A new approach to satellite-based estimation of precipitation over snow cover

Current satellite-based remote-sensing approaches are largely incapable of estimating precipitation over snow cover. This note reports a proof-of-concept study of a new satellite-based approach to the estimation of precipitation over snow-covered surfaces. The method is based on the principle that precipitation can be inferred from the changes in the snow water equivalent of the snowpack. Using satellite-based snow water equivalent measurements, we derived daily precipitation amounts for the northern hemisphere for three snow-accumulation seasons, and evaluated these against independent reference datasets. The new precipitation estimates captured realistic-looking storm events over largely un-instrumented regions. However, the data are noisy and, on a seasonal scale, the amount of precipitation is believed to be underestimated. Nevertheless, current uncertainty in snow measurements, albeit large (50–100%), is still lower than direct precipitation measurements over snow (100–140%) and therefore this approach is still useful. The method will become more feasible as the quality of remotely sensed snow measurements improves.     

Lidar remote sensing of forest biomass: A scale-invariant estimation approach using airborne lasers

Researchers in lidar (Light Detection And Ranging) strive to search for the most appropriate laser-based metrics as predictors in regression models for estimating forest structural variables. Many previously developed models are scale-dependent that need to be fitted and then applied both at the same scale or pixel size. The objective of this paper is to develop methods for scale-invariant estimation of forest biomass using lidar data. We proposed two scale-invariant models for biomass: a linear functional model and an equivalent nonlinear model that use lidar-derived canopy height distributions (CHD) and canopy height quantile functions (CHQ) as predictors, respectively. The two models are called functional regression models because the predictors CHD and CHQ are themselves functions or functional data. The model formulation was justified mathematically under moderate assumptions. We also created a fine-resolution biomass map by mapping individual tree component biomass in a temperate forest of eastern Texas with a lidar tree-delineation approach. The map was used as reference data to synthesize training and test datasets at multiple scales for validating the two scale-invariant models. Results suggest that the models can accurately predict biomass and yield consistent predictive performances across a variety of scales with an R² ranging from 0.80 to 0.95 (RMSE: from 14. 3 Mg/ha to 33.7 Mg/ha) among all the fitted models. Results also show that a training data size of around 50 plots or less was enough to guarantee a good fitting of the linear functional model. Our findings demonstrate the effectiveness of CHD and CHQ as lidar metrics for estimating biomass as well as the capability of lidar for mapping biomass at a range of scales. The functional regression models of this study are useful for lidar-based forest inventory tasks where the analysis units vary in size and shape. They also hold promise for estimating other forest characteristics such as below-ground biomass, timber volume, crown fuel weight, and Leaf Area Index.     

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.     

Fuzzy reliability estimation using Bayesian approach

The Bayesian reliability estimation under fuzzy environments is proposed in this paper. In order to apply the Bayesian approach, the fuzzy parameters are assumed to be fuzzy random variables with fuzzy prior distributions. The (conventional) Bayesian estimation method will be used to create the fuzzy Bayes point estimator of reliability by invoking the well-known theorem called ‘Resolution Identity’ in fuzzy sets theory. On the other hand, we also provide the computational procedures to evaluate the membership degree of any given Bayes point estimate of reliability. In order to achieve this purpose, we transform the original problem into a nonlinear programming problem. This nonlinear programming problem is then divided into four subproblems for the purpose of simplifying computation. Finally, the subproblems can be solved by using any commercial optimizers, e.g. GAMS or LINGO.     

Satellite estimation of forest carbon using regression models

Periodic monitoring of forest carbon is important, since forest cover is changing rapidly in many parts of the world, and becomes a major source of terrestrial carbon emission that may be one of the main drivers of global climate change. Regression is often used to estimate forest variables (including carbon) using satellite sensor data though a low coefficient of determination (R ²) is apparent and this research was designed to investigate both traditional and alternate regression approaches to increase the magnitude of R ². The study area was located in southeastern Bangladesh. Data from Landsat Enhanced Thematic Mapper Plus (ETM+) and ground‐based forest survey were used. This research explored the use of dummy variables in regression models to increase R ², while the dummies were set from the optimal stratification of forestland. The finding will heighten the accuracy of forest attribute estimation and help to understand terrestrial carbon dynamics and global climate change.     

