. Perennial pepperweed and water hyacinth both exhibited significant spectral variation related to plant phenology. Such variation must be accounted for in order to optimally map these species, and this was done for the water hyacinth case study. Submerged aquatic vegetation was not mapped to the species level due to complex non-linear mixing problems between the water column and its constituents, which was beyond the scope of the current study. We discuss our study in the context of providing guidelines for future remote sensing studies of aquatic systems.     

A multi-scale analysis of dynamic optical signals in a Southern California chaparral ecosystem: A comparison of field, AVIRIS and MODIS data

Using field data, Airborne Visible Infrared Imaging Spectrometer (AVIRIS) imagery, and Moderate-Resolution Imaging Spectroradiometer (MODIS) data, a multi-scale analysis of ecosystem optical properties was performed for Sky Oaks, a Southern California chaparral ecosystem in the spectral network (SpecNet) and FLUXNET networks. The study covered a 4-year period (2000-2004), which included a severe drought in 2002 and a subsequent wildfire in July 2003, leading to extreme perturbation in ecosystem productivity and optical properties. Two vegetation greenness indices (Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)), and a measure of the fraction of photosynthetically active radiation absorbed by vegetation (fPAR), were compared across sampling platforms, which ranged in pixel size from 1 m (tram system in the field) to 1000 m (MODIS satellite sensor). The three MODIS products closely followed the same seasonal trends as the tram and AVIRIS data, but tended to be higher than the tram and AVIRIS values, particularly for fPAR and NDVI. Following a wildfire that removed all green vegetation, the overestimation in MODIS fPAR values was particularly clear. The MODIS fPAR algorithm (version 4 vs. v.4.1) had a significant effect on the degree of overestimation, with v. 4.1 improving the agreement with the other sensors (AVIRIS and tram) for vegetated conditions, but not for low, post-fire values. The differences between MODIS products and the products from the other platform sensors could not be entirely attributed to differences in sensor spectral responses or sampling scale. These results are consistent with several other recently published studies that indicate that MODIS overestimates fPAR and thus net primary production (NPP) for many terrestrial ecosystems, and demonstrates the need for proper validation of MODIS terrestrial biospheric products by direct comparison against optical signals at other spatial scales, as is now possible at several SpecNet sites. The study also demonstrates the utility of in-situ field sampling (e.g. tram systems) and hyperspectral aircraft imagery for proper interpretation of satellite data taken at coarse spatial scales.     

Statistical analysis of soil colour and spectroradiometric data for hyperspectral remote sensing of soil properties (example in a southern Italy Mediterranean ecosystem)

Munsell hue, value and chroma of 69 surface soil samples were both visually estimated by four observers under diffuse daylight and computed from laboratory reflectance spectra by applying the CIE 1931 standard method. Significant relationships were found between 'observed' and 'computed' colour components, and between the latter and some soil properties. Using a correspondence analysis, soil colour was shown to be important in differentiating between soil types. From the original spectra, the visible bands of the MIVIS hyperspectral sensor were simulated and related to the colour components through single and multiple regression analyses. The R² values for hue, value and chroma were 0.58, 0.81 and 0.87 respectively. The results were compared with those obtained using simulated visible Thematic Mapper (TM) bands. For each sample, a curve was fitted to both the MIVIS and TM bands. From these curves, values of colour components were computed and compared with those obtained from the original spectra. Results showed a clear improvement in colour determination. Nevertheless, the complexity and variability of the best fitting curves makes this approach difficult to apply to the images. Remote sensing of soil colour is expected to improve with future launches of higher resolution hyperspectral sensors.     

