Characterize the spatial heterogeneity of fragmented land-surface and its relationship with spatial scale in southwest china

HUANG Yajun; ZHOU Wei; MA Mingguo

doi:10.11834/jrs.20232134

Algorithm Test And Products Validation | Views : 0 下载量: 264 CSCD: 0 更多指标

R-PDF
PDF
Export
Share
Collection
Album

Characterize the spatial heterogeneity of fragmented land-surface and its relationship with spatial scale in southwest china
Vol. 27, Issue 3, Pages: 802-809(2023)
Published： 07 March 2023 ，
DOI： 10.11834/jrs.20232134

扫描看全文

黄雅君，周伟，马明国.2023.西南地区破碎地表空间异质性刻画与空间尺度关系初探.遥感学报，27（3）： 802-809

Huang Y J，Zhou W and Ma M G. 2023. Characterize the spatial heterogeneity of fragmented land-surface and its relationship with spatial scale in southwest china. National Remote Sensing Bulletin， 27（3）：802-809
黄雅君，周伟，马明国.2023.西南地区破碎地表空间异质性刻画与空间尺度关系初探.遥感学报，27（3）： 802-809 DOI： 10.11834/jrs.20232134.

Huang Y J，Zhou W and Ma M G. 2023. Characterize the spatial heterogeneity of fragmented land-surface and its relationship with spatial scale in southwest china. National Remote Sensing Bulletin， 27（3）：802-809 DOI： 10.11834/jrs.20232134.

摘要

尺度效应是定量遥感领域的经典且重要问题之一，其中地表异质性的判断和明确对地表真实性检验和场站优化布设问题的前置工作。并且地表异质性的判断和计算，一般是通过一景低分辨率待检验产品与同步获取的地面测量结果或者高分辨率产品进行尺度转换实现间接表达。然而，由于待检低分辨率与高分辨率遥感影像之间几乎很难做到完全同步。那么如何在缺乏同步产品的基础上，仅利用高分辨率产品去刻画地表异质性，是为下一步对空间异质性进行进一步探索和分析的前提条件。本文针对该问题使用优于0.2 m空间分辨率的无人机光谱反射率数据，计算得到归一化差值植被指数NDVI（Normalized Difference Vegetation Index）数据，通过三次卷积升尺度算法计算获取了0.2—30 m共39个不同空间分辨率结果，通过目视解译获得土地利用/覆盖变化LULC（Land Use and Land Cover change）数据，结合地理探测器对1 km×1 km图幅内的空间异质性行评价。结果表明：3个地形破碎的喀斯特槽谷区，其空间异质性评价值

的阈值存在差异，但总体上

值都随着空间分辨率的提高（30—0.2 m）由震荡趋于平稳； Mann-Kendall突变检测发现，柑橘研究所和虎头村的空间异质性突变点和

值震荡曲线的稳点阈值基本一致。

Abstract

Scale effect is one of the classical and important problems in the field of quantitative remote sensing especially in surface validation field

in which the judgment of surface heterogeneity is a precursor to the problems of surface validation and station optimized layout

and is also one of the important error sources of surface parameters validation. The first way is to calculate the accuracy evaluation of the scale transformation results between the medium resolution remote sensing products and the ground measurement results or the very high resolution products acquired at the same time to express spatial heterogeneity indirectly

and a series of errors

such as different sensors optical parameters

different measurement angles

spatial and temporal scale inconsistency

geometric mismatching etc.

they all affect the results directly or jointly

and the error contributions are difficult to quantitatively

it means that is difficult to describe the spatial heterogeneity clearly. The second way is to use geostatistical methods to describe the images for evaluation the spatial heterogeneity directly. Then how to express the surface heterogeneity with only very high resolution remote sensing measurement image based on the lack of moderation satellite retrieval products is a workable way to describe to spatial heterogeneity for further exploration and analysis of spatial heterogeneity in the next step. Therefore

this paper uses a typical algorithm to portray spatial heterogeneity and discusses the relationship between spatial resolution and spatial heterogeneity in the absence of a reference base of medium-resolution data

with a view to reflecting the relationship between resolution and spatial heterogeneity and conducting a preliminary analysis. Specifically

this paper calculates Normalized Difference Vegetation Index (NDVI) data using Unmanned Aerial Vehicle（UAV） spectral reflectance data with spatial resolution better than 0.2 m that has been Radiation calibration by reflector plates

and obtains results for 39 different spatial resolutions from 0.2 m to 30 m by cubic convolution upscaling algorithm

and obtains land use and land cover change (LULC) by visual interpretation. The spatial heterogeneity of the 1km×1km map area was evaluated with GeoDetector algorithm

and then the regional spatial heterogeneity was described to explore the relationship between resolution and spatial heterogeneity. The results showed that the thresholds of spatial heterogeneity evaluation q value were different in three regions with fragmented land-surface

but the overall q value tended were oscillate to stable with the increase of spatial resolution (30 m to 0.2 m)

and the minimum threshold from oscillation to stability was 2 m resolution; then the change curve of

value with spatial resolution and done M-K mutation detection found that the thresholds and q values of spatial heterogeneity mutation points in Ganyansuo and Hutou Village oscillation curve existed for the oscillation to stable points basically matched

but there were multiple mutation points and mismatched in the Caoshang. There were pass the 5% significance test of M-K test for all three areas

which tested the relationship between

value and spatial resolution in the aforementioned in statistical significance. In conclusion

all this classification system was now regionally stable when the resolution was lower than 2 m

i.e.

when the resolution was higher than 2 m

its spatial heterogeneity tends to stable

and its could provide some reference for the sampling of ground and space-based platforms.

