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| Titel |
Effects of active forest fire on terrestrial ecosystem production and greenhouse gas emissions |
| VerfasserIn |
Srikanta Sannigrahi, Shahid Rahmat, Sandeep Bhatt, Virendra Rana |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250143846
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| Publikation (Nr.) |
 EGU/EGU2017-7607.pdf |
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| Zusammenfassung |
| The forest fire is one of the most catalysing agents which degrade an ecosystems
leading to the loss of net and gross primary productivity (NPP & GPP) and carbon
sequestration service. Additionally, it can suppress the efficiency of service providing
capacity of an ecosystem throughout the time and space. Remote sensing-based forest
fire estimation in a diverse ecosystem is very much essential for mitigating the
biodiversity and productivity losses due to the forest fire. Satellite-based Land Surface
Temperature (LST) has been calculated for the pre-fire and fire years to identify the
burn severity hotspot across all eco-regions in the Lower Himalaya region. Several
burn severity indices: Normalized Burn Ratio (NBR), Burnt Area Index (BAI),
Normalized Multiband Drought Index (NMDI), Soil Adjusted Vegetation Index
(SAVI), Global Environmental Monitoring Index (GEMI), Enhance Vegetation Index
(EVI) have been used in this study to quantify the spatial and temporal changes
(delta) of the selected indices. Two Light Use Efficiency (LUE) models: Carnegie-
Ames-Stanford-Approach (CASA) and Vegetation Photosynthesis Model (VPM) have been
used to quantify the terrestrial Net Primary Productivity (NPP) in the pre-fire and
fire years across all biomes of the region. A novel approach has been preceded
in this field to demonstrate the correlation between forest fire density (FFD) and
NPP. A strong positive correlation was found between burn severity indices and
predicted NPP: BAI and NPP (r = 0.49), NBR and NPP: (r = 0.58), EVI and NPP:
(r = 0.72), SAVI and NPP: (r = 0.67), whereas, a negative association has noted
between the NMDI and NPP: (r = -0.36) during the both studied years. Results
have shown that the NPP is highly correlated with the forest fire density (R2 =
0.75, RMSE = 5.03 gC m−2 month−1). The estimated LST of the individual fire
days has witnessed a sharp temperature increase by > 6oC – 9oC in comparison to
the non-fire days clearly indicates high fire risk (in Uttarakhand) due to the subtle
water stress condition with lesser soil moisture content into the ground. Among the
13 districts, the maximum net emissions of carbon and nitrogen compounds have
been observed in 7 districts (accounting for high biomass and forest cover loss by
the 2016 forest fire), whereas, the rest of the 6 districts acts as the sequester of
greenhouse compounds. This new approach having the potentiality of quantifying the
losses of ecosystem productivity due to forest fires and could be used in broader
aspects if more accurate field based observation can be obtained in the near future. |
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