College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, Liaoning Province, China; Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN, USA.
USDA-ARS, Grassland, Soil and Water Research Laboratory, Temple, TX 76502, USA.
Sci Total Environ. 2020 Jun 15;721:137814. doi: 10.1016/j.scitotenv.2020.137814. Epub 2020 Mar 9.
Dynamic changes in soil organic carbon pools have significant impacts on regional and global carbon balance. Due to rapid development in urbanized areas, the land use changes dramatically, impacting soil organic carbon (SOC) stocks in topsoil. This study aimed to document the impacts of urbanization on SOC stocks in a rapidly urbanized area from northeastern China. A total of 12 auxiliary variables were as SOC predictors including elevation, slope aspect, slope gradient, topographic wetness index, Landsat TM band3, Landsat TM band4, Landsat TM5, and normalized difference vegetation index. Urban-specific variables including population (POP), gross domestic product (GDP), distance to the socio-economic center, and distance to the roads are also considered. A set of 523 (in 1990) and 847 (in 2015) top soil samples with SOC measurement were collected. Two random forest (RF) models, one with all auxiliary variables except urban-specific variable (MA) and the other with all auxiliary variables (MB) were used to map the spatial distribution of SOC stocks in the two periods. Ten-fold cross-validation was conducted to evaluate the performance of RF models. We find that the full auxiliary variables model had a better performance for the both periods. POP and GDP were key auxiliary variables affecting spatial variability of SOC stocks in 2015. Over a 25-year period, SOC stocks decreased from 2.77 ± 1.09 kg m to 2.16 ± 0.93 kg m, resulting in 3.78 Tg SOC loss in this region. Rapid urbanization led to drastic land- use change, which was the main reason for the decrease of SOC stocks. Additionally, urban-specific variables should be used as the main auxiliary variables when predicting SOC stocks in the areas that experience rapid urbanization. We believe that accurate prediction and mapping of SOC stocks will help manage land use and facilitate soil quality assessment so as to increase soil carbon sequestration in the region.
土壤有机碳库的动态变化对区域和全球碳平衡有重大影响。由于城市化地区的快速发展，土地利用发生了巨大变化，影响了表土中的土壤有机碳（SOC）储量。本研究旨在记录中国东北快速城市化地区城市化对 SOC 储量的影响。共选择了 12 个辅助变量作为 SOC 预测因子，包括海拔、坡度方向、坡度梯度、地形湿度指数、Landsat TM 波段 3、Landsat TM 波段 4、Landsat TM 波段 5 和归一化植被指数。还考虑了城市化特有变量，包括人口（POP）、国内生产总值（GDP）、距社会经济中心的距离和距道路的距离。收集了一组 1990 年（523 个）和 2015 年（847 个）的表层土壤样本，进行 SOC 测量。使用两个随机森林（RF）模型，一个模型使用所有辅助变量（除城市化特有变量外）（MA），另一个模型使用所有辅助变量（MB），分别对两个时期的 SOC 储量空间分布进行制图。采用十折交叉验证评估 RF 模型的性能。结果表明，全辅助变量模型在两个时期的性能都更好。POP 和 GDP 是影响 2015 年 SOC 储量空间变化的关键辅助变量。在 25 年的时间里，SOC 储量从 2.77±1.09kg/m 减少到 2.16±0.93kg/m，导致该地区 SOC 损失 3.78Tg。快速城市化导致土地利用发生剧烈变化，这是 SOC 储量减少的主要原因。此外，在预测快速城市化地区的 SOC 储量时，应将城市化特有变量作为主要辅助变量。我们相信，SOC 储量的准确预测和制图将有助于管理土地利用，促进土壤质量评估，从而增加该地区的土壤碳固存。
