Forest carbon sinks are crucial for mitigating urban climate change.Their effectiveness depends on the balance between gross carbon losses and gains.However,quantitative and continuous monitoring of forest change/dist...Forest carbon sinks are crucial for mitigating urban climate change.Their effectiveness depends on the balance between gross carbon losses and gains.However,quantitative and continuous monitoring of forest change/disturbance carbon fluxes is still insufficient.To address this gap,we integrated an improved spatial carbon bookkeeping(SBK)model with the continuous change detection and classification(CCDC)algorithm,long-term Landsat observations,and ground measurements to track carbon emissions,uptakes,and net changes from forest cover changes in the Yangtze River Delta(YRD)of China from 2000 to 2020.The SBK model was refined by incorporating heterogeneous carbon response functions.Our results reveal that carbon emissions(-3.88 Tg C·year^(-1))were four times greater than carbon uptakes(0.93 Tg C·year^(-1))from forest cover changes in the YRD during 2000-2020,despite a net forest cover gain of 10.95×10^(4) ha.These findings indicate that the carbon effect per hectare of forest cover loss is approximately 4.5 times that of forest cover gain.The asymmetric carbon effect suggests that forest cover change may act as a carbon source even with net-zero or net-positive forest cover change.Furthermore,carbon uptakes from forest gains in the YRD during 2000-2020 could only offset 0.28% of energy-related carbon emissions from 2000 to 2019.Urban and agricultural expansions accounted for 37% and 10% of carbon emissions,respectively,while the Grain for Green Project contributed to 45% of carbon uptakes.Our findings underscore the necessity of understanding the asymmetric carbon effects of forest cover loss and gain to accurately assess the capacity of forest carbon sinks.展开更多
This study examined wetland trends in the St.Lawrence Seaway(~500,000 km^(2))in Canada over the past four decades.To this end,historical Landsat data within the Google Earth Engine(GEE)big geo data platform were proce...This study examined wetland trends in the St.Lawrence Seaway(~500,000 km^(2))in Canada over the past four decades.To this end,historical Landsat data within the Google Earth Engine(GEE)big geo data platform were processed.Reference samples were scrutinized using the Continuous Change Detection and Classification(CCDC)algorithm to identify spectrally unchanged samples.These spectrally unchanged samples were subsequently employed as training data within an object-based Random Forest(RF)model to generate wetland maps from 1984 to 2021.Subsequently,a change analysis was conducted to calculate the loss and gain of different wetland types.Overall,it was observed that approximately 45%(184,434 km^(2))and 55%(220,778 km^(2))of the entire study area are covered by wetland and non-wetland categories,respectively.It was also observed that 2.46%(12,495 km^(2))of the study area was changed during 40 years.Overall,there was a decline in the Bog and Fen classes,while the Marsh,Swamp,Forest,Grassland/Shrubland,Cropland,and Barren classes had an increase.Finally,the wetland gain and loss were 6,793 km^(2)and 5,701 km^(2),respectively.This study demonstrated that the use of Landsat data,along with advanced machine learning and GEE,could provide valuable assistance for wetland classification and change studies.展开更多
基金supported by the Natural Science Foundation of Zhejiang Province(No.ZCLQN25C0301)the National Key Research and Development Program of China(No.2016YFC0502700)the General Program of Education Department of Zhejiang(No.23056209-F).
文摘Forest carbon sinks are crucial for mitigating urban climate change.Their effectiveness depends on the balance between gross carbon losses and gains.However,quantitative and continuous monitoring of forest change/disturbance carbon fluxes is still insufficient.To address this gap,we integrated an improved spatial carbon bookkeeping(SBK)model with the continuous change detection and classification(CCDC)algorithm,long-term Landsat observations,and ground measurements to track carbon emissions,uptakes,and net changes from forest cover changes in the Yangtze River Delta(YRD)of China from 2000 to 2020.The SBK model was refined by incorporating heterogeneous carbon response functions.Our results reveal that carbon emissions(-3.88 Tg C·year^(-1))were four times greater than carbon uptakes(0.93 Tg C·year^(-1))from forest cover changes in the YRD during 2000-2020,despite a net forest cover gain of 10.95×10^(4) ha.These findings indicate that the carbon effect per hectare of forest cover loss is approximately 4.5 times that of forest cover gain.The asymmetric carbon effect suggests that forest cover change may act as a carbon source even with net-zero or net-positive forest cover change.Furthermore,carbon uptakes from forest gains in the YRD during 2000-2020 could only offset 0.28% of energy-related carbon emissions from 2000 to 2019.Urban and agricultural expansions accounted for 37% and 10% of carbon emissions,respectively,while the Grain for Green Project contributed to 45% of carbon uptakes.Our findings underscore the necessity of understanding the asymmetric carbon effects of forest cover loss and gain to accurately assess the capacity of forest carbon sinks.
文摘This study examined wetland trends in the St.Lawrence Seaway(~500,000 km^(2))in Canada over the past four decades.To this end,historical Landsat data within the Google Earth Engine(GEE)big geo data platform were processed.Reference samples were scrutinized using the Continuous Change Detection and Classification(CCDC)algorithm to identify spectrally unchanged samples.These spectrally unchanged samples were subsequently employed as training data within an object-based Random Forest(RF)model to generate wetland maps from 1984 to 2021.Subsequently,a change analysis was conducted to calculate the loss and gain of different wetland types.Overall,it was observed that approximately 45%(184,434 km^(2))and 55%(220,778 km^(2))of the entire study area are covered by wetland and non-wetland categories,respectively.It was also observed that 2.46%(12,495 km^(2))of the study area was changed during 40 years.Overall,there was a decline in the Bog and Fen classes,while the Marsh,Swamp,Forest,Grassland/Shrubland,Cropland,and Barren classes had an increase.Finally,the wetland gain and loss were 6,793 km^(2)and 5,701 km^(2),respectively.This study demonstrated that the use of Landsat data,along with advanced machine learning and GEE,could provide valuable assistance for wetland classification and change studies.