关键词

地表真实性检验NDVIUAV空间异质性地理探测器Mann-Kendall突变检测

Keywords

surface validationNDVIUAVspatial heterogeneitygeographical detectorM-K test

references

Aman A, Randriamanantena H P, Podaire A and Frouin R. 1992. Upscale integration of normalized difference vegetation index: the problem of spatial heterogeneity. IEEE Transactions on Geoscience and Remote Sensing, 30(2): 326-338 [DOI: 10.1109/36.134082http://dx.doi.org/10.1109/36.134082]

Anselin L. 1995. Local indicators of spatial association-LISA. Geographical Analysis, 27(2): 93-115 [DOI:10.1111/j.1538-4632.1995. tb00338.xhttp://dx.doi.org/10.1111/j.1538-4632.1995.tb00338.x]

Fotheringham A S, Brunsdon C and Charlton M. 2000. Quantitative Geography: Perspectives on Spatial Data Analysis. London: Sage

Garrigues S, Allard D, Baret F and Weiss M. 2006. Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data. Remote Sensing of Environment, 105(4): 286-298 [DOI: 10.1016/j.rse.2006.07.013http://dx.doi.org/10.1016/j.rse.2006.07.013]

Geng L Y and Ma M G. 2014. Advance in method comparison of reconstructing remote sensing time series data sets. Remote Sensing Technology and Application, 29(2): 362-368

耿丽英, 马明国. 2014. 长时间序列NDVI数据重建方法比较研究进展. 遥感技术与应用, 29(2): 362-368 [DOI:10.11873j.iss n.1004-0323.2014.2.0362http://dx.doi.org/10.11873j.issn.1004-0323.2014.2.0362]

Jiang J L, Ji X S, Yao X, Tian Y C, Zhu Y, Cao W X and Cheng T. 2018. Evaluation of three techniques for correcting the spatial scaling bias of leaf area index. Remote Sensing, 10(2): 221 [DOI: 10.3390/rs10020221http://dx.doi.org/10.3390/rs10020221]

Jin R, Li X, Ma M G, Ge Y, Liu S M, Xiao Q, Wen J G, Zhao K, Xin X P, Ran Y H, Liu Q H and Zhang R H. 2017. Key methods and experiment verification for the validation of quantitative remote sensing products. Advances in Earth Science, 32(6): 630-642

晋锐, 李新, 马明国, 葛咏, 刘绍民, 肖青, 闻建光, 赵凯, 辛晓平, 冉有华, 柳钦火, 张仁华. 2017. 陆地定量遥感产品的真实性检验关键技术与试验验证. 地球科学进展, 32(6): 630-642 [DOI: 10.11867/j.issn.1001-8166.2017.06.0630http://dx.doi.org/10.11867/j.issn.1001-8166.2017.06.0630]

Jin Y, Ge Y, Wang J H, Heuvelink G B M and Wang L. 2018. Geographically weighted area-to-point regression kriging for spatial downscaling in remote sensing. Remote Sensing, 10(4): 579 [DOI: 10.3390/rs10040579http://dx.doi.org/10.3390/rs10040579]

Li L F, Wang J F, Cao Z D and Zhong E S. 2008. An information-fusion method to identify pattern of spatial heterogeneity for improving the accuracy of estimation. Stochastic Environmental Research and Risk Assessment, 22(6): 689-704 [DOI: 10.1007/s00477-007-0179-1http://dx.doi.org/10.1007/s00477-007-0179-1]

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]

Liu L Y. 2014. Simulation and correction of spatial scaling 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, Zhang X X, Nie Q and Zhu X L. 2018. Trends on scaling research for land surface parameters in quantitative remote sensing. Advances in Earth Science, 33(5): 483-492

栾海军, 田庆久, 章欣欣, 聂芹, 朱晓玲. 2018. 定量遥感地表参数尺度转换研究趋势探讨. 地球科学进展, 33(5): 483-492 [DOI: 10.11867/j.issn.1001-8166.2018.05.0483http://dx.doi.org/10.11867/j.issn.1001-8166.2018.05.0483]

Moran P A P. 1950. Notes on continuous stochastic phenomena. Biometrika, 37(1/2): 17-23 [DOI: 10.1093/biomet/37.1-2.17http://dx.doi.org/10.1093/biomet/37.1-2.17]

Raffy M. 1992. Change of scale in models of remote sensing: a general method for spatialization of models. Remote Sensing of Environment, 40(2): 101-112 [DOI: 10.1016/0034-4257(92)90008-8http://dx.doi.org/10.1016/0034-4257(92)90008-8]

