Fire season affects the dynamic changes of post-fire vegetation communities and carbon emissions.Analyzing its global patterns supports understanding of the ecological impacts of fires and responses of fires to climat...Fire season affects the dynamic changes of post-fire vegetation communities and carbon emissions.Analyzing its global patterns supports understanding of the ecological impacts of fires and responses of fires to climate change.Meteorological variables have been widely used to quantify fire season in current studies.However,their results can not be used to assess climate impacts on the seasonality of fire activities.Here we utilized satellite-based Moderate Resolution Imaging Spectroradiometer(MODIS)burned area data from 2001 to 2022 to identify global fire season types based on the number of peaks within a year.Using satellite data and innovatively processing the data to obtain a more accurate length of the fire season.We divided fire season types and examined the spatial distribution of fire season types across the Koppen-Geiger climate(KGC)zones.At a global scale,we identified three major fire season types,including unimodal(31.25%),bimodal(52.07%),and random(16.69%).The unimodal fire season primarily occurs in boreal and tropical regions lasting about 2.7 mon.In comparison,temperate ecosystems tend to have a longer fire season(3 mon)with two peaks throughout the year.The KGC zones show divergent contributions from the fire season types,indicating potential impacts of the climatic conditions on fire seasonality in these regions.展开更多
Cotton is one of the most significant cash crops in the world,and it is also the main source of natural fiber for textiles.It is crucial for cotton management to identify the spatiotemporal distribution of cotton plan...Cotton is one of the most significant cash crops in the world,and it is also the main source of natural fiber for textiles.It is crucial for cotton management to identify the spatiotemporal distribution of cotton planting areas timely and accurately on a fine scale.However,previous research studies have predominantly concentrated on specific years using remote sensing data.Challenges still exist in the extraction of cotton areas for long time series with high accuracy.To address this issue,a novel cotton sample selection method was proposed and the machine learning method is employed to effectively identify the long time series cotton planting areas at a 30-m resolution scale.Bortala and Shuanghe in Xinjiang,China,were selected as the study cases to demonstrate the approach.Specifically,the cropland in this study was extracted by using an object-oriented classification method with Landsat images and the results were optimized as the vectorized boundary of croplands.Then,the cotton samples were selected using the Normalized Difference Vegetation Index(NDVI)series of Moderate Resolution Imaging Spectroradiometer(MODIS)based on its phenological characteristics.Next,cotton was identified based on the croplands from 2000 to 2020 by using the machine learning model.Finally,the performance was evaluated,and the spatiotemporal distribution characteristics of cotton planting areas were analyzed.The results showed that the proposed approach can achieve high accuracy at a fine spatial resolution.The performance evaluation indicated the applicability and suitability of the method,there is a good correlation between the extracted cotton areas and statistical data,and the cotton area of the study area showed an increasing trend.The cotton spatial distribution pattern developed from dispersion to agglomeration.The proposed approach and the derived 30-m cotton maps can provide a scientific reference for the optimization of agricultural management.展开更多
基金Under the auspices of the National Key Research and Development Program of China(No.2019YFA0606603)。
文摘Fire season affects the dynamic changes of post-fire vegetation communities and carbon emissions.Analyzing its global patterns supports understanding of the ecological impacts of fires and responses of fires to climate change.Meteorological variables have been widely used to quantify fire season in current studies.However,their results can not be used to assess climate impacts on the seasonality of fire activities.Here we utilized satellite-based Moderate Resolution Imaging Spectroradiometer(MODIS)burned area data from 2001 to 2022 to identify global fire season types based on the number of peaks within a year.Using satellite data and innovatively processing the data to obtain a more accurate length of the fire season.We divided fire season types and examined the spatial distribution of fire season types across the Koppen-Geiger climate(KGC)zones.At a global scale,we identified three major fire season types,including unimodal(31.25%),bimodal(52.07%),and random(16.69%).The unimodal fire season primarily occurs in boreal and tropical regions lasting about 2.7 mon.In comparison,temperate ecosystems tend to have a longer fire season(3 mon)with two peaks throughout the year.The KGC zones show divergent contributions from the fire season types,indicating potential impacts of the climatic conditions on fire seasonality in these regions.
基金supported by the National Natural Science Foundation of China[grant number 42101342]Third Comprehensive Scientific Expedition to Xinjiang[grant number 2021XJKK1403].
文摘Cotton is one of the most significant cash crops in the world,and it is also the main source of natural fiber for textiles.It is crucial for cotton management to identify the spatiotemporal distribution of cotton planting areas timely and accurately on a fine scale.However,previous research studies have predominantly concentrated on specific years using remote sensing data.Challenges still exist in the extraction of cotton areas for long time series with high accuracy.To address this issue,a novel cotton sample selection method was proposed and the machine learning method is employed to effectively identify the long time series cotton planting areas at a 30-m resolution scale.Bortala and Shuanghe in Xinjiang,China,were selected as the study cases to demonstrate the approach.Specifically,the cropland in this study was extracted by using an object-oriented classification method with Landsat images and the results were optimized as the vectorized boundary of croplands.Then,the cotton samples were selected using the Normalized Difference Vegetation Index(NDVI)series of Moderate Resolution Imaging Spectroradiometer(MODIS)based on its phenological characteristics.Next,cotton was identified based on the croplands from 2000 to 2020 by using the machine learning model.Finally,the performance was evaluated,and the spatiotemporal distribution characteristics of cotton planting areas were analyzed.The results showed that the proposed approach can achieve high accuracy at a fine spatial resolution.The performance evaluation indicated the applicability and suitability of the method,there is a good correlation between the extracted cotton areas and statistical data,and the cotton area of the study area showed an increasing trend.The cotton spatial distribution pattern developed from dispersion to agglomeration.The proposed approach and the derived 30-m cotton maps can provide a scientific reference for the optimization of agricultural management.
