淮海经济区生态系统服务价值演变与安全格局优化（英文）

土地利用变化引起的生态环境效应受到全球关注。作为淮河生态经济带核心组成的淮海经济区,其土地利用、生态安全与区域经济发展发生着互馈演变。基于Google Earth Engine (GEE)提取多期土地利用数据,借助生态系统服务价值（ESV）评估以及最小累计阻力模型（MCR）,探讨了1998–2018年淮海经济区的ESV演变的时空分异特征,优化了区域生态安全格局（ESP）。研究表明：ESV强度存在明显的空间分异,呈东北高、西南低的特征。中值ESV区面积比重最大,高、低值ESV区的占比较小。ESV呈现等级下降与升高并存的时空分异特征。从发展时段看,ESV等级变化呈现向好趋势。生态安全格局优化中,优化识别重要生态源地26个,主要景观生态廊道22条、生态战略节点65个,中水平生态安全区占比最大。ESV和ESP发生变化的主要原因与当地自然资源的变化和政府保护政策的改变与调整密切相关。研究结果可为跨省区的国土空间保护、制定可持续发展战略提供参考。     

C3、C4作物的光保护机制差异的光谱探测研究

本文设计了大豆(C3作物)和玉米(C4作物)日变化光谱探测试验,提取了太阳光照条件下叶绿素荧光信号和光化学指数PRI。结果表明,首先大豆和玉米叶绿素荧光强度的日变化特征有较大差异,上午大豆的叶绿素荧光比例呈现快速增加的趋势,而玉米的叶绿素荧光未出现增加趋势;其次,下午高温胁迫条件下C3、C4叶绿素荧光光谱比值的变化差异显著,大豆在760nm和688nm波段的叶绿素荧光比值快速增加,而玉米未出现显著变化;最后,研究证实了C3作物中午受到强光抑制出现光合作用"午休"现象,即午后光合恢复后其光合效率快速增加,表现为C3作物的热耗散降低、PRI快速增加,而C4作物无"午休"现象,午后PRI增加较小。本文研究表明,强光和高温环境胁迫下C3、C4作物的叶绿素荧光和热耗散变化特征有较大差异,利用胁迫条件下的多时相、高光谱遥感数据,提取C3、C4作物的叶绿素荧光和PRI,有可能实现C3、C4作物的遥感分类目标。     

锡林浩特草原区域MODIS LAI产品真实性检验与误差分析

本文研究了LAI产品真实性检验的指标和方法,建立了LAI产品真实性检验的流程,将遥感产品真实性检验误差分解为模型误差、数据定量化差异和尺度效应3个方面。以内蒙古锡林浩特草原为研究区,结合实测数据和Landsat TM数据建立NDVI-LAI模型,得到LAI验证参考"真值",据此"真值"按照本文的流程对MODIS LAI产品进行验证,分析了研究区MODIS LAI产品真实性检验的误差来源。研究表明,该研究区的MODIS LAI(MOD15A2)产品相对高估约25%。各个误差因素中,LAI遥感模型差异对于结果影响最大,MODIS LAI模型高估了该区域草地LAI(高估约44.2%);数据定量差异的影响也比较大,MODIS地表反射率数据与Landsat TM地表反射率数据的差异造成了约16.2%的低估;尺度效应的影响较小,造成约3.1%的低估,其中NDVI-LAI模型的尺度效应带来2.4%的低估,NDVI数据的尺度效应造成约0.7%的低估。     

任意方向对称差值核的SAR图像边缘提取算法

由于现有边缘检测方法多存在对图像噪声的敏感性以及检测边缘的有限方向性等问题,因此在斑点噪声的影响下难以有效检测SAR图像中具有任意方向性且强度相近的区域间边缘。本文提出了一种任意方向对称窗口的SAR图像边缘提取算法。首先,以任意像素在图像中的格点位置为对称中心构建任意方向上的对称窗口;定义各窗口内像素到中心像素距离的核函数;以像素核函数为权重分别计算两对称窗口内像素光谱测度的加权平均值,并定义两者之差的绝对值为该方向上该像素隶属边缘的强度;选取所有方向中最大边缘强度作为边缘像素的判别标准。为去除错误提取的边缘像素,设计基于滤波操作的后处理过程。对模拟图像提取结果的定性及定量分析表明提出算法可在一定程度上克服噪声影响,较为准确地提取任意方向的边缘。     

