[en] Timely and accurate data on forest change within Indonesia is required to provide government, private and civil society interests with the information needed to improve forest management. The forest clearing rate in Indonesia is among the highest reported by the United Nations Food and Agriculture Organization (FAO), behind only Brazil in terms of forest area lost. While the rate of forest loss reported by FAO was constant from 1990 through 2005 (1.87 Mha yr^-1), the political, economic, social and environmental drivers of forest clearing changed at the close of the last century. We employed a consistent methodology and data source to quantify forest clearing from 1990 to 2000 and from 2000 to 2005. Results show a dramatic reduction in clearing from a 1990s average of 1.78 Mha yr^-1 to an average of 0.71 Mha yr^-1 from 2000 to 2005. However, annual forest cover loss indicator maps reveal a near-monotonic increase in clearing from a low in 2000 to a high in 2005. Results illustrate a dramatic downturn in forest clearing at the turn of the century followed by a steady resurgence thereafter to levels estimated to exceed 1 Mha yr^-1 by 2005. The lowlands of Sumatra and Kalimantan were the site of more than 70% of total forest clearing within Indonesia for both epochs; over 40% of the lowland forests of these island groups were cleared from 1990 to 2005. The method employed enables the derivation of internally consistent, national-scale changes in the rates of forest clearing, results that can inform carbon accounting programs such as the Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD) initiative.

[en] Highlights: • We developed models to estimate willow chlorophyll concentration from CM readings. • The model incorporating growing degree days considers seasonal variation. • We improved the model performance among willow growth stages. • The model reduced the complexity of field data needed compared to previous studies. • The study supported shrub willow time-series health status monitoring. Perennial shrub willow crops can simultaneously address environmental issues and produce biomass for biofuels, bioproducts and bioenergy. Chlorophyll is an essential biochemical property for characterizing plant health and growth and remote sensing techniques have been developed to quantify plant chlorophyll concentration. However, these techniques are highly dependent on the ability to obtain a large number of ground-based observations to train and validate the models. Given the species-specific and growth stage-dependent nature of leaf chlorophyll concentration (LCC), and the limited research about LCC estimation for shrub willow, we proposed a new model for quantifying shrub willow LCC from chlorophyll meter (CM) readings. Results indicated that there were statistically significant interaction effects in CM readings between cultivar and root age across willow growth stages. However, neither the cultivar, root age, nor their interaction were statistically significant predictor variables in a model for LCC, so separate response curves for different cultivars or root ages were not necessary. To consider changes in LCC across the growing season, we included growing degree days (GDD) as an additional predictor for estimating LCC from CM readings. The model including GDD performed better (R² = 0.92, RMSE = 4.81 μg/cm²) than the model (R² = 0.90, RMSE = 5.38 μg/cm²) using only CM readings. Compared to previous studies, this model reduced complexity and achieved more accurate predictions of seasonal LCC, thus providing a readily applicable approach for mapping willow LCC using remote sensing technologies.