2000—2015年柴达木盆地植被覆盖度时空变化及其与环境因子的关系

基于MODIS-NDVI、DEM和气象数据,分析柴达木盆地2000—2015年植被覆盖度(FVC)时空变化特征,并与降水、温度、日照时数、相对湿度、蒸散量和海拔进行相关、偏相关或叠加分析,探讨FVC与各环境因子的关系。结果表明:FVC整体自东南向西北内陆呈半环状递减,FVC集中在20%以下,人类活动及径流等打破植被地带性规律;2000—2015年FVC明显改善,广泛分布于盆地中西部地区,2001—2002年年际变化最显著;FVC与降水、相对湿度以正相关为主,与温度关系不显著,与日照时数和蒸散量主要为负相关,降水对FVC贡献最大,温度通过影响蒸散量等间接影响FVC,而土壤蒸发对蒸散量的影响大于植物蒸腾;FVC与等高线空间分布较吻合,FVC在2 800~2 900 m和4 600~4 700 m出现两个峰值,4 700 m以上FVC迅速降低。     

不同植被盖度沙质草地生长季土壤水分动态

水分是干旱半干旱沙地生态系统最大的限制因子,研究植被盖度和土壤水分之间的关系有助于沙地生态恢复和保护。基于生长季科尔沁沙质草地不同植被盖度下土壤水分动态和降水的观测试验,分析了沙质草地植被盖度和土壤水分的耦合关系。结果表明：在土壤剖面上土壤水分存在明显的分层结构,依次为水分剧变层（0~40 cm）、缓变层（40~100 cm）和稳定层（100~180 cm）;植被盖度对土壤水分有很大影响,不同植被盖度下土壤含水量存在显著差异,土壤水分与盖度之间呈倒“V”型关系,土壤水分状况在28%的盖度下最优;不同的植被盖度下土壤水分对降水的响应也存在差异,在13%盖度下响应最敏感,28%和46%的盖度下响应微弱,后二者的土壤水分也相对稳定。在沙地生态恢复建设过程中合理的植被盖度配置可提高降水利用效率,并能使土壤水分和植被达到一种良好的平衡状态,从而有利于生态系统的稳定。     

新疆地区土地覆被遥感数据的一致性研究

鉴于新疆地区对中国乃至中亚有着特殊的战略意义,本文针对不同数据源及分类系统在土地覆被数据的空间分布上缺乏互通性问题,结合2010年目视解译土地利用现状遥感监测数据、GlobeLand30和GlobCover2009共3种土地覆被数据,采用类型相似分析、类型混淆分析、混淆矩阵分析、空间一致性分析4种方法开展精度评价及一致性分析,以期对土地覆被数据在中国西北干旱区的适用性及适用范围提供有效建议。结果表明,3种土地覆被数据对新疆地区土地覆被类型构成基本一致,且对裸地类型的辨识度最高;新疆地区中高度一致区域占新疆总面积的95%;3种数据两两对比时,总体精度在64.11%~72.57%之间,其中目视解译数据/GlobeLand 30组合表现出最高水平,且仍有提高空间,反映出目前相同卫星传感器是提升精度评价结果的重要因素之一,且不同分类系统、分类方法、空间分辨率及卫星过境时间等因素对精度评价结果也会产生巨大影响。为解决此类问题,利用多源土地覆被遥感数据的融合技术提高数据精度,或是利用深度学习对遥感影像资料进行精确地解译和判读,将是今后全球土地覆被制图及应用领域的主要发展趋势。     

Agriculture development-induced surface albedo changes and climatic implications across northeastern China

To improve the understandings on regional climatic effects of past human-induced land cover changes,the surface albedo changes caused by conversions from natural vegetation to cropland were estimated across northeastern China over the last 300 years,and its climatic effects were simulated by using the Weather Research and Forecasting (WRF) model.Essential natural vegetation records compiled from historical documents and regional optimal surface albedo dataset were used.The results show that the surface albedo decreased by 0.01-0.03 due to conversions from grassland to cropland in the Northeast China Plain and it increased by 0.005-0.015 due to conversions from forests to cropland in the surrounding mountains.As a consequence,in the Northeast China Plain,the surface net radiation increased by 4-8 W/m 2,2-5 W/m 2,and 1-3 W/m 2,and the climate was therefore warmed by 0.1℃-0.2℃、0.1℃-0.2℃、 0.1℃-0.3 ℃ in the spring,autumn and winter,respectively.In the surrounding mountain area,the net radiation decreased by less than 1.5 W/m 2,and the climate was therefore cooled too slight to be detected.In summer,effects of surface albedo changes on climate were closely associated with moisture dynamics,such as evapotranspiration and cloud,instead of being merely determined by surface radiation budget.The simulated summer climatic effects have large uncertainties.These findings demonstrate that surface albedo changes resulted in warming climate effects in the non-rainy seasons in Northeast China Plain through surface radiation processes while the climatic effects in summer could hardly be concluded so far.     

Classifier ensembles for land cover mapping using multitemporal SAR imagery

SAR data are almost independent from weather conditions, and thus are well suited for mapping of seasonally changing variables such as land cover. In regard to recent and upcoming missions, multitemporal and multi-frequency approaches become even more attractive. In the present study, classifier ensembles (i.e., boosted decision tree and random forests) are applied to multi-temporal C-band SAR data, from different study sites and years. A detailed accuracy assessment shows that classifier ensembles, in particularly random forests, outperform standard approaches like a single decision tree and a conventional maximum likelihood classifier by more than 10% independently from the site and year. They reach up to almost 84% of overall accuracy in rural areas with large plots. Visual interpretation confirms the statistical accuracy assessment and reveals that also typical random noise is considerably reduced. In addition the results demonstrate that random forests are less sensitive to the number of training samples and perform well even with only a small number. Random forests are computationally highly efficient and are hence considered very well suited for land cover classifications of future multifrequency and multitemporal stacks of SAR imagery.     

Optimizing support vector machine learning for semi-arid vegetation mapping by using clustering analysis

In remote sensing communities, support vector machine (SVM) learning has recently received increasing attention. SVM learning usually requires large memory and enormous amounts of computation time on large training sets. According to SVM algorithms, the SVM classification decision function is fully determined by support vectors, which compose a subset of the training sets. In this regard, a solution to optimize SVM learning is to efficiently reduce training sets. In this paper, a data reduction method based on agglomerative hierarchical clustering is proposed to obtain smaller training sets for SVM learning. Using a multiple angle remote sensing dataset of a semi-arid region, the effectiveness of the proposed method is evaluated by classification experiments with a series of reduced training sets. The experiments show that there is no loss of SVM accuracy when the original training set is reduced to 34% using the proposed approach. Maximum likelihood classification (MLC) also is applied on the reduced training sets. The results show that MLC can also maintain the classification accuracy. This implies that the most informative data instances can be retained by this approach.     

MODIS数据的水浇地提取

水浇地是土地覆盖分类系统中的一个重要类型, 依据水分亏缺指数(WDI)与土壤水分的相关性, 计算了监测时间内WDI的变化情况, 用降雨资料去除了由于降雨引起的WDI的变化, 进而提取了研究区的水浇地分布。结果表明：(1)提取出的水浇地在数量上与统计资料相比, 除山西省偏差相对较大外, 其余各省偏差均小于7%, 提取出的结果与统计资料具有可比性。(2)提取出的水浇地在空间上多成片出现在河流、湖泊、水库附近、灌区和绿洲上, 与已知的水浇地集中区一致。(3)利用TM影像对提取结果进行了初步判断, 其中山西省的正确率较低, 只有64%, 其余各省正确率均在70%以上, 新疆的正确率最高为92%。     

Research activities on land-use/cover change in the past ten years in China using space technology

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黄河源区玛多县土地利用/覆被及景观格局变化的遥感分析

采用了黄河源地区1977、1990及2003年MSS、TM的3个时相遥感影像,通过人机交互的解译与GIS的空间分析,提取了玛多县3个时期的土地利用/覆被信息。分析了玛多县各地类的数量变化和空间变化特征。另外对景观生态空间分布格局,利用FRAGSTATS软件对玛多县景观级别的动态特征进行了分析。结果表明:玛多县的土地利用/覆被类型以草地为主,占全区总面积77.93%;退化现象十分严重,主要是草地覆盖度的降低以及草地沙化;景观破碎化程度在前期呈下降趋势,而后期呈上升趋势,景观斑块形态越来越偏离规则而变得复杂、多样。     

Sensitivity of streamflow from a Himalayan catchment to plausible changes in land cover and climate

Global climate change will likely increase temperature and variation in precipitation in the Himalayas, modifying both supply of and demand for water. This study assesses combined impacts of land‐cover and climate changes on hydrological processes and a rainfall‐to‐streamflow buffer indicator of watershed function using the Soil Water Assessment Tool (SWAT) in Kejie watershed in the eastern Himalayas. The Hadley Centre Coupled Model Version 3 (HadCM3) was used for two Intergovernmental Panel on Climate Change (IPCC) emission scenarios (A2 and B2), for 2010–2099. Four land‐cover change scenarios increase forest, grassland, crops, or urban land use, respectively, reducing degraded land. The SWAT model predicted that downstream water resources will decrease in the short term but increase in the long term. Afforestation and expansion in cropland will probably increase actual evapotranspiration (ET) and reduce annual streamflow but will also, through increased infiltration, reduce the overland flow component of streamflow and increase groundwater release. An expansion in grassland will decrease actual ET, increase annual streamflow and groundwater release, while decreasing overland flow. Urbanization will result in increases in streamflow and overland flow and reductions in groundwater release and actual ET. Land‐cover change dominated over effects on streamflow of climate change in the short and middle terms. The predicted changes in buffer indicator for land‐use plus climate‐change scenarios reach up to 50% of the current (and future) range of inter‐annual variability. Copyright © 2010 John Wiley & Sons, Ltd.