Studies on the distribution of the shortest linear recurring sequences

The distribution of the shortest linear recurrence (SLR) sequences in the Z/(p) field and over the Z/(p^e) ring is studied. It is found that the length of the shortest linear recurrent (SLRL) is always equal to n/2, if n is even and n/2 + 1 if n is odd in the Z/(p) field, respectively. On the other hand, over the Z/(p^e) ring, the number of sequences with length n can also be calculated. The recurring distribution regulation of the shortest linear recurring sequences is also found. To solve the problem of calculating the SLRL, a new simple representation of the Berlekamp-Massey algorithm is developed as well.     

Fast, prime factor, discrete Fourier transform algorithms over GF(2) for 8 ? m ? 10

In this paper it is shown that Winograd’s algorithm for computing convolutions and a fast, prime factor, discrete Fourier transform (DFT) algorithm can be modified to compute Fourier-like transforms of long sequences of 2^m − 1 points over GF(2^m), for 8 ? m ? 10. These new transform techniques can be used to decode Reed-Solomon (RS) codes of block length 2^m − 1. The complexity of this new transform algorithm is reduced substantially from more conventional methods. A computer simulation verifies these new results.     

Amperometric biosensor for catechol using electrochemical template process

A novel amperometric biosensor for the determination of catechol was developed accordingly to the electrochemical template procedure. The optimum fabricating conditions of the biosensor were studied. The resulting biosensor with the limit of less than 0.05 μM can be used for detection of catechol in the linear range of 2.5-140 μM. The maximum response current (I_max) and the Michaelis-Menten constant (k′_m) are 3.08 μA and 77.52 μM, respectively. The activation energy (E_a) of the polyphenol oxidase (PPO) catalytic reaction is 25.56 kJ mol⁻¹ in the B-R buffer. The stability of the PANI-CA biosensor fabricated with the electrochemical template process (retains 86% of the original activity after four months) is much higher than that fabricated with one-step and two-step processes (retains 75% of the original activity after four months). The effects of potential and pH on the response current of the biosensor are also described.     

Hashing via finite field

In order to produce full length probe sequences, the table size m for many existing open addressing hash functions, for example, the widely used double hashing, must be prime, i.e., m = p where p is prime. In this paper, we propose a new and efficient open addressing technique, called hashing via finite field, to construct a new class of hash functions with table size m = pⁿ, where p is prime and n ? 1. It is clear that it includes prime m as a special case when n = 1. We show that the new class of hash functions constructed via finite field produces full length probe sequences on all table elements. Also, some theoretic analysis is provided along with concrete examples.     

Preparation and performance of IPMC actuators with electrospun Nafion-MWNT composite electrodes

A new ionic polymer actuator was prepared with Nafion^®-117 membrane and electrodes made of an electrospun Nafion^®/multiwalled carbon nanotube (MWNT) web. The surfaces of composite electrodes were ion-beam coated with gold layers of 2-3 μm thickness to reduce the surface resistance. The composite electrodes offer several advantages over conventional platinum electrodes prepared via electroless plating process, i.e. flexibility, simple processability in large scales, and batch-to-batch reproducibility. The new ionic polymer-metal composite (IPMC) actuators showed a rapid and large bending motion. Under an applied potential of 3 V dc, the maximum horizontal displacement (δ_max) measured at the tip of IPMC strip (cantilever length: 20 mm) was 16.7 mm, the tip velocity in the initial linear region was 10.5 mm/s, 88% of the δ_max was reached within initial 5 s, and the generated strain% was 0.79 (13.6 mm, 7.2 mm/s, 85%, and 0.88, respectively for a conventional Nafion^®-IPMC made via the electroless plating of platinum). It was noted that the energy efficiency of strain was over 10 times higher than that of the conventional Nafion^®-IPMC. And the crack formation of metal electrode after repeated bending deformation significantly reduced with the introduction of relatively flexible electrode assembly into the IPMC architecture. The remarkable improvements in its performance were considered to be due to the efficient quantum chemical and double-layer electrostatic effects in a charge injection model, induced by the good dispersion of MWNTs through a typical electrospinning technique.     

