Above-ground biomass mapping in West African dryland forest using Sentinel-1 and 2 datasets - A case study
Introduction
West Africa has been identified as a climate change hotspot where an increased probability of hazards, vulnerability, and exposure meet (Heubes et al., 2013; Sylla et al., 2015). Climate change and variability (CCV) and its effects are inducing significant land use/land cover (LULC) changes in the sub-region, resulting in unprecedented deforestation rates, degradation of arable lands and deterioration of ecological systems (Zoungrana et al., 2018). These challenges call for appropriate adaptation and mitigation strategies to reduce the adverse effects of CCV on the sub-region's socio-ecological systems. Several global programs have been established to tackle the effects of CCV at varying scales.
The United Nations Sustainable Development Goals (SDGs) aim to, among other things, protect, restore and promote sustainable use of terrestrial ecosystems by ensuring sustainable management of forests and reverse land degradation (SDG 15). Monitoring the spatio-temporal changes in biomass and carbon stocks is essential for the realization of several SDGs and global programs such as Reducing Emissions from Deforestation and Forest Degradation (REDD+) (Herold et al., 2011; Saatchi et al., 2011). Above and below ground biomass stocks are important sub-indicators for achieving land degradation neutrality target (15.3.1) (UNEP/CBD/SBSTTA, 2016) and sustainable forest management (FAO, 2017). Consequently, determination and monitoring of carbon stocks in forests and other land uses are important for reducing the effects of CCV and achieving sustainable development in the sub-region (Asner et al., 2013; Skutsch and Ba, 2010).
Remote Sensing (RS) data are suitable for mapping the spatial distribution of above-ground biomass (AGB) or carbon stocks over large areas. Previous mapping efforts at the scale of Africa mostly used coarse spatial resolution RS imagery (ca. 0.5 to 1 km) with limited ground reference data (Avitabile et al., 2016; Baccini et al., 2012; Dobos et al., 2001; Baccini et al., 2008). Few studies recently used relatively high resolution (30 m) optical (Baccini et al., 2017) and Synthetic Aperture Radar (SAR) data (25 m) (Bouvet et al., 2018) for large scale mapping. Despite these improvements, existing products/maps give conflicting and inconsistent AGB estimates, especially for the savanna and woodland regions of dry forests (Bouvet et al., 2018). The fragmented and heterogeneous nature of tropical dry forest areas require further efforts by way of data and methodological approaches to improve AGB estimation. Exploring the complementary use of higher resolution optical and SAR data, and incorporating significant and representative field inventory data, can enhance the accuracy and consistency of AGB estimation in tropical dry forests.
The Copernicus program of the European Commission (Moreno et al., 2012) provides open access high resolution optical (Sentinel-2, S-2) (Drusch et al., 2012) and SAR (Sentinel-1, S-1) (Torres et al., 2012) data for terrestrial research applications. Recent studies have explored these datasets, singularly or complementarily, to map forest AGB stocks in a variety of biomes. Chen et al. (2018) used S-1, S-2 and their derivatives (e.g. texture, spectral indices, biophysical variables) to map forest AGB in Jilin Province, northeast China. One parametric (Geographically weighted regression, GWR) and three non-parametric machine learning algorithms (Support Vector Machines for Regression, SVR; Random Forest, RF and Artificial Neural Networks, ANN) were compared in modeling AGB. They found both datasets to be suitable for forest AGB estimation, especially textural properties from S-1 and biophysical variables from S-2. SVR outperformed the other tested algorithms. A similar study by (Chen et al., 2019, Chen et al., 2019), which combined S-1, S-2 and elevation data from the Shuttle Radar Topographic Mission (SRTM), further emphasized the suitability of S-1 and S-2 for forest AGB mapping. However, they found RF to have outperformed GWR, SVR, ANN and linear multiple regression. Pandit et al. (2018) applied S-2 data and derived spectral indices to map AGB in sub-tropical buffer zone community forests in Nepal using RF algorithm. They found that compared to the use of only spectral bands, the inclusion of vegetation indices improved AGB prediction accuracy. The study noted the possible contribution of red-edge derived vegetation indices in enhancing the performance of the regression. An integration of Unmanned Aerial Vehicle (UAV) data, S-1 and S-2 was performed by Navarro et al. (2019) to map AGB in a mangrove plantation in Senegal. Results of a UAV-based field AGB estimation procedure (response) were regressed against predictors from S-1 (VH backscatter) and S-2 (spectral bands and vegetation indices) to predict AGB values for the entire study area. SVR was selected for the modelling and prediction. Comparison of the models using the Akaike information criterion (AIC) showed S-1 to perform better than S-2, although a combination of the two datasets produced the best results. The study further revealed that the spectral indices from S-2 were more important than spectral bands in AGB estimation. Other studies that used S-1 and/or S-2 affirmed their suitability for AGB estimation (Chen et al., 2019, Chen et al., 2019; Haywood et al., 2018; Jay Labadisos Argamosa et al., 2018).
