扫 描 看 全 文
Quote
耿云龙，汤玉奇，尹芝勇，邹滨，冯徽徽.2023.顾及斑块面积比的国产卫星土地覆盖产品尺度效应研究.遥感学报，27（3）： 810-820
Geng Y L，Tang Y Q，Yin Z Y，Zou B and Feng H H. 2023. Research on scale effect of domestic satellite land cover products considering patch area ratio. National Remote Sensing Bulletin， 27（3）：810-820
尺度转换可获取不同应用需求下的土地覆盖产品最优表达尺度。但现有针对尺度转换伴随的尺度效应斑块级模型研究通常易受斑块分类的影响。因此，本文通过构建跨类型斑块面积比尺度效应模型，进一步探索斑块形态指标在尺度转换中的作用机制，实现对特定尺度土地覆盖产品中各地类面积的预测，为尺度转换方案提供合理性评价。首先利用信息度量技术定义斑块形态指标集，并通过数学变换将尺度纳入形态指标；然后联合斑块形态指标集和尺度转换前后的斑块面积比构建跨类型斑块面积比尺度效应模型。本文利用国家重点研发计划专项项目生产的国产卫星30 m土地覆盖产品对提出的斑块级尺度效应模型进行构建与验证。结果显示，该模型对林地、耕地、草地和不透水面四种地类在90—750 m共12个目标尺度（以60 m为间隔）上的平均预测误差分别为0.036、0.033、0.034和0.035，对目标尺度土地覆盖产品各地类面积具有较好的预测精度，可为国产土地覆盖产品尺度转换提供理论依据，为高精度产品生产提供技术支持。
The optimal scale of land cover products varies by different applications. Consequently, it needs the scale conversion of land cover products from the base resolution to the objective one. However, the scale effects accompanying the scale conversion often lead to information distortion of land cover products. The state-of-art patch-level research on scale effect, which is vulnerable to patch classification accuracy, often classifies the patches of land cover products into different types. Consequently, a cross-type scale effect model (CSEM) with Patch Area Ratio (PAR) is proposed in this study.,The CSEM is aimed to explore further the effect mechanism of Patch Morphological Indexes (PMIs) on scale conversion, predict the areas of land classes in land cover products with specific scales, and provide a rationality evaluation for the scale conversion. First, a candidate index library is defined with seven indexes to describe the patch morphology based on patch size, shape, edge complexity, and internal connectivity. Then, a set of PMIs is defined by information metric techniques. The scale is incorporated into the PMIs by mathematical transformation. Moreover, the CSEM with PAR is constructed by associating the PMIs with PAR, calculated according to the patch areas before and after scale conversion.,In this study, a 30 m land cover product from domestic satellites is used with the support of a National Key Research and Development Program to construct and validate the proposed CSEM. According to the experimental results, the CSEM fits the relationship among Filling, logRsr, and PAR: the logRsr is the main factor affecting the value of PAR when logRsr > -0.05, whereas Filling is the main factor on PAR while logRsr < -0.05. The prediction results show that the maximum prediction errors for the four land-cover types of forest, cropland, grassland, and impervious surface on 12 converted scales, varying from 90 m to 750 m with an interval of 60 m, are 0.056, 0.051, 0.051, and 0.053; their average prediction errors are 0.036, 0.033, 0.034, and 0.035, respectively.,The results confirm that the CESM explores the effects of Filling and logRsr on PAR and reveals the effect mechanism during the scale conversion, providing instruction for subsequent research of other indexes. The proposed CSEM has satisfactory prediction accuracy for various area types in land cover products with specific scales. It can also provide theoretical guidance for scale conversion and production of land cover products.
