Raghunandan Hills Reserve is an important protected area in Bangladesh that supports some remnant patches of natural forest and is the habitat of several globally threatened primates including Western Hoolock Gibbon, Northern Pig-tailed Macaque, and Capped Langur. However, deforestation and forest degradation due to anthropogenic factors, such as illegal logging and fuelwood collection are age-old problems at Raghunandan. The areas of the reserve vulnerable to future conversions due to the possible proximate or underlaying causes were unknown. This study analyzed the historical trend of forest and land-use/landcover transitions at Raghunandan Hills Reserve from 1995 until 2015 at a 10-year interval using Monte Carlo spectral unmixing and knowledge-based classification approaches to Landsat satellite images in Claslite and ArcGIS software. Based on the past trend, it then predicted the future trend of forest land-use/landcover transitions for 2025 and 2035 using an artificial multi-layer perceptron neural network with Markov Chain machine learning algorithm integrated into the land change modeler module of IDRISI/TerrSet software. Results indicated that ∼30 % to 35% of the total area of the reserve was covered by forest, which included patches of natural forest and plantations, whereas the remaining area was occupied by non-forest categories like scattered degraded forests, grasses, and shrubs. Forest cover declined during 1995–2005, and then increased slightly during 2005–2015 due to afforestation activities. This trend is likely to continue in the future with forest cover occupying nearly 40% of the reserve by 2025 and 2035. Along with identifying the areas where the forest is likely to be expanded, the areas of the reserve vulnerable to deforestation (hotspots) were also highlighted and quantified in the form of maps and statistics. The findings have useful implications for any forest conservation initiatives including the global climate change mitigation program reducing emissions from deforestation and forest degradation+, which requires identifying at-risk areas of planned and unplanned deforestation.

中文翻译：

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使用带有马尔可夫链的多层感知器神经网络对孟加拉国的森林覆盖动态进行建模

Raghunandan Hills Reserve 是孟加拉国的一个重要保护区，支持着一些残余的天然森林，是几种全球受威胁的灵长类动物的栖息地，包括西部白头长臂猿、北猪尾猕猴和盖叶叶猴。然而，由于非法采伐和收集薪柴等人为因素导致的森林砍伐和森林退化是 Raghunandan 的古老问题。由于可能的近因或潜在原因，保护区内易受未来转换影响的区域尚不清楚。本研究在 Claslite 和 ArcGIS 软件中使用 Monte Carlo 光谱分解和基于知识的 Landsat 卫星图像分类方法，分析了 1995 年至 2015 年期间 Raghunandan Hills Reserve 森林和土地利用/土地覆盖转变的历史趋势，每 10 年一次。在过去趋势的基础上，利用集成在 IDRISI/TerrSet 土地变化建模器模块中的马尔可夫链机器学习算法的人工多层感知神经网络预测 2025 年和 2035 年林地土地利用/土地覆盖转变的未来趋势软件。结果表明，保护区总面积的约30％至35％被森林覆盖，其中包括斑块的天然林和人工林，而其余区域则被分散的退化森林、草和灌木等非森林类别占据。 . 由于植树造林活动，森林覆盖率在 1995 年至 2005 年期间下降，然后在 2005 年至 2015 年期间略有增加。这一趋势很可能在未来继续下去，到 2025 年和 2035 年，森林覆盖率将占保护区的近 40%。除了确定森林可能扩大的区域外，还以地图和统计数据的形式突出和量化了保护区内易毁林的区域（热点）。这些发现对任何森林保护举措都有有益的影响，包括减少森林砍伐和森林退化造成的排放的全球气候变化减缓计划+，这需要确定有计划和计划外砍伐森林的风险区域。