Land use and cover change(LUCC) is an important indicator of the human-earth system under climate/environmental change,which also serves as a key impact factor of carbon balance,and a major source/sink of soil carbon cycles.The Heihe River Basin(HRB) is known as a typical ecologically fragile area in the arid/semi-arid regions of northwestern China,which makes it more sensitive to the LUCC.However,its sensitivity varies in a broad range of controlling factors,such as soil layers,LUCCs and calculation methods(e.g.the fixed depth method,FD,and the equivalent mass method,ESM).In this study,we performed a meta-analysis to assess the response of soil organic carbon(SOC) and total nitrogen(TN) storage to the LUCC as well as method bias based on 383 sets of SOC data and 148 sets of TN data from the HRB.We first evaluated the calculation methods and found that based on the FD method,the LUCC caused SOC and TN storage to decrease by 17.39% and 14.27%,respectively;while the losses estimated using the ESM method were 19.31% and 18.52%,respectively.The deviations between two methods were mainly due to the fact that the FD method ignores the heterogeneity of soil bulk density(BD),which may underestimate the results subsequently.We then analyzed the response of SOC and TN storage to various types of the LUCC.In particular,when woodland and grassland were converted into cultivated land or other land types,SOC and TN suffered from heavy losses,while other LUCCs had minor influences.Finally,we showed that increasing the depth of the soil layers would reduce the losses of SOC and TN storage.In summary,we identified a series of controlling factors(e.g.soil layer,the LUCC and calculation method) to evaluate the impact of the LUCC on SOC and TN storage in the HRB,which should be considered in future research. 相似文献
We analyzed the spatial local accuracy of land cover (LC) datasets for the Qiangtang Plateau, High Asia, incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere Programme Data and Information System (IGBPDIS), Global Land cover mapping at 30 m resolution (GlobeLand30), MODIS Land Cover Type product (MCD12Q1), Climate Change Initiative Land Cover (CCI-LC), Global Land Cover 2000 (GLC2000), University of Maryland (UMD), and GlobCover 2009 (Glob-Cover). We initially compared resultant similarities and differences in both area and spatial patterns and analyzed inherent relationships with data sources. We then applied a geographically weighted regression (GWR) approach to predict local accuracy variation. The results of this study reveal that distinct differences, even inverse time series trends, in LC data between CCI-LC and MCD12Q1 were present between 2001 and 2015, with the exception of category areal discordance between the seven datasets. We also show a series of evident discrepancies amongst the LC datasets sampled here in terms of spatial patterns, that is, high spatial congruence is mainly seen in the homogeneous southeastern region of the study area while a low degree of spatial congruence is widely distributed across heterogeneous northwestern and northeastern regions. The overall combined spatial accuracy of the seven LC datasets considered here is less than 70%, and the GlobeLand30 and CCI-LC datasets exhibit higher local accuracy than their counterparts, yielding maximum overall accuracy (OA) values of 77.39% and 61.43%, respectively. Finally, 5.63% of this area is characterized by both high assessment and accuracy (HH) values, mainly located in central and eastern regions of the Qiangtang Plateau, while most low accuracy regions are found in northern, northeastern, and western regions.
