Single-variable model for a dynamic soil structure

Identifying Subalpine Fir (Abies lasiocarpa) Attacked by the Balsam Woolly Adelgid (Adelges piceae) Using Spectral Measurements of the Foliage

International Journal of Forestry Research ◽

10.1155/2010/498189 ◽

2010 ◽

Vol 2010 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Stephen P. Cook ◽

Karen S. Humes ◽

Ryan Hruska ◽

Grant Fraley ◽

Christopher J. Williams

Keyword(s):

Discriminant Analysis ◽

The United States ◽

High Spectral Resolution ◽

Invasive Pest ◽

Stepwise Discriminant Analysis ◽

Single Variable ◽

Remotely Sensed Data ◽

Variable Model ◽

Balsam Woolly Adelgid ◽

Simple Ratio

Balsam woolly adelgid is an invasive pest of firs in the United States. Aerial surveys are conducted for detection of adelgid infestations but other remotely sensed data may also be useful. Our objective was to determine if high spectral resolution, branch-level data can be used to distinguish infested from noninfested trees. Stepwise discriminant analysis yielded a three-variable model (the red-green index and two narrow-bands (one at 670 nm and the other at 1912 nm)) that classified infested versus non-infested trees with 94% accuracy compared with the 83% accuracy obtained with a single-variable model. The response of trees in narrow spectral bands was integrated across wavebands to simulate measurements from the multispectral SPOT5-HRVIR sensor. Stepwise discriminant analysis again yielded a three-variable model (simple ratio, the SPOT5-HRVIR band in the SWIR region and NDVI) with similar accuracy (93%) at discriminating infested from non-infested trees compared with the 83% accuracy obtained with a single-variable model.

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Multivariate Regression Modeling

Journal of Solar Energy Engineering ◽

10.1115/1.2888067 ◽

1998 ◽

Vol 120 (3) ◽

pp. 177-184 ◽

Cited By ~ 38

Author(s):

S. Katipamula ◽

T. A. Reddy ◽

D. E. Claridge

Keyword(s):

Energy Consumption ◽

Regression Models ◽

Energy Use ◽

Complex Function ◽

Climatic Conditions ◽

Regression Modeling ◽

Commercial Buildings ◽

Coefficient Of Determination ◽

Single Variable ◽

Variable Model

An empirical or regression modeling approach is simple to develop and easy to use compared to detailed hourly simulations of energy use in commercial buildings. Therefore, regression models developed from measured energy data are becoming an increasingly popular method for determining retrofit savings or identifying operational and maintenance (O&M) problems. Because energy consumption in large commercial buildings is a complex function of climatic conditions, building characteristics, building usage, system characteristics and type of heating, ventilation, and air conditioning (HVAC) equipment used, a multiple linear regression (MLR) model provides better accuracy than a single-variable model for modeling energy consumption. Also, when hourly monitored data are available, an issue which arises is what time resolution to adopt for regression models to be most accurate. This paper addresses both these topics. This paper reviews the literature on MLR models of building energy use, describes the methodology to develop MLR models, and highlights the usefulness of MLR models as baseline models and in detecting deviations in energy consumption resulting from major operational changes. The paper first develops the functional basis of cooling energy use for two commonly used HVAC systems: dual-duct constant volume (DDCV) and dual-duct variable air volume (DDVAV). Using these functional forms, the cooling energy consumption in five large commercial buildings located in central Texas were modeled at monthly, daily, hourly, and hour-of-day (HOD) time scales. Compared to the single-variable model (two-parameter model with outdoor dry-bulb as the only variable), MLR models showed a decrease in coefficient of variation (CV) between 10 percent to 60 percent, with an average decrease of about 33 percent, thus clearly indicating the superiority of MLR models. Although the models at the monthly time scale had higher coefficient of determination (R2) and lower CV than daily, hourly, and HOD models, the daily and HOD models proved more accurate at predicting cooling energy use.

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Forest Growing Stock Volume Estimation in Subtropical Mountain Areas Using PALSAR-2 L-Band PolSAR Data

Forests ◽

10.3390/f10030276 ◽

2019 ◽

Vol 10 (3) ◽

pp. 276 ◽

Cited By ~ 8

Author(s):

Haibo Zhang ◽

Jianjun Zhu ◽

Changcheng Wang ◽

Hui Lin ◽

Jiangping Long ◽

...

Keyword(s):

Synthetic Aperture Radar ◽

Correlation Coefficient ◽

Terrain Correction ◽

Central China ◽

Synthetic Aperture ◽

Single Variable ◽

Variable Model ◽

Growing Stock ◽

L Band ◽

Stock Volume

Forest growing stock volume (GSV) extraction using synthetic aperture radar (SAR) images has been widely used in climate change research. However, the relationships between forest GSV and polarimetric SAR (PolSAR) data in the mountain region of central China remain unknown. Moreover, it is challenging to estimate GSV due to the complex topography of the region. In this paper, we estimated the forest GSV from advanced land observing satellite-2 (ALOS-2) phased array-type L-band synthetic aperture radar (PALSAR-2) full polarimetric SAR data based on ground truth data collected in Youxian County, Central China in 2016. An integrated three-stage (polarization orientation angle, POA; effective scattering area, ESA; and angular variation effect, AVE) correction method was used to reduce the negative impact of topography on the backscatter coefficient. In the AVE correction stage, a strategy for fine terrain correction was attempted to obtain the optimum correction parameters for different polarization channels. The elements on the diagonal of covariance matrix were used to develop forest GSV prediction models through five single-variable models and a multi-variable model. The results showed that the integrated three-stage terrain correction reduced the negative influence of topography and improved the sensitivity between the forest GSV and backscatter coefficients. In the three stages, the POA compensation was limited in its ability to reduce the impact of complex terrain, the ESA correction was more effective in low-local incidence angles area than high-local incidence angles, and the effect of the AVE correction was opposite to the ESA correction. The data acquired on 14 July 2016 was most suitable for GSV estimation in this study area due to its correlation with GSV, which was the strongest at HH, HV, and VV polarizations. The correlation coefficient values were 0.489, 0.643, and 0.473, respectively, which were improved by 0.363, 0.373, and 0.366 in comparison to before terrain correction. In the five single-variable models, the fitting performance of the Water-Cloud analysis model was the best, and the correlation coefficient R2 value was 0.612. The constructed multi-variable model produced a better inversion result, with a root mean square error (RMSE) of 70.965 m3/ha, which was improved by 22.08% in comparison to the single-variable models. Finally, the space distribution map of forest GSV was established using the multi-variable model. The range of estimated forest GSV was 0 to 450 m3/ha, and the mean value was 135.759 m3/ha. The study expands the application potential of PolSAR data in complex topographic areas; thus, it is helpful and valuable for the estimation of large-scale forest parameters.

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