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Background and aims: The quantitative retrieval of soil organic carbon (SOC) storage, particularly for soils with a large potential for carbon sequestration, is of global interest due to its link with the carbon cycle and the mitigation of climate change. However, complex ecosystems with good soil qualities for SOC storage are poorly studied. Methods: The interrelation between SOC and various vegetation remote sensing drivers is understood to demonstrate the link between the carbon stored in the vegetation layer and SOC of the top soil layers. Based on the mapping of SOC in two horizons (0–30 cm and 30–60 cm) we predict SOC with high accuracy in the complex and mountainous heterogeneous páramo system in Ecuador. A large SOC database (in weight % and in Mg/ha) of 493 and 494 SOC sampling data points from 0–30 cm and 30–60 cm soil profiles, respectively, were used to calibrate GPR models using Sentinel-2 and GIS predictors (i.e., Temperature, Elevation, Soil Taxonomy, Geological Unit, Slope Length and Steepness (LS Factor), Orientation and Precipitation). Results: In the 0–30 cm soil profile, the models achieved a R2 of 0.85 (SOC%) and a R2 of 0.79 (SOC Mg/ha). In the 30–60 cm soil profile, models achieved a R2 of 0.86 (SOC%), and a R2 of 0.79 (SOC Mg/ha). Conclusions: The used Sentinel-2 variables (FVC, CWC, LCC/Cab, band 5 (705 nm) and SeLI index) were able to improve the estimation accuracy between 3–21% compared to previous results of the same study area. CWC emerged as the most relevant biophysical variable for SOC prediction. [ABSTRACT FROM AUTHOR]

Abstract Background Soil organic carbon (SOC) affects essential biological, biochemical, and physical soil functions such as nutrient cycling, water retention, water distribution, and soil structure stability. The Andean páramo known as such a high carbon and water storage capacity ecosystem is a complex, heterogeneous and remote ecosystem complicating field studies to collect SOC data. Here, we propose a multi-predictor remote quantification of SOC using Random Forest Regression to map SOC stock in the herbaceous páramo of the Chimborazo province, Ecuador. Results Spectral indices derived from the Landsat-8 (L8) sensors, OLI and TIRS, topographic, geological, soil taxonomy and climate variables were used in combination with 500 in situ SOC sampling data for training and calibrating a suitable predictive SOC model. The final predictive model selected uses nine predictors with a RMSE of 1.72% and a R2 of 0.82 for SOC expressed in weight %, a RMSE of 25.8 Mg/ha and a R2 of 0.77 for the model in units of Mg/ha. Satellite-derived indices such as VARIG, SLP, NDVI, NDWI, SAVI, EVI2, WDRVI, NDSI, NDMI, NBR and NBR2 were not found to be strong SOC predictors. Relevant predictors instead were in order of importance: geological unit, soil taxonomy, precipitation, elevation, orientation, slope length and steepness (LS Factor), Bare Soil Index (BI), average annual temperature and TOA Brightness Temperature. Conclusions Variables such as the BI index derived from satellite images and the LS factor from the DEM increase the SOC mapping accuracy. The mapping results show that over 57% of the study area contains high concentrations of SOC, between 150 and 205 Mg/ha, positioning the herbaceous páramo as an ecosystem of global importance. The results obtained with this study can be used to extent the SOC mapping in the whole herbaceous ecosystem of Ecuador offering an efficient and accurate methodology without the need for intensive in situ sampling.

Terrestrial ecosystems of Canada store a large amount of organic carbon (C) in soils, peats and plant materials, yet little is known about the C stock size and distributions, both spatially and in various C pools. As temperature rises, C is becoming available for disturbance, decomposition and eventual release into the atmosphere, which makes the quantification of C stocks in terrestrial ecosystems of Canada of high interest for the assessment of climate change impacts and conservation efforts. We used a large number of field measurements, multisource satellite, climate and topographic data and a machine learning algorithm to produce the first wall‐to‐wall estimates of C stocks and uncertainties in plants and soils of Canada at 250 m spatial resolution. Our findings show that above and belowground live biomass and detritus store a total of 21.1 Pg of carbon (Pg C). Whereas the Canadian soils store 306 (±147, 90% confidence interval) Pg organic C in the top 1 m, 98 Pg C of which are stored in peatlands, confirming that the soil organic C dominates terrestrial carbon stocks in Canada. We also find previously under‐reported large soil organic C stocks in forested peatlands on the boreal shields of Canada. Given that Canada is warming twice the global average rate and Canadian soils store approximately 20.4% of world soil C stocks in top 1 m, initiatives to understand their vulnerabilities to climate change and disturbance are indispensable not only for Canada but also for the global C cycle. Plain Language Summary: Soil and plants of Canada store a large amount of organic carbon (C), yet little is known about the C stock size and distributions over the country. As temperature rises, more C is being released into the atmosphere, which makes the quantification of C stocks in soil and plants of Canada of high interest for the assessment of climate change impacts and conservation efforts. Here, we use satellite data and machine‐learning models to produce the first maps of C stocks in plants and soils of Canada. We determined that Canadian forests store around 6% of all the carbon stored in the world's forests. Whereas the Canadian soils store 20% of world soil C stocks in top 1 m, confirming that soil organic C dominates terrestrial carbon stocks. We also find previously under‐reported large soil organic C stock in forested peatlands on the boreal shields of Canada. Given that Canada is warming twice the global average rate, initiatives to understand their vulnerabilities to climate change and disturbance are indispensable not only for Canada but also for the global C budget and cycle. Key Points: Soils of Canada store 306 ± 147 Pg organic carbon in the top 1 m, which is 20.4% of the global soil carbon storagePeatlands, covering ∼12% of Canada, store 32% of the total amount of the soil organic carbon stock of the nationWe found previously unreported large organic carbon stocks in forested boreal peatlands of Canada [ABSTRACT FROM AUTHOR]

