Global validation of the collection 6 MODIS burned area product.

This paper presents a Stage 3 validation of the recently released Collection 6 NASA MCD64A1 500 m global burned area product. The product is validated by comparison with Landsat 8 Operational Land Imager (OLI) image pairs acquired 16 days apart that were visually interpreted. These independent reference data were selected using a stratified random sampling approach that allows for probability sampling of Landsat data in both time and in space. A total of 558 Landsat 8 OLI image pairs (1116 images), acquired between March 1st, 2014 and March 19th , 2015, were selected and used to validate the MCD64A1 product. The areal accuracy of the MCD64A1 product was characterized at the 30 m resolution of the Landsat independent reference data using standard accuracy metrics derived from global and from biome specific confusion matrices. Because a probability based Stage 3 sampling protocol was followed, unbiased estimators of the accuracy metrics and associated standard errors could be used. Globally, the MCD64A1 product had an estimated 40.2% commission error and 72.6% omission error; the prevalence of omission errors is reflected by a negative estimated bias of the mapped global area burned relative to the Landsat independent reference data (-54.1%). Globally, the standard errors of the accuracy metrics were less than 6%. The lowest errors were observed in the boreal forest biome (27.0% omission and 23.9% estimated commission errors) where burned areas tend to be large and distinct, and remain on the landscape for long periods, and the highest errors were in the Tropical Forest, Temperate Forest, and Mediterranean biomes (estimated > 90% omission error and > 50% commission error). The product accuracy was also characterized at coarser scale using metrics derived from the regression between the proportion of coarse resolution grid cells detected as burned by MCD64A1 and the proportion mapped in the Landsat 8 interpreted maps. The errors of omission and commission observed at 30 m resolution compensate to a considerable extent at coarser resolution, as indicated by the coefficient of determination (r2 > 0.70), slope (> 0.79) and intercept (-0.0030) of the regression between the MCD64A1 product and the Landsat independent reference data in 3 km, 4 km, 5 km, and 6 km coarse resolution cells. The Boreal Forest, Desert and Xeric Shrublands, Temperate Savannah and Tropical Savannah biomes had higher r 2 and slopes closer to unity than the Temperate Forest, Mediterranean, and Tropical Forest biomes. The analysis of the deviations between the proportion of area burned mapped by the MCD64A1 product and by the independent reference data, performed using 3 km × 3 km and 6 km × 6 km coarse resolution cells, indicates that the large negative bias in global area burned is primarily due to the systematic underestimation of smaller burned areas in the MCD64A1 product.

The Collection 6 MODIS burned area mapping algorithm and product.

The two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on-board NASA's Terra and Aqua satellites have provided nearly two decades of global fire data. Here, we describe refinements made to the 500-m global burned area mapping algorithm that were implemented in late 2016 as part of the MODIS Collection 6 (C6) land-product reprocessing. The updated algorithm improves upon the heritage Collection 5.1 (C5.1) MCD64A1 and MCD45A1 algorithms by offering significantly better detection of small burns, a modest reduction in burn-date temporal uncertainty, and a large reduction in the extent of unmapped areas. Comparison of the C6 and C5.1 MCD64A1 products for fifteen years (2002-2016) on a regional basis shows that the C6 product detects considerably more burned area globally (26%) and in almost every region considered. The sole exception was in Boreal North America, where the mean annual area burned was 6% lower for C6, primarily as a result of a large increase in the number of small lakes mapped (and subsequently masked) at high latitudes in the upstream C6 input data. With respect to temporal reporting accuracy, 44% of the C6 MCD64A1 burned grid cells were de-tected on the same day as an active fire, and 68% within 2 days, which represents a substantial reduction in temporal uncertainty compared to the C5.1 MCD64A1 and MCD45A1 products. In addition, an areal accuracy assessment of the C6 burned area product undertaken using high resolution burned area reference maps derived from 108 Landsat image pairs is reported.

A stratified random sampling design in space and time for regional to global scale burned area product validation.

The potential research, policy and management applications of global burned area products place a high priority on rigorous, quantitative assessment of their accuracy. Such an assessment can be achieved by implementing validation methods employing design-based inference in which the independent reference data are selected via a probability sampling design. The majority of global burned area validation exercises use Landsat data to derive the independent reference data. This paper presents a three-dimensional sampling grid that allows for probability sampling of Landsat data in both space and time. To sample the globe in the spatial domain with non-overlapping sampling units, the Thiessen Scene Area (TSA) tessellation of the Landsat path/row geometry is used. The TSA grid is combined in time with the 16-day Landsat acquisition calendar to provide three-dimensional elements (voxels).This allows for implementation of stratified random sampling designs, where not only the location but also the time interval of the independent reference data is explicitly drawn by probability sampling. To illustrate this, we use a stratification methodology based on the Olson global ecoregion map and on the MODIS global active fire product. Using the global MODIS burned area product to establish a hypothetical population of reference data, we show that a sampling scheme based on the proposed stratification with equal sample allocation among strata is effective in reducing the standard errors of accuracy and area estimators compared to simple random sampling. Globally, the standard errors were reduced by 63%, 54%, 22% and 53% for overall accuracy, omission error, commission error and total burned area estimates respectively. By incorporating probability sampling in both the spatial and temporal domains, the present study establishes the foundation for rigorous design-based validation of global burned area products and, more generally, of terrestrial thematic products that have high temporal variability.