Reliability of LiDAR derived predictors of forest inventory attributes: A case study with Norway spruce

To increase the application domain (re-use) of LiDAR-based models the random replication effects in the predictor(s) must be considered. We quantify these effects in a linear predictor (X) of four forest inventory attributes (Lorey's height HT, basal area BA, volume VOL, and stem density TPH) with LiDAR data acquired over 40 spruce-dominated large plots in southeastern Norway. A grid-based random thinning of the raw multi-echo LiDAR data, to five target densities between 0.25 m^− 2 and 2.0 m^− 2, generated 100 replications with each density. A DTM was estimated for each replicate and target pulse density. The four linear predictors were constructed from two indicators of canopy density and a posited average effect of a power-transform of echoes classified as canopy returns. Replication variance varied significantly among plots but the reliability ratio of X was high (≥ 0.92) for HT, BA and VOL but lower for TPH, especially at low pulse densities. Reliability ratios increased with pulse density. Replication variance attenuated the linear regression coefficients by about 10% and inflated the residual variance by 3-6%. A proposed calibration was effective in reducing the impact of replication effects. A proposed bootstrap procedure can be used in practice to obtain good approximations of the replication variance. With echo-densities of approximately 1 m^− 2 or higher the replication effects do not warrant the effort of a calibration.     

Parallel comparison of run-length-encoded strings on a linear systolic array

The length of the longest common subsequence (LCS) between two strings of M and N characters can be computed by an O(M × N) dynamic programming algorithm, that can be executed in O(M + N) steps by a linear systolic array. It has been recently shown that the LCS between run-length-encoded (RLE) strings of m and n runs can be computed by an O(nM + Nm − nm) algorithm that could be executed in O(m + n) steps by a parallel hardware. However, the algorithm cannot be directly mapped on a linear systolic array because of its irregular structure.In this paper, we propose a modified algorithm that exhibits a more regular structure at the cost of a marginal reduction of the efficiency of RLE. We outline the algorithm and we discuss its mapping on a linear systolic array.     

Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN)

In this paper the possibility of predicting salt concentrations in soils from measured reflectance spectra is studied using partial least squares regression (PLSR) and artificial neural network (ANN). Performance of these two adaptive methods has been compared in order to examine linear and non-linear relationship between soil reflectance and salt concentration.Experiment-, field- and image-scale data sets were prepared consisting of soil EC measurements (dependent variable) and their corresponding reflectance spectra (independent variables). For each data set, PLSR and ANN predictive models of soil salinity were developed based on soil reflectance data. The predictive accuracies of PLSR and ANN models were assessed against independent validation data sets not included in the calibration or training phase.The results of PLSR analyses suggest that an accurate to good prediction of EC can be made based on models developed from experiment-scale data (R² > 0.81 and RPD (ratio of prediction to deviation) > 2.1) for soil samples salinized by bischofite and epsomite minerals. For field-scale data sets, the PLSR predictive models provided approximate quantitative EC estimations (R² = 0.8 and RPD = 2.2) for grids 1 and 6 and poor estimations for grids 2, 3, 4 and 5. The salinity predictions from image-scale data sets by PLSR models were very reliable to good (R² between 0.86 and 0.94 and RPD values between 2.6 and 4.1) except for sub-image 2 (R² = 0.61 and RPD = 1.2).The ANN models from experiment-scale data set revealed similar network performances for training, validation and test data sets indicating a good network generalization for samples salinized by bischofite and epsomite minerals. The RPD and the R² between reference measurements and ANN outputs of theses models suggest an accurate to good prediction of soil salinity (R² > 0.92 and RPD > 2.3). For the field-scale data set, prediction accuracy is relatively poor (0.69 > R² > 0.42). The ANN predictive models estimating soil salinity from image-scale data sets indicate a good prediction (R² > 0.86 and RPD > 2.5) except for sub-image 2 (R² = 0.6 and RPD = 1.2).The results of this study show that both methods have a great potential for estimating and mapping soil salinity. Performance indexes from both methods suggest large similarity between the two approaches with PLSR advantages. This indicates that the relation between soil salinity and soil reflectance can be approximated by a linear function.     

