Purpose of Review This review presents cutting-edge methods and current and forthcoming satellite remote sensing technologies to map aboveground biomass (AGB). Recent Findings The monitoring of carbon stored in living AGB of forest is of key importance to understand the global carbon cycle and for the functioning of international economic mechanisms aiming to protect and enhance forest carbon stocks. The main challenge of monitoring AGB lies in the difficulty of obtaining field measurements and allometric models in several parts of the world due to geographical remoteness, lack of capacity, data paucity or armed conflicts. Space-borne remote sensing in combination with ground measurements is the most cost-efficient technology to undertake the monitoring of AGB. Summary These approaches face several challenges: lack of ground data for calibration/validation purposes, signal saturation in high AGB, coverage of the sensor, cloud cover persistence or complex signal retrieval due to topography. New space-borne sensors to be launched in the coming years will allow accurate measurements of AGB in high biomass forests (>200 t ha −1 ) for the first time across large areas.

Radar backscatter from forest canopies is related to forest cover, canopy structure and aboveground biomass (AGB). The S-band frequency (3.1-3.3 GHz) lies between the longer L-band (1-2 GHz) and the shorter C-band (5-6 GHz) and has been insufficiently studied for forest applications due to limited data availability. In anticipation of the British built NovaSAR-S satellite mission, this study evaluates the benefits of polarimetric S-band SAR for forest biophysical properties. To understand the scattering mechanisms in forest canopies at S-band the Michigan Microwave Canopy Scattering (MIMICS-I) radiative transfer model was used. S-band backscatter was found to have high sensitivity to the forest canopy characteristics across all polarisations and incidence angles. This sensitivity originates from ground/trunk interaction as the dominant scattering mechanism related to broadleaved species for co-polarised mode and specific incidence angles. The study was carried out in the temperate mixed forest at Savernake Forest and Wytham Woods in southern England, where airborne S-band SAR imagery and field data are available from the recent AirSAR campaign. Field data from the test sites revealed wide ranges of forest parameters, including average canopy height (6-23 m), diameter at breast-height (7-42 cm), basal area (0.2-56 m 2 /ha), stem density (20-350 trees/ha) and woody biomass density (31-520 t/ha). S-band backscatter-biomass relationships suggest increasing backscatter sensitivity to forest AGB with least error between 90.63 and 99.39 t/ha and coefficient of determination (r 2 ) between 0.42 and 0.47 for the co-polarised channel at 0.25 ha resolution. The conclusion is that S-band SAR data such as from NovaSAR-S is suitable for monitoring forest aboveground biomass less than 100 t/ha at 25 m resolution in low to medium incidence angle range.

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.

<p><strong>Abstract.</strong> An assessment on the amount and spatial distribution of forest aboveground biomass (AGB) for the forests in Colombia was generated using in-situ national forest inventory data (IDEAM, 2018), in combination with multispectral optical data and synthetic aperture radar (SAR) satellite imagery. ALOS-2 PALSAR-2 gamma-0 backscatter annual mosaics (2015&amp;ndash;2017) provided by JAXA were normalised and corrected using previous ALOS PALSAR annual mosaics (2007&amp;ndash;2010) as reference. A multi-temporal Landsat 7 &amp;amp; 8 composite over the whole of Colombia was used for the year 2016&amp;thinsp;&amp;plusmn;&amp;thinsp;1. The national forest inventory in-situ plots used to train our model consisted of 5-subplots each and were collected during the period 2015&amp;ndash;2017 in the main biomes of the country. A sample of permanent 1ha plots (PPMs) were also measured. Nationally developed allometries (Alvarez et al., 2012) were used to estimate AGB. A non-parametric random forests (RF) algorithm was used within a k-fold framework to retrieve AGB at 30&amp;thinsp;m spatial resolution for the whole of Colombia. The algorithm was trained using forest inventory plots and validated at plot (0.35&amp;thinsp;ha) and PPM level (1&amp;thinsp;ha). The accuracy assessment found coefficients of determination (R²) of 0.68 and 0.61, and relative root mean square errors (Rel. RMSE) of 49% and 34% at plot and at PPM level, respectively. The results showed that the average AGB for the country was 118.1&amp;thinsp;t&amp;thinsp;ha^&amp;minus;1 (45.6&amp;thinsp;t&amp;thinsp;ha^&amp;minus;1 for Caribe, 75.4&amp;thinsp;t&amp;thinsp;ha^&amp;minus;1 Andes, 122.5&amp;thinsp;t&amp;thinsp;ha^&amp;minus;1 Pacifico, 32.7&amp;thinsp;t&amp;thinsp;ha^&amp;minus;1 Orinoquia, and 200.5&amp;thinsp;t&amp;thinsp;ha^&amp;minus;1 for the Amazonia, regionally), and that the total carbon stocks for the country were 6.7&amp;thinsp;Pg C for the period 2015&amp;ndash;2017.</p>

The commitment by over 100 governments covering over 90% of the world’s forests at the COP26 in Glasgow to end deforestation by 2030 requires more effective forest monitoring systems. The near real-time (NRT) change detection of forest cover loss enables forest landowners, government agencies and local communities to monitor natural and anthropogenic disturbances in a much timelier fashion than the thematic maps that are released every year. NRT deforestation alerts enable the establishment of more up-to-date forest inventories and rapid responses to unlicensed logging. The Copernicus Sentinel-2 satellites provide operational Earth observation (EO) data from multi-spectral optical/near-infrared wavelengths every five days at a global scale and at 10 m resolution. The amount of acquired data requires cloud computing or high-performance computing for ongoing monitoring systems and an automated system for processing, analyzing and delivering the information promptly. Here, we present a Sentinel-2-based NRT change detection system, assess its performance over two study sites, Manantlán in Mexico and Cartagena del Chairá in Colombia, and evaluate the forest changes that occurred in 2018. An independent validation with very high-resolution PlanetScope (~3 m) and RapidEye (~5 m) data suggests that the proposed NRT change detection system can accurately detect forest cover loss (> 87%), other vegetation loss (> 76%) and other vegetation gain (> 71%). Furthermore, the proposed NRT change detection system is designed to be attuned using in situ data. Therefore, it is scalable to larger regions, entire countries and even continents.

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&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;