This study seeks to contribute to the existing knowledge on the suitability of S-1, S-2 and their derivatives for AGB mapping in the tropical dry forest of the Sudanian Savanna (SS) agro-ecological zone of West Africa. Four main vegetation types are considered – agroforestry parklands, shrub savannas, woodland/tree savannas and forest. Previous efforts at AGB estimation in sub-Saharan Africa focused on tropical humid forest areas (Akindele and LeMay 2006; Bakayoko et al., 2012; Lindsell and Klop, 2013) due to their high carbon storage potential (193–200 tons carbon/ha). On the other hand, dry forests and their associated vegetation types, estimated to have carbon storage potential of 17–70 tons carbon/ha have received less attention (Gibbs et al., 2007; Skutsch and Ba, 2010). But there are several reasons why tropical dry forests can no longer be neglected in terms of improving AGB estimation and understanding its dynamics: (1) their estimated coverage of about 22% of Africa's land area (Simons et al., 2001), (2) widespread degradation from high population growth vis-à-vis scarce resources (Campbell et al., 2007) and (3) the signing on of most countries in this biome to the REDD + program and international conventions such as the Paris Agreement (Skutsch and Ba, 2010).
To improve biomass and carbon stock estimation in the tropical dry forest regions of the SS in West Africa, we utilized high resolution multi-temporal optical (S-2, Landsat 8) and annual monthly time-series SAR (S-1) data together with a comprehensive field dataset of inventory plots to map AGB stocks (White, 1986). S-1 and S-2 were selected because they are new, open access and have superior spatial, spectral and temporal resolution compared to other open access datasets. Landsat 8 (L-8) data were used to fill-in areas that S-2 data were not available. RF (Breiman, 2001), a machine learning non-parametric algorithm, was used for modeling AGB. Previous studies have confirmed the superior performance of non-parametric machine learning algorithms (MLA) (e.g. ANN, SVR, RF) over parametric algorithms (GWR, linear regression) (Chen et al., 2018; Wålinder, 2014). This is mainly due to the ability of the former to handle complex non-linear relationships between variables from multi-source data. However, comparisons between MLAs in estimating forest AGB and other terrestrial properties (e.g. soil) have been mostly inconclusive. For example, whereas Chen et al. (2018) found SVR to outperform RF and ANN in forest AGB estimation, Chen et al., 2019, Chen et al., 2019 found RF to outperform SVR and ANN in a similar study. In addition, MLA comparative studies that tested for significance found the differences between them to be minimal and statistically insignificant (Adam et al., 2014; Freeman et al., 2016). These notwithstanding, RF was selected in this study due to the relative ease of tuning, robustness against noise and its inherent ability to extract variable importance measures.
