基于时序NDVI图谱库提高土地覆盖分类精度的方法

为提高MODIS土地覆盖产品的分类精度,该文以河南省为试验区,首先将MODIS土地覆盖产品(MCD12Q1)分为高精度区域和低精度区域,然后通过构建时序NDVI图谱库并利用图谱曲线相似性测定方法,改进MCD12Q1低精度区域的分类精度。结果表明:1)时序NDVI是土地覆盖的重要分类特征,二者之间具有较强的关联性。2)利用时序NDVI图谱库能够明显提高MODIS土地覆盖产品的分类精度,改进后的MCD12Q1的总体分类精度分别由72.76%(比较评价)、64.52%(样本评价)提高到83.05%和81.72%。3)不同土地覆盖类别精度提高的程度不同,林地、草地、耕地、人工地表以及水体的生产者精度分别提高35.36%、29.51%、2.98%、6.96%和6.11%。4)对于判定时序NDVI曲线相似度的2种具体方法而言,最小距离法(minimum distance,MD)总体上优于光谱角度匹配法(spectral angle mapper,SAM)。综上,保留现有土地覆盖产品中分类精度较高的部分,基于时序NDVI图谱库改进分类精度较低的部分,是提高现有土地覆盖产品分类精度的有效方法。

DMSP-OLS与NPP-VIIRS夜间灯光数据测算统计指标能力评估——以京津冀地区县域GDP、人口及能源消耗为例

夜间灯光数据记录了地球表面的人造灯光强度,是估计社会统计指标的有效手段之一。为了评估DMSP-OLS和NPP-VIIRS2种夜间灯光数据对社会统计指标的模拟潜力,采用4种常用的灯光校正方法分别对2种夜间灯光数据进行灯光饱和性校正,根据校正后的夜间灯光数据分别建立与京津冀地区县域GDP、人口和能源消耗3种社会统计指标间的线性回归模型,从模型拟合的相关系数、F统计量值与概率p值中分析并评价了2种夜间灯光数据对GDP、人口和能源消耗3种社会统计指标的测算能力。本文研究结果表明:EANTLI法是2种夜间灯光数据的最佳校正方式,而HSI法不适用于夜间灯光数据校正后与县域社会统计指标的线性关系拟合2种夜间灯光数据对GDP的拟合效果都较好,NPP-VIIRS夜间灯光数据估算社会统计指标的拟合能力要优于DMSP-OLS数据。

利用地形参数提高土地覆被分类精度方法的改进

为提高MODIS卫星影像土地覆被产品的分类精度,以京津冀为研究区,在1∶25万土地覆被数据与MODIS土地覆被产品(MCD12Q1)分类一致区内,构建土地覆被类型面积占比与地形因子之间的多元回归模型,并据此改进MODIS土地覆被产品中分类精度较低区域的分类。用面积构成比例和空间一致性比率两个评价指标对改进结果进行评价。结果表明:林地、草地、耕地三种地类的回归模型适合用来改进MODIS土地覆被产品的分类,三种地类与参考数据的空间一致性比率比改进前分别提高了30.02%、40.87%和4.94%;对于与地形因子关系密切的林地和草地,两个评价指标均显示,基于分类一致区建模来改进目标产品的分类精度,比基于整个区域建模改进目标产品的分类精度的效果更加明显。其中,林地的空间一致性比率的提升幅度由8.55%升到30.02%,草地由27.44%升到40.87%。由此可见,地形地貌对土地覆被类型的形成具有重要影响,土地覆被类型面积占比与地形因子之间具有很强的相关关系,基于这种定量关系对土地覆被分类进行改进是完全可行的。

Errors Prediction for Vector-to-Raster Conversion Based on Map Load and Cell Size

Vector-to-raster conversion is a process accompanied with errors.The errors are classified into predicted errors before rasterization and actual errors after that.Accurate prediction of the errors is beneficial to developing reasonable rasterization technical schemes and to making products of high quality.Analyzing and establishing a quantitative relationship between the error and its affecting factors is the key to error prediction.In this study,land cover data of China at a scale of 1:250 000 were taken as an example for analyzing the relationship between rasterization errors and the density of arc length(DA),the density of polygon(DP) and the size of grid cells(SG).Significant correlations were found between the errors and DA,DP and SG.The correlation coefficient(R2) of a model established based on samples collected in a small region(Beijing) reaches 0.95,and the value of R2 is equal to 0.91 while the model was validated with samples from the whole nation.On the other hand,the R2 of a model established based on nationwide samples reaches 0.96,and R2 is equal to 0.91 while it was validated with the samples in Beijing.These models depict well the relationships between rasterization errors and their affecting factors(DA,DP and SG).The analyzing method established in this study can be applied to effectively predicting rasterization errors in other cases as well.

Scale effect and methods for accuracy evaluation of attribute information loss in rasterization

Rasterization is a conversion process accompanied with information loss, which includes the loss of features＇ shape, structure, position, attribute and so on. Two chief factors that affect estimating attribute accuracy loss in rasterization are grid cell size and evaluating method. That is, attribute accuracy loss in rasterization has a close relationship with grid cell size; besides, it is also influenced by evaluating methods. Therefore, it is significant to analyze these two influencing factors comprehensively. Taking land cover data of Sichuan at the scale of 1：250,000 in 2005 as a case, in view of data volume and its processing time of the study region, this study selects 16 spatial scales from 600 m to 30 km, uses rasterizing method based on the Rule of Maximum Area （RMA） in ArcGIS and two evaluating methods of attribute accuracy loss, which are Normal Analysis Method （NAM） and a new Method Based on Grid Cell （MBGC）, respectively, and analyzes the scale effect of attribute （it is area here） accuracy loss at 16 different scales by these two evaluating methods comparatively. The results show that： （1） At the same scale, average area accuracy loss of the entire study region evaluated by MBGC is significantly larger than the one estimated using NAM. Moreover, this discrepancy between the two is obvious in the range of 1 km to 10 km. When the grid cell is larger than 10 km, average area accuracy losses calculated by the two evaluating methods are stable, even tended to parallel. （2） MBGC can not only estimate RMA rasterization attribute accuracy loss accurately, but can express the spatial distribution of the loss objectively. （3） The suitable scale domain for RMA rasterization of land cover data of Sichuan at the scale of 1：250,000 in 2005 is better equal to or less than 800 m, in which the data volume is favorable and the processina time is not too Iona. as well as the area accuracv loss is less than 2.5%.