Forest degradation and biomass damage resulting from logging is currently difficult to evaluate with satellite images, but contributes substantially to carbon emissions in the tropics. To address this situation, we mo...Forest degradation and biomass damage resulting from logging is currently difficult to evaluate with satellite images, but contributes substantially to carbon emissions in the tropics. To address this situation, we modelled how changes in the minimum felling diameter affect stem density, basal area and the related carbon biomass at the end of the felling cycle （30 years） in a semi-deciduous natural forest in Cameroon. With new MFDs estimates, at 7% logging damage rate, we found that the stem density of initially harvestable trees reduces from 12.3 （50.4 MgC·ha^-1） to 6.7 （32.5 MgC·ha^-1） trees per ha and the number of initial residual trees increases from 80 （18.9MgC·ha^-1） to 85.7 （36.8 MgC·ha^-1） trees per ha. This corresponds to an avoided damage estimated at 17.9 MgC·ha^-1. We also found that increasing mortality and damage intensity also increases the damage on carbon biomass estimated to be 8.9 MgC·ha^-1 at 10% or to be 17.4 MgC.hal at 15% logging damage. Overall, our study shows that proper determination of MFD of logged species taking into consideration their capacity of reconstitution and the Reduced Impact Logging can avoid the loss of up to 35 MgC·ha^-1.展开更多
Grassland fires results in carbon emissions,which directly affects the carbon cycle of ecosystems and the carbon balance.The grassland area of Inner Mongolia accounts for 22%of the total grassland area in China,and ma...Grassland fires results in carbon emissions,which directly affects the carbon cycle of ecosystems and the carbon balance.The grassland area of Inner Mongolia accounts for 22%of the total grassland area in China,and many fires occur in the area every year.However,there are few models for estimation of carbon emissions from grassland fires.Accurate estimation of direct carbon emissions from grassland fires is critical to quantifying the contribution of grassland fires to the regional balance of atmospheric carbon.In this study,the regression equations for aboveground biomass(AGB)of grassland in growing season and MODIS NDVI(Normalized Difference Vegetation Index)were established through field experiments,then AGB during Nov.–Apr.were retrieved based on that in Oct.and decline rate,finally surface fuel load was obtained for whole year.Based on controlled combustion experiments of different grassland types in Inner Mongolia,the carbon emission rate of grassland fires for each grassland type were determined,then carbon emission was estimated using proposed method and carbon emission rate.Results revealed that annual average surface fuel load of grasslands in Inner Mongolia during 2000–2016 was approximately 1.1978×1012 kg.The total area of grassland which was burned in the Inner Mongolia region over the 17-year period was 5298.75 km2,with the annual average area of 311.69 km2.The spatial distribution of grassland surface fuel loads is characterized by decreasing from northeast to southwest in Inner Mongolia.The total carbon emissions from grassland fires amounted to 2.24×107 kg with an annual average of 1.32×106 for the study area.The areas with most carbon emissions were mainly concentrated in Old Barag Banner and New Barag Right Banner and on the right side of the Oroqin Autonomous Banner.The spatial characteristics of carbon emission depend on the location of grassland fire,mainly in the northeast of Inner Mongolia include Hulunbuir City,Hinggan League,Xilin Gol League and Ulanqab City.The area and spatial location of grassland fires can directly affect the total amount and spatial distribution of carbon emissions.This study provides a reference for estimating carbon emissions from steppe fires.The model and framework for estimation of carbon emissions from grassland fires established can provide a reference value for estimation of carbon emissions from grassland fires in other regions.展开更多
Aiming at the shortage of sufficient continuous parameters for using models to estimate farmland soil organic carbon(SOC) content, an acquisition method of factors influencing farmland SOC and an estimation method of ...Aiming at the shortage of sufficient continuous parameters for using models to estimate farmland soil organic carbon(SOC) content, an acquisition method of factors influencing farmland SOC and an estimation method of farmland SOC content with Internet of Things(IOT) are proposed in this paper. The IOT sensing device and transmission network were established in a wheat demonstration base in Yanzhou Distict of Jining City, Shandong Province, China to acquire data in real time. Using real-time data and statistics data, the dynamic changes of SOC content between October 2012 and June 2015 was simulated in the experimental area with SOC dynamic simulation model. In order to verify the estimation results, potassium dichromate external heating method was applied for measuring the SOC content. The results show that: 1) The estimated value matches the measured value in the lab very well. So the method is feasible in this paper. 2) There is a clear dynamic variation in the SOC content at 0.2 m soil depth in different growing periods of wheat. The content reached the highest level during the sowing period, and is lowest in the flowering period. 3) The SOC content at 0.2 m soil depth varies in accordance with the amount of returned straw. The larger the amount of returned straw is, the higher the SOC content.展开更多