Evaluation of Landsat-8 and Sentinel-2A aerosol optical depth retrievals across Chinese cities and implications for medium spatial resolution urban aerosol monitoring.

In urban environments, aerosol distributions may change rapidly due to building and transport infrastructure and human population density variations. The recent availability of medium resolution Landsat-8 and Sentinel-2 satellite data provide the opportunity for aerosol optical depth (AOD) estimation at higher spatial resolution than provided by other satellites. A year of 30 m Landsat-8 and 10 m Sentinel-2A AOD data retrieved using the Land Surface Reflectance Code (LaSRC) were compared with coincident ground-based Aerosol Robotic Network (AERONET) Version 3 AOD data for 20 Chinese cities. Stringent selection criteria were used to select contemporaneous data - only satellite and AERONET data acquired within 10 minutes were considered. The average satellite retrieved AOD over a 1470 m × 1470 m window centered on each AERONET site was derived to capture fine scale urban AOD variations. AERONET Level 1.5 (cloud-screened) and Level 2.0 (cloud-screened and also quality assured) data were considered. For the 20 urban AERONET sites in 2016 there were 106 (Level 1.5) and 67 (Level 2.0) Landsat-8 AERONET AOD contemporaneous data pairs, and 118 (Level 1.5) and 89 (Level 2.0) Sentinel-2A AOD data pairs. The greatest AOD values (>1.5) occurred in Beijing, suggesting that the Chinese capital was one of the most polluted cities in China in 2016. The LaSRC Landsat-8 and Sentinel-2A AOD retrievals agreed well with the AERONET AOD data (linear regression slopes > 0.96; coefficient of determination r2 > 0.90; root mean square deviation < 0.175) and demonstrate that the LaSRC is an effective and applicable medium resolution AOD retrieval algorithm over urban environments. The Sentinel-2A AOD retrievals had better accuracy than the Landsat-8 AOD retrievals, which is consistent with previously published research. The implications of the research and the potential for urban aerosol monitoring by combining the freely available Landsat-8 and Sentinel-2 satellite data are discussed.

A contemporary decennial examination of changing agricultural field sizes using Landsat time series data.

Field size distributions and their changes have not been studied over large areas as field size change datasets are not available. This study quantifies agricultural field size changes in a consistent manner using Landsat satellite data that also provide geographic context for the observed decadal scale changes. Growing season cloud-free Landsat 30 m resolution images acquired from 9 to 25 years apart were used to extract field object classifications at seven sites located by examination of a global agricultural yield map, agricultural production statistics, literature review, and analysis of the imagery in the US Landsat archive. High spatial resolution data were used to illustrate issues identifying small fields that are not reliably discernible at 30 m Landsat resolution. The predominant driver of field size change was attributed by literature review. Significant field size changes were driven by different factors, including technological advancements (Argentina and USA), government land use and agricultural policies (Malaysia, Brazil, France), and political changes (Albania and Zimbabwe). While observed local field size changes were complex, the reported results suggest that median field sizes are increasing due to technological advancements and changes to government policy, but may decrease where abrupt political changes affect the agricultural sector and where pastures are converted to arable land uses. In the limited sample considered, median field sizes increased from 45% (France) to 159% (Argentina) and decreased from 47% (Brazil) to 86% (Albania). These changes imply significant impacts on landscape spatial configuration and land use diversity with ecological and biogeochemical consequences.

Large seasonal swings in leaf area of Amazon rainforests.

Despite early speculation to the contrary, all tropical forests studied to date display seasonal variations in the presence of new leaves, flowers, and fruits. Past studies were focused on the timing of phenological events and their cues but not on the accompanying changes in leaf area that regulate vegetation-atmosphere exchanges of energy, momentum, and mass. Here we report, from analysis of 5 years of recent satellite data, seasonal swings in green leaf area of approximately 25% in a majority of the Amazon rainforests. This seasonal cycle is timed to the seasonality of solar radiation in a manner that is suggestive of anticipatory and opportunistic patterns of net leaf flushing during the early to mid part of the light-rich dry season and net leaf abscission during the cloudy wet season. These seasonal swings in leaf area may be critical to initiation of the transition from dry to wet season, seasonal carbon balance between photosynthetic gains and respiratory losses, and litterfall nutrient cycling in moist tropical forests.