Effects of chlorophyll fluorescence on the estimation of microphytobenthos biomass using spectral reflectance indices

The communities of benthic microalgae that form dense biofilms at the surface of aquatic sediments, or microphytobenthos, are important primary producers in estuarine intertidal flats and shallow coastal waters. The microalgal biomass present in the photic zone of the sediment is a key parameter for ecological and photophysiological studies on microphytobenthos, and has been routinely estimated using hyperspectral reflectance indices based on the chlorophyll (Chl) a red absorption peak at 675 nm, usually the Normalised Difference Vegetation Index (NDVI). This study reports that red region-based biomass indices measured on microphytobenthos biofilms can be significantly affected by the enrichment of reflected light with solar-induced Chl fluorescence emitted by the microalgae. Chl fluorescence emission peaks at 683 nm, counterbalancing the decrease in reflectance centered at 675 nm, thus causing the underestimation of NDVI. The interference of Chl fluorescence was found to be easily identified by a conspicuous double-peak feature in the 670-700 nm region of the second-derivative reflectance spectra. The fluorescence-induced NDVI underestimation was shown to be most pronounced for high surface biomass levels and low incident solar irradiance. Particular aspects of microphytobenthos biofilms, such as the increase in surface Chl fluorescence due the contribution of emission by subsurface layers, and vertical migratory responses by motile microalgae to changes in ambient light, further complicate the effects on biomass estimation using NDVI-like indices. By comparing NDVI with a fluorescence-independent biomass index for a wide range of natural light conditions, it was found that Chl fluorescence interference may cause the underestimation of microalgal biomass to reach over 25%, with errors above 10% being expected for more than half of the measuring occasions. These results indicate that the use of NDVI may compromise the correct assessment of important aspects of microphytobenthos ecology, such as the characterisation of migratory behaviour or the determination of biomass-specific productivity rates, and call for the use of alternative biomass indices, not based on the Chl a red absorption peak.     

Nonlinear motion estimation using the supercoupling approach

This paper presents the application of a very efficient multiresolution transformation, which is related to the renormalization group approach of physics, to the problem of motion segmentation. The approach proposed is much faster and yields much better results than the full resolution approach. The problem is formulated as one of global optimization where a cost function is constructed to combine the information obtained by various processors as well as the constraints we impose to the problem. The cost function is optimized using the supercoupling multiresolution approach     

Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon

The complicated forest stand structure and associated abundant tree species in the Amazon often induce difficulty in estimating aboveground biomass (AGB) using remotely sensed data. This paper explores AGB estimation using Landsat Thematic Mapper (TM) data in the eastern and western Brazilian Amazon, and discusses the impacts of forest stand structure on AGB estimation. Estimating AGB is still a challenging task, especially for the sites with complicated biophysical environments. The TM spectral responses are more suitable for AGB estimation in the sites with relatively simple forest stand structure than for the sites with complicated forest stand structure. Conversely, textures appear more important than spectral responses in AGB estimation in the sites with complicated forest stand structure. A combination of spectral responses and textures improves AGB estimation performance. Different study areas having various biophysical conditions affect AGB estimation performance.     

Savanna and tropical rainforest biomass estimation and spatialization using JERS-1 data

The objective of this study is to show the relation among backscatter signals of JERS-1 images and biophysical parameters (biomass values) of forest and savanna formations. Two contact zones involving these vegetation units in Brazilian Amazonia (Roraima and Mato Grosso States) were selected. A regression model was applied during the analysis of these two variables, based on the best fit function and taking into account the data dispersion. Maps were generated showing biomass spatialization of the vegetation typology found in the study areas. The importance of this study is the innovation referring to the joint analysis of JERS-1 data of these two contact zones in Amazonia, representing both an abrupt contact and a smooth contact along a transition zone of savanna/tropical rainforests formations.     