Post-fire recovery of leaf area index in California chaparral: A remote sensing-chronosequence approach

Fire is a major driver of land surface transformation in California Mediterranean-type shrublands (i.e. chaparral). The re-growth of leaves following fire impacts a wide variety of ecosystem processes and information on the post-fire recovery of leaf area index (LAI) is often required in eco-hydrologic modelling studies. A few studies have reported LAI values for chaparral, but none have tracked LAI dynamics over the entire post-fire recovery sequence. In this study we used a chronosequence approach with satellite imagery to determine the post-fire development sequence of LAI for chaparral shrublands in central California. Moreover, we explored how LAI varied with differences in annual antecedent precipitation conditions (APC) and physical site factors. LAI recovery following fire was most rapid during the first 15 years, after which it remained relatively constant with increasing stand age. For a given stand age, LAI varied nonlinearly with annual APC, while spatial variations in LAI were associated with differences in topographic aspect and landscape wetness potential. However, a better understanding of the nature and interaction of these controls on LAI is needed if realistic post-fire LAI trajectories (for historic, present and future periods) for eco-hydrological modelling studies in chaparral catchments are to be developed in the future.     

BP-ANN在荒漠草地高光谱分类研究中的应用

利用高分辨率光谱仪在实地测得的光谱数据来识别新疆阜康地区的7种典型荒漠草种,对原始高光谱数据作预处理（微分和平滑）,选取典型荒漠植被的光谱特征（红边、绿峰、红谷、RVI等）作为输入数据,植被类型作为输出数据,构建基于BP神经网络模型的典型荒漠草地分类器,进行了三组基于高光谱特征的草地类型分类实验,结果表明：（1）红边特征较其余吸收特征更能获得精确的分类结果;（2）波段550~790 nm间的窄波段光谱分类间隔中,20 nm优于10 nm的间隔;（3）草地分类器中BP网络模型的输入层、隐藏层神经元个数与BP网络训练时间、精度具有复杂的耦合关系,不可一概而论。     

Characterizing patterns of plant distribution in a southern California salt marsh using remotely sensed topographic and hyperspectral data and local tidal fluctuations

We used LiDAR topographic data, AVIRIS hyperspectral data, and locally measured tidal fluctuations to characterize patterns of plant distribution within a southern California salt marsh (Carpinteria Salt Marsh (CSM)). LiDAR data required ground truthing and correction before they were suitable for use. Twenty to forty percent of the uncertainty associated with LiDAR was due to variance in the elevation of the target surface, the balance was attributed to error inherent in the LiDAR system. The incidence of LiDAR penetration of plant canopy cover (i.e., registration of ground elevation) was only three percent. The depth of LiDAR penetration into the plant canopy varied according to plant species composition; plant species-specific corrections significantly improved LiDAR accuracy (58% reduction in overall uncertainty) and with the use of ground-based surveys, reduced overall RMSE to an average of 6.3 cm in vegetated areas. A supervised classification of AVIRIS data was used to generate a vegetation map with six classification types; overall classification accuracy averaged 59% with a kappa coefficient of 0.40. The vegetation classification map was overlaid with a LiDAR-based digital elevation model (DEM) to compute elevation distributions and frequencies of tidal inundation. The average elevations of the dominant plant classifications found in CSM (e.g., Salicornia virginica, Jaumea carnosa, and salt-grass mix, a mixture of multiple marsh plant species) occurred within a 17 cm range, a vertical change that resulted in a 7% difference in the period of tidal inundation.     

Mapping carbon and water vapor fluxes in a chaparral ecosystem using vegetation indices derived from AVIRIS

Using simple models derived from spectral reflectance, we mapped the patterns of ecosystem CO₂ and water fluxes in a semi-arid site in southern California during a period of extreme disturbance, marked by drought and fire. Employing a combination of low (∼ 2 km) and high (∼ 16 km) altitude images from the hyperspectral Airborne Visible Infrared Imaging Spectrometer (AVIRIS), acquired between April 2002 and September 2003, and ground data collected from an automated tram system, several vegetation indices were calculated for Sky Oaks field station, a FLUXNET and SpecNet site located in northern San Diego County (CA, USA). Based on the relationships observed between the fluxes measured by the eddy covariance tower and the vegetation indices, net CO₂ and water vapor flux maps were derived for the region around the flux tower. Despite differences in the scale of the images (from ∼ 2 m to 16 m pixel size) as well as marked differences in environmental conditions (drought in 2002, recovery in early 2003, and fire in mid 2003), net CO₂ and water flux modeled from AVIRIS-derived reflectance indices (NDVI, PRI and WBI) effectively tracked changes in tower fluxes across both drought and fire, and readily revealed spatial variation in fluxes within this landscape. After an initial period of net carbon uptake, drought and fire caused the ecosystem to lose carbon to the atmosphere during most of the study period. Our study shows the power of integrating optical and flux data in LUE models to better understand factors driving surface-atmosphere carbon and water vapor flux cycles, one of the main goals of SpecNet.     

