Biological Earth observation with animal sensors.

Space-based tracking technology using low-cost miniature tags is now delivering data on fine-scale animal movement at near-global scale. Linked with remotely sensed environmental data, this offers a biological lens on habitat integrity and connectivity for conservation and human health; a global network of animal sentinels of environmen-tal change

A Range of Earth Observation Techniques for Assessing Plant Diversity

AbstractVegetation diversity and health is multidimensional and only partially understood due to its complexity. So far there is no single monitoring approach that can sufficiently assess and predict vegetation health and resilience. To gain a better understanding of the different remote sensing (RS) approaches that are available, this chapter reviews the range of Earth observation (EO) platforms, sensors, and techniques for assessing vegetation diversity. Platforms include close-range EO platforms, spectral laboratories, plant phenomics facilities, ecotrons, wireless sensor networks (WSNs), towers, air- and spaceborne EO platforms, and unmanned aerial systems (UAS). Sensors include spectrometers, optical imaging systems, Light Detection and Ranging (LiDAR), and radar. Applications and approaches to vegetation diversity modeling and mapping with air- and spaceborne EO data are also presented. The chapter concludes with recommendations for the future direction of monitoring vegetation diversity using RS

Biological Earth observation with animal sensors

Space-based tracking technology using low-cost miniature tags is now delivering data on fine-scale animal movement at near-global scale. Linked with remotely sensed environmental data, this offers a biological lens on habitat integrity and connectivity for conservation and human health; a global network of animal sentinels of environmen-tal change

Coal Fire Quantification and Detection Using the DLR Experimental Bi-Spectral Infrared Detection (BIRD) Small Satellite

Uncontrolled coal seam fires cause severe environmental and economic problems worldwide. They result in a reduction of the coal reserve, lead to atmospheric pollution through the emission of greenhouse gases, cause land subsidence and negatively impact human health in the nearby areas. Within the framework of a Sino-German geo-scientific Coal Fire Research Initiative the German Aerospace Centre (DLR) has investigated the potential of the BIRD small satellite to detect and quantify coal fires in Northern China. The BIRD satellite is a technology demonstrating mission of new infrared pushbroom channels, specifically designed to support the detection and quantification of thermal anomalies on the earths surface. In this paper we present BIRD data acquired over two coal fire areas in Northern China. Based on field observations of coal fires in the investigated areas, we visually outline potential coal fire areas on BIRD data and apply the Bi-spectral technique (Dozier, 1981) to estimate potential coal fire temperatures, sizes and radiative energy releases. We compare the BIRD derived quantitative coal fire parameters to field measurements derived during two field campaigns in September 2003 and September 2003. In addition, we compare the results of the BIRD data analysis to a coal fire analysis performed via Enhanced Thematic Mapper (ETM) data. This study demonstrates that in particular BIRD night-time data have a high potential to register and analysis coal fires. The majority of the coal fire areas in the investigated coalfields can be clearly outlined on BIRD night-time data. In addition, BIRD derived coal fire temperatures correlate well with field observations and thus indicate the high potential of the BIRD technology to derive physical meaningful fire parameters. The comparison of ETM and BIRD data reveals that the BIRD MIR spectral band is radiometrically more sensitive to coal fire anomalies than the ETM TIR spectral channel. However, the six time higher spatial resolution of the ETM allows it to perform better than BIRD at night-time