Forest carbon densities and uncertainties from Lidar, QuickBird, and field measurements in California

Greenhouse gas inventories and emissions reduction programs require robust methods to quantify carbon sequestration in forests. We compare forest carbon estimates from Light Detection and Ranging (Lidar) data and QuickBird high-resolution satellite images, calibrated and validated by field measurements of individual trees. We conducted the tests at two sites in California: (1) 59 km² of secondary and old-growth coast redwood (Sequoia sempervirens) forest (Garcia-Mailliard area) and (2) 58 km² of old-growth Sierra Nevada forest (North Yuba area). Regression of aboveground live tree carbon density, calculated from field measurements, against Lidar height metrics and against QuickBird-derived tree crown diameter generated equations of carbon density as a function of the remote sensing parameters. Employing Monte Carlo methods, we quantified uncertainties of forest carbon estimates from uncertainties in field measurements, remote sensing accuracy, biomass regression equations, and spatial autocorrelation. Validation of QuickBird crown diameters against field measurements of the same trees showed significant correlation (r = 0.82, P < 0.05). Comparison of stand-level Lidar height metrics with field-derived Lorey's mean height showed significant correlation (Garcia-Mailliard r = 0.94, P < 0.0001; North Yuba R = 0.89, P < 0.0001). Field measurements of five aboveground carbon pools (live trees, dead trees, shrubs, coarse woody debris, and litter) yielded aboveground carbon densities (mean ± standard error without Monte Carlo) as high as 320 ± 35 Mg ha^− 1 (old-growth coast redwood) and 510 ± 120 Mg ha^− 1 (red fir [Abies magnifica] forest), as great or greater than tropical rainforest. Lidar and QuickBird detected aboveground carbon in live trees, 70-97% of the total. Large sample sizes in the Monte Carlo analyses of remote sensing data generated low estimates of uncertainty. Lidar showed lower uncertainty and higher accuracy than QuickBird, due to high correlation of biomass to height and undercounting of trees by the crown detection algorithm. Lidar achieved uncertainties of < 1%, providing estimates of aboveground live tree carbon density (mean ± 95% confidence interval with Monte Carlo) of 82 ± 0.7 Mg ha^− 1 in Garcia-Mailliard and 140 ± 0.9 Mg ha^− 1 in North Yuba. The method that we tested, combining field measurements, Lidar, and Monte Carlo, can produce robust wall-to-wall spatial data on forest carbon.     

Polynomial pseudo-random number generator via cyclic phase

Fast and reliable pseudo-random number generator (PRNG) is required for simulation and other applications in scientific computing. In this work, a polynomial PRNG algorithm, based on a linear feedback shift register (LFSR) is presented. LFSR generator of order k   determines a 2^k−1$2^k-1$ cyclic sequence period when the associated polynomial is primitive. The main drawback of this generator is the cyclicality of the shifted binary sequence. A non-linear transformation is proposed, which eliminates the underlying cyclicality and maintains both the characteristics of the original generator and the feedback function. The modified generator assures a good trade off between fastness and reliability and passes both graphical and statistical tests.     

Estimating forest canopy height and terrain relief from GLAS waveform metrics

Quantifying aboveground biomass in forest ecosystems is required for carbon stock estimation, aspects of forest management, and further developing a capacity for monitoring carbon stocks over time. Airborne Light Detection And Ranging (LiDAR) systems, of all remote sensing technologies, have been demonstrated to yield the most accurate estimates of aboveground biomass for forested areas over a wide range of biomass values. However, these systems are limited by considerations including large data volumes and high costs. Within the constraints imposed by the nature of the satellite mission, the GeoScience Laser Altimeter System (GLAS) aboard ICESat has provided data conferring information regarding forest vertical structure for large areas at a low end user cost. GLAS data have been demonstrated to accurately estimate forest height and aboveground biomass especially well in topographically smooth areas with homogeneous forested conditions. However in areas with dense forests, high relief, or heterogeneous vegetation cover, GLAS waveforms are more complex and difficult to consistently characterize. We use airborne discrete return LiDAR data to simulate GLAS waveforms and to subsequently deconstruct coregistered GLAS waveforms into vegetation and ground returns. A series of waveform metrics was calculated and compared to topography and vegetation information gleaned from the airborne data. A model to estimate maximum relief directly from waveform metrics was developed with an R² of 0.76 (n = 110), and used for the classification of the maximum relief of the areas sensed by GLAS. Discriminant analysis was also conducted as an alternative classification technique. A model was also developed estimating forest canopy height from waveform metrics for all of the data (R² = 0.81, n = 110) and for the three separate relief classes; maximum relief 0-7 m (R² = 0.83, n = 44), maximum relief 7-15 m (R² = 0.88, n = 41) and maximum relief > 15 m (R² = 0.75, n = 25). The moderate relief class model yielded better predictions of forest height than the low relief class model which is attributed to the increasing variability of waveform metrics with terrain relief. The moderate relief class model also yielded better predictions than the high relief class model because of the mixing of vegetation and terrain signals in waveforms from high relief footprints. This research demonstrates that terrain can be accurately modeled directly from GLAS waveforms enabling the inclusion of terrain relief, on a waveform specific basis, as supplemental model input to improve estimates of canopy height.     

