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Home PE&RS Journals In Press Peer Reviewed Articles

PE&RS Journals

In Press Peer Reviewed Articles

As a convenience to ASPRS members, in-press peer reviewed articles approved for publication in forthcoming issues of PE&RS have been made available for members of the society.


April 2014 Issue

Road Extraction from Lidar Data Using Support Vector Machine Classification

Ali Akbar Matkan, Mohammad Hajeb, and Saeed Sadeghian

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This paper presents a method for road extraction from lidar data based on SVM classification. The lidar data are used exclusively to evaluate the potential in the road extraction process. First, the SVM algorithm is used to classify the lidar data into five classes: road, tree, building, grassland, and cement. Then, some misclassified pixels in the road class is removed using the road values in the normalized Digital Surface Model and Normalized Difference Distance features. In the postprocessing stage, a method based on Radon transform and Spline interpolation is employed to automatically locate and fill the gaps in the road network. The experimental results show that the proposed algorithm for gap filling works well on straight roads. The proposed road extraction algorithm is tested on three datasets. An accuracy assessment indicated 63.7 percent, 60.26 percent and 66.71 percent quality for three datasets. Finally, centerline of the detected roads is extracted using mathematical morphology.

 


Daily Temperature Oscillation Enhancement of Multitemporal LST Imagery

George Ch. Miliaresis

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This paper addresses a major limitation of remote sensing in biophysical modeling-capturing the diurnal temperature range (DTR) with global datasets at moderate resolution scale. DTR relates to the variation in temperature that occurs between daytime and nighttime daily temperatures. A new context for MODIS land surface temperature modeling is proposed on the basis of the 01:30, 10:30, 13:30, and 22:30 local time Aqua and Terra acquisitions. First, cubic spline interpolation produces an image time series with a uniform six hours sampling per day. Second, the inverse Fourier transform considers the harmonics with period less than or equal to a day, to reconstruct a new image time series, and enhance DTR. Finally, cluster analysis of the reconstructed data set identifies eight clusters that are spatially arranged into a Southern and a Northern group. The temporal variation for each cluster reveals a season dependent DTR that is a key issue in supporting land cover studies in Greece.

 


An Extended Approach for Biomass Estimation in a Mixed Vegetation Area Using ASAR and TM Data

Minfeng Xing, Binbin He, Xingwen Quan, and Xiaowen Li

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The use of microwave remote sensing for estimating vegetation biomass is limited in arid regions because of the heterogeneous distribution of vegetation, variable scattering mechanisms from different vegetation components, and the strong influence from underlying ground surface. In order to minimize this problem, a synergistic method of optical and microwave remote sensing data for the retrieval of aboveground biomass (AGB) based on the modified water cloud model (WCM) was developed in this paper. Vegetation coverage which can be easily estimated from optical data as additional information was combined in this method. Dimidiate pixel model (DPM) and phenological subtraction methodology (PSM) were used to estimate vegetation coverage and differentiate vegetation types in the sub-pixel domain, respectively. The percentage cover of unmixed vegetation was incorporated to minimize problems associated with heterogeneous vegetation and sparse vegetation cover. Finally, the accuracy and sources of error in this novel AGB retrieval method were evaluated. The results showed that the predicted AGB correlated with the measured AGB (R2 = 0.8007; RMSE = 0.2808 kg/m2).

 


Wetland Mapping in the Upper Midwest United States: An Object-Based Approach Integrating Lidar and Imagery Data

Lian P. Rampi, Joseph F. Knight, and Keith C. Pelletier

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This study investigated the effectiveness of using high resolution data to map wetlands in three ecoregions in Minnesota. High resolution data included multispectral leaf-off aerial imagery and lidar elevation data. These data were integrated using an Object-Based Image Analysis (OBIA) approach. Results for each study area were compared against field and image interpreted reference data using error matrices, accuracy estimates, and the kappa statistic. Producer's and user's accuracies were in the range of 92 to 96 percent and 91 to 96 percent, respectively, and overall accuracies ranged from 96-98 percent for wetlands larger than 0.20 ha (0.5 acres). The results of this study may allow for increased accuracy of mapping wetlands efforts over traditional remote sensing methods.

 


Laboratory Measurements of Plant Drying: Implications to Estimate Moisture Content from Radiative Transfer Models in Two Temperate Species

Sara Jurdao, Marta Yebra, Patricia Oliva, and Emilio Chuvieco

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The estimation of live fuel moisture content (LFMC) is necessary for fire danger assessment. Several studies have successfully used satellite imagery to estimate LFMC, both using empirical and simulation approaches (Yebra et al., 2013). The latter are based on Radiative Transfer Models (RTM). They are generally more robust and easier to generalize, but they rely heavily on the proper parameterization. Since some of the input parameters are associated with different physiological processes, a better understanding of how those parameters co-vary is necessary for constraining the simulation scenarios, thus avoiding combinations of parameters that are unlikely to occur (for instance, in temperate ecosystems, it is unlikely to find simultaneously high values of leaf chlorophyll and low values of leaf moisture).To improve parameterization of RTM models for LFMC estimation, we conducted a laboratory experiment to measure trends in leaf and canopy variables of two tree species broadly distributed in Eurosiberian climates: Beech (Fagus sylvatica L.) and pedunculate Oak (Quercus robur L.). Measurements of LFMC, equivalent water thickness (EWT), dry matter content (DMC), chlorophyll (Ca+b), leaf area index (LAI), leaf angle distribution (LIDF), crown height to width ratio (CHW) and plant reflectance were performed. Significant positive correlations were found between LFMC and EWT (Rs >0.5), and negative ones were found between both parameters and Ca+b (Rs <-0.3). LFMC and EWT were positively related to DMC and LAI, with lower correlation coefficients for the latter. The effect of moisture variation in spectral reflectance was also analyzed using two indices: the spectral angle (SA) and the root mean square error (RMSE).The former contributed the most to the estimation of LFMC variations. Spearman correlation coefficients (Rs) between SA and LFMC were 0.656 and 0.554 for F. sylvatica and Q. robur, respectively; while for RMSE and LFMC they were 0.366 and 0.430, respectively.

 


A Comparative Study of Land Cover Classification Techniques for "Farmscapes" Using Very High Resolution Remotely Sensed Data

Niva Kiran Verma, David W. Lamb, Nick Reid, and Brian Wilson

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High spatial resolution images (~10 cm) are routinely available from airborne platforms. Few studies have examined the applicability of using such data to characterize land cover in "farmscapes" comprising open pasture and remnant vegetation communities of varying density. Very high spatial resolution remotely sensed imagery has been used to classify land cover classes on a ~5000 ha extensive grazing farm in Australia. This "farmscape" consisted of open pasture fields, scattered trees, and remnant vegetation (woodlands). The relative performances of object-based and pixel-based approaches to classification were tested for accuracy and applicability. Maximum likelihood classification (MLC) was used for pixel-based classification while the k-nearest neighbor (k-NN) technique was used for object-based classification. A range of image sampling scales was tested for image segmentation. At an optimal sampling scale, the pixel-based classification resulted in an overall accuracy of 77 percent, while the object-based classification achieved an overall accuracy of 86 percent. While both the object- and pixel-based classification techniques yielded higher quantitative accuracies, a "more realistic" land cover classification, with few errors due to intermixing of similar classes, was achieved using the object-based method.

 

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