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
A critical problem associated with the southern part of Nigeria is the rapid alteration of the landscape as a result of logging, agricultural practices, human migration and expansion, oil exploration, exploitation and...A critical problem associated with the southern part of Nigeria is the rapid alteration of the landscape as a result of logging, agricultural practices, human migration and expansion, oil exploration, exploitation and production activities. These processes have had both positive and negative effects on the economic and socio-political development of the country in general. The negative impacts have led not only to the degradation of the ecosystem but also posing hazards to human health and polluting surface and ground water resources. This has created the need for the development of a rapid, cost effective and efficient land use/land cover (LULC) classification technique to monitor the biophysical dynamics in the region. Due to the complex land cover patterns existing in the study area and the occasionally indistinguishable relationship between land cover and spectral signals, this paper introduces a combined use of unsupervised and supervised image classification for detecting land use/land cover (LULC) classes. With the continuous conflict over the impact of oil activities in the area, this work provides a procedure for detecting LULC change, which is an important factor to consider in the design of an environmental decision-making framework. Results from the use of this technique on Landsat TM and ETM+ of 1987 and 2002 are discussed. The results reveal the pros and cons of the two methods and the effects of their overall accuracy on post-classification change detection.展开更多
Monitoring alpine wetland degradation on the Qinghai-Tibet Plateau is crucial for understanding the responses to and resilience against climate change but has been challenging due to limited images in cloudy high-moun...Monitoring alpine wetland degradation on the Qinghai-Tibet Plateau is crucial for understanding the responses to and resilience against climate change but has been challenging due to limited images in cloudy high-mountain areas.Based on 3 elements,spectral-temporal characterization,classification,and degradation detection for wetland covers,this study proposes a continuous classification and degradation detection algorithm for alpine wetlands(AW-CCD).This algorithm relates to water-related landscape change processes,including multiscenario detection of snowmelt,lake,and river shrinkage and the transition of a swampy meadow to an alpine meadow with decreased soil wetness.AW-CCD uses the spectral-temporal index features to classify wetlands on an annual basis and then capture wetland degradation processes to combine long-time-series inter-annual parameters and seasonal soil wetness.This study detected snow cover from clouds based on the Landsat Quality Assessment band and spectral changes during snow-bare rock transition.Through the meadow spectral ratio vegetation index and seasonal soil wetness frequency across years,swampy and alpine meadow dynamics are tracked by wetness loss and increasing grass signal.By effectively characterizing multiple surface changes through spectral-temporal analysis,AW-CCD provides annual wetland mapping and monitoring metrics for multiscenario degradation.Results show an improvement in snow and meadow mapping accuracy by 5%and 3%,respectively,with a mapping accuracy of 94.9%in the Maidika Wetland in 2022.Spatial-temporal patterns demonstrated multiscenario degradation during 2 decades,with snow and river areas decreasing by 5.04%and 16.74%,respectively,and 3.23%of swampy meadows transitioning to alpine meadows.Degradation was most pronounced before 2009,followed by stability until 2015 and renewed degradation thereafter.This study highlights the effectiveness of AW-CCD in capturing the multiscenario responses of alpine wetlands to climatic changes on the Qinghai-Tibet Plateau.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
