Spatial Assessment of forest litterfall in Central Himalayas (India): Comparison of geospatial, remote sensing and data-driven estimates

Abstract

Abstract A comparison of multiple approaches for annual litterfall estimation and spatial assessment of forests was carried out for the state of Uttarakhand (Geog. Area = 54533 sq. km, Forest Area- 24652.32 sq. km) in Central Himalayas, India. Non-spatial approach used meta-analysis of published litterfall studies in Uttarakhand (29 studies with 115 measurements over sites/years) classified by forest types and area under forest types estimated by remote sensing by Forest Survey of India. The measured mean litterfall ranged from a high of 7.88 t/ha/yr for the sub-tropical broad- leaved forests to a low of 3.70 t/ha/yr in plantations. Spatial models of litterfall used a data-driven approach with 100 measurements and a random forest (RF) model that used bioclimate, elevation and forest type as covariates at a spatial grid of 1km resolution. This estimate was compared with published global (Li et al., 2019) and European (Neumann et al., 2018) spatial models. The total litterfall with five different forest-type area and estimated mean litterfall varied between 12.34 to 14.69 Mt/yr and with spatial allocation to forest type map estimated 14.02 Mt/yr litterfall. Data-driven spatial model using Random Forest approach estimated 13.305 Mt/yr of total litterfall. Use of spatial litterfall models developed for other study areas resulted in estimates that ranged from 9.11–15.81 Mt/yr. The study provides important insights towards developing a spatial gridded annual litterfall dataset for India and its use for studying the dynamics of forest carbon cycle.