Sunflower biomass estimation using a scattering model and a neural network algorithm

Inversion of biomass for sunflower fields using radar backscattering data has been carried out with neural network algorithms. An electromagnetic model is used to generate the scattering coefficients for training and testing of the net. The model is validated with experimental data obtained from the Montespertoli test site during the Remote Sensing Campaign Mac-Europe 91. The inversion results show that the neural network is capable of performing the retrieval with good accuracy. By optimizing the structural complexity of the net, a better inversion result is obtained.     

Optimal estimation in distributed processes using the innovations approach

An application of the innovations approach for tackling the state estimation problem for a class of linear distributed parameter systems is considered. This, in effect, leads to a unified treatment of filtering, smoothing, and prediction problems.     

Mangrove biomass carbon stock mapping of the Karimunjawa Islands using multispectral remote sensing

Among vegetated coastal habitats, mangrove forests are among the densest carbon pools. They store their organic carbon in the surrounding soil and thus the sequestered carbon stays in the sediment for a long time and cannot be easily returned to the atmosphere. Additionally, mangroves also provide various important ecosystem services in coastal areas and surroundings. Accordingly, it is important to understand the distribution of biomass carbon stock in mangrove habitats in a spatial and temporal context, not only to reduce CO₂ concentrations in the atmosphere, but also for their sustainability. The objectives of this research are to map the mangrove carbon stock and estimate the total biomass carbon stock sheltered by mangrove forests, with the Karimunjawa Islands as a study site, using the widely available passive remote sensing system ALOS AVNIR-2. The modelling and mapping of mangrove carbon stock incorporates the integration of image pixel values and mangroves field data via empirical modelling. Vegetation indices and PC bands at different levels of radiometric corrections were all used as the input in the mangrove carbon stock modelling so that the effectiveness and sensitivity of different image transformations to particular radiometric correction levels could be analysed and understood. Afterward, the accuracy and effectiveness of each mangrove carbon stock-mapping routine was compared and evaluated. The accuracy of the best mangrove above-ground carbon stock (AGC) map modelled from vegetation index is 77.1% (EVI1, SE 5.89 kg C m^?2), and for mangrove below-ground carbon stock (BGC) it is 60.0% (GEMI, SE 2.54 kg C m^?2). The mangrove carbon stock map from ALOS AVNIR-2 PC bands showed a maximum accuracy of 77.8% (PC2, SE 5.71 kg C m^?2) and 60.8% (PC2, SE 2.48 kg C m^?2) for AGC and BGC respectively. From the resulting maps, the Karimunjawa Islands are estimated to shelter 96,482 tonnes C of mangroves AGC with a mean value of 21.64 kg C m^?2 and 24,064 tonnes C of mangroves BGC with a mean value of 5.39 kg C m^?2. Potentially, there are approximately 120,546 tonnes C of mangrove biomass carbon stock in the Karimunjawa Islands. Remote-sensing reflectance can successfully model mangrove carbon stock based on the relationship between mangrove canopy properties, represented by leaf area index (LAI) and the tree or root biomass carbon stock. The accuracy of the mangrove carbon stock map is subject to errors, which are sourced mainly from: (1) the absence of a species-specific biomass allometric equation for several species present in the study area; (2) the generalized standard conversion value of mangrove biomass to mangrove carbon stock; (3) the relationship between mangrove reflectance and mangrove LAI; (4) the relationship between mangrove reflectance and above-ground mangrove biomass and carbon stock due to its relationship with LAI; (5) the relationship between mangrove LAI and mangrove below-ground parts; (6) the inability to perform mangrove carbon stock modelling at the species level due to the complexities of the mangrove forest in the study area; (7) background reflectance and atmospheric path radiance that could not be completely minimized using image radiometric corrections and transformations; and (8) spatial displacement between the actual location of the mangrove forest in the field and the corresponding pixel in the image. The availability of mangrove biomass carbon stock maps is beneficial for carrying out various management activities, and is also very important for the resilience of mangroves to changing environments.     

