Innovative Technologies for Terrestrial Remote Sensing

Abstract

Characterizing and monitoring terrestrial, or land, surface features, such as forests, deserts, and cities, are fundamental and continuing goals of Earth Observation (EO). EO imagery and related technologies are essential for increasing our scientific understanding of environmental processes, such as carbon capture and albedo change, and to manage and safeguard environmental resources, such as tropical forests, particularly over large areas or the entire globe. This measurement or observation of some property of the land surface is central to a wide range of scientific investigations and industrial operations, involving individuals and organizations from many different backgrounds and disciplines. However, the process of observing the land provides a unifying theme for these investigations, and in practice there is much consistency in the instruments used for observation and the techniques used to map and model the environmental phenomena of interest. There is therefore great potential benefit in exchanging technological knowledge and experience among the many and diverse members of the terrestrial EO community.The Earth Observation Technology Cluster (EOTC) is a major knowledge exchange initiative, established to promote development, understanding and communication about innovative technology used in remote sensing of the land surface among the wide community of interested researchers, technology developers and end-users (Figure 1). To ensure broad relevance and widespread interest in the initiative, the EOTC covers the full range of remote sensing operation, from new platform and sensor development, through image retrieval and analysis, to environmental modelling and data applications. (For more details about the achievements of the EOTC, see [1].) Following a public consultation, certain topical and strategic themes were identified for detailed investigation: unmanned aircraft systems, terrestrial laser scanning, field-based Fourier transform infrared spectroscopy, hypertemporal image analysis, and circumpolar and cryospheric applications. This paper provides an introduction to the OPEN ACCESS

A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture

“…Multiple image sources increase the chances to acquire more observations. Hypertemporal imaging, a term coined to indicate a pool of multi-temporal data collected frequently enough to capture the phenomenon dynamics [27], is possible in most cases of optical remote sensing only by making use of a virtual constellation.…”

“…Multiple image sources increase the chances to acquire more observations. Hypertemporal imaging, a term coined to indicate a pool of multi-temporal data collected frequently enough to capture the phenomenon dynamics [27], is possible in most cases of optical remote sensing only by making use of a virtual constellation. Given the above conditions, we conducted a tasked acquisition of high spatial resolution satellite images (Table 1) over two consecutive years (2014 to 2015) and within an active time window, expressed as the start and end year according to the agricultural conditions of each area.…”