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。     

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%。     

Contribution of a pathway through the Arctic Ocean to the re cent reduction in the ice cover

The sea ice cover in the Arctic Ocean has been reducing and hit the low record in the summer of 2007. The anomaly was extremely large in the Pacific sector. The sea level height in the Bering Sea vs. the Greenland Sea has been analyzed and compared with the current meter data through the Bering Strait. A recent peak existed as a consequence of atmospheric circulation and is considered to contribute to inflow of the Pacific Water into the Arctic Basin. The timing of the Pacific Water inflow matched with the sea ice reduction in the Pacific sector and suggests a significant increase in heat flux. This component should be included in the model prediction for answering the question when the Arctic sea ice becomes a seasonal ice cover.     

ERA5再分析云量在大气环境评估中的适用性研究

以河北省7个国家级气象站为例,利用1979—2013年ERA5云量和地面实测云量数据,依国标推荐法计算混合层高度,通过公式推导出通风量和大气自净能力指数,比较两套数据的计算结果,对ERA5再分析云量在大气环境评估中的适用性进行了研究;采用ERA5云量计算2013年3月至2021年2月的大气自净能力指数,对其与PM2.5浓度之间的关系进行分析。结果表明：①两种云量资料计算的月平均混合层高度、通风量和大气自净能力指数的相对误差近似符合正态分布,主要集中在0～5%;各月混合层高度：相对误差＞通风量＞大气自净能力指数,相关性则相反。秋冬季的相对误差（相关系数）大都低（高）于春夏季,邢台、石家庄和保定的误差明显小于其他站。②利用两种云量数据计算的大气自净能力指数的多年平均相对误差最小,通风量的次之,历年值的相关性和偏差也是中南部站点的更为理想。③2013年3月至2021年2月,各站月平均大气自净能力指数和PM2.5浓度呈反向相关,中南部站点的相关性更为显著。     

基于3S的荣成湾沿岸土地覆被变化及驱动力研究

运用3S技术对最近40年来荣成湾沿岸土地覆被动态变化及驱动力进行研究.结果表明,该区海岸湿地土地覆被总面积和陆域土地覆被总面积变化不大,但其各自类型结构和分布均发生了显著调整,而且二者变化在时间上不同步.从面积上讲,前者以1981年为界,渴湖和沙地先减少后波动增加,滩涂和沼泽地先增加后波动减少;养殖池从无到有并迅速增加...     

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。     

基于改进Markov-CA模型的黄土高原土地利用多情景模拟

土地利用/覆被的时空变化研究能为区域生态环境恢复和生态系统集成管理提供科学支持.集成Logistic回归模型、改进的Markov与FLUS模型模拟黄土高原2020-2050年3种典型情景土地利用变化.发现各情景土地利用面积变化及空间置换转移主要集中在农用地、草地和城镇用地;历史趋势延续情景下农用地减少15 205 km...     

地理国情监测水面数据时空一致性优化方法

地理国情监测利用遥感、地理信息等技术,动态获取地表覆盖等多样化的地理要素,经统计、分析、评价和应用,服务于政治、经济、文化、资源、环境等多个领域,为政府提供全面、准确、基础的地理信息情报。针对地理国情监测在全国范围基于多时相遥感影像采集水面信息存在时空不一致性的现状,提出一种基于精细DEM的水面数据时空一致性优化方法。利用栅格图形区域生长算法,采用8邻域算子,对水面种子点进行迭代生长计算,得出基于精细DEM的区域生长结果;通过与地理国情监测水面数据对比分析,实现空间化结果的修正,从而达到时空一致性优化的目的。分析了典型研究区水面数据特征,利用该方法进行了优化处理,结果显示：研究区水面数据空间范围相对于时点监测修正了7.99%,满足了地理国情监测时点一致性需求。研究表明：该方法的应用,能够使得在全国尺度上统计的水面数据反映同一季节或可接受时段内的状况,避免或缩小了由于影像数据源的差异造成的时间和空间上的不一致性带来的误差,满足了地理国情监测时点一致性需求,能够在地理国情监测等地表覆盖水面信息提取、优化中推广应用,为政府有效决策提供客观、准确和基础的水面信息。