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

  • role： First author第一作者
  • Affiliation：

    Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China

    Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China

  • Email：hyj1368376@email.swu.edu.cn
  • Introduction：黄雅君，研究方向为地表真实性检验与场站优化布设。E-mail： hyj1368376@email.swu.edu.cn
HUANG Yajun12,  
  • Affiliation：

    Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China

    Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China

ZHOU Wei12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China

    Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China

  • Email：mmg@swu.edu.cn
  • Introduction：马明国，研究方向为生态与陆面过程遥感。E-mail： mmg@swu.edu.cn
MA Mingguo12*

Resümee

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 q 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 q 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.

Schlüsselwort

surface validation;NDVI;UAV;spatial heterogeneity;geographical detector;M-K test

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