Abstract

The collection of high-quality field measurements of ground cover is critical for calibration and validation of fractional ground cover maps derived from satellite imagery. Field-based hyperspectral ground cover sampling is a potential alternative to traditional in situ techniques. This study aimed to develop an effective sampling design for spectral ground cover surveys in order to estimate fractional ground cover in the Australian arid zone. To meet this aim, we addressed two key objectives: (1) Determining how spectral surveys and traditional step-point sampling compare when conducted at the same spatial scale and (2) comparing these two methods to current Australian satellite-derived fractional cover products. Across seven arid, sparsely vegetated survey sites, six 500-m transects were established. Ground cover reflectance was recorded taking continuous hyperspectral readings along each transect while step-point surveys were conducted along the same transects. Both measures of ground cover were converted into proportions of photosynthetic vegetation, non-photosynthetic vegetation, and bare soil for each site. Comparisons were made of the proportions of photosynthetic vegetation, non-photosynthetic vegetation, and bare soil derived from both in situ methods as well as MODIS and Landsat fractional cover products. We found strong correlations between fractional cover derived from hyperspectral and step-point sampling conducted at the same spatial scale at our survey sites. Comparison of the in situ measurements and image-derived fractional cover products showed that overall, the Landsat product was strongly related to both in situ methods for non-photosynthetic vegetation and bare soil whereas the MODIS product was strongly correlated with both in situ methods for photosynthetic vegetation. This study demonstrates the potential of the spectral transect method, both in its ability to produce results comparable to the traditional transect measures, but also in its improved objectivity and relative logistic ease. Future efforts should be made to include spectral ground cover sampling as part of Australia’s plan to produce calibration and validation datasets for remotely sensed products.

Highlights

  • Satellite image-derived fractional ground cover mapping has proven to be an essential source of information for applications, including analysis of spatial and temporal vegetation dynamics [1], monitoring urban greenness [2], mapping bushfire burn severity levels [3], forest cover change [4], and deforestation [5]

  • When the in situ methods were compared to the image-based models (MODIS and Landsat), spectral transect sampling showed a strong relationship to the MODIS image for photosynthetic vegetation (PV) and was the strongest relationship observed

  • For bare soil (BS), the correlation between the MODIS imagery and the in situ methods was moderate and moderate to low for non-photosynthetic vegetation (NPV)

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Summary

Introduction

Satellite image-derived fractional ground cover mapping has proven to be an essential source of information for applications, including analysis of spatial and temporal vegetation dynamics [1], monitoring urban greenness [2], mapping bushfire burn severity levels [3], forest cover change [4], and deforestation [5]. Algorithms, including spectral mixture analysis [6,7,8], multiple endmember spectral mixture analysis [9], and relative spectral mixture analysis [10], are used to produce fractional cover (FC) maps. These algorithms can be applied to multispectral and hyperspectral imagery, decomposing each image pixel into a measure of similarity to two or more spectrally distinct land cover types. In Australia, time series of fractional cover have been produced from MODIS [17] and Landsat [18,19] and are being used widely for environmental assessment and monitoring applications

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