A framework for natural resource management with geospatial machine learning: a case study of the 2021 Almora forest fires

IF 3.6 3区 环境科学与生态学 Q1 ECOLOGY Fire Ecology Pub Date : 2024-08-27 DOI:10.1186/s42408-024-00293-9
Arpit Tiwari, Preethi Nanjundan, Ravi Ranjan Kumar, Vijay Kumar Soni
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Abstract

Wildfires have a substantial impact on air quality and ecosystems by releasing greenhouse gases (GHGs), trace gases, and aerosols into the atmosphere. These wildfires produce both light-absorbing and merely scattering aerosols that can act as cloud condensation nuclei, altering cloud reflectivity, cloud lifetime, and precipitation frequency. Uttarakhand province in India experiences frequent wildfires that affect its protected ecosystems. Thus, a natural resource management system is needed in this region to assess the impact of wildfire hazards on land and atmosphere. We conducted an analysis of a severe fire event that occurred between January and April 2021 in the Kumaun region of Uttarakhand, by utilizing open-source geospatial data. Near-real-time satellite observations of pre- and post-fire conditions within the study area were used to detect changes in land and atmosphere. Supervised machine learning algorithm was also implemented to estimate burned above ground biomass (AGB) to monitor biomass stock. The study found that 21.75% of the total burned area burned with moderate to high severity, resulting in a decreased Soil Adjusted Vegetation Index value (> 0.3), a reduced Normalized Differential Moisture Index value (> 0.4), and a lowered Normalized Differential Vegetation Index (> 0.5). The AGB estimate demonstrated a significant simple determination (r2 = 0.001702) and probability (P < 2.2 10−16), along with a positive correlation (r ≤ 0.24) with vegetation and soil indices. The algorithm predicted that 17.56 tonnes of biomass per hectare burned in the Kumaun forests. This fire incident resulted in increased emissions of carbon dioxide (CO2; ~ 0.8 10−4 kg carbon h−1), methane (CH4; ~ 200 10−9 mol fraction in dry air), carbon monoxide (CO; 2000 1015 molecules cm−2 total column), and formaldehyde (HCHO; 3500 1013 molecules cm−2 total column), along with increased aerosol optical thickness (varying from 0.2 to 0.5). We believe that our proposed operational framework for managing natural resources and assessing the impact of natural hazards can be used to efficiently monitor near-real-time forest-fire-caused changes in land and atmosphere. This method makes use of openly accessible geospatial data that can be employed for several objectives, including monitoring carbon stocks, greenhouse gas emissions, criterion air pollution, and radiative forcing of the climate, among many others. Our proposed framework will assist policymakers and the scientific community in mitigating climate change problems and in developing adaptation policies.
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利用地理空间机器学习的自然资源管理框架:2021 年阿尔莫拉森林火灾案例研究
野火会向大气中释放温室气体(GHGs)、微量气体和气溶胶,从而对空气质量和生态系统产生重大影响。这些野火既会产生光吸收气溶胶,也只会产生散射气溶胶,这些气溶胶会成为云的凝结核,改变云的反射率、云的寿命和降水频率。印度北阿坎德邦经常发生野火,影响其受保护的生态系统。因此,该地区需要一个自然资源管理系统来评估野火危害对土地和大气的影响。我们利用开源地理空间数据,对 2021 年 1 月至 4 月间发生在北阿坎德邦库马恩地区的严重火灾事件进行了分析。研究区域内火灾发生前后的近实时卫星观测数据被用来检测土地和大气的变化。此外,还采用了有监督的机器学习算法来估算烧毁的地上生物量(AGB),以监测生物量存量。研究发现,21.75% 的烧毁总面积为中度至高度烧毁,导致土壤调整植被指数值降低(> 0.3)、归一化差异水分指数值降低(> 0.4)和归一化差异植被指数降低(> 0.5)。AGB 估计值显示出显著的简单确定性(r2 = 0.001702)和概率(P < 2.2 10-16),同时与植被和土壤指数呈正相关(r ≤ 0.24)。该算法预测库马恩森林每公顷燃烧了 17.56 吨生物质。这次火灾事件导致二氧化碳(CO2;约 0.8 10-4 kg carbon h-1)、甲烷(CH4;约 200 10-9 mol fraction in dry air)、一氧化碳(CO;2000 1015 molecules cm-2 total column)和甲醛(HCHO;3500 1013 molecules cm-2 total column)排放量增加,同时气溶胶光学厚度增加(从 0.2 到 0.5 不等)。我们相信,我们提出的管理自然资源和评估自然灾害影响的操作框架可用于有效监测近实时森林火灾引起的土地和大气变化。该方法利用可公开访问的地理空间数据,可用于多个目标,包括监测碳储量、温室气体排放、标准空气污染和气候辐射强迫等。我们提出的框架将有助于决策者和科学界缓解气候变化问题和制定适应政策。
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来源期刊
Fire Ecology
Fire Ecology ECOLOGY-FORESTRY
CiteScore
6.20
自引率
7.80%
发文量
24
审稿时长
20 weeks
期刊介绍: Fire Ecology is the international scientific journal supported by the Association for Fire Ecology. Fire Ecology publishes peer-reviewed articles on all ecological and management aspects relating to wildland fire. We welcome submissions on topics that include a broad range of research on the ecological relationships of fire to its environment, including, but not limited to: Ecology (physical and biological fire effects, fire regimes, etc.) Social science (geography, sociology, anthropology, etc.) Fuel Fire science and modeling Planning and risk management Law and policy Fire management Inter- or cross-disciplinary fire-related topics Technology transfer products.
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