Interpretation and topographic compensation of conifer canopy self-shadowing

The self-shadowing of conifer canopies results from the size and arrangement of trees within a stand and is a first-order term controlling radiance from forested terrain at common pixel scales of tens of meters. Although self-shadowing is a useful attribute for forest remote-sensing classification, compensation for the topographic effects of self-shadowing has proven problematic. This study used airborne canopy LiDAR measurements of 80 Pacific Northwest, USA conifer stands ranging in development stage from pre-canopy closure to old-growth in order to model canopy self-shadowing for four solar zenith angles (SZA). The shadow data were compared to physical measurements used to characterize forest stands, and were also used to test and improve terrain compensation models for remotely sensed images of forested terrain. Canopy self-shadowing on flat terrain strongly correlates with the canopy's geometric complexity as measured by the rumple index (canopy surface area/ground surface area) (R² = 0.94–0.87 depending on SZA), but is less correlated with other stand measurements: 95th percentile canopy height (R² = 0.68), mean diameter at breast height (dbh) (R² = 0.65), basal area ha^? 1 (R² = 0.18), and canopy stem count ha^? 1 (R² = 0.18). The results in this paper support interpretation of self-shadowing as a function of canopy complexity, which is an important ecological characteristic in its own right. Modeling of canopy self-shadowing was used to assess the accuracy of the Sun-Canopy-Sensor (SCS) topographic correction, and to develop a new empirical Adaptive Shade Compensation (ASC) topographic compensation model. ASC used measured shadow (as an estimate of canopy complexity) and the SCS term (to describe the illumination geometry) as independent variables in multiple regressions to determine the topographic correction. The ASC model provided more accurate radiance corrections with limited variation in results across the full range of canopy complexities and incidence angles.     

Consensus scoring model for the molecular docking study of mTOR kinase inhibitor

The discovery of mammalian target of rapamycin (mTOR) kinase inhibitors has always been a research hotspot of antitumor drugs. Consensus scoring used in the docking study of mTOR kinase inhibitors usually improves hit rate of virtual screening. Herein, we attempt to build a series of consensus scoring models based on a set of the common scoring functions. In this paper, twenty-five kinds of mTOR inhibitors (16 clinical candidate compounds and 9 promising preclinical compounds) are carefully collected, and selected for the molecular docking study used by the Glide docking programs within the standard precise (SP) mode. The predicted poses of these ligands are saved, and revaluated by twenty-six available scoring functions, respectively. Subsequently, consensus scoring models are trained based on the obtained rescoring results by the partial least squares (PLS) method, and validated by Leave-one-out (LOO) method. In addition, three kinds of ligand efficiency indices (BEI, SEI, and LLE) instead of pIC₅₀ as the activity could greatly improve the statistical quality of build models. Two best calculated models 10 and 22 using the same BEI indice have following statistical parameters, respectively: for model 10, training set R² = 0.767, Q² = 0.647, RMSE = 0.024, and for test set R² = 0.932, RMSE = 0.026; for model 22, raining set R² = 0.790, Q² = 0.627, RMSE = 0.023, and for test set R² = 0.955, RMSE = 0.020. These two consensus scoring model would be used for the docking virtual screening of novel mTOR inhibitors.     

Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels

Variation in the foliar chemistry of humid tropical forests is poorly understood, and airborne imaging spectroscopy could provide useful information at leaf and canopy scales. However, variation in canopy structure affects our ability to estimate foliar properties from airborne spectrometer data, yet these structural affects remain poorly quantified. Using leaf spectral (400–2500 nm) and chemical data collected from 162 Australian tropical forest species, along with partial least squares (PLS) analysis and canopy radiative transfer modeling, we determined the strength of the relationship between canopy reflectance and foliar properties under conditions of varying canopy structure.At the leaf level, chlorophylls, carotenoids and specific leaf area (SLA) were highly correlated with leaf spectral reflectance (r = 0.90–0.91). Foliar nutrients and water were also well represented by the leaf spectra (r = 0.79–0.85). When the leaf spectra were incorporated into the canopy radiative transfer simulations with an idealistic leaf area index (LAI) = 5.0, correlations between canopy reflectance spectra and leaf properties increased in strength by 4–18%. The effects of random LAI (= 3.0–6.5) variation on the retrieval of leaf properties remained minimal, particularly for pigments and SLA (r = 0.92–0.93). In contrast, correlations between leaf nitrogen (N) and canopy reflectance estimates decreased from r = 0.87 at constant LAI = 5 to r = 0.65 with randomly varying LAI = 3.0–6.5. Progressive increases in the structural variability among simulated tree crowns had relatively little effect on pigment, SLA and water predictions. However, N and phosphorus (P) were more sensitive to canopy structural variability. Our modeling results suggest that multiple leaf chemicals and SLA can be estimated from leaf and canopy reflectance spectroscopy, and that the high-LAI canopies found in tropical forests enhance the signal via multiple scattering. Finally, the two factors we found to most negatively impact leaf chemical predictions from canopy reflectance were variation in LAI and viewing geometry, which can be managed with new airborne technologies and analytical methods.     