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.     

A novel approach to robust parameter estimation using neurofuzzy systems

A novel approach for solving robust parameter estimation problems is presented for processes with unknown-but-bounded errors and uncertainties. An artificial neural network is developed to calculate a membership set for model parameters. Techniques of fuzzy logic control lead the network to its equilibrium points. Simulated examples are presented as an illustration of the proposed technique. The result represent a significant improvement over previously proposed methods.     

A two-level parameter estimation algorithm using the multiple projection approach

An efficient two-level algorithm is developed for parameter estimation using the multiple projection approach. The optimal minimum variance estimate is achieved using a fixed number of iterations. Both the recursive and non-recursive versions of the algorithm are presented. Simulation results of two examples have indicated that the new two-level algorithm provides accurate estimates whilst needing a reduced amount of computational effort.     

纳米材料在GOD电极中应用的近期研究进展

最近有关纳米材料应用于葡萄糖氧化酶电极中检测葡萄糖的研究较多,纳米材料的应用使葡萄糖氧化酶电极的性能得到了改善.该文综述了近三年纳米材料在葡萄糖氧化酶电极(GOD)中应用的最新研究进展.     

Development of real-time reference evapotranspiration at the regional scale using satellite-based observations

Real-time data of reference evapotranspiration (ET₀) at different space-time scales are essential to regional agricultural drought assessment, water accounting at the watershed to basin scale, and provide irrigation advisory to farmers. Here, we present a data-fusion approach that integrates satellite-based insolation product (8 km) from an Indian geostationary satellite (Kalpana-1) sensor (VHRR; Very High Resolution Radiometer) and high-resolution (~ 5 km) short-range weather forecast into an FAO56 model based on the classical Penman–Monteith (P-M) formulation. Five year (2009–2013) mean monthly estimates from the daily ET₀ product over the Indian landmass were found to vary between 10 and 350 mm. It increased from January to May (70–350 mm), followed by a decrease to reach the lowest in November (10–140 mm), thus typically showing unimodal distribution. The comparison of daily space-based and station-based estimates (at six ground stations) produced a root mean square deviation (RMSD) ranging from 21% to 38% for 977 paired data sets with the correlation coefficient (r) varying from 0.32 to 0.82. The error was reduced from 25% to 10% with an increase in ‘r’ from 0.43 to 0.98 for daily to 10 day summation period. Spatial grid-to-grid comparison of monthly ET₀ estimates with Global Data Assimilation System (GDAS) potential evapotranspiration (PET) showed RMSD within a range of 1.4–18.4% for most of the months, except for two. Further ET₀ analysis over normal and drought years showed that it could be used for comprehensive drought assessment with other existing indicators.     

A new methodology for the estimation of biomass of coniferdominated boreal forest using NOAA AVHRR data

In this paper, a new methodology to estimate the biomass (organic matter) of conifer-dominated boreal forests is developed. It aims to estimate biomass of extensive areas where ground data are limited. First, the principal models are computed using ground measurements and high resolution satellite images. Spectral models are then applied directly to a calibrated AVHRR image mosaic covering the entire area of interest. This methodology was tested quantitatively in Finland, where detailed forest measurement data are available, on an area reaching from the west coast of Norway to the Ural mountains. The methodology appeared to perform beyond pre-test expectation.     

A two-stage short-term load forecasting approach using temperature daily profiles estimation

Neural Computing and Applications - Electrical load forecasting plays an important role in the regular planning of power systems, in which load is influenced by several factors that must be...