土地覆盖产品尺度上推尺度效应斑块面积比形态指标
land cover productupscalingscale effectPatch area Ratio (PAR)Patch Morphology Index (PMI)
Bicheron P, Defourny P, Brockmann C, Schouten L, Vancutsem C, Huc M, Bontemps S, Leroy M, Achard F, Herold M, Ranera F and Arino O. 2011. GLOBCOVER - Products description and validation report. Foro Mundial de la Salud, 17(3): 285-287
Buyantuyev A and Wu J G. 2007. Effects of thematic resolution on landscape pattern analysis. Landscape Ecology, 22(1): 7-13 [DOI: 10.1007/s10980-006-9010-5http://dx.doi.org/10.1007/s10980-006-9010-5]
Chen J, Chen J, Liao A P, Cao X, Chen L J, Chen X H, He C Y, Han G, Peng S, Lu M, Zhang W W, Tong X H and Mills J. 2015. Global land cover mapping at 30 m resolution: a POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103: 7-27 [DOI: 10.1016/j.isprsjprs.2014.09.002http://dx.doi.org/10.1016/j.isprsjprs.2014.09.002]
de Fries R S, Hansen M, Townshend J R G and Sohlberg R. 1998. Global land cover classifications at 8 km spatial resolution: the use of training data derived from Landsat imagery in decision tree classifiers. International Journal of Remote Sensing, 19(16): 3141-3168 [DOI: 10.1080/014311698214235http://dx.doi.org/10.1080/014311698214235]
Friedl M A, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A and Huang X M. 2010. MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(1): 168-182 [DOI: 10.1016/j.rse.2009.08.016http://dx.doi.org/10.1016/j.rse.2009.08.016]
Gong P, Liu H, Zhang M N, Li C C, Wang J, Huang H B, Clinton N, Ji LY, Li W Y, Bai Y Q, Chen B, Xu B, Zhu Z L, Yuan C, Suen H P, Guo J, Xu N, Li W J, Zhao Y Y, Yang J, Yu C Q, Wang X, Fu H H, Yu L, Dronova I, Hui F M, Cheng X, Shi X L, Xiao F J, Liu Q F and Song L C. 2019. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Science Bulletin, 64(6): 370-373 [DOI: 10.1016/j.scib.2019.03.002http://dx.doi.org/10.1016/j.scib.2019.03.002]
He H S, Ventura S J and Mladenoff D J. 2002. Effects of spatial aggregation approaches on classified satellite imagery. International Journal of Geographical Information Science, 16(1): 93-109 [DOI: 10.1080/13658810110075978http://dx.doi.org/10.1080/13658810110075978]
Li D R, Ding L and Shao Z F. 2016. Reflections on issues in national geographical conditions monitoring. Geomatics and Information Science of Wuhan University, 41(2): 143-147
李德仁, 丁霖, 邵振峰. 2016. 关于地理国情监测若干问题的思考. 武汉大学学报(信息科学版), 41(2): 143-147 [DOI: 10.13203/j.whugis20150555http://dx.doi.org/10.13203/j.whugis20150555]
Li X W and Wang Y T. 2013. Prospects on future developments of quantitative remote sensing. Acta Geographica Sinica, 68(9): 1163-1169
李小文, 王祎婷. 2013. 定量遥感尺度效应刍议. 地理学报, 68(9): 1163-1169 [DOI: 10.11821/dlxb201309001http://dx.doi.org/10.11821/dlxb201309001]
Li Z L. 2008. Multi-scale digital terrain modelling and analysis//Zhou Q M, Lees B and Tang G A, eds. Advances in Digital Terrain Analysis. Berlin, Heidelberg: Springer: 59-83 [DOI: 10.1007/978-3-540-77800-4_4http://dx.doi.org/10.1007/978-3-540-77800-4_4]
Li Z L, Wang J C, Tan S T and Xu Z. 2018. Scale in geo-information science: an overview of thirty-year development. Geomatics and Information Science of Wuhan University, 43(12): 2233-2242
李志林, 王继成, 谭诗腾, 徐柱. 2018. 地理信息科学中尺度问题的30年研究现状. 武汉大学学报(信息科学版), 43(12): 2233-2242 [DOI: 10.13203/j.whugis20180303http://dx.doi.org/10.13203/j.whugis20180303]
Liu L Y. 2014. Simulation and correction of spatialscaling effects for leaf area index. Journal of Remote Sensing, 18(6): 1158-1168
刘良云. 2014. 叶面积指数遥感尺度效应与尺度纠正. 遥感学报, 18(6): 1158-1168 [DOI: 10.11834/jrs.20144103http://dx.doi.org/10.11834/jrs.20144103]
Luan H J, Tian Q J, Gu X F, Yu T and Hu X L. 2013. Establishing continuous scaling of NDVI based on fractal theory and GEOEYE-1 image. Journal of Infrared and Millimeter Waves, 32(6): 538-544, 549
