The measurement of tree height has long been an important tree attribute for the purpose of calculating tree growth, volume, and biomass, which in turn deliver important ecological and economical information to decision makers. Tree height has traditionally been measured by indirect field-based techniques, however these methods are rarely contested. With recent advances in Unmanned Aerial Vehicle (UAV) remote sensing technologies, the possibility to acquire accurate tree heights semi-automatically has become a reality. In this study, photogrammetric and field-based tree height measurements of a Scots Pine stand were validated using destructive methods. The intensive forest monitoring site implemented for the study was configured with permanent ground control points (GCPs) measured with a Total Station (TS). Field-based tree height measurements resulted in a similar level of error to that of the photogrammetric measurements, with root mean square error (RMSE) values of 0.304 m (1.82%) and 0.34 m (2.07%), respectively (n = 34). A conflicting bias was, however, discovered where field measurements tended to overestimate tree heights and photogrammetric measurements were underestimated. The photogrammetric tree height measurements of all trees (n = 285) were validated against the field-based measurements and resulted in a RMSE of 0.479 m (2.78%). Additionally, two separate photogrammetric tree height datasets were compared (n = 251), and a very low amount of error was observed with a RMSE of 0.138 m (0.79%), suggesting a high potential for repeatability. This study shows that UAV photogrammetric tree height measurements are a viable option for intensive forest monitoring plots and that the possibility to acquire within-season tree growth measurements merits further study. Additionally, it was shown that negative and positive biases evident in field-based and UAV-based photogrammetric tree height measurements could potentially lead to misinterpretation of results when field-based measurements are used as validation.

The vegetation of small granitic rock outcrops (geomorphologically small‐sized inselbergs) which do not reach the canopy was studied in the Taı rain forest (southwestern Ivory Coast) under aspects of species diversity and phytogeographical affinities. Rock outcrops form edaphically arid (due to absent or very sparse soil cover) and microclimatologically xeric (i.e. low air humidity, temperature regularly exceeding 50°C) islands with cryptogamic crusts, succulents and poikilohydric vascular plants as characteristic elements of their vegetation which differs totally from the surrounding forest. Altogether sixty‐six species of vascular plants out of twenty‐nine families occur, the number of species correlates positively with inselberg size. Compared with large inselbergs the microclimatic attributes of small‐sized rock outcrops are less pronounced. This is accompanied by a decrease of typical inselberg taxa (i.e. species mainly occurring on inselbergs). Low beta diversity between inselbergs indicates deterministic influences as important regulators of species composition. Annual Poaceae and Cyperaceae are richly represented. It can be hypothesized that inselbergs may represent natural growing sites of widely distributed tropical weeds today. Inselbergs might provide habitat resources for savanna elements in rain forest zones.

The paper presents an international multidisciplinary initiative, a Namibia SensorWeb Pilot Project, that was created as a testbed for evaluating and prototyping key technologies for rapid acquisition and distribution of data products for decision support systems to monitor floods. Those key technologies include SensorWebs, Grids and Computation Clouds. This pilot project aims at developing an operational trans-boundary flood management decision support system for the Southern African region to provide useful flood and water-borne disease forecasting tools for local decision makers. This effort integrates space-based and ground sensor data along with higher level geospatial data products to enable risk assessment and ultimately risk maps related to flood disaster management and water-related disease management. We present an overall architecture of the Pilot along with components and services being developed. Additionally, case-studies and results achieved so far are discussed. The presented work is being carried out within GEO 2009-2011 Work Plan as CEOS WGISS contribution.

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Since two decades, the use of terrestrial laser scanning (TLS) and Airborne Light Detection and Ranging (LIDAR) has become very prominent in analysing 3D forest structures (AKAY et al. 2009). The potential of full waveform analysis of high density Airborne LiDAR data (ALS) for the detection and structural analysis of multi-layered forest stands is not yet well investigated (JASKIERNIAK et al. 2011), although ALS data provide exact information on tree heights of multi-layered forest stands using particular laser pulse characteristics (GAULTON & MALTHUS 2010).