Change detection of gravel mining on riverbeds from the multi‐temporal and high‐spatial‐resolution formosat‐2 imagery

Abstract

Gravel mining from river channels is conducted in many countries around the world, yet ground-based monitoring of these activities requires considerable manpower and is not very effective. Therefore, innovative and effective approaches to monitoring gravel mining are urgently required. Deployed as a high spatial resolution sensor in a daily revisit orbit, Formosat-2 has proved to be an ideal satellite for site surveillance. Using one known event of gravel mining in the TsengWen River, Taiwan, between March 2004 and March 2005, we analysed nine Formosat-2 images taken during this period to summarize three characteristics of gravel mining in the images. Based on these characteristics, a standard procedure for processing Formosat-2 imagery to detect gravel mining is proposed. This procedure is validated against ground truthing collected by an unmanned helicopter flying at low altitude. The evolution of this gravel-mining event in the Tseng-Wen River is described by processing all historical Formosat-2 imagery using the proposed procedure. This standard procedure has been successfully incorporated into the Formosat-2 automatic image processing system and has been used to monitor gravel mining on a daily-basis.

Emissions Inventory for Rice Straw Open Burning in Taiwan Based on Burned Area Classification and Mapping Using Formosat-2 Satellite Imagery

“…It is the second satellite owned and operated by the National Space Organization (NSPO) of Taiwan. Studies have demonstrated the potential of FS-2 for site surveillance (Liu, 2006), rapid disaster response detection (Liu et al, 2009a), illegal gravel mining (Liu et al, 2009b), and environmental monitoring (Chang et al, 2009;Liu et al, 2009c), supporting the use of FS-2 imagery for monitoring RSOB and visualizing the B value in Taiwan.…”

“…The availability of daily images for a particular area is uncertain and depends on the weather conditions and job priority of the NSPO. For example, although we requested intensive FS-2 image acquisition during the study period ( All raw images were preprocessed by the automatic Formosat-2 image processing system (F2-AIPS; Liu, 2006), including band-to-band coregistration , orthorectification (Liu and Chen, 2009), geometrical registration of the multi-temporal imagery (Liu et al, 2009b), and radiometric normalization of the multi-temporal imagery (Chang et al, 2009). Following image orthorectification, a root mean square error (RMSE) of 1.5 pixels (3 m) for the geometrical registration was obtained using F2-AIPS.…”

Classification of non-vegetated areas using Formosat-2 high spatiotemporal imagery: the case of Tseng-Wen Reservoir catchment area (Taiwan)

“…The successful operation of Formosat-2 has proved the concept that the temporal resolution of a remote sensing system can be much improved by deploying a high-spatial-resolution sensor in a daily revisit orbit, as each accessible scene can be systematically observed from the same angle under similar illumination conditions (Liu 2006). These characteristics make Formosat-2 an ideal satellite for site surveillance and its images have been successfully applied in environmental monitoring , 2009a, Scambos et al 2009), hazard assessment (Liu et al 2007(Liu et al , 2009c, orthomap generation and land use management (Liu et al 2009b). A detailed description of the daily revisit orbit (the accessible areas and the ground track) and the spectral characteristics of the remote sensing instruments on-board Formosat-2 are given in Liu (2006).…”

Near Real-Time Browsable Landsat-8 Imagery

“…To fully exploit the advantages of Formosat-2 daily-revisit imagery and meet the requirement of serving as an image application and distribution center, the Formosat-2 automatic image processing system (F-2 AIPS) was developed [5] and implemented in 2005. F-2 AIPS is able to digest raw data in the Gerald format, apply the basic radiometric and geometric correction, output the level-1A product, conduct rigorous band-to-band coregistration [6], automatic orthorectification [7], multi-temporal image geometrical registration [8], multi-temporal image radiometric normalization [9], Spectral Summation Intensity Modulation pan-sharpening [6], edge enhancement and adaptive contrast enhancement, the absolute radiometric calibration [10], as well as the superoverlay output for displaying on the Google Earth platform [11]. Experience acquired from F-2 AIPS motivated us to develop a Landsat-8 automatic image processing system (L-8 AIPS) that is able to process and share near-real-time Landsat-8 imagery via the internet.…”