As urban carbon neutrality initiatives accelerate,green spaces in cities are playing an increasingly critical role as natural carbon sinks in mitigating greenhouse gas emissions.However,conventional carbon estimation ...As urban carbon neutrality initiatives accelerate,green spaces in cities are playing an increasingly critical role as natural carbon sinks in mitigating greenhouse gas emissions.However,conventional carbon estimation approaches struggle with spatial fragmentation and temporal variability in urban green areas,resulting in limited accuracy and poor adaptability.To address this challenge,this study proposes a deep spatiotemporal modeling framework combining Convolutional Neural Networks(CNN)and Temporal Convolutional Networks(TCN),integrating multi-source remote sensing data from Landsat-8,Sentinel-2,and Moderate Resolution Imaging Spectroradiometer(MODIS)to estimate carbon storage in Guangzhou's green spaces from 2018 to 2023.Experimental results demonstrate that the model achieves robust performance across diverse land types and seasonal conditions,with an overall Root Mean Square Error(RMSE)of 2.71 tC/ha,R^(2)of 0.926,and Structural Similarity Index Measure(SSIM)of 0.841,significantly outperforming traditional statistical and machine learning methods.The study confirms the effectiveness of deep fusion modeling in urban carbon sink estimation and offers a scalable technical pathway to support carbon asset management,green space planning,and low-carbon policy development in complex urban contexts.展开更多
To generate carbon credits under the Reducing Emissions from Deforestation and forest Degradation program（REDD＋）, accurate estimates of forest carbon stocks are needed. Carbon accounting efforts have focused on car...To generate carbon credits under the Reducing Emissions from Deforestation and forest Degradation program（REDD＋）, accurate estimates of forest carbon stocks are needed. Carbon accounting efforts have focused on carbon stocks in aboveground biomass（AGB）.Although wood specific gravity（WSG） is known to be an important variable in AGB estimates, there is currently a lack of data on WSG for Malagasy tree species. This study aimed to determine whether estimates of carbon stocks calculated from literature-based WSG values differed from those based on WSG values measured on wood core samples. Carbon stocks in forest biomass were assessed using two WSG data sets：（i） values measured from 303 wood core samples extracted in the study area,（ii） values derived from international databases. Results suggested that there is difference between the field and literaturebased WSG at the 0.05 level. The latter data set was on average 16 % higher than the former. However, carbon stocks calculated from the two data sets did not differ significantly at the 0.05 level. Such findings could be attributed to the form of the allometric equation used which gives more weight to tree diameter and tree height than to WSG. The choice of dataset should depend on the level of accuracy（Tier II or III） desired by REDD＋. As higher levels of accuracy are rewarded by higher prices, speciesspecific WSG data would be highly desirable.展开更多
Quantifying tree resources is essential for effectively implementing climate adaptation strategies and supporting local communities.In the Sahel,where tree presence is scattered,measuring carbon becomes challenging.We...Quantifying tree resources is essential for effectively implementing climate adaptation strategies and supporting local communities.In the Sahel,where tree presence is scattered,measuring carbon becomes challenging.We present an approach to estimating aboveground carbon(AGC)at the individual tree level using a combination of very high-resolution imagery,field-collected data,and machine learning algorithms.We populated an AGC database from in situ measurements using allometric equations and carbon conversion factors.We extracted satellite spectral information and tree crown area upon segmenting each tree crown.We then trained and validated an artificial neural network to predict AGC from these variables.The validation at the tree level resulted in an R^(2)of 0.66,a root mean square error(RMSE)of 373.85 kg,a relative RMSE of 78.6%,and an overestimation bias of 47 kg.When aggregating results at coarser spatial resolutions,the relative RMSE decreased for all areas,with the median value at the plot level being under 30%in all cases.Within our areas of study,we obtained a total of 3,900 Mg,with an average carbon content per tree of 330 kg.A benchmarking analysis against published carbon maps showed that 9 out of 10 underestimate AGC stocks,in comparison to our results,in the areas of study.An additional comparison against a method using only crown area to determine AGC showed an improved performance,including spectral signature.This study improves crown-based biomass estimations for areas where unmanned aerial vehicle or height data are not available and validates at the individual tree level using solely satellite imagery.展开更多
文摘Forest degradation and biomass damage resulting from logging is currently difficult to evaluate with satellite images, but contributes substantially to carbon emissions in the tropics. To address this situation, we modelled how changes in the minimum felling diameter affect stem density, basal area and the related carbon biomass at the end of the felling cycle （30 years） in a semi-deciduous natural forest in Cameroon. With new MFDs estimates, at 7% logging damage rate, we found that the stem density of initially harvestable trees reduces from 12.3 （50.4 MgC·ha^-1） to 6.7 （32.5 MgC·ha^-1） trees per ha and the number of initial residual trees increases from 80 （18.9MgC·ha^-1） to 85.7 （36.8 MgC·ha^-1） trees per ha. This corresponds to an avoided damage estimated at 17.9 MgC·ha^-1. We also found that increasing mortality and damage intensity also increases the damage on carbon biomass estimated to be 8.9 MgC·ha^-1 at 10% or to be 17.4 MgC.hal at 15% logging damage. Overall, our study shows that proper determination of MFD of logged species taking into consideration their capacity of reconstitution and the Reduced Impact Logging can avoid the loss of up to 35 MgC·ha^-1.