Impact of test-driven development on productivity, code and tests: A controlled experiment

Context

Test-driven development is an approach to software development, where automated tests are written before production code in highly iterative cycles. Test-driven development attracts attention as well as followers in professional environment; however empirical evidence of its superiority regarding its effect on productivity, code and tests compared to test-last development is still fairly limited. Moreover, it is not clear if the supposed benefits come from writing tests before code or maybe from high iterativity/short development cycles.

Objective

This paper describes a family of controlled experiments comparing test-driven development to micro iterative test-last development with emphasis on productivity, code properties (external quality and complexity) and tests (code coverage and fault-finding capabilities).

Method

Subjects were randomly assigned to test-driven and test-last groups. Controlled experiments were conducted for two years, in an academic environment and in different developer contexts (pair programming and individual programming contexts). Number of successfully implemented stories, percentage of successful acceptance tests, McCabe’s code complexity, code coverage and mutation score indicator were measured.

Results

Experimental results and their selective meta-analysis show no statistically significant differences between test-driven development and iterative test-last development regarding productivity (χ²(6) = 4.799, p = 1.0, r = .107, 95% CI (confidence interval): −.149 to .349), code complexity (χ²(6) = 8.094, p = .46, r = .048, 95% CI: −.254 to .341), branch coverage (χ²(6) = 13.996, p = .059, r = .182, 95% CI: −.081 to .421), percentage of acceptance tests passed (one experiment, Mann-Whitney U = 125.0, p = .98, r = .066) and mutation score indicator (χ²(4) = 3.807, p = .87, r = .128, 95% CI: −.162 to .398).

Conclusion

According to our findings, the benefits of test-driven development compared to iterative test-last development are small and thus in practice relatively unimportant, although effects are positive. There is an indication of test-driven development endorsing better branch coverage, but effect size is considered small.     

One-pot electrodeposition of 3-aminopropyltriethoxysilane-chitosan hybrid gel film to immobilize glucose oxidase for biosensing

We report on the electrodeposition of a 3-aminopropyltriethoxysilane-chitosan (APTES-CS) hybrid gel film for in situ immobilization of glucose oxidase (GOx) on an Au or platinized Au (Pt_nano/Au) electrode for biosensing of glucose. Controllable electroreduction of p-benzoquinone is used to lift the electrode-surface pH for the GOx-APTES-CS codeposition, which was monitored by an electrochemical quartz crystal microbalance. The fabrication procedures of the biosensor and the parameters influencing the biosensing performance were optimized. The prepared porous GOx-APTES-CS/Pt_nano/Au and GOx-APTES-CS/Au electrodes can be used to detect the enzymatically generated H₂O₂ at 0.5 and 0.7 V vs SCE, respectively. The enzyme electrodes exhibited linear responses to glucose concentration from 0.2 μM to 8.2 mM (R = 0.998, at Pt_nano/Au substrate) and from 0.2 μM to 5.5 mM (R = 0.998, at Au substrate), with current sensitivities of 69.5 (Pt_nano/Au) and 65 (Au) μA mM⁻¹ cm⁻², respectively, and a detection limit of 0.2 μM (S/N = 3) was achieved for each electrode. The response time was less than 5 (Pt_nano/Au) or 8 (Au) s. It is striking that the enzyme electrodes remained their initial response sensitivity after storage for 5 (Au) and >6 (Pt_nano/Au) months in 0.10 M PBS (pH 7.0) at 4 °C.     

