Zhang Yanbin, Wang Ronghua
Vocational and Technical College, Inner Mongolia Agricultural University, Baotou, 014109, China.
College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010070, China.
Heliyon. 2022 Oct 15;8(11):e11099. doi: 10.1016/j.heliyon.2022.e11099. eCollection 2022 Nov.
Remote sensing estimation of aboveground biomass for desert oasis vegetation in arid area is an important means to monitor land desertification, it is of great significance to accurately evaluate the carbon sink change of desert oasis ecosystem, and maintain the stability of oasis ecosystem. The aboveground biomass information of vegetation, such as vegetation index and band factor in a delta oasis area is obtained by using Landsat 8 OLI image data; Based on the combination with the measured aboveground biomass data of vegetation, the optimal estimation model of aboveground biomass of four vegetation types (, , and ) in this area is established, and the above ground biomass of vegetation was retrieved and verified. The results showed that: (1) there was a very significant correlation between the remote sensing factors of aboveground biomass of four vegetation types and the measured aboveground biomass, and the correlation coefficient ranged from 0.711 to 0.756 (P < 0.01); (2) multiple stepwise regression () model is the optimal estimation model of aboveground biomass of arbors and shrubs, and partial least squares regression () model is the optimal estimation model of aboveground biomass of herbs and crops, the estimation results have a good linear fitting relationship with the measured results; (3) the order of aboveground biomass of vegetation in oasis area from low to high is: herbs < shrubs < arbors < crops. Among them, the aboveground biomass of grass is mainly below 280 g m, the aboveground biomass of shrub is mainly 280-950 g m, and the aboveground biomass of four vegetation is mainly distributed in 280-1450 g m. Based on the Landsat 8 OLI image data, the remote sensing estimation model can accurately estimate the aboveground biomass of four oasis vegetation types (, , and ), and reveal the spatial distribution characteristics of aboveground biomass of oasis vegetation.
干旱区沙漠绿洲植被地上生物量的遥感估算是监测土地沙漠化的重要手段，对于准确评估沙漠绿洲生态系统碳汇变化、维持绿洲生态系统稳定性具有重要意义。利用Landsat 8 OLI影像数据获取某三角洲绿洲地区植被的植被指数、波段因子等地上生物量信息；结合实测植被地上生物量数据，建立该地区4种植被类型（乔木、灌木、草本、农作物）地上生物量的最优估算模型，并对植被地上生物量进行反演与验证。结果表明：（1）4种植被类型地上生物量遥感因子与实测地上生物量之间存在极显著相关性，相关系数在0.711～0.756之间（P＜0.01）；（2）多元逐步回归（MLR）模型是乔木和灌木地上生物量的最优估算模型，偏最小二乘回归（PLSR）模型是草本和农作物地上生物量的最优估算模型，估算结果与实测结果具有良好的线性拟合关系；（3）绿洲地区植被地上生物量由低到高的顺序为：草本＜灌木＜乔木＜农作物。其中，草本地上生物量主要在280 g/m以下，灌木地上生物量主要在280～950 g/m，4种植被地上生物量主要分布在280～1450 g/m。基于Landsat 8 OLI影像数据的遥感估算模型能够准确估算4种绿洲植被类型（乔木、灌木、草本、农作物）的地上生物量，并揭示绿洲植被地上生物量的空间分布特征。
