氮(N)、磷(P)等养分添加是提高草地生态系统生产力的重要策略,但其对土壤氧化亚氮(N2O)排放的影响尚不明确。该研究以南疆昆仑山北坡高山草地为研究对象,设置氮添加、磷添加、氮磷交互以及不施肥(CK) 4个处理,采用静态箱-气象色谱法连...氮(N)、磷(P)等养分添加是提高草地生态系统生产力的重要策略,但其对土壤氧化亚氮(N2O)排放的影响尚不明确。该研究以南疆昆仑山北坡高山草地为研究对象,设置氮添加、磷添加、氮磷交互以及不施肥(CK) 4个处理,采用静态箱-气象色谱法连续监测2017年生长季草地的N2O排放,研究不同氮、磷添加处理下的N2O排放特征,并利用Pearson相关分析对影响N2O排放的主要环境因子进行定性识别及定量解析。结果表明:氮添加处理与氮磷交互处理在施肥后约3周引起显著的N2O排放峰,分别为42.3和15.4 g N·hm–2·d–1。与其他处理相比,氮添加处理生长季N2O排放通量显著增加了1.8–3.2倍,而磷添加以及氮磷交互处理与CK之间没有显著差异。Pearson相关分析结果表明:N2O排放与微生物生物量碳呈负相关关系,与溶解性有机碳含量、pH值呈正相关关系,而与其他环境因子关系不显著。以上结果表明,与单施氮肥相比,在该地区草场采用氮磷混施可显著减少N2O的排放。展开更多
Salt-affected soils classification using remotely sensed images is one of the most common applications in remote sensing,and many algorithms have been developed and applied for this purpose in the literature.This stud...Salt-affected soils classification using remotely sensed images is one of the most common applications in remote sensing,and many algorithms have been developed and applied for this purpose in the literature.This study takes the Delta Oasis of Weigan and Kuqa Rivers as a study area and discusses the prediction of soil salinization from ETM +Landsat data.It reports the Support Vector Machine(SVM) classification method based on Independent Component Analysis(ICA) and Texture features.Meanwhile,the letter introduces the fundamental theory of SVM algorithm and ICA,and then incorporates ICA and texture features.The classification result is compared with ICA-SVM classification,single data source SVM classification,maximum likelihood classification(MLC) and neural network classification qualitatively and quantitatively.The result shows that this method can effectively solve the problem of low accuracy and fracture classification result in single data source classification.It has high spread ability toward higher array input.The overall accuracy is 98.64%,which increases by10.2% compared with maximum likelihood classification,even increases by 12.94% compared with neural net classification,and thus acquires good effectiveness.Therefore,the classification method based on SVM and incorporating the ICA and texture features can be adapted to RS image classification and monitoring of soil salinization.展开更多
文摘氮(N)、磷(P)等养分添加是提高草地生态系统生产力的重要策略,但其对土壤氧化亚氮(N2O)排放的影响尚不明确。该研究以南疆昆仑山北坡高山草地为研究对象,设置氮添加、磷添加、氮磷交互以及不施肥(CK) 4个处理,采用静态箱-气象色谱法连续监测2017年生长季草地的N2O排放,研究不同氮、磷添加处理下的N2O排放特征,并利用Pearson相关分析对影响N2O排放的主要环境因子进行定性识别及定量解析。结果表明:氮添加处理与氮磷交互处理在施肥后约3周引起显著的N2O排放峰,分别为42.3和15.4 g N·hm–2·d–1。与其他处理相比,氮添加处理生长季N2O排放通量显著增加了1.8–3.2倍,而磷添加以及氮磷交互处理与CK之间没有显著差异。Pearson相关分析结果表明:N2O排放与微生物生物量碳呈负相关关系,与溶解性有机碳含量、pH值呈正相关关系,而与其他环境因子关系不显著。以上结果表明,与单施氮肥相比,在该地区草场采用氮磷混施可显著减少N2O的排放。
基金Supported by the National Key Basic Research Development Pro-gram (2009CB421302 )National Natural Science Foundation ofChina (40861020,40961025,40901163)+1 种基金Natural Science Foun-dation of Xinjiang (200821128 )Open Foundation of State KeyLaboratory of Resources and Environment Information ystems(2010KF0003SA)
文摘Salt-affected soils classification using remotely sensed images is one of the most common applications in remote sensing,and many algorithms have been developed and applied for this purpose in the literature.This study takes the Delta Oasis of Weigan and Kuqa Rivers as a study area and discusses the prediction of soil salinization from ETM +Landsat data.It reports the Support Vector Machine(SVM) classification method based on Independent Component Analysis(ICA) and Texture features.Meanwhile,the letter introduces the fundamental theory of SVM algorithm and ICA,and then incorporates ICA and texture features.The classification result is compared with ICA-SVM classification,single data source SVM classification,maximum likelihood classification(MLC) and neural network classification qualitatively and quantitatively.The result shows that this method can effectively solve the problem of low accuracy and fracture classification result in single data source classification.It has high spread ability toward higher array input.The overall accuracy is 98.64%,which increases by10.2% compared with maximum likelihood classification,even increases by 12.94% compared with neural net classification,and thus acquires good effectiveness.Therefore,the classification method based on SVM and incorporating the ICA and texture features can be adapted to RS image classification and monitoring of soil salinization.
