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Ten Dragon 3 projects deal with hydrologic and cryosphere processes, with a focus on the Himalayas and Qinghai – Tibet Plateau, but not limited to that. At the 1st Dragon 3 Progress Symposium in 2013 a significant potential for a better and deeper integration appeared very clearly and we worked out an overview of the ten projects identifying specific issues and objectives shared by at least two projects. At the Mid Term Symposium in 2014 a joint session was held over two days. As regards cryospheric processes science highlights covered: Glacier flow velocity by optical and SAR features tracking and InSAR; Patterns in space and time of glacier flow velocity; Mass change estimated with DTM-s and altimetry; Reflectance and LST used to classify glacier surface and understand surface processes, Inventory and changes in the number and area of lakes in the Qinghai – Tibet Plateau 1970, 1990, 2000 and 2010; Deformation of permafrost along the Qinghai – Tibet railway. Highlights on hydrologic processes included: Global comparison of SMOS, ASCAT and ERA soil moisture data products; Relative deviations evaluated by climate zone; Soil moisture data products improved with ancillary data; Assimilation of FY - , TRMM and GPM precipitation data products in WRF; Improved algorithm and data products on fractional snow cover; Improvement of MODIS ET with assimilation of LST; TRMM data products evaluated in the Yangtze; Calibration of river basin models using LST; System to calibrate, correct and normalize (spatial, spectral) data collected by imaging spectral radiometers; Integration of data acquired by different sensors, e.g. ET Monitor with optical and microwave (SMOS, FY – 3) data; Hydrological data products used both for forcing and evaluation of Qinghai – Tibet Plateau hydrological model; Wetlands vulnerability assessed through changes in land cover 1987 – 2013; Multi incidence angle and multi – temporal SAR to monitor water extent. In the general session a proposal for a Dragon Water Cycle Initiative was presented.

Quick and economical ways of detecting of planimetric and volumetric changes of forest areas are in high demand. A research platform, called FORSAT (A satellite processing platform for high resolution forest assessment), was developed for the extraction of 3D geometric information from VHR (very-high resolution) imagery from satellite optical sensors and automatic change detection.This 3D forest information solution was developed during a Eurostars project. FORSAT includes two main units. The first one is dedicated to the geometric and radiometric processing of satellite optical imagery and 2D/3D information extraction. This includes: image radiometric pre-processing, image and ground point measurement, improvement of geometric sensor orientation, quasi-epipolar image generation for stereo measurements, digital surface model (DSM) extraction by using a precise and robust image matching approach specially designed for VHR satellite imagery, generation of orthoimages, and 3D measurements in single imagesusing mono-plotting and in stereo images as well as triplets. FORSAT supports most of the VHR optically imagery commonly used for civil applications: IKONOS, OrbView – 3, SPOT – 5 HRS, SPOT – 5 HRG, QuickBird, GeoEye-1, WorldView-1/2, Pléiades 1A/1B, SPOT 6/7, and sensors of similar type to be expected in the future. The second unit of FORSAT is dedicated to 3D surfacecomparison for change detection. It allows users to import digital elevation models (DEMs), align them using an advanced 3D surface matching approach and calculate the 3D differences and volume changes between epochs. To this end our 3D surface matching method LS3D is being used. FORSAT is a single source and flexible forest information solution with a very competitive price/quality ratio, allowing expert and non-expert remote sensing users to monitor forests in three and four dimensions from VHR optical imagery for many forest information needs. The capacity and benefits of FORSAT have been tested in six case studies located in Austria, Cyprus, Spain, Switzerland and Turkey, using optical data from different sensors and with the purpose to monitor forest with different geometric characteristics. The validation run on Cyprus dataset is reported and commented., International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI (B8), ISSN:1682-1750, ISSN:2194-9034, ISSN:1682-1777

Saltmarsh is one of the important communities of wetlands. Due to a range of pressures, it has been declared as an EEC (Ecological Endangered Community) in Australia. In order to correctly identify different saltmarsh species, development of distinct spectral characteristics is essential to monitor this EEC. This research was conducted to classify saltmarsh species based on spectral characteristics in the VNIR wavelength of Hyperion Hyperspectral and Worldview 2 multispectral remote sensing data. Signal Noise Ratio (SNR) and Principal Component Analysis (PCA) were applied in Hyperion data to test data quality and to reduce data dimensionality respectively. FLAASH atmospheric correction was done to get surface reflectance data. Based on spectral and spatial information a supervised classification followed by Mapping Accuracy (%) was used to assess the classification result. SNR of Hyperion data was varied according to season and wavelength and it was higher for all land cover in VNIR wavelength. There was a significant difference between radiance and reflectance spectra. It was found that atmospheric correction improves the spectral information. Based on the PCA of 56 VNIR band of Hyperion, it was possible to segregate 16 bands that contain 99.83 % variability. Based on reference 16 bands were compared with 8 bands of Worldview 2 for classification accuracy. Overall Accuracy (OA) % for Worldview 2 was increased from 72 to 79 while for Hyperion, it was increased from 70.47 to 71.66 when bands were added orderly. Considering the significance test with z values and kappa statistics at 95% confidence level, Worldview 2 classification accuracy was higher than Hyperion data.