基金Under the auspices of National Natural Science Foundation of China (No. 4176110141771450+2 种基金41871330)National Natural Science Foundation of Inner Mongolia (No. 2017MS0409)Fundamental Research Funds for the Central Universities (No. 2412019BJ001)
文摘Grassland fires results in carbon emissions,which directly affects the carbon cycle of ecosystems and the carbon balance.The grassland area of Inner Mongolia accounts for 22%of the total grassland area in China,and many fires occur in the area every year.However,there are few models for estimation of carbon emissions from grassland fires.Accurate estimation of direct carbon emissions from grassland fires is critical to quantifying the contribution of grassland fires to the regional balance of atmospheric carbon.In this study,the regression equations for aboveground biomass(AGB)of grassland in growing season and MODIS NDVI(Normalized Difference Vegetation Index)were established through field experiments,then AGB during Nov.–Apr.were retrieved based on that in Oct.and decline rate,finally surface fuel load was obtained for whole year.Based on controlled combustion experiments of different grassland types in Inner Mongolia,the carbon emission rate of grassland fires for each grassland type were determined,then carbon emission was estimated using proposed method and carbon emission rate.Results revealed that annual average surface fuel load of grasslands in Inner Mongolia during 2000–2016 was approximately 1.1978×1012 kg.The total area of grassland which was burned in the Inner Mongolia region over the 17-year period was 5298.75 km2,with the annual average area of 311.69 km2.The spatial distribution of grassland surface fuel loads is characterized by decreasing from northeast to southwest in Inner Mongolia.The total carbon emissions from grassland fires amounted to 2.24×107 kg with an annual average of 1.32×106 for the study area.The areas with most carbon emissions were mainly concentrated in Old Barag Banner and New Barag Right Banner and on the right side of the Oroqin Autonomous Banner.The spatial characteristics of carbon emission depend on the location of grassland fire,mainly in the northeast of Inner Mongolia include Hulunbuir City,Hinggan League,Xilin Gol League and Ulanqab City.The area and spatial location of grassland fires can directly affect the total amount and spatial distribution of carbon emissions.This study provides a reference for estimating carbon emissions from steppe fires.The model and framework for estimation of carbon emissions from grassland fires established can provide a reference value for estimation of carbon emissions from grassland fires in other regions.
基金Under the auspices of National High-tech R&D Program of China(No.2013AA102301)National Natural Science Foundation of China(No.71503148)
文摘Aiming at the shortage of sufficient continuous parameters for using models to estimate farmland soil organic carbon(SOC) content, an acquisition method of factors influencing farmland SOC and an estimation method of farmland SOC content with Internet of Things(IOT) are proposed in this paper. The IOT sensing device and transmission network were established in a wheat demonstration base in Yanzhou Distict of Jining City, Shandong Province, China to acquire data in real time. Using real-time data and statistics data, the dynamic changes of SOC content between October 2012 and June 2015 was simulated in the experimental area with SOC dynamic simulation model. In order to verify the estimation results, potassium dichromate external heating method was applied for measuring the SOC content. The results show that: 1) The estimated value matches the measured value in the lab very well. So the method is feasible in this paper. 2) There is a clear dynamic variation in the SOC content at 0.2 m soil depth in different growing periods of wheat. The content reached the highest level during the sowing period, and is lowest in the flowering period. 3) The SOC content at 0.2 m soil depth varies in accordance with the amount of returned straw. The larger the amount of returned straw is, the higher the SOC content.