Background: Soil organic carbon (SOC) affects essential biological, biochemical, and physical soil functions such as nutrient cycling, water retention, water distribution, and soil structure stability. The Andean páramo known as such a high carbon and water storage capacity ecosystem is a complex, heterogeneous and remote ecosystem complicating field studies to collect SOC data. Here, we propose a multi-predictor remote quantification of SOC using Random Forest Regression to map SOC stock in the herbaceous páramo of the Chimborazo province, Ecuador. Results: Spectral indices derived from the Landsat-8 (L8) sensors, OLI and TIRS, topographic, geological, soil taxonomy and climate variables were used in combination with 500 in situ SOC sampling data for training and calibrating a suitable predictive SOC model. The final predictive model selected uses nine predictors with a RMSE of 1.72% and a R2 of 0.82 for SOC expressed in weight %, a RMSE of 25.8 Mg/ha and a R2 of 0.77 for the model in units of Mg/ha. Satellite-derived indices such as VARIG, SLP, NDVI, NDWI, SAVI, EVI2, WDRVI, NDSI, NDMI, NBR and NBR2 were not found to be strong SOC predictors. Relevant predictors instead were in order of importance: geological unit, soil taxonomy, precipitation, elevation, orientation, slope length and steepness (LS Factor), Bare Soil Index (BI), average annual temperature and TOA Brightness Temperature. Conclusions: Variables such as the BI index derived from satellite images and the LS factor from the DEM increase the SOC mapping accuracy. The mapping results show that over 57% of the study area contains high concentrations of SOC, between 150 and 205 Mg/ha, positioning the herbaceous páramo as an ecosystem of global importance. The results obtained with this study can be used to extent the SOC mapping in the whole herbaceous ecosystem of Ecuador offering an efficient and accurate methodology without the need for intensive in situ sampling. [ABSTRACT FROM AUTHOR]

Mangrove ecosystem is one of coastal wetlands that have experienced significant anthropogenic driven degradation and conversion, which have resulted in substantial carbon releases to the atmosphere and the oceans. However, carbon stock assessments and monitoring for mangroves are mostly confined to in situ methodologies, which has limited further understanding of wider-scale spatial variations and additional driving factors of carbon loss. Here,we report a novel application using unmanned aerial vehicles (UAVs) to estimate and compare aboveground biomass (AGB) carbon stocks of mangrove forests with two alternate replacement commercial land uses (oil palm and coconut plantations) in Lubuk Kertang and Pulau Sembilan, North Sumatra, Indonesia. We generated high-accuracy digital surface models (DSMs) and digital terrain models (DTMs), as well as elevation data from a global navigation satellite system (GNSS). Through analysis, we highlight how mangrove forest conversion to oil palm and coconut plantations significantly decreases the aboveground biomass carbon (AGB) and carbon stocks (AGC) of selected mangrove ecosystems in Indonesia. In this study, spatially mapped biomass carbon measurements were found to be more accurate when cross-validated with in situ measurements. AGC stocks were found to be over 10- to 12-fold that of converted oil palm and coconut plantations across selected study sites. The generated biomass carbon stock product provides new knowledge of carbon stock distribution in Indonesia across mangrove forests and two alternative commercial land use types within similar topographical landscapes. This combined methodological approach may be a useful tool to assist with blue carbon inventories as well as for selecting specific mangrove conservation and restoration areas across larger spatial scales. • Biomass carbon stocks across different coastal land uses are spatially mapped. • The application of UAV photogrammetry for biomass carbon stocks mapping is cost effective. • Combined field survey and UAV photogrammetry improve biomass carbon stocks spatial resolution. • This study is important to support carbon accounting methodology for future blue carbon project. [ABSTRACT FROM AUTHOR]

Recently, the severe intensification of atmospheric carbon has highlighted the importance of urban tree contributions in atmospheric carbon mitigations in city areas considering sustainable urban green planning and management systems. Explicit and timely information on urban trees and their roles in the atmospheric Carbon Stock (CS) are essential for policymakers to take immediate actions to ameliorate the effects of deforestation and their worsening outcomes. In this study, a detailed methodology for urban tree CS calibration and mapping was developed for the small urban area of Sassuolo in Italy. For dominant tree species classification, a remote sensing approach was applied, utilizing a high-resolution WV3 image. Five dominant species were identified and classified by applying the Object-Based Image Analysis (OBIA) approach with an overall accuracy of 78%. The CS calibration was done by utilizing an allometric model based on the field data of tree dendrometry—i.e., Height (H) and Diameter at Breast Height (DBH). For geometric measurements, a terrestrial photogrammetric approach known as Structure-from-Motion (SfM) was utilized. Out of 22 randomly selected sample plots of 100 square meters (10 m × 10 m) each, seven plots were utilized to validate the results of the CS calibration and mapping. In this study, CS mapping was done in an efficient and convenient way, highlighting higher CS and lower CS zones while recognizing the dominant tree species contributions. This study will help city planners initiate CS mapping and predict the possible CS for larger urban regions to ensure a sustainable urban green management system. [ABSTRACT FROM AUTHOR]