栾海军, 田庆久, 顾行发, 余涛, 胡新礼. 2013. 基于分形理论与GEOEYE-1影像的NDVI连续空间尺度转换模型构建及应用. 红外与毫米波学报, 32(6): 538-544, 549 [DOI: 10.3724/sp.j.1010.2013.00538http://dx.doi.org/10.3724/sp.j.1010.2013.00538]
Luan H J, Tian Q J, Yu T, Gu X F, Huang Y, Hu X L and Yang Y J. 2015. Establishing continuous spatial scaling model of NDVI on fractal theory and five-index estimation system. Journal of Remote Sensing, 19(1): 116-125
栾海军, 田庆久, 余涛, 顾行发, 黄彦, 胡新礼, 杨闫君. 2015. 根据分形理论与五指标评价体系构建NDVI连续空间尺度转换模型. 遥感学报, 19(1): 116-125 [DOI: 10.11834/jrs.20153340http://dx.doi.org/10.11834/jrs.20153340]
McGarigal K. 2015. FRAGSTATS Help. Amherst: University of Massachusetts: 77-80
Muchoney D, Borak J, Chi H, Friedl M, Gopal S, Hodges J, Morrow N and Strahler A. 2000. Application of the MODIS global supervised classification model to vegetation and land cover mapping of Central America. International Journal of Remote Sensing, 21(6/7): 1115-1138 [DOI: 10.1080/014311600210100http://dx.doi.org/10.1080/014311600210100]
Peterson G D. 2000. Scaling ecological dynamics: self-organization, hierarchical structure, and ecological resilience. Climatic Change, 44(3): 291-309 [DOI: 10.1023/A:1005502718799http://dx.doi.org/10.1023/A:1005502718799]
Riitters K, Wickham J, O'Neill R, Jones B and Smith E. 2000. Global-scale patterns of forest fragmentation. Conservation Ecology, 4(2): 3 [DOI: 10.5751/ES-00209-040203http://dx.doi.org/10.5751/ES-00209-040203]
Riitters K H, O'Neill R V, Hunsaker C T, Wickham J D, Yankee D H, Timmins S P, Jones K B and Jackson B L. 1995. A factor analysis of landscape pattern and structure metrics. Landscape Ecology, 10(1): 23-39 [DOI: 10.1007/BF00158551http://dx.doi.org/10.1007/BF00158551]
Tan S T, Xu Z, Ti P and Li Z L. 2018. Morphology-based modeling of aggregation effect on the patch area size for GlobeLand30 data. Transactions in GIS, 22(1): 98-118 [DOI: 10.1111/tgis.12298http://dx.doi.org/10.1111/tgis.12298]
Wu H, Jiang X G, Xi X H, Li C R and Li Z L. 2009. Comparison and analysis of two general scaling methods for remotely sensed information. Journal of Remote Sensing, 13(2): 183-189
吴骅, 姜小光, 习晓环, 李传荣, 李召良. 2009. 两种普适性尺度转换方法比较与分析研究. 遥感学报, 13(2): 183-189 [DOI: 10.11834/jrs.20090235http://dx.doi.org/10.11834/jrs.20090235]
Wu J G. 2004. Effects of changing scale on landscape pattern analysis: scaling relations. Landscape Ecology, 19(2): 125-138 [DOI: 10.1023/B:LAND.0000021711.40074.aehttp://dx.doi.org/10.1023/B:LAND.0000021711.40074.ae]
Xu Y H, Du B and Zhang L P. 2020. Beyond the patchwise classification: spectral-spatial fully convolutional networks for hyperspectral image classification. IEEE Transactions on Big Data, 6(3): 492-506 [DOI: 10.1109/TBDATA.2019.2923243http://dx.doi.org/10.1109/TBDATA.2019.2923243]
Xu Z Y, Hu Y F, Liu Y and Yan Y. 2012. A review on the accuracy analysis of spatial scaling data. Progress in Geography, 31(12): 1574-1582
徐芝英, 胡云锋, 刘越, 艳燕. 2012. 空间尺度转换数据精度评价的准则和方法. 地理科学进展, 31(12): 1574-1582 [DOI: 10.11820/dlkxjz.2012.12.002http://dx.doi.org/10.11820/dlkxjz.2012.12.002]
Zhang Q N, Tan S T, Xu Z and Huang Z C. 2017. Applicability and simplification study of patch level landscape metrics based on GLC30. Remote Sensing for Land and Resources, 29(4): 98-105
张倩宁, 谭诗腾, 徐柱, 黄泽纯. 2017. 基于GLC30数据的斑块级别景观指标适用性及简化研究. 国土资源遥感, 29(4): 98-105 [DOI: 10.6046/gtzyyg.2017.04.15http://dx.doi.org/10.6046/gtzyyg.2017.04.15]
Zhang X, Liu L Y, Chen X D, Xie S and Gao Y. 2019. Fine land-cover mapping in China using landsat datacube and an operational SPECLib-based approach. Remote Sensing, 11(9): 1056 [DOI: 10.3390/rs11091056http://dx.doi.org/10.3390/rs11091056]
相关文章
相关作者
相关机构
京公网安备11010802024621