Modelling Aboveground Biomass of Miombo Woodlands in Niassa Special Reserve, Northern Mozambique

编号 010033302

推送时间 20220307

研究领域 森林生态 

年份 2022 

类型 期刊 

语种 英语

标题 Modelling Aboveground Biomass of Miombo Woodlands in Niassa Special Reserve, Northern Mozambique

来源期刊 forest

期 第333期

发表时间 20220214

关键词 remote sensing;  allometric models;  biomass;  REDD+; 

摘要 Aboveground biomass (AGB) estimation plays a crucial role in forest management and carbon emission reporting, especially for developing countries wishing to address REDD+ projects. Both passive and active remote-sensing technologies can provide spatially explicit information of AGB by using a limited number of field samples, thus reducing the substantial budgetary cost of field inventories. The aim of the current study was to estimate AGB in the Niassa Special Reserve (NSR) using fusion of optical (Landsat 8/OLI and Sentinel 2A/MSI) and radar (Sentinel 1B and ALOS/PALSAR-2) data. The performance of multiple linear regression models to relate ground biomass with different combinations of sensor data was assessed using root-mean-square error (RMSE), and the Akaike and Bayesian information criteria (AIC and BIC). The mean AGB and carbon stock (CS) estimated from field data were estimated at 56 Mg ha?1 (ranging from 11 to 95 Mg ha?1) and 28 MgC ha?1, respectively. The best model estimated AGB at 63 ± 20.3 Mg ha?1 for NSR, ranging from 0.6 to 200 Mg ha?1 (r2 = 87.5%, AIC = 123, and BIC = 51.93). We obtained an RMSE % of 20.46 of the mean field estimate of 56 Mg ha?1. The estimation of AGB in this study was within the range that was reported in the existing literature for the miombo woodlands. The fusion of vegetation indices derived from Landsat/OLI and Sentinel 2A/MSI, and backscatter from ALOS/PALSAR-2 is a good predictor of AGB. View Full-Text

服务人员 王璐

服务院士 蒋有绪

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