Department of Civil Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, India.
Environ Monit Assess. 2022 Nov 14;195(1):104. doi: 10.1007/s10661-022-10728-y.
In many regions across the world, including river basins, population growth and land development have enhanced the demand for land and other natural resources. The anthropogenic activities can be detrimental to the vital ecosystems that sustain the river basin region. This work assessed the impact of human modification on land surface temperature (LST) for the Ramganga basin in India. It has been hypothesised that the footprints of anthropogenic activities in the region have been connected to the LST fluctuation for the region, which could indicate environmental degradation. The LST variation between 2000 and 2016 has been estimated to test this hypothesis. The spatio-temporal correlation between human modification and LST has been computed. LST has been calculated with MODIS satellite data in the Google earth engine (GEE) platform, and anthropogenic activities can be visualised using an LU/LC map of the basin created by the Classification and Regression (CART) technique. The statistical parameters (average, maximum and standard deviation) of annual temperature for each pixel in 17 years (2000-2016) have been assessed to establish the links with human modification. The result of this work portrays a positive correlation of 0.705 between maximum LST and human modification. The forest class in the basin region has the lowest average human modification value (0.37), and it also possesses the lowest mean LST of 26.72 °C. Similarly, the settlement class has the highest average human modification value (0.85), and the mean LST temperature of this class has been on the higher side, having a value of 31.07 °C.
在世界上许多地区，包括河流流域，人口增长和土地开发增加了对土地和其他自然资源的需求。人为活动可能会对维持河流流域地区的重要生态系统造成不利影响。本工作评估了人为活动对印度拉姆甘加流域地表温度（LST）的影响。有人假设，该地区人类活动的足迹与该地区的 LST 波动有关，这可能表明环境退化。为了验证这一假设，估计了 2000 年至 2016 年之间的 LST 变化。计算了人为活动与 LST 之间的时空相关性。在 Google earth engine（GEE）平台上使用 MODIS 卫星数据计算了 LST，并使用流域分类和回归（CART）技术创建的 LU/LC 地图可视化了人为活动。评估了 17 年（2000-2016 年）中每个像素的年温度的统计参数（平均值、最大值和标准偏差），以建立与人为活动的联系。该工作的结果描绘了最大 LST 与人为活动之间的正相关关系为 0.705。流域地区的森林类别具有最低的平均人为活动值（0.37），并且其平均 LST 也最低，为 26.72°C。同样，定居点类别具有最高的平均人为活动值（0.85），并且该类别的平均 LST 温度较高，为 31.07°C。
