Convolutional neural network(CNN)has excellent ability to model locally contextual information.However,CNNs face challenges for descripting long-range semantic features,which will lead to relatively low classification...Convolutional neural network(CNN)has excellent ability to model locally contextual information.However,CNNs face challenges for descripting long-range semantic features,which will lead to relatively low classification accuracy of hyperspectral images.To address this problem,this article proposes an algorithm based on multiscale fusion and transformer network for hyperspectral image classification.Firstly,the low-level spatial-spectral features are extracted by multi-scale residual structure.Secondly,an attention module is introduced to focus on the more important spatialspectral information.Finally,high-level semantic features are represented and learned by a token learner and an improved transformer encoder.The proposed algorithm is compared with six classical hyperspectral classification algorithms on real hyperspectral images.The experimental results show that the proposed algorithm effectively improves the land cover classification accuracy of hyperspectral images.展开更多
Land use and land cover are essential for maintaining and managing the natural resources on the earth surface. A complex set of economic, demographic, social, cultural, technological, and environmental processes usual...Land use and land cover are essential for maintaining and managing the natural resources on the earth surface. A complex set of economic, demographic, social, cultural, technological, and environmental processes usually result in the change in the land use/land cover change (LULC). Pokhara Metropolitan is influenced mainly by the combination of various driving forces: geographical location, high rate of population growth, economic opportunity, globalization, tourism activities, and political activities. In addition to this, geographically steep slope, rugged terrain, and fragile geomorphic conditions and the frequency of earthquakes, floods, and landslides make the Pokhara Metropolitan region a disaster-prone area. The increment of the population along with infrastructure development of a given territory leads towards the urbanization. It has been rapidly changing due to urbanization, industrialization and internal migration since the 1970s. The landscapes and ground patterns are frequently changing on time and prone to disaster. Here a study has been carried to study on LULC for the last 18 years (2000-2018). The supervised classification on Landsat Imagery was performed and verified the classification through computing the error matrix. Besides, the water bodies and vegetation area were extracted through the Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDWI) respectively. This research shows that during the last 18 years the agricultural areas diminishing by 15.66% while urban area is increasing by 13.2%. This research is beneficial for preparing the plan and policy in the sustainable development of Pokhara Metropolitan.展开更多
Human-induced land use changes and the resulting alterations in vegetation features are major but poorly recognized drivers of regional climatic patterns.In order to investigate the impacts of anthropogenically-induce...Human-induced land use changes and the resulting alterations in vegetation features are major but poorly recognized drivers of regional climatic patterns.In order to investigate the impacts of anthropogenically-induced seasonal vegetation cover changes on regional climate in China,harmonic analysis is applied to 1982-2000 National Oceanic and Atmospheric Administration（NOAA） Advanced Very High Resolution Radiometer（AVVHRR）-derived normalized difference vegetation index（NDVI） time series（ten day interval data）.For two climatic divisions of South China,it is shown that the first harmonic term is in phase with air temperature,while the second and third harmonics are in phase with agricultural cultivation.The Penman-Monteith Equation and the Complementary Relationship Areal Evapotranspiration（CRAE） model suggest that monthly mean evapotranspiration is out of phase with temperature and precipitation in regions with signiffcant second or third harmonics.Finally,seasonal vegetation cover changes associated with agricultural cultivation are identiffed：for cropped areas,the temperature and precipitation time series have a single maximum value,while the monthly evapotranspiration time series has a bimodal distribution.It is hypothesized that multi-cropping causes the land surface albedo to sharply increase during harvesting,thereby altering the energy distribution ratio and contributing to observed seasonal vegetation cover changes.展开更多