A lidar-based hierarchical approach for assessing MODIS fPAR

The purpose of this study was to estimate the fraction of photosynthetically active radiation absorbed by the canopy (fPAR) from point measurements to airborne lidar for hierarchical scaling up and assessment of the Moderate Resolution Imaging Spectroradiometer (MODIS) fPAR product within a “medium-sized” (7 km × 18 km) watershed. Nine sites across Canada, containing one or more (of 11) distinct species types and age classes at varying stages of regeneration and seasonal phenology were examined using a combination of discrete pulse airborne scanning Light Detection And Ranging (lidar) and coincident analog and digital hemispherical photography (HP). Estimates of fPAR were first compared using three methods: PAR radiation sensors, HP, and airborne lidar. HP provided reasonable estimates of fPAR when compared with radiation sensors. A simplified fractional canopy cover ratio from lidar based on the number of within canopy returns to the total number of returns was then compared with fPAR estimated from HP at 486 geographically registered measurement locations. The return ratio fractional cover method from lidar compared well with HP-derived fPAR (coefficient of determination = 0.72, RMSE = 0.11), despite varying the lidar survey configurations, canopy structural characteristics, seasonal phenologies, and possible slight inaccuracies in location using handheld GPS at some sites. Lidar-derived fractional cover estimates of fPAR were ～ 10% larger than those obtained using HP (after removing wood components), indicating that lidar likely provides a more realistic estimate of fPAR than HP when compared with radiation sensors. Finally, fPAR derived from lidar fractional cover was modelled at 1 m resolution and averaged over 99 1 km areas for comparison with MODIS fPAR. The following study is one of the first to scale between plot measurements and MODIS pixels using airborne lidar.     

Remote sensing of the distribution and abundance of host species for spruce budworm in Northern Minnesota and Ontario

Insects and disease affect large areas of forest in the U.S. and Canada. Understanding ecosystem impacts of such disturbances requires knowledge of host species distribution patterns on the landscape. In this study, we mapped the distribution and abundance of host species for the spruce budworm (Choristoneura fumiferana) to facilitate landscape scale planning and modeling of outbreak dynamics. We used multi-temporal, multi-seasonal Landsat data and 128 ground truth plots (and 120 additional validation plots) to map basal area (BA), for 6.4 million hectares of forest in northern Minnesota and neighboring Ontario. Partial least-squares (PLS) regression was used to determine relationships between ground data and Landsat sensor data. Subsequently, BA was mapped for all forests, as well as for two specific host tree genera (Picea and Abies). These PLS regression analyses yielded estimates for overall forest BA with an R² of 0.62 and RMSE of 4.67 m² ha^? 1 (20% of measured BA), white spruce relative BA with an R² of 0.88 (RMSE = 12.57 m² ha^? 1 [20% of measured]), and balsam fir relative BA with an R² of 0.64 (RMSE = 6.08 m² ha^? 1 [33% of measured]). We also used this method to estimate the relative BA of deciduous and coniferous species, each with R² values of 0.86 and RMSE values of 9.89 m² ha^? 1 (23% of measured) and 9.78 m² ha^? 1 (16% of measured), respectively. Of note, winter imagery (with snow cover) and shortwave infrared-based indices – especially the shortwave infrared/visible ratio – strengthened the models we developed. Because ground measurements were made largely in forest stands containing spruce and fir, modeled results are not applicable to stands dominated by non-target conifers such as pines and cedar. PLS regression has proven to be an effective modeling tool for regional characterization of forest structure within spatially heterogeneous forests using multi-temporal Landsat sensor data.     

Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content,leaf mass per area,leaf area index and leaf canopy biomass

This article aims at finding efficient hyperspectral indices for the estimation of forest sun leaf chlorophyll content (CHL, µg cm_leaf^? 2), sun leaf mass per area (LMA, g_{dry matter} m_leaf^? 2), canopy leaf area index (LAI, m²_leaf m_soil^? 2) and leaf canopy biomass (B_leaf, g_{dry matter} m_soil^? 2). These parameters are useful inputs for forest ecosystem simulations at landscape scale. The method is based on the determination of the best vegetation indices (index form and wavelengths) using the radiative transfer model PROSAIL (formed by the newly-calibrated leaf reflectance model PROSPECT coupled with the multi-layer version of the canopy radiative transfer model SAIL). The results are tested on experimental measurements at both leaf and canopy scales. At the leaf scale, it is possible to estimate CHL with high precision using a two wavelength vegetation index after a simulation based calibration. At the leaf scale, the LMA is more difficult to estimate with indices. At the canopy scale, efficient indices were determined on a generic simulated database to estimate CHL, LMA, LAI and Bleaf in a general way. These indices were then applied to two Hyperion images (50 plots) on the Fontainebleau and Fougères forests and portable spectroradiometer measurements. They showed good results with an RMSE of 8.2 µg cm^? 2 for CHL, 9.1 g m^? 2 for LMA, 1.7 m² m^? 2 for LAI and 50.6 g m^? 2 for Bleaf. However, at the canopy scale, even if the wavelengths of the calibrated indices were accurately determined with the simulated database, the regressions between the indices and the biophysical characteristics still had to be calibrated on measurements. At the canopy scale, the best indices were: for leaf chlorophyll content: ND_chl = (ρ₉₂₅ ? ρ₇₁₀)/(ρ₉₂₅ + ρ₇₁₀), for leaf mass per area: ND_LMA = (ρ₂₂₆₀ ? ρ₁₄₉₀)/(ρ₂₂₆₀ + ρ₁₄₉₀), for leaf area index: D_LAI = ρ₁₇₂₅ ? ρ₉₇₀, and for canopy leaf biomass: ND_Bleaf = (ρ₂₁₆₀ ? ρ₁₅₄₀)/(ρ₂₁₆₀ + ρ₁₅₄₀).     

A pseudo-waveform technique to assess forest structure using discrete lidar data

The use of airborne laser scanning systems (lidar) to describe forest structure has increased dramatically since height profiling experiments nearly 30 years ago. The analyses in most studies employ a suite of frequency-based metrics calculated from the lidar height data, which are systematically eliminated from a full model using stepwise multiple linear regression. The resulting models often include highly correlated predictors with little physical justification for model formulation. We propose a method to aggregate discrete lidar height and intensity measurements into larger footprints to create “pseudo-waves”. Specifically, the returns are first sorted into height bins, sliced into narrow discrete elements, and finally smoothed using a spline function. The resulting “pseudo-waves” have many of the same characteristics of traditional waveform lidar data. We compared our method to a traditional frequency-based method to estimate tree height, canopy structure, stem density, and stand biomass in coniferous and deciduous stands in northern Wisconsin (USA). We found that the pseudo-wave approach had strong correlations for nearly all tree measurements including height (cross validated adjusted R² (R²_cv) = 0.82, RMSE_cv = 2.09 m), mean stem diameter (R²_cv = 0.64, RMSE_cv = 6.15 cm), total aboveground biomass (R²_cv = 0.74, RMSE_cv = 74.03 kg ha^− 1), and canopy coverage (R²_cv = 0.79, RMSE_cv = 5%). Moreover, the type of wave (derived from height and intensity or from height alone) had little effect on model formulation and fit. When wave-based and frequency-based models were compared, fit and mean square error were comparable, leading us to conclude that the pseudo-wave approach is a viable alternative because it has 1) an increased breadth of available metrics; 2) the potential to establish new meaningful metrics that capture unique patterns within the waves; 3) the ability to explain metric selection based on the physical structure of forests; and 4) lower correlation among independent variables.     

