Temperate forests are vital for maintaining ecological security and regulating the global climate.Despite considerable controversy surrounding the biophysical impacts of temperate forests on mid-latitude temperatures,...Temperate forests are vital for maintaining ecological security and regulating the global climate.Despite considerable controversy surrounding the biophysical impacts of temperate forests on mid-latitude temperatures,we analyzed the effects of forest cover change on local temperature using the Weather Research and Forecasting(WRF)model from 2010 to 2020 in the Greater and Lesser Khingan Mountains(GLKM),Northeastern China,and explored the related driving factors.The conversions between forest and open lands(i.e.,cropland and grassland)were predominant.During the growing season,the conversion of cropland and grassland to forest resulted in warming(0.38±0.10 and 0.41±0.09℃,respectively)in air temperature(Ta),while the reverse conversion caused cooling(-0.31 peratur±0.08 and e-0.24±0.07℃,respectively),which was less than the changes observed in land surface tem(LST).Conversion of forest to impervious land caused warming(1.16 the±0.11℃),and opposite conversion resulted in cooling(can-0.88 t±0.17℃).These results indicate that radiative effects like albedo and net radiation drive the signifi net warming effect from afforestation on open lands within the temperate forest ecoregion.Conversely,conversion to impervious land produced the most substantial net warming impacts,driven by non-radiative effects like sensible heat,latent heat,and ground heat flux(GH).In these conversions,temperature can indirectly influence precipitation(Pre)through vapor pressure deficit(VPD),and Pre can also indirectly affect temperature via evapotranspiration(ET).This study highlights the need to thoroughly understand the impacts of afforestation in temperate forests while avoiding deforestation to regulate the climate effectively.展开更多
Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications.Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satell...Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications.Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satellite images of several bands at various frequencies,the mix of high-and low-resolution images that make object classification difficult because of the mixed pixel problem.Accuracy is impacted by the mixed pixel problem,which occurs when pixels belong to different classes and makes detection challenging.To identify mixed pixels,Band Math is used to merge numerous bands to generate a new band NDVI.Thresholding is used to analyze the edges of deforested and non-deforested areas.Segmentation is then used to analyze the pixels which helps to identify the number of mixed pixels to compute the deforested and non-deforested areas.Segmented image pixels are used to categorize the deforestation of the Brazilian Amazon Forest between 2019 and 2023.Verify how many pixels are mixed to improve accuracy and identify mixed pixel issues;compare the mixed and pure pixels of fuzzy clustering with the subtracted morphological image pixels.With the help of segmentation and clustering researchers effectively validate mixed pixels in a specific area.The proposed methodology is easy to analyze and helpful for an appropriate calculation of deforested and non-deforested areas.展开更多
Forestation projects have been identified as an important component of climate mitigation strategy to reduce greenhouse gas emissions worldwide.However,most previous studies ignore the impacts of potential forestation...Forestation projects have been identified as an important component of climate mitigation strategy to reduce greenhouse gas emissions worldwide.However,most previous studies ignore the impacts of potential forestation projects on livestock production,which is crucial to the livelihoods of local people.In this study,we identified potential forestation areas in China by integrating random forest regression model and LPJ-GUESS model.The impacts of potential forestation on carbon storage and pasture-based livestock production were then analyzed.The results showed that China has a potential forestation area of 43.2 million hectares,accounting for about 19.6% of the country's forest area as reported in the 9th National Forest Inventory.If all these regions are reforested,China's forest cover will increase to 27.4%.Furthermore,1.58 Pg C of new above-and below-ground carbon would be sequestered,about an increase of 17.2% of current forest carbon storage.However,the potential forestation may result in a significant negative impact on existing pastures and the amount of livestock.It can reduce 4.7% of beef,and 0.8% of mutton products from China's livestock sector each year.These significant declines will result in a huge gap in China's livestock products supply,posing a serious threat to food security and the livelihoods of many people.Our findings highlight that potential forestation projects should further consider a reasonable pasture protection strategy to balance the potential carbon sequestration and the socio-economic benefits of livestock production.展开更多
Enhancing the carbon sink of terrestrial ecosystems is an essential nature-based solution to mitigate global warming and achieve the target of carbon neutrality.Over recent decades,China has launched a series of long-...Enhancing the carbon sink of terrestrial ecosystems is an essential nature-based solution to mitigate global warming and achieve the target of carbon neutrality.Over recent decades,China has launched a series of long-running and large-scale ambitious forestation projects.However,there is still a lack of year-to-year evaluation on the effects of afforestation on carbon sequestration.Satellite remote sensing provides continuous observations of vegetation dynamics and land use and land cover change,is becoming a practical tool to evaluate the changes of vegetation productivity driven by afforestation.Here,a spatially-explicit analysis was conducted by combining Moderate Resolution Imaging Spectroradiometer(MODIS)land cover and three up-to-date remote sensing gross primary productivity(GPP)datasets of China.The results showed that the generated afforestation maps have similar spatial distribution with the national forest inventory data at the provincial level.The accumulative areas of afforestation were 3.02×10^(5)km^(2)in China from 2002 to 2018,it was mainly distributed in Southwest(SW),South(Sou),Southeast(SE)and Northeast(NE)of China.Among them,SW possesses the largest afforestation sub-region,with an area of 9.38×10^(4)km^(2),accounting for 31.06%of the total.There were divergent trends of affores-tation area among different sub-regions.The southern sub-regions showed increasing trends,while the northern sub-regions showed decreasing trends.In keeping with these,the center of annual afforestation moved to the south after 2009.The southern sub-regions were the majority of the cumula-tive GPP,accounting for nearly 70%of the total.The GPP of new afforestation showed an increasing trend from 2002 to 2018,and the increasing rate was higher than existing forests.After afforestation,the GPP change of afforestation was higher than adjacent non-forest over the same period.Our work provides new evidence that afforestation of China has enhanced the carbon assimilation and will deepen our understanding of dynamics of carbon sequestration driven by afforestation.展开更多
基金funded or supported by the National Natural Science Foundation of China(Nos.32371878,32001251)the Natural Science Foundation of Jiangsu Province(No.BK20200781)+1 种基金the Youth Science and Technology Talent Lifting Project of Jiangsu Province(No.JSTJ-2024-324)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)。
文摘Temperate forests are vital for maintaining ecological security and regulating the global climate.Despite considerable controversy surrounding the biophysical impacts of temperate forests on mid-latitude temperatures,we analyzed the effects of forest cover change on local temperature using the Weather Research and Forecasting(WRF)model from 2010 to 2020 in the Greater and Lesser Khingan Mountains(GLKM),Northeastern China,and explored the related driving factors.The conversions between forest and open lands(i.e.,cropland and grassland)were predominant.During the growing season,the conversion of cropland and grassland to forest resulted in warming(0.38±0.10 and 0.41±0.09℃,respectively)in air temperature(Ta),while the reverse conversion caused cooling(-0.31 peratur±0.08 and e-0.24±0.07℃,respectively),which was less than the changes observed in land surface tem(LST).Conversion of forest to impervious land caused warming(1.16 the±0.11℃),and opposite conversion resulted in cooling(can-0.88 t±0.17℃).These results indicate that radiative effects like albedo and net radiation drive the signifi net warming effect from afforestation on open lands within the temperate forest ecoregion.Conversely,conversion to impervious land produced the most substantial net warming impacts,driven by non-radiative effects like sensible heat,latent heat,and ground heat flux(GH).In these conversions,temperature can indirectly influence precipitation(Pre)through vapor pressure deficit(VPD),and Pre can also indirectly affect temperature via evapotranspiration(ET).This study highlights the need to thoroughly understand the impacts of afforestation in temperate forests while avoiding deforestation to regulate the climate effectively.
文摘Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications.Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satellite images of several bands at various frequencies,the mix of high-and low-resolution images that make object classification difficult because of the mixed pixel problem.Accuracy is impacted by the mixed pixel problem,which occurs when pixels belong to different classes and makes detection challenging.To identify mixed pixels,Band Math is used to merge numerous bands to generate a new band NDVI.Thresholding is used to analyze the edges of deforested and non-deforested areas.Segmentation is then used to analyze the pixels which helps to identify the number of mixed pixels to compute the deforested and non-deforested areas.Segmented image pixels are used to categorize the deforestation of the Brazilian Amazon Forest between 2019 and 2023.Verify how many pixels are mixed to improve accuracy and identify mixed pixel issues;compare the mixed and pure pixels of fuzzy clustering with the subtracted morphological image pixels.With the help of segmentation and clustering researchers effectively validate mixed pixels in a specific area.The proposed methodology is easy to analyze and helpful for an appropriate calculation of deforested and non-deforested areas.
基金supported by the National Science Fund for Distinguished Young Scholars(Grant No.42225107)the Natural Science Foundation of Guangdong Province of China(Grant No.2022A1515012207)the National Natural Science Foundation of China(Grant No.42401505)。
文摘Forestation projects have been identified as an important component of climate mitigation strategy to reduce greenhouse gas emissions worldwide.However,most previous studies ignore the impacts of potential forestation projects on livestock production,which is crucial to the livelihoods of local people.In this study,we identified potential forestation areas in China by integrating random forest regression model and LPJ-GUESS model.The impacts of potential forestation on carbon storage and pasture-based livestock production were then analyzed.The results showed that China has a potential forestation area of 43.2 million hectares,accounting for about 19.6% of the country's forest area as reported in the 9th National Forest Inventory.If all these regions are reforested,China's forest cover will increase to 27.4%.Furthermore,1.58 Pg C of new above-and below-ground carbon would be sequestered,about an increase of 17.2% of current forest carbon storage.However,the potential forestation may result in a significant negative impact on existing pastures and the amount of livestock.It can reduce 4.7% of beef,and 0.8% of mutton products from China's livestock sector each year.These significant declines will result in a huge gap in China's livestock products supply,posing a serious threat to food security and the livelihoods of many people.Our findings highlight that potential forestation projects should further consider a reasonable pasture protection strategy to balance the potential carbon sequestration and the socio-economic benefits of livestock production.
基金funded by the National Key Research and Development Program of China(Grant No.2020YFA0608103)the National Science Foundation of China(Grant Nos.42265012 and 31770765).
文摘Enhancing the carbon sink of terrestrial ecosystems is an essential nature-based solution to mitigate global warming and achieve the target of carbon neutrality.Over recent decades,China has launched a series of long-running and large-scale ambitious forestation projects.However,there is still a lack of year-to-year evaluation on the effects of afforestation on carbon sequestration.Satellite remote sensing provides continuous observations of vegetation dynamics and land use and land cover change,is becoming a practical tool to evaluate the changes of vegetation productivity driven by afforestation.Here,a spatially-explicit analysis was conducted by combining Moderate Resolution Imaging Spectroradiometer(MODIS)land cover and three up-to-date remote sensing gross primary productivity(GPP)datasets of China.The results showed that the generated afforestation maps have similar spatial distribution with the national forest inventory data at the provincial level.The accumulative areas of afforestation were 3.02×10^(5)km^(2)in China from 2002 to 2018,it was mainly distributed in Southwest(SW),South(Sou),Southeast(SE)and Northeast(NE)of China.Among them,SW possesses the largest afforestation sub-region,with an area of 9.38×10^(4)km^(2),accounting for 31.06%of the total.There were divergent trends of affores-tation area among different sub-regions.The southern sub-regions showed increasing trends,while the northern sub-regions showed decreasing trends.In keeping with these,the center of annual afforestation moved to the south after 2009.The southern sub-regions were the majority of the cumula-tive GPP,accounting for nearly 70%of the total.The GPP of new afforestation showed an increasing trend from 2002 to 2018,and the increasing rate was higher than existing forests.After afforestation,the GPP change of afforestation was higher than adjacent non-forest over the same period.Our work provides new evidence that afforestation of China has enhanced the carbon assimilation and will deepen our understanding of dynamics of carbon sequestration driven by afforestation.
