## 3 Mapping of Land Cover and Physiological or Structural Variables
### Physiological Variables
#### 3.2.3 Forest Age Class  
Apart from vegetation structural classes (e.g., Forest, Shrub, and Grass), successional age groups of forests are important for the estimation of changes in NPP, NEP, biomass as well as carbon flux, especially in recovering tropical secondary forests [61]. These maps could be prepared through sequential analysis of high-resolution aerial photographs or satellite images [12; 122; 123] and/or spectral profiles of different age groups of the forests [124], and spectral recovery indicators from time series of medium-resolution multispectral images. Earlier, multispectral analysis of multitemporal Landsat images is used to estimate the age of the recovering topical forests in Amazon [125]. Another Landsat-based study estimated the age of the regenerating tropical forest in the Tapajos National Forest of Brazil by performing multitemporal analysis [126]. Both the studies [125; 126] documented the utility of NDVI in determining the age classes of secondary forests. A recent study documented a significant difference in fluorescence emissions (which is a proxy variable for plant biomass or carbon flux measurement) among different age groups of Hong Kong's recovering tropical forests [127]. They observed higher productivity up to the age of 61 years old and then a decline in productivity in the old-growth forests. This accords with fairly recent pronouncements by scientists that older forests are not as efficient as younger rapidly growing forests as carbon sinks. [PERSON] et al. [128] presented a multitemporal semi-automatic classification approach to map successional stages of the recovering tropical forest in Brazilian Amazonia using Landsat images. Another study in the Brazilian Amazon presented an integrated analysis of multispectral Landsat and ALOS PALSAR images to determine the age of secondary forests [129]. Recently, annual forest age maps of a 33-year (1985-2018) secondary forest succession in Brazil have been produced by using the time series of LCLU maps (MapBiomas Project Collection 4.1) derived from Landsat satellite images [71]. Apart from satellite remote sensing time-series studies, multitemporal photogrammetric analysis of aerial photographs has shown strong potential to determine the structural changes and age of tropical forests [130].  
Recent studies are indicating changes in vegetation productivity along successional gradients from new young forest to old-growth primary forests. For example, [PERSON] et al. [131] quantified a relationship between forest age and carbon sink and found that carbon sequestration capacity gradually increases and peaks after 22 years, then start declining. Another study found a temporal pattern of the rapid increase in NPP in USA forests at the young ages, peak growth in the middle ages, and a slow decline in the mature ages [132]. [PERSON] et al. [133] linked the declining patterns of forest growth with a decline in photosynthesis and dry matter production once a canopy achieves a maximum leaf area. Besides, it has been documented that the forest-age and height are both controlling factors of photosynthesis in Amazonian forests [134]. They also found that the younger and shorter forests are three times more sensitive than the older forests to precipitation variability, due to lesser biomass and lack of deeper roots to access deeper moisture soil. Thus, successional age classification map provided by remote sensing can improve the ability of NPP and NEP simulation models, by simulating carbon cycles processes along successional age gradients [135]. Consequently, the availability of long-term records of low- and high-resolution satellite records of the past five decades, together with a cloud computing platform, would enable the production of high-resolution forest age maps that would play a significant role in future studies of the remote-sensing-based estimate of carbon stock assessment in the tropical secondary forests.