Evaluation of a non-point source pollution model,AnnAGNPS, in a tropical watershed

Impaired water quality caused by human activity and the spread of invasive plant and animal species has been identified as a major factor of degradation of coastal ecosystems in the tropics. The main goal of this study was to evaluate the performance of AnnAGNPS (Annualized Non-Point Source Pollution Model), in simulating runoff and soil erosion in a 48 km² watershed located on the Island of Kauai, Hawaii. The model was calibrated and validated using 2 years of observed stream flow and sediment load data. Alternative scenarios of spatial rainfall distribution and canopy interception were evaluated. Monthly runoff volumes predicted by AnnAGNPS compared well with the measured data (R² = 0.90, P < 0.05); however, up to 60% difference between the actual and simulated runoff were observed during the driest months (May and July). Prediction of daily runoff was less accurate (R² = 0.55, P < 0.05). Predicted and observed sediment yield on a daily basis was poorly correlated (R² = 0.5, P < 0.05). For the events of small magnitude, the model generally overestimated sediment yield, while the opposite was true for larger events. Total monthly sediment yield varied within 50% of the observed values, except for May 2004. Among the input parameters the model was most sensitive to the values of ground residue cover and canopy cover. It was found that approximately one third of the watershed area had low sediment yield (0–1 t ha⁻¹ y⁻¹), and presented limited erosion threat. However, 5% of the area had sediment yields in excess of 5 t ha⁻¹ y⁻¹. Overall, the model performed reasonably well, and it can be used as a management tool on tropical watersheds to estimate and compare sediment loads, and identify “hot spots” on the landscape.     

Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff

This study investigated the effects of upstream stations’ flow records on the performance of artificial neural network (ANN) models for predicting daily watershed runoff. As a comparison, a multiple linear regression (MLR) analysis was also examined using various statistical indices. Five streamflow measuring stations on the Cahaba River, Alabama, were selected as case studies. Two different ANN models, multi layer feed forward neural network using Levenberg–Marquardt learning algorithm (LMFF) and radial basis function (RBF), were introduced in this paper. These models were then used to forecast one day ahead streamflows. The correlation analysis was applied for determining the architecture of each ANN model in terms of input variables. Several statistical criteria (RMSE, MAE and coefficient of correlation) were used to check the model accuracy in comparison with the observed data by means of K-fold cross validation method. Additionally, residual analysis was applied for the model results. The comparison results revealed that using upstream records could significantly increase the accuracy of ANN and MLR models in predicting daily stream flows (by around 30%). The comparison of the prediction accuracy of both ANN models (LMFF and RBF) and linear regression method indicated that the ANN approaches were more accurate than the MLR in predicting streamflow dynamics. The LMFF model was able to improve the average of root mean square error (RMSE_ave) and average of mean absolute percentage error (MAPE_ave) values of the multiple linear regression forecasts by about 18% and 21%, respectively. In spite of the fact that the RBF model acted better for predicting the highest range of flow rate (flood events, RMSE_ave/RBF = 26.8 m³/s vs. RMSE_ave/LMFF = 40.2 m³/s), in general, the results suggested that the LMFF method was somehow superior to the RBF method in predicting watershed runoff (RMSE/LMFF = 18.8 m³/s vs. RMSE/RBF = 19.2 m³/s). Eventually, statistical differences between measured and predicted medians were evaluated using Mann-Whitney test, and differences in variances were evaluated using the Levene's test.     

Scaling-based forest structural change detection using an inverted geometric-optical model in the Three Gorges region of China

