A new global 1‐km dataset of percentage tree cover derived from remote sensing

TLDR

Accurate assessment of forest cover is crucial for quantifying carbon sources and sinks and for meeting Kyoto Protocol baseline estimates, yet country‑level data vary in forest definitions, leading to inconsistencies. The dataset is intended for use in terrestrial carbon cycle models alongside climate and soil data to improve carbon stock estimates. The dataset is provided via the Global Land Cover Facility at the University of Maryland (http://glcf.umiacs.umd.edu). Combining two 1‑km AVHRR datasets, the authors produced a prototype global map of percentage tree cover and leaf‑type proportions.

Abstract

Summary Accurate assessment of the spatial extent of forest cover is a crucial requirement for quantifying the sources and sinks of carbon from the terrestrial biosphere. In the more immediate context of the United Nations Framework Convention on Climate Change, implementation of the Kyoto Protocol calls for estimates of carbon stocks for a baseline year as well as for subsequent years. Data sources from country level statistics and other ground‐based information are based on varying definitions of ‘forest’ and are consequently problematic for obtaining spatially and temporally consistent carbon stock estimates. By combining two datasets previously derived from the Advanced Very High Resolution Radiometer (AVHRR) at 1 km spatial resolution, we have generated a prototype global map depicting percentage tree cover and associated proportions of trees with different leaf longevity (evergreen and deciduous) and leaf type (broadleaf and needleleaf). The product is intended for use in terrestrial carbon cycle models, in conjunction with other spatial datasets such as climate and soil type, to obtain more consistent and reliable estimates of carbon stocks. The percentage tree cover dataset is available through the Global Land Cover Facility at the University of Maryland at http://glcf.umiacs.umd.edu .