Apart from testing the suitability of S-1 and S-2 for AGB mapping in the SS, this study advances the knowledge in AGB estimation using multi-sensor satellite data in three unique ways. First, we investigated the optimal image acquisition period (within a year) for AGB estimation by analyzing annual multi-temporal (monthly) time-series of S-1 and S-2. This knowledge can reduce image processing efforts in future mapping exercises as well as financial burden of image acquisition if commercial sensors are to be used. Second, we demonstrated the complementary use of S-1 and S-2 according to rainfall seasons in the SS. Specifically, experiments were set up to determine the optimal combination of S-1 and S-2 data for improved AGB mapping. Third, a red-edge dependent index and stress-related vegetation indices (Thenkabail et al., 1994) that have not been tested with S-1/S-2 data in recent AGB estimation studies were used. To our knowledge, no study has tested S-1 and S-2 data for AGB mapping in the SS biome. Thus, we focused on the following objectives: (1) determine the potential of S-1 and S-2, individually and in combination, to map AGB in the SS agro-ecological zone of West Africa, (2) determine optimal image acquisition period for AGB modelling and (3) investigate the contribution of derivatives (e.g. indices and biophysical parameters), to AGB mapping, and (4) establish the importance of accurate AGB maps to achieving targeted indicators in the SDGs.
Section snippets
Study area
The study was conducted in a 170,000 km2 area in the SS agro-climatic region of West Africa (Fig. 1). The area covers parts of Burkina Faso, Ghana, Togo and Benin, and represents almost the combined total area of Benin (114,763 km2) and Togo (56,785 km2). These countries have all signed up to the UN-REDD + program and are at different stages of program implementation (Isyaku et al., 2017; Lund et al., 2017). They are additionally signatories to the Paris Agreement and the UN SDGs. Improving the
AGB statistics in major vegetation types
Table 4 presents AGB statistics in the four major vegetation types considered. Shrub savannas had the least standard deviation (SD) and standard error (SE), indicating a relatively high level of uniformity within this vegetation type. Tree savanna/woodland had the highest coefficient of variation (CV) and range of AGB values, which is indicative of high variability in AGB values of plots. Mean AGB ranged from a low of 24.56 Mg/ha in shrub savanna to a high of 72.12 Mg/ha in agroforestry
AGB in agroforestry parklands
Mean AGB values for agroforestry parklands were found to be higher than the other major vegetation types. This can be attributed to the predominance of large Vitellaria paradoxa and Parkia biglobosa tree species on most parklands. Vitellaria paradoxa provides a range of ecosystem services for rural communities and therefore has high socio-economic importance (Bayala et al., 2014; Dimobe et al., 2018b). Consequently, rural communities protect these tree species against anthropogenic effects such
Conclusion
This study mapped AGB in the dry forest of the SS using multi-temporal S-1, S-2, their derivatives (indices, biophysical parameters) and inventory data in a Random Forest Regression (RFR). Vegetation types considered are agroforestry parklands, shrub savannas, woodlands and forest. Eight experiments involving different combinations of the data were conducted to achieve the objectives of the study. Analysis of multi-temporal S-2 achieved better accuracy than S-1, although a complementary use of
Acknowledgements
The authors are grateful to the European Space Agency and the United States Geological Survey for their open data policy. We acknowledge financial support from the German Federal Ministry of Education and Research (BMBF), Germany, under the research grant 01LG1001A.
References (108)
- et al.
Development of tree volume equations for common timber species in the tropical rain forest area of Nigeria
For. Ecol. Manage.
(2006) - et al.
GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: principles of development and production
Remote Sens. Environ.
(2013) - et al.
Parklands for buffering climate risk and sustaining agricultural production in the Sahel of West Africa
Curr. Opin. Environ. Sustain.
(2014) - et al.
An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR
Remote Sens. Environ.
(2018) - et al.
Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery
ISPRS J. Photogrammetry Remote Sens.
(2017) - et al.
Estimation of forest above-ground biomass by geographically weighted regression and machine learning with sentinel imagery
Forests
(2018) - et al.
Diversity-carbon stock relationship across vegetation types in W National park in Burkina Faso
For. Ecol. Manage.
(2019) - et al.
Aboveground biomass partitioning and additive models for Combretum glutinosum and Terminalia laxiflora in West Africa
Biomass Bioenergy
(2018) - et al.
A regional scale soil mapping approach using integrated AVHRR and DEM data
Int. J. Appl. Earth Obs. Geoinf.
(2001) - et al.
Sentinel-2: ESA's optical high-resolution mission for GMES operational services
Remote Sens. Environ.
(2012)
New estimates of CO2 forest emissions and removals: 1990--2015
For. Ecol. Manage.
