Integration of Optical and X-Band Radar Data for Pasture Biomass Estimation in an Open Savannah Woodland

Pasture biomass is an important quantity globally in livestock industries, carbon balances, and bushfire management. Quantitative estimates of pasture biomass or total standing dry matter (TSDM) at the field scale are much desired by land managers for land-resource management, forage budgeting, and conservation purposes. Estimates from optical satellite imagery alone tend to saturate in the cover-to-mass relationship and fail to differentiate standing dry matter from litter. X-band radar imagery was added to complement optical imagery with a structural component to improve TSDM estimates in rangelands. High quality paddock-scale field data from a northeastern Australian cattle grazing trial were used to establish a statistical TSDM model by integrating optical satellite image data from the Landsat sensor with observations from the TerraSAR-X (TSX) radar satellite. Data from the dry season of 2014 and the wet season of 2015 resulted in models with adjusted r2 of 0.81 in the dry season and 0.74 in the wet season. The respective models had a mean standard error of 332 kg/ha and 240 kg/ha. The wet and dry season conditions were different, largely due to changed overstorey vegetation conditions, but not greatly in a pasture ‘growth’ sense. A more robust combined-season model was established with an adjusted r2 of 0.76 and a mean standard error of 358 kg/ha. A clear improvement in the model performance could be demonstrated when integrating HH polarised TSX imagery with optical satellite image products. View Full-Tex

Remote Sensing of Savannas and Woodlands

Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome

Evaluation of low-cost Earth observations to scale-up national forest monitoring in Miombo Woodlands of Malawi

This study explored the extent that low-cost Earth Observations (EO) data could effectively be combined with in-situ tree-level measurements to support national estimates of Above Ground Biomass (AGB) and Carbon (C) in Malawi’s Miombo Woodlands. The specific objectives were to; (i) investigate the effectiveness of low-cost optical UAV orthomosaics in geo-locating individual trees and estimating AGB and C, (ii) scale-up the AGB estimates using the canopy height model derived from the UAV imagery, and crown diameter measurements; and (iii) compare results from (ii), ALOS-PALSAR-2, Sentinel1, ESA CCI Biomass Map datasets, and Sentinel 2 vis/NIR/SWIR band combination datasets in mapping biomass. Data were acquired in 2019 from 13 plots over Ntchisi Forest in 3-fold, vis-a-vis; (i) individual tree measurements from 0.1ha ground-based (gb) plots, (ii) 3-7cm pixel resolution optical airborne imagery from 50ha plots, and (iii) SAR backscatter and Vis/NIR/SWIR bands imagery. Results demonstrate a strong correlational relationship (R2 = 0.7, RMSE = 11tCha-1) between gb AGB and gb fractional cover percent (FC %), more importantly (R2 = 0.7) between gb AGB and UAV-based FC. Similarly, another set of high correlation (R2 = 0.9, RMSE = 7tCha-1; R2 = 0.8, RMSE = 8tCha-1; and R2 = 0.7) was observed between the gb AGB and EO-based AGB from; (i) ALOS-PALSAR-2, (ii) ESA-CCI-Biomass Map, and (iii) S1-C-band, respectively. Under the measurement conditions, these findings reveal that; (i) FC is more indicative of AGB and C pattern than CHM, (ii) the UAV can collect optical data of very high resolution (3-7cm resolution with ±13m horizontal geolocation error), and (iii) provides the cost-effective means of bridging the ground datasets to the wall-to-wall satellite EO data (£7 ha-1 compared to £30 ha-1, per person, provided by the gb system). The overall better performance of the SAR backscatter (R2 = 0.7 to 0.9) establishes the suitability of the SAR backscatter to infer the Miombo AGB and fractional cover with high accuracy. However, the following factors compromised the accuracy for both the SAR and optical measurements; leaf-off and seasonality (fire, aridness), topography (steep slopes of 18-74%), and sensing angle. Inversely, the weak to moderate correlation observed between the gb height and UAV FC % measurements (R2 = 0.4 to 0.7) are attributable to the underestimation systematic error that UAV height datasets are associated with. The visual lacunarity analysis on S2-Vis/NIR/SWIR composite band and SAR backscatter measurements demonstrated robust, consistent and homogenous spatial crown patterns exhibited particularly by the leaf-on tree canopies along riverine tree belts and cohorts. These results reveal the potential of vis/NIR/SWIR band combination in determining the effect of fire, rock outcrops and bare land/soil common in these woodlands. Coarsening the EO imagery to ≥50m pixel resolution compromised the accuracy of the estimations, hence <50m resolution is the ideal scale for these Miombo. Careful consideration of the aforementioned factors and incorporation of FC parameter in during estimation of AGB and C will go a long way in not only enhancing the accuracy of the measurements, but also in bolstering Malawi’s NFMS standards to yield carbon off-set payments under the global REDD+ mechanism

