Radiative forcing over China due to albedo change caused by land cover change during 1990–2010

Land cover change affects surface radiation budget and energy balance by chang- ing surface albedo and further impacts the regional and global climate. In this article, high spatial and temporal resolution satellite products were used to analyze the driving mechanism for surface albedo change caused by land cover change during 1990-2010. In addition, the annual-scale radiative forcing caused by surface albedo changes in China＇s 50 ecological regions were calculated to reveal the biophysical mechanisms of land cover change affecting climate change at regional scale. Our results showed that the national land cover changes were mainly caused by land reclamation, grassland desertification and urbanization in past 20 years, which were almost induced by anthropogenic activities. Grassland and forest area decreased by 0.60% and 0.11%, respectively. The area of urban and farmland increased by 0.60% and 0.19%, respectively. The mean radiative forcing caused by land cover changes during 1990-2010 was 0.062 W/m2 in China, indicating a warming climate effect. However, spatial heterogeneity of radiative forcing was huge among different ecological regions. Farmland conversing to urban construction land, the main type of land cover change for the urban and suburban agricultural ecological region in Beijing-Tianjin-Tangshan region, caused an albedo reduction by 0.00456 and a maximum positive radiative forcing of 0.863 WIm2, which was presented as warming climate effects. Grassland and forest conversing to farmland, the main type of land cover change for the temperate humid agricultural and wetland ecological region in Sanjiang Plain, caused an albedo increase by 0.00152 and a maximum negative radiative forcing of 0.184 W/m2, implying cooling climate effects.     

东北地区历史时期森林空间格局网格化重建方法研究(英文)

The spatially explicit reconstruction of historical land-cover datasets plays an important role in studying the climatic and ecological effects of land-use and land-cover change(LUCC). Using potential natural vegetation(PNV) and satellite-based land use data, we determined the possible maximum distribution extent of forest cover in the absence of human disturbance. Subsequently, topography and climate factors were selected to assess the suitability of land for cultivation. Finally, a historical forest area allocation model was devised on the basis of the suitability of land for cultivation. As a case study, we used the historical forest area allocation model to reconstruct forest cover for 1780 and 1940 in Northeast China with a 10-km resolution. To validate the model, we compared satellite-based forest cover data with our reconstruction for 2000. A one-sample t-test of absolute bias showed that the two-tailed significance was 0.12, larger than the significant level 0.05, suggesting that the model has strong ability to capture the spatial distribution of forests. In addition, we calculated the relative difference of our reconstruction at the county scale for 1780 in Northeast China. The number of counties whose relative difference ranged from-30% to 30% is 99, accounting for 74.44% of all counties. These findings demonstrated that the provincial forest area could be transformed into forest cover maps well using the model.     

The total operating characteristic to measure diagnostic ability for multiple thresholds

The relative operating characteristic (ROC) is a popular statistical method to measure the association between observed and diagnosed presence of a characteristic. The diagnosis of presence or absence depends on whether the value of an index variable is above a threshold. ROC considers multiple possible thresholds. Each threshold generates a two-by-two contingency table, which contains four central entries: hits, misses, false alarms, and correct rejections. ROC reveals for each threshold only two ratios, hits/(hits + misses) and false alarms/(false alarms + correct rejections). This article introduces the total operating characteristic (TOC), which shows the total information in the contingency table for each threshold. TOC maintains desirable properties of ROC, while TOC reveals strictly more information than ROC in a manner that makes TOC more useful than ROC. We illustrate the concepts with an application to land change science.     

A new MODIS daffy cloud free snow cover mapping algorithm on the Tibetan Plateau

Because of similar reflective characteristics of snow and cloud, the weather status seriously affects snow monitoring using optical remote sensing data. Cloud amount analysis during 2010 to 2011 snow seasons shows that cloud cover is the major limitation for snow cover monitoring using MOD10A1 and MYD10A1. By use of MODIS daily snow cover products and AMSR-E snow wa- ter equivalent products （SWE）, several cloud elimination methods were integrated to produce a new daily cloud flee snow cover product, and information of snow depth from 85 climate stations in Tibetan Plateau area （TP） were used to validate the accuracy of the new composite snow cover product. The results indicate that snow classification accuracy of the new daily snow cover product reaches 91.7% when snow depth is over 3 cm. This suggests that the new daily snow cover mapping algorithm is suitable for monitoring snow cover dynamic changes in TP.     

Thermodynamic properties of snow cover on sea ice during the austral summer in Prydz Bay,East Antarctica

The thermodynamic properties of snow cover on sea ice play a key role in the ice-ocean-atmosphere system and have been a focus of recent scientific research. In this study, we investigated the thermodynamic properties of snow cover on sea ice in the Nella Fjord, Prydz Bay, East Antarctica(69°20′S, 76°07′E), near the Chinese Antarctic Zhongshan Station. Our observations were carried out during the 29th Chinese National Antarctic Research Expedition. We found that the vertical temperature profile of snow cover changed considerably in response to changes in air temperature and solar radiation during the summer. Associated with the changes in the temperature profile were fluctuations in the temperature gradient within the upper 10 cm of the snow cover. Results of previous research have shown that the thermal conductivity of snow is strongly correlated with snow density. To calculate the thermal conductivity in this study, we measured densities in three snow pits. The calculated thermal conductivity ranged from 0.258–0.569 W?m-1?K-1. We present these datasets to show how involved parameters changed, and to contribute to a better understanding of melting processes in the snow cover on sea ice.     

大伙房库区近30年来植被覆盖变化分析

根据像元二分模型原理,利用1983年、2000年、2011年大伙房库区遥感影像数据提取植被覆盖遥感信息,结合植被指数计算植被覆盖度,分析比较大伙房库区不同时期植被覆盖的变化,研究大伙房库区这三个时期植被覆盖动态变化规律,为大伙房库区的生态建设和可持续发展提供决策支持。结果显示:1近30年以来,大伙房库区植被覆盖等级分布不均衡,高植被覆盖区过分集中在北部地带,西部则多为低植被覆盖区;2大伙房库区高植被覆盖面积在减少、低植被覆盖面积在增加;3大伙房库区植被覆盖总面积在下降,库区的生态环境已经遭到了破坏;4政策建议:加强监测与监管力度,合理开发利用矿产资源,积极响应国家"退耕还林、退耕还草"号召。     

Results of a CLM4 Land Surface Simulation over China Using a Multisource Integrated Land Cover Dataset

In this study, the high-accuracy multisource integrated Chinese land cover （MICLCover） dataset was used in version 4 of the Community Land Model （CLM4） to assess how the new land cover information affected land surface simulation over China. Compared to the default land cover dataset in CLM4, the MICL data indicated lower values for bare soil （14.6% reduction）, nee- dleleaf tree （3.6%）, and broadleaf tree （1.9%）; higher values for shrub cover （1.8% increase）, grassland （9.9%）, cropland （5.0%）, glaciers （0.5%）, lakes （1.6%）, and wetland （1.1%）; and unchanged for urban areas. Two comparative CLM4 simulations were conducted for the 33-yr period from 1972 to 2004, one using the MICL dataset and the other using the default dataset. The results revealed that the MICL data produced a 0.3% lower mean annual surface albedo over China than the original data. The largest contributor to the reduced value was semiarid regions （2.1% reduction）. The MICL-data albedo value agreed more closely with observations （MODIS broad- band black-sky albedo products） over arid and semiarid regions than for the original data to some extent. The simulated average sensible heat flux over China increased by only 0.1 W m 2 owing to the reduced values in arid and semiarid regions, as opposed to increases in humid and semihumid regions, while an increased latent heat flux of I W m-2 was reflected in almost identical changes over the whole region. In addition, the mean annual runoff simulated by CLM4 using MICL data decreased by 6.8 mm yr-1, primarily due to large simulated decreases in humid regions.     

A Dipole Pattern of Summer Precipitation over Mid-high Latitude Asia and Related Snow Cover Anomalies in the Preceding Spring

A dipole pattern of summer precipitation over the mid-high latitudes of Asia, which is characterized by opposing summer precipitation variations between the Mongolian and Northeast China （MNC） region and the West Siberian Plain （WSP）, is found to be clear and stable on both interdecadal and interannual scales during 1981- 2011. Spring snow cover anomalies over a small region within the WSP and the Heilongjiang River （HR） region are closely related to the variation of this dipole mode during the subsequent summer, and they can therefore be considered as forecasting factors. Our statistical results imply a potential process explaining the relationship between the spring snow anomalies and the summer rainfall dipole. Corresponding to the snow anomalies, Rossby waves propagate along a path from the WSP region, via the Mongolian Plateau, to the Stanovoy Range during summer. At the same time, Rossby-wave energy divergences and convergences along this path maintain and reinforce an anomalous cyclone and anticyclone pairing over the Asian continent, which is significantly linked to opposite summer precipitation anomalies between the MNC and WSP regions. Numerical experiments are need- ed to further confirm the above conjecture and demonstrate the detailed physical mechanisms linking the spring snow cover anomalies and summer precipitation dipole.     

Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data

Land cover classification of finer resolution remote sensing data is always difficult to acquire high-frequency time series data which contains temporal features for improving classification accuracy. This paper proposed a method of land cover classification with finer resolution remote sensing data integrating temporal features extracted from time series coarser resolution data. The coarser resolution vegetation index data is first fused with finer resolution data to obtain time series finer resolution data. Temporal features are extracted from the fused data and added to improve classification accuracy. The result indicates that temporal features extracted from coarser resolution data have significant effect on improving classification accuracy of finer resolution data, especially for vegetation types. The overall classification accuracy is significantly improved approximately 4% from 90.4% to 94.6% and 89.0% to 93.7% for using Landsat 8 and Landsat 5 data, respectively. The user and producer accuracies for all land cover types have been improved.     

Assessing integration of intensity,polarimetric scattering,interferometric coherence and spatial texture metrics in PALSAR-derived land cover classification

Synthetic aperture radar (SAR) is an important alternative to optical remote sensing due to its ability to acquire data regardless of weather conditions and day/night cycle. The Phased Array type L-band SAR (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) provided new opportunities for vegetation and land cover mapping. Most previous studies employing PALSAR investigated the use of one or two feature types (e.g. intensity, coherence); however, little effort has been devoted to assessing the simultaneous integration of multiple types of features. In this study, we bridged this gap by evaluating the potential of using numerous metrics expressing four feature types: intensity, polarimetric scattering, interferometric coherence and spatial texture. Our case study was conducted in Central New York State, USA using multitemporal PALSAR imagery from 2010. The land cover classification implemented an ensemble learning algorithm, namely random forest. Accuracies of each classified map produced from different combinations of features were assessed on a pixel-by-pixel basis using validation data obtained from a stratified random sample. Among the different combinations of feature types evaluated, intensity was the most indispensable because intensity was included in all of the highest accuracy scenarios. However, relative to using only intensity metrics, combining all four feature types increased overall accuracy by 7%. Producer’s and user’s accuracies of the four vegetation classes improved considerably for the best performing combination of features when compared to classifications using only a single feature type.