Variation of biomass and carbon pool with NDVI and altitude in sub-tropical forests of northwestern Himalaya

Abstract

In the present study, forests at three altitudes, viz., A (600-900 m), A (900-1200 m) and A (1200-1500 m) above mean sea level having normalised differential vegetation index (NDVI) values of N (0.0-0.1), N (0.1-0.2), N (0.2-0.3), N (0.3-0.4) and N (0.4-0.5) were selected for studying their relationship with the biomass and carbon pool in the state of Himachal Pradesh, India. The study reported maximum stem density of (928 trees ha) at the A altitude and minimum in the A and A with 600 trees ha each. The stem densities in relation to NDVIs were observed in the order N > N > N > N > N and did not show any definite trend with increasing altitude. Highest stem volume (295.7 m ha) was observed in N NDVI and minimum (194.1 m ha) in N index. The trend observed for stem biomass at different altitudes was A > A > A and for NDVIs, it was N > N > N > N > N. Maximum aboveground biomass (265.83 t ha) was recorded in the 0.0-0.1 NDVI and minimum (169.05 t ha) in 0.2-0.3 NDVI index. Significantly, maximum total soil carbon density (90.82 t C ha) was observed in 0.4-0.5 NDVI followed by 0.3-0.4 NDVI (77.12 t C ha). The relationship between soil carbon and other studied parameters was derived through different functions simultaneously. Cubic function showed highest r in most cases, followed by power, inverse and exponential function. The relationship with NDVI showed highest r (0.62) through cubic functions. In relationship between ecosystem carbon with other parameters of different altitudinal gradient and NDVI, only one positively significant relation was formed with total density (0.579) through cubic function. The present study thus reveals that soil carbon density was directly related to altitude and NDVIs, but the vegetation carbon density did not bear any significant relation with altitude and NDVI.

Influence of Aspect and Elevational Gradient on Vegetation Pattern, Tree Characteristics and Ecosystem Carbon Density in Northwestern Himalayas

“…Similarly, the SD varied from 283 to 116 in northern and southern aspects, respectively. The SD in the present study is on the lower side than the values reported by Bhardwaj et al [37], but more or less comparable with the findings of Banday et al [70] under similar forest types of the Indian Himalayas. Pala et al [72] have also reported higher tree densities for the central Himalayan region, which may be due to variation in species composition and other environmental factors.…”

“…In most of the studies, the parameters pertaining to forest productiveness are overestimated because of the unaccountability of slope and aspects [36]. The ecological studies and biomass estimation along altitudinal gradients in forests of western Himalaya have been well documented over the years [37][38][39]. However, the literature reveals that very few studies report the effect of aspect in the northwestern Himalayas on forest ecosystems.…”

Estimation of Above Ground Biomass in a Tropical Mountain Forest in Southern Ecuador Using Airborne LiDAR Data

“…For this task, the AGB must be monitored in space and time, which is particularly challenging in Tropical Mountain Forests (TMF; elevation >1000 m above sea level), because of the more complex terrain in comparison to lowland tropical forests [16]. The fast-changing forest structure in tropical mountains is hardly detectable by field measurements, where the forest AGB is estimated by means of individual tree samples taken in relatively small field plots [8,12,17]. Field observations traditionally estimate the mean forest AGB manually by means of random sampling of felled trees in the forest ecosystems [18].…”

Remote Sensing of Tropical Rainforest Biomass Changes in Hainan Island, China from 2003 to 2018

“…However, NDVI is easily affected by the forest canopy, soil, atmospheric molecules, and excessive forest biomass [42,43], especially in tropical rainforests, which is close to saturation due to the complex canopy structure [44][45][46]. Bhardwaj also confirmed that NDVI might not be a reliable method for estimating forest biomass carbon storage in Himalayan subtropical forests [1]. For improving the sensitivity of tropical rainforest biomass estimation, the EVI was selected in this study.…”

“…The rapidly increasing global population, land-use change, and large-area forest fires have resulted in a downward trend in global biomass in recent years [1][2][3][4][5]. With global warming, forest biomass could effectively delay the increase in carbon dioxide, which has attracted much attention [6,7].…”