Wetlands provide vital ecological services for both humans and environment,necessitating continuous,refined and up-to-date mapping of wetlands for conservation and management.in this study,we developed an automated an...Wetlands provide vital ecological services for both humans and environment,necessitating continuous,refined and up-to-date mapping of wetlands for conservation and management.in this study,we developed an automated and refined wetland mapping framework integrating training sample migration method,supervised machine learning and knowledge-driven rules using Google Earth Engine(GEE)platform and open-source geospatial tools.We applied the framework to temporally dense Sentinel-1/2 imagery to produce annual refined wetland maps of the Dongting Lake Wetland(DLW)during 2015-2021.First,the continuous change detection(CCD)algorithm was utilized to migrate stable training samples.Then,annual 10 m preliminary land cover maps with 9 classes were produced using random forest algorithm and migrated samples.Ultimately,annual 10 m refined wetland maps were generated based on preliminary land cover maps via knowledge-driven rules from geometric features and available water-related inventories,with Overall Accuracy(OA)ranging from 81.82%(2015)to 93.84%(2020)and Kappa Coefficient(KC)between 0.73(2015)and 0.91(2020),demonstrating satisfactory performance and substantial potential for accurate,timely and type-refined wetland mapping.Our methodological framework allows rapid and accurate monitoring of wetland dynamics and could provide valuable information and methodological support for monitoring,conservation and sustainable development of wetland ecosystem.展开更多
A semi supervised image classification method for satellite images is proposed in this paper.The satellite images contain enormous data that can be used in various applications.The analysis of the data is a tedious ta...A semi supervised image classification method for satellite images is proposed in this paper.The satellite images contain enormous data that can be used in various applications.The analysis of the data is a tedious task due to the amount of data and the heterogeneity of the data.Thus,in this paper,a Radial Basis Function Neural Network(RBFNN)trained using Manta Ray Foraging Optimization algorithm(MRFO)is proposed.RBFNN is a three-layer network comprising of input,output,and hidden layers that can process large amounts.The trained network can discover hidden data patterns in unseen data.The learning algorithm and seed selection play a vital role in the performance of the network.The seed selection is done using the spectral indices to further improve the performance of the network.The manta ray foraging optimization algorithm is inspired by the intelligent behaviour of manta rays.It emulates three unique foraging behaviours namelys chain,cyclone,and somersault foraging.The satellite images contain enormous amount of data and thus require exploration in large search space.The spiral movement of the MRFO algorithm enables it to explore large search spaces effectively.The proposed method is applied on pre and post flooding Landsat 8 Operational Land Imager(OLI)images of New Brunswick area.The method was applied to identify and classify the land cover changes in the area induced by flooding.The images are classified using the proposed method and a change map is developed using post classification comparison.The change map shows that a large amount of agricultural area was washed away due to flooding.The measurement of the affected area in square kilometres is also performed for mitigation activities.The results show that post flooding the area covered by water is increased whereas the vegetated area is decreased.The performance of the proposed method is done with existing state-of-the-art methods.展开更多