Phenology has become a good indicator for illustrating the long-term changes in the natural resources of the Yangtze River Delta.However,two issues can be observed from previous studies.On the one hand,existing time-s...Phenology has become a good indicator for illustrating the long-term changes in the natural resources of the Yangtze River Delta.However,two issues can be observed from previous studies.On the one hand,existing time-series classification methods mainly using a single classifier,the discrimination power,can become deteriorated due to fluctuations characterizing the time series.On the other hand,previous work on the Yangtze River Delta was limited in the spatial domain (usually to 16 cities)and in the temporal domain (usually 2000-2010).To address these issues,this study attempts to analyze the spatiotemporal variation in phenology in the Yangtze River Delta (with 26 cities,enlarged by the state council in June 2016), facilitated by classifying the land cover types and extracting the phenological metrics based on Moderate Resolution Imaging Spectrometer (MODIS)Normalized Difference Vegetation Index (NDVI)time series collected from 2001 to 2015.First,ensemble learning (EL)-based classifiers are used for land cover classification,where the training samples (a total of 201,597)derived from visual interpretation based on GlobelLand30 are further screened using vertex component analysis (VCA),resulting in 600 samples for training and the remainder for validating. Then,eleven phenological metrics are extracted by TIMESAT (a package name)based on the time series, where a seasonal-trend decomposition procedure based on loess (STL-decomposition)is used to remove spikes and a Savitzky-Golay filter is used for filtering.Finally,the spatio-temporal phenology variation is analyzed by considering the classification maps and the phenological metrics.The experimental results indicate that:1)random forest (R.F)obtains the most accurate classification map (with an overall accuracy higher than 96%);2)different land cover types illustrate the various seasonalities;3)the Yangtze River Delta has two obvious regions,i.e.,the north and the south parts,resulting from different rainfall, temperature,and ecosystem conditions;4)the phenology variation over time is not significant in the study area;5)the correlation between gross spring greenness (GSG) and gross primary productivity (GPP)is very high, indicating the potential use of GSG for assessing the carbon flux.展开更多
该文采用M OD IS N DV I时序数据对东北区土地覆盖分类进行研究,以验证M OD IS区域土地覆盖制图的可靠性。通过试验发现经过Sav izky-G o lay滤波处理能有效去除云、缺失数据及异常值的影响,使得N DV I时序曲线能更好的反映植被季相变...该文采用M OD IS N DV I时序数据对东北区土地覆盖分类进行研究,以验证M OD IS区域土地覆盖制图的可靠性。通过试验发现经过Sav izky-G o lay滤波处理能有效去除云、缺失数据及异常值的影响,使得N DV I时序曲线能更好的反映植被季相变化特征,分类结果表明N DV I时序数列能较好的区分植被与非植被、草本(一年生)与木本(多年生)覆盖类型。但研究区内一年一熟的农作物与高盖度草地、落叶针叶林与落叶阔叶林具有相似的物候特征,混分现象比较严重。该研究通过添加地表温度(land surface tem perature,LST)数据解决这一问题,利用所得温度/植被指数TV I对研究区进行土地覆盖分类。所得结果用363个野外调查样区进行验证,N DV I及TV I时序数据的分类精度分别为62.26%与71.63%。结果表明TV I比N DV I对土地覆盖类型中的植被类型识别更有效。展开更多
基金National Natural Science Foundation of China(No.62201457)Natural Science Foundation of Shaanxi Province(Nos.2022JQ-668,2022JQ-588)。
文摘Convolutional neural network(CNN)has excellent ability to model locally contextual information.However,CNNs face challenges for descripting long-range semantic features,which will lead to relatively low classification accuracy of hyperspectral images.To address this problem,this article proposes an algorithm based on multiscale fusion and transformer network for hyperspectral image classification.Firstly,the low-level spatial-spectral features are extracted by multi-scale residual structure.Secondly,an attention module is introduced to focus on the more important spatialspectral information.Finally,high-level semantic features are represented and learned by a token learner and an improved transformer encoder.The proposed algorithm is compared with six classical hyperspectral classification algorithms on real hyperspectral images.The experimental results show that the proposed algorithm effectively improves the land cover classification accuracy of hyperspectral images.
文摘Land use and land cover are essential for maintaining and managing the natural resources on the earth surface. A complex set of economic, demographic, social, cultural, technological, and environmental processes usually result in the change in the land use/land cover change (LULC). Pokhara Metropolitan is influenced mainly by the combination of various driving forces: geographical location, high rate of population growth, economic opportunity, globalization, tourism activities, and political activities. In addition to this, geographically steep slope, rugged terrain, and fragile geomorphic conditions and the frequency of earthquakes, floods, and landslides make the Pokhara Metropolitan region a disaster-prone area. The increment of the population along with infrastructure development of a given territory leads towards the urbanization. It has been rapidly changing due to urbanization, industrialization and internal migration since the 1970s. The landscapes and ground patterns are frequently changing on time and prone to disaster. Here a study has been carried to study on LULC for the last 18 years (2000-2018). The supervised classification on Landsat Imagery was performed and verified the classification through computing the error matrix. Besides, the water bodies and vegetation area were extracted through the Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDWI) respectively. This research shows that during the last 18 years the agricultural areas diminishing by 15.66% while urban area is increasing by 13.2%. This research is beneficial for preparing the plan and policy in the sustainable development of Pokhara Metropolitan.
