Recent studies on glaciers in the West Kunlun Shan, northwest Tibetan Plateau, have shown that they may be stable or retreating slightly. Here, we assess changes in the mass of the glaciers in the West Kunlun Shan(WKS...Recent studies on glaciers in the West Kunlun Shan, northwest Tibetan Plateau, have shown that they may be stable or retreating slightly. Here, we assess changes in the mass of the glaciers in the West Kunlun Shan(WKS) in an attempt to understand the processes that control their behavior. Glaciers over the recent 40 years(1970-2010) have shrunk 3.4±3.1%in area, based on a comparison between two Chinese glacier inventories. Variations of surface elevations, derived from ICESat-GLAS(Ice, Cloud, and Land Elevation Satellite-Geoscience Laser Altimeter System) elevation products(GLA14 data) using the robust linear-fit method, indicate that the glaciers have been gaining mass at a rate of 0.23±0.24 m w.e./a since 2003. The annual mass budget for the whole WKS range from 2003 to 2009 is estimated to be 0.71±0.62 Gt/a. This gain trend is confirmed by MOD10A1 albedo for the WKS region which shows a descent of the mean snowline altitude from 2003 to 2009.展开更多
Soil diagnostic horizons, which each have a set of quantified properties, play a key role in soil classification. However, they are difficult to predict, and few attempts have been made to map their spatial occurrence...Soil diagnostic horizons, which each have a set of quantified properties, play a key role in soil classification. However, they are difficult to predict, and few attempts have been made to map their spatial occurrence. We evaluated and compared four machine learning algorithms, namely, the classification and regression tree(CART), random forest(RF), boosted regression trees(BRT), and support vector machine(SVM), to map the occurrence of the soil mattic horizon in the northeastern Qinghai-Tibetan Plateau using readily available ancillary data. The mechanisms of resampling and ensemble techniques significantly improved prediction accuracies(measured based on area under the receiver operator characteristic curve score(AUC)) and produced more stable results for the BRT(AUC of 0.921 ± 0.012, mean ± standard deviation) and RF(0.908 ± 0.013) algorithms compared to the CART algorithm(0.784 ± 0.012), which is the most commonly used machine learning method. Although the SVM algorithm yielded a comparable AUC value(0.906 ± 0.006) to the RF and BRT algorithms, it is sensitive to parameter settings, which are extremely time-consuming.Therefore, we consider it inadequate for occurrence-distribution modeling. Considering the obvious advantages of high prediction accuracy, robustness to parameter settings, the ability to estimate uncertainty in prediction, and easy interpretation of predictor variables, BRT seems to be the most desirable method. These results provide an insight into the use of machine learning algorithms to map the mattic horizon and potentially other soil diagnostic horizons.展开更多
基金supported by a National Science Foundation of China major project (Grant No. 41190084) funded by the National Natural Science Foundation of Chinathe National Key Technology R&D Program (Grant No. 2012BAC19B07)+2 种基金the International S&T Cooperation Program of the Ministry of Science and Technology of China (Grant No. 2010DFA92720-23)provided by the MOST (Grant No. 2006FY110200)CAS projects (Grant No. KZCX2-YW-301)
文摘Recent studies on glaciers in the West Kunlun Shan, northwest Tibetan Plateau, have shown that they may be stable or retreating slightly. Here, we assess changes in the mass of the glaciers in the West Kunlun Shan(WKS) in an attempt to understand the processes that control their behavior. Glaciers over the recent 40 years(1970-2010) have shrunk 3.4±3.1%in area, based on a comparison between two Chinese glacier inventories. Variations of surface elevations, derived from ICESat-GLAS(Ice, Cloud, and Land Elevation Satellite-Geoscience Laser Altimeter System) elevation products(GLA14 data) using the robust linear-fit method, indicate that the glaciers have been gaining mass at a rate of 0.23±0.24 m w.e./a since 2003. The annual mass budget for the whole WKS range from 2003 to 2009 is estimated to be 0.71±0.62 Gt/a. This gain trend is confirmed by MOD10A1 albedo for the WKS region which shows a descent of the mean snowline altitude from 2003 to 2009.
基金supported by the National Natural Science Foundation of China (Nos. 41501229, 41371224, 41130530, and 91325301)the China Postdoctoral Science Foundation (No. 2015M581876)
文摘Soil diagnostic horizons, which each have a set of quantified properties, play a key role in soil classification. However, they are difficult to predict, and few attempts have been made to map their spatial occurrence. We evaluated and compared four machine learning algorithms, namely, the classification and regression tree(CART), random forest(RF), boosted regression trees(BRT), and support vector machine(SVM), to map the occurrence of the soil mattic horizon in the northeastern Qinghai-Tibetan Plateau using readily available ancillary data. The mechanisms of resampling and ensemble techniques significantly improved prediction accuracies(measured based on area under the receiver operator characteristic curve score(AUC)) and produced more stable results for the BRT(AUC of 0.921 ± 0.012, mean ± standard deviation) and RF(0.908 ± 0.013) algorithms compared to the CART algorithm(0.784 ± 0.012), which is the most commonly used machine learning method. Although the SVM algorithm yielded a comparable AUC value(0.906 ± 0.006) to the RF and BRT algorithms, it is sensitive to parameter settings, which are extremely time-consuming.Therefore, we consider it inadequate for occurrence-distribution modeling. Considering the obvious advantages of high prediction accuracy, robustness to parameter settings, the ability to estimate uncertainty in prediction, and easy interpretation of predictor variables, BRT seems to be the most desirable method. These results provide an insight into the use of machine learning algorithms to map the mattic horizon and potentially other soil diagnostic horizons.
