Pinliang Dong

Test of a new lacunarity estimation method for image texture analysis

Based on a differential box counting method and a gliding-box algorithm, a new method for estimating the lacunarity of grey scale digital image surfaces is introduced, and directionality of lacunarity defined. To test the performance of the new lacunarity measure, a Brodatz texture image mosaic is employed and several other texture analysis approaches are also applied to the texture mosaic. Quantitative comparison shows that the new lacunarity estimation method for grey-scale images can provide more accurate texture measurements than some existing lacunarity measures, the grey level co-occurrence matrix based texture measures, the Min-Max operator, and the fractal dimension.

Generating and updating multiplicatively weighted Voronoi diagrams for point, line and polygon features in GIS

A Voronoi diagram is an interdisciplinary concept that has been applied to many fields. In geographic information systems (GIS), existing capabilities for generating Voronoi diagrams normally focus on ordinary (not weighted) point (not linear or area) features. For better integration of Voronoi diagram models and GIS, a raster-based approach is developed, and implemented seamlessly as an ArcGIS extension using ArcObjects. In this paper, the methodology and implementation of the extension are described, and examples are provided for ordinary or weighted point, line, and polygon features. Advantages and limitations of the extensions are also discussed. The extension has the following features: (1) it works for point, line, and polygon vector features; (2) it can generate both ordinary and multiplicatively weighted Voronoi diagrams in vector format; (3) it can assign non-spatial attributes of input features to Voronoi cells through spatial joining; and (4) it can produce an ordinary or a weighted Euclidean distance raster dataset for spatial modeling applications. The results can be conveniently combined with other GIS datasets to support both vector-based spatial analysis and raster-based spatial modeling.

Evaluation of small-area population estimation using LiDAR, Landsat TM and parcel data

This paper presents methods and results of small-area population estimation using a combined Light Detection And Ranging (LiDAR), Landsat Thematic Mapper (TM) and parcel dataset for a study area in Denton, Texas, USA. A normalized digital surface model (nDSM) was created from a digital surface model (DSM) and a digital elevation model (DEM) built from LiDAR point data. Residential and commercial parcels were selected from parcel data and used as a mask to remove non-residential and non-commercial pixels from the nDSM. Classification results of residential areas from Landsat TM images acquired on two dates were used to further refine the nDSM. Using continuous and random census blocks as samples, building count, building area and building volume were calculated from the nDSM through mathematical morphological operations, zonal statistics, data conversion and spatial joining in a geographic information system (GIS). Combined with census 2000 data, a total of 10 ordinary least squares (OLS) regression models and geographically weighted regression (GWR) models were built and applied to the census blocks in the study area. Finally, accuracy assessments were carried out. The results show that the sign and magnitude of the relative estimation errors at the census-block level lead to underestimation of the total population in the study area. Possible reasons for the relatively low accuracies and problems for further investigation are also discussed.

Lacunarity analysis of raster datasets and 1D, 2D, and 3D point patterns

Spatial scale plays an important role in many fields. As a scale-dependent measure for spatial heterogeneity, lacunarity describes the distribution of gaps within a set at multiple scales. In Earth science, environmental science, and ecology, lacunarity has been increasingly used for multiscale modeling of spatial patterns. This paper presents the development and implementation of a geographic information system (GIS) software extension for lacunarity analysis of raster datasets and 1D, 2D, and 3D point patterns. Depending on the application requirement, lacunarity analysis can be performed in two modes: global mode or local mode. The extension works for: (1) binary (1-bit) and grey-scale datasets in any raster format supported by ArcGIS and (2) 1D, 2D, and 3D point datasets as shapefiles or geodatabase feature classes. For more effective measurement of lacunarity for different patterns or processes in raster datasets, the extension allows users to define an area of interest (AOI) in four different ways, including using a polygon in an existing feature layer. Additionally, directionality can be taken into account when grey-scale datasets are used for local lacunarity analysis. The methodology and graphical user interface (GUI) are described. The application of the extension is demonstrated using both simulated and real datasets, including Brodatz texture images, a Spaceborne Imaging Radar (SIR-C) image, simulated 1D points on a drainage network, and 3D random and clustered point patterns. The options of lacunarity analysis and the effects of polyline arrangement on lacunarity of 1D points are also discussed. Results from sample data suggest that the lacunarity analysis extension can be used for efficient modeling of spatial patterns at multiple scales.

An Updating System for the Gridded Population Database of China Based on Remote Sensing, GIS and Spatial Database Technologies

The spatial distribution of population is closely related to land use and land cover (LULC) patterns on both regional and global scales. Population can be redistributed onto geo-referenced square grids according to this relation. In the past decades, various approaches to monitoring LULC using remote sensing and Geographic Information Systems (GIS) have been developed, which makes it possible for efficient updating of geo-referenced population data. A Spatial Population Updating System (SPUS) is developed for updating the gridded population database of China based on remote sensing, GIS and spatial database technologies, with a spatial resolution of 1 km by 1 km. The SPUS can process standard Moderate Resolution Imaging Spectroradiometer (MODIS L1B) data integrated with a Pattern Decomposition Method (PDM) and an LULC-Conversion Model to obtain patterns of land use and land cover, and provide input parameters for a Population Spatialization Model (PSM). The PSM embedded in SPUS is used for generating 1 km by 1 km gridded population data in each population distribution region based on natural and socio-economic variables. Validation results from finer township-level census data of Yishui County suggest that the gridded population database produced by the SPUS is reliable.

