Stem number estimation by kernel smoothing of aerial photos

1996 ◽  
Vol 26 (7) ◽  
pp. 1228-1236 ◽  
Author(s):  
Kim Dralle ◽  
Mats Rudemo
Keyword(s):  
Kernel Smoothing ◽  
Estimation Method ◽  
Gaussian Kernel ◽  
Bandwidth Estimation ◽  
Stem Number ◽  
Aerial Photos ◽  
Step Procedure ◽  
Nonlinear Regression Method ◽  
Number Of Stems ◽  
Old Trees

A digitized aerial panchromatic photo of a thinning experiment in pure even-aged Norway spruce (Piceaabies (L.) Karst.) is smoothed by a two-dimensional isotropic Gaussian kernel. The number of stems per hectare is estimated from the number of maxima above a certain level of the smoothed image. For the crucial kernel bandwidth estimation problem we suggest a two-step procedure where the first step consists of computing (for each homogeneous stand (or subplot)) an "internal curve" corresponding to the number of maxima at a series of bandwidths. The estimated stem number and the optimal bandwidth is then obtained as the crossing of the internal curve with an "external curve," here assumed to be of a simple parametric form. If a series of stands (or subplots) with different known stem numbers is available, the external curve can be estimated by use of a nonlinear regression method. An experiment with 48-year-old trees and six subplots with varying thinning treatments is analyzed. The stem number estimation method, checked by cross validation, performs satisfactorily for all thinning grades except the unthinned control.

The Cumulant Generating Function Estimation Method

1997 ◽  
Vol 13 (2) ◽  
pp. 170-184 ◽  
Author(s):  
John L. Knight ◽  
Stephen E. Satchell
Keyword(s):  
Characteristic Function ◽  
Density Function ◽  
Generating Function ◽  
Estimation Method ◽  
Empirical Characteristic Function ◽  
Function Estimation ◽  
Asymptotic Equivalence ◽  
Cumulant Generating Function ◽  
Worked Example ◽  
Step Procedure

This paper deals with the use of the empirical cumulant generating function to consistently estimate the parameters of a distribution from data that are independent and identically distributed (i.i.d.). The technique is particularly suited to situations where the density function is unknown or unbounded in parameter space. We prove asymptotic equivalence of our technique to that of the empirical characteristic function and outline a six-step procedure for its implementation. Extensions of the approach to non-i.i.d. situations are considered along with a discussion of suitable applications and a worked example.

scholarly journals Analysis of morphological traits of bird's foot trefoil plants cv. Bokor transformed with Agrobacterium rhizogenes

Genetika ◽  
2005 ◽  
Vol 37 (1) ◽  
pp. 33-38
Author(s):  
Radomirka Nikolic ◽  
Nevena Mitic
Keyword(s):  
Genetic Transformation ◽  
High Frequency ◽  
Morphological Traits ◽  
Field Analysis ◽  
Stem Number ◽  
Transformed Plants ◽  
Chemical Content ◽  
Number Of Stems ◽  
Nitrogen Contents ◽  
Plant Length

An efficient method for genetic transformation and shoot regeneration was achieved in bird's foot trefoil cv. Bokor using A. rhizogens. The transformed shoots were regenerated on hairy root segments in high frequency. After rooting and acclimation, transformed To plants were grown in experimental field. Analysis of morphological traits and chemical content in ten unintentionally chosen To bird's foot trefoil plants (genotypes no. 2 and no. 5) was performed. They were compared to those of control non-transformed plants. The traits as a number of stems per plant, length of internodes in longest stem, number of flowers per plant and plan high were very significant differed than the same traits in control plants, while there were no significant differences in the leaf area. No signs of the rol genes genotype and "T" phenotype were present. The transformed plants had significantly higher content of cellulose, while the protein and nitrogen contents of are in the range of control plants.

scholarly journals Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS)

2017 ◽  
Author(s):  
Nur Ahmadi ◽  
Timothy G. Constandinou ◽  
Christos-Savvas Bouganis
Keyword(s):  
Firing Rate ◽  
Spike Train ◽  
Prior Distribution ◽  
Kernel Smoothing ◽  
Neuronal Firing ◽  
Gaussian Kernel ◽  
Single Trial ◽  
Adaptive Kernel ◽  
Neuronal Firing Rate ◽  
Spike Train Data

AbstractNeurons use sequences of action potentials (spikes) to convey information across neuronal networks. In neurophysiology experiments, information about external stimuli or behavioral tasks has been frequently characterized in term of neuronal firing rate. The firing rate is conventionally estimated by averaging spiking responses across multiple similar experiments (or trials). However, there exist a number of applications in neuroscience research that require firing rate to be estimated on a single trial basis. Estimating firing rate from a single trial is a challenging problem and current state-of-the-art methods do not perform well. To address this issue, we develop a new method for estimating firing rate based on kernel smoothing technique that considers the bandwidth as a random variable with prior distribution that is adaptively updated under a Bayesian framework. By carefully selecting the prior distribution together with Gaussian kernel function, an analytical expression can be achieved for the kernel bandwidth. We refer to the proposed method as Bayesian Adaptive Kernel Smoother (BAKS). We evaluate the performance of BAKS using synthetic spike train data generated by biologically plausible models: inhomogeneous Gamma (IG) and inhomogeneous inverse Gaussian (IIG). We also apply BAKS to real spike train data from non-human primate (NHP) motor and visual cortex. We benchmark the proposed method against the established and previously reported methods. These include: optimized kernel smoother (OKS), variable kernel smoother (VKS), local polynomial fit (Locfit), and Bayesian adaptive regression splines (BARS). Results using both synthetic and real data demonstrate that the proposed method achieves better performance compared to competing methods. This suggests that the proposed method could be useful for understanding the encoding mechanism of neurons in cognitive-related tasks. The proposed method could also potentially improve the performance of brain-machine interface (BMI) decoder that relies on estimated firing rate as the input.