We use the Li-Strahler geometric-optical model combined with a scaling-based approach to detect forest structural changes in the Three Gorges region of China. The physical-based Li-Strahler model can be inverted to retrieve forest structural properties. One of the main input variables for the inverted model is the fractional component of sunlit background, which is calculated by using pure reflectance spectra (endmembers) of surface components. In this study, we extract these endmembers from moderate spatial resolution MODIS data using two scaling-based methods (namely, a regional based linear unmixing and a purest-pixel approach) relying on corresponding high spatial resolution Landsat TM images. Then, the forest structural property crown closure (CC) is estimated by inverting the Li-Strahler model based on the extracted endmembers. Changes in CC are mapped using MODIS mosaics dated 2002 and 2004 for the whole Three Gorges region. Validation of the estimated CC using 25 sample sites indicates that the regional scaling-based endmembers extracted using linear unmixing are more suitable to be used in combination with the inverted Li-Strahler model for monitoring the forest CC than the purest-pixel approach, and results in significantly better estimates in both years (R²₂₀₀₂ = 0.614, RMSE₂₀₀₂ = 6%, R²₂₀₀₄ = 0.631 and RMSE₂₀₀₄ = 5.2%). A change detection map of the model derived CC in 2002 and 2004 shows a decrease in CC in the eastern counties of the Three Gorges region located close to the Three Gorges Dam. An increase in CC has been observed in other counties of the Three Gorges region, implying a preliminary positive feedback on certain policy measures taken safeguarding forest structure.     

ANN based simulation and experimental verification of analytical four- and five-parameters models of PV modules

In this article, artificial neural network (ANN) is adopted to predict photovoltaic (PV) panel behaviors under realistic weather conditions. ANN results are compared with analytical four and five parameter models of PV module. The inputs of the models are the daily total irradiation, air temperature and module voltage, while the outputs are the current and power generated by the panel. Analytical models of PV modules, based on the manufacturer datasheet values, are simulated through Matlab/Simulink environment. Multilayer perceptron is used to predict the operating current and power of the PV module. The best network configuration to predict panel current had a 3–7–4–1 topology. So, this two hidden layer topology was selected as the best model for predicting panel current with similar conditions. Results obtained from the PV module simulation and the optimal ANN model has been validated experimentally. Results showed that ANN model provide a better prediction of the current and power of the PV module than the analytical models. The coefficient of determination (R²), mean square error (MSE) and the mean absolute percentage error (MAPE) values for the optimal ANN model were 0.971, 0.002 and 0.107, respectively. A comparative study among ANN and analytical models was also carried out. Among the analytical models, the five-parameter model, with MAPE = 0.112, MSE = 0.0026 and R² = 0.919, gave better prediction than the four-parameter model (with MAPE = 0.152, MSE = 0.0052 and R² = 0.905). Overall, the 3–7–4–1 ANN model outperformed four-parameter model, and was marginally better than the five-parameter model.     

Remote sensing of canopy light use efficiency in temperate and boreal forests of North America using MODIS imagery

Light use efficiency (LUE) is an important variable characterizing plant eco-physiological functions and refers to the efficiency at which absorbed solar radiation is converted into photosynthates. The estimation of LUE at regional to global scales would be a significant advantage for global carbon cycle research. Traditional methods for canopy level LUE determination require meteorological inputs which cannot be easily obtained by remote sensing. Here we propose a new algorithm that incorporates the enhanced vegetation index (EVI) and a modified form of land surface temperature (T_m) for the estimation of monthly forest LUE based on Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Results demonstrate that a model based on EVI × T_m parameterized from ten forest sites can provide reasonable estimates of monthly LUE for temperate and boreal forest ecosystems in North America with an R² of 0.51 (p < 0.001) for the overall dataset. The regression coefficients (a, b) of the LUE–EVI × T_m correlation for these ten sites have been found to be closely correlated with the average EVI (EVI_ave, R² = 0.68, p = 0.003) and the minimum land surface temperature (LST_min, R² = 0.81, p = 0.009), providing a possible approach for model calibration. The calibrated model shows comparably good estimates of LUE for another ten independent forest ecosystems with an overall root mean square error (RMSE) of 0.055 g C per mol photosynthetically active radiation. These results are especially important for the evergreen species due to their limited variability in canopy greenness. The usefulness of this new LUE algorithm is further validated for the estimation of gross primary production (GPP) at these sites with an RMSE of 37.6 g C m^? 2 month^? 1 for all observations, which reflects a 28% improvement over the standard MODIS GPP products. These analyses should be helpful in the further development of ecosystem remote sensing methods and improving our understanding of the responses of various ecosystems to climate change.     