Three-dimensional surface reconstruction of tree canopy from lidar point clouds using a region-based level set method

In this article, a novel method is proposed for three-dimensional (3D) canopy surface reconstruction of trees using a region-based level set method. Both individual tree crowns and clusters of trees are first marked for further exploration. Multiple horizontal slices corresponding to different heights are obtained. The 3D structure of tree canopy is built using raw data from lidar point clouds. Also, new applications are proposed based on the new method for 3D forest reconstruction. The biomass parameters of the forest, including tree intersection area, tree equivalent crown radius, and canopy volume, can be calculated from stacking 2D slices of trees. Tree types are also identified and classified. The results indicate that this approach is effective for 3D surface reconstruction of forests including individual trees and clusters of trees, and that critical forest parameters (such as tree intersection area, tree position, and canopy volume) can be derived for the evaluation and measurement of biophysical parameters of forests.

Modeling Herds and Their Evolvements from Trajectory Data

A trajectory is the time-stamped path of a moving entity through space. Given a set of trajectories, this paper proposes new conceptual definitions for a spatio-temporal pattern named Herdand four types of herd evolvements: expand, join, shrink,and leavebased on the definition of a related term flock. Herd evolvements are identified through measurements of Precision, Recall,and F-score. A graph-based representation, Herd Interaction Graph, or Herding, for herd evolvements is described and an algorithm to generate the graph is proposed and implemented in a Geographic Information System (GIS) environment. A data generator to simulate herd movements and their interactions is proposed and implemented as well. The results suggest that herds and their interactions can be effectively modeled through the proposed measurements and the herd interaction graph from trajectory data.

Implementation of mathematical morphological operations for spatial data processing

Abstract Mathematical morphology is a theory of image transformations which is based on set-theoretical, geometrical, and topological concepts. The methodology is useful particularly for the analysis of the geometrical structure in an image. Since many data types in the geosciences are related to spatial location, it is expected that the mathematical morphological operations are particularly important for geoscientific spatial data processing. In this paper, the binary mathematical morphological operations of dilation, erosion, opening, closing, hit/miss transform, and thinning are implemented using the C programming language and the PCIDSK C Toolbox. Dilation, erosion, opening, and closing operations are extended to gray-scale imagery. As an example, gray-scale image dilation and erosion operations are applied to an aeromagnetic image from the File Lake area, Manitoba, Canada for edge and linear feature detection. The detected edge and linear features are scaled into a binary image through thresholding, which allows for further binary morphological operations. Thinning operations are applied to binary images to obtain connected one-pixel thick skeletons. Trimming operations are tested to remove skeletal legs resulting from the thinning operation. Results from both binary and gray-scale images show that mathematical morphological operations can be applied efficiently to the processing of spatial data in the geosciences. The thinning operation makes it possible for edge and linear features to be vectorized and put directly into a geographic information system (GIS).

Estimating leaf area index of maize using airborne full-waveform lidar data

ABSTRACT The leaf area index (LAI) is a key input parameter in ecosystem models and plays a vital role in gas–vegetation exchange processes. Several studies have recently been conducted to estimate the LAI of low-stature vegetation using airborne discrete-return light detection and ranging (lidar) data. However, few studies have been carried out to estimate the LAI of low-stature vegetation using airborne full-waveform lidar data. The objective of this research is to explore the potential of airborne full-waveform lidar for LAI estimation of maize. First, waveform processing was conducted for better extraction of waveform-derived metrics for LAI estimation. A method of faint returns retrieval was also proposed to obtain ground returns. Second, the LAIs of maize were estimated based on the Beer–Lambert law. Finally, the LAI estimates were validated using field-measured LAIs in Huailai, Hebei Province of China. Results indicated that maize LAI could be successfully retrieved with high accuracy (R2 = 0.724, RMSE = 0.449) using full-waveform lidar data by the method proposed in this study.

Characterization of individual tree crowns using three-dimensional shape signatures derived from LiDAR data

Three-dimensional (3D) shape signatures based on the distance distribution of random point pairs are introduced and the effectiveness evaluated using computer simulations and samples of oak and Douglas fir crowns selected from Light Detection and Ranging (LiDAR) point clouds and Digital Surface Models (DSMs). The results suggest that comparison of 3D crown shapes can be effectively reduced to the comparison of frequency distributions of distances between random points, and that it is more computationally efficient when shape signatures are derived from raster surfaces. The results also suggest that the statistically based 3D shape signatures are relatively insensitive to noise and other small local variations, which is important for crown shape analysis in real-world environments.