Efficient bit-parallel multipliers over finite fields GF(2)

Hardware implementation of multiplication in finite field GF(2^m) based on sparse polynomials is found to be advantageous in terms of space-complexity as well as the time-complexity. In this paper, we present a new permutation method to construct the irreducible like-trinomials of the form (x + 1)^m + (x + 1)ⁿ + 1 for the implementation of efficient bit-parallel multipliers. For implementing the multiplications based on such polynomials, we have defined a like-polynomial basis (LPB) as an alternative to the original polynomial basis of GF(2^m). We have shown further that the modular arithmetic for the binary field based on like-trinomials is equivalent to the arithmetic for the field based on trinomials. In order to design multipliers for composite fields, we have found another permutation polynomial to convert irreducible polynomials into like-trinomials of the forms (x² + x + 1)^m + (x² + x + 1)ⁿ + 1, (x² + x)^m + (x² + x)ⁿ + 1 and (x⁴ + x + 1)^m + (x⁴ + x + 1)ⁿ + 1. The proposed bit-parallel multiplier over GF(2^4m) is found to offer a saving of about 33% multiplications and 42.8% additions over the corresponding existing architectures.     

Pancyclicity and bipancyclicity of conditional faulty folded hypercubes

A graph is said to be pancyclic if it contains cycles of every length from its girth to its order inclusive; and a bipartite graph is said to be bipancyclic if it contains cycles of every even length from its girth to its order. The pancyclicity or the bipancyclicity of a given network is an important factor in determining whether the network’s topology can simulate rings of various lengths. An n-dimensional folded hypercube FQ_n is an attractive variant of an n-dimensional hypercube Q_n that is obtained by establishing some extra edges between the vertices of Q_n. FQ_n for any odd n is known to be bipartite. In this paper, we explore the pancyclicity and bipancyclicity of FQ_n. For any FQ_n (n ? 2) with at most 2n − 3 faulty edges, where each vertex is incident to at least two fault-free edges, we prove that there exists a fault-free cycle of every even length from 4 to 2ⁿ; and when n ? 2 is even, we prove there also exists a fault-free cycle of every odd length from n + 1 to 2ⁿ − 1. The result is optimal with respect to the number of faulty edges tolerated.     

Three-dimensional canopy fuel loading predicted using upward and downward sensing LiDAR systems

We calibrated upward sensing profiling and downward sensing scanning LiDAR systems to estimates of canopy fuel loading developed from field plots and allometric equations, and then used the LiDAR datasets to predict canopy bulk density (CBD) and crown fuel weight (CFW) in wildfire prone stands in the New Jersey Pinelands. LiDAR-derived height profiles were also generated in 1-m layers, and regressed on CBD estimates calculated for 1-m layers from field plots to predict three-dimensional canopy fuel loading. We then produced maps of canopy fuel metrics for three 9 km² forested areas in the Pinelands.Correlations for standard LiDAR-derived parameters between the two LiDAR systems were all highly significant, with correlation coefficients ranging between 0.82 and 0.98. Stepwise linear regression models developed from the profiling LiDAR data predicted maximum CBD and CFW (r² = 0.94 and 0.92) better than those developed from the scanning LiDAR data (r² = 0.83 and 0.71, respectively). A single regression for the prediction of CBD at all canopy layers had r² values of 0.93 and 0.82 for the profiling and scanning datasets, respectively. Individual bin regressions for predicting CBD at each canopy height layer were also highly significant at most canopy heights, with r² values for each layer ranging between 0.36 and 0.89, and 0.44 and 0.99 for the profiling and scanning datasets, respectively. Relationships were poorest mid-canopy, where highest average values and highest variability in fuel loading occurred. Fit of data to Gaussian distributions of canopy height profiles facilitated a simpler expression of these parameters for analysis and mapping purposes, with overall r² values of 0.86 and 0.92 for the profiling and scanning LiDAR datasets, respectively. Our research demonstrates that LiDAR data can be used to generate accurate, three-dimensional representations of canopy structure and fuel loading at high spatial resolution by linking 1-m return height profiles to biometric estimates from field plots.     

The m-pancycle-connectivity of a WK-Recursive network

In this paper, we study the m-pancycle-connectivity of a WK-Recursive network. We show that a WK-Recursive network with amplitude W and level L is strictly (5 × 2^L−1 − 2)-pancycle-connected for W ? 3. That is, each pair of vertices in a WK-recursive network with amplitude greater than or equal to 3 resides in a common cycle of every length ranging from 5 × 2^L−1 − 2 to N, where N is the size of the interconnection network; and the value 5 × 2^L−1 − 2 reaches the lower bound of the problem.     

