, NDVI was important in the estimation of carbon stock inside reforested
, NDVI was worthwhile in the estimation of carbon stock within reforested urban landscape. This could possibly be attributed to the reality that NDVI is an critical indicator of green-biomass, which is usually efficiently made use of for deriving and monitoring spatio-temporal dynamics of aboveground carbon stock/sequestration [591]. The findings in this study are constant with those of Moumouni et al. [61] who predicted aboveground carbon stock variability across Pinacidil manufacturer distinctive forest biomes to a R2 of 0.91 making use of an NDVI. Meanwhile, in a related study, Bindu et al. [59] attained an R2 of 0.71 in estimating carbon stock of mangroves trees making use of NDVI. Such a powerful predictive efficiency of NDVI in carbon stock estimation is usually explained by the sensitivity from the near-infrared region towards the internal leaf mesophyll, that is a major indicator of vegetation overall health and is responsible for maximum biomass productivity [62,63], and therefore critical for simulating the quantity of carbon stored in forest ecosystems. NDVI include robust spectral facts derived from Red and NIR bands, that are sensitive in detecting vegetation health and productivity, which are valuable carbon accumulation indicators. According to Moumouni et al. [61], the spatiotemporal variability in green-biomass reflectance as measured by NDVI is proportional to the simulated carbon flux. Interestingly, the inclusion of new and one of a kind red-edge indices such as NDVIRE boosted the predictive performance of carbon stock within reforested urban landscape. The robustness of red-edge indices (i.e., NDVIRE ) lies together with the potential to provide spectral reflectance that have less atmospheric, soil background and water absorption influence or effects [25,29]. The findings within this study are congruous with prior studies, which also established that red-edge indices are very sensitive to vegetation metrics (e.g., leaf region index and biomass) [25,28,29,64]. For instance, Xie et al. [64] located that the red-edge derived spectral indices are far better prospects for improving estimation coefficient of leaf area index in agroecosystems. Whilst Mutanga et al. [25] established thatRemote Sens. 2021, 13,11 ofred-edge indices can considerably increase biomass estimation of wetland vegetation. These studies recommended that red-edge indices may very well be efficiently utilised to measure vegetation productivity and overall health (which consists of carbon sequestration and stock). Red-edge derived indices are significantly less prone to saturation which is prevalent to standard NDVI [28,29], and hence may be effectively applied in dense vegetation cover. In addition, red-edge indices contain sensitive spectral data as red-edge wavebands record speedy variations in plants chlorophyll content and leaf structure, therefore essential for monitoring the spatial and temporal dynamics of vegetation wellness and productivity [65,66]. Additionally, the results on the carbon stock map show the variability of carbon stock across the study region, which decreases with all the lower in canopy density. This variability in carbon stock within the study location might be attributed for the variations in landscape topographic traits, which influence vegetation Alvelestat custom synthesis density and productivity. As an example, research have shown that slope, elevation and aspect can significantly influence the spatial distribution of carbon stock across forest landscapes [9,67,68]. Variations may also be triggered by forest species composition because of the differences in biophysical (i.e., leaf region, stomata and canopy structu.
