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Assessment of remote sensing and GIS application in identification of land suitability for agroforestry: A case study of Samastipur, Bihar, India

机译:遥感和GIS在评估农用林地适宜性方面的评估:以印度比哈尔省Samastipur为例

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Agroforestry provides the foundation for climate-smart agriculture to withstand the extreme weather events. The aim of the present study was to identify the land of Samastipur, Bihar, India for agroforestry, based on GIS modeling concept utilizing various ancillary (soil fertility) and satellite data (DEM, wetness, NDVI and LULC) sets. This was achieved by integrating various thematic layers logically in GIS domain. Agroforestry suitability maps were generated for the Samastipur district of Bihar, India which showed 48.22 % as very high suitable, 22.83 % as high suitable, 23.32% as moderate suitable and 5.63% as low suitable. The cross evaluation of agroforestry suitability with LULC categories revealed that the 86.4 % (agriculture) and 30.2% (open area) of land fall into a very high agroforestry suitability category which provides the huge opportunity to harness agroforestry practices if utilized scientifically. Such analysis/results will certainly assist agroforestry policymakers and planner in the state of Bihar, India to implement and extend it to new areas. The potentiality of Remote Sensing and GIS can be exploited in accessing suitable land for agroforestry which will significantly help to rural poor people/farmers in ensuring food and ecological security, resilience in livelihoods.
机译:农用林业为气候智能型农业抵御极端天气事件提供了基础。本研究的目的是基于GIS建模概念,利用各种辅助(土壤肥力)和卫星数据(DEM,湿度,NDVI和LULC)集,确定印度比哈尔邦Samastipur的农林业用地。这是通过在GIS领域中逻辑上集成各种主题层来实现的。生成了印度比哈尔邦Samastipur区的农林业适宜性图,结果显示极高适宜性为48.22%,高度适宜为22.83%,中度为23.32%,低度为5.63%。对农林适宜性与土地利用,土地利用变化和土地分类的交叉评估表明,86.4%(农业)和30.2%(空地)的土地属于非常高的农林业适宜性类别,如果科学地加以利用,将为利用农林业实践提供巨大的机会。这样的分析/结果无疑将帮助印度比哈尔邦的农林业政策制定者和计划者实施并将其扩展到新的领域。可以利用遥感和GIS的潜力来获取合适的农林业用地,这将极大地帮助农村贫困人口/农民确保粮食和生态安全以及生计适应力。

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