Reducing Uncertainties in Applying Remotely Sensed Land Use and Land Cover Maps in Land-Atmosphere Interaction: Identifying Change in Space and Time

Yaqian He,Eungul Lee,Timothy Warner,Brenden Mcneil

doi:10.3390/rs10040506

Abstract

Land use and land cover (LULC) data are a central component of most land-atmosphere interaction studies, but there are two common and highly problematic scale mismatches between LULC and climate data. First, in the spatial domain, researchers rarely consider the impact of scaling up fine-scale LULC data to match coarse-scale climate datasets. Second, in the temporal domain, climate data typically have sub-daily, daily, monthly, or annual resolution, but LULC datasets often have much coarser (e.g., decadal) resolution. We first explored the effect of three spatial scaling methods on correlations among LULC data and a land surface climatic variable, latent heat flux in China. Scaling by a fractional method preserved significant correlations among LULC data and latent heat flux at all three studied scales (0.5°, 1.0°, and 2.5°), whereas nearest-neighbor and majority-aggregation methods caused these correlations to diminish and even become statistically non-significant at coarser spatial scales (i.e., 2.5°). In the temporal domain, we identified fractional changes in croplands, forests, and grasslands in China using a recently developed and annually resolved time series of LULC maps from 1982 to 2012. Relative to common LULC change (LULCC) analyses conducted over two-time steps or several time periods, this annually resolved, 31-year time series of LULC maps enables robust interpretation of LULCC. Specifically, the annual resolution of these data enabled us to more precisely observe three key and statistically significant LULCC trends and transitions that could have consequential effects on land-atmosphere interaction: (1) decreasing grasslands to increasing croplands in the Northeast China plain and the Yellow river basin, (2) decreasing croplands to increasing forests in the Yangtze river basin, and (3) decreasing grasslands to increasing forests in Southwest China. Our study not only demonstrates the importance of using a fractional spatial rescaling method, but also illustrates the value of annually resolved LULC time series for detecting significant trends and transitions in LULCC, thus potentially facilitating a more robust use of remotely sensed data in land-atmosphere interaction studies.

Highlights

Human activities have transformed a large proportion of the planet’s land surface [1] through processes such as the deforestation of tropical forests, as well as intensified agricultural land use and urbanization in China and India
The correlation coefficients for the association of fraction of croplands and latent heat flux in both the North China plain and the Sichuan basin change minimally as the spatial scale coarsened for all six years, and in each case are significant at the 1% level (Table 1)
Both categorical land use and land cover (LULC) maps and fractional maps capture the overall pattern of croplands in the relatively homogenous North China plain and the comparatively heterogeneous Sichuan basin at all of the three spatial resolutions studied (0.5◦, 1.0◦, and 2.5◦)

Summary

Introduction

Human activities have transformed a large proportion of the planet’s land surface [1] through processes such as the deforestation of tropical forests, as well as intensified agricultural land use and urbanization in China and India. Human-induced land use and land cover change (LULCC) can alter surface roughness, surface wetness, the partitioning of surface energy between sensible and latent heat fluxes, and terrestrial carbon storage [2,3,4,5,6]. These changes are increasingly becoming a focus of concern because of their potential to influence the climate system [7,8] and, as a consequence, the Intergovernmental Panel on Climate Change (IPCC) has emphasized the importance of understanding the climate response to LULCC at local, regional, and global scales [9]. Resampling LULC maps into the same resolution as climate data is problematic, as LULCC is complex, with heterogeneous patterns that may not be evident in simple measures, such as dominant change type, in coarse resolution data

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