基金supported by State Key Laboratory of Earth Surface Processes and Resource Ecology, National Basic Research Program of China (Grant No. 2010CB951101)the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) 21st Century COE Program for DPRI, Kyoto University and the National Natural Science Foundation of China (Grant No. 40675047)
文摘Human-induced land use changes and the resulting alterations in vegetation features are major but poorly recognized drivers of regional climatic patterns.In order to investigate the impacts of anthropogenically-induced seasonal vegetation cover changes on regional climate in China,harmonic analysis is applied to 1982-2000 National Oceanic and Atmospheric Administration（NOAA） Advanced Very High Resolution Radiometer（AVVHRR）-derived normalized difference vegetation index（NDVI） time series（ten day interval data）.For two climatic divisions of South China,it is shown that the first harmonic term is in phase with air temperature,while the second and third harmonics are in phase with agricultural cultivation.The Penman-Monteith Equation and the Complementary Relationship Areal Evapotranspiration（CRAE） model suggest that monthly mean evapotranspiration is out of phase with temperature and precipitation in regions with signiffcant second or third harmonics.Finally,seasonal vegetation cover changes associated with agricultural cultivation are identiffed：for cropped areas,the temperature and precipitation time series have a single maximum value,while the monthly evapotranspiration time series has a bimodal distribution.It is hypothesized that multi-cropping causes the land surface albedo to sharply increase during harvesting,thereby altering the energy distribution ratio and contributing to observed seasonal vegetation cover changes.
基金the National Natural Science Foundation of China (Grant No.41601347)the Natural Science Foundation of Jiangsu Province (BK20160860)+2 种基金the Fundamental Research Funds for:the Central Universities (2018B17814)the Open Research Found of State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University (17R04)the Fundamental Research Funds for the Central Universities,and the Open Research Fund in 2018 of Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense (3091801410406).
文摘Phenology has become a good indicator for illustrating the long-term changes in the natural resources of the Yangtze River Delta.However,two issues can be observed from previous studies.On the one hand,existing time-series classification methods mainly using a single classifier,the discrimination power,can become deteriorated due to fluctuations characterizing the time series.On the other hand,previous work on the Yangtze River Delta was limited in the spatial domain (usually to 16 cities)and in the temporal domain (usually 2000-2010).To address these issues,this study attempts to analyze the spatiotemporal variation in phenology in the Yangtze River Delta (with 26 cities,enlarged by the state council in June 2016), facilitated by classifying the land cover types and extracting the phenological metrics based on Moderate Resolution Imaging Spectrometer (MODIS)Normalized Difference Vegetation Index (NDVI)time series collected from 2001 to 2015.First,ensemble learning (EL)-based classifiers are used for land cover classification,where the training samples (a total of 201,597)derived from visual interpretation based on GlobelLand30 are further screened using vertex component analysis (VCA),resulting in 600 samples for training and the remainder for validating. Then,eleven phenological metrics are extracted by TIMESAT (a package name)based on the time series, where a seasonal-trend decomposition procedure based on loess (STL-decomposition)is used to remove spikes and a Savitzky-Golay filter is used for filtering.Finally,the spatio-temporal phenology variation is analyzed by considering the classification maps and the phenological metrics.The experimental results indicate that:1)random forest (R.F)obtains the most accurate classification map (with an overall accuracy higher than 96%);2)different land cover types illustrate the various seasonalities;3)the Yangtze River Delta has two obvious regions,i.e.,the north and the south parts,resulting from different rainfall, temperature,and ecosystem conditions;4)the phenology variation over time is not significant in the study area;5)the correlation between gross spring greenness (GSG) and gross primary productivity (GPP)is very high, indicating the potential use of GSG for assessing the carbon flux.
文摘该文采用M OD IS N DV I时序数据对东北区土地覆盖分类进行研究,以验证M OD IS区域土地覆盖制图的可靠性。通过试验发现经过Sav izky-G o lay滤波处理能有效去除云、缺失数据及异常值的影响,使得N DV I时序曲线能更好的反映植被季相变化特征,分类结果表明N DV I时序数列能较好的区分植被与非植被、草本(一年生)与木本(多年生)覆盖类型。但研究区内一年一熟的农作物与高盖度草地、落叶针叶林与落叶阔叶林具有相似的物候特征,混分现象比较严重。该研究通过添加地表温度(land surface tem perature,LST)数据解决这一问题,利用所得温度/植被指数TV I对研究区进行土地覆盖分类。所得结果用363个野外调查样区进行验证,N DV I及TV I时序数据的分类精度分别为62.26%与71.63%。结果表明TV I比N DV I对土地覆盖类型中的植被类型识别更有效。