Modelling damping ratio and shear modulus of sand–mica mixtures using genetic programming

This study presents two Genetic Programming (GP) models for damping ratio and shear modulus of sand–mica mixtures based on experimental results. The experimental database used for GP modelling is based on a laboratory study of dynamic properties of saturated coarse rotund sand and mica mixtures with various mix ratios under different effective stresses. In the tests, shear modulus, and damping ratio of the geomaterials have been measured for a strain range of 0.001% up to 0.1% using a Stokoe resonant column testing apparatus. The input variables in the developed NN models are the mica content, effective stress and strain, and the outputs are damping ratio and shear modulus. The performance of accuracies of proposed NN models are quite satisfactory (R² = 0.95 for damping ratio and R² = 0.98 for shear modulus).     

Lidar-based mapping of leaf area index and its use for validating GLOBCARBON satellite LAI product in a temperate forest of the southern USA

Lidar provides enhanced abilities to remotely map leaf area index (LAI) with improved accuracies. We aim to further explore the capability of discrete-return lidar for estimating LAI over a pine-dominated forest in East Texas, with a secondary goal to compare the lidar-derived LAI map and the GLOBCARBON moderate-resolution satellite LAI product. Specific problems we addressed include (1) evaluating the effects of analysts and algorithms on in-situ LAI estimates from hemispherical photographs (hemiphoto), (2) examining the effectiveness of various lidar metrics, including laser penetration, canopy height and foliage density metrics, to predict LAI, (3) assessing the utility of integrating Quickbird multispectral imagery with lidar for improving the LAI estimate accuracy, and (4) developing a scheme to co-register the lidar and satellite LAI maps and evaluating the consistency between them. Results show that the use of different analysts or algorithms in analyzing hemiphotos caused an average uncertainty of 0.35 in in-situ LAI, and that several laser penetration metrics in logarithm models were more effective than other lidar metrics, with the best one explaining 84% of the variation in the in-situ LAI (RMSE = 0.29 LAI). The selection of plot size and height threshold in calculating laser penetration metrics greatly affected the effectiveness of these metrics. The combined use of NDVI and lidar metrics did not significantly improve estimation over the use of lidar alone. We also found that mis-registration could induce a large artificial discrepancy into the pixelwise comparison between the coarse-resolution satellite and fine-resolution lidar-derived LAI maps. By compensating for a systematic sub-pixel shift error, the correlation between two maps increased from 0.08 to 0.85 for pines (n = 24 pixels). However, the absolute differences between the two LAI maps still remained large due to the inaccuracy in accounting for clumping effects. Overall, our findings imply that lidar offers a superior tool for mapping LAI at local to regional scales as compared to optical remote sensing, accuracies of lidar-estimate LAI are affected not only by the choice of models but also by the absolute accuracy of in-situ reference LAI used for model calibration, and lidar-derived LAI maps can serve as reliable references for validating moderate-resolution satellite LAI products over large areas.     

Relationship of a Landsat cumulative disturbance index to canopy nitrogen and forest structure

Spatially-explicit knowledge of the timing, frequency, and intensity of forest disturbances is essential for forest management, yet little is known about how disturbances such as forest harvests and insect outbreaks might accumulate in their effects over time. Capturing the many forest harvest and insect defoliation events occurring over twenty-five years, we transformed a series of Landsat images into cumulative disturbance maps covering Green Ridge State Forest (GRSF) and Savage River State Forest (SRSF) in western Maryland. These maps summed yearly ΔDI images, which were defined as the change in a yearly tasseled cap disturbance index (DI), relative to a synthetic reference condition map created by finding the minimum DI value for all years. Intensive field-plot surveys and AVIRIS imagery collected during the summer of 2009 provided measurements of forest structure and canopy nitrogen. With these data, we found that while the most recent year's ΔDI had little relation, increases in the cumulative DI were related to decreased field-measured current canopy cover (R² = 0.66 at GRSF, 0.67 at SRSF and 0.34 combined) and watershed-averaged AVIRIS canopy N (R² = 0.40 at GRSF, 0.57 at SRSF and 0.54 combined). The latter relationship was obscured at the field-plot level of analysis, suggesting that fine scale studies will also need to account for other drivers (e.g. species composition) of variability in canopy N. Nevertheless, our study demonstrates that Landsat time series data can be synthesized into cumulative metrics incorporating multiple disturbance types, which help explain important cumulative disturbance-mediated changes in ecosystem functioning.     