Impact of pixel size on mapping surface water in subsolar imagery

We observed surface water in a wetland, imaging in the subsolar or specular direction the exceptionally bright specular reflection of sunlight at a ground resolution of 0.3 m. We then simulated ground resolutions between 1.7 m and 1.2 km through aggregation of the 0.3 m pixels. Contrary to the expectations of some of our colleagues in the wetlands community, for these data, the accuracy of spectral mixture analysis (SMA) estimates of surface water increases as pixel ground footprint size increases. Our results suggest that regional to global scale assessments of flooded landscapes and wetlands that do not involve issues requiring 1 m resolution per se may be addressed with acceptable accuracy by applying SMA techniques to low resolution imagery. Our results indicate within-pixel estimates of surface water area derived from data measured by subsolar viewing sensors with large ground pixel footprints, such as satellite POLarization and Directionality of Earth Radiance (POLDER) data, may be highly accurate under strong surface wind conditions.     

Multidate MESMA for monitoring vegetation growth forms in southern California shrublands

Identifying habitats that should be protected from further disturbance or conversion and isolating high-risk areas is a focus of community habitat plans in southern California shrublands. Larger wildfires are occurring at shorter intervals in recent decades, contributing to degradation and conversion of shrubland vegetation. Multitemporal remote-sensing approaches can bridge the gap between vegetation mapping and field sampling in habitats where frequent quantification and mapping of vegetation growth forms over large extents is required. The objective of this study is to examine the reliability and stability of a multiple endmember spectral mixture analysis (MESMA) approach with moderate spatial resolution imagery for monitoring changes in growth form fractional cover in shrubland habitats. Estimates from visual interpretation of high spatial resolution image were used as reference data for validating MESMA-derived maps and as basis for providing complementary monitoring protocols that may be accurate and cost-effective across multiple scales. Growth form proportions modelled in burned and unburned management areas compare well with expected fractional cover in mature and regenerating shrublands. In the management areas recovering from fire, herbaceous cover fraction exceeded 0.40 for all three study dates, suggesting that large portions of those management areas may already be invaded. From 2008 to 2011 overall herbaceous cover fraction in shrubland area increased by 2%. Herbaceous cover fraction was modelled with an overall mean absolute error (MAE) of 0.08, a smaller percentage than the percentage of herbaceous cover change recorded in areas recovering from fire (increase in herbaceous cover fraction from 0.09 to 0.13). This MESMA approach would be effective for quantifying changes in fractional cover that exceed 0.10, providing a way to delineate and quantify herbaceous invasions and expansions following disturbance or succession.     

Close-range hyperspectral imaging for geological field studies: workflow and methods

Close-range hyperspectral imaging is a new method for geological research, in which imaging spectrometry is applied from the ground, allowing the mineralogy and lithology in near-vertical cliff sections to be studied in detail. Contemporary outcrop studies often make use of photorealistic three-dimensional (3D) models, derived from terrestrial laser scanning (lidar), that facilitate geological interpretation of geometric features. Hyperspectral imaging provides complementary geochemical information that can be combined with lidar models, enhancing quantitative and qualitative analyses. This article describes a complete workflow for applying close-range hyperspectral imaging, from planning the optimal scan conditions and data acquisition, through pre-processing the hyperspectral imagery and spectral mapping, integration with lidar photorealistic 3D models, and analysis of the geological results. Pre-processing of the hyperspectral images involves the reduction of scanner artefacts and image discontinuities, as well as relative reflectance calibration using empirical line correction, based on two calibrated reflection targets. Signal-to-noise ratios better than 70:1 are achieved for materials with 50% reflectance. The lidar-based models are textured with products such as hyperspectral classification maps. Examples from carbonate and siliciclastic geological environments are presented, with results showing that spectrally similar material, such as different dolomite types or sandstone and siltstone, can be distinguished and spectrally mapped. This workflow offers a novel and flexible technique for applications, in which a close-range instrument setup is required and the spatial distribution of minerals or chemical variations is valuable.     

