• increase font size
  • Default font size
  • decrease font size
books join My ASPRS
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.

October 2015 Issue

A Geometric Method for Wood-Leaf Separation Using Terrestrial and Simulated Lidar Data

Shengli Tao, Qinghua Guo, Shiwu Xu, Yanjun Su, Yumei Li, and Fangfang Wu

Abstract Download Full Article (members only)

Terrestrial light detection and ranging (lidar) can be used to record the three-dimensional structures of trees. Wood-leaf separation, which aims to classify lidar points into wood and leaf components, is an essential prerequisite for deriving individual tree characteristics. Previous research has tended to use intensity (including a multi-wavelength approach) and waveform information for wood-leaf separation, but use of the most fundamental information from a lidar point cloud, i.e., the x-, y-, and z- coordinates of each point, for this purpose has been poorly explored. In this study, we introduce a geometric method for wood-leaf separation using the x-, y-, and z-coordinates of each point. The separation results indicate that first-, second-, and third-order branches can be extracted from the raw point cloud by this new method, suggesting that it might provide a promising solution for wood-leaf separation.


Two Dimensional Linear Discriminant Analyses for Hyperspectral Data

Maryam Imani and Hassan Ghassemian

Abstract Download Full Article (members only)

Most supervised feature extraction methods like linear discriminant analysis (LDA) suffer from the limited number of available training samples. The singularity problem causes LDA to fail in small sample size (SSS) situations. Two dimensional linear discriminant analysis (2DLDA) for feature extraction of hyperspectral images is proposed in this paper which has good efficiency with small training sample size. In this approach, the feature vector of each pixel of hyperspectral image is transformed into a feature matrix. As a result, the data matrices lie in a low-dimensional space. Then, the between-class and within-class scatter matrices are calculated using the matrix form of training samples. The proposed approach has two main advantages: it deals with the SSS problem in hyperspectral data, and also it can extract each number of features (with no limitation) from the original high dimensional data. The proposed method is tested on four widely used hyperspectral datasets. Experimental results confirm that the proposed 2DLDA feature extraction method provides better classification accuracy, with a reasonable computation time, compared to popular supervised feature extraction methods such as generalized discriminant analysis (GDA) and nonparametric weighted feature extraction (NWFE) particularly compared to the 1DLDA in the SSS situation. The experiments show that two dimensional linear discriminant analysis + support vector machine (2DLDA+SVM) is an appropriate choice for feature extraction and classification of hyperspectral images using limited training samples.


Applying ASPRS Accuracy Standards to Surveys from Small Unmanned Aircraft Systems (UAS)

Ken Whitehead and Chris H. Hugenholtz

Abstract Download Full Article (members only)

We present a first assessment of UAS-derived orthoimagery and digital elevation data in the context of newly-released accuracy standards for digital geospatial data developed by the American Society for Photogrammetry and Remote Sensing. We outline results from two case studies using a commercially-available UAS, photogrammetry software, and an array of ground control and checkpoints. Radial horizontal and vertical root-mean-square-errors (RMSE) were calculated as 0.05 m and 0.06 m, respectively, for one site, and 0.08 m and 0.03 m, respectively, for the other. Under the 1990 ASPRS standards, both surveys meet the requirements for Class 1 accuracy at the 1:500 map scale and at the 0.50 m contour interval. Under the newly-developed ASPRS standards, the reported errors fulfill the requirements for both horizontal and vertical mapping at the 10 cm RMSE level. Overall, these results provide initial direction for practitioners considering UAS surveying in the context of accuracy standards.


Discriminating Saltcedar (Tamarix ramosissima) from Sparsely Distributed Cottonwood (Populus euphratica) Using a Summer Season Satellite Image

Wenjie Ji and Le Wang

Abstract Download Full Article (members only)

Accurate mapping of saltcedar (Tamarix ramosissima) and cottonwood (Populus euphratica) using remote sensing images is required to study the dynamic relationship between these two species. Our study used pixel-based and semi-object-based methods to classify a high spatial resolution QuickBird image acquired during the summer in northern China, where both saltcedar and cottonwood are native species. The pixel-based classification results revealed that spectral bands alone were not sufficient to discriminate saltcedar from cottonwood trees due to their similar foliage reflectance in the summer. Including texture measures did not improve the result. The unique crown shapes and shadows associated with sparsely distributed cottonwood were used to facilitate the semi object-based method. The overall accuracy of the object-based classification result increased 15 percent compared to that of the pixel-based classification results and showed significant improvement in the discrimination between saltcedar and cottonwood.


Marsh Canopy Leaf Area and Orientation Calculated for Improved Marsh Structure Mapping

Yang Shen, Yong Wang, Haitao Lv, and Hong Li

Abstract Download Full Article (members only)

An approach is presented for producing the spatiotemporal estimation of leaf area index (LAI) of a highly heterogeneous coastal marsh without reliance on user estimates of marsh leaf-stem orientation. The canopy LAI profile derivation used three years of field measured photosynthetically active radiation (PAR) vertical profiles at seven S. alterniflora marsh sites and iterative transform of those PAR attenuation profiles to best-fit light extinction coefficients (KM). KM sun zenith dependency was removed obtaining the leaf angle distribution (LAD) representing the average marsh orientation and the LAD used to calculate the LAI canopy profile. LAI and LAD reproduced measured PAR profiles with 99 percent accuracy and corresponded to field documented structures. LAI and LAD better reflect marsh structure and results substantiate the need to account for marsh orientation. The structure indexes are directly amenable to remote sensing spatiotemporal mapping and offer a more meaningful representation of wetland systems promoting biophysical function understanding. 


Click Here to Report a Problem on this Page
Home PE&RS Journals In Press Peer Reviewed Articles