Monitoring land degradation risk using ASTER data: The non-evaporative fraction as an indicator of ecosystem function

There is a need to develop operational land degradation indicators for large regions to prevent losses of biological and economic productivity. Disturbance events press ecosystems beyond resilience and modify the associated hydrological and surface energy balance. Therefore, new indicators for water-limited ecosystems can be based on the partition of the surface energy into latent (λE) and sensible heat flux (H).In this study, a new methodology for monitoring land degradation risk for regional scale application is evaluated in a semiarid area of SE Spain. Input data include ASTER surface temperature and reflectance products, and other ancillary data. The methodology employs two land degradation indicators, one related to ecosystem water use derived from the non-evaporative fraction (NEF = H / (λE + H)), and another related to vegetation greenness derived from the NDVI. The surface energy modeling approach used to estimate the NEF showed errors within the range of similar studies (R² = 0.88; RMSE = 0.18 (22%)).To create quantitative indicators suitable for regional analysis, the NEF and NDVI were standardized between two possible extremes of ecosystem status: extremely disturbed and undisturbed in each climatic region to define the NEFS (NEF Standardized) and NDVIS (NDVI Standardized). The procedure was successful, as it statistically identified ecosystem status extremes for both indicators without supervision. Evaluation of the indicators at disturbed and undisturbed (control) sites, and intermediate surface variables such as albedo or surface temperature, provided insights on the main surface energy status controls following disturbance events. These results suggest that ecosystem functional indicators, such as the NEFS, can provide information related to the surface water deficit, including the role of soil properties.     

Precipitation forecasting by using wavelet-support vector machine conjunction model

A new wavelet-support vector machine conjunction model for daily precipitation forecast is proposed in this study. The conjunction method combining two methods, discrete wavelet transform and support vector machine, is compared with the single support vector machine for one-day-ahead precipitation forecasting. Daily precipitation data from Izmir and Afyon stations in Turkey are used in the study. The root mean square errors (RMSE), mean absolute errors (MAE), and correlation coefficient (R) statistics are used for the comparing criteria. The comparison results indicate that the conjunction method could increase the forecast accuracy and perform better than the single support vector machine. For the Izmir and Afyon stations, it is found that the conjunction models with RMSE=46.5 mm, MAE=13.6 mm, R=0.782 and RMSE=21.4 mm, MAE=9.0 mm, R=0.815 in test period is superior in forecasting daily precipitations than the best accurate support vector regression models with RMSE=71.6 mm, MAE=19.6 mm, R=0.276 and RMSE=38.7 mm, MAE=14.2 mm, R=0.103, respectively. The ANN method was also employed for the same data set and found that there is a slight difference between ANN and SVR methods.     

Computer-based analysis of explicit approximations to the implicit Colebrook–White equation in turbulent flow friction factor calculation

The implicit Colebrook–White equation has been widely used to estimate the friction factor for turbulent fluid-flow in rough-pipes. In this paper, the state-of-the-art review for the most currently available explicit alternatives to the Colebrook–White equation, is presented. An extensive comparison test was established on the 20 × 500 grid, for a wide range of relative roughness (ε/D) and Reynolds number (R) values (1 × 10^?6 ? ε/D ? 5 × 10^?2; 4 × 10³ ? R ? 10⁸), covering a large portion of turbulent flow zone in Moody’s diagram. Based on the comprehensive error analysis, the magnitude points in which the maximum absolute and the maximum relative error are occurred at the pair of ε/D and R values, are observed. A limiting case of the most of these approximations provided friction factor estimates that are characterized by a mean absolute error of 5 × 10^?4, a maximum absolute error of 4 × 10^?3 whereas, a mean relative error of 1.3% and a maximum relative error of 5.8%, over the entire range of ε/D and R values, respectively. For practical purposes, the complete results for the maximum and the mean relative errors versus the 20 sets of ε/D value, are also indicated in two comparative figures. The examination results for error properties of these approximations gives one an opportunity to practically evaluate the most accurate formula among of all the previous explicit models; and showing in this way its great flexibility for estimating turbulent flow friction factor. Comparative analysis for the mean relative error profile revealed, the classification for the best-fitted six equations examined was in a good agreement with those of the best model selection criterion claimed in the recent literature, for all performed simulations.