Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultiv...Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultivation management and promoting the sustainable development of the cotton industry.Xinjiang is the primary cotton-producing region in China.However,long-term data of cotton cultiv-ation areas with high spatial resolution are unavailable for Xinjiang,China.Therefore,this study aimed to identify and map an accurate 30-m cotton cultivation area dataset in Xinjiang from 2000 to 2020 by applying a Random Forest(RF)-based method that integrates Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)images,and validated the applicability and accuracy of dataset at a large spatial scale.Then,this study analyzed the spatiotemporal variations and influencing factors of cotton cultivation in the study period.The results showed that a high classification accuracy was achieved(overall accuracy>85%,F1>0.80),strongly agreeing with county-level agricultural statistical yearbook data(R2>0.72).Significant spatiotemporal variation in the cotton cultivation areas was found in Xinjiang,with a total increase of 1131.26 kha from 2000 to 2020.Notably,cotton cultivation area in southern Xinjiang expan-ded substantially,with that in Aksu increasing from 20.10%in 2000 to 28.17%in 2020,representing an expansion of 374.29 kha.In northern Xinjiang,the cotton areas in the Tacheng region also exhibited significant increased by almost ten percentage points in the same period.In contrast,cotton cultivation in eastern Xinjiang declined,decreasing from 2.22%in 2000 to merely 0.24%in 2020.Standard deviation ellipse analysis revealed a‘northeast-southwest’spatial distribution,with the centroid consistently located in Aksu and shifting 102.96 km over the 20-yr period.Pearson correlation analysis indicated that socioeconomic factors had a stronger influence on cotton cultivation than climatic factors,with effective irrigation area(r=0.963,P<0.05)and total agricultural machinery power(r=0.823)showing significant positive correlations,whereas climatic variables exhibiting weak associations(r<0.200).These results provide valuable scientific data for informed agricultural management,sustainable development,and policymaking.展开更多
天山北坡是西北地区的重要水源涵养区及草原畜牧业基地,其积雪融水对生态系统维持、农业灌溉及城市供水至关重要。为解决MODIS积雪产品易受云层干扰而导致的数据缺失问题,论文通过扩展MODIS数据输入,以已有积雪数据共同识别为积雪或非...天山北坡是西北地区的重要水源涵养区及草原畜牧业基地,其积雪融水对生态系统维持、农业灌溉及城市供水至关重要。为解决MODIS积雪产品易受云层干扰而导致的数据缺失问题,论文通过扩展MODIS数据输入,以已有积雪数据共同识别为积雪或非积雪的像元为“真值”,采用随机森林、支持向量机及BP神经网络等机器学习算法,确定积雪识别最佳方案。结合多种数据协同去云方法与隐马尔可夫随机场(hidden Markov random field,HMRF)算法,对去云效果进行对比分析,并使用高分辨率Landsat数据对实验结果的准确性进行验证。研究表明:(1)随机森林模型在积雪二分类任务中的表现最佳,准确率达90.15%,精确率达91.95%;(2)多种数据协同去云方法可以取得较好效果,Kappa系数为0.729,但结合HMRF方法的去云效果最佳,总体精度达82.84%,生产者精度为88.46%,Kappa系数为0.795;(3)年均积雪天数、积雪覆盖天数与海拔之间关系、月均积雪覆盖率与年均积雪覆盖面积变化趋势均与已有数据保持较高一致性。研究结果表明该方法能够有效提升积雪监测精度与时空连续性,为天山北坡及相似地区的积雪监测、冰雪水资源评估和生态环境管理提供了可靠的技术支撑。展开更多
基金Under the auspices of the National Natural Science Foundation of China(No.42101342,U2243205)the Third Comprehensive Scientific Expedition to Xinjiang(No.2021XJKK1403)。
文摘Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultivation management and promoting the sustainable development of the cotton industry.Xinjiang is the primary cotton-producing region in China.However,long-term data of cotton cultiv-ation areas with high spatial resolution are unavailable for Xinjiang,China.Therefore,this study aimed to identify and map an accurate 30-m cotton cultivation area dataset in Xinjiang from 2000 to 2020 by applying a Random Forest(RF)-based method that integrates Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)images,and validated the applicability and accuracy of dataset at a large spatial scale.Then,this study analyzed the spatiotemporal variations and influencing factors of cotton cultivation in the study period.The results showed that a high classification accuracy was achieved(overall accuracy>85%,F1>0.80),strongly agreeing with county-level agricultural statistical yearbook data(R2>0.72).Significant spatiotemporal variation in the cotton cultivation areas was found in Xinjiang,with a total increase of 1131.26 kha from 2000 to 2020.Notably,cotton cultivation area in southern Xinjiang expan-ded substantially,with that in Aksu increasing from 20.10%in 2000 to 28.17%in 2020,representing an expansion of 374.29 kha.In northern Xinjiang,the cotton areas in the Tacheng region also exhibited significant increased by almost ten percentage points in the same period.In contrast,cotton cultivation in eastern Xinjiang declined,decreasing from 2.22%in 2000 to merely 0.24%in 2020.Standard deviation ellipse analysis revealed a‘northeast-southwest’spatial distribution,with the centroid consistently located in Aksu and shifting 102.96 km over the 20-yr period.Pearson correlation analysis indicated that socioeconomic factors had a stronger influence on cotton cultivation than climatic factors,with effective irrigation area(r=0.963,P<0.05)and total agricultural machinery power(r=0.823)showing significant positive correlations,whereas climatic variables exhibiting weak associations(r<0.200).These results provide valuable scientific data for informed agricultural management,sustainable development,and policymaking.
文摘天山北坡是西北地区的重要水源涵养区及草原畜牧业基地,其积雪融水对生态系统维持、农业灌溉及城市供水至关重要。为解决MODIS积雪产品易受云层干扰而导致的数据缺失问题,论文通过扩展MODIS数据输入,以已有积雪数据共同识别为积雪或非积雪的像元为“真值”,采用随机森林、支持向量机及BP神经网络等机器学习算法,确定积雪识别最佳方案。结合多种数据协同去云方法与隐马尔可夫随机场(hidden Markov random field,HMRF)算法,对去云效果进行对比分析,并使用高分辨率Landsat数据对实验结果的准确性进行验证。研究表明:(1)随机森林模型在积雪二分类任务中的表现最佳,准确率达90.15%,精确率达91.95%;(2)多种数据协同去云方法可以取得较好效果,Kappa系数为0.729,但结合HMRF方法的去云效果最佳,总体精度达82.84%,生产者精度为88.46%,Kappa系数为0.795;(3)年均积雪天数、积雪覆盖天数与海拔之间关系、月均积雪覆盖率与年均积雪覆盖面积变化趋势均与已有数据保持较高一致性。研究结果表明该方法能够有效提升积雪监测精度与时空连续性,为天山北坡及相似地区的积雪监测、冰雪水资源评估和生态环境管理提供了可靠的技术支撑。