Spatial and temporal patterns of remotely-sensed and field-measured rainfall in southern California

Quantification of spatial and temporal patterns of rainfall is an important step toward developing regional hydrological models. However, traditionally used rain gauge data are sparse and do not always provide adequate spatial representation of rainfall. In this study, we evaluated the daily 1-degree resolution remotely-sensed atmospheric precipitation data provided by Global Precipitation Climatology Project (GPCP) as an alternative to rain gauge-measured data. We analyzed data from the watersheds of southern California during the period of 1996-2003, focusing on the comparison of patterns of spatial, seasonal, and interannual rainfall dynamics. We used Empirical Orthogonal Functions to discern the patterns of precipitation and atmospheric circulation at different time scales, from synoptic to interannual. The correlation between the daily rain gauge-measured and remotely-sensed precipitation was poor and the resulting patterns of remotely-sensed precipitation are different than the temporal patterns of precipitation accumulated by rain gauges. These differences likely result from the fact that the precipitable water concentration measured by satellites is not always highly correlated to rainfall reaching the earth surface. Differences in the spatial resolution and coverage of the two methods and the differential influence of orographic effects and wind patterns on each also contribute to low correlations. We conclude that daily remotely-sensed precipitation produced at GPCP is not currently appropriate for use in assessing fine-scale hydrological processes in arid zones like southern California, and would not be a recommended surrogate for event-based hydrologic modeling. At the same time, the interannual variabilities of remotely-sensed and gauge-measured precipitation were highly correlated and the regional patterns of gauge-measured and remotely-sensed precipitation variability were similar; though the total precipitation estimated from satellite data was substantially lower than the gauge-measured data. Therefore, remotely-sensed precipitation data may be appropriate for use in long-term regional hydrologic or climate modeling focused on trends and patterns of rainfall in southern California. Both data sets showed that precipitation generally decreases from the northern to the southern watersheds. At interannual time-scale, the rainfall is related to the ENSO cycle. At synoptic time-scales, the rainfall patterns in southern California result from atmospheric moisture transport from the south-southwest.     

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.     

A novel data hiding for color images based on pixel value difference and modulus function

This paper proposes a novel data hiding method using pixel-value difference and modulus function for color image with the large embedding capacity(hiding 810757 bits in a 512×512 host image at least) and a high-visual-quality of the cover image. The proposed method has fully taken into account the correlation of the R, G and B plane of a color image. The amount of information embedded the R plane and the B plane determined by the difference of the corresponding pixel value between the G plane and the median of G pixel value in each pixel block. Furthermore, two sophisticated pixel value adjustment processes are provided to maintain the division consistency and to solve underflow and overflow problems. The most importance is that the secret data are completely extracted through the mathematical theoretical proof.     

Analysis of energy fluxes and land surface parameters in a grassland ecosystem: a remote sensing perspective

A remote sensing‐based land surface characterization and flux estimation study was conducted using Landsat data from 1997 to 2003 on two grazing land experimental sites located at the Agricultural Research Services (ARS), Mandan, North Dakota. Spatially distributed surface energy fluxes [net radiation (R _n), soil heat flux (G), sensible heat (H), latent heat (LE)] and surface parameters [emissivity (ε), albedo (α), normalized difference vegetation index (NDVI) and surface temperature (T _sur)] were estimated and mapped at a pixel level from Landsat images and weather information using the Surface Energy Balance Algorithm for Land (SEBAL) procedure as a function of grazing land management: heavily grazed (HGP) and moderately grazed pastures (MGP). Energy fluxes and land surface parameters were mapped and comparisons were made between the two sites. Over the study period, H, ε and T _sur from HGP were higher by 6.7%, 18.2% and 2.9% than in MGP, respectively. The study also showed that G, LE and NDVI were higher by 1.3%, 1.6% and 7.4% for MGP than in HGP, respectively. The results of this study are beneficial in understanding the trends of land surface parameters, energy and water fluxes as a function of land management.     

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.