GEOSPATIAL ESTIMATION OF AGROFORESTRY AREA, SUITABILITY MAPPING AND CARBON SEQUESTRATION POTENTIAL OF AGROFORESTRY SYSTEMS IN SHIMLA DISTRICT OF HIMACHAL PRADESH
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Date
2024-03-04
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UHF Nauni
Abstract
The current research, entitled “Geospatial estimation of agroforestry area, suitability mapping
and carbon sequestration potential of agroforestry systems in Shimla district of Himachal Pradesh”
was carried out in Shimla district of Himachal Pradesh during the year 2021-2023 for delineation of area
and estimation of carbon sequestration under different land use systems along with assessment of land
suitability to agroforestry. The study's findings revealed that the D7 dataset combination, encompassing
thirteen spectral bands, three biophysical parameters, ten vegetation indices, four water indices and four
soil indices achieved superior performance with a random forest classifier compared to other dataset
combinations. The land area estimates derived from the D7 dataset combination revealed the highest
proportions in forests (45.20%), followed by agroforestry (28.57%), agriculture (15.13%), built-up areas
(3.84%), barren land (2.50%), grassland (2.22%), snow (1.89%) and water bodies (0.65%). The
classification model demonstrated an overall accuracy of 81.55%, a kappa coefficient of 0.78, with
producer and user accuracy reaching 89.71% and 85.31%, for the agroforestry class. The carbon
sequestration analysis was carried out in different land use systems for 2001, 2011 and 2021 by using
InVEST model. It was found that dense forest exhibited the maximum carbon density followed by
moderate forest, agroforestry, open forest, agriculture, grassland and barrenland while built up has the
minimum carbon density. Over the years, an overall increase in carbon density was observed due to the
expansion of various land uses such as agriculture, agroforestry, built-up areas, dense forests and moderate
forests. Conversely, the area under barren land, open forests, grassland, snow and water bodies decreased.
Furthermore, using analytic hierarchy process (AHP), about 19.97% of the area was categorized as highly
suitable, 24.03% as moderately suitable, 4.36% as marginal suitable, 10.47% as currently not suitable and
41.17% as permanently not suitable. Whereas, using Fuzzy-AHP technique, about 16.24% of the area was
classified as highly suitable, 25.14% as moderately suitable, 5.54% as marginal suitable, 16.53% as
currently not suitable and 36.54% as permanently not suitable for agroforestry in the Shimla district. This
study therefore provides valuable insights for policymakers, planners and scientists, offering guidance for
the adoption, development and expansion of agroforestry policies in Shimla district which will contribute
significantly to carbon neutrality, climate change mitigation and align with sustainable development goals