Imphal Valley, situated in Manipur, India, stands as an intermontane valley of great ecological significance, notably hosting Loktak Lake. This research delves into the Land Use Land Cover Change (LULCC) within the Imphal Valley from 2016 to 2021 and assesses their impact on air quality across distinct land cover types. We used Sentinel-1 & 2 data, ALOS PALSAR Digital Elevation Model, and applied Random Forest (RF), a machine learning algorithm for effective land use and land cover (LULC) mapping. Additionally, Sentinel-5P data was utilized to monitor air quality parameters (CO, HCHO, NO₂, SO₂, Aerosol Index) spanning 2019 to 2021. The overall accuracies for the LULC maps employing a k (k = 3) fold approach for accuracy assessment varied between 88% and 92%, with corresponding Kappa coefficient ranging from 0.85 to 0.90. Noteworthy trends emerged from our analysis, revealing an increase in settlements and horticulture farms and a decline in forested areas and phumdis (floating biomass). Our findings highlight a mean concentration of CO ranging between 0.045 mol/m² and 0.055 mol/m² in different land cover types during February and March (2019–2021). Furthermore, we observed maximum mean HCHO, NO₂, SO₂, and aerosol index concentrations in March. Pollution levels surged during forest fires and shifting agriculture seasons, while aerosol levels declined during the lockdown period. This integrated approach emphasized on the comprehensive analysis of the dynamic interplay between LULCC and air quality in the Imphal Valley. This intricate relationship between LULC changes and air quality dynamics in the Imphal Valley, contribute to our understanding of the environmental dynamics in this ecologically vital region.

中文翻译：

---

基于 ML 的混合 SAR 和光学图像 LULC 测绘以及印度东北部因帕尔山谷空气质量变化的变化分析

因帕尔山谷位于印度曼尼普尔邦，是一个具有重要生态意义的山间山谷，尤其是洛克塔克湖。这项研究深入研究了 2016 年至 2021 年因帕尔山谷内的土地利用土地覆盖变化 (LULCC)，并评估了其对不同土地覆盖类型的空气质量的影响。我们使用 Sentinel-1 和 2 数据、ALOS PALSAR 数字高程模型，并应用随机森林 (RF)，这是一种用于有效土地利用和土地覆盖 (LULC) 绘图的机器学习算法。此外，Sentinel-5P 数据还用于监测 2019 年至 2021 年的空气质量参数（CO、HCHO、NO ₂、SO _{2 、气溶胶指数）。采用}k ( k  = 3) 倍方法的LULC 地图的总体精度为准确度评估在88%到92%之间变化，相应的Kappa系数在0.85到0.90之间。我们的分析出现了值得注意的趋势，揭示了定居点和园艺农场的增加以及森林面积和 phumdis（漂浮生物量）的减少。我们的研究结果强调，2019-2021 年 2 月和 3 月期间，不同土地覆盖类型的 CO 平均浓度在 0.045 mol/m ²至 0.055 mol/m ^2之间。此外，我们还观察到 3 月份 HCHO、NO ₂、SO ₂和气溶胶指数浓度的最高平均浓度。森林火灾和农业季节变化期间污染水平飙升，而封锁期间气溶胶水平下降。这种综合方法强调对因帕尔山谷 LULCC 与空气质量之间动态相互作用的综合分析。因帕尔山谷土地利用和土地利用变化变化与空气质量动态之间的这种错综复杂的关系，有助于我们了解这个生态重要地区的环境动态。