Remote Sensing in Mangroves

The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the worl

Above-ground carbon stocks, species diversity and fire dynamics in the Bateke Plateau

Savannas are heterogeneous systems characterised by a high spatial and temporal variation in ecosystem structure. Savannas dominate the tropics, with important ecological functions, and play a prominent role in the global carbon cycle, in particular responsible for much of its inter-annual variability. They are shaped by resource availability, soil characteristics and disturbance events, particularly fire. Understanding and predicting the demographic structure and woody cover of savannas remains a challenge, as it is currently poorly understood due to the complex interactions and processes that determine them. A predictive understanding of savanna ecosystems is critical in the context of land use management and global change. Fire is an essential ecological disturbance in savannas, and forest-savanna mosaics are maintained by fire-mediated positive feedbacks. Over half of the world’s savannas are found in Africa, and over a quarter Africa’s surface burns every year, with fires occurring principally in the savanna biome. These have strong environmental and social impacts. Most fires in Africa are anthropogenic and occur during the late dry season, but their dynamics and effects remain understudied. The main objective of this research is to understand the floristic composition, carbon storage, woody cover and fire regime of the mesic savannas of the Bateke Plateau. The Bateke Plateau is savanna-forest mosaic ecosystem, situated mainly in the Republic of Congo, with sandy Kalahari soils and enough precipitation for potential forest establishment (1600 mm/yr). Despite occupying 89,800 km2, its ecology and ecosystem functions are poorly understood. This study combines two approaches: firstly experimental, setting up long term field experiments where the fire regime is manipulated, and then observational, using remote sensing to estimate the carbon storage and study the past history of the fire regime in the region. I established four large (25 ha) plots at two savanna sites, measured their carbon stocks, spatial structure and floristic composition, and applied different annual fire treatments (early and late dry season burns). These treatments were applied annually during 3 years (2015, 2016 and 2017), and the plots were re-measured every year to estimate tree demographic rates and the identification of the key processes that impact them, including fire and competition. Field data were combined with satellite radar data from ALOS PALSAR, and the fire products of the MODIS satellites, to estimate carbon stocks and fire regimes for the entire Bateke Plateau. I also analyse the underlying biophysical and anthropogenic processes that influence the patterns in Above-Ground Woody Biomass (AGWB) and their spatial variability in the Bateke landscape. The total plant carbon stocks (above-ground and below-ground) were low, averaging only 6.5 ± 0.3 MgC/ha, with grass representing over half the biomass. Soil organic matter dominate the ecosystem carbon stocks, with 16.7 ± 0.9 Mg/ha found in the top 20 cm alone. We identified 49 plant species (4 trees, 13 shrubs, 4 sedges, 17 forbs and 11 grass species), with a tree hyperdominance of Hymenocardia acida, and a richer herbaceous species composition. These savannas showed evidence of tree clustering, and also indications of tree-tree competition. Trees had low growth rates (averaging 1.21 mm/yr), and mortality was relatively low (3.24 %/yr) across all plots. The experiment showed that late dry season fires significantly reduced tree growth compared to early dry season fires, but also reduced stem mortality rates. Results show that these mesic savannas had very low tree biomass, with tree cover held far below its climate potential closed-canopy maximum, likely due to nutrient poor sandy soils and frequent fires. Results from the remote sensing analysis indicated that multiple explanatory variables had a significant effect on AGWB in the Bateke Plateau. Overall, the frequency of fire had the largest impact on AGWB (with higher fire frequency resulting in lower AGWB), with sand content the next most important explanatory variable (with more sand reducing AGWB). Fires in the Bateke are very frequent, and show high seasonality. The proportion of fires that occurred in the late dry season, though smaller predictor, was also more important than other factors (including soil carbon proportion, whether or not the savanna area was in a protected area, annual rainfall, or distance to the nearest town, river or road), with a larger proportion of late dry season fires associated with a small increase in AGWB. The results give pointers for management of the savannas of the Bateke Plateau, as well as improving our understanding of vegetation dynamics in this understudied ecosystem and help orient policy and conservation