Above ground biomass estimation in the upper Blue Nile basin forests, North-Western Ethiopia

Environmental Systems Research Kerebeh et al. Environmental Systems Research (2024) 13:48 https://doi.org/10.1186/s40068-024-00376-1 Open Access RESEARCH Above ground biomass estimation in the upper Blue Nile basin forests, North‑Western Ethiopia Habtamu Kerebeh1*, Matthias Forkel2 and Worku Zewdie1 Abstract Forest ecosystems play a decisive role in the global climatic condition, as well as, provides a wide range of societal benefits, including fuel-wood, tourism, and ecosystem services are considered as one of the major sources of livelihood for the local people in the upper Blue Nile Basin. Therefore, rapid and accurate estimation of forest biomass is crucial for greatly reducing the uncertainty in carbon stock assessments, and for designing strategic forest management plans. Because, above-ground biomass (AGB) estimation is important in determining the management, environmental, and economic roles of forests in the Blue Nile basin. The study was aimed at estimating above-ground biomass in the Upper Blue Nile Basin forests by integrating field-measured data with predictors from Sentinel-2 image. The relationship between measured AGB and sentinel-2 derived vegetation indices and biophysical parameters showed a good correlation result (r value ranging from 0.67 to 0.74). A stepwise regression analysis was carried out in order to develop AGB estimation model by identifying the most important variable. The result demonstrated that, green normalized difference vegetation index, leaf area index, fraction of absorbed photosynthetic active radiation and fractional vegetation cover achieved good performance in predicting AGB with R2 value > 0.5. AGB was estimated with a coefficient of determination (R2) of 0.59 adjusted R2 of 0.618 and root mean square error of (RMSE) 38.36 t/ha in comparison to field observations. The maximum AGB value of 268.32 t/ha was estimated in the Alemsaga natural forest, which is a highly protected dense forest stand from any entrance and disturbance. Generally, integrating field data with optical remote sensing data provides more reliable result for AGB estimation. Moreover, it is also recommended to employ RADAR and LiDAR remote sensing data products together in order to attain more precise estimate results of AGB with great potential for forest resource monitoring and management. Keywords Biophysical parameters, Field-measured data, Regression Analysis, Vegetation indices Introduction Forest ecosystems are a vital component in the exchange of carbon between the earth’s surface and the atmosphere (Bonan 2008; Tao et al. 2016), serving as both carbon sinks by storing about 80% of *Correspondence: Habtamu Kerebeh 1 Space Science and Geospatial Institute, Entoto Observatory and Research Center, Addis Ababa, Ethiopia 2 TUD Dresden University of Technology, Faculty for Environmental Sciences, Dresden, Germany terrestrial biosphere carbon, and sources of carbon through deforestation and forest degradation (Dixon et al. 1994). Climate change associated disasters such as flooding, landslides and locust outbreaks are prevailing in Ethiopia specifically in the Upper Blue Nile basin(Kim & Kaluarachchi 2009), which result damage to homes, agricultural lands, infrastructure and livelihoods (Kassegn & Endris 2021). Forest biomass plays a crucial role in monitoring these disasters through monitoring climate changes by sequestering and storing atmospheric carbon-dioxide (CO2) (Nunes et al. 2020). As a result, forests are instrumental in combating © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Kerebeh et al. Environmental Systems Research (2024) 13:48 global climate change (Yue et al., 2017). Above-ground biomass (AGB) accounts for between 70 and 90% of total forest biomass and is one of the important carbon pools in forest ecosystems, besides carbon stored in soil and litter (Chen et al. 2018). The Blue Nile Basin has many unique terrestrial (forest) ecosystems with thousands of plant and animal species, most of them endemic to the basin. Important vegetation types ranges from lowland Acacia woodland to highland rainforest and afro-alpine forests. Forests in basin provide a wide range of environmental goods and services. Such as fuel, clean water, control of floods and erosion, sustainability of biodiversity and genetic resources, and providing opportunities for recreation and education (El‐Fadel et al., 2003). Information on AGB is vital for the management and monitoring of forest ecosystems. Rapid and accurate estimation and monitoring of AGB over various scales of space and time are crucial for greatly reducing the uncertainty in carbon stock assessments, and for designing strategic forest management plans (Deo et al. 2017). Therefore, biomass estimation in the Upper Blue Nile Basin forests is important for studying subsequent disturbances in the forest ecosystem (Baccini et al., 2012); because it is a critical tool for measuring, reporting and verifying carbon stocks (Baker et al., 2010). AGB estimation methods basically grouped in to ground based and remote sensing based methods, which were developed and are being in the development process (Addo-Fordjour & Rahmad 2013; Segura et al. 2018; Tetemke et al. 2019; Zhao et al. 2019). The ground based AGB measurement belongs to employing an allometric equation by using field measured tree parameters such as tree height, diameter at breast height, crown cover, and density. While, remote sensing data based on relies on, data derived from various remote sensing sensors, such as, multi-spectral remote sensing, Radio Detection and Ranging (RADAR) and Light Detection and Ranging (LiDAR) (Lu 2006; Lu et al. 2016). Various remote sensing data products have been widely employed to facilitate rapid and reliable assessment of AGB, across various spatial and temporal scales by reducing the level of uncertainty (Pan, et al., 2011; Dou and Yang, 2018). These includes, spectral bands, vegetation indices and biophysical parameters of optic (...truncated)