## 3 Remote Sensing of Ecosystem Health
### Remote Sensing of Organization
#### 3.2.1 Species Richness and Biodiversity  
Species richness is a primary measurement of regional or community biodiversity [99]. Due to the ecological importance of biodiversity, considerable research and a few reviews [42; 100; 101; 102; 103] have been completed on remote sensing of species richness. To date, species richness studies have been conducted using one sensor imagery at specific time of periods [104], and more recently using images of multiple passive sensors over multiple time periods [105]. Generally remote sensing of species richness can be classified as direct mapping and indirect modeling approaches [100; 101]. Direct mapping species distribution using remote sensing is similar to mapping invasive plant species in terms of approaches and potential and limitations of remote sensing data. Indirect modeling approaches have been widely used to predict species richness based on the empirical relationships between field species investigation and information derived from remote sensing, such as land cover and landscape metrics, NPP, and spectral variation [100; 101]. The rationale on why land cover and landscape metrics can be correlated to species richness or biodiversity is that land cover and landscape metrics including fragmentation [12] have certain associations with species existence. Such land cover information has been used for predicting species richness [12; 106]. This method may be suitable for species richness investigation at large spatial scales. However, prediction accuracy of species richness using such method is arguable due to three aspects: (1) environmental factors including temperature, precipitation, disturbance, and others were neglected [107]; (2) this method is highly affected by spatial resolution of remote sensing imagery [108]; and (3) the derived landscape-metrics do not contain internal information of the metrics [104]. The relationship between NPP and species richness was established based on the species-energy theory which hypothesizes that species richness is correlated with NPP [109]. Thus, NDVI with a close relationship with NPP [36] has been widely used to predict species richness [12, 100]. The utilization of NDVI is mainly based on NDVI variation, and positive relationship between NDVI and specie richness was found in [105, 110]. However, there are inconsistent conclusions that state little correlation between NDVI variation and species richness. Research indicated that NDVI variation has negative relationship with species richness [104] based on the hypothesis that low NDVI variation, higher homogeneity, and consequently higher species richness [111]. Although there are inconsistent conclusions, using NDVI for species richness prediction is still an effective method at large spatial scales [111]. The application of spectral variation for predicting species richness is based on spectral variation hypothesis (SVH) proposed by [112] which assumes that the higher variation in spectra, the higher heterogeneity of habitats allowing coexistence of more species, and consequently higher species richness [113]. In this domain, spectral indices, land-cover heterogeneity, and spectral variability derived from optical remote sensing data have been used to predict species richness [114]. SVH approaches will be an important direction of optical remote sensing of species richness [101]. Overall, the landscape metrics and NDVI approach are more suitable for species richness estimation at large spatial scales, while SVH can be used at a fine scale. However, prediction of species richness is affected by both spatial and spectral resolutions of satellite imagery [114]. For the SVH approach, multispectral imagery generally has difficulty in providing sufficient information for species richness prediction as it is hard to be used for retrieving biochemical and canopy structure information [115]. Hyperspectral images have an advantage as they can provide information on canopy biochemical elements including Ch, N, and cellulose content [116].