文摘As urban carbon neutrality initiatives accelerate,green spaces in cities are playing an increasingly critical role as natural carbon sinks in mitigating greenhouse gas emissions.However,conventional carbon estimation approaches struggle with spatial fragmentation and temporal variability in urban green areas,resulting in limited accuracy and poor adaptability.To address this challenge,this study proposes a deep spatiotemporal modeling framework combining Convolutional Neural Networks(CNN)and Temporal Convolutional Networks(TCN),integrating multi-source remote sensing data from Landsat-8,Sentinel-2,and Moderate Resolution Imaging Spectroradiometer(MODIS)to estimate carbon storage in Guangzhou's green spaces from 2018 to 2023.Experimental results demonstrate that the model achieves robust performance across diverse land types and seasonal conditions,with an overall Root Mean Square Error(RMSE)of 2.71 tC/ha,R^(2)of 0.926,and Structural Similarity Index Measure(SSIM)of 0.841,significantly outperforming traditional statistical and machine learning methods.The study confirms the effectiveness of deep fusion modeling in urban carbon sink estimation and offers a scalable technical pathway to support carbon asset management,green space planning,and low-carbon policy development in complex urban contexts.
基金supported by TWAS (The World Academy of Sciences) and CIRAD (Centre de Coopération Internationale en Recherche Agronomique pour le Développement)
文摘To generate carbon credits under the Reducing Emissions from Deforestation and forest Degradation program（REDD＋）, accurate estimates of forest carbon stocks are needed. Carbon accounting efforts have focused on carbon stocks in aboveground biomass（AGB）.Although wood specific gravity（WSG） is known to be an important variable in AGB estimates, there is currently a lack of data on WSG for Malagasy tree species. This study aimed to determine whether estimates of carbon stocks calculated from literature-based WSG values differed from those based on WSG values measured on wood core samples. Carbon stocks in forest biomass were assessed using two WSG data sets：（i） values measured from 303 wood core samples extracted in the study area,（ii） values derived from international databases. Results suggested that there is difference between the field and literaturebased WSG at the 0.05 level. The latter data set was on average 16 % higher than the former. However, carbon stocks calculated from the two data sets did not differ significantly at the 0.05 level. Such findings could be attributed to the form of the allometric equation used which gives more weight to tree diameter and tree height than to WSG. The choice of dataset should depend on the level of accuracy（Tier II or III） desired by REDD＋. As higher levels of accuracy are rewarded by higher prices, speciesspecific WSG data would be highly desirable.
基金funded by Intermon Oxfam Spain,and under Industrial PhD grants AGAUR(2021 DI 121)and DIN2020-010982 financed by MCIN AEI 10.13039/501100011033 and by European Union“NextGenerationEU/PRTR”Aitor Ameztegui is funded by a Serra-Húnter fellowship from Generalitat de Catalunyasupported by the ESA Network of Resources Initiative.
文摘Quantifying tree resources is essential for effectively implementing climate adaptation strategies and supporting local communities.In the Sahel,where tree presence is scattered,measuring carbon becomes challenging.We present an approach to estimating aboveground carbon(AGC)at the individual tree level using a combination of very high-resolution imagery,field-collected data,and machine learning algorithms.We populated an AGC database from in situ measurements using allometric equations and carbon conversion factors.We extracted satellite spectral information and tree crown area upon segmenting each tree crown.We then trained and validated an artificial neural network to predict AGC from these variables.The validation at the tree level resulted in an R^(2)of 0.66,a root mean square error(RMSE)of 373.85 kg,a relative RMSE of 78.6%,and an overestimation bias of 47 kg.When aggregating results at coarser spatial resolutions,the relative RMSE decreased for all areas,with the median value at the plot level being under 30%in all cases.Within our areas of study,we obtained a total of 3,900 Mg,with an average carbon content per tree of 330 kg.A benchmarking analysis against published carbon maps showed that 9 out of 10 underestimate AGC stocks,in comparison to our results,in the areas of study.An additional comparison against a method using only crown area to determine AGC showed an improved performance,including spectral signature.This study improves crown-based biomass estimations for areas where unmanned aerial vehicle or height data are not available and validates at the individual tree level using solely satellite imagery.
