Landslide poses severe threats to the natural landscape of the Lesser Himalayas and the lives and economy of the communities residing in that mountainous topography. This study aims to investigate whether the landscape change has any impact on landslide occurrences in the Kalsi-Chakrata road corridor by detailed investigation through correlation of the landslide susceptibility zones and the landscape change, and finally to demarcate the hotspot villages where influence of landscape on landslide occurrence may be more in future. The rational of this work is to delineate the areas with higher landslide susceptibility using the ensemble model of GIS-based multi-criteria decision making through fuzzy landslide numerical risk factor model along the Kalsi-Chakrata road corridor of Uttarakhand where no previous detailed investigation was carried out applying any contemporary statistical techniques. The approach includes the correlation of the landslide conditioning factors in the study area with the changes in land use and land cover (LULC) over the past decade to understand whether frequent landslides have any link with the physical and hydro-meteorological or, infrastructure, and socioeconomic activities. It was performed through LULC change detection and landslide susceptibility mapping (LSM), and spatial overlay analysis to establish statistical correlation between the said parameters. The LULC change detection was performed using the object-oriented classification of satellite images acquired in 2010 and 2019. The inventory of the past landslides was formed by visual interpretation of high-resolution satellite images supported by an intensive field survey of each landslide area. To assess the landslide susceptibility zones for 2010 and 2019 scenarios, the geo-environmental or conditioning factors such as slope, rainfall, lithology, normalized differential vegetation index (NDVI), proximity to road and land use and land cover (LULC) were considered, and the fuzzy LNRF technique was applied. The results indicated that the LULC in the study area was primarily transformed from forest cover and sparse vegetation to open areas and arable land, which is increased by 6.7% in a decade. The increase in built-up areas and agricultural land by 2.3% indicates increasing human interference that is continuously transforming the natural landscape. The landslide susceptibility map of 2019 shows that about 25% of the total area falls under high and very high susceptibility classes. The result shows that 80% of the high landslide susceptible class is contained by LULC classes of open areas, scrubland, and sparse vegetation, which point out the profound impact of landscape change that aggravate landslide occurrence in that area. The result acclaims that specific LULC classes, such as open areas, barren-rocky lands, are more prone to landslides in this Lesser Himalayan road corridor, and the LULC-LSM correlation can be instrumental for landslide probability assessment concerning the changing landscape. The fuzzy LNRF model applied has 89.6% prediction accuracy at 95% confidence level which is highly satisfactory. The present study of the connection of LULC change with the landslide probability and identification of the most fragile landscape at the village level has been instrumental in delineation of landslide susceptible areas, and such studies may help the decision-makers adopt appropriate mitigation measures in those villages where the landscape changes have mainly resulted in increased landslide occurrences and formulate strategic plans to promote ecologically sustainable development of the mountainous communities in India's Lesser Himalayas.

滑坡对小喜马拉雅山的自然景观以及该山区地形的社区的生活和经济构成了严重威胁。本研究旨在通过对滑坡易感性区域与景观变化的相关性进行详细调查，调查景观变化是否对卡尔西－查克拉塔公路走廊的滑坡发生有任何影响，并最终划定热点村庄，在这些村庄中，景观对滑坡的影响将来可能会更多。这项工作的合理性是使用基于GIS的多准则集成模型通过模糊的滑坡数值风险因子模型沿着北阿坎德邦的Kalsi-Chakrata道路走廊描绘高滑坡易感性的区域，之前未进行过详细调查淘汰使用任何当代统计技术。该方法包括研究区域中滑坡条件因素与过去十年中土地利用和土地覆被（LULC）的变化之间的相关性，以了解频繁的滑坡是否与物理和水文气象或基础设施有任何联系，以及社会经济活动。它是通过LULC变化检测和滑坡敏感性地图（LSM）进行的，空间重叠分析以建立所述参数之间的统计相关性。使用2010年和2019年获得的卫星图像的面向对象分类来进行LULC变化检测。过去的滑坡清单是通过对高分辨率的卫星图像进行视觉解释而形成的，该图像由每个滑坡区域的密集实地调查支持。为了评估2010年和2019年情景的滑坡敏感性区，考虑了地球环境或条件因素，例如坡度，降雨，岩性，归一化植被指数（NDVI），接近道路和土地利用以及土地覆盖（LULC），并应用了模糊LNRF技术。结果表明，研究区的土地利用变化主要是由森林覆盖和稀疏植被转变为开阔地带和耕地，十年间增长了6.7％。建筑面积和农业用地增加2.3％，表明人为干扰不断加剧，不断改变着自然景观。2019年的滑坡敏感性地图显示，总面积的约25％属于高和非常高的敏感性类别。结果表明，开放区域，灌丛和稀疏植被的LULC类包含80％的高滑坡易感性类别，这表明景观变化对加剧该地区滑坡发生的深远影响。结果称赞特定的LULC类别，例如开阔地带，荒芜的土地，该小喜马拉雅道路走廊更容易发生滑坡，LULC-LSM相关性可有助于评估有关不断变化的景观的滑坡概率。所应用的模糊LNRF模型在95％置信度下具有89.6％的预测准确度，非常令人满意。目前关于土地利用变化和土地利用变化与滑坡发生率的联系的研究以及确定村庄一级最脆弱的景观对界定滑坡易感地区很有帮助，这些研究可能有助于决策者在这些村庄采取适当的缓解措施该地区的景观变化主要导致滑坡发生率增加，并制定了战略计划，以促进印度小喜马拉雅山山区社区的生态可持续发展。