Regrowth of forest understory species following seasonal burning

1988 ◽  
Vol 66 (1) ◽  
pp. 150-155 ◽  
Author(s):  
Marguerite A. Flinn ◽  
Ross W. Wein
Keyword(s):  
Fold Increase ◽  
Forest Stand ◽  
Forest Understory ◽  
Stem Number ◽  
Tree Seedling ◽  
Vaccinium Angustifolium ◽  
Understory Species ◽  
Number Of Stems ◽  
Maianthemum Canadense ◽  
Vaccinium Myrtilloides

Small experimental plots in mixed-wood stands of the Acadian Forest were burned in the spring, summer, and autumn to obtain an estimate of the regrowth potential of common forest understory species. The number of stems was measured before burning and then monthly for 5 months thereafter. Supportive experiments on seasonal transplanting were conducted at the same time to determine regrowth potential after interspecific competition had been removed. Regrowth potential of species varied among seasonal burning treatments as expected and was strongest for Maianthemum canadense, Vaccinium myrtilloides, Andromeda glaucophylla, Vaccinium angustifolium, Viburnum cassinoides, and Betula populifolia. These species, which showed a 10-fold increase in stem number, could compete successfully with tree seedling number and composition and thus ultimately alter forest stand composition.

scholarly journals A Hybrid GWR-Based Height Estimation Method for Building Detection in Urban Environments

2014 ◽  
Vol II-2 ◽  
pp. 23-29 ◽  
Author(s):  
X. Wei ◽  
X. Yao
Keyword(s):  
Urban Areas ◽  
Estimation Method ◽  
Urban Environments ◽  
Lidar Data ◽  
Building Detection ◽  
Building Roof ◽  
Aerial Photos ◽  
The Road ◽  
Urban Modelling ◽  
Gwr Model

LiDAR has become important data sources in urban modelling. Traditional methods of LiDAR data processing for building detection require high spatial resolution data and sophisticated methods. The aerial photos, on the other hand, provide continuous spectral information of buildings. But the segmentation of the aerial photos cannot distinguish between the road surfaces and the building roof. This paper develops a geographically weighted regression (GWR)-based method to identify buildings. The method integrates characteristics derived from the sparse LiDAR data and from aerial photos. In the GWR model, LiDAR data provide the height information of spatial objects which is the dependent variable, while the brightness values from multiple bands of the aerial photo serve as the independent variables. The proposed method can thus estimate the height at each pixel from values of its surrounding pixels with consideration of the distances between the pixels and similarities between their brightness values. Clusters of contiguous pixels with higher estimated height values distinguish themselves from surrounding roads or other surfaces. A case study is conducted to evaluate the performance of the proposed method. It is found that the accuracy of the proposed hybrid method is better than those by image classification of aerial photos along or by height extraction of LiDAR data alone. We argue that this simple and effective method can be very useful for automatic detection of buildings in urban areas.

scholarly journals TAKSASI PRODUKSI MATA TUNAS SEBAGAI BENIH TEBU (SACHARRUM OFFICINARUM L.) DENGAN PENDEKATAN ANALISA REGRESI

2018 ◽  
Vol 25 (2) ◽  
pp. 273
Author(s):  
Ahmad Dhiaul Khuluq ◽  
Ruly Hamida
Keyword(s):  
Regression Analysis ◽  
Standard Error ◽  
Block Design ◽  
Stem Number ◽  
Central Java ◽  
Randomized Complete Block Design ◽  
Analysis Calculation ◽  
Independent Variable ◽  
Number Of Stems ◽  
Planting Method

<p>One of the problems encountered in the development of sugarcane (Sacharrum officinarum L.) includes the availability of sugarcane seed both in quality and quantity. Evaluation of bud sett planting method in seed production was required in order to achieve the expected results. The study was conducted at the experiment station Muktiharjo, Central Java in 2012 using PSJT 941 varieties. Treatments applied were the different number of buds on bud sett which were at 3 levels, 1 bud, 2 buds or 3 buds. Research was arranged in a randomized complete block design (RCBD) with 5 replications. Observations were conducted on germination, tillering, plant height, number of stems, number of suckers and number of buds. The data obtained were analyzed with ANOVA and further tested using the Duncan test. Production assessment modeling approach was performed by a regression analysis. Calculation of stem number on 2 buds showed the highest with 9.6 stems/m, 9.2 buds/stem and with the sucker numbers lowest at 0.38 suckers/m. The highest production buds was obtained at planting 2 buds with 847,848.06 buds/ha which can be used as 8.83 ha for the milled sugarcane plantation. Assessment of bud production per hectare could use equation Y = 159655,48.e0,171.X with the independent variable of stem numbers per meter with a correlation coefficient of 0,9007 and a standard error of 1,0699.</p>

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