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지표화피해지 산불피해목 생존예측을 위한 위성영상 지수의 적용성 검토

Evaluating the Applicability of Satellite Imagery Indices for Predicting Tree Mortality in Surface Fire Damaged Forests

배정현|강원석|이영근|정유경
(사)위기관리이론과실천 (Crisis and Emergency Management: Theory and Praxis)|2024.03|Crisisonomy|제20권 제3호|pp. 55-65|DOI https://doi.org/10.14251/crisisonomy.2024.20.3.55

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키워드

정규식생지수로지스틱 회귀모델정규탄화지수logistic regression model임목고사율지표화tree mortalitysurface firendvinbr

초록

본 연구는 지표화피해를 입은 소나무를 대상으로 생존예측의 적용성을 높이기 위해 통합형 모델을개발하고, 위성영상기법에 대한 활용성을 검토하였다. 지표화 발생 후 3년이 경과한 두 개의 연구대상지를 대상으로 로지스틱 회귀모형을 이용하여 통합 생존예측모델을 개발하였다. 생존예측을 위한 주요인자로는 흉고직경과 그을음지수, 사면향이 도출되었으며, 통합모델은 예측정확도가 0.82 나타나 비교적높은 정확도를 보였다. 대면적 산불피해지의 임분 단위 고사율을 추정하기 위해 원격탐사 기법을 이용하여 생존예측을 위한 적정 인자를 도출하였다. dNBR의 초기변화는 고사에 영향을 미치는 인자로 도출되었으며, 고사율과 상관성이 높은 것으로 나타났다. 두 인자들은 모델 개선을 위해 새로운 지표로 적용할수 있을 것으로 보여지나, 범례 기준 하에서는 뚜렷한 경향을 확인하기 어려웠다. 높은 공간적, 분광학적해상도를 확보하여 정확도를 높이고, 산불피해지 기준에 적합한 분류 등급이 필요하다.

Abstract

This study aimed to develop a comprehensive model for predicting fire-damaged pine tree survival and assess the usefulness of satellite imagery techniques. Logistic regression models were used to create integrated survival prediction models at two sites three years after fire damage. Diameter at breast height, char height index, and aspect were identified as key factors for survival prediction. The integrated model achieved a high accuracy level, with a predictive power of 82%. Remote sensing techniques were employed to estimate mortality rates at the stand level and identify relevant factors for survival prediction. Initial changes in dNBR, which showed a strong correlation with mortality rates, were found to be influential factors. While these factors could potentially improve the model as new indicators, clear trends under legend criteria were challenging to establish. High spatial and spectral resolution are necessary to enhance accuracy, along with classification categories specifically designed for fire-damaged areas.

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