Integrating Remote Sensing Data with Directional Two- Dimensional Wavelet Analysis and Open Geospatial Techniques for Efficient Disaster Monitoring and Management.

Yun-Bin Lin,Dong-Po Deng,Kuan-Wei Chen,Yu-Pin Lin

doi:10.3390/s8021070

Abstract

In Taiwan, earthquakes have long been recognized as a major cause oflandslides that are wide spread by floods brought by typhoons followed. Distinguishingbetween landslide spatial patterns in different disturbance regimes is fundamental fordisaster monitoring, management, and land-cover restoration. To circumscribe landslides,this study adopts the normalized difference vegetation index (NDVI), which can bedetermined by simply applying mathematical operations of near-infrared and visible-redspectral data immediately after remotely sensed data is acquired. In real-time disastermonitoring, the NDVI is more effective than using land-cover classifications generatedfrom remotely sensed data as land-cover classification tasks are extremely time consuming.Directional two-dimensional (2D) wavelet analysis has an advantage over traditionalspectrum analysis in that it determines localized variations along a specific direction whenidentifying dominant modes of change, and where those modes are located in multi-temporal remotely sensed images. Open geospatial techniques comprise a series ofsolutions developed based on Open Geospatial Consortium specifications that can beapplied to encode data for interoperability and develop an open geospatial service for sharing data. This study presents a novel approach and framework that uses directional 2Dwavelet analysis of real-time NDVI images to effectively identify landslide patterns andshare resulting patterns via open geospatial techniques. As a case study, this study analyzedNDVI images derived from SPOT HRV images before and after the ChiChi earthquake(7.3 on the Richter scale) that hit the Chenyulan basin in Taiwan, as well as images aftertwo large typhoons (Xangsane and Toraji) to delineate the spatial patterns of landslidescaused by major disturbances. Disturbed spatial patterns of landslides that followed theseevents were successfully delineated using 2D wavelet analysis, and results of patternrecognitions of landslides were distributed simultaneously to other agents using geographymarkup language. Real-time information allows successive platforms (agents) to work withlocal geospatial data for disaster management. Furthermore, the proposed is suitable fordetecting landslides in various regions on continental, regional, and local scales usingremotely sensed data in various resolutions derived from SPOT HRV, IKONOS, andQuickBird multispectral images.

Highlights

Earthquakes have long been recognized as a major cause of landslides [1]
This study presents a novel approach and framework that uses directional 2D wavelet analysis of real-time normalized difference vegetation index (NDVI) images to effectively identify landslide patterns and share resulting patterns via open geospatial techniques
This study presents a novel procedure combined with integrated techniques, including wavelet analysis, remote sensing, and open geospatial standards, for efficient disaster monitoring and management using a rapid algorithm for landslide detection and an architecture implemented for encoding, transporting and storing geospatial data

Summary

Introduction

Earthquakes have long been recognized as a major cause of landslides [1]. Landslides are only the first in a series of processes by which materials can be removed from slopes and transported out of a region by fluvial action [2]. Since 1999, after the ChiChi earthquake, the expansion rate of landslide areas has grown 20 times in central Taiwan [3] due to the numerous extension cracks, which accelerate landslides during downpours, generated on hill slopes during the ChiChi earthquake [4]. Management, and land-cover restoration have become high priority tasks that reduce damage to livelihoods and economies. Landslides monitoring via field surveys is time consuming because of the widespread distribution of landslides and limited accessibility to disaster regions. Sensed data has become an efficient means of quantifying the spatial characteristics of landscape changes both regionally and globally [5]

Methods

Results

Discussion

Conclusion