Impact of nutrients on peatland GPP estimations using MODIS time series data

Time series of satellite sensor-derived data can be used in the light use efficiency (LUE) model for gross primary productivity (GPP). The LUE model and a closely related linear regression model were studied at an ombrotrophic peatland in southern Sweden. Eddy covariance and chamber GPP, incoming and reflected photosynthetic photon flux density (PPFD), field-measured spectral reflectance, and data from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used in this study. The chamber and spectral reflectance measurements were made on four experimental treatments: unfertilized control (Ctrl), nitrogen fertilized (N), phosphorus fertilized (P), and nitrogen plus phosphorus fertilized (NP). For Ctrl, a strong linear relationship was found between GPP and the photosynthetically active radiation absorbed by vegetation (APAR) (R² = 0.90). The slope coefficient (ε_s, where s stands for “slope”) for the linear relationship between seasonal time series of GPP and the product of the normalized difference vegetation index (NDVI) and PPFD was used as a proxy for the light use efficiency factor (ε). There were differences in ε_s depending on the treatments with a significant effect for N compared to Ctrl (ANOVA: p = 0.042, Tukey's: p ≤ 0.05). Also, ε_s was linearly related to the cover degree of vascular plants (R² = 0.66). As a sensitivity test, the regression coefficients (ε_s and intercept) for each treatment were used to model time series of 16-day GPP from the product of MODIS NDVI and PPFD. Seasonal averages of GPP were calculated for 2005, 2006, and 2007, which resulted in up to 19% higher average GPP for the fertilization treatments compared to Ctrl. The main conclusion is that the LUE model and the regression model can be applied in peatlands but also that temporal and spatial changes in ε or the regression coefficients should be considered.     

Remote sensing of structural complexity indices for habitat and species distribution modeling

Spatial distribution models are increasingly used in ecological studies, but are limited by the poor accuracy of remote sensing (RS) for mapping microhabitat (< 0.1 ha) features. Mapping accuracy can be improved by combining advanced RS image-processing techniques with microhabitat data expressed as a structural complexity index (SCI). To test this idea, we used principal components analysis (PCA) and an additive SCI method developed for forest ecology (calculated by re-scaling and summing representative structural variables) to summarize 13 microhabitat-scale (0.04 ha) vegetation structure attributes describing the rare mountain bongo antelope's (Tragelaphus eurycerus isaaci) habitat in Kenya's Aberdare mountains. Microhabitat data were collected in 127 plots: 37 related to bongo habitat use, 90 from 1 km-spaced grid points representing overall habitat availability and bongo non-presence. We then assessed each SCI's effectiveness for discerning microhabitat variability and bongo habitat selection, using Wilcoxon Rank Sum tests for differences in mean SCI scores among plots divided into 4 vegetation classes, and the Area Under the Curve (AUC) of receiver operating characteristics from logistic regressions. We also examined the accuracy of predicted SCI scores resulting from regression models based on variables derived from a) ASTER imagery processed with spectral mixture and texture analysis, b) an SRTM DEM and c) rainfall data, using the 90 grid plots for model training and the bongo plots as an independent test dataset. Of the five SCIs derived, two performed best: the PCA-derived Canopy Structure Index (CSI) and an additive index summarizing 8 structural variables (AI₈). CSI and AI₈ showed significant differences between 5 of 6 vegetation class pairs, strong abilities to distinguish bongo-selected from available habitat (AUCs = 0.71 (CSI); 0.70 (AI₈)), and predicted scores 60-110% more accurate than reported by other studies using RS to quantify individual microhabitat structural attributes (CSI model R² = 0.51, RMSE = 0.19 (training) and 0.21 (test); AI₈ model R² = 0.46, RMSE = 0.17 (training) and 0.19 (test)). Repeating the Wilcoxon tests and logistic regressions with RS-predicted SCI values showed that AI₈ most effectively preserved the patterns found with the observed SCIs. These results demonstrate that SCIs effectively characterize microhabitat structure and selection, and boost microhabitat mapping accuracy when combined with enhanced RS image-processing techniques. This approach can improve distribution models and broaden their applicability, makes RS more relevant to applied ecology, and shows that processing field data to be more compatible with RS can improve RS-based habitat mapping accuracy.