Water balance of small reservoirs in the Volta basin: a case study of Boura reservoir in Burkina Faso
Agric. Water Manag.
Random Forests for land cover classification
Pattern Recognit. Lett.
The projected impact of climate and land use change on plant diversity: an example from West Africa
J. Arid Environ.
Opportunities and constraints for farmers of west Africa to use seasonal precipitation forecasts with Burkina Faso as a case study
Agric. Syst.
Spatial and temporal variation of carbon stocks in a lowland tropical forest in West Africa
For. Ecol. Manage.
Promising change, delivering continuity: REDD+ as conservation fad
World Dev.
The influence of stand variables and human use on biomass and carbon stocks of a transitional African forest: implications for forest carbon projects
For. Ecol. Manage.
Sentinels for science: potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land
Remote Sens. Environ.
Decrease of L-band SAR backscatter with biomass of dense forest
Remote Sensing of Environment
L-band Synthetic Aperture Radar imagery performs better than optical datasets at retrieving woody fractional cover in deciduous, dry savannahs
Int. J. Appl. Earth Obs. Geoinf.
Population structure of the widespread species, Anogeissus leiocarpa (DC.) Guill. & Perr. across the climatic gradient in West Africa semi-arid area
South Afr. J. Bot.
Retrieval of growing stock volume in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR backscatter measurements
Remote Sens. Environ.
Crediting carbon in dry forests: the potential for community forest management in West Africa. For
For. Policy Econ.
GMES Sentinel-1 mission
Remote Sens. Environ.
Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa
Int. J. Appl. Earth Obs. Geoinf.
Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers
Int. J. Remote Sens.
High-fidelity national carbon mapping for resource management and REDD+
Carbon Balance Manag.
An integrated pan-tropical biomass map using multiple reference datasets
Glob. Chang. Biol.
Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps
Nat. Clim. Chang.
A first map of tropical Africa’s above-ground biomass derived from satellite imagery
Environ. Res. Lett.
Tropical Forests Are a Net Carbon Source Based on Aboveground Measurements of Gain and Loss 5962
Stockage de Carbone dans des Peuplements de Cedrela Odorata et de Gmelina Arborea en Côte D'ivoire
Eur. J. Sci. Res.
Estimation OF mangrove forest aboveground biomass using multispectral bands, vegetation indices and biophysical variables derived from optical satellite imageries: rapideye, planetscope and SENTINEL-2
Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.
Random forests
Mach. Learn.
Farming and Cropping Systems in the West African Sudanian Savanna (No. 100)
Miombo Woodlands-Oopportunities and Barriers to Sustainable Forest Management
Erratum : allometric models and aboveground biomass stocks of a West African Sudan Savannah watershed in Benin
Carbon Balance Manag.
Improved allometric models to estimate the aboveground biomass of tropical trees
Glob. Chang. Biol.
Optimal combination of predictors and algorithms for forest above-ground biomass mapping from sentinel and SRTM data
Remote Sens.
Exploring bamboo forest aboveground biomass estimation using Sentinel-2 data
Remote Sens.
Important variables of a rapideye time series for modelling biophysical parameters of winter wheat
Photogramm. Fernerkund. GeoInf. (PFG)
Gene selection and classification of microarray data using random forest
BMC Bioinf.
Farmersʼ preferred tree species and their potential carbon stocks in southern Burkina Faso: implications for biocarbon initiatives
PLoS One
KEEPING an EYE on SDG 15. Rome, Italy
Agricultural Land Use Mapping in West Africa Using Multi-Sensor Satellite Imagery
Integration of optical and synthetic aperture radar imagery for improving crop mapping in northwestern Benin, West Africa
Remote Sens.
High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: a comparison of machine learning and multiple linear regression models
PLoS One
Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performance 1
Can. J. For. Res.
Pattern and Trends of Poverty in Ghana, 1991-2006
Cited by (103)
Tackling sustainable development goals through new space
2024, Project Leadership and SocietyVegetation fuel characterization using machine learning approach over southern Portugal
2023, Remote Sensing Applications: Society and Environment
- 1
These authors contributed equally to this work.