精准快速识别小麦-玉米轮作区域对于中国北方地区耕地非粮化动态监测、主粮作物产能保障及农业可持续发展具有重要战略意义。该研究以河南省安阳市滑县为研究区,基于GEE云平台集成2018—2024年关键物候期Sentinel-2时序数据,构建光谱反...精准快速识别小麦-玉米轮作区域对于中国北方地区耕地非粮化动态监测、主粮作物产能保障及农业可持续发展具有重要战略意义。该研究以河南省安阳市滑县为研究区,基于GEE云平台集成2018—2024年关键物候期Sentinel-2时序数据,构建光谱反射率及植被指数时间序列多维特征集,分别使用传统单时相方法和改进的连续变化检测和分类(continuous change detection and classification,CCDC)算法对研究区域内主粮-主粮、主粮-非主粮、非主粮-主粮、非主粮-非主粮等4种轮作模式进行动态分类识别。结果表明:1)传统单时相方法在两个生长季的主粮作物分类总体精度(OA)最高可达96.8%、Kappa系数最高为0.96,两季影像叠加后的轮作模式识别平均OA和Kappa系数分别为71.3%、0.63;2)改进的CCDC-ANN算法对4种轮作模式识别的平均总体精度为91.8%、Kappa系数为0.891,较传统方法提升约20%;3)研究区种植结构呈现出明显的空间异质性,西部丘陵地区以主粮–非主粮轮作为主,东部平原以主粮–主粮、非主粮–主粮为主;4类轮作模式在2018—2024年均呈“先增后降再回升”动态:主粮-非主粮模式波动最剧烈,主粮-主粮模式最为平稳(波动<5%),非主粮-非主粮与非主粮-主粮模式亦表现出明显的阶段性涨落。该研究方法实现了小麦-玉米轮作区域的精准提取,为中国北方地区开展耕地非粮化监测提供了方法支撑。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A critical problem associated with the southern part of Nigeria is the rapid alteration of the landscape as a result of logging, agricultural practices, human migration and expansion, oil exploration, exploitation and production activities. These processes have had both positive and negative effects on the economic and socio-political development of the country in general. The negative impacts have led not only to the degradation of the ecosystem but also posing hazards to human health and polluting surface and ground water resources. This has created the need for the development of a rapid, cost effective and efficient land use/land cover (LULC) classification technique to monitor the biophysical dynamics in the region. Due to the complex land cover patterns existing in the study area and the occasionally indistinguishable relationship between land cover and spectral signals, this paper introduces a combined use of unsupervised and supervised image classification for detecting land use/land cover (LULC) classes. With the continuous conflict over the impact of oil activities in the area, this work provides a procedure for detecting LULC change, which is an important factor to consider in the design of an environmental decision-making framework. Results from the use of this technique on Landsat TM and ETM+ of 1987 and 2002 are discussed. The results reveal the pros and cons of the two methods and the effects of their overall accuracy on post-classification change detection.
基金supported by the National Natural Science Foundation of China(grant number 42071399)the Second Tibetan Plateau Scientific Expedition and Research,(grant number 2019QZKK060)+1 种基金the Humanities and Social Science Fund of Ministry of Education of China(grant number 23YJAZH019)the Science and Technology Plan Projects of Tibet Autonomous Region(grant number XZ202301ZY0021G).
文摘Monitoring alpine wetland degradation on the Qinghai-Tibet Plateau is crucial for understanding the responses to and resilience against climate change but has been challenging due to limited images in cloudy high-mountain areas.Based on 3 elements,spectral-temporal characterization,classification,and degradation detection for wetland covers,this study proposes a continuous classification and degradation detection algorithm for alpine wetlands(AW-CCD).This algorithm relates to water-related landscape change processes,including multiscenario detection of snowmelt,lake,and river shrinkage and the transition of a swampy meadow to an alpine meadow with decreased soil wetness.AW-CCD uses the spectral-temporal index features to classify wetlands on an annual basis and then capture wetland degradation processes to combine long-time-series inter-annual parameters and seasonal soil wetness.This study detected snow cover from clouds based on the Landsat Quality Assessment band and spectral changes during snow-bare rock transition.Through the meadow spectral ratio vegetation index and seasonal soil wetness frequency across years,swampy and alpine meadow dynamics are tracked by wetness loss and increasing grass signal.By effectively characterizing multiple surface changes through spectral-temporal analysis,AW-CCD provides annual wetland mapping and monitoring metrics for multiscenario degradation.Results show an improvement in snow and meadow mapping accuracy by 5%and 3%,respectively,with a mapping accuracy of 94.9%in the Maidika Wetland in 2022.Spatial-temporal patterns demonstrated multiscenario degradation during 2 decades,with snow and river areas decreasing by 5.04%and 16.74%,respectively,and 3.23%of swampy meadows transitioning to alpine meadows.Degradation was most pronounced before 2009,followed by stability until 2015 and renewed degradation thereafter.This study highlights the effectiveness of AW-CCD in capturing the multiscenario responses of alpine wetlands to climatic changes on the Qinghai-Tibet Plateau.
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.
基金supported by the National Natural Science Foundation of China(grant numbers 42071393,U1901219 and U21A2022).
文摘Wetlands provide vital ecological services for both humans and environment,necessitating continuous,refined and up-to-date mapping of wetlands for conservation and management.in this study,we developed an automated and refined wetland mapping framework integrating training sample migration method,supervised machine learning and knowledge-driven rules using Google Earth Engine(GEE)platform and open-source geospatial tools.We applied the framework to temporally dense Sentinel-1/2 imagery to produce annual refined wetland maps of the Dongting Lake Wetland(DLW)during 2015-2021.First,the continuous change detection(CCD)algorithm was utilized to migrate stable training samples.Then,annual 10 m preliminary land cover maps with 9 classes were produced using random forest algorithm and migrated samples.Ultimately,annual 10 m refined wetland maps were generated based on preliminary land cover maps via knowledge-driven rules from geometric features and available water-related inventories,with Overall Accuracy(OA)ranging from 81.82%(2015)to 93.84%(2020)and Kappa Coefficient(KC)between 0.73(2015)and 0.91(2020),demonstrating satisfactory performance and substantial potential for accurate,timely and type-refined wetland mapping.Our methodological framework allows rapid and accurate monitoring of wetland dynamics and could provide valuable information and methodological support for monitoring,conservation and sustainable development of wetland ecosystem.
文摘A semi supervised image classification method for satellite images is proposed in this paper.The satellite images contain enormous data that can be used in various applications.The analysis of the data is a tedious task due to the amount of data and the heterogeneity of the data.Thus,in this paper,a Radial Basis Function Neural Network(RBFNN)trained using Manta Ray Foraging Optimization algorithm(MRFO)is proposed.RBFNN is a three-layer network comprising of input,output,and hidden layers that can process large amounts.The trained network can discover hidden data patterns in unseen data.The learning algorithm and seed selection play a vital role in the performance of the network.The seed selection is done using the spectral indices to further improve the performance of the network.The manta ray foraging optimization algorithm is inspired by the intelligent behaviour of manta rays.It emulates three unique foraging behaviours namelys chain,cyclone,and somersault foraging.The satellite images contain enormous amount of data and thus require exploration in large search space.The spiral movement of the MRFO algorithm enables it to explore large search spaces effectively.The proposed method is applied on pre and post flooding Landsat 8 Operational Land Imager(OLI)images of New Brunswick area.The method was applied to identify and classify the land cover changes in the area induced by flooding.The images are classified using the proposed method and a change map is developed using post classification comparison.The change map shows that a large amount of agricultural area was washed away due to flooding.The measurement of the affected area in square kilometres is also performed for mitigation activities.The results show that post flooding the area covered by water is increased whereas the vegetated area is decreased.The performance of the proposed method is done with existing state-of-the-art methods.
文摘精准快速识别小麦-玉米轮作区域对于中国北方地区耕地非粮化动态监测、主粮作物产能保障及农业可持续发展具有重要战略意义。该研究以河南省安阳市滑县为研究区,基于GEE云平台集成2018—2024年关键物候期Sentinel-2时序数据,构建光谱反射率及植被指数时间序列多维特征集,分别使用传统单时相方法和改进的连续变化检测和分类(continuous change detection and classification,CCDC)算法对研究区域内主粮-主粮、主粮-非主粮、非主粮-主粮、非主粮-非主粮等4种轮作模式进行动态分类识别。结果表明:1)传统单时相方法在两个生长季的主粮作物分类总体精度(OA)最高可达96.8%、Kappa系数最高为0.96,两季影像叠加后的轮作模式识别平均OA和Kappa系数分别为71.3%、0.63;2)改进的CCDC-ANN算法对4种轮作模式识别的平均总体精度为91.8%、Kappa系数为0.891,较传统方法提升约20%;3)研究区种植结构呈现出明显的空间异质性,西部丘陵地区以主粮–非主粮轮作为主,东部平原以主粮–主粮、非主粮–主粮为主;4类轮作模式在2018—2024年均呈“先增后降再回升”动态:主粮-非主粮模式波动最剧烈,主粮-主粮模式最为平稳(波动<5%),非主粮-非主粮与非主粮-主粮模式亦表现出明显的阶段性涨落。该研究方法实现了小麦-玉米轮作区域的精准提取,为中国北方地区开展耕地非粮化监测提供了方法支撑。