Reichenbach S E and Haake K. 1996. Cubic convolution for one-pass restoration and resampling//1996 International Geoscience and Remote Sensing Symposium. Lincoln, NE, USA: IEEE: 1597-1599 [DOI: 10.1109/IGARSS.1996.516742http://dx.doi.org/10.1109/IGARSS.1996.516742]

Su L H, Li X W and Huang Y X. 2001. An review on scale in remote sensing. Advances in Earth Science, 16(4): 544-548

苏理宏, 李小文, 黄裕霞. 2001. 遥感尺度问题研究进展. 地球科学进展, 16(4): 544-548 [DOI: 10.3321/j.issn:1001-8166.2001.04.016http://dx.doi.org/10.3321/j.issn:1001-8166.2001.04.016]

Wang J F and Xu C D. 2017. Geodetector: Principle and prospective. Acta Geographica Sinica, 72(1): 116-134

王劲峰, 徐成东. 2017. 地理探测器: 原理与展望. 地理学报, 72(1): 116-134 [DOI: 10.11821/dlxb201701010http://dx.doi.org/10.11821/dlxb201701010]

Wang J F, Zhang T L and Fu B J. 2016. A measure of spatial stratified heterogeneity. Ecological Indicators, 67: 250-256 [DOI: 10.1016/j.ecolind.2016.02.052http://dx.doi.org/10.1016/j.ecolind.2016.02.052]

Wang Y, Ryu D, Park S, Fuentes S and O’Connell M. 2017. Upscaling UAV-borne high resolution vegetation index to satellite resolutions over a vineyard//22nd International Congress on Modelling and Simulation. Hobart, Tasmania, Australia

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 X D. 2017. Validation of Quantitative Remote Sensing Products Over Heterogeneous Land Surfaces——A Case Study with Surface Albedo. Beijing: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences

吴小丹. 2017. 异质性地表定量遥感产品真实性检验方法研究——以地表反照率为例. 北京: 中国科学院遥感与数字地球研究所

Wu X D, Wen J G, Xiao Q, Li X, Liu Q H, Tang Y, Dou B C, Peng J J, You D Q and Li X W. 2015. Advances in validation methods for remote sensing products of land surface parameters. Journal of Remote Sensing, 19(1): 75-92

吴小丹, 闻建光, 肖青, 李新, 刘强, 唐勇, 窦宝成, 彭菁菁, 游冬琴, 李小文. 2015. 关键陆表参数遥感产品真实性检验方法研究进展. 遥感学报, 19(1): 75-92 [DOI: 10.11834/jrs.20154009http://dx.doi.org/10.11834/jrs.20154009]

Wu X D, Xiao Q, Wen J G, You D Q and Hueni A. 2019. Advances in quantitative remote sensing product validation: overview and current status. Earth-Science Reviews, 196: 102875 [DOI: 10.1016/j.earscirev.2019.102875http://dx.doi.org/10.1016/j.earscirev.2019.102875]

Xu J H. 2006. Quantitative Geography. Beijing: Higher Education Press

徐建华. 2006. 计量地理学. 北京: 高等教育出版社

Yu W T, Li J, Liu Q H, Zeng Y L, Yin G F, Zhao J and Xu B D. 2016. Extraction and analysis of land cover heterogeneity over China. Advances in Earth Science, 31(10): 1067-1077

于文涛, 李静, 柳钦火, 曾也鲁, 尹高飞, 赵静, 徐保东. 2016. 中国地表覆盖异质性参数提取与分析. 地球科学进展, 31(10): 1067-1077 [DOI: 10.11867/j.issn.1001-8166.2016.10.1067http://dx.doi.org/10.11867/j.issn.1001-8166.2016.10.1067]

Yu W T, Li J, Liu Q H, Zeng Y L, Zhao J, Xu B D and Yin G F. 2018. Global land cover heterogeneity characteristics at moderate resolution for mixed pixel modeling and inversion. Remote Sensing, 10(6): 856 [DOI: 10.3390/rs10060856http://dx.doi.org/10.3390/rs10060856]

Zhang R H, Tian J, Li Z L, Su Z B, Chen S H. Principles and methods for the validation of quantitative remote sensing products. Sci China Ser Earth Sci, 2010. (2):211-222

张仁华, 田静, 李召良, 苏红波, 陈少辉. 2010. 定量遥感产品真实性检验的基础与方法. 中国科学: 地球科学. (2):211-222 [DOI:10.1007/s1143 0-010-0021-3http://dx.doi.org/10.1007/s11430-010-0021-3]

Alert me when the article has been cited

提交

Extraction method of burned area using GF-1 WFV images and FY-3D MERSI fire-point products

Analysis of the thermal infrared radiation characteristics of a typical land surface stable target and applicability assessments

Response of vegetation to climate change in Central Asia with remote sensing and meteorological data

Analysis of the influence of key-phase growth-environment-landscape features on the accuracy of county-level winter wheat yield estimation in Hebei Province

Research on multispectral-image-based NDVI shadow-effect-eliminating model