Changlin Wang

Next-generation Digital Earth

A speech of then-Vice President Al Gore in 1998 created a vision for a Digital Earth, and played a role in stimulating the development of a first generation of virtual globes, typified by Google Earth, that achieved many but not all the elements of this vision. The technical achievements of Google Earth, and the functionality of this first generation of virtual globes, are reviewed against the Gore vision. Meanwhile, developments in technology continue, the era of “big data” has arrived, the general public is more and more engaged with technology through citizen science and crowd-sourcing, and advances have been made in our scientific understanding of the Earth system. However, although Google Earth stimulated progress in communicating the results of science, there continue to be substantial barriers in the public’s access to science. All these factors prompt a reexamination of the initial vision of Digital Earth, and a discussion of the major elements that should be part of a next generation.

Digital Earth 2020: towards the vision for the next decade

Abstract This position paper is the outcome of a brainstorming workshop organised by the International Society for Digital Earth (ISDE) in Beijing in March 2011. It argues that the vision of Digital Earth (DE) put forward by Vice-President Al Gore 13 years ago needs to be re-evaluated in the light of the many developments in the fields of information technology, data infrastructures and earth observation that have taken place since. The paper identifies the main policy, scientific and societal drivers for the development of DE and illustrates the multi-faceted nature of a new vision of DE grounding it with a few examples of potential applications. Because no single organisation can on its own develop all the aspects of DE, it is essential to develop a series of collaborations at the global level to turn the vision outlined in this paper into reality.

Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks

Many biologists, ecologists, and conservationists are interested in the possibilities that remote sensing offers for their daily work and study site analyses as well as for the assessment of biodiversity. However, due to differing technical backgrounds and languages, cross-sectorial communication between this group and remote-sensing scientists is often hampered. Hardly any really comprehensive studies exist that are directed towards the conservation community and provide a solid overview of available Earth observation sensors and their different characteristics. This article presents, categorizes, and discusses what spaceborne remote sensing has contributed to the study of animal and vegetation biodiversity, which different types of variables of value for the biodiversity community can be derived from remote-sensing data, and which types of spaceborne sensor data are available for which time spans, and at which spatial and temporal resolution. We categorize all current and important past sensors with respect to application fields relevant for biologists, ecologists, and conservationists. Furthermore, sensor gaps and current challenges for Earth observation with respect to data access and provision are presented.

Big Earth Data: a new challenge and opportunity for Digital Earth’s development

ABSTRACT Digital Earth has seen great progress during the last 19 years. When it entered into the era of big data, Digital Earth developed into a new stage, namely one characterized by ‘Big Earth Data’, confronting new challenges and opportunities. In this paper we give an overview of the development of Digital Earth by summarizing research achievements and marking the milestones of Digital Earth’s development. Then, the opportunities and challenges that Big Earth Data faces are discussed. As a data-intensive scientific research approach, Big Earth Data provides a new vision and methodology to Earth sciences, and the paper identifies the advantages of Big Earth Data to scientific research, especially in knowledge discovery and global change research. We believe that Big Earth Data will advance and promote the development of Digital Earth.

Building segmentation and modeling from airborne LiDAR data

Due to the high accuracy and fast acquisition speed offered by airborne Light Detection and Ranging (LiDAR) technology, airborne LiDAR point clouds have been widely used in three-dimensional building model reconstruction. This paper presents a novel approach to segment building roofs from point clouds using a Gaussian mixture model in which buildings are represented by a mixture of Gaussians (MoG). The Expectation-Maximization (EM) algorithm with the minimum description length (MDL) principle is employed to obtain the optimal parameters of the MoG model for separating building roofs. To separate complete planar building roofs, coplanar Gaussian components are merged according to their distances to the corresponding planes. In addition, shape analysis is utilized to remove nonplanar objects caused by trees and irregular artifacts. Building models are obtained by combining segmented planar roofs, topological relationships, and regularized building boundaries. Roof intersection segments and points are derived by the segmentation results, and a raster-based regularization method is employed to obtain geometrically correct and regular building models. Experimental results suggest that the segmentation method is able to separate building roofs with high accuracy while maintaining correct topological relationships among roofs.

DEM generation in the densely vegetated area of Hotan, north-west China using SIR-C repeat pass polarimetric SAR interferometry

The potential use of the coherence optimization method of polarimetric SAR interferometry (Pol-InSAR) to increase the accuracy of digital elevation models (DEMs) was investigated in a densely vegetated area of the Hotan region, Xinjiang Province, north-west China, using SIR-C data. Under the same experimental conditions, the results indicate that the accuracy of a DEM generated from L-band fully polarimetric single baseline interferometric data is significantly greater than that derived from L-band HH-HH single baseline interferometric data, and the rms. error is less than 8 m.

Multilayer perceptron classification for ENVISAT-ASAR imagery

This paper describes the application of neural networks to targets classification from multi-polarization ENVISAT-ASAR imagery. The used neural network is multilayer perception (MLP) with fast learning (FL), which is fully interconnected network. Accordingly, the training data sets may be taken from a known truth data in the ground. And finally, the results of proposed method are compared with that of the other classification ones, the in situ test data are from Zhaoqing in Guangdong Province of China

Generation and error analysis of DEM using spaceborne polarimetric SAR interferometry data

From the SIR-C polarimetric L-band data of Hotan, China, on 9 and 10 October 1994, the DEM of polarimetric SAR interferometry (Pol-InSAR) and the conventional L-band HH-HH interferometric pair are extracted. The difference of DEM generation using Pol-InSAR and conventional InSAR is discussed in detail. Based on the result of comparison and analysis of two DEM, it is conclude that the accuracy of DEM generated from the polarimetric SAR Interferometry data is significantly improved, especially for the area covered by rich vegetation with enough high coherence, the error less than 10 m. Finally, error sources of DEM generated by polarimetric SAR interferometry are further analyzed.

Extraction of polymetallic mineralization information from multispectral Thematic Mapper data using the Gram-Schmidt Orthogonal Projection (GSOP) method

In order to accelerate the reconnaissance of mineral resources in Xingjiang Uygur Autonomous Region of China, multispectral Thematic Mapper (TM) data were used to delineate hydrothermal alteration related to polymetallic mineralization. In addition to the use of commercial image processing packages, new image processing software based on geological environment and spectral information have been developed. The Gram-Schmidt Orthogonal Projection (GSOP) method used in linear algebra for signal processing was adopted for our study. The kernel part of the algorithm is that the GSOP uses least mean square deviation of mathematical expectation in linear vector space (Hilbert space). In a vector space, one base axis is established and other transitional vectors are inputted to evaluate the effectiveness of GSOP. Satisfactory results have been achieved by applying this method to differentiate hydrothermal alterations in the Kangxiwa area of the Kunlun mountains, western China. The red colour in the image shows Pb-Zn-Cu-Au-Ag polymetallic mineralization zones, which were confirmed by field investigations and sample analyses in 1998.

Chinese SAR for Yangtze river flood monitoring in 1998

In the summer of 1998, a disastrous flood swept across central and northeast of China, along the Yangtze River and Song-Neng River. The water level broke the historical records. A numerous farmland was inundated. Most of residential areas and industrial sites within the flooding region were affected or totally inundated. Radar remote sensing played a important role in flood monitoring in summer of 1998. The Chinese L-band airborne radar system-L-SAR radar system provided the extent and distribution of flood in Poyang Lake, Dongting Lake, and Jingjiang segment of Yangtze River. This paper presents the results of the extent, distribution, development and damage assessment of flood in 1998 delivered from L-SAR and Canadian RADARSAT data.