This research focuses on analyzing land use and cover changes in Metropolitan Area of Wuhan between 1988 and 2023,utilizing a comprehensive data set from Landsat remote sensing and machine learning techniques to under...This research focuses on analyzing land use and cover changes in Metropolitan Area of Wuhan between 1988 and 2023,utilizing a comprehensive data set from Landsat remote sensing and machine learning techniques to understand their implications for carbon storage.It finds that the Random Forest(RF)algorithm outperforms others like Support Vector Machine(SVM),Gradient Boosting Trees(GBT),and Classification and Regression Trees in identifying land use types,achieving high accuracy and a Kappa coefficient exceeding 0.98.Significant changes in Wuhan’s landscape have been noted,especially the marked decrease in arable land and increase in urban construction,reflecting the pressures of economic development and urban expansion on natural resources and their impact on the ecosystem.The study uses the InVEST model to assess how these land use transformations affect carbon storage,revealing a significant decrease in carbon storage from 1988 to 2023,with a total reduction of approximately 428.59×104 t from 1988 to 2023,largely attributed to the conversion of key carbon sequestering lands such as arable lands and forests into urban areas.This transition,particularly from arable land to urban construction land,underscores the challenges faced in managing land use changes without compromising environmental sustainability and carbon storage capacities.展开更多
The study examines the changes of land cover/use resources for the period under investigation.An unsupervised vegetation classification is being performed that provides five distinctive classes and thus assesses these...The study examines the changes of land cover/use resources for the period under investigation.An unsupervised vegetation classification is being performed that provides five distinctive classes and thus assesses these changes in five broad land cover classes-high/moist forests,forest regrowth,mixed savanna,bare land/ grass and water.The remote sensing images used in this work are both images of TM and ETM+in different time periods(1986 to 2001)to determine land cover/use changes.A fairly accuracy report is recorded after performing the unsupervised classification,which shows vegetation has been depleted for over the years.Changes created are mostly human and to a lesser extent environment.Human activities are mainly encroachment thus altering the landscape through activities such as population growth,agriculture,settlements,etc.and environment due to some perceive climatic changes.This vegetation classification highlights the importance to acquire and publish information about the country's partial vegetation cover and vegetation change including vegetation maps and other basic vegetation influencing factors,leading to an understanding of its evolution for a period.展开更多
Global land cover(LC)maps have been widely employed as the base layer for a number of applications including climate change,food security,water quality,biodiversity,change detection,and environmental planning.Due to t...Global land cover(LC)maps have been widely employed as the base layer for a number of applications including climate change,food security,water quality,biodiversity,change detection,and environmental planning.Due to the importance of LC,there is a pressing need to increase the temporal and spatial resolution of global LC maps.A recent advance in this direction has been the GlobeLand30 dataset derived from Landsat imagery,which has been developed by the National Geomatics Center of China(NGCC).Although overall accuracy is greater than 80%,the NGCC would like help in assessing the accuracy of the product in different regions of the world.To assist in this process,this study compares the GlobeLand30 product with existing public and online datasets,that is,CORINE,Urban Atlas(UA),OpenStreetMap,and ATKIS for Germany in order to assess overall and per class agreement.The results of the analysis reveal high agreement of up to 92%between these datasets and GlobeLand30 but that large disagreements for certain classes are evident,in particular wetlands.However,overall,GlobeLand30 is shown to be a useful product for characterizing LC in Germany,and paves the way for further regional and national validation efforts.展开更多
基金supported by National Key R&D Program of China(No.2021YFB3900400).
文摘This research focuses on analyzing land use and cover changes in Metropolitan Area of Wuhan between 1988 and 2023,utilizing a comprehensive data set from Landsat remote sensing and machine learning techniques to understand their implications for carbon storage.It finds that the Random Forest(RF)algorithm outperforms others like Support Vector Machine(SVM),Gradient Boosting Trees(GBT),and Classification and Regression Trees in identifying land use types,achieving high accuracy and a Kappa coefficient exceeding 0.98.Significant changes in Wuhan’s landscape have been noted,especially the marked decrease in arable land and increase in urban construction,reflecting the pressures of economic development and urban expansion on natural resources and their impact on the ecosystem.The study uses the InVEST model to assess how these land use transformations affect carbon storage,revealing a significant decrease in carbon storage from 1988 to 2023,with a total reduction of approximately 428.59×104 t from 1988 to 2023,largely attributed to the conversion of key carbon sequestering lands such as arable lands and forests into urban areas.This transition,particularly from arable land to urban construction land,underscores the challenges faced in managing land use changes without compromising environmental sustainability and carbon storage capacities.
文摘The study examines the changes of land cover/use resources for the period under investigation.An unsupervised vegetation classification is being performed that provides five distinctive classes and thus assesses these changes in five broad land cover classes-high/moist forests,forest regrowth,mixed savanna,bare land/ grass and water.The remote sensing images used in this work are both images of TM and ETM+in different time periods(1986 to 2001)to determine land cover/use changes.A fairly accuracy report is recorded after performing the unsupervised classification,which shows vegetation has been depleted for over the years.Changes created are mostly human and to a lesser extent environment.Human activities are mainly encroachment thus altering the landscape through activities such as population growth,agriculture,settlements,etc.and environment due to some perceive climatic changes.This vegetation classification highlights the importance to acquire and publish information about the country's partial vegetation cover and vegetation change including vegetation maps and other basic vegetation influencing factors,leading to an understanding of its evolution for a period.
基金The authors would also like to acknowledge the support and contribution of COST Action TD1202‘Mapping and the Citizen Sensor’as well as COST Action IC1203‘European Network Exploring Research into Geospatial Information Crowdsourcing’(ENERGIC).
文摘Global land cover(LC)maps have been widely employed as the base layer for a number of applications including climate change,food security,water quality,biodiversity,change detection,and environmental planning.Due to the importance of LC,there is a pressing need to increase the temporal and spatial resolution of global LC maps.A recent advance in this direction has been the GlobeLand30 dataset derived from Landsat imagery,which has been developed by the National Geomatics Center of China(NGCC).Although overall accuracy is greater than 80%,the NGCC would like help in assessing the accuracy of the product in different regions of the world.To assist in this process,this study compares the GlobeLand30 product with existing public and online datasets,that is,CORINE,Urban Atlas(UA),OpenStreetMap,and ATKIS for Germany in order to assess overall and per class agreement.The results of the analysis reveal high agreement of up to 92%between these datasets and GlobeLand30 but that large disagreements for certain classes are evident,in particular wetlands.However,overall,GlobeLand30 is shown to be a useful product for characterizing LC in Germany,and paves the way for further regional and national validation efforts.
