Refined burned-area mapping protocol using Sentinel-2 data increases estimate of 2019 Indonesian burning

Many nations are challenged by landscape fires. A confident knowledge of the area and distribution of burning is crucial to monitor these fires and to assess how they might best be reduced. Given the differences that arise using different detection approaches, and the uncertainties surrounding burned-area estimates, their relative merits require evaluation. Here we propose, illustrate, and examine one promising approach for Indonesia where recurring forest and peatland fires have become an international crisis. Drawing on Sentinel-2 satellite time-series analysis, we present and validate new 2019 burned-area estimates for Indonesia. The corresponding burned-area map is available at https://doi.org/10.5281/zenodo.4551243 (Gaveau et al., 2021a). We show that >3.11 million hectares (Mha) burned in 2019. This burned-area extent is double the Landsat-derived official estimate of 1.64 Mha from the Indonesian Ministry of Environment and Forestry and 50 % more that the MODIS MCD64A1 burned-area estimate of 2.03 Mha. Though we observed proportionally less peatland burning (31 % vs. 39 % and 40 % for the official and MCD64A1 products, respectively), in absolute terms we still observed a greater area of peatland affected (0.96 Mha) than the official estimate (0.64 Mha). This new burned-area dataset has greater reliability than these alternatives, attaining a user accuracy of 97.9 % (CI: 97.1 %–98.8 %) compared to 95.1 % (CI: 93.5 %–96.7 %) and 76 % (CI: 73.3 %–78.7 %), respectively. It omits fewer burned areas, particularly smaller- (<100 ha) to intermediate-sized (100–1000 ha) burns, attaining a producer accuracy of 75.6 % (CI: 68.3 %–83.0 %) compared to 49.5 % (CI: 42.5 %–56.6 %) and 53.1 % (CI: 45.8 %–60.5 %), respectively. The frequency–area distribution of the Sentinel-2 burn scars follows the apparent fractal-like power law or Pareto pattern often reported in other fire studies, suggesting good detection over several magnitudes of scale. Our relatively accurate estimates have important implications for carbon-emission calculations from forest and peatland fires in Indonesia.

The size of the informal economy in Nigeria: a structural equation approach

PurposeThis paper contributes to the literature concerning the Nigerian informal economy (IE) by estimating its size from 1991 to 2017 and identifying the major causes.Design/methodology/approachA structural equation approach in the form of the multiple indicators multiple causes (MIMIC) method is used to estimate the size of the Nigerian IE.FindingsThe results indicate that vulnerable employment and urban population as a percentage of the total population are the main drivers of the IE in Nigeria. The IE in Nigeria ranges from 38.83% to 57.55% of gross domestic product (GDP).Research limitations/implicationsAs a result of the empirical challenges in the estimation of the IE, the estimates of Nigeria's IE are considered to be rough estimates.Originality/valueThe authors calibrated the MIMIC model with the official estimate of the informal sector published by the Nigerian National Bureau of Statistics (NBS). This was an attempt to combine the national accounting approach, to estimate the size of IE, with the MIMIC approach, and to estimate the trend of informality.

A REVIEW OF THE GRASS SPECIES INTRODUCTIONS INTO THE TUSSOCK GRASSLANDS OF THE SOUTH ISLAND, NEW ZEALAND

T HE EARLY SETTLERS and their stock moved into the inland South Island tussock grasslands in the 1850s. It was an area of rich pasture and the official estimate of the time (1857) was that the superior grasses could carry one sheep to two acres, good grasses one sheep to three acres, and inferior one sheep to four to five acres (Beettie, 1947).

Section III. The Demand for Energy

The original article, prepared in 1960 and published in September of that year, took as its initial text the statement of the National Coal Board in April 1956 : “ Even in the longer term, the problem of overproduction for the coal industry can scarcely arise ”. The article was concerned to try to explain the divergence, from 1956 onwards, in the movement of gross domestic product on the one hand, and the demand for energy and particularly for coal on the other (chart 9). The two main explanations of the divergence were that the temperature in the 1956-59 period was higher than average, and that fuel efficiency improved much faster than before. As a by-product of these calculations, some conditional forecasts were made for total energy demand and coal demand in 1965 and 1970. These forecasts took three rates of growth of gross domestic product, in real terms—2, 3 and 4 per cent—and three rates of fuel-efficiency improvements—at 1950-59 rates, at 1957-59 rates, and a further slow improvement in fuel efficiency rates. The main conclusion about the future was that “ it would need a very slow rate of efficiency improvement, and a very rapid rate of increase in national output, for the demand for energy to reach 300 million tons (coal equivalent) by 1965; this is still the official estimate. It also does not seem likely that the demand for coal will rise over the next five years ”.