## 5 Conclusions/Outlook  
A healthy ecosystem can sustainably provide the best quality ecological services to human beings, yet ecosystems worldwide have been impacted by climate change and anthropological activities. A comprehensive and dynamic ecosystem health assessment with the involvement of both ecologists and remote sensing specialists is needed. The intrinsic temporal and spatial properties of remote sensing data provide an opportunity for developing a spatially explicit health ecosystem assessment and monitoring system. Currently, the issue of remote sensing ecosystem health is that the assessment is only based on a single indicator of one ecosystem attribute; however a comprehensive assessment should be a dynamic measurement of the three ecosystem attributes: vigor, organization, and resilience. The retrieval of different ecosystem health indicators may need diverse remote sensing data sources in terms of optical, radar, and LiDAR data, or optical data with different temporal, spectral, and spatial resolutions. To develop a comprehensive health ecosystem system with indicators of ecosystem vigor, organization, and resilience retrieved from different remote sensing imagery, currently one has to face the challenges of scale issue, transportability issue, data availability, and uncertainties in the estimation of indicators. Moreover, retrieval of some indicators, such as Non-photosynthetic Vegetation (NPV) biomass and Biological Soil Crust (BSC) cover, are still challenging. As the technology in developing LiDAR, radar, and multi-angle optical sensors and methodologies for information retrieval improves, the uncertainties of estimation of health indicators are expected to decrease [162]. The integration of multi-sensoral data provides an opportunity to relieve the effects of scale issue. As the methods to develop the integrated models increase, the advantages of multiple sensors can be taken while minimizing disadvantages of each data source [162]. The multi-sensoral data may provide an opportunity to minimize the scale issues and reduce the uncertainties of health indicators. In addition, newly operational sensors and the upcoming sensors will also provide more opportunities for remote sensing of ecosystem health. Newly operational 10-day syntheses PROBA-V 333 m NDVI products since November 2013 are free for use to fill the gap of data discontinuity of SPOT 4 and SPOT vegetation-1 and vegetation-2 sensors [163]. In the near future, upcoming satellites, such as WorldView-3 satellite, Sentinel-2, the proposed Radarsat-2 constellation, and NovaSAR-S will provide more low cost opportunities for developing a spatially explicit ecosystem health assessment. As technological innovation in acquiring radar, LiDAR, and hyperspectral and multi-angle optical remote sensing data, and in algorithms and methods for retrieving information and integrating multi-sensoral data, developing a comprehensive, dynamic, and spatially explicit ecosystem health assessment and monitoring system will face fewer challenges. The upcoming new sensors and upgraded technology on data processing will increase availability of remote sensing data and provide ecologists remote sensing products (e.g., biophysical parameter estimation, canopy structure, NPV biomass and BSC cover estimation _etc._) with fewer uncertainties. This is expected to contribute to ecological studies by providing more accurate temporal and spatial details of ecological indicators, and thus aid ecologists to select more representative ecosystem health indicators. In turn, the progress of ecological studies in the regard of ecosystem health will provide feedbacks on the application of remote sensing data, which may facilitate the design of remote sensing sensors and the processing of remote sensing data. Through close collaboration, ecologists can obtain useful information on remote sensing data in terms of data attributes, application, limitation, and cost, while remote sensing specialists can acquire the most effective ecosystem health indicators in an efficient way.