Evaluation of long term forest fires in India with respect to state administrative boundary, forest category of LULC and future climate change scenario: A Geospatial Perspective

Q4 Agricultural and Biological Sciences

USDA Forest Service - Research Papers PNW-RP Pub Date : 2018-12-01 DOI:10.2478/frp-2018-0034

F. Ahmad, Md Meraj Uddin, L. Goparaju

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引用次数: 3

Abstract

Abstract Analysing the forest fires events in climate change scenario is essential for protecting the forest from further degradation. Geospatial technology is one of the advanced tools that has enormous capacity to evaluate the number of data sets simultaneously and to analyse the hidden relationships and trends. This study has evaluated the long term forest fire events with respect to India’s state boundary, its seasonal monthly trend, all forest categories of LULC and future climate anomalies datasets over the Indian region. Furthermore, the spatial analysis revealed the trend and their relationship. The state wise evaluation of forest fire events reflects that the state of Mizoram has the highest forest fire frequency percentage (11.33%) followed by Chhattisgarh (9.39%), Orissa (9.18%), Madhya Pradesh (8.56%), Assam (8.45%), Maharashtra (7.35%), Manipur (6.94%), Andhra Pradesh (5.49%), Meghalaya (4.86%) and Telangana (4.23%) when compared to the total country’s forest fire counts. The various LULC categories which represent the forest show some notable forest fire trends. The category ‘Deciduous Broadleaf Forest’ retain the highest fire frequency equivalent to 38.1% followed by ‘Mixed Forest’ (25.6%), ‘Evergreen Broadleaf Forest’ (16.5%), ‘Deciduous Needle leaf Forest’ (11.5%), ‘Shrub land’ (5.5%), ‘Evergreen Needle leaf Forest’ (1.5%) and ‘Plantations’ (1.2%). Monthly seasonal variation of forest fire events reveal the highest forest fire frequency percentage in the month of ‘March’ (55.4%) followed by ‘April’ (28.2%), ‘February’ (8.1%), ‘May’ (6.7%), ‘June’ (0.9%) and ‘January’ (0.7%). The evaluation of future climate data for the year 2030 shows significant increase in forest fire seasonal temperature and abrupt annual rainfall pattern; therefore, future forest fires will be more intensified in large parts of India, whereas it will be more crucial for some of the states such as Orissa, Chhattisgarh, Mizoram, Assam and in the lower Sivalik range of Himalaya. The deciduous forests will further degrade in future. The highlight/results of this study have very high importance because such spatial relationship among the various datasets is analysed at the country level in view of the future climate scenario. Such analysis gives insight to the policymakers to make sustainable future plans for prioritization of the various state forests suffering from forest fire keeping in mind the future climate change scenario.

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基于邦行政边界、LULC森林类别和未来气候变化情景的印度长期森林火灾评价:地理空间视角

摘要分析气候变化情景下的森林火灾事件对保护森林免受进一步退化至关重要。地理空间技术是一种先进的工具，它具有同时评估数据集数量和分析隐藏关系和趋势的巨大能力。本研究评估了印度邦界的长期森林火灾事件、其季节性月度趋势、LULC的所有森林类别以及印度地区未来气候异常数据集。空间分析进一步揭示了其变化趋势及其相互关系。邦对森林火灾事件的评估表明，与全国森林火灾总数相比，米佐拉姆邦的森林火灾频率百分比最高(11.33%)，其次是恰蒂斯加尔邦(9.39%)、奥里萨邦(9.18%)、中央邦(8.56%)、阿萨姆邦(8.45%)、马哈拉施特拉邦(7.35%)、曼尼普尔邦(6.94%)、安得拉邦(5.49%)、梅加拉亚邦(4.86%)和特伦甘纳邦(4.23%)。代表森林的各种LULC类别显示出一些显著的森林火灾趋势。“落叶阔叶林”类别保持最高的火灾频率，相当于38.1%，其次是“混交林”(25.6%)，“常绿阔叶林”(16.5%)，“落叶针叶林”(11.5%)，“灌木地”(5.5%)，“常绿针叶林”(1.5%)和“人工林”(1.2%)。森林火灾事件的月度季节变化显示，森林火灾频率百分比最高的月份是“3月”(55.4%)，其次是“4月”(28.2%)，“2月”(8.1%)，“5月”(6.7%)，“6月”(0.9%)和“1月”(0.7%)。对2030年未来气候数据的评估表明，森林火灾、季节温度和年突变降水模式显著增加;因此，未来印度大部分地区的森林火灾将更加严重，而对奥里萨邦、恰蒂斯加尔邦、米佐拉姆邦、阿萨姆邦和喜马拉雅山脉的西瓦利克山脉下游等一些邦来说，森林火灾将更加关键。这些落叶森林将来还会进一步退化。本研究的重点/结果非常重要，因为各种数据集之间的空间关系是根据未来气候情景在国家一级进行分析的。这种分析为决策者提供了洞察力，以便制定可持续的未来计划，优先考虑遭受森林火灾的各种国家森林，同时考虑到未来的气候变化情景。

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USDA Forest Service - Research Papers PNW-RP Agricultural and Biological Sciences-Forestry

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