## 4 Challenges to Developing a Remote Sensing Based EHA System
### Scale Issue  
Species distribution and ecological processes are scale dependent on the growing conditions of species [135] partially controlled by soil and topography. The spatial scale issue has been identified as a major challenge in ecological assessment of remote sensing [135]. In part, the accuracy of the retrieval of vegetation properties using remote sensing depends upon sensor spatial resolution [136]. Using remote sensing data, especially low spatial resolution data, such as the 1 km spatial resolution Advanced Very High Resolution Radiometer (AVHRR), for ecosystem health assessment may introduce uncertainties resulting from land surface heterogeneity and mixed pixels containing more land cover types [137]. GPP calculated from the Region Production Efficiency Model (REG-PEM, [138]) with all model inputs obtained from AVHRR 1 km remote sensing data is significantly different from the GPP calculated using Landsat TM 30 m data [137]. However, finer spatial resolution remote sensing data cannot guarantee higher accuracy of ecosystem assessment [26]. For example, MODIS EVI at 250 m resolution cannot be used for estimating GPP of coniferous forests, while MODIS 1 km EVI can [42]. It was also found that when the spatial resolution of remote sensing data is higher than 60-80 m, the accuracy of forest classification decrease [139]. At the same time, a suitable spatial scale or satellite image at optimal spatial resolution can improve vegetation vertical structure (e.g., LAI) estimation in grassland because the land surface heterogeneity was minimized [140; 141; 142]. An optimum resolution can be detected using wavelet or semivariagram analysis, while considering a specific study goal.  
In addition, some retrieval algorithms and models for ecosystem health indicators, such as biochemical properties, are derived at small scales for homogeneous land surfaces [143]. Applying those algorithms and models to large scales (typically imply heterogeneous land surface), may incur scale effects. Besides heterogeneity, the linearity or nonlinearity of retrieval models is the other factor on scale effects, and generally the former may result in smaller scale effects than the latter for mixed pixels with unknown mixture of different land covers [144]. Therefore, cautions should be made to select up-scaling approaches for the purpose of minimizing such scale effects. Some scaling methods have been summarized, although no universal scaling method was found [143]. More recently, a conceptual framework was proposed to scale up biochemical content in semi-arid mixed grassland from leaf to canopy level [76], and the estimation of grassland chlorophyll content at leaf, canopy, and landscape scales is fairly accurate [77].  
Besides spatial resolution and scaling methods, the accuracy of ecological assessment using optical remote sensing also relies on spectral and temporal resolutions of sensors [135; 136]. As discussed in Section 3, some ecological health indicators may be retrieved from remote sensing at high spatial resolution imagery, while others may need higher spectral resolution or temporal resolution data. In this regard, to develop a comprehensive ecosystem health assessment and monitoring system, data fusion can be a solution. The fused imagery can provide the maximum amount of useful information [145], and thus have significant advantages over each single source data [146].