Modelling forest resilience in Hindu Kush Himalaya using geoinformation

Abstract

Resilience is the capacity of an ecosystem to absorb disturbance and undergo change while maintaining its essential structure, functions, identity and feedbacks. The forests of the Hindu Kush Himalayan (HKH) region are vulnerable to both natural and anthropogenic changes, and the forest land conversion and degradation. Using satellite-derived tree canopy cover percent data and precipitation as the explaining variable, we studied the forest cover resilience in a geospatial framework employing the logistic regression and polynomial equation fitting. Out of the 4.3 million km 2 geographical areas, 873,650 km 2 (20.20%) was under the forest in 2000 and experienced loss of 11,250 km 2 during 2000-2010. We could model the forest cover and treeless areas fairly than the scrub and grassland owing to the variation in precipitation pattern. The majority of the forest cover (59.3%) has been estimated to have less resilience owing to the receipt of <1650 mm of total annual precipitation, whereas only 375 km 2 forest area could change to scrub that shows the least resilience. About 94.4% of treeless areas were estimated to be stable, while only 1% (25,200 km 2) area could accommodate the grassland. The resilient forest areas estimated and observed, owing to the mapping and modelling protocols used in this study, shall be useful in conservation planning in the HKH region.

Climate change projections of terrestrial primary productivity over the Hindu Kush Himalayan forests

“…The forests of HKH are undergoing changes of varied intensity as a result of climatic and human disturbances, alongside the various forest management policies practised in the different countries (Behera et al, 2018;Pulakesh et al, 2017). The rate of deforestation along the HKH has been reported to be 0.5 % yr −1 in Bhutan and 1.7 % yr −1 in Myanmar from 2000 to 2014 (Brandt et al, 2017). The warming trend observed over recent decades of the 20th century is attributed to the increase in anthropogenic GHG concentrations.…”

Forest resilience research using remote sensing and GIS – A systematic literature review

“…Some indicator thresholds used the Enhanced Vegetation Index (EVI), detrended Vegetation Index, forest area loss percentage, and tree cover state relative to a stable state of reference forests. A study in the Hindu Kush, Himalaya [10] built a formulation to quantify forest resilience based on forest gain-forest loss of forest cover into three classes: less, moderate, and high resilience (see Table S1).…”

“…Resilience thinking is greatly needed for natural resource conservation because resilience thinking integrates science, management, and policy to respond to uncertainty, manage risk, and adapt to the changing world [9]. Adopting resilience thinking in forest monitoring and assessment is helpful for forest conservation planning [10], forest restoration [11], and forest dynamics caused by multiple disasters [12], to name some.…”

Modeling Precipitation Dependent Forest Resilience in India

“…Resilience state of Indian forest cover according to the climatic conditions is the aim of the current study. In previous global to regional studies on forest resilience, the tree canopy cover density map have been used as the input map of forest cover distribution (Hirota et al, 2011;Staver et al, 2011;Keersmaecker et al, 2015;Behera et al, 2018). The forest type map of Champion and Seth (1968) was used study of forest cover resilience in India (Chaturvedi et al, 2010).…”

“…with three vegetation type as forest, savannah and treeless, the forest cover shows change proneness towards the its nearest stable state of savannah only, whereas the savannah shows change proneness to its either states as forest or treeless state and the treeless state shows change proneness towards the savannah. The probability of change proneness or complementarily the resilience can be modelled using the vegetation type map, driving forcing or simply drivers through statistical approaches or models as logistic regression, generalized additive model (GAM), Integrated Biosphere Simulator (IBIS) (Hirota et al, 2011;Staver et al, 2011;Behera et al, 2018;Chaturvedi et al, 2010). In the current study the binary logistic regression technique was adopted for estimating the forest cover resilience.…”