基于5种植被指数的荒漠区植被生物量提取研究
收稿日期: 2022-07-31
修回日期: 2023-03-02
网络出版日期: 2023-04-28
基金资助
甘肃省大学生创新创业训练计划项目(S202210733015);甘肃农业大学大学生创新创业计划项目(202102047);国家自然科学基金项目(41907406);甘肃农业大学科技创新基金(GAU-KYQD-2018-23)
Extraction of desert vegetation information based on five vegetation indices
Received date: 2022-07-31
Revised date: 2023-03-02
Online published: 2023-04-28
荒漠区植被地上生物量是土地荒漠化监测和荒漠植被遥感信息提取的重要指标。本研究以甘肃民勤县为试验区，以哨兵2号（Sentinel-2）影像为数据源，构建了比值植被指数RVI、归一化植被指数NDVI、差值植被指数DVI、土壤调节植被指数SAVI及优化型土壤调节植被指数OSAVI 5种植被指数与植被实测地上生物量的估算模型（一元线性、指数、对数和二项式模型），并利用所选的最优模型，估算了研究区的地上生物量。结果表明： SAVI相较于RVI、NDVI、DVI和OSAVI指数同地上生物量之间的相关性最高（r=0.79），基于SAVI指数的二项式模型是研究区地上生物量估算的最优模型（R2=0.76），且精度较高（R2=0.73，RMSE=0.12）。民勤县的植被相对密集区主要分布于四大灌区（红崖山、环河、昌宁、南湖）、青土湖周边以及红沙岗镇西北区域，其他地域植被较为稀疏，无植被区[<0.005 kg·(100m2)-1]、低植被区[0.005~0.2 kg·(100m2)-1]、中植被区[0.2~0.5 kg·(100m2)-1]和高植被区[>0.5 kg·(100m2)-1]的占比分别为66%、21%、5%和8%。
关键词： 荒漠区植被; 地上生物量; 植被指数; 信息提取; Sentinel数据
梁博明 , 刘新 , 郝媛媛 , 楚彬 , 唐庄生 . 基于5种植被指数的荒漠区植被生物量提取研究[J]. 干旱区研究, 2023 , 40(4) : 647 -654 . DOI: 10.13866/j.azr.2023.04.13
The aboveground biomass of vegetation in desert areas serves as a crucial indicator for monitoring land desertification and extracting desert vegetation information using remote sensing techniques. In this study, the Minqin County of Gansu Province was selected as the experimental area and Sentinel-2 images were used as the data source. We constructed estimation models (unitary linear, exponential, logarithmic, and binomial models) for the planted index and the aboveground biomass of vegetation, which were measured by us. These models include five vegetation indices: ratio vegetation index (RVI), normalized difference vegetation index (NDVI), difference vegetation index (DVI), soil-adjusted vegetation index (SAVI), and optimized soil-adjusted vegetation index (OSAVI). The aboveground biomass in the study area was estimated using the selected optimal model. The results demonstrated that SAVI had the highest correlation with the aboveground biomass (r = 0.79) compared with RVI, NDVI, DVI, and OSAVI. The binomial model based on SAVI was the best model (R2 = 0.76) for the aboveground biomass estimation in the study area, with higher accuracy (R2 = 0.73, RMSE = 0.12). In the Minqin County, the relatively dense areas of vegetation were mainly distributed in the four major irrigation districts (Hongyashan, Huanhe, Changning, and Nanhu), the surrounding area of Qingtu Lake, and the northwest region of Hongshagang Town, whereas the vegetation in other regions was relatively sparse. The proportions of nonvegetation area <[0.005 kg·(100m2)-1], low vegetation area [0.005-0.2 kg·(100m2)-1], medium vegetation area [0.2-0.5 kg·(100m2)-1], and high vegetation area [>0.5 kg·(100m2)-1] were 66%, 21%, 5%, and 8%, respectively.
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