基于降斑各向异性扩散的SAR图像海岸线提取

受SAR图像相干斑噪声的影响,传统基于阈值的SAR图像海岸线提取方法难以迅速、精确地提取结构复杂精细的海岸线。为此,提出一种结合降斑各向异性扩散和大津法的SAR图像海岸线提取方法。首先,利用降斑各向异性扩散方法对SAR图像进行滤波,使其在尽可能滤除掉相干斑噪声的同时保持精细边缘和纹理结构;然后,以类间方差最大为准则,自适应地确定阈值,实现滤波结果图像二值化分割。在分割结果中,利用连通域标定法标定图像域,去除误分割的连通域,得到准确的海陆分割结果,以实现精确的SAR图像海岸线提取。对具有不同特点的真实大尺度SAR图像的实验结果和定量评价结果表明本文方法可以高精度地提取出光滑、连续的海岸线,相较其它方法具有更强的实用性。     

Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR,MODIS and Landsat data using the BFAST algorithm

Annual land use land cover(LULC) change information at medium spatial resolution(i.e. at 30 m) is required in numerous subjects, such as biophysical modelling, land management and global change studies. Annual LULC information,however, is usually not available at continental or national scale due to reasons such as insufficient remote sensing data coverage or lack of computational capabilities. Here we integrate high temporal resolution and coarse spatial resolution satellite images(i.e., Moderate Resolution Imaging Spectroradiometer(MODIS) and Global Inventory Modelling and Mapping Studies(GIMMS) normalized difference vegetation index(NDVI)) with high spatial resolution datasets(China's Land-Use/cover Datasets(CLUDs) derived from 30-meter Landsat TM/ETM+/OLI) to generate reliable annual nominal 30 m LULC maps for the whole of China between 1980 and 2015. We also test the performance of a statistical based change detection algorithm(Breaks for Additive Seasonal and Trend), originally designed for tracking forest change, in classifying all-type LULC change.As a result, a nominal 30 m annual land use/land cover datasets(CLUD-A) from 1980 to 2015 was developed for the whole China. The mapping results were assessed with a change sample dataset, a regional annual validation sample set and a three-year China sample set. Of the detected change years, 75.61% matched the exact time of conversion within ±1 year. Annual mapping results provided a detail process of urbanization, deforestation, afforestation, water and cropland dynamics over the past 36 years. The consistent characterization of land change dynamics for China can be further used in scientific research and to support land management for policy-makers.     

Coastline Extraction from SAR Images Using Robust Ridge Tracing

Although ridge tracing has the advantages of continuity and high positioning accuracy compared with other edge-based methods, it is difficult to use ridge tracing to extract coastlines from Synthetic Aperture Radar (SAR) images because of the speckle noise that occurs in SAR images. This paper presents a new coastline extraction method for SAR images based on a more robust ridge tracing method. First, according to the statistical properties of the pixel intensities in the land and sea regions in a SAR image, an edge magnitude map that characterizes the boundary between them is produced by the ratio of the variance to the mean such that the magnitude at the land-sea boundary is much higher than that at other locations. Second, the pixel with the maximum magnitude in the map is adopted as the starting point for tracing, and strip windows, which reduce tracing failures, are adopted to obtain different average magnitudes corresponding to the eight neighborhood pixels around the starting point. Then, the neighborhood pixel with the maximum magnitude is adopted as the next tracing point. The above procedure is repeated to determine the direction of the next point. This process achieves part of the tracing operation. The complete coastline is then extracted by performing the other part of the tracing operation. The experimental results show that the proposed method is more robust than traditional methods, and we demonstrate its effectiveness with RADARSAT-2 and Sentinel-1A data.     

Detection and estimation of mixed paddy rice cropping patterns with MODIS data

In this paper, we developed a more sophisticated method for detection and estimation of mixed paddy rice agriculture from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. Previous research demonstrated that MODIS data can be used to map paddy rice fields and to distinguish rice from other crops at large, continental scales with combined Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) analysis during the flooding and rice transplanting stage. Our approach improves upon this methodology by incorporating mixed rice cropping patterns that include single-season rice crops, early-season rice, and late-season rice cropping systems. A variable EVI/LSWI threshold function, calibrated to more local rice management practices, was used to recognize rice fields at the flooding stage. We developed our approach with MODIS data in Hunan Province, China, an area with significant flooded paddy rice agriculture and mixed rice cropping patterns. We further mapped the aerial coverage and distribution of early, late, and single paddy rice crops for several years from 2000 to 2007 in order to quantify temporal trends in rice crop coverage, growth and management systems. Our results were validated with finer resolution (2.5 m) Satellite Pour l’Observation de la Terre 5 High Resolution Geometric (SPOT 5 HRG) data, land-use data at the scale of 1/10,000 and with county-level rice area statistical data. The results showed that all three paddy rice crop patterns could be discriminated and their spatial distribution quantified. We show the area of single crop rice to have increased annually and almost doubling in extent from 2000 to 2007, with simultaneous, but unique declines in the extent of early and late paddy rice. These results were significantly positive correlated and consistent with agricultural statistical data at the county level (P < 0.01).