Spatio-temporal dynamics of soil organic carbon and total nitrogen: evidenced from 2000 to 2020 in a mixed ecosystem

土壤碳 归一化差异植被指数 环境科学 数字高程模型 生态系统 地形 土壤科学 空间分布 仰角(弹道) 氮气 植被(病理学) 空间变异性 自然地理学 水文学(农业) 大气科学 土壤水分 生态学 遥感 气候变化 地理 地质学 数学 化学 统计 生物 医学 几何学 有机化学 病理 岩土工程
作者
Baig Abdullah Al Shoumik,Md. Zulfikar Khan
出处
期刊:Environmental Earth Sciences [Springer Nature]
卷期号:82 (3) 被引量:4
标识
DOI:10.1007/s12665-023-10756-y
摘要

The southwestern part of Bangladesh has a mixed ecosystem, which is directly affected by climate change and undergoing remarkable changes in agricultural activities that are exacerbating the dynamics of soil organic carbon (SOC) and soil total nitrogen (STN). To study the spatial distribution of SOC and STN dynamics in such ecosystem, Dacope Upazila was chosen, and different interpolation methods were applied and compared to find out the best approach to demonstrate the spatial pattern of soil carbon and nitrogen status in 2000 and 2020 and correlated them with the terrain and environmental variables. Soil resource development institute (SRDI) database was used to obtain the SOC and STN of the area in 2000 and 2020. To identify the elevation, aspect, slope, land surface temperature (LST), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI), the digital elevation model (DEM) and satellite images of 2000 and 2020 were downloaded. The results showed that the applied interpolation methods and models did not perform well as the model efficiency (R2) was very low. Furthermore, in 2000, SOC and STN were only positively correlated with EC (p < 0.01) whereas after two decades, SOC and STN showed a positive correlation with elevation (p < 0.01), NDVI (p < 0.01), and negative correlation with NDWI (p < 0.01). In both years, SOC and STN showed a strong positive correlation at p < 0.001 with each other. Due to the poor model performance, it is recommended using extended spatial techniques coupling with environmental variables to increase the accuracy of spatial distribution for a complex ecosystem.

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