A New Indicator for Enhancing Fire Fuel Mapping in Marivan Forests, West of Iran

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Rahimi, I (Author) Other; Lia Duarte (Author) FCUP; Ana Teodoro (Author) FCUP

Conference proceedings International

Title: Proceedings of SPIE - The International Society for Optical Engineering

Earth Resources and Environmental Remote Sensing/GIS Applications XIV 2023

Amsterdam, 2023

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-00Z-J7K

DOI: 10.1117/12.2678997

Abstract (EN): Hundreds of forest fires have been occurred, every year, in the oak forests of Zagros mountain chains, especially in Marivan region, Kurdistan province, western Iran. Vegetation condition is considered as one of the critical dynamic factors in estimating fire susceptibility. Geographical Information System (GIS) integrated with Remote Sensing (RS) data provides significant geospatial information of environmental conditions before, during and after a fire occurrence, accurately, easy, not expensive, and with reduced risky fieldworks. This study aims to introduce a local method to differentiate between grass-covered surface and tree canopy in Marivan forests to recognize grass-covered area, as one of the most vulnerable fuel types to fire. The methodology uses Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) product, as an input, to develop a new indicator named Zagros Grass Indicator (ZGI). The ZGI intends to reduce the effect of tree canopy to reach the NDVI value which is created only by grass cover by employing the phenological characteristics of the forest trees and grass species. ZGI is supposed to be more efficient than NDVI for mapping grass-covered area which regarded as one of the most important factors in estimating fire susceptibility in the study area. The results revealed that the ZGI-based map is highly correlated with the distribution of fire occurrences recorded by official authorities. © 2023 SPIE. All rights reserved.

Language: English

Type (Professor's evaluation): Scientific