The tropical savanna in Brazil known as the Cerrado covers circa 23% of the Brazilian territory, but only 3% of this area is protected. High rates of deforestation and degradation in the woodland and forest areas have made the Cerrado the second-largest source of carbon emissions in Brazil. However, data on these emissions are highly uncertain because of the spatial and temporal variability of the aboveground biomass (AGB) in this biome. Remote-sensing data combined with local vegetation inventories provide the means to quantify the AGB at large scales. Here, we quantify the spatial distribution of woody AGB in the Rio Vermelho watershed, located in the centre of the Cerrado, at a high spatial resolution of 30 metres, with a random forest (RF) machine-learning approach. We produced the first high-resolution map of the AGB for a region in the Brazilian Cerrado using a combination of vegetation inventory plots, airborne light detection and ranging (LiDAR) data, and multispectral and radar satellite images (Landsat 8 and ALOS-2/PALSAR-2). A combination of random forest (RF) models and jackknife analyses enabled us to select the best remote-sensing variables to quantify the AGB on a large scale. Overall, the relationship between the ground data from vegetation inventories and remote-sensing variables was strong (R2 = 0.89), with a root-mean-square error (RMSE) of 7.58 Mg ha−1 and a bias of 0.43 Mg ha−1.

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Abstract. The Ross Ice Shelf (RIS) is currently stable but recent observations have indicated that basal melt rates beneath the ice shelf are expected to increase. It is important to know which areas of the RIS are more sensitive to enhanced basal melting as well as other external forcings or internal material properties of the ice to understand how climate change will influence RIS mass balance. In this paper, we use automatic differentiation and the Ice Sheet and Sea-level System Model to quantify the sensitivity of the RIS to changes in basal friction, ice rigidity, surface mass balance, and basal melting. Using volume above flotation (VAF) as our quantity of interest, we find that the RIS is most sensitive to changes in basal friction and ice rigidity close to grounding lines and along shear margins of the Siple Coast Ice Streams and Transantarctic Mountains Outlet Glaciers. The RIS sensitivity to surface mass balance is uniform over grounded ice, while the sensitivity to basal melting is more spatially variable. Changes in basal melting close to the grounding lines of the Siple Coast Ice Streams and Transantarctic Mountains outlet glaciers have a larger impact on the final VAF compared to elsewhere. Additionally, the pinning points and ice shelf shear margins are highly sensitive to changes in basal melt. Our sensitivity maps allow areas of greatest future vulnerability to be identified.

<p>In recent decades, global warming has driven significant mass losses across the Antarctic Ice Sheet (AIS). Global warming of 1.5◦C and 2◦C is expected to beexceeded in the coming decades, which will trigger further AIS instabilities (Pattyn et al., 2018; Pörtner et al., 2022). The AIS has the potential to be the largest contributor to global sea level rise; thus, it is essential to understand the dynamics of the AIS in a warming world to aid governmental policies. The most significant mass losses in the AIS are driven by ocean-forced basal melting reducing the buttressing ability of ice shelves. The Ross Ice Shelf (RIS) is the largest cold water ice shelf on the AIS and buttresses the West and East Antarctic Ice Sheet. Understanding the current dynamics of the RIS in a warming world is important as the ice shelf has a large control over the mass balance of the AIS. Seasonal changes in sea ice cover have recently been found to elevate basal melt rates at the calving front of the RIS (Stewart et al., 2019). This thesis sets out to understand the influence of short-term environmental variability on RIS flow dynamics. This will be achieved through observing the RIS flow rates over seasonal and interannual timescales using GNSS and remote sensing methods. Exploration of environmental drivers of the observed flow variability is carried out using the Ice-sheet and Sea-level System Model (ISSM). Furthermore, ISSM is used to quantify sensitive areas of the RIS to changes in glaciology and environmental controls. The results showed that the RIS flow rates do not vary significantly on seasonal or interannual timescales, suggesting that the RIS dynamics are insensitive to external forcings at seasonal and interannual frequencies. However, basal melting was found to drive seasonal variations in ice flow dynamics with similar patterns to the GNSS velocities. The sensitivity maps highlighted that changes in basal melt in sensitive areas (i.e., grounding lines and shear margins) would impact the mass balance substantially and should be monitored in a warming world. The simulations could not replicate the observed velocity variations suggesting that some other environmental forcing not considered is driving these variations. Projections of the RIS and AIS using a range of climate models and present-day basal melt conditions were also performed in ISSM to understand whether short-term basal melt variability should be included in projections and the influence basal melt parameterisation inputs have on the final projected sea level contribution. The results showed that including short-term basal melt variability was not needed to project the mass changes of the RIS, with variability in surface mass balance driving larger changes in the mass balance over a 100 year timescale. However, using high spatio-temporal resolution datasets within the basal melt parameterisation projected larger basal melt rates, mass loss and sea level contributions for the AIS. Thus, uncertainties still exist in ice sheet modelled projections of sea level rise estimates, and policymakers should use multiple model simulations when planning adaptive strategies.</p>

&lt;p&gt;The Brazilian Savanna, known as Cerrado (Cerrado sensu lato (s.l.)), is the second largest biome in South America. It comprises different physiognomies due to variations of soil, topography and human impacts. The gradients of tree density, tree height, above ground biomass (AGB) and wood species cover vary according to the Cerrado formation, ranging from different grassland formations (Campo limpo, campo sujo), savanna intermediary formations (Campo cerrado and Cerrado sensu stricto - s.s) and forest formations (Cerrad&amp;#227;o, Mata ciliar, Mata de galeria and Mata Seca).&lt;/p&gt;&lt;p&gt;Although the carbon stock in Cerrado is lower than in the Brazilian Amazon, the conversion of this biome to other types of land use is occurring much faster. In the last ten years, the degradation of Cerrado forest was the second largest source of carbon emissions in Brazil. Therefore, effective methods for assessing and monitoring aboveground woody biomass and carbon stocks are needed. A multi-sensor Earth observation approach and machine learning techniques have shown potential for the large-scale characterization of Cerrado forest structure.The aim of this study is to present a method to estimate the AGB of an area of the Brazilian Cerrado using ALOS-PALSAR (L-band SAR), Landsat, LIDAR (LIght Detection And Ranging) and field datasets. Field data consisted of 15 plots of 1 ha area located in Rio Vermelho watershed in Goi&amp;#225;s-State (Brazil). We used a 2-step AGB estimation (i) from the field AGB using LIDAR metrics and (ii) from LIDAR-AGB to satellite Earth Observation scales following a Random Forest regression algorithm. &amp;#160;The methodology to estimate ABG of Cerrado Stricto Sensu vegetation is part of the Forests 2020 project which is the largest investment by the UK Space Agency, as part of the International Partnerships Programme (IPP), to support in the improvement of the forest monitoring in six partner countries through